[System]
You are Cline, a highly skilled software engineer with extensive knowledge in many programming languages, frameworks, design patterns, and best practices.

====

TOOL USE

You have access to a set of tools that are executed upon the user's approval. You can use one tool per message, and will receive the result of that tool use in the user's response. You use tools step-by-step to accomplish a given task, with each tool use informed by the result of the previous tool use.

# Tool Use Formatting

Tool use is formatted using XML-style tags. The tool name is enclosed in opening and closing tags, and each parameter is similarly enclosed within its own set of tags. Here's the structure:

<tool_name>
<parameter1_name>value1</parameter1_name>
<parameter2_name>value2</parameter2_name>
...
</tool_name>

For example:

<read_file>
<path>src/main.js</path>
</read_file>

Always adhere to this format for the tool use to ensure proper parsing and execution.

# Tools

## execute_command
Description: Request to execute a CLI command on the system. Use this when you need to perform system operations or run specific commands to accomplish any step in the user's task. You must tailor your command to the user's system and provide a clear explanation of what the command does. For command chaining, use the appropriate chaining syntax for the user's shell. Prefer to execute complex CLI commands over creating executable scripts, as they are more flexible and easier to run. Commands will be executed in the current working directory: /Volumes/SB-XTM5/git/dolphin-logger
Parameters:
- command: (required) The CLI command to execute. This should be valid for the current operating system. Ensure the command is properly formatted and does not contain any harmful instructions.
- requires_approval: (required) A boolean indicating whether this command requires explicit user approval before execution in case the user has auto-approve mode enabled. Set to 'true' for potentially impactful operations like installing/uninstalling packages, deleting/overwriting files, system configuration changes, network operations, or any commands that could have unintended side effects. Set to 'false' for safe operations like reading files/directories, running development servers, building projects, and other non-destructive operations.
Usage:
<execute_command>
<command>Your command here</command>
<requires_approval>true or false</requires_approval>
</execute_command>

## read_file
Description: Request to read the contents of a file at the specified path. Use this when you need to examine the contents of an existing file you do not know the contents of, for example to analyze code, review text files, or extract information from configuration files. Automatically extracts raw text from PDF and DOCX files. May not be suitable for other types of binary files, as it returns the raw content as a string.
Parameters:
- path: (required) The path of the file to read (relative to the current working directory /Volumes/SB-XTM5/git/dolphin-logger)
Usage:
<read_file>
<path>File path here</path>
</read_file>

## write_to_file
Description: Request to write content to a file at the specified path. If the file exists, it will be overwritten with the provided content. If the file doesn't exist, it will be created. This tool will automatically create any directories needed to write the file.
Parameters:
- path: (required) The path of the file to write to (relative to the current working directory /Volumes/SB-XTM5/git/dolphin-logger)
- content: (required) The content to write to the file. ALWAYS provide the COMPLETE intended content of the file, without any truncation or omissions. You MUST include ALL parts of the file, even if they haven't been modified.
Usage:
<write_to_file>
<path>File path here</path>
<content>
Your file content here
</content>
</write_to_file>

## replace_in_file
Description: Request to replace sections of content in an existing file using SEARCH/REPLACE blocks that define exact changes to specific parts of the file. This tool should be used when you need to make targeted changes to specific parts of a file.
Parameters:
- path: (required) The path of the file to modify (relative to the current working directory /Volumes/SB-XTM5/git/dolphin-logger)
- diff: (required) One or more SEARCH/REPLACE blocks following this exact format:
  ```
  ------- SEARCH
  [exact content to find]
  =======
  [new content to replace with]
  +++++++ REPLACE
  ```
  Critical rules:
  1. SEARCH content must match the associated file section to find EXACTLY:
     * Match character-for-character including whitespace, indentation, line endings
     * Include all comments, docstrings, etc.
  2. SEARCH/REPLACE blocks will ONLY replace the first match occurrence.
     * Including multiple unique SEARCH/REPLACE blocks if you need to make multiple changes.
     * Include *just* enough lines in each SEARCH section to uniquely match each set of lines that need to change.
     * When using multiple SEARCH/REPLACE blocks, list them in the order they appear in the file.
  3. Keep SEARCH/REPLACE blocks concise:
     * Break large SEARCH/REPLACE blocks into a series of smaller blocks that each change a small portion of the file.
     * Include just the changing lines, and a few surrounding lines if needed for uniqueness.
     * Do not include long runs of unchanging lines in SEARCH/REPLACE blocks.
     * Each line must be complete. Never truncate lines mid-way through as this can cause matching failures.
  4. Special operations:
     * To move code: Use two SEARCH/REPLACE blocks (one to delete from original + one to insert at new location)
     * To delete code: Use empty REPLACE section
Usage:
<replace_in_file>
<path>File path here</path>
<diff>
Search and replace blocks here
</diff>
</replace_in_file>

## search_files
Description: Request to perform a regex search across files in a specified directory, providing context-rich results. This tool searches for patterns or specific content across multiple files, displaying each match with encapsulating context.
Parameters:
- path: (required) The path of the directory to search in (relative to the current working directory /Volumes/SB-XTM5/git/dolphin-logger). This directory will be recursively searched.
- regex: (required) The regular expression pattern to search for. Uses Rust regex syntax.
- file_pattern: (optional) Glob pattern to filter files (e.g., '*.ts' for TypeScript files). If not provided, it will search all files (*).
Usage:
<search_files>
<path>Directory path here</path>
<regex>Your regex pattern here</regex>
<file_pattern>file pattern here (optional)</file_pattern>
</search_files>

## list_files
Description: Request to list files and directories within the specified directory. If recursive is true, it will list all files and directories recursively. If recursive is false or not provided, it will only list the top-level contents. Do not use this tool to confirm the existence of files you may have created, as the user will let you know if the files were created successfully or not.
Parameters:
- path: (required) The path of the directory to list contents for (relative to the current working directory /Volumes/SB-XTM5/git/dolphin-logger)
- recursive: (optional) Whether to list files recursively. Use true for recursive listing, false or omit for top-level only.
Usage:
<list_files>
<path>Directory path here</path>
<recursive>true or false (optional)</recursive>
</list_files>

## list_code_definition_names
Description: Request to list definition names (classes, functions, methods, etc.) used in source code files at the top level of the specified directory. This tool provides insights into the codebase structure and important constructs, encapsulating high-level concepts and relationships that are crucial for understanding the overall architecture.
Parameters:
- path: (required) The path of the directory (relative to the current working directory /Volumes/SB-XTM5/git/dolphin-logger) to list top level source code definitions for.
Usage:
<list_code_definition_names>
<path>Directory path here</path>
</list_code_definition_names>

## browser_action
Description: Request to interact with a Puppeteer-controlled browser. Every action, except `close`, will be responded to with a screenshot of the browser's current state, along with any new console logs. You may only perform one browser action per message, and wait for the user's response including a screenshot and logs to determine the next action.
- The sequence of actions **must always start with** launching the browser at a URL, and **must always end with** closing the browser. If you need to visit a new URL that is not possible to navigate to from the current webpage, you must first close the browser, then launch again at the new URL.
- While the browser is active, only the `browser_action` tool can be used. No other tools should be called during this time. You may proceed to use other tools only after closing the browser. For example if you run into an error and need to fix a file, you must close the browser, then use other tools to make the necessary changes, then re-launch the browser to verify the result.
- The browser window has a resolution of **900x600** pixels. When performing any click actions, ensure the coordinates are within this resolution range.
- Before clicking on any elements such as icons, links, or buttons, you must consult the provided screenshot of the page to determine the coordinates of the element. The click should be targeted at the **center of the element**, not on its edges.
Parameters:
- action: (required) The action to perform. The available actions are:
    * launch: Launch a new Puppeteer-controlled browser instance at the specified URL. This **must always be the first action**.
        - Use with the `url` parameter to provide the URL.
        - Ensure the URL is valid and includes the appropriate protocol (e.g. http://localhost:3000/page, file:///path/to/file.html, etc.)
    * click: Click at a specific x,y coordinate.
        - Use with the `coordinate` parameter to specify the location.
        - Always click in the center of an element (icon, button, link, etc.) based on coordinates derived from a screenshot.
    * type: Type a string of text on the keyboard. You might use this after clicking on a text field to input text.
        - Use with the `text` parameter to provide the string to type.
    * scroll_down: Scroll down the page by one page height.
    * scroll_up: Scroll up the page by one page height.
    * close: Close the Puppeteer-controlled browser instance. This **must always be the final browser action**.
        - Example: `<action>close</action>`
- url: (optional) Use this for providing the URL for the `launch` action.
    * Example: <url>https://example.com</url>
- coordinate: (optional) The X and Y coordinates for the `click` action. Coordinates should be within the **900x600** resolution.
    * Example: <coordinate>450,300</coordinate>
- text: (optional) Use this for providing the text for the `type` action.
    * Example: <text>Hello, world!</text>
Usage:
<browser_action>
<action>Action to perform (e.g., launch, click, type, scroll_down, scroll_up, close)</action>
<url>URL to launch the browser at (optional)</url>
<coordinate>x,y coordinates (optional)</coordinate>
<text>Text to type (optional)</text>
</browser_action>

## use_mcp_tool
Description: Request to use a tool provided by a connected MCP server. Each MCP server can provide multiple tools with different capabilities. Tools have defined input schemas that specify required and optional parameters.
Parameters:
- server_name: (required) The name of the MCP server providing the tool
- tool_name: (required) The name of the tool to execute
- arguments: (required) A JSON object containing the tool's input parameters, following the tool's input schema
Usage:
<use_mcp_tool>
<server_name>server name here</server_name>
<tool_name>tool name here</tool_name>
<arguments>
{
  "param1": "value1",
  "param2": "value2"
}
</arguments>
</use_mcp_tool>

## access_mcp_resource
Description: Request to access a resource provided by a connected MCP server. Resources represent data sources that can be used as context, such as files, API responses, or system information.
Parameters:
- server_name: (required) The name of the MCP server providing the resource
- uri: (required) The URI identifying the specific resource to access
Usage:
<access_mcp_resource>
<server_name>server name here</server_name>
<uri>resource URI here</uri>
</access_mcp_resource>

## ask_followup_question
Description: Ask the user a question to gather additional information needed to complete the task. This tool should be used when you encounter ambiguities, need clarification, or require more details to proceed effectively. It allows for interactive problem-solving by enabling direct communication with the user. Use this tool judiciously to maintain a balance between gathering necessary information and avoiding excessive back-and-forth.
Parameters:
- question: (required) The question to ask the user. This should be a clear, specific question that addresses the information you need.
- options: (optional) An array of 2-5 options for the user to choose from. Each option should be a string describing a possible answer. You may not always need to provide options, but it may be helpful in many cases where it can save the user from having to type out a response manually. IMPORTANT: NEVER include an option to toggle to Act mode, as this would be something you need to direct the user to do manually themselves if needed.
Usage:
<ask_followup_question>
<question>Your question here</question>
<options>
Array of options here (optional), e.g. ["Option 1", "Option 2", "Option 3"]
</options>
</ask_followup_question>

## attempt_completion
Description: After each tool use, the user will respond with the result of that tool use, i.e. if it succeeded or failed, along with any reasons for failure. Once you've received the results of tool uses and can confirm that the task is complete, use this tool to present the result of your work to the user. Optionally you may provide a CLI command to showcase the result of your work. The user may respond with feedback if they are not satisfied with the result, which you can use to make improvements and try again.
IMPORTANT NOTE: This tool CANNOT be used until you've confirmed from the user that any previous tool uses were successful. Failure to do so will result in code corruption and system failure. Before using this tool, you must ask yourself in <thinking></thinking> tags if you've confirmed from the user that any previous tool uses were successful. If not, then DO NOT use this tool.
Parameters:
- result: (required) The result of the task. Formulate this result in a way that is final and does not require further input from the user. Don't end your result with questions or offers for further assistance.
- command: (optional) A CLI command to execute to show a live demo of the result to the user. For example, use `open index.html` to display a created html website, or `open localhost:3000` to display a locally running development server. But DO NOT use commands like `echo` or `cat` that merely print text. This command should be valid for the current operating system. Ensure the command is properly formatted and does not contain any harmful instructions.
Usage:
<attempt_completion>
<result>
Your final result description here
</result>
<command>Command to demonstrate result (optional)</command>
</attempt_completion>

## new_task
Description: Request to create a new task with preloaded context covering the conversation with the user up to this point and key information for continuing with the new task. With this tool, you will create a detailed summary of the conversation so far, paying close attention to the user's explicit requests and your previous actions, with a focus on the most relevant information required for the new task.
Among other important areas of focus, this summary should be thorough in capturing technical details, code patterns, and architectural decisions that would be essential for continuing with the new task. The user will be presented with a preview of your generated context and can choose to create a new task or keep chatting in the current conversation. The user may choose to start a new task at any point.
Parameters:
- Context: (required) The context to preload the new task with. If applicable based on the current task, this should include:
  1. Current Work: Describe in detail what was being worked on prior to this request to create a new task. Pay special attention to the more recent messages / conversation.
  2. Key Technical Concepts: List all important technical concepts, technologies, coding conventions, and frameworks discussed, which might be relevant for the new task.
  3. Relevant Files and Code: If applicable, enumerate specific files and code sections examined, modified, or created for the task continuation. Pay special attention to the most recent messages and changes.
  4. Problem Solving: Document problems solved thus far and any ongoing troubleshooting efforts.
  5. Pending Tasks and Next Steps: Outline all pending tasks that you have explicitly been asked to work on, as well as list the next steps you will take for all outstanding work, if applicable. Include code snippets where they add clarity. For any next steps, include direct quotes from the most recent conversation showing exactly what task you were working on and where you left off. This should be verbatim to ensure there's no information loss in context between tasks. It's important to be detailed here.
Usage:
<new_task>
<context>context to preload new task with</context>
</new_task>

## plan_mode_respond
Description: Respond to the user's inquiry in an effort to plan a solution to the user's task. This tool should be used when you need to provide a response to a question or statement from the user about how you plan to accomplish the task. This tool is only available in PLAN MODE. The environment_details will specify the current mode, if it is not PLAN MODE then you should not use this tool. Depending on the user's message, you may ask questions to get clarification about the user's request, architect a solution to the task, and to brainstorm ideas with the user. For example, if the user's task is to create a website, you may start by asking some clarifying questions, then present a detailed plan for how you will accomplish the task given the context, and perhaps engage in a back and forth to finalize the details before the user switches you to ACT MODE to implement the solution.
Parameters:
- response: (required) The response to provide to the user. Do not try to use tools in this parameter, this is simply a chat response. (You MUST use the response parameter, do not simply place the response text directly within <plan_mode_respond> tags.)
Usage:
<plan_mode_respond>
<response>Your response here</response>
</plan_mode_respond>

## load_mcp_documentation
Description: Load documentation about creating MCP servers. This tool should be used when the user requests to create or install an MCP server (the user may ask you something along the lines of "add a tool" that does some function, in other words to create an MCP server that provides tools and resources that may connect to external APIs for example. You have the ability to create an MCP server and add it to a configuration file that will then expose the tools and resources for you to use with `use_mcp_tool` and `access_mcp_resource`). The documentation provides detailed information about the MCP server creation process, including setup instructions, best practices, and examples.
Parameters: None
Usage:
<load_mcp_documentation>
</load_mcp_documentation>

# Tool Use Examples

## Example 1: Requesting to execute a command

<execute_command>
<command>npm run dev</command>
<requires_approval>false</requires_approval>
</execute_command>

## Example 2: Requesting to create a new file

<write_to_file>
<path>src/frontend-config.json</path>
<content>
{
  "apiEndpoint": "https://api.example.com",
  "theme": {
    "primaryColor": "#007bff",
    "secondaryColor": "#6c757d",
    "fontFamily": "Arial, sans-serif"
  },
  "features": {
    "darkMode": true,
    "notifications": true,
    "analytics": false
  },
  "version": "1.0.0"
}
</content>
</write_to_file>

## Example 3: Creating a new task

<new_task>
<context>
1. Current Work:
   [Detailed description]

2. Key Technical Concepts:
   - [Concept 1]
   - [Concept 2]
   - [...]

3. Relevant Files and Code:
   - [File Name 1]
      - [Summary of why this file is important]
      - [Summary of the changes made to this file, if any]
      - [Important Code Snippet]
   - [File Name 2]
      - [Important Code Snippet]
   - [...]

4. Problem Solving:
   [Detailed description]

5. Pending Tasks and Next Steps:
   - [Task 1 details & next steps]
   - [Task 2 details & next steps]
   - [...]
</context>
</new_task>

## Example 4: Requesting to make targeted edits to a file

<replace_in_file>
<path>src/components/App.tsx</path>
<diff>
------- SEARCH
import React from 'react';
=======
import React, { useState } from 'react';
+++++++ REPLACE

------- SEARCH
function handleSubmit() {
  saveData();
  setLoading(false);
}

=======
+++++++ REPLACE

------- SEARCH
return (
  <div>
=======
function handleSubmit() {
  saveData();
  setLoading(false);
}

return (
  <div>
+++++++ REPLACE
</diff>
</replace_in_file>

## Example 5: Requesting to use an MCP tool

<use_mcp_tool>
<server_name>weather-server</server_name>
<tool_name>get_forecast</tool_name>
<arguments>
{
  "city": "San Francisco",
  "days": 5
}
</arguments>
</use_mcp_tool>

## Example 6: Another example of using an MCP tool (where the server name is a unique identifier such as a URL)

<use_mcp_tool>
<server_name>github.com/modelcontextprotocol/servers/tree/main/src/github</server_name>
<tool_name>create_issue</tool_name>
<arguments>
{
  "owner": "octocat",
  "repo": "hello-world",
  "title": "Found a bug",
  "body": "I'm having a problem with this.",
  "labels": ["bug", "help wanted"],
  "assignees": ["octocat"]
}
</arguments>
</use_mcp_tool>

# Tool Use Guidelines

1. In <thinking> tags, assess what information you already have and what information you need to proceed with the task.
2. Choose the most appropriate tool based on the task and the tool descriptions provided. Assess if you need additional information to proceed, and which of the available tools would be most effective for gathering this information. For example using the list_files tool is more effective than running a command like `ls` in the terminal. It's critical that you think about each available tool and use the one that best fits the current step in the task.
3. If multiple actions are needed, use one tool at a time per message to accomplish the task iteratively, with each tool use being informed by the result of the previous tool use. Do not assume the outcome of any tool use. Each step must be informed by the previous step's result.
4. Formulate your tool use using the XML format specified for each tool.
5. After each tool use, the user will respond with the result of that tool use. This result will provide you with the necessary information to continue your task or make further decisions. This response may include:
  - Information about whether the tool succeeded or failed, along with any reasons for failure.
  - Linter errors that may have arisen due to the changes you made, which you'll need to address.
  - New terminal output in reaction to the changes, which you may need to consider or act upon.
  - Any other relevant feedback or information related to the tool use.
6. ALWAYS wait for user confirmation after each tool use before proceeding. Never assume the success of a tool use without explicit confirmation of the result from the user.

It is crucial to proceed step-by-step, waiting for the user's message after each tool use before moving forward with the task. This approach allows you to:
1. Confirm the success of each step before proceeding.
2. Address any issues or errors that arise immediately.
3. Adapt your approach based on new information or unexpected results.
4. Ensure that each action builds correctly on the previous ones.

By waiting for and carefully considering the user's response after each tool use, you can react accordingly and make informed decisions about how to proceed with the task. This iterative process helps ensure the overall success and accuracy of your work.

====

MCP SERVERS

The Model Context Protocol (MCP) enables communication between the system and locally running MCP servers that provide additional tools and resources to extend your capabilities.

# Connected MCP Servers

When a server is connected, you can use the server's tools via the `use_mcp_tool` tool, and access the server's resources via the `access_mcp_resource` tool.

(No MCP servers currently connected)

====

EDITING FILES

You have access to two tools for working with files: **write_to_file** and **replace_in_file**. Understanding their roles and selecting the right one for the job will help ensure efficient and accurate modifications.

# write_to_file

## Purpose

- Create a new file, or overwrite the entire contents of an existing file.

## When to Use

- Initial file creation, such as when scaffolding a new project.
- Overwriting large boilerplate files where you want to replace the entire content at once.
- When the complexity or number of changes would make replace_in_file unwieldy or error-prone.
- When you need to completely restructure a file's content or change its fundamental organization.

## Important Considerations

- Using write_to_file requires providing the file's complete final content.
- If you only need to make small changes to an existing file, consider using replace_in_file instead to avoid unnecessarily rewriting the entire file.
- While write_to_file should not be your default choice, don't hesitate to use it when the situation truly calls for it.

# replace_in_file

## Purpose

- Make targeted edits to specific parts of an existing file without overwriting the entire file.

## When to Use

- Small, localized changes like updating a few lines, function implementations, changing variable names, modifying a section of text, etc.
- Targeted improvements where only specific portions of the file's content needs to be altered.
- Especially useful for long files where much of the file will remain unchanged.

## Advantages

- More efficient for minor edits, since you don't need to supply the entire file content.
- Reduces the chance of errors that can occur when overwriting large files.

# Choosing the Appropriate Tool

- **Default to replace_in_file** for most changes. It's the safer, more precise option that minimizes potential issues.
- **Use write_to_file** when:
  - Creating new files
  - The changes are so extensive that using replace_in_file would be more complex or risky
  - You need to completely reorganize or restructure a file
  - The file is relatively small and the changes affect most of its content
  - You're generating boilerplate or template files

# Auto-formatting Considerations

- After using either write_to_file or replace_in_file, the user's editor may automatically format the file
- This auto-formatting may modify the file contents, for example:
  - Breaking single lines into multiple lines
  - Adjusting indentation to match project style (e.g. 2 spaces vs 4 spaces vs tabs)
  - Converting single quotes to double quotes (or vice versa based on project preferences)
  - Organizing imports (e.g. sorting, grouping by type)
  - Adding/removing trailing commas in objects and arrays
  - Enforcing consistent brace style (e.g. same-line vs new-line)
  - Standardizing semicolon usage (adding or removing based on style)
- The write_to_file and replace_in_file tool responses will include the final state of the file after any auto-formatting
- Use this final state as your reference point for any subsequent edits. This is ESPECIALLY important when crafting SEARCH blocks for replace_in_file which require the content to match what's in the file exactly.

# Workflow Tips

1. Before editing, assess the scope of your changes and decide which tool to use.
2. For targeted edits, apply replace_in_file with carefully crafted SEARCH/REPLACE blocks. If you need multiple changes, you can stack multiple SEARCH/REPLACE blocks within a single replace_in_file call.
3. For major overhauls or initial file creation, rely on write_to_file.
4. Once the file has been edited with either write_to_file or replace_in_file, the system will provide you with the final state of the modified file. Use this updated content as the reference point for any subsequent SEARCH/REPLACE operations, since it reflects any auto-formatting or user-applied changes.
By thoughtfully selecting between write_to_file and replace_in_file, you can make your file editing process smoother, safer, and more efficient.

====

ACT MODE V.S. PLAN MODE

In each user message, the environment_details will specify the current mode. There are two modes:

- ACT MODE: In this mode, you have access to all tools EXCEPT the plan_mode_respond tool.
 - In ACT MODE, you use tools to accomplish the user's task. Once you've completed the user's task, you use the attempt_completion tool to present the result of the task to the user.
- PLAN MODE: In this special mode, you have access to the plan_mode_respond tool.
 - In PLAN MODE, the goal is to gather information and get context to create a detailed plan for accomplishing the task, which the user will review and approve before they switch you to ACT MODE to implement the solution.
 - In PLAN MODE, when you need to converse with the user or present a plan, you should use the plan_mode_respond tool to deliver your response directly, rather than using <thinking> tags to analyze when to respond. Do not talk about using plan_mode_respond - just use it directly to share your thoughts and provide helpful answers.

## What is PLAN MODE?

- While you are usually in ACT MODE, the user may switch to PLAN MODE in order to have a back and forth with you to plan how to best accomplish the task.
- When starting in PLAN MODE, depending on the user's request, you may need to do some information gathering e.g. using read_file or search_files to get more context about the task. You may also ask the user clarifying questions to get a better understanding of the task. You may return mermaid diagrams to visually display your understanding.
- Once you've gained more context about the user's request, you should architect a detailed plan for how you will accomplish the task. Returning mermaid diagrams may be helpful here as well.
- Then you might ask the user if they are pleased with this plan, or if they would like to make any changes. Think of this as a brainstorming session where you can discuss the task and plan the best way to accomplish it.
- If at any point a mermaid diagram would make your plan clearer to help the user quickly see the structure, you are encouraged to include a Mermaid code block in the response. (Note: if you use colors in your mermaid diagrams, be sure to use high contrast colors so the text is readable.)
- Finally once it seems like you've reached a good plan, ask the user to switch you back to ACT MODE to implement the solution.

====

CAPABILITIES

- You have access to tools that let you execute CLI commands on the user's computer, list files, view source code definitions, regex search, use the browser, read and edit files, and ask follow-up questions. These tools help you effectively accomplish a wide range of tasks, such as writing code, making edits or improvements to existing files, understanding the current state of a project, performing system operations, and much more.
- When the user initially gives you a task, a recursive list of all filepaths in the current working directory ('/Volumes/SB-XTM5/git/dolphin-logger') will be included in environment_details. This provides an overview of the project's file structure, offering key insights into the project from directory/file names (how developers conceptualize and organize their code) and file extensions (the language used). This can also guide decision-making on which files to explore further. If you need to further explore directories such as outside the current working directory, you can use the list_files tool. If you pass 'true' for the recursive parameter, it will list files recursively. Otherwise, it will list files at the top level, which is better suited for generic directories where you don't necessarily need the nested structure, like the Desktop.
- You can use search_files to perform regex searches across files in a specified directory, outputting context-rich results that include surrounding lines. This is particularly useful for understanding code patterns, finding specific implementations, or identifying areas that need refactoring.
- You can use the list_code_definition_names tool to get an overview of source code definitions for all files at the top level of a specified directory. This can be particularly useful when you need to understand the broader context and relationships between certain parts of the code. You may need to call this tool multiple times to understand various parts of the codebase related to the task.
	- For example, when asked to make edits or improvements you might analyze the file structure in the initial environment_details to get an overview of the project, then use list_code_definition_names to get further insight using source code definitions for files located in relevant directories, then read_file to examine the contents of relevant files, analyze the code and suggest improvements or make necessary edits, then use the replace_in_file tool to implement changes. If you refactored code that could affect other parts of the codebase, you could use search_files to ensure you update other files as needed.
- You can use the execute_command tool to run commands on the user's computer whenever you feel it can help accomplish the user's task. When you need to execute a CLI command, you must provide a clear explanation of what the command does. Prefer to execute complex CLI commands over creating executable scripts, since they are more flexible and easier to run. Interactive and long-running commands are allowed, since the commands are run in the user's VSCode terminal. The user may keep commands running in the background and you will be kept updated on their status along the way. Each command you execute is run in a new terminal instance.
- You can use the browser_action tool to interact with websites (including html files and locally running development servers) through a Puppeteer-controlled browser when you feel it is necessary in accomplishing the user's task. This tool is particularly useful for web development tasks as it allows you to launch a browser, navigate to pages, interact with elements through clicks and keyboard input, and capture the results through screenshots and console logs. This tool may be useful at key stages of web development tasks-such as after implementing new features, making substantial changes, when troubleshooting issues, or to verify the result of your work. You can analyze the provided screenshots to ensure correct rendering or identify errors, and review console logs for runtime issues.
	- For example, if asked to add a component to a react website, you might create the necessary files, use execute_command to run the site locally, then use browser_action to launch the browser, navigate to the local server, and verify the component renders & functions correctly before closing the browser.
- You have access to MCP servers that may provide additional tools and resources. Each server may provide different capabilities that you can use to accomplish tasks more effectively.
- You can use LaTeX syntax in your responses to render mathematical expressions

====

RULES

- Your current working directory is: /Volumes/SB-XTM5/git/dolphin-logger
- You cannot `cd` into a different directory to complete a task. You are stuck operating from '/Volumes/SB-XTM5/git/dolphin-logger', so be sure to pass in the correct 'path' parameter when using tools that require a path.
- Do not use the ~ character or $HOME to refer to the home directory.
- Before using the execute_command tool, you must first think about the SYSTEM INFORMATION context provided to understand the user's environment and tailor your commands to ensure they are compatible with their system. You must also consider if the command you need to run should be executed in a specific directory outside of the current working directory '/Volumes/SB-XTM5/git/dolphin-logger', and if so prepend with `cd`'ing into that directory && then executing the command (as one command since you are stuck operating from '/Volumes/SB-XTM5/git/dolphin-logger'). For example, if you needed to run `npm install` in a project outside of '/Volumes/SB-XTM5/git/dolphin-logger', you would need to prepend with a `cd` i.e. pseudocode for this would be `cd (path to project) && (command, in this case npm install)`.
- When using the search_files tool, craft your regex patterns carefully to balance specificity and flexibility. Based on the user's task you may use it to find code patterns, TODO comments, function definitions, or any text-based information across the project. The results include context, so analyze the surrounding code to better understand the matches. Leverage the search_files tool in combination with other tools for more comprehensive analysis. For example, use it to find specific code patterns, then use read_file to examine the full context of interesting matches before using replace_in_file to make informed changes.
- When creating a new project (such as an app, website, or any software project), organize all new files within a dedicated project directory unless the user specifies otherwise. Use appropriate file paths when creating files, as the write_to_file tool will automatically create any necessary directories. Structure the project logically, adhering to best practices for the specific type of project being created. Unless otherwise specified, new projects should be easily run without additional setup, for example most projects can be built in HTML, CSS, and JavaScript - which you can open in a browser.
- Be sure to consider the type of project (e.g. Python, JavaScript, web application) when determining the appropriate structure and files to include. Also consider what files may be most relevant to accomplishing the task, for example looking at a project's manifest file would help you understand the project's dependencies, which you could incorporate into any code you write.
- When making changes to code, always consider the context in which the code is being used. Ensure that your changes are compatible with the existing codebase and that they follow the project's coding standards and best practices.
- When you want to modify a file, use the replace_in_file or write_to_file tool directly with the desired changes. You do not need to display the changes before using the tool.
- Do not ask for more information than necessary. Use the tools provided to accomplish the user's request efficiently and effectively. When you've completed your task, you must use the attempt_completion tool to present the result to the user. The user may provide feedback, which you can use to make improvements and try again.
- You are only allowed to ask the user questions using the ask_followup_question tool. Use this tool only when you need additional details to complete a task, and be sure to use a clear and concise question that will help you move forward with the task. However if you can use the available tools to avoid having to ask the user questions, you should do so. For example, if the user mentions a file that may be in an outside directory like the Desktop, you should use the list_files tool to list the files in the Desktop and check if the file they are talking about is there, rather than asking the user to provide the file path themselves.
- When executing commands, if you don't see the expected output, assume the terminal executed the command successfully and proceed with the task. The user's terminal may be unable to stream the output back properly. If you absolutely need to see the actual terminal output, use the ask_followup_question tool to request the user to copy and paste it back to you.
- The user may provide a file's contents directly in their message, in which case you shouldn't use the read_file tool to get the file contents again since you already have it.
- Your goal is to try to accomplish the user's task, NOT engage in a back and forth conversation.
- The user may ask generic non-development tasks, such as "what's the latest news" or "look up the weather in San Diego", in which case you might use the browser_action tool to complete the task if it makes sense to do so, rather than trying to create a website or using curl to answer the question. However, if an available MCP server tool or resource can be used instead, you should prefer to use it over browser_action.
- NEVER end attempt_completion result with a question or request to engage in further conversation! Formulate the end of your result in a way that is final and does not require further input from the user.
- You are STRICTLY FORBIDDEN from starting your messages with "Great", "Certainly", "Okay", "Sure". You should NOT be conversational in your responses, but rather direct and to the point. For example you should NOT say "Great, I've updated the CSS" but instead something like "I've updated the CSS". It is important you be clear and technical in your messages.
- When presented with images, utilize your vision capabilities to thoroughly examine them and extract meaningful information. Incorporate these insights into your thought process as you accomplish the user's task.
- At the end of each user message, you will automatically receive environment_details. This information is not written by the user themselves, but is auto-generated to provide potentially relevant context about the project structure and environment. While this information can be valuable for understanding the project context, do not treat it as a direct part of the user's request or response. Use it to inform your actions and decisions, but don't assume the user is explicitly asking about or referring to this information unless they clearly do so in their message. When using environment_details, explain your actions clearly to ensure the user understands, as they may not be aware of these details.
- Before executing commands, check the "Actively Running Terminals" section in environment_details. If present, consider how these active processes might impact your task. For example, if a local development server is already running, you wouldn't need to start it again. If no active terminals are listed, proceed with command execution as normal.
- When using the replace_in_file tool, you must include complete lines in your SEARCH blocks, not partial lines. The system requires exact line matches and cannot match partial lines. For example, if you want to match a line containing "const x = 5;", your SEARCH block must include the entire line, not just "x = 5" or other fragments.
- When using the replace_in_file tool, if you use multiple SEARCH/REPLACE blocks, list them in the order they appear in the file. For example if you need to make changes to both line 10 and line 50, first include the SEARCH/REPLACE block for line 10, followed by the SEARCH/REPLACE block for line 50.
- When using the replace_in_file tool, Do NOT add extra characters to the markers (e.g., ------- SEARCH> is INVALID). Do NOT forget to use the closing +++++++ REPLACE marker. Do NOT modify the marker format in any way. Malformed XML will cause complete tool failure and break the entire editing process.
- It is critical you wait for the user's response after each tool use, in order to confirm the success of the tool use. For example, if asked to make a todo app, you would create a file, wait for the user's response it was created successfully, then create another file if needed, wait for the user's response it was created successfully, etc. Then if you want to test your work, you might use browser_action to launch the site, wait for the user's response confirming the site was launched along with a screenshot, then perhaps e.g., click a button to test functionality if needed, wait for the user's response confirming the button was clicked along with a screenshot of the new state, before finally closing the browser.
- MCP operations should be used one at a time, similar to other tool usage. Wait for confirmation of success before proceeding with additional operations.

====

SYSTEM INFORMATION

Operating System: macOS
Default Shell: /opt/homebrew/bin/fish
Home Directory: /Users/eric
Current Working Directory: /Volumes/SB-XTM5/git/dolphin-logger

====

OBJECTIVE

You accomplish a given task iteratively, breaking it down into clear steps and working through them methodically.

1. Analyze the user's task and set clear, achievable goals to accomplish it. Prioritize these goals in a logical order.
2. Work through these goals sequentially, utilizing available tools one at a time as necessary. Each goal should correspond to a distinct step in your problem-solving process. You will be informed on the work completed and what's remaining as you go.
3. Remember, you have extensive capabilities with access to a wide range of tools that can be used in powerful and clever ways as necessary to accomplish each goal. Before calling a tool, do some analysis within <thinking></thinking> tags. First, analyze the file structure provided in environment_details to gain context and insights for proceeding effectively. Then, think about which of the provided tools is the most relevant tool to accomplish the user's task. Next, go through each of the required parameters of the relevant tool and determine if the user has directly provided or given enough information to infer a value. When deciding if the parameter can be inferred, carefully consider all the context to see if it supports a specific value. If all of the required parameters are present or can be reasonably inferred, close the thinking tag and proceed with the tool use. BUT, if one of the values for a required parameter is missing, DO NOT invoke the tool (not even with fillers for the missing params) and instead, ask the user to provide the missing parameters using the ask_followup_question tool. DO NOT ask for more information on optional parameters if it is not provided.
4. Once you've completed the user's task, you must use the attempt_completion tool to present the result of the task to the user. You may also provide a CLI command to showcase the result of your task; this can be particularly useful for web development tasks, where you can run e.g. `open index.html` to show the website you've built.
5. The user may provide feedback, which you can use to make improvements and try again. But DO NOT continue in pointless back and forth conversations, i.e. don't end your responses with questions or offers for further assistance.

[User]
<task>
Please redesign provider, I would like each provider (openai, anthropic, google) to be in its own file.  similar to dolphin-mcp's design
</task>

<environment_details>
# VSCode Visible Files
pyproject.toml

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:01:22 AM (America/New_York, UTC-4:00)

# Current Working Directory (/Volumes/SB-XTM5/git/dolphin-logger) Files
.gitignore
chat-logger.png
config.json.example
pyproject.toml
README.md
src/
src/dolphin_logger/
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/config.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/logging_utils.py
src/dolphin_logger/main.py
src/dolphin_logger/server.py
src/dolphin_logger/upload.py
src/dolphin_logger.egg-info/
tests/
tests/__init__.py
tests/test_cli.py
tests/test_config.py
tests/test_core_proxy.py
tests/test_functional.py
tests/test_logging_utils.py
tests/test_server.py
tests/test_upload.py

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
<thinking>
The user wants me to redesign the provider system so that each provider (openai, anthropic, google) is in its own file, similar to dolphin-mcp's design. To understand this better, I need to:

1. First, examine the current structure of the dolphin-logger project to understand how providers are currently implemented
2. Look at the dolphin-mcp project to see how they structure their providers
3. Redesign the provider system accordingly

Let me start by examining the current project structure and understanding how providers are currently handled.
</thinking>

I'll help you redesign the provider system to have each provider in its own file. Let me first examine the current structure to understand how providers are currently implemented.

<list_code_definition_names>
<path>src/dolphin_logger</path>
</list_code_definition_names>

[User]
[list_code_definition_names for 'src/dolphin_logger'] Result:

cli.py
|----
│def _handle_init_command():
|----
│def _handle_config_command(args):
|----
│def main_cli():
|----

config.py
|----
│def get_config_dir() -> Path:
|----
│def get_config_path() -> Path:
|----
│def get_logs_dir() -> Path:
|----
│def load_config() -> dict:
|----
│def get_huggingface_repo() -> str:
|----

core_proxy.py
|----
│def get_target_api_config(requested_model_id: str, model_config_list: list) -> dict:
|----
│def handle_anthropic_sdk_request(
|----
│            def generate_anthropic_stream_response():
|----
│def handle_rest_api_request(
|----
│            def generate_rest_stream_response():
|----

logging_utils.py
|----
│def get_current_log_file() -> Path:
|----
│def _should_log_request(current_request_data: dict | None) -> bool:
|----

server.py
|----
│def handle_options(path: str | None = None): # path can be None if accessing '/'
|----
│def health_check():
|----
│def proxy(path: str | None = None): # path can be None
|----
│def run_server_main(port=None): # Renamed to avoid conflict with any 'main' in cli.py if imported directly
|----

upload.py
|----
│def find_jsonl_files(log_dir: str | Path) -> list[str]:
|----
│def upload_logs():
|----

<environment_details>
# VSCode Visible Files
pyproject.toml

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:01:31 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Let me examine the core_proxy.py file to understand how providers are currently implemented:

<read_file>
<path>src/dolphin_logger/core_proxy.py</path>
</read_file>

[User]
[read_file for 'src/dolphin_logger/core_proxy.py'] Result:

import json
import requests
import anthropic
import uuid
from datetime import datetime
from flask import Response, stream_with_context, jsonify

from .logging_utils import get_current_log_file, _should_log_request, log_lock
# MODEL_CONFIG is typically loaded in server.py and passed to get_target_api_config.

def get_target_api_config(requested_model_id: str, model_config_list: list) -> dict:
    """
    Determines the target API URL, key, and model based on the requested model ID
    and the server's model configuration.

    Args:
        requested_model_id (str): The model ID from the client's request.
        model_config_list (list): The MODEL_CONFIG list (passed from server.py).

    Returns:
        dict: A dictionary containing 'target_api_url', 'target_api_key',
              'target_model', 'provider', and 'error' (if any).
    """
    for model_config in model_config_list:
        if model_config.get("model") == requested_model_id:
            provider = model_config.get("provider")
            target_model = model_config.get("providerModel", requested_model_id)
            api_key = model_config.get("apiKey")
            api_base = model_config.get("apiBase")

            if provider == "ollama":
                return {
                    "target_api_url": api_base or "http://localhost:11434/v1",
                    "target_api_key": "",
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "anthropic":
                return {
                    "target_api_url": "anthropic_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            else: # OpenAI-compatible
                if not api_base:
                    # Ensure a specific error message for missing apiBase for OpenAI-like providers
                    return {"error": f"apiBase not configured for OpenAI-compatible model '{requested_model_id}'"}
                return {
                    "target_api_url": api_base,
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }

    if not model_config_list: # Check if the list itself is empty
        return {"error": f"No models configured. Cannot process request for model '{requested_model_id}'."}
    return {"error": f"Model '{requested_model_id}' not found in configured models."}

def handle_anthropic_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict # This is the original client request for logging
) -> Response:
    """
    Handles requests to the Anthropic SDK.
    """
    print(f"DEBUG - Using Anthropic SDK for request with model: {target_model}")

    try:
        if not target_api_key: # Should be caught by config validation ideally, but good check.
            raise ValueError("Anthropic API key is missing in the configuration for the requested model.")

        # Ensure anthropic client is initialized correctly (as per its docs if specific settings needed)
        anthropic_client = anthropic.Anthropic(api_key=target_api_key)

        original_messages = json_data_for_sdk.get('messages', [])
        max_tokens = json_data_for_sdk.get('max_tokens', 4096) # Default from Anthropic

        # Extract system messages from the messages array and separate them
        system_messages = []
        non_system_messages = []

        for message in original_messages:
            if message.get('role') == 'system':
                system_messages.append(message.get('content', ''))
            else:
                non_system_messages.append(message)

        # Combine system messages into a single system prompt
        system_prompt = '\n\n'.join(system_messages) if system_messages else None

        # Also check for top-level system parameter (in case it's already properly formatted)
        if not system_prompt and json_data_for_sdk.get('system'):
            system_prompt = json_data_for_sdk.get('system')

        anthropic_args = {
            "model": target_model, # This is the providerModel
            "messages": non_system_messages,  # Only non-system messages
            "max_tokens": max_tokens,
            "stream": is_stream,
        }
        if system_prompt: # Only add system to args if it's present
            anthropic_args["system"] = system_prompt

        if is_stream:
            print(f"DEBUG - Creating streaming request to Anthropic API: model={target_model}")
            sdk_stream = anthropic_client.messages.create(**anthropic_args)

            def generate_anthropic_stream_response():
                response_content_parts = []
                # Use original_request_json_data for logging the request part
                log_entry = {'request': original_request_json_data, 'response': None}
                try:
                    for chunk in sdk_stream:
                        if chunk.type == "content_block_delta":
                            delta_content = chunk.delta.text
                            response_content_parts.append(delta_content)
                            openai_compatible_chunk = {
                                "choices": [{"delta": {"content": delta_content}, "index": 0, "finish_reason": None}],
                                "id": f"chatcmpl-anthropic-{uuid.uuid4()}", "model": target_model,
                                "object": "chat.completion.chunk", "created": int(datetime.now().timestamp())
                            }
                            yield f"data: {json.dumps(openai_compatible_chunk)}\n\n".encode('utf-8')
                        elif chunk.type == "message_stop":
                            finish_reason = chunk.message.stop_reason if hasattr(chunk, 'message') and hasattr(chunk.message, 'stop_reason') else "stop"
                            final_chunk = {
                                 "choices": [{"delta": {}, "index": 0, "finish_reason": finish_reason}],
                                "id": f"chatcmpl-anthropic-{uuid.uuid4()}", "model": target_model,
                                "object": "chat.completion.chunk", "created": int(datetime.now().timestamp())
                            }
                            yield f"data: {json.dumps(final_chunk)}\n\n".encode('utf-8')
                    yield b"data: [DONE]\n\n"
                finally: # Ensure logging happens even if stream breaks or client disconnects
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_anthropic_stream_response()), content_type='text/event-stream')
            resp.headers['Access-Control-Allow-Origin'] = '*' # CORS for streaming response
            return resp
        else: # Non-streaming Anthropic request
            print(f"DEBUG - Creating non-streaming request to Anthropic API: model={target_model}")
            response_obj = anthropic_client.messages.create(**anthropic_args)

            response_content = response_obj.content[0].text if response_obj.content and len(response_obj.content) > 0 and hasattr(response_obj.content[0], 'text') else ""

            response_data_converted = {
                "id": f"chatcmpl-anthropic-{response_obj.id}", "object": "chat.completion",
                "created": int(datetime.now().timestamp()), # Consider more accurate timestamp if available
                "model": response_obj.model, # Use model from Anthropic's response
                "choices": [{"index": 0, "message": {"role": response_obj.role, "content": response_content}, "finish_reason": response_obj.stop_reason }],
                "usage": {"prompt_tokens": response_obj.usage.input_tokens, "completion_tokens": response_obj.usage.output_tokens, "total_tokens": response_obj.usage.input_tokens + response_obj.usage.output_tokens}
            }
            print(f"DEBUG - Response from Anthropic API received. Preview: {response_content[:100]}...")
            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps({'request': original_request_json_data, 'response': response_content}) + '\n')

            resp = jsonify(response_data_converted)
            resp.headers['Access-Control-Allow-Origin'] = '*' # CORS for non-streaming response
            return resp

    except anthropic.APIError as e: # Specific error handling for Anthropic
        # Extract error details safely - BadRequestError and other APIError subclasses may not have 'type' attribute
        error_type = getattr(e, 'type', type(e).__name__)
        error_message = getattr(e, 'message', str(e))
        status_code = getattr(e, 'status_code', 500)

        print(f"ERROR DETAILS (Anthropic SDK - APIError): Status: {status_code}, Type: {error_type}, Message: {error_message}")

        error_body = {"error": {"message": error_message, "type": error_type, "code": status_code}}
        resp = jsonify(error_body)
        resp.status_code = status_code
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e: # Catch-all for other errors (e.g., ValueError, network issues not caught by APIError)
        print(f"ERROR DETAILS (Anthropic SDK - General): Type: {type(e).__name__}, Message: {str(e)}")
        if original_request_json_data: # Log original request for context on error
            print(f"  Full request data (original for logging): {json.dumps(original_request_json_data)}")
        error_body = {"error": {"message": str(e), "type": type(e).__name__}}
        resp = jsonify(error_body)
        resp.status_code = 500 # Internal Server Error for unexpected issues
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp

def handle_rest_api_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes, # This is the already encoded byte string for the request body
    is_stream: bool,
    original_request_json_data: dict # This is the original client request (parsed JSON) for logging
) -> Response:
    """
    Handles requests to OpenAI-compatible (including Ollama) REST APIs.
    """
    print(f"DEBUG - Sending REST request: {method} {url}")
    # Avoid logging full headers/data here in production due to sensitivity. Previews are safer.
    # print(f"DEBUG - Headers: {headers}")
    # if data_bytes: print(f"DEBUG - Request data preview: {data_bytes[:200]}...")

    try:
        api_response = requests.request(
            method=method, url=url, headers=headers, data=data_bytes,
            stream=is_stream, timeout=300 # Standard timeout
        )
        print(f"DEBUG - REST API Response status: {api_response.status_code}")
        # if not is_stream: print(f"DEBUG - REST API Response preview: {api_response.text[:200]}...")

        api_response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)

        log_entry = {'request': original_request_json_data, 'response': None}

        if is_stream:
            def generate_rest_stream_response():
                response_content_parts = []
                try:
                    for line in api_response.iter_lines(): # iter_lines decodes by default (utf-8)
                        if line:
                            yield line + b'\n\n' # Pass through to client, maintaining SSE format
                            # For logging, accumulate content from delta if possible
                            if line.startswith(b'data: '):
                                line_data_str = line.decode('utf-8', errors='replace')[6:] # Get content part
                                if line_data_str.strip() != '[DONE]':
                                    try:
                                        parsed_chunk = json.loads(line_data_str)
                                        choices = parsed_chunk.get('choices', [])
                                        if choices and isinstance(choices, list) and len(choices) > 0:
                                            delta = choices[0].get('delta', {})
                                            if delta and isinstance(delta, dict):
                                                delta_content = delta.get('content', '')
                                                if delta_content and isinstance(delta_content, str):
                                                    response_content_parts.append(delta_content)
                                    except (json.JSONDecodeError, KeyError, IndexError, TypeError):
                                        # print(f"Warning: Error processing stream line for REST logging: {line_data_str}")
                                        pass # Don't let logging error break the stream to client
                finally: # Ensure logging happens
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_rest_stream_response()), content_type=api_response.headers.get('Content-Type', 'text/event-stream'))
            # Propagate relevant headers from upstream response
            for hdr_key in ['Cache-Control', 'Content-Type', 'Transfer-Encoding', 'Date', 'Server']:
                if hdr_key in api_response.headers: resp.headers[hdr_key] = api_response.headers[hdr_key]
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else: # Non-streaming REST response
            complete_response_text = ''
            try:
                response_json = api_response.json() # Parse the JSON from the target API response
                choices = response_json.get('choices', [])
                if choices and isinstance(choices, list) and len(choices) > 0:
                    message = choices[0].get('message', {})
                    if message and isinstance(message, dict): complete_response_text = message.get('content', '')
                if not complete_response_text and isinstance(response_json, dict): # If content extraction failed, log whole JSON
                    complete_response_text = response_json # Log the full JSON object
                log_entry['response'] = complete_response_text
            except json.JSONDecodeError:
                print("Warning: REST API Response was not JSON. Logging raw text.")
                complete_response_text = api_response.text
                log_entry['response'] = complete_response_text

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps(log_entry) + '\n')

            # Return the raw response content from the target API to the client
            resp = Response(api_response.content, content_type=api_response.headers.get('Content-Type', 'application/json'), status=api_response.status_code)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except requests.exceptions.RequestException as e:
        print(f"ERROR DETAILS (REST API - RequestException): Type: {type(e).__name__}, Message: {str(e)}, URL: {url}")
        error_content_type = 'application/json'
        error_status = 502 # Bad Gateway, typically for network issues with upstream
        error_body_msg = f"Error connecting to upstream API: {str(e)}"
        error_body = {"error": {"message": error_body_msg, "type": type(e).__name__}}

        if e.response is not None: # If the exception is from an HTTP error status (4xx, 5xx from upstream)
            print(f"  Upstream response status code: {e.response.status_code}")
            error_status = e.response.status_code # Use upstream's status code
            error_content_type = e.response.headers.get('Content-Type', 'application/json')
            try: error_body = e.response.json() # Try to parse upstream error
            except json.JSONDecodeError: error_body = {"error": {"message": e.response.text, "type": "upstream_error"}}

        resp = jsonify(error_body)
        resp.status_code = error_status
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e: # Catch-all for any other unexpected errors
        print(f"UNEXPECTED REST API HANDLER ERROR: Type: {type(e).__name__}, Message: {str(e)}")
        # import traceback; traceback.print_exc() # Good for debugging
        resp = jsonify({"error": {"message": "An unexpected error occurred in the REST API handler.", "type": type(e).__name__}})
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp

<environment_details>
# VSCode Visible Files
pyproject.toml

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:01:37 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now let me check the dolphin-mcp project structure to understand their provider design pattern:

<execute_command>
<command>find /Volumes/SB-XTM5/git -name "dolphin-mcp" -type d 2>/dev/null | head -1</command>
<requires_approval>false</requires_approval>
</execute_command>

[User]
[TASK RESUMPTION] This task was interrupted just now. It may or may not be complete, so please reassess the task context. Be aware that the project state may have changed since then. The current working directory is now '/Volumes/SB-XTM5/git/dolphin-logger'. If the task has not been completed, retry the last step before interruption and proceed with completing the task.

Note: If you previously attempted a tool use that the user did not provide a result for, you should assume the tool use was not successful and assess whether you should retry. If the last tool was a browser_action, the browser has been closed and you must launch a new browser if needed.

New instructions for task continuation:
<user_message>
/Volumes/SB-XTM5/git/dolphin-mcp
</user_message>

<environment_details>
# VSCode Visible Files
pyproject.toml

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:02:16 AM (America/New_York, UTC-4:00)

# Current Working Directory (/Volumes/SB-XTM5/git/dolphin-logger) Files
.gitignore
chat-logger.png
config.json.example
pyproject.toml
README.md
src/
src/dolphin_logger/
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/config.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/logging_utils.py
src/dolphin_logger/main.py
src/dolphin_logger/server.py
src/dolphin_logger/upload.py
src/dolphin_logger.egg-info/
tests/
tests/__init__.py
tests/test_cli.py
tests/test_config.py
tests/test_core_proxy.py
tests/test_functional.py
tests/test_logging_utils.py
tests/test_server.py
tests/test_upload.py

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Thanks for providing the path to dolphin-mcp. Let me examine its provider structure to understand the design pattern you want to follow.

<list_files>
<path>/Volumes/SB-XTM5/git/dolphin-mcp</path>
<recursive>true</recursive>
</list_files>

[User]
[list_files for '/Volumes/SB-XTM5/git/dolphin-mcp'] Result:

.env.example
.gitignore
config.yml
dolphin_mcp.py
pyproject.toml
README.md
requirements.txt
setup_db.py
build/
examples/
examples/filesystem-fetch-mcp.json
examples/filesystem-fetch-mcp.sh
examples/README.md
examples/sqlite-mcp.json
examples/sqlite-mcp.sh
examples/stocklist.txt
src/
src/dolphin_mcp/
src/dolphin_mcp/__init__.py
src/dolphin_mcp/cli.py
src/dolphin_mcp/client.py
src/dolphin_mcp/utils.py
src/dolphin_mcp/providers/
src/dolphin_mcp/providers/__init__.py
src/dolphin_mcp/providers/anthropic.py
src/dolphin_mcp/providers/lmstudio.py
src/dolphin_mcp/providers/msazureopenai.py
src/dolphin_mcp/providers/ollama.py
src/dolphin_mcp/providers/openai.py
src/dolphin_mcp.egg-info/

<environment_details>
# VSCode Visible Files
pyproject.toml

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:02:29 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Perfect! I can see dolphin-mcp has a clean provider structure with each provider in its own file under `src/dolphin_mcp/providers/`. Let me examine their provider structure to understand the pattern:

<read_file>
<path>/Volumes/SB-XTM5/git/dolphin-mcp/src/dolphin_mcp/providers/__init__.py</path>
</read_file>

[User]
[read_file for '/Volumes/SB-XTM5/git/dolphin-mcp/src/dolphin_mcp/providers/__init__.py'] Result:

"""
Provider-specific implementations for different LLM services.
"""

__all__ = ["openai", "anthropic", "ollama", "lmstudio"]

<environment_details>
# VSCode Visible Files
pyproject.toml

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:02:41 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
<read_file>
<path>/Volumes/SB-XTM5/git/dolphin-mcp/src/dolphin_mcp/providers/anthropic.py</path>
</read_file>

[User]
[read_file for '/Volumes/SB-XTM5/git/dolphin-mcp/src/dolphin_mcp/providers/anthropic.py'] Result:

"""
Anthropic provider implementation for Dolphin MCP.
"""

import os
import logging
import json
import hashlib
import re
import atexit
import asyncio
import sys
import time
from typing import Dict, List, Any

# Set up logger
logger = logging.getLogger(__name__)

# Keep track of client resources that need cleanup
_active_clients = set()

# Track last request time for rate limiting
_last_request_time = 0.0

# Get rate limit from env var (in seconds) or default to 60 seconds (1 minute)
def get_rate_limit_seconds():
    try:
        return float(os.getenv("ANTHROPIC_RATE_LIMIT_SECONDS", "60"))
    except (ValueError, TypeError):
        logger.warning("Invalid ANTHROPIC_RATE_LIMIT_SECONDS value, using default of 60 seconds")
        return 60.0

def get_caching_enabled():
    try:
        return bool(os.getenv("ANTHROPIC_CACHING_ENABLED", "true"))
    except (ValueError, TypeError):
        logger.warning("Invalid ANTHROPIC_CACHING_ENABLED value, using default of True")
        return True

def _cleanup_clients():
    """Ensure all active clients are closed during interpreter shutdown"""
    # Skip if no clients to clean up
    if not _active_clients:
        return

    try:
        # Try to get or create an event loop for cleanup
        try:
            loop = asyncio.get_event_loop()
            if loop.is_closed():
                # If the main loop is closed, create a new one
                loop = asyncio.new_event_loop()
                asyncio.set_event_loop(loop)
        except RuntimeError:
            # No event loop in thread, create a new one
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)

        # Now use the loop to clean up clients
        for client in list(_active_clients):
            try:
                if hasattr(client, 'close'):
                    if asyncio.iscoroutinefunction(client.close):
                        try:
                            loop.run_until_complete(client.close())
                        except Exception as e:
                            logger.error(f"Error in async client close: {e}")
                    else:
                        client.close()
            except Exception as e:
                logger.error(f"Error cleaning up client during shutdown: {e}")
            finally:
                _active_clients.discard(client)

        # Close our temporary loop if we created one
        try:
            if not loop.is_closed():
                loop.close()
        except:
            pass

    except Exception as e:
        # Last resort - make sure we don't crash during interpreter shutdown
        logger.error(f"Error during client cleanup at exit: {e}")

    # Clear the set to help with garbage collection
    _active_clients.clear()

# Register the cleanup function to run at exit
atexit.register(_cleanup_clients)

def generate_tool_id(tool_name: str) -> str:
    """
    Generate a deterministic tool ID from the tool name.

    Args:
        tool_name: The name of the tool

    Returns:
        A string ID for the tool
    """
    # Create a hash from just the tool name
    name_underscored = re.sub(r'[^a-zA-Z0-9]', '_', tool_name)
    return name_underscored

def format_tools(all_functions: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Format functions into Anthropic's tool format.

    Args:
        all_functions: List of function definitions

    Returns:
        List of formatted tools for Anthropic API
    """
    anthropic_tools = []

    for i, func in enumerate(all_functions):
        # Extract required fields
        name = func.get("name")
        description = func.get("description", "")
        parameters = func.get("parameters", {})

        if not name:
            logger.warning(f"Function at index {i} has no name, skipping")
            continue

        # Format exactly according to Anthropic's documentation
        tool = {
            "name": name,
            "description": description,
            "input_schema": parameters
        }

        # Verify input_schema is properly formatted
        if not isinstance(tool["input_schema"], dict):
            logger.warning(f"Invalid input_schema for function {name}, using default")
            tool["input_schema"] = {"type": "object", "properties": {}}

        # Ensure input_schema has required type field
        if "type" not in tool["input_schema"]:
            tool["input_schema"]["type"] = "object"

        anthropic_tools.append(tool)

    return anthropic_tools

async def generate_with_anthropic(conversation, model_cfg, all_functions):
    """
    Generate text using Anthropic's API.

    Args:
        conversation: The conversation history
        model_cfg: Configuration for the model
        all_functions: Available functions for the model to call

    Returns:
        Dict containing assistant_text and tool_calls
    """
    from anthropic import AsyncAnthropic, APIError as AnthropicAPIError

    global _last_request_time

    # Apply rate limiting
    rate_limit_seconds = get_rate_limit_seconds()
    current_time = time.time()
    time_since_last_request = current_time - _last_request_time

    if time_since_last_request < rate_limit_seconds:
        # Need to wait before making a new request
        wait_time = rate_limit_seconds - time_since_last_request
        logger.info(f"Rate limiting: Waiting {wait_time:.2f} seconds before making Anthropic API request")
        await asyncio.sleep(wait_time)

    # Update last request time after waiting (if needed)
    _last_request_time = time.time()

    anthro_api_key = model_cfg.get("apiKey", os.getenv("ANTHROPIC_API_KEY"))

    # Initialize result outside context manager
    result = {"assistant_text": "", "tool_calls": []}

    # Create the client
    client = None
    try:
        client = AsyncAnthropic(api_key=anthro_api_key)
        # Register for cleanup at interpreter shutdown - only if we need to
        if client:
            _active_clients.add(client)

        # Store tool ID mappings to ensure consistency
        tool_id_map = {}

        # Helper function to get or create a tool ID
        def get_or_create_tool_id(tool_name):
            if tool_name in tool_id_map:
                return tool_id_map[tool_name]
            else:
                new_id = generate_tool_id(tool_name)
                tool_id_map[tool_name] = new_id
                return new_id

        model_name = model_cfg["model"]
        temperature = model_cfg.get("temperature", 0.7)
        top_k = model_cfg.get("top_k", None)
        top_p = model_cfg.get("top_p", None)
        max_tokens = model_cfg.get("max_tokens", 1024)

        # Extract system messages and non-system messages
        system_messages = []
        non_system_messages = []
        last_assistant_content = None

        # Process conversation messages for Anthropic format
        for i, msg in enumerate(conversation):
            role = msg.get("role", "")
            content = msg.get("content", "")

            if role == "system":
                system_messages.append({
                    "type": "text",
                    "text": content,
                })
            elif role == "tool":
                new_msg = {
                    "role": "user",
                    "content": [{
                        "type": "tool_result",
                        "tool_use_id": msg.get("tool_call_id"),
                        "content": msg.get("content")
                    }]
                }
                non_system_messages.append(new_msg)
            elif role == "assistant" and isinstance(content, str) and msg.get("tool_calls"):
                # Create a new message with content blocks for text and tool_use
                new_msg = {"role": "assistant", "content": []}

                # Add text block if there's content
                if content:
                    last_assistant_content = {"type": "text", "text": content}
                    new_msg["content"].append(last_assistant_content)

                # Add tool_use blocks for each tool call
                for tool_call in msg.get("tool_calls", []):
                    if tool_call.get("type") == "function":
                        func = tool_call.get("function", {})
                        func_name = func.get("name", "")
                        tool_id = tool_call.get("id")

                        # Parse arguments from string if needed
                        arguments = func.get("arguments", "{}")
                        if isinstance(arguments, str):
                            try:
                                tool_input = json.loads(arguments)
                            except:
                                tool_input = {"raw_input": arguments}
                        else:
                            tool_input = arguments

                        # Create tool_use block
                        tool_use = {
                            "type": "tool_use",
                            "id": tool_id,
                            "name": func_name,
                            "input": tool_input
                        }
                        new_msg["content"].append(tool_use)

                non_system_messages.append(new_msg)
            else:
                # Keep user and assistant messages as they are
                non_system_messages.append(msg)

        if get_caching_enabled() and last_assistant_content:
            last_assistant_content["cache_control"] = {"type": "ephemeral"}

        # Prepare API parameters, excluding None values
        api_params = {
            "model": model_name,
            "messages": non_system_messages,
            "max_tokens": max_tokens,
            "temperature": temperature,
        }

        # Format tools for Anthropic API
        if all_functions:
            # Format tools for Anthropic API
            anthropic_tools = format_tools(all_functions)

            # Only add tools if we have valid ones
            if anthropic_tools:
                api_params["tools"] = anthropic_tools

                # cache last tool (because this should be stable)
                if get_caching_enabled() and not last_assistant_content:
                    anthropic_tools[-1]["cache_control"] = {"type": "ephemeral"}

                # Let Claude decide when to use tools instead of forcing it
                api_params["tool_choice"] = {"type": "auto"}
            else:
                logger.warning("No valid tools to add to the request")

        # Only add parameters if they have valid values
        if system_messages:
            api_params["system"] = system_messages
            if get_caching_enabled() and not last_assistant_content:
                for msg in system_messages:
                    # do not cache if the first line contains "TODO.md"
                    if "TODO.md" not in msg["text"].split("\n")[0]:
                        msg["cache_control"] = {"type": "ephemeral"}
        if top_p is not None:
            api_params["top_p"] = top_p
        if top_k is not None and isinstance(top_k, int):
            api_params["top_k"] = top_k

        try:
            create_resp = await client.messages.create(**api_params)

            # Handle the case where content might be a list of TextBlock objects
            if create_resp.content:
                # Extract text properly from Anthropic response
                if isinstance(create_resp.content, list):
                    # If content is a list of blocks, extract text from each block
                    assistant_text = ""
                    for block in create_resp.content:
                        if hasattr(block, 'text'):
                            assistant_text += block.text
                        elif isinstance(block, dict) and 'text' in block:
                            assistant_text += block['text']
                        elif isinstance(block, str):
                            assistant_text += block
                else:
                    # If content is a single item
                    if hasattr(create_resp.content, 'text'):
                        assistant_text = create_resp.content.text
                    elif isinstance(create_resp.content, dict) and 'text' in create_resp.content:
                        assistant_text = create_resp.content['text']
                    else:
                        assistant_text = str(create_resp.content)
            else:
                assistant_text = ""

            # Check for tool calls in the response
            tool_calls = []
            if hasattr(create_resp, 'content') and create_resp.content:
                # Look for tool calls in content blocks
                content_blocks = create_resp.content if isinstance(create_resp.content, list) else [create_resp.content]
                for block in content_blocks:
                    if hasattr(block, 'type') and block.type == 'text':
                        assistant_text = block.text

                    # Check if this is a tool use block
                    if hasattr(block, 'type') and block.type == 'tool_use':
                        # Get the tool name and input
                        tool_name = block.name
                        tool_input = block.input
                        tool_id = block.id

                        # Generate a tool ID if one is not provided
                        if not tool_id:
                            tool_id = get_or_create_tool_id(tool_name)

                        # Format as a function call for our system
                        tool_call = {
                            "id": tool_id,
                            "type": "function",
                            "function": {
                                "name": tool_name,
                                "arguments": json.dumps(tool_input) if isinstance(tool_input, dict) else tool_input
                            }
                        }
                        tool_calls.append(tool_call)
                        print(f"{assistant_text}")

            # Store the result to return after client is closed
            result = {"assistant_text": assistant_text, "tool_calls": tool_calls}

        except AnthropicAPIError as e:
            error_msg = str(e)
            logger.error(f"Anthropic API error: {error_msg}")
            result = {"assistant_text": f"Anthropic error: {error_msg}", "tool_calls": []}

        except Exception as e:
            import traceback
            logger.error(f"Unexpected error in Anthropic provider: {str(e)}")
            logger.error(traceback.format_exc())
            result = {"assistant_text": f"Unexpected Anthropic error: {str(e)}", "tool_calls": []}

    finally:
        # Always clean up the client
        if client:
            try:
                # First remove from active clients to prevent double cleanup
                _active_clients.discard(client)
                # Then close the client
                await client.close()
            except Exception as e:
                logger.error(f"Error closing Anthropic client: {e}")

    # Return result after everything is cleaned up
    return result

<environment_details>
# VSCode Visible Files
pyproject.toml

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:02:47 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Let me also examine the OpenAI provider to understand the pattern better:

<read_file>
<path>/Volumes/SB-XTM5/git/dolphin-mcp/src/dolphin_mcp/providers/openai.py</path>
</read_file>

[User]
[read_file for '/Volumes/SB-XTM5/git/dolphin-mcp/src/dolphin_mcp/providers/openai.py'] Result:

"""
OpenAI provider implementation for Dolphin MCP.
"""

import os
import json
from typing import Dict, List, Any, AsyncGenerator, Optional, Union

from openai import AsyncOpenAI, APIError, RateLimitError

async def generate_with_openai_stream(client: AsyncOpenAI, model_name: str, conversation: List[Dict],
                                    formatted_functions: List[Dict], temperature: Optional[float] = None,
                                    top_p: Optional[float] = None, max_tokens: Optional[int] = None) -> AsyncGenerator:
    """Internal function for streaming generation"""
    try:
        response = await client.chat.completions.create(
            model=model_name,
            messages=conversation,
            temperature=temperature,
            top_p=top_p,
            max_tokens=max_tokens,
            tools=[{"type": "function", "function": f} for f in formatted_functions],
            tool_choice="auto",
            stream=True
        )

        current_tool_calls = []
        current_content = ""

        async for chunk in response:
            delta = chunk.choices[0].delta

            if delta.content:
                # Immediately yield each token without buffering
                yield {"assistant_text": delta.content, "tool_calls": [], "is_chunk": True, "token": True}
                current_content += delta.content

            # Handle tool call updates
            if delta.tool_calls:
                for tool_call in delta.tool_calls:
                    # Initialize or update tool call
                    while tool_call.index >= len(current_tool_calls):
                        current_tool_calls.append({
                            "id": "",
                            "function": {
                                "name": "",
                                "arguments": ""
                            }
                        })

                    current_tool = current_tool_calls[tool_call.index]

                    # Update tool call properties
                    if tool_call.id:
                        current_tool["id"] = tool_call.id

                    if tool_call.function.name:
                        current_tool["function"]["name"] = (
                            current_tool["function"]["name"] + tool_call.function.name
                        )

                    if tool_call.function.arguments:
                        # Properly accumulate JSON arguments
                        current_args = current_tool["function"]["arguments"]
                        new_args = tool_call.function.arguments

                        # Handle special cases for JSON accumulation
                        if new_args.startswith("{") and not current_args:
                            current_tool["function"]["arguments"] = new_args
                        elif new_args.endswith("}") and current_args:
                            # If we're receiving the end of the JSON object
                            if not current_args.endswith("}"):
                                current_tool["function"]["arguments"] = current_args + new_args
                        else:
                            # Middle part of JSON - append carefully
                            current_tool["function"]["arguments"] += new_args

            # If this is the last chunk, yield final state with complete tool calls
            if chunk.choices[0].finish_reason is not None:
                # Clean up and validate tool calls
                final_tool_calls = []
                for tc in current_tool_calls:
                    if tc["id"] and tc["function"]["name"]:
                        try:
                            # Ensure arguments is valid JSON
                            args = tc["function"]["arguments"].strip()
                            if not args or args.isspace():
                                args = "{}"
                            # Parse and validate JSON
                            parsed_args = json.loads(args)
                            tc["function"]["arguments"] = json.dumps(parsed_args)
                            final_tool_calls.append(tc)
                        except json.JSONDecodeError:
                            # If arguments are malformed, try to fix common issues
                            args = tc["function"]["arguments"].strip()
                            # Remove any trailing commas
                            args = args.rstrip(",")
                            # Ensure proper JSON structure
                            if not args.startswith("{"):
                                args = "{" + args
                            if not args.endswith("}"):
                                args = args + "}"
                            try:
                                # Try parsing again after fixes
                                parsed_args = json.loads(args)
                                tc["function"]["arguments"] = json.dumps(parsed_args)
                                final_tool_calls.append(tc)
                            except json.JSONDecodeError:
                                # If still invalid, default to empty object
                                tc["function"]["arguments"] = "{}"
                                final_tool_calls.append(tc)

                yield {
                    "assistant_text": current_content,
                    "tool_calls": final_tool_calls,
                    "is_chunk": False
                }

    except Exception as e:
        yield {"assistant_text": f"OpenAI error: {str(e)}", "tool_calls": [], "is_chunk": False}

async def generate_with_openai_sync(client: AsyncOpenAI, model_name: str, conversation: List[Dict],
                                  formatted_functions: List[Dict], temperature: Optional[float] = None,
                                  top_p: Optional[float] = None, max_tokens: Optional[int] = None) -> Dict:
    """Internal function for non-streaming generation"""
    try:
        response = await client.chat.completions.create(
            model=model_name,
            messages=conversation,
            temperature=temperature,
            top_p=top_p,
            max_tokens=max_tokens,
            tools=[{"type": "function", "function": f} for f in formatted_functions],
            tool_choice="auto",
            stream=False
        )

        choice = response.choices[0]
        assistant_text = choice.message.content or ""
        tool_calls = []

        if hasattr(choice.message, 'tool_calls') and choice.message.tool_calls:
            for tc in choice.message.tool_calls:
                if tc.type == 'function':
                    tool_call = {
                        "id": tc.id,
                        "function": {
                            "name": tc.function.name,
                            "arguments": tc.function.arguments or "{}"
                        }
                    }
                    # Ensure arguments is valid JSON
                    try:
                        json.loads(tool_call["function"]["arguments"])
                    except json.JSONDecodeError:
                        tool_call["function"]["arguments"] = "{}"
                    tool_calls.append(tool_call)
        return {"assistant_text": assistant_text, "tool_calls": tool_calls}

    except APIError as e:
        return {"assistant_text": f"OpenAI API error: {str(e)}", "tool_calls": []}
    except RateLimitError as e:
        return {"assistant_text": f"OpenAI rate limit: {str(e)}", "tool_calls": []}
    except Exception as e:
        return {"assistant_text": f"Unexpected OpenAI error: {str(e)}", "tool_calls": []}

async def generate_with_openai(conversation: List[Dict], model_cfg: Dict,
                             all_functions: List[Dict], stream: bool = False) -> Union[Dict, AsyncGenerator]:
    """
    Generate text using OpenAI's API.

    Args:
        conversation: The conversation history
        model_cfg: Configuration for the model
        all_functions: Available functions for the model to call
        stream: Whether to stream the response

    Returns:
        If stream=False: Dict containing assistant_text and tool_calls
        If stream=True: AsyncGenerator yielding chunks of assistant text and tool calls
    """
    api_key = model_cfg.get("apiKey") or os.getenv("OPENAI_API_KEY")
    if "apiBase" in model_cfg:
        client = AsyncOpenAI(api_key=api_key, base_url=model_cfg["apiBase"])
    else:
        client = AsyncOpenAI(api_key=api_key)

    model_name = model_cfg["model"]
    temperature = model_cfg.get("temperature", None)
    top_p = model_cfg.get("top_p", None)
    max_tokens = model_cfg.get("max_tokens", None)

    # Format functions for OpenAI API
    formatted_functions = []
    for func in all_functions:
        formatted_func = {
            "name": func["name"],
            "description": func["description"],
            "parameters": func["parameters"]
        }
        formatted_functions.append(formatted_func)

    if stream:
        return generate_with_openai_stream(
            client, model_name, conversation, formatted_functions,
            temperature, top_p, max_tokens
        )
    else:
        return await generate_with_openai_sync(
            client, model_name, conversation, formatted_functions,
            temperature, top_p, max_tokens
        )

<environment_details>
# VSCode Visible Files
pyproject.toml

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:02:57 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Let me also check the Ollama provider to understand the pattern for REST API providers:

<read_file>
<path>/Volumes/SB-XTM5/git/dolphin-mcp/src/dolphin_mcp/providers/ollama.py</path>
</read_file>

[User]
[read_file for '/Volumes/SB-XTM5/git/dolphin-mcp/src/dolphin_mcp/providers/ollama.py'] Result:

"""
Ollama provider implementation for Dolphin MCP.

This module provides integration with Ollama models for text generation and tool usage,
including proper formatting of tool calls and their arguments.
"""

import json
import logging
import sys
import traceback
import copy
from typing import Dict, List, Any, Optional, Union, Mapping, TypeVar, cast, Callable

# Third-party imports
import httpx
from pydantic import BaseModel, ValidationError
# Import Ollama types for response parsing
from ollama import ResponseError
from ollama._types import ChatResponse, Message

# Configure logging
logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[logging.StreamHandler()]
)
logger = logging.getLogger('dolphin_mcp.providers.ollama')

# Constants
DEFAULT_API_HOST = "http://localhost:11434"
DEFAULT_TEMPERATURE = 0.7
DEFAULT_MAX_TOKENS = 1024
EMPTY_JSON_VALUES = ('', '{}')

# Type definitions
JsonDict = Dict[str, Any]
OllamaToolType = Dict[str, Any]
MessageType = Dict[str, Any]
T = TypeVar('T')

# Global mapping to track original tool names
tool_name_mapping: Dict[str, str] = {}

def sanitize_tool_name(name: str) -> str:
    """
    Sanitize tool name for compatibility with various systems.

    Args:
        name: The original tool name

    Returns:
        A sanitized version of the name with problematic characters replaced
    """
    return name.replace("-", "_").replace(" ", "_").lower()

def parse_json_safely(json_str: Union[str, Any]) -> JsonDict:
    """
    Parse a JSON string safely, handling edge cases and returning an empty dict for invalid inputs.

    Args:
        json_str: String containing JSON or any other value

    Returns:
        Parsed JSON dict or empty dict if parsing fails
    """
    # Handle non-string inputs
    if not isinstance(json_str, str):
        return {}

    # Handle empty inputs
    json_str = json_str.strip()
    if not json_str or json_str in EMPTY_JSON_VALUES:
        return {}

    # Attempt to parse
    try:
        return json.loads(json_str)
    except json.JSONDecodeError:
        logger.warning(f"Failed to parse JSON string: {json_str}")
        return {}

def preprocess_messages(messages: List[MessageType]) -> List[MessageType]:
    """
    Preprocess conversation messages to ensure tool_calls.function.arguments are dictionaries.
    The Ollama server expects arguments as objects, not strings.

    Args:
        messages: List of message objects from the conversation history

    Returns:
        Processed copy of messages with tool call arguments converted to dictionaries
    """
    if not messages:
        return messages

    # Create a deep copy to avoid modifying the original
    msgs_copy = copy.deepcopy(messages)
    modified_count = 0

    for msg in msgs_copy:
        # Check if the message has tool_calls
        if isinstance(msg, dict) and 'tool_calls' in msg and msg['tool_calls']:
            for tool_call in msg['tool_calls']:
                if isinstance(tool_call, dict) and 'function' in tool_call:
                    if 'arguments' in tool_call['function']:
                        # If arguments is a string, parse it to a dict
                        if isinstance(tool_call['function']['arguments'], str):
                            try:
                                parsed = parse_json_safely(tool_call['function']['arguments'])
                                # If parsing results in an empty dict, remove the key instead
                                if not parsed:
                                    del tool_call['function']['arguments']
                                    logger.debug("Removed empty arguments key during preprocessing.")
                                else:
                                    tool_call['function']['arguments'] = parsed
                                modified_count += 1
                            except Exception as e:
                                logger.error(f"Error parsing tool call arguments: {e}")
                                # If error, ensure key is removed or set to empty dict
                                if 'arguments' in tool_call['function']:
                                    del tool_call['function']['arguments']

    if modified_count > 0:
        logger.debug(f"Preprocessed {modified_count} tool call arguments from strings to dicts")

    return msgs_copy

def convert_mcp_tools_to_ollama_format(mcp_tools: Union[List[Any], Dict[str, Any], Any]) -> List[OllamaToolType]:
    """
    Convert MCP tool format to Ollama tool format according to the Ollama SDK docs.

    Args:
        mcp_tools: Tools in MCP format (list, dict with 'tools' key, or object with 'tools' attribute)

    Returns:
        List of tools formatted for Ollama's API
    """
    logger.debug("Converting MCP tools to Ollama format")
    ollama_tools = []

    # Clear the global mapping before processing
    tool_name_mapping.clear()

    # Extract tools from the input based on its type
    tools_list = extract_tools_list(mcp_tools)

    # Process each tool in the list
    if isinstance(tools_list, list):
        for tool_idx, tool in enumerate(tools_list):
            if "name" in tool and "description" in tool:
                # Process valid tool
                ollama_tool = process_single_tool(tool, tool_idx)
                if ollama_tool:
                    ollama_tools.append(ollama_tool)
            else:
                logger.warning(f"Tool missing required attributes: has name = {'name' in tool}, has description = {'description' in tool}")
    else:
        logger.warning(f"Tools list is not a list, it's a {type(tools_list)}")

    logger.debug(f"Converted {len(ollama_tools)} tools to Ollama format")
    return ollama_tools

def extract_tools_list(mcp_tools: Union[List[Any], Dict[str, Any], Any]) -> List[Any]:
    """
    Extract the actual tools list from various possible input formats.

    Args:
        mcp_tools: The input that contains tools in some format

    Returns:
        List of tools
    """
    if hasattr(mcp_tools, 'tools'):
        tools_list = mcp_tools.tools
        logger.debug("Extracted tools from object.tools attribute")
    elif isinstance(mcp_tools, dict):
        tools_list = mcp_tools.get('tools', [])
        logger.debug("Extracted tools from dict['tools']")
    else:
        tools_list = mcp_tools
        logger.debug(f"Using tools directly from input of type {type(mcp_tools)}")

    return tools_list

def process_single_tool(tool: Dict[str, Any], tool_idx: int) -> Optional[OllamaToolType]:
    """
    Process a single tool definition into Ollama format.

    Args:
        tool: Tool definition
        tool_idx: Index for logging purposes

    Returns:
        Tool in Ollama format or None if processing fails
    """
    try:
        # Store the original name in our mapping
        original_name = tool["name"]
        logger.debug(f"Processing tool [{tool_idx}]: {original_name}")

        # For server_name_tool_name format used in client.py
        server_tool_name = f"{original_name}"
        tool_name_mapping[original_name] = server_tool_name

        # Get parameter properties
        properties, required = extract_tool_parameters(tool)

        # Create tool in Ollama's expected format based on docs
        ollama_tool = {
            "type": "function",
            "function": {
                "name": original_name,
                "description": tool.get("description", ""),
                "parameters": {
                    "type": "object",
                    "properties": properties,
                    "required": required
                }
            }
        }

        logger.debug(f"Added tool to Ollama format: {original_name}")
        return ollama_tool

    except Exception as e:
        logger.error(f"Error processing tool: {e}")
        return None

def extract_tool_parameters(tool: Dict[str, Any]) -> tuple[dict, list]:
    """
    Extract parameter properties and required fields from a tool definition.

    Args:
        tool: Tool definition

    Returns:
        Tuple of (properties dict, required list)
    """
    properties = {}
    required = []

    if "parameters" in tool:
        if isinstance(tool["parameters"], dict):
            properties = tool["parameters"].get("properties", {})
            required = tool["parameters"].get("required", [])
            logger.debug(f"Tool has parameters: properties={list(properties.keys())}, required={required}")
        else:
            logger.warning(f"Tool parameters not a dict: {type(tool['parameters'])}")
    else:
        logger.debug("Tool has no parameters defined")

    return properties, required

def prepare_ollama_options(model_cfg: Dict[str, Any]) -> tuple[Dict[str, Any], Optional[Any], str]:
    """
    Prepare options for Ollama API call.

    Args:
        model_cfg: Model configuration

    Returns:
        Tuple of (options dict, client object, keep_alive value)
    """
    # Import here to avoid import errors when module is loaded
    from ollama import Client

    options = {}
    client = None
    keep_alive_seconds = "0"

    # Set model parameters from config
    if "temperature" in model_cfg:
        options["temperature"] = model_cfg.get("temperature", DEFAULT_TEMPERATURE)
    if "top_k" in model_cfg:
        options["top_k"] = model_cfg.get("top_k")
    if "repetition_penalty" in model_cfg:
        options["repeat_penalty"] = model_cfg.get("repetition_penalty")
    if "max_tokens" in model_cfg:
        options["num_predict"] = model_cfg.get("max_tokens", DEFAULT_MAX_TOKENS)
    if "client" in model_cfg:
        client = Client(host=model_cfg.get("client", DEFAULT_API_HOST))
    if "keep_alive_seconds" in model_cfg:
        keep_alive_seconds = model_cfg.get("keep_alive_seconds") + "s"

    return options, client, keep_alive_seconds

def format_tool_calls(response_tool_calls: List[Any]) -> List[Dict[str, Any]]:
    """
    Format tool calls from Ollama response for Dolphin MCP.

    Args:
        response_tool_calls: Tool calls from Ollama response

    Returns:
        Formatted tool calls for client.py
    """
    tool_calls = []

    for i, tool in enumerate(response_tool_calls):
        # Get function details
        func_name = tool.function.name
        func_args = {}

        # Extract arguments (should be a dict in the response)
        if hasattr(tool.function, 'arguments'):
            if isinstance(tool.function.arguments, dict):
                func_args = tool.function.arguments
                logger.debug(f"Tool {i} arguments: {func_args}")
            else:
                # If somehow not a dict, try to convert
                if isinstance(tool.function.arguments, str):
                    func_args = parse_json_safely(tool.function.arguments)
                    logger.debug(f"Converted string arguments to dict: {func_args}")

        # Format the function name for client.py
        formatted_name = format_function_name(func_name)

        # Convert arguments back to a JSON string for client.py
        args_str = json.dumps(func_args) if func_args else "{}"

        # Create the tool object in the format expected by client.py
        tool_obj = {
            "id": f"call_ollama_{i}",
            "function": {
                "name": formatted_name,
                "arguments": args_str  # Must be string for client.py
            }
        }

        tool_calls.append(tool_obj)
        logger.debug(f"Added tool call: {formatted_name}")

    return tool_calls

def format_function_name(func_name: str) -> str:
    """
    Format function name with server prefix if needed.

    Args:
        func_name: Original function name

    Returns:
        Formatted function name
    """
    formatted_name = func_name
    if "_" not in func_name and tool_name_mapping:
        first_server_prefix = next(iter(tool_name_mapping.values()), "unknown_server")
        formatted_name = f"{first_server_prefix}_{func_name}"
        logger.debug(f"Formatted name: {formatted_name}")

    return formatted_name

async def generate_with_ollama(
    conversation: List[MessageType],
    model_cfg: Dict[str, Any],
    all_functions: Union[List[Any], Dict[str, Any], Any]
) -> Dict[str, Any]:
    """
    Generate text using Ollama's API.

    Args:
        conversation: The conversation history as a list of message objects
        model_cfg: Configuration for the model including parameters and options
        all_functions: Available functions for the model to call

    Returns:
        Dict containing assistant_text and tool_calls
    """
    logger.debug("===== Starting generate_with_ollama =====")

    # Import required components from Ollama
    try:
        ollama_imports = import_ollama_components()
        if not ollama_imports:
            return {"assistant_text": "Failed to import required Ollama components", "tool_calls": []}
        chat, Client, ResponseError = ollama_imports
    except Exception as e:
        logger.error(f"Unexpected error during Ollama import: {e}")
        return {"assistant_text": f"Unexpected Ollama import error: {str(e)}", "tool_calls": []}

    # Get model name from config
    model_name = model_cfg.get("model", "")
    if not model_name:
        error_msg = "Model name is required but was not provided in configuration"
        logger.error(error_msg)
        return {"assistant_text": error_msg, "tool_calls": []}

    logger.debug(f"Using model: {model_name}")

    # Convert tools to Ollama format
    converted_all_functions = convert_mcp_tools_to_ollama_format(all_functions)

    # Prepare options dictionary for Ollama
    options, client, keep_alive_seconds = prepare_ollama_options(model_cfg)

    # Preprocess conversation messages to ensure arguments are dictionaries
    processed_conversation = preprocess_messages(conversation)

    # Define a baseline chat params dict
    chat_params = {
        "model": model_name,
        "messages": processed_conversation,
        "options": options,
        "stream": False,
        "tools": converted_all_functions
    }

    # Add keep_alive if needed
    if keep_alive_seconds != "0":
        chat_params["keep_alive"] = keep_alive_seconds

    logger.debug(f"Chat parameters prepared with {len(converted_all_functions)} tools")

    # Log conversation for debugging (abbreviated)
    log_conversation_sample(processed_conversation)

    # Call Ollama API
    try:
        # Make the API call
        response = await call_ollama_api(chat, client, chat_params)
        if isinstance(response, dict) and "assistant_text" in response:
            # This is an error response from call_ollama_api
            return response

        # Extract assistant text
        assistant_text = response.message.content or ""
        logger.debug(f"Assistant text (abbreviated): {assistant_text[:100]}...")

        # Process tool calls if present
        tool_calls = []
        if hasattr(response.message, 'tool_calls') and response.message.tool_calls:
            logger.debug(f"Found {len(response.message.tool_calls)} tool calls in response")
            tool_calls = format_tool_calls(response.message.tool_calls)

        return {"assistant_text": assistant_text, "tool_calls": tool_calls}

    except Exception as e:
        logger.error(f"Unexpected error in generate_with_ollama: {e}")
        traceback.print_exc()
        return {"assistant_text": f"Unexpected error: {str(e)}", "tool_calls": []}

def import_ollama_components() -> Optional[tuple]:
    """
    Import the necessary Ollama components.

    Returns:
        Tuple of (chat, Client, ResponseError) or None if import fails
    """
    try:
        from ollama import chat, Client, ResponseError
        logger.debug("Imported Ollama SDK successfully")

        # Try to get the version if available
        try:
            import importlib.metadata
            ollama_version = importlib.metadata.version('ollama')
            logger.debug(f"Ollama SDK version: {ollama_version}")
        except (ImportError, importlib.metadata.PackageNotFoundError):
            logger.debug("Could not determine Ollama SDK version")

        return chat, Client, ResponseError
    except ImportError as e:
        logger.error(f"Failed to import Ollama SDK: {e}")
        return None

def log_conversation_sample(conversation: List[MessageType]) -> None:
    """
    Log a sample of the conversation for debugging.

    Args:
        conversation: The conversation to log
    """
    if not conversation:
        return

    try:
        if len(conversation) > 0:
            first_msg = json.dumps(conversation[0])[:150]
            logger.debug(f"First message (abbreviated): {first_msg}...")

        if len(conversation) > 1:
            last_msg = json.dumps(conversation[-1])[:150]
            logger.debug(f"Last message (abbreviated): {last_msg}...")
    except Exception as e:
        logger.debug(f"Could not log conversation sample: {e}")

async def call_ollama_api(
    chat: Callable,
    client: Optional[Any],
    chat_params: Dict[str, Any]
) -> Union[ChatResponse, Dict[str, Any]]:
    """
    Call the Ollama API using httpx to manually handle the response and fix potential issues
    before Pydantic validation.

    Args:
        chat: The chat function from ollama (unused, kept for signature compatibility for now)
        client: Optional ollama client object to get host info
        chat_params: Parameters for the chat call

    Returns:
        Either a parsed ChatResponse object or an error dict
    """
    logger.debug("Calling Ollama API via httpx...")

    # Determine host and construct URL
    host = DEFAULT_API_HOST
    if client and hasattr(client, 'host'):
        host = client.host
    api_url = f"{host}/api/chat"
    logger.debug(f"Target API URL: {api_url}")

    try:
        async with httpx.AsyncClient(timeout=600.0) as http_client: # Increased timeout
            http_response = await http_client.post(api_url, json=chat_params)
            http_response.raise_for_status() # Raise exception for 4xx/5xx errors

            raw_response_data = http_response.json()
            logger.debug("Received raw JSON response from Ollama API.")

            # --- Manual Fix for Missing 'arguments' ---
            if 'message' in raw_response_data and 'tool_calls' in raw_response_data['message'] and raw_response_data['message']['tool_calls']:
                corrected_count = 0
                for tool_call in raw_response_data['message']['tool_calls']:
                    if isinstance(tool_call, dict) and 'function' in tool_call:
                        if 'arguments' not in tool_call['function']:
                            tool_call['function']['arguments'] = {}
                            corrected_count += 1
                            logger.debug(f"Manually added missing 'arguments: {{}}' to tool call: {tool_call.get('function', {}).get('name')}")
                if corrected_count > 0:
                     logger.info(f"Corrected {corrected_count} tool calls with missing 'arguments'.")
            # --- End Manual Fix ---

            # Attempt to parse the corrected data using the SDK's Pydantic model
            try:
                parsed_response = ChatResponse(**raw_response_data)
                logger.debug("Successfully parsed corrected JSON into ChatResponse model.")
                return parsed_response
            except ValidationError as pydantic_error:
                logger.error(f"Pydantic validation failed even after manual correction: {pydantic_error}")
                logger.debug(f"Corrected JSON data that failed validation: {raw_response_data}")
                return {
                     "assistant_text": f"Ollama SDK Validation Error after manual correction: {str(pydantic_error)}",
                     "tool_calls": []
                 }

    except httpx.HTTPStatusError as e:
        logger.error(f"Ollama API HTTP Error: {e.response.status_code} - {e.response.text}")
        # Try to parse error details from response if possible
        error_detail = e.response.text
        try:
            error_json = e.response.json()
            error_detail = error_json.get('error', error_detail)
        except Exception:
            pass # Keep original text if JSON parsing fails
        return {"assistant_text": f"Ollama API HTTP Error {e.response.status_code}: {error_detail}", "tool_calls": []}
    except httpx.RequestError as e:
        logger.error(f"Ollama API Request Error: {e}")
        return {"assistant_text": f"Ollama API Request Error: {str(e)}", "tool_calls": []}
    except json.JSONDecodeError as e:
        logger.error(f"Failed to decode JSON response from Ollama API: {e}")
        return {"assistant_text": f"Ollama API JSON Decode Error: {str(e)}", "tool_calls": []}
    except ValidationError as e: # Catch Pydantic errors during initial raw parsing if they occur
        # This specific check might be less likely now, but kept for safety
        error_str = str(e)
        logger.error(f"Caught Pydantic validation error during httpx handling: {e}")
        return {
            "assistant_text": f"Ollama SDK Validation Error during httpx handling: {error_str}",
            "tool_calls": []
        }
    except Exception as e:
        logger.error(f"Unexpected error during Ollama API call via httpx: {e}")
        traceback.print_exc()
        return {"assistant_text": f"Unexpected error during httpx API call: {str(e)}", "tool_calls": []}

<environment_details>
# VSCode Visible Files
pyproject.toml

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:03:04 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I understand the pattern from dolphin-mcp. Let me examine the current core_proxy.py file to see exactly what needs to be refactored, then I'll create the new provider structure.

Based on the dolphin-mcp pattern, I need to:

1. Create a `providers` directory under `src/dolphin_logger`
2. Split the provider logic from `core_proxy.py` into separate files for each provider
3. Create a provider interface that each provider implements
4. Update `core_proxy.py` to use the new provider structure

Let me start by creating the providers directory and the individual provider files:

<execute_command>
<command>mkdir -p src/dolphin_logger/providers</command>
<requires_approval>false</requires_approval>
</execute_command>

[User]
[TASK RESUMPTION] This task was interrupted just now. It may or may not be complete, so please reassess the task context. Be aware that the project state may have changed since then. The current working directory is now '/Volumes/SB-XTM5/git/dolphin-logger'. If the task has not been completed, retry the last step before interruption and proceed with completing the task.

Note: If you previously attempted a tool use that the user did not provide a result for, you should assume the tool use was not successful and assess whether you should retry. If the last tool was a browser_action, the browser has been closed and you must launch a new browser if needed.

IMPORTANT: If the last tool use was a replace_in_file or write_to_file that was interrupted, the file was reverted back to its original state before the interrupted edit, and you do NOT need to re-read the file as you already have its up-to-date contents.

New instructions for task continuation:
<user_message>
the directory is creaated.
</user_message>

<environment_details>
# VSCode Visible Files
pyproject.toml

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:03:39 AM (America/New_York, UTC-4:00)

# Current Working Directory (/Volumes/SB-XTM5/git/dolphin-logger) Files
.gitignore
chat-logger.png
config.json.example
pyproject.toml
README.md
src/
src/dolphin_logger/
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/config.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/logging_utils.py
src/dolphin_logger/main.py
src/dolphin_logger/server.py
src/dolphin_logger/upload.py
src/dolphin_logger/providers/
src/dolphin_logger.egg-info/
tests/
tests/__init__.py
tests/test_cli.py
tests/test_config.py
tests/test_core_proxy.py
tests/test_functional.py
tests/test_logging_utils.py
tests/test_server.py
tests/test_upload.py

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Great! The providers directory has been created. Now I'll create the provider structure similar to dolphin-mcp. Let me start by creating the `__init__.py` file for the providers package:

<write_to_file>
<path>src/dolphin_logger/providers/__init__.py</path>
<content>
"""
Provider-specific implementations for different LLM services.
"""

__all__ = ["openai", "anthropic", "google"]
</content>
</write_to_file>

[User]
[write_to_file for 'src/dolphin_logger/providers/__init__.py'] Result:

The content was successfully saved to src/dolphin_logger/providers/__init__.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/providers/__init__.py">
"""
Provider-specific implementations for different LLM services.
"""

__all__ = ["openai", "anthropic", "google"]

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/providers/__init__.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:03:52 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I'll create the Anthropic provider by extracting the Anthropic-specific logic from core_proxy.py:

<write_to_file>
<path>src/dolphin_logger/providers/anthropic.py</path>
<content>
"""
Anthropic provider implementation for Dolphin Logger.
"""

import json
import anthropic
import uuid
from datetime import datetime
from flask import Response, stream_with_context, jsonify

from ..logging_utils import get_current_log_file, _should_log_request, log_lock

def handle_anthropic_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Anthropic SDK.

    Args:
        json_data_for_sdk: The request data formatted for the Anthropic SDK
        target_model: The target model name
        target_api_key: The API key for Anthropic
        is_stream: Whether this is a streaming request
        original_request_json_data: The original client request for logging

    Returns:
        Flask Response object
    """
    print(f"DEBUG - Using Anthropic SDK for request with model: {target_model}")

    try:
        if not target_api_key:
            raise ValueError("Anthropic API key is missing in the configuration for the requested model.")

        # Initialize Anthropic client
        anthropic_client = anthropic.Anthropic(api_key=target_api_key)

        original_messages = json_data_for_sdk.get('messages', [])
        max_tokens = json_data_for_sdk.get('max_tokens', 4096)

        # Extract system messages from the messages array and separate them
        system_messages = []
        non_system_messages = []

        for message in original_messages:
            if message.get('role') == 'system':
                system_messages.append(message.get('content', ''))
            else:
                non_system_messages.append(message)

        # Combine system messages into a single system prompt
        system_prompt = '\n\n'.join(system_messages) if system_messages else None

        # Also check for top-level system parameter
        if not system_prompt and json_data_for_sdk.get('system'):
            system_prompt = json_data_for_sdk.get('system')

        anthropic_args = {
            "model": target_model,
            "messages": non_system_messages,
            "max_tokens": max_tokens,
            "stream": is_stream,
        }
        if system_prompt:
            anthropic_args["system"] = system_prompt

        if is_stream:
            print(f"DEBUG - Creating streaming request to Anthropic API: model={target_model}")
            sdk_stream = anthropic_client.messages.create(**anthropic_args)

            def generate_anthropic_stream_response():
                response_content_parts = []
                log_entry = {'request': original_request_json_data, 'response': None}
                try:
                    for chunk in sdk_stream:
                        if chunk.type == "content_block_delta":
                            delta_content = chunk.delta.text
                            response_content_parts.append(delta_content)
                            openai_compatible_chunk = {
                                "choices": [{"delta": {"content": delta_content}, "index": 0, "finish_reason": None}],
                                "id": f"chatcmpl-anthropic-{uuid.uuid4()}",
                                "model": target_model,
                                "object": "chat.completion.chunk",
                                "created": int(datetime.now().timestamp())
                            }
                            yield f"data: {json.dumps(openai_compatible_chunk)}\n\n".encode('utf-8')
                        elif chunk.type == "message_stop":
                            finish_reason = chunk.message.stop_reason if hasattr(chunk, 'message') and hasattr(chunk.message, 'stop_reason') else "stop"
                            final_chunk = {
                                 "choices": [{"delta": {}, "index": 0, "finish_reason": finish_reason}],
                                "id": f"chatcmpl-anthropic-{uuid.uuid4()}",
                                "model": target_model,
                                "object": "chat.completion.chunk",
                                "created": int(datetime.now().timestamp())
                            }
                            yield f"data: {json.dumps(final_chunk)}\n\n".encode('utf-8')
                    yield b"data: [DONE]\n\n"
                finally:
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_anthropic_stream_response()), content_type='text/event-stream')
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else:
            print(f"DEBUG - Creating non-streaming request to Anthropic API: model={target_model}")
            response_obj = anthropic_client.messages.create(**anthropic_args)

            response_content = response_obj.content[0].text if response_obj.content and len(response_obj.content) > 0 and hasattr(response_obj.content[0], 'text') else ""

            response_data_converted = {
                "id": f"chatcmpl-anthropic-{response_obj.id}",
                "object": "chat.completion",
                "created": int(datetime.now().timestamp()),
                "model": response_obj.model,
                "choices": [{"index": 0, "message": {"role": response_obj.role, "content": response_content}, "finish_reason": response_obj.stop_reason }],
                "usage": {"prompt_tokens": response_obj.usage.input_tokens, "completion_tokens": response_obj.usage.output_tokens, "total_tokens": response_obj.usage.input_tokens + response_obj.usage.output_tokens}
            }
            print(f"DEBUG - Response from Anthropic API received. Preview: {response_content[:100]}...")
            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps({'request': original_request_json_data, 'response': response_content}) + '\n')

            resp = jsonify(response_data_converted)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except anthropic.APIError as e:
        error_type = getattr(e, 'type', type(e).__name__)
        error_message = getattr(e, 'message', str(e))
        status_code = getattr(e, 'status_code', 500)

        print(f"ERROR DETAILS (Anthropic SDK - APIError): Status: {status_code}, Type: {error_type}, Message: {error_message}")

        error_body = {"error": {"message": error_message, "type": error_type, "code": status_code}}
        resp = jsonify(error_body)
        resp.status_code = status_code
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e:
        print(f"ERROR DETAILS (Anthropic SDK - General): Type: {type(e).__name__}, Message: {str(e)}")
        if original_request_json_data:
            print(f"  Full request data (original for logging): {json.dumps(original_request_json_data)}")
        error_body = {"error": {"message": str(e), "type": type(e).__name__}}
        resp = jsonify(error_body)
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
</content>
</write_to_file>

[User]
[write_to_file for 'src/dolphin_logger/providers/anthropic.py'] Result:

The content was successfully saved to src/dolphin_logger/providers/anthropic.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/providers/anthropic.py">
"""
Anthropic provider implementation for Dolphin Logger.
"""

import json
import anthropic
import uuid
from datetime import datetime
from flask import Response, stream_with_context, jsonify

from ..logging_utils import get_current_log_file, _should_log_request, log_lock

def handle_anthropic_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Anthropic SDK.

    Args:
        json_data_for_sdk: The request data formatted for the Anthropic SDK
        target_model: The target model name
        target_api_key: The API key for Anthropic
        is_stream: Whether this is a streaming request
        original_request_json_data: The original client request for logging

    Returns:
        Flask Response object
    """
    print(f"DEBUG - Using Anthropic SDK for request with model: {target_model}")

    try:
        if not target_api_key:
            raise ValueError("Anthropic API key is missing in the configuration for the requested model.")

        # Initialize Anthropic client
        anthropic_client = anthropic.Anthropic(api_key=target_api_key)

        original_messages = json_data_for_sdk.get('messages', [])
        max_tokens = json_data_for_sdk.get('max_tokens', 4096)

        # Extract system messages from the messages array and separate them
        system_messages = []
        non_system_messages = []

        for message in original_messages:
            if message.get('role') == 'system':
                system_messages.append(message.get('content', ''))
            else:
                non_system_messages.append(message)

        # Combine system messages into a single system prompt
        system_prompt = '\n\n'.join(system_messages) if system_messages else None

        # Also check for top-level system parameter
        if not system_prompt and json_data_for_sdk.get('system'):
            system_prompt = json_data_for_sdk.get('system')

        anthropic_args = {
            "model": target_model,
            "messages": non_system_messages,
            "max_tokens": max_tokens,
            "stream": is_stream,
        }
        if system_prompt:
            anthropic_args["system"] = system_prompt

        if is_stream:
            print(f"DEBUG - Creating streaming request to Anthropic API: model={target_model}")
            sdk_stream = anthropic_client.messages.create(**anthropic_args)

            def generate_anthropic_stream_response():
                response_content_parts = []
                log_entry = {'request': original_request_json_data, 'response': None}
                try:
                    for chunk in sdk_stream:
                        if chunk.type == "content_block_delta":
                            delta_content = chunk.delta.text
                            response_content_parts.append(delta_content)
                            openai_compatible_chunk = {
                                "choices": [{"delta": {"content": delta_content}, "index": 0, "finish_reason": None}],
                                "id": f"chatcmpl-anthropic-{uuid.uuid4()}",
                                "model": target_model,
                                "object": "chat.completion.chunk",
                                "created": int(datetime.now().timestamp())
                            }
                            yield f"data: {json.dumps(openai_compatible_chunk)}\n\n".encode('utf-8')
                        elif chunk.type == "message_stop":
                            finish_reason = chunk.message.stop_reason if hasattr(chunk, 'message') and hasattr(chunk.message, 'stop_reason') else "stop"
                            final_chunk = {
                                 "choices": [{"delta": {}, "index": 0, "finish_reason": finish_reason}],
                                "id": f"chatcmpl-anthropic-{uuid.uuid4()}",
                                "model": target_model,
                                "object": "chat.completion.chunk",
                                "created": int(datetime.now().timestamp())
                            }
                            yield f"data: {json.dumps(final_chunk)}\n\n".encode('utf-8')
                    yield b"data: [DONE]\n\n"
                finally:
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_anthropic_stream_response()), content_type='text/event-stream')
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else:
            print(f"DEBUG - Creating non-streaming request to Anthropic API: model={target_model}")
            response_obj = anthropic_client.messages.create(**anthropic_args)

            response_content = response_obj.content[0].text if response_obj.content and len(response_obj.content) > 0 and hasattr(response_obj.content[0], 'text') else ""

            response_data_converted = {
                "id": f"chatcmpl-anthropic-{response_obj.id}",
                "object": "chat.completion",
                "created": int(datetime.now().timestamp()),
                "model": response_obj.model,
                "choices": [{"index": 0, "message": {"role": response_obj.role, "content": response_content}, "finish_reason": response_obj.stop_reason }],
                "usage": {"prompt_tokens": response_obj.usage.input_tokens, "completion_tokens": response_obj.usage.output_tokens, "total_tokens": response_obj.usage.input_tokens + response_obj.usage.output_tokens}
            }
            print(f"DEBUG - Response from Anthropic API received. Preview: {response_content[:100]}...")
            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps({'request': original_request_json_data, 'response': response_content}) + '\n')

            resp = jsonify(response_data_converted)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except anthropic.APIError as e:
        error_type = getattr(e, 'type', type(e).__name__)
        error_message = getattr(e, 'message', str(e))
        status_code = getattr(e, 'status_code', 500)

        print(f"ERROR DETAILS (Anthropic SDK - APIError): Status: {status_code}, Type: {error_type}, Message: {error_message}")

        error_body = {"error": {"message": error_message, "type": error_type, "code": status_code}}
        resp = jsonify(error_body)
        resp.status_code = status_code
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e:
        print(f"ERROR DETAILS (Anthropic SDK - General): Type: {type(e).__name__}, Message: {str(e)}")
        if original_request_json_data:
            print(f"  Full request data (original for logging): {json.dumps(original_request_json_data)}")
        error_body = {"error": {"message": str(e), "type": type(e).__name__}}
        resp = jsonify(error_body)
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/providers/anthropic.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:04:26 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I'll create the OpenAI provider (which will handle OpenAI-compatible APIs including OpenAI itself):

<write_to_file>
<path>src/dolphin_logger/providers/openai.py</path>
<content>
"""
OpenAI provider implementation for Dolphin Logger.
Handles OpenAI and OpenAI-compatible APIs (including Ollama).
"""

import json
import requests
from flask import Response, stream_with_context, jsonify

from ..logging_utils import get_current_log_file, _should_log_request, log_lock

def handle_openai_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to OpenAI-compatible (including Ollama) REST APIs.

    Args:
        method: HTTP method for the request
        url: Target API URL
        headers: Request headers
        data_bytes: Request body as bytes
        is_stream: Whether this is a streaming request
        original_request_json_data: The original client request for logging

    Returns:
        Flask Response object
    """
    print(f"DEBUG - Sending REST request: {method} {url}")

    try:
        api_response = requests.request(
            method=method, url=url, headers=headers, data=data_bytes,
            stream=is_stream, timeout=300
        )
        print(f"DEBUG - REST API Response status: {api_response.status_code}")

        api_response.raise_for_status()

        log_entry = {'request': original_request_json_data, 'response': None}

        if is_stream:
            def generate_rest_stream_response():
                response_content_parts = []
                try:
                    for line in api_response.iter_lines():
                        if line:
                            yield line + b'\n\n'
                            # For logging, accumulate content from delta if possible
                            if line.startswith(b'data: '):
                                line_data_str = line.decode('utf-8', errors='replace')[6:]
                                if line_data_str.strip() != '[DONE]':
                                    try:
                                        parsed_chunk = json.loads(line_data_str)
                                        choices = parsed_chunk.get('choices', [])
                                        if choices and isinstance(choices, list) and len(choices) > 0:
                                            delta = choices[0].get('delta', {})
                                            if delta and isinstance(delta, dict):
                                                delta_content = delta.get('content', '')
                                                if delta_content and isinstance(delta_content, str):
                                                    response_content_parts.append(delta_content)
                                    except (json.JSONDecodeError, KeyError, IndexError, TypeError):
                                        pass
                finally:
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_rest_stream_response()),
                          content_type=api_response.headers.get('Content-Type', 'text/event-stream'))
            # Propagate relevant headers from upstream response
            for hdr_key in ['Cache-Control', 'Content-Type', 'Transfer-Encoding', 'Date', 'Server']:
                if hdr_key in api_response.headers:
                    resp.headers[hdr_key] = api_response.headers[hdr_key]
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else:
            complete_response_text = ''
            try:
                response_json = api_response.json()
                choices = response_json.get('choices', [])
                if choices and isinstance(choices, list) and len(choices) > 0:
                    message = choices[0].get('message', {})
                    if message and isinstance(message, dict):
                        complete_response_text = message.get('content', '')
                if not complete_response_text and isinstance(response_json, dict):
                    complete_response_text = response_json
                log_entry['response'] = complete_response_text
            except json.JSONDecodeError:
                print("Warning: REST API Response was not JSON. Logging raw text.")
                complete_response_text = api_response.text
                log_entry['response'] = complete_response_text

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps(log_entry) + '\n')

            # Return the raw response content from the target API to the client
            resp = Response(api_response.content,
                          content_type=api_response.headers.get('Content-Type', 'application/json'),
                          status=api_response.status_code)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except requests.exceptions.RequestException as e:
        print(f"ERROR DETAILS (REST API - RequestException): Type: {type(e).__name__}, Message: {str(e)}, URL: {url}")
        error_content_type = 'application/json'
        error_status = 502  # Bad Gateway, typically for network issues with upstream
        error_body_msg = f"Error connecting to upstream API: {str(e)}"
        error_body = {"error": {"message": error_body_msg, "type": type(e).__name__}}

        if e.response is not None:
            print(f"  Upstream response status code: {e.response.status_code}")
            error_status = e.response.status_code
            error_content_type = e.response.headers.get('Content-Type', 'application/json')
            try:
                error_body = e.response.json()
            except json.JSONDecodeError:
                error_body = {"error": {"message": e.response.text, "type": "upstream_error"}}

        resp = jsonify(error_body)
        resp.status_code = error_status
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e:
        print(f"UNEXPECTED REST API HANDLER ERROR: Type: {type(e).__name__}, Message: {str(e)}")
        resp = jsonify({"error": {"message": "An unexpected error occurred in the REST API handler.", "type": type(e).__name__}})
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
</content>
</write_to_file>

[User]
[write_to_file for 'src/dolphin_logger/providers/openai.py'] Result:

The content was successfully saved to src/dolphin_logger/providers/openai.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/providers/openai.py">
"""
OpenAI provider implementation for Dolphin Logger.
Handles OpenAI and OpenAI-compatible APIs (including Ollama).
"""

import json
import requests
from flask import Response, stream_with_context, jsonify

from ..logging_utils import get_current_log_file, _should_log_request, log_lock

def handle_openai_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to OpenAI-compatible (including Ollama) REST APIs.

    Args:
        method: HTTP method for the request
        url: Target API URL
        headers: Request headers
        data_bytes: Request body as bytes
        is_stream: Whether this is a streaming request
        original_request_json_data: The original client request for logging

    Returns:
        Flask Response object
    """
    print(f"DEBUG - Sending REST request: {method} {url}")

    try:
        api_response = requests.request(
            method=method, url=url, headers=headers, data=data_bytes,
            stream=is_stream, timeout=300
        )
        print(f"DEBUG - REST API Response status: {api_response.status_code}")

        api_response.raise_for_status()

        log_entry = {'request': original_request_json_data, 'response': None}

        if is_stream:
            def generate_rest_stream_response():
                response_content_parts = []
                try:
                    for line in api_response.iter_lines():
                        if line:
                            yield line + b'\n\n'
                            # For logging, accumulate content from delta if possible
                            if line.startswith(b'data: '):
                                line_data_str = line.decode('utf-8', errors='replace')[6:]
                                if line_data_str.strip() != '[DONE]':
                                    try:
                                        parsed_chunk = json.loads(line_data_str)
                                        choices = parsed_chunk.get('choices', [])
                                        if choices and isinstance(choices, list) and len(choices) > 0:
                                            delta = choices[0].get('delta', {})
                                            if delta and isinstance(delta, dict):
                                                delta_content = delta.get('content', '')
                                                if delta_content and isinstance(delta_content, str):
                                                    response_content_parts.append(delta_content)
                                    except (json.JSONDecodeError, KeyError, IndexError, TypeError):
                                        pass
                finally:
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_rest_stream_response()),
                          content_type=api_response.headers.get('Content-Type', 'text/event-stream'))
            # Propagate relevant headers from upstream response
            for hdr_key in ['Cache-Control', 'Content-Type', 'Transfer-Encoding', 'Date', 'Server']:
                if hdr_key in api_response.headers:
                    resp.headers[hdr_key] = api_response.headers[hdr_key]
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else:
            complete_response_text = ''
            try:
                response_json = api_response.json()
                choices = response_json.get('choices', [])
                if choices and isinstance(choices, list) and len(choices) > 0:
                    message = choices[0].get('message', {})
                    if message and isinstance(message, dict):
                        complete_response_text = message.get('content', '')
                if not complete_response_text and isinstance(response_json, dict):
                    complete_response_text = response_json
                log_entry['response'] = complete_response_text
            except json.JSONDecodeError:
                print("Warning: REST API Response was not JSON. Logging raw text.")
                complete_response_text = api_response.text
                log_entry['response'] = complete_response_text

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps(log_entry) + '\n')

            # Return the raw response content from the target API to the client
            resp = Response(api_response.content,
                          content_type=api_response.headers.get('Content-Type', 'application/json'),
                          status=api_response.status_code)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except requests.exceptions.RequestException as e:
        print(f"ERROR DETAILS (REST API - RequestException): Type: {type(e).__name__}, Message: {str(e)}, URL: {url}")
        error_content_type = 'application/json'
        error_status = 502  # Bad Gateway, typically for network issues with upstream
        error_body_msg = f"Error connecting to upstream API: {str(e)}"
        error_body = {"error": {"message": error_body_msg, "type": type(e).__name__}}

        if e.response is not None:
            print(f"  Upstream response status code: {e.response.status_code}")
            error_status = e.response.status_code
            error_content_type = e.response.headers.get('Content-Type', 'application/json')
            try:
                error_body = e.response.json()
            except json.JSONDecodeError:
                error_body = {"error": {"message": e.response.text, "type": "upstream_error"}}

        resp = jsonify(error_body)
        resp.status_code = error_status
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e:
        print(f"UNEXPECTED REST API HANDLER ERROR: Type: {type(e).__name__}, Message: {str(e)}")
        resp = jsonify({"error": {"message": "An unexpected error occurred in the REST API handler.", "type": type(e).__name__}})
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/providers/openai.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:04:58 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I'll create the Google provider. Since the current code doesn't have specific Google handling, I'll create a basic structure that follows the same pattern and can be extended later:

<write_to_file>
<path>src/dolphin_logger/providers/google.py</path>
<content>
"""
Google provider implementation for Dolphin Logger.
Handles Google AI APIs (Gemini, etc.).
"""

import json
import requests
from flask import Response, stream_with_context, jsonify

from ..logging_utils import get_current_log_file, _should_log_request, log_lock

def handle_google_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to Google AI APIs.

    Args:
        method: HTTP method for the request
        url: Target API URL
        headers: Request headers
        data_bytes: Request body as bytes
        is_stream: Whether this is a streaming request
        original_request_json_data: The original client request for logging

    Returns:
        Flask Response object
    """
    print(f"DEBUG - Sending Google API request: {method} {url}")

    try:
        api_response = requests.request(
            method=method, url=url, headers=headers, data=data_bytes,
            stream=is_stream, timeout=300
        )
        print(f"DEBUG - Google API Response status: {api_response.status_code}")

        api_response.raise_for_status()

        log_entry = {'request': original_request_json_data, 'response': None}

        if is_stream:
            def generate_google_stream_response():
                response_content_parts = []
                try:
                    for line in api_response.iter_lines():
                        if line:
                            yield line + b'\n\n'
                            # For logging, accumulate content from delta if possible
                            if line.startswith(b'data: '):
                                line_data_str = line.decode('utf-8', errors='replace')[6:]
                                if line_data_str.strip() != '[DONE]':
                                    try:
                                        parsed_chunk = json.loads(line_data_str)
                                        # Google AI might have different response structure
                                        # This is a placeholder that can be adapted based on actual API
                                        choices = parsed_chunk.get('choices', [])
                                        if choices and isinstance(choices, list) and len(choices) > 0:
                                            delta = choices[0].get('delta', {})
                                            if delta and isinstance(delta, dict):
                                                delta_content = delta.get('content', '')
                                                if delta_content and isinstance(delta_content, str):
                                                    response_content_parts.append(delta_content)
                                    except (json.JSONDecodeError, KeyError, IndexError, TypeError):
                                        pass
                finally:
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_google_stream_response()),
                          content_type=api_response.headers.get('Content-Type', 'text/event-stream'))
            # Propagate relevant headers from upstream response
            for hdr_key in ['Cache-Control', 'Content-Type', 'Transfer-Encoding', 'Date', 'Server']:
                if hdr_key in api_response.headers:
                    resp.headers[hdr_key] = api_response.headers[hdr_key]
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else:
            complete_response_text = ''
            try:
                response_json = api_response.json()
                # Google AI might have different response structure
                # This is a placeholder that can be adapted based on actual API
                choices = response_json.get('choices', [])
                if choices and isinstance(choices, list) and len(choices) > 0:
                    message = choices[0].get('message', {})
                    if message and isinstance(message, dict):
                        complete_response_text = message.get('content', '')
                if not complete_response_text and isinstance(response_json, dict):
                    complete_response_text = response_json
                log_entry['response'] = complete_response_text
            except json.JSONDecodeError:
                print("Warning: Google API Response was not JSON. Logging raw text.")
                complete_response_text = api_response.text
                log_entry['response'] = complete_response_text

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps(log_entry) + '\n')

            # Return the raw response content from the target API to the client
            resp = Response(api_response.content,
                          content_type=api_response.headers.get('Content-Type', 'application/json'),
                          status=api_response.status_code)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except requests.exceptions.RequestException as e:
        print(f"ERROR DETAILS (Google API - RequestException): Type: {type(e).__name__}, Message: {str(e)}, URL: {url}")
        error_content_type = 'application/json'
        error_status = 502  # Bad Gateway, typically for network issues with upstream
        error_body_msg = f"Error connecting to upstream API: {str(e)}"
        error_body = {"error": {"message": error_body_msg, "type": type(e).__name__}}

        if e.response is not None:
            print(f"  Upstream response status code: {e.response.status_code}")
            error_status = e.response.status_code
            error_content_type = e.response.headers.get('Content-Type', 'application/json')
            try:
                error_body = e.response.json()
            except json.JSONDecodeError:
                error_body = {"error": {"message": e.response.text, "type": "upstream_error"}}

        resp = jsonify(error_body)
        resp.status_code = error_status
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e:
        print(f"UNEXPECTED GOOGLE API HANDLER ERROR: Type: {type(e).__name__}, Message: {str(e)}")
        resp = jsonify({"error": {"message": "An unexpected error occurred in the Google API handler.", "type": type(e).__name__}})
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
</content>
</write_to_file>

[User]
[write_to_file for 'src/dolphin_logger/providers/google.py'] Result:

The content was successfully saved to src/dolphin_logger/providers/google.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/providers/google.py">
"""
Google provider implementation for Dolphin Logger.
Handles Google AI APIs (Gemini, etc.).
"""

import json
import requests
from flask import Response, stream_with_context, jsonify

from ..logging_utils import get_current_log_file, _should_log_request, log_lock

def handle_google_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to Google AI APIs.

    Args:
        method: HTTP method for the request
        url: Target API URL
        headers: Request headers
        data_bytes: Request body as bytes
        is_stream: Whether this is a streaming request
        original_request_json_data: The original client request for logging

    Returns:
        Flask Response object
    """
    print(f"DEBUG - Sending Google API request: {method} {url}")

    try:
        api_response = requests.request(
            method=method, url=url, headers=headers, data=data_bytes,
            stream=is_stream, timeout=300
        )
        print(f"DEBUG - Google API Response status: {api_response.status_code}")

        api_response.raise_for_status()

        log_entry = {'request': original_request_json_data, 'response': None}

        if is_stream:
            def generate_google_stream_response():
                response_content_parts = []
                try:
                    for line in api_response.iter_lines():
                        if line:
                            yield line + b'\n\n'
                            # For logging, accumulate content from delta if possible
                            if line.startswith(b'data: '):
                                line_data_str = line.decode('utf-8', errors='replace')[6:]
                                if line_data_str.strip() != '[DONE]':
                                    try:
                                        parsed_chunk = json.loads(line_data_str)
                                        # Google AI might have different response structure
                                        # This is a placeholder that can be adapted based on actual API
                                        choices = parsed_chunk.get('choices', [])
                                        if choices and isinstance(choices, list) and len(choices) > 0:
                                            delta = choices[0].get('delta', {})
                                            if delta and isinstance(delta, dict):
                                                delta_content = delta.get('content', '')
                                                if delta_content and isinstance(delta_content, str):
                                                    response_content_parts.append(delta_content)
                                    except (json.JSONDecodeError, KeyError, IndexError, TypeError):
                                        pass
                finally:
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_google_stream_response()),
                          content_type=api_response.headers.get('Content-Type', 'text/event-stream'))
            # Propagate relevant headers from upstream response
            for hdr_key in ['Cache-Control', 'Content-Type', 'Transfer-Encoding', 'Date', 'Server']:
                if hdr_key in api_response.headers:
                    resp.headers[hdr_key] = api_response.headers[hdr_key]
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else:
            complete_response_text = ''
            try:
                response_json = api_response.json()
                # Google AI might have different response structure
                # This is a placeholder that can be adapted based on actual API
                choices = response_json.get('choices', [])
                if choices and isinstance(choices, list) and len(choices) > 0:
                    message = choices[0].get('message', {})
                    if message and isinstance(message, dict):
                        complete_response_text = message.get('content', '')
                if not complete_response_text and isinstance(response_json, dict):
                    complete_response_text = response_json
                log_entry['response'] = complete_response_text
            except json.JSONDecodeError:
                print("Warning: Google API Response was not JSON. Logging raw text.")
                complete_response_text = api_response.text
                log_entry['response'] = complete_response_text

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps(log_entry) + '\n')

            # Return the raw response content from the target API to the client
            resp = Response(api_response.content,
                          content_type=api_response.headers.get('Content-Type', 'application/json'),
                          status=api_response.status_code)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except requests.exceptions.RequestException as e:
        print(f"ERROR DETAILS (Google API - RequestException): Type: {type(e).__name__}, Message: {str(e)}, URL: {url}")
        error_content_type = 'application/json'
        error_status = 502  # Bad Gateway, typically for network issues with upstream
        error_body_msg = f"Error connecting to upstream API: {str(e)}"
        error_body = {"error": {"message": error_body_msg, "type": type(e).__name__}}

        if e.response is not None:
            print(f"  Upstream response status code: {e.response.status_code}")
            error_status = e.response.status_code
            error_content_type = e.response.headers.get('Content-Type', 'application/json')
            try:
                error_body = e.response.json()
            except json.JSONDecodeError:
                error_body = {"error": {"message": e.response.text, "type": "upstream_error"}}

        resp = jsonify(error_body)
        resp.status_code = error_status
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e:
        print(f"UNEXPECTED GOOGLE API HANDLER ERROR: Type: {type(e).__name__}, Message: {str(e)}")
        resp = jsonify({"error": {"message": "An unexpected error occurred in the Google API handler.", "type": type(e).__name__}})
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/providers/google.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/core_proxy.py

# Current Time
6/14/2025, 8:05:28 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I need to update the `core_proxy.py` file to use the new provider structure. I'll refactor it to import and use the individual provider modules:

<replace_in_file>
<path>src/dolphin_logger/core_proxy.py</path>
<diff>
------- SEARCH
import json
import requests
import anthropic
import uuid
from datetime import datetime
from flask import Response, stream_with_context, jsonify

from .logging_utils import get_current_log_file, _should_log_request, log_lock
# MODEL_CONFIG is typically loaded in server.py and passed to get_target_api_config.
=======
import json
from flask import Response

from .providers.anthropic import handle_anthropic_request
from .providers.openai import handle_openai_request
from .providers.google import handle_google_request
# MODEL_CONFIG is typically loaded in server.py and passed to get_target_api_config.
+++++++ REPLACE
</diff>
</replace_in_file>

[User]
[replace_in_file for 'src/dolphin_logger/core_proxy.py'] Result:

The content was successfully saved to src/dolphin_logger/core_proxy.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/core_proxy.py">
import json
from flask import Response

from .providers.anthropic import handle_anthropic_request
from .providers.openai import handle_openai_request
from .providers.google import handle_google_request
# MODEL_CONFIG is typically loaded in server.py and passed to get_target_api_config.

def get_target_api_config(requested_model_id: str, model_config_list: list) -> dict:
    """
    Determines the target API URL, key, and model based on the requested model ID
    and the server's model configuration.

    Args:
        requested_model_id (str): The model ID from the client's request.
        model_config_list (list): The MODEL_CONFIG list (passed from server.py).

    Returns:
        dict: A dictionary containing 'target_api_url', 'target_api_key',
              'target_model', 'provider', and 'error' (if any).
    """
    for model_config in model_config_list:
        if model_config.get("model") == requested_model_id:
            provider = model_config.get("provider")
            target_model = model_config.get("providerModel", requested_model_id)
            api_key = model_config.get("apiKey")
            api_base = model_config.get("apiBase")

            if provider == "ollama":
                return {
                    "target_api_url": api_base or "http://localhost:11434/v1",
                    "target_api_key": "",
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "anthropic":
                return {
                    "target_api_url": "anthropic_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            else: # OpenAI-compatible
                if not api_base:
                    # Ensure a specific error message for missing apiBase for OpenAI-like providers
                    return {"error": f"apiBase not configured for OpenAI-compatible model '{requested_model_id}'"}
                return {
                    "target_api_url": api_base,
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }

    if not model_config_list: # Check if the list itself is empty
        return {"error": f"No models configured. Cannot process request for model '{requested_model_id}'."}
    return {"error": f"Model '{requested_model_id}' not found in configured models."}

def handle_anthropic_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict # This is the original client request for logging
) -> Response:
    """
    Handles requests to the Anthropic SDK.
    """
    print(f"DEBUG - Using Anthropic SDK for request with model: {target_model}")

    try:
        if not target_api_key: # Should be caught by config validation ideally, but good check.
            raise ValueError("Anthropic API key is missing in the configuration for the requested model.")

        # Ensure anthropic client is initialized correctly (as per its docs if specific settings needed)
        anthropic_client = anthropic.Anthropic(api_key=target_api_key)

        original_messages = json_data_for_sdk.get('messages', [])
        max_tokens = json_data_for_sdk.get('max_tokens', 4096) # Default from Anthropic

        # Extract system messages from the messages array and separate them
        system_messages = []
        non_system_messages = []

        for message in original_messages:
            if message.get('role') == 'system':
                system_messages.append(message.get('content', ''))
            else:
                non_system_messages.append(message)

        # Combine system messages into a single system prompt
        system_prompt = '\n\n'.join(system_messages) if system_messages else None

        # Also check for top-level system parameter (in case it's already properly formatted)
        if not system_prompt and json_data_for_sdk.get('system'):
            system_prompt = json_data_for_sdk.get('system')

        anthropic_args = {
            "model": target_model, # This is the providerModel
            "messages": non_system_messages,  # Only non-system messages
            "max_tokens": max_tokens,
            "stream": is_stream,
        }
        if system_prompt: # Only add system to args if it's present
            anthropic_args["system"] = system_prompt

        if is_stream:
            print(f"DEBUG - Creating streaming request to Anthropic API: model={target_model}")
            sdk_stream = anthropic_client.messages.create(**anthropic_args)

            def generate_anthropic_stream_response():
                response_content_parts = []
                # Use original_request_json_data for logging the request part
                log_entry = {'request': original_request_json_data, 'response': None}
                try:
                    for chunk in sdk_stream:
                        if chunk.type == "content_block_delta":
                            delta_content = chunk.delta.text
                            response_content_parts.append(delta_content)
                            openai_compatible_chunk = {
                                "choices": [{"delta": {"content": delta_content}, "index": 0, "finish_reason": None}],
                                "id": f"chatcmpl-anthropic-{uuid.uuid4()}", "model": target_model,
                                "object": "chat.completion.chunk", "created": int(datetime.now().timestamp())
                            }
                            yield f"data: {json.dumps(openai_compatible_chunk)}\n\n".encode('utf-8')
                        elif chunk.type == "message_stop":
                            finish_reason = chunk.message.stop_reason if hasattr(chunk, 'message') and hasattr(chunk.message, 'stop_reason') else "stop"
                            final_chunk = {
                                 "choices": [{"delta": {}, "index": 0, "finish_reason": finish_reason}],
                                "id": f"chatcmpl-anthropic-{uuid.uuid4()}", "model": target_model,
                                "object": "chat.completion.chunk", "created": int(datetime.now().timestamp())
                            }
                            yield f"data: {json.dumps(final_chunk)}\n\n".encode('utf-8')
                    yield b"data: [DONE]\n\n"
                finally: # Ensure logging happens even if stream breaks or client disconnects
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_anthropic_stream_response()), content_type='text/event-stream')
            resp.headers['Access-Control-Allow-Origin'] = '*' # CORS for streaming response
            return resp
        else: # Non-streaming Anthropic request
            print(f"DEBUG - Creating non-streaming request to Anthropic API: model={target_model}")
            response_obj = anthropic_client.messages.create(**anthropic_args)

            response_content = response_obj.content[0].text if response_obj.content and len(response_obj.content) > 0 and hasattr(response_obj.content[0], 'text') else ""

            response_data_converted = {
                "id": f"chatcmpl-anthropic-{response_obj.id}", "object": "chat.completion",
                "created": int(datetime.now().timestamp()), # Consider more accurate timestamp if available
                "model": response_obj.model, # Use model from Anthropic's response
                "choices": [{"index": 0, "message": {"role": response_obj.role, "content": response_content}, "finish_reason": response_obj.stop_reason }],
                "usage": {"prompt_tokens": response_obj.usage.input_tokens, "completion_tokens": response_obj.usage.output_tokens, "total_tokens": response_obj.usage.input_tokens + response_obj.usage.output_tokens}
            }
            print(f"DEBUG - Response from Anthropic API received. Preview: {response_content[:100]}...")
            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps({'request': original_request_json_data, 'response': response_content}) + '\n')

            resp = jsonify(response_data_converted)
            resp.headers['Access-Control-Allow-Origin'] = '*' # CORS for non-streaming response
            return resp

    except anthropic.APIError as e: # Specific error handling for Anthropic
        # Extract error details safely - BadRequestError and other APIError subclasses may not have 'type' attribute
        error_type = getattr(e, 'type', type(e).__name__)
        error_message = getattr(e, 'message', str(e))
        status_code = getattr(e, 'status_code', 500)

        print(f"ERROR DETAILS (Anthropic SDK - APIError): Status: {status_code}, Type: {error_type}, Message: {error_message}")

        error_body = {"error": {"message": error_message, "type": error_type, "code": status_code}}
        resp = jsonify(error_body)
        resp.status_code = status_code
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e: # Catch-all for other errors (e.g., ValueError, network issues not caught by APIError)
        print(f"ERROR DETAILS (Anthropic SDK - General): Type: {type(e).__name__}, Message: {str(e)}")
        if original_request_json_data: # Log original request for context on error
            print(f"  Full request data (original for logging): {json.dumps(original_request_json_data)}")
        error_body = {"error": {"message": str(e), "type": type(e).__name__}}
        resp = jsonify(error_body)
        resp.status_code = 500 # Internal Server Error for unexpected issues
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp

def handle_rest_api_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes, # This is the already encoded byte string for the request body
    is_stream: bool,
    original_request_json_data: dict # This is the original client request (parsed JSON) for logging
) -> Response:
    """
    Handles requests to OpenAI-compatible (including Ollama) REST APIs.
    """
    print(f"DEBUG - Sending REST request: {method} {url}")
    # Avoid logging full headers/data here in production due to sensitivity. Previews are safer.
    # print(f"DEBUG - Headers: {headers}")
    # if data_bytes: print(f"DEBUG - Request data preview: {data_bytes[:200]}...")

    try:
        api_response = requests.request(
            method=method, url=url, headers=headers, data=data_bytes,
            stream=is_stream, timeout=300 # Standard timeout
        )
        print(f"DEBUG - REST API Response status: {api_response.status_code}")
        # if not is_stream: print(f"DEBUG - REST API Response preview: {api_response.text[:200]}...")

        api_response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)

        log_entry = {'request': original_request_json_data, 'response': None}

        if is_stream:
            def generate_rest_stream_response():
                response_content_parts = []
                try:
                    for line in api_response.iter_lines(): # iter_lines decodes by default (utf-8)
                        if line:
                            yield line + b'\n\n' # Pass through to client, maintaining SSE format
                            # For logging, accumulate content from delta if possible
                            if line.startswith(b'data: '):
                                line_data_str = line.decode('utf-8', errors='replace')[6:] # Get content part
                                if line_data_str.strip() != '[DONE]':
                                    try:
                                        parsed_chunk = json.loads(line_data_str)
                                        choices = parsed_chunk.get('choices', [])
                                        if choices and isinstance(choices, list) and len(choices) > 0:
                                            delta = choices[0].get('delta', {})
                                            if delta and isinstance(delta, dict):
                                                delta_content = delta.get('content', '')
                                                if delta_content and isinstance(delta_content, str):
                                                    response_content_parts.append(delta_content)
                                    except (json.JSONDecodeError, KeyError, IndexError, TypeError):
                                        # print(f"Warning: Error processing stream line for REST logging: {line_data_str}")
                                        pass # Don't let logging error break the stream to client
                finally: # Ensure logging happens
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_rest_stream_response()), content_type=api_response.headers.get('Content-Type', 'text/event-stream'))
            # Propagate relevant headers from upstream response
            for hdr_key in ['Cache-Control', 'Content-Type', 'Transfer-Encoding', 'Date', 'Server']:
                if hdr_key in api_response.headers: resp.headers[hdr_key] = api_response.headers[hdr_key]
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else: # Non-streaming REST response
            complete_response_text = ''
            try:
                response_json = api_response.json() # Parse the JSON from the target API response
                choices = response_json.get('choices', [])
                if choices and isinstance(choices, list) and len(choices) > 0:
                    message = choices[0].get('message', {})
                    if message and isinstance(message, dict): complete_response_text = message.get('content', '')
                if not complete_response_text and isinstance(response_json, dict): # If content extraction failed, log whole JSON
                    complete_response_text = response_json # Log the full JSON object
                log_entry['response'] = complete_response_text
            except json.JSONDecodeError:
                print("Warning: REST API Response was not JSON. Logging raw text.")
                complete_response_text = api_response.text
                log_entry['response'] = complete_response_text

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps(log_entry) + '\n')

            # Return the raw response content from the target API to the client
            resp = Response(api_response.content, content_type=api_response.headers.get('Content-Type', 'application/json'), status=api_response.status_code)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except requests.exceptions.RequestException as e:
        print(f"ERROR DETAILS (REST API - RequestException): Type: {type(e).__name__}, Message: {str(e)}, URL: {url}")
        error_content_type = 'application/json'
        error_status = 502 # Bad Gateway, typically for network issues with upstream
        error_body_msg = f"Error connecting to upstream API: {str(e)}"
        error_body = {"error": {"message": error_body_msg, "type": type(e).__name__}}

        if e.response is not None: # If the exception is from an HTTP error status (4xx, 5xx from upstream)
            print(f"  Upstream response status code: {e.response.status_code}")
            error_status = e.response.status_code # Use upstream's status code
            error_content_type = e.response.headers.get('Content-Type', 'application/json')
            try: error_body = e.response.json() # Try to parse upstream error
            except json.JSONDecodeError: error_body = {"error": {"message": e.response.text, "type": "upstream_error"}}

        resp = jsonify(error_body)
        resp.status_code = error_status
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e: # Catch-all for any other unexpected errors
        print(f"UNEXPECTED REST API HANDLER ERROR: Type: {type(e).__name__}, Message: {str(e)}")
        # import traceback; traceback.print_exc() # Good for debugging
        resp = jsonify({"error": {"message": "An unexpected error occurred in the REST API handler.", "type": type(e).__name__}})
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/core_proxy.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:05:42 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I need to replace the old Anthropic handler function with a call to the new provider:

<replace_in_file>
<path>src/dolphin_logger/core_proxy.py</path>
<diff>
------- SEARCH
def handle_anthropic_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict # This is the original client request for logging
) -> Response:
    """
    Handles requests to the Anthropic SDK.
    """
    print(f"DEBUG - Using Anthropic SDK for request with model: {target_model}")

    try:
        if not target_api_key: # Should be caught by config validation ideally, but good check.
            raise ValueError("Anthropic API key is missing in the configuration for the requested model.")

        # Ensure anthropic client is initialized correctly (as per its docs if specific settings needed)
        anthropic_client = anthropic.Anthropic(api_key=target_api_key)

        original_messages = json_data_for_sdk.get('messages', [])
        max_tokens = json_data_for_sdk.get('max_tokens', 4096) # Default from Anthropic

        # Extract system messages from the messages array and separate them
        system_messages = []
        non_system_messages = []

        for message in original_messages:
            if message.get('role') == 'system':
                system_messages.append(message.get('content', ''))
            else:
                non_system_messages.append(message)

        # Combine system messages into a single system prompt
        system_prompt = '\n\n'.join(system_messages) if system_messages else None

        # Also check for top-level system parameter (in case it's already properly formatted)
        if not system_prompt and json_data_for_sdk.get('system'):
            system_prompt = json_data_for_sdk.get('system')

        anthropic_args = {
            "model": target_model, # This is the providerModel
            "messages": non_system_messages,  # Only non-system messages
            "max_tokens": max_tokens,
            "stream": is_stream,
        }
        if system_prompt: # Only add system to args if it's present
            anthropic_args["system"] = system_prompt

        if is_stream:
            print(f"DEBUG - Creating streaming request to Anthropic API: model={target_model}")
            sdk_stream = anthropic_client.messages.create(**anthropic_args)

            def generate_anthropic_stream_response():
                response_content_parts = []
                # Use original_request_json_data for logging the request part
                log_entry = {'request': original_request_json_data, 'response': None}
                try:
                    for chunk in sdk_stream:
                        if chunk.type == "content_block_delta":
                            delta_content = chunk.delta.text
                            response_content_parts.append(delta_content)
                            openai_compatible_chunk = {
                                "choices": [{"delta": {"content": delta_content}, "index": 0, "finish_reason": None}],
                                "id": f"chatcmpl-anthropic-{uuid.uuid4()}", "model": target_model,
                                "object": "chat.completion.chunk", "created": int(datetime.now().timestamp())
                            }
                            yield f"data: {json.dumps(openai_compatible_chunk)}\n\n".encode('utf-8')
                        elif chunk.type == "message_stop":
                            finish_reason = chunk.message.stop_reason if hasattr(chunk, 'message') and hasattr(chunk.message, 'stop_reason') else "stop"
                            final_chunk = {
                                 "choices": [{"delta": {}, "index": 0, "finish_reason": finish_reason}],
                                "id": f"chatcmpl-anthropic-{uuid.uuid4()}", "model": target_model,
                                "object": "chat.completion.chunk", "created": int(datetime.now().timestamp())
                            }
                            yield f"data: {json.dumps(final_chunk)}\n\n".encode('utf-8')
                    yield b"data: [DONE]\n\n"
                finally: # Ensure logging happens even if stream breaks or client disconnects
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_anthropic_stream_response()), content_type='text/event-stream')
            resp.headers['Access-Control-Allow-Origin'] = '*' # CORS for streaming response
            return resp
        else: # Non-streaming Anthropic request
            print(f"DEBUG - Creating non-streaming request to Anthropic API: model={target_model}")
            response_obj = anthropic_client.messages.create(**anthropic_args)

            response_content = response_obj.content[0].text if response_obj.content and len(response_obj.content) > 0 and hasattr(response_obj.content[0], 'text') else ""

            response_data_converted = {
                "id": f"chatcmpl-anthropic-{response_obj.id}", "object": "chat.completion",
                "created": int(datetime.now().timestamp()), # Consider more accurate timestamp if available
                "model": response_obj.model, # Use model from Anthropic's response
                "choices": [{"index": 0, "message": {"role": response_obj.role, "content": response_content}, "finish_reason": response_obj.stop_reason }],
                "usage": {"prompt_tokens": response_obj.usage.input_tokens, "completion_tokens": response_obj.usage.output_tokens, "total_tokens": response_obj.usage.input_tokens + response_obj.usage.output_tokens}
            }
            print(f"DEBUG - Response from Anthropic API received. Preview: {response_content[:100]}...")
            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps({'request': original_request_json_data, 'response': response_content}) + '\n')

            resp = jsonify(response_data_converted)
            resp.headers['Access-Control-Allow-Origin'] = '*' # CORS for non-streaming response
            return resp

    except anthropic.APIError as e: # Specific error handling for Anthropic
        # Extract error details safely - BadRequestError and other APIError subclasses may not have 'type' attribute
        error_type = getattr(e, 'type', type(e).__name__)
        error_message = getattr(e, 'message', str(e))
        status_code = getattr(e, 'status_code', 500)

        print(f"ERROR DETAILS (Anthropic SDK - APIError): Status: {status_code}, Type: {error_type}, Message: {error_message}")

        error_body = {"error": {"message": error_message, "type": error_type, "code": status_code}}
        resp = jsonify(error_body)
        resp.status_code = status_code
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e: # Catch-all for other errors (e.g., ValueError, network issues not caught by APIError)
        print(f"ERROR DETAILS (Anthropic SDK - General): Type: {type(e).__name__}, Message: {str(e)}")
        if original_request_json_data: # Log original request for context on error
            print(f"  Full request data (original for logging): {json.dumps(original_request_json_data)}")
        error_body = {"error": {"message": str(e), "type": type(e).__name__}}
        resp = jsonify(error_body)
        resp.status_code = 500 # Internal Server Error for unexpected issues
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
=======
def handle_anthropic_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Anthropic SDK.
    Delegates to the Anthropic provider.
    """
    return handle_anthropic_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )
+++++++ REPLACE
</diff>
</replace_in_file>

[User]
[replace_in_file for 'src/dolphin_logger/core_proxy.py'] Result:

The content was successfully saved to src/dolphin_logger/core_proxy.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/core_proxy.py">
import json
from flask import Response

from .providers.anthropic import handle_anthropic_request
from .providers.openai import handle_openai_request
from .providers.google import handle_google_request
# MODEL_CONFIG is typically loaded in server.py and passed to get_target_api_config.

def get_target_api_config(requested_model_id: str, model_config_list: list) -> dict:
    """
    Determines the target API URL, key, and model based on the requested model ID
    and the server's model configuration.

    Args:
        requested_model_id (str): The model ID from the client's request.
        model_config_list (list): The MODEL_CONFIG list (passed from server.py).

    Returns:
        dict: A dictionary containing 'target_api_url', 'target_api_key',
              'target_model', 'provider', and 'error' (if any).
    """
    for model_config in model_config_list:
        if model_config.get("model") == requested_model_id:
            provider = model_config.get("provider")
            target_model = model_config.get("providerModel", requested_model_id)
            api_key = model_config.get("apiKey")
            api_base = model_config.get("apiBase")

            if provider == "ollama":
                return {
                    "target_api_url": api_base or "http://localhost:11434/v1",
                    "target_api_key": "",
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "anthropic":
                return {
                    "target_api_url": "anthropic_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            else: # OpenAI-compatible
                if not api_base:
                    # Ensure a specific error message for missing apiBase for OpenAI-like providers
                    return {"error": f"apiBase not configured for OpenAI-compatible model '{requested_model_id}'"}
                return {
                    "target_api_url": api_base,
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }

    if not model_config_list: # Check if the list itself is empty
        return {"error": f"No models configured. Cannot process request for model '{requested_model_id}'."}
    return {"error": f"Model '{requested_model_id}' not found in configured models."}

def handle_anthropic_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Anthropic SDK.
    Delegates to the Anthropic provider.
    """
    return handle_anthropic_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )

def handle_rest_api_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes, # This is the already encoded byte string for the request body
    is_stream: bool,
    original_request_json_data: dict # This is the original client request (parsed JSON) for logging
) -> Response:
    """
    Handles requests to OpenAI-compatible (including Ollama) REST APIs.
    """
    print(f"DEBUG - Sending REST request: {method} {url}")
    # Avoid logging full headers/data here in production due to sensitivity. Previews are safer.
    # print(f"DEBUG - Headers: {headers}")
    # if data_bytes: print(f"DEBUG - Request data preview: {data_bytes[:200]}...")

    try:
        api_response = requests.request(
            method=method, url=url, headers=headers, data=data_bytes,
            stream=is_stream, timeout=300 # Standard timeout
        )
        print(f"DEBUG - REST API Response status: {api_response.status_code}")
        # if not is_stream: print(f"DEBUG - REST API Response preview: {api_response.text[:200]}...")

        api_response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)

        log_entry = {'request': original_request_json_data, 'response': None}

        if is_stream:
            def generate_rest_stream_response():
                response_content_parts = []
                try:
                    for line in api_response.iter_lines(): # iter_lines decodes by default (utf-8)
                        if line:
                            yield line + b'\n\n' # Pass through to client, maintaining SSE format
                            # For logging, accumulate content from delta if possible
                            if line.startswith(b'data: '):
                                line_data_str = line.decode('utf-8', errors='replace')[6:] # Get content part
                                if line_data_str.strip() != '[DONE]':
                                    try:
                                        parsed_chunk = json.loads(line_data_str)
                                        choices = parsed_chunk.get('choices', [])
                                        if choices and isinstance(choices, list) and len(choices) > 0:
                                            delta = choices[0].get('delta', {})
                                            if delta and isinstance(delta, dict):
                                                delta_content = delta.get('content', '')
                                                if delta_content and isinstance(delta_content, str):
                                                    response_content_parts.append(delta_content)
                                    except (json.JSONDecodeError, KeyError, IndexError, TypeError):
                                        # print(f"Warning: Error processing stream line for REST logging: {line_data_str}")
                                        pass # Don't let logging error break the stream to client
                finally: # Ensure logging happens
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_rest_stream_response()), content_type=api_response.headers.get('Content-Type', 'text/event-stream'))
            # Propagate relevant headers from upstream response
            for hdr_key in ['Cache-Control', 'Content-Type', 'Transfer-Encoding', 'Date', 'Server']:
                if hdr_key in api_response.headers: resp.headers[hdr_key] = api_response.headers[hdr_key]
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else: # Non-streaming REST response
            complete_response_text = ''
            try:
                response_json = api_response.json() # Parse the JSON from the target API response
                choices = response_json.get('choices', [])
                if choices and isinstance(choices, list) and len(choices) > 0:
                    message = choices[0].get('message', {})
                    if message and isinstance(message, dict): complete_response_text = message.get('content', '')
                if not complete_response_text and isinstance(response_json, dict): # If content extraction failed, log whole JSON
                    complete_response_text = response_json # Log the full JSON object
                log_entry['response'] = complete_response_text
            except json.JSONDecodeError:
                print("Warning: REST API Response was not JSON. Logging raw text.")
                complete_response_text = api_response.text
                log_entry['response'] = complete_response_text

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps(log_entry) + '\n')

            # Return the raw response content from the target API to the client
            resp = Response(api_response.content, content_type=api_response.headers.get('Content-Type', 'application/json'), status=api_response.status_code)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except requests.exceptions.RequestException as e:
        print(f"ERROR DETAILS (REST API - RequestException): Type: {type(e).__name__}, Message: {str(e)}, URL: {url}")
        error_content_type = 'application/json'
        error_status = 502 # Bad Gateway, typically for network issues with upstream
        error_body_msg = f"Error connecting to upstream API: {str(e)}"
        error_body = {"error": {"message": error_body_msg, "type": type(e).__name__}}

        if e.response is not None: # If the exception is from an HTTP error status (4xx, 5xx from upstream)
            print(f"  Upstream response status code: {e.response.status_code}")
            error_status = e.response.status_code # Use upstream's status code
            error_content_type = e.response.headers.get('Content-Type', 'application/json')
            try: error_body = e.response.json() # Try to parse upstream error
            except json.JSONDecodeError: error_body = {"error": {"message": e.response.text, "type": "upstream_error"}}

        resp = jsonify(error_body)
        resp.status_code = error_status
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e: # Catch-all for any other unexpected errors
        print(f"UNEXPECTED REST API HANDLER ERROR: Type: {type(e).__name__}, Message: {str(e)}")
        # import traceback; traceback.print_exc() # Good for debugging
        resp = jsonify({"error": {"message": "An unexpected error occurred in the REST API handler.", "type": type(e).__name__}})
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/core_proxy.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:06:27 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I need to replace the old REST API handler function with calls to the appropriate provider based on the provider type. Let me update the `handle_rest_api_request` function to route to the correct provider:

<replace_in_file>
<path>src/dolphin_logger/core_proxy.py</path>
<diff>
------- SEARCH
def handle_rest_api_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes, # This is the already encoded byte string for the request body
    is_stream: bool,
    original_request_json_data: dict # This is the original client request (parsed JSON) for logging
) -> Response:
    """
    Handles requests to OpenAI-compatible (including Ollama) REST APIs.
    """
    print(f"DEBUG - Sending REST request: {method} {url}")
    # Avoid logging full headers/data here in production due to sensitivity. Previews are safer.
    # print(f"DEBUG - Headers: {headers}")
    # if data_bytes: print(f"DEBUG - Request data preview: {data_bytes[:200]}...")

    try:
        api_response = requests.request(
            method=method, url=url, headers=headers, data=data_bytes,
            stream=is_stream, timeout=300 # Standard timeout
        )
        print(f"DEBUG - REST API Response status: {api_response.status_code}")
        # if not is_stream: print(f"DEBUG - REST API Response preview: {api_response.text[:200]}...")

        api_response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)

        log_entry = {'request': original_request_json_data, 'response': None}

        if is_stream:
            def generate_rest_stream_response():
                response_content_parts = []
                try:
                    for line in api_response.iter_lines(): # iter_lines decodes by default (utf-8)
                        if line:
                            yield line + b'\n\n' # Pass through to client, maintaining SSE format
                            # For logging, accumulate content from delta if possible
                            if line.startswith(b'data: '):
                                line_data_str = line.decode('utf-8', errors='replace')[6:] # Get content part
                                if line_data_str.strip() != '[DONE]':
                                    try:
                                        parsed_chunk = json.loads(line_data_str)
                                        choices = parsed_chunk.get('choices', [])
                                        if choices and isinstance(choices, list) and len(choices) > 0:
                                            delta = choices[0].get('delta', {})
                                            if delta and isinstance(delta, dict):
                                                delta_content = delta.get('content', '')
                                                if delta_content and isinstance(delta_content, str):
                                                    response_content_parts.append(delta_content)
                                    except (json.JSONDecodeError, KeyError, IndexError, TypeError):
                                        # print(f"Warning: Error processing stream line for REST logging: {line_data_str}")
                                        pass # Don't let logging error break the stream to client
                finally: # Ensure logging happens
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_rest_stream_response()), content_type=api_response.headers.get('Content-Type', 'text/event-stream'))
            # Propagate relevant headers from upstream response
            for hdr_key in ['Cache-Control', 'Content-Type', 'Transfer-Encoding', 'Date', 'Server']:
                if hdr_key in api_response.headers: resp.headers[hdr_key] = api_response.headers[hdr_key]
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else: # Non-streaming REST response
            complete_response_text = ''
            try:
                response_json = api_response.json() # Parse the JSON from the target API response
                choices = response_json.get('choices', [])
                if choices and isinstance(choices, list) and len(choices) > 0:
                    message = choices[0].get('message', {})
                    if message and isinstance(message, dict): complete_response_text = message.get('content', '')
                if not complete_response_text and isinstance(response_json, dict): # If content extraction failed, log whole JSON
                    complete_response_text = response_json # Log the full JSON object
                log_entry['response'] = complete_response_text
            except json.JSONDecodeError:
                print("Warning: REST API Response was not JSON. Logging raw text.")
                complete_response_text = api_response.text
                log_entry['response'] = complete_response_text

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps(log_entry) + '\n')

            # Return the raw response content from the target API to the client
            resp = Response(api_response.content, content_type=api_response.headers.get('Content-Type', 'application/json'), status=api_response.status_code)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except requests.exceptions.RequestException as e:
        print(f"ERROR DETAILS (REST API - RequestException): Type: {type(e).__name__}, Message: {str(e)}, URL: {url}")
        error_content_type = 'application/json'
        error_status = 502 # Bad Gateway, typically for network issues with upstream
        error_body_msg = f"Error connecting to upstream API: {str(e)}"
        error_body = {"error": {"message": error_body_msg, "type": type(e).__name__}}

        if e.response is not None: # If the exception is from an HTTP error status (4xx, 5xx from upstream)
            print(f"  Upstream response status code: {e.response.status_code}")
            error_status = e.response.status_code # Use upstream's status code
            error_content_type = e.response.headers.get('Content-Type', 'application/json')
            try: error_body = e.response.json() # Try to parse upstream error
            except json.JSONDecodeError: error_body = {"error": {"message": e.response.text, "type": "upstream_error"}}

        resp = jsonify(error_body)
        resp.status_code = error_status
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e: # Catch-all for any other unexpected errors
        print(f"UNEXPECTED REST API HANDLER ERROR: Type: {type(e).__name__}, Message: {str(e)}")
        # import traceback; traceback.print_exc() # Good for debugging
        resp = jsonify({"error": {"message": "An unexpected error occurred in the REST API handler.", "type": type(e).__name__}})
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
=======
def handle_rest_api_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict,
    provider: str = "openai"
) -> Response:
    """
    Handles requests to REST APIs by routing to the appropriate provider.
    Delegates to the specific provider based on the provider parameter.
    """
    # Route to the appropriate provider
    if provider == "google":
        return handle_google_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
    else:
        # Default to OpenAI provider for OpenAI-compatible APIs (including Ollama)
        return handle_openai_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
+++++++ REPLACE
</diff>
</replace_in_file>

[User]
[replace_in_file for 'src/dolphin_logger/core_proxy.py'] Result:

The content was successfully saved to src/dolphin_logger/core_proxy.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/core_proxy.py">
import json
from flask import Response

from .providers.anthropic import handle_anthropic_request
from .providers.openai import handle_openai_request
from .providers.google import handle_google_request
# MODEL_CONFIG is typically loaded in server.py and passed to get_target_api_config.

def get_target_api_config(requested_model_id: str, model_config_list: list) -> dict:
    """
    Determines the target API URL, key, and model based on the requested model ID
    and the server's model configuration.

    Args:
        requested_model_id (str): The model ID from the client's request.
        model_config_list (list): The MODEL_CONFIG list (passed from server.py).

    Returns:
        dict: A dictionary containing 'target_api_url', 'target_api_key',
              'target_model', 'provider', and 'error' (if any).
    """
    for model_config in model_config_list:
        if model_config.get("model") == requested_model_id:
            provider = model_config.get("provider")
            target_model = model_config.get("providerModel", requested_model_id)
            api_key = model_config.get("apiKey")
            api_base = model_config.get("apiBase")

            if provider == "ollama":
                return {
                    "target_api_url": api_base or "http://localhost:11434/v1",
                    "target_api_key": "",
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "anthropic":
                return {
                    "target_api_url": "anthropic_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            else: # OpenAI-compatible
                if not api_base:
                    # Ensure a specific error message for missing apiBase for OpenAI-like providers
                    return {"error": f"apiBase not configured for OpenAI-compatible model '{requested_model_id}'"}
                return {
                    "target_api_url": api_base,
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }

    if not model_config_list: # Check if the list itself is empty
        return {"error": f"No models configured. Cannot process request for model '{requested_model_id}'."}
    return {"error": f"Model '{requested_model_id}' not found in configured models."}

def handle_anthropic_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Anthropic SDK.
    Delegates to the Anthropic provider.
    """
    return handle_anthropic_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )

def handle_rest_api_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict,
    provider: str = "openai"
) -> Response:
    """
    Handles requests to REST APIs by routing to the appropriate provider.
    Delegates to the specific provider based on the provider parameter.
    """
    # Route to the appropriate provider
    if provider == "google":
        return handle_google_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
    else:
        # Default to OpenAI provider for OpenAI-compatible APIs (including Ollama)
        return handle_openai_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/core_proxy.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:08:24 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I need to update the server.py file to pass the provider information to the `handle_rest_api_request` function. Let me check how it's currently being called:

<read_file>
<path>src/dolphin_logger/server.py</path>
</read_file>

[User]
[read_file for 'src/dolphin_logger/server.py'] Result:

import os
import json
from flask import Flask, request, Response, jsonify
from flask_cors import CORS

# Imports from our refactored modules
from .config import load_config, get_config_path, get_logs_dir
from .core_proxy import get_target_api_config, handle_anthropic_sdk_request, handle_rest_api_request
# logging_utils are used by core_proxy, not directly by server.py usually.

app = Flask(__name__)

# Enable CORS with explicit configuration
CORS(app, resources={r"/*": {"origins": "*", "allow_headers": ["Content-Type", "Authorization"]}})

# Global for model config, loaded when server starts
MODEL_CONFIG: list = []

# Handle preflight OPTIONS requests explicitly
@app.route('/', defaults={'path': ''}, methods=['OPTIONS'])
@app.route('/<path:path>', methods=['OPTIONS'])
def handle_options(path: str | None = None): # path can be None if accessing '/'
    resp = app.make_default_options_response()
    return resp

@app.route('/health', methods=['GET'])
def health_check():
    """Provides a health check endpoint for the server."""
    global MODEL_CONFIG # Ensure we are referring to the global
    if MODEL_CONFIG:
        response_data = {"status": "ok", "message": "Server is healthy, configuration loaded."}
        status_code = 200
    else:
        response_data = {"status": "error", "message": "Server is running, but configuration might have issues (e.g., no models loaded)."}
        status_code = 500

    resp = jsonify(response_data)
    resp.status_code = status_code
    return resp

@app.route('/', defaults={'path': ''}, methods=['GET', 'POST', 'PUT', 'DELETE'])
@app.route('/<path:path>', methods=['GET', 'POST', 'PUT', 'DELETE'])
def proxy(path: str | None = None): # path can be None
    global MODEL_CONFIG # Ensure we are referring to the global

    # Check if client is trying to use HTTPS on HTTP server
    if request.headers.get('X-Forwarded-Proto') == 'https' or \
       request.url.startswith('https://') or \
       request.headers.get('X-Forwarded-Ssl') == 'on':
        print("\n" + "="*60)
        print("🚨 HTTPS CONNECTION DETECTED!")
        print("="*60)
        print("You probably want to change https://localhost to http://localhost")
        print("Dolphin Logger runs on HTTP, not HTTPS.")
        print("Update your client configuration to use:")
        print(f"   http://localhost:{request.environ.get('SERVER_PORT', '5001')}")
        print("="*60 + "\n")
        return jsonify({
            "error": "HTTPS not supported. Use http://localhost instead of https://localhost",
            "suggestion": f"Change your client configuration to: http://localhost:{request.environ.get('SERVER_PORT', '5001')}"
        }), 400

    # Handle /v1/models endpoint directly here
    # Ensure path is not None before calling endswith
    if path and (path.endswith('/models') or path.endswith('/models/')):
        models_response = []
        if MODEL_CONFIG:
            for model_config_entry in MODEL_CONFIG:
                model_id = model_config_entry.get("model")
                provider = model_config_entry.get("provider", "unknown")
                if model_id:
                    models_response.append({
                        "id": model_id, "object": "model", "created": 1686935002,
                        "owned_by": provider, "provider": provider,
                        "provider_model": model_config_entry.get("providerModel", "")
                    })

        resp_data = {"data": models_response, "object": "list"}
        return jsonify(resp_data)

    # --- Main Proxy Logic ---
    raw_data_bytes = request.get_data()

    try:
        decoded_data_str = raw_data_bytes.decode('utf-8') if raw_data_bytes else "{}"
        original_request_json = json.loads(decoded_data_str) if decoded_data_str else {}
    except json.JSONDecodeError:
        return jsonify({"error": "Invalid JSON in request body"}), 400

    if request.method != 'POST' or 'model' not in original_request_json:
        error_msg_detail = "POST request JSON body must include a 'model' field for this endpoint." \
                           if request.method == 'POST' else \
                           "This endpoint primarily proxies POST requests with a 'model' field."
        return jsonify({"error": error_msg_detail}), 400

    requested_model_id = original_request_json.get('model')
    print(f"Requested model ID: {requested_model_id}")

    api_config_result = get_target_api_config(requested_model_id, MODEL_CONFIG)

    if api_config_result.get("error"):
        error_msg = api_config_result["error"]
        print(f"Error getting API config: {error_msg}")
        return jsonify({"error": error_msg}), 400

    target_api_url = api_config_result["target_api_url"]
    target_api_key = api_config_result["target_api_key"]
    target_model_for_provider = api_config_result["target_model"]

    json_data_for_downstream_call = original_request_json.copy()
    if target_model_for_provider != requested_model_id:
        json_data_for_downstream_call['model'] = target_model_for_provider

    data_to_send_final_bytes = json.dumps(json_data_for_downstream_call).encode('utf-8') if json_data_for_downstream_call else raw_data_bytes
    is_stream = original_request_json.get('stream', False)

    if target_api_url == "anthropic_sdk":
        return handle_anthropic_sdk_request(
            json_data_for_sdk=json_data_for_downstream_call,
            target_model=target_model_for_provider,
            target_api_key=target_api_key,
            is_stream=is_stream,
            original_request_json_data=original_request_json
        )
    else:
        # Handle URL construction more intelligently
        # If the target_api_url already contains a path (like /private/chat/completions),
        # don't append the original request path
        if path and path.startswith('v1/'):
            # For requests to /v1/chat/completions, extract just the endpoint part
            endpoint_part = path[3:]  # Remove 'v1/' prefix
            downstream_url = f"{target_api_url.rstrip('/')}/{endpoint_part}"
        else:
            # For other paths, use them as-is
            downstream_path_segment = path.lstrip('/') if path else ''
            downstream_url = f"{target_api_url.rstrip('/')}/{downstream_path_segment}"
        print(f"Proxying REST request to: {downstream_url}")

        downstream_headers = {
            k: v for k, v in request.headers.items()
            if k.lower() not in ['host', 'authorization', 'content-length', 'connection', 'user-agent']
        }
        downstream_headers['Host'] = target_api_url.split('//')[-1].split('/')[0]
        if target_api_key:
            downstream_headers['Authorization'] = f'Bearer {target_api_key}'

        return handle_rest_api_request(
            method=request.method,
            url=downstream_url,
            headers=downstream_headers,
            data_bytes=data_to_send_final_bytes,
            is_stream=is_stream,
            original_request_json_data=original_request_json
        )

def run_server_main(port=None): # Renamed to avoid conflict with any 'main' in cli.py if imported directly
    """Sets up and runs the Flask development server."""
    global MODEL_CONFIG
    try:
        config_data = load_config()
        MODEL_CONFIG = config_data.get('models', [])

        # Enhanced startup status summary
        print("\n" + "="*50)
        print("🐬 Dolphin Logger Server Starting")
        print("="*50)

        if MODEL_CONFIG:
            print(f"✅ Loaded {len(MODEL_CONFIG)} model(s):")
            for model in MODEL_CONFIG:
                model_name = model.get('model', 'unknown')
                provider = model.get('provider', 'unknown')
                provider_model = model.get('providerModel', 'unknown')
                print(f"   • {model_name} ({provider}: {provider_model})")
        else:
            print("⚠  Warning: No models found in configuration.")

    except Exception as e:
        print(f"❌ Critical Error loading config: {e}. Server might not function correctly.")
        MODEL_CONFIG = []

    # Port priority: CLI argument > environment variable > default (5001)
    if port is None:
        port = int(os.environ.get('PORT', 5001))

    base_url = f"http://localhost:{port}"

    print(f"\n📍 Server Details:")
    print(f"   • URL: {base_url}")
    print(f"   • Health Check: {base_url}/health")
    print(f"   • Models Endpoint: {base_url}/v1/models")
    print(f"   • Chat Endpoint: {base_url}/v1/chat/completions")
    print(f"\n📁 File Locations:")
    print(f"   • Configuration: {get_config_path()}")
    print(f"   • Logs Directory: {get_logs_dir()}")
    print(f"\n🚀 Server ready! Use Ctrl+C to stop.")
    print("="*50 + "\n")

    app.run(host='0.0.0.0', port=port, debug=False)

<environment_details>
# VSCode Visible Files
src/dolphin_logger/core_proxy.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
src/dolphin_logger/server.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:08:36 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I need to update the server.py file to pass the provider information to the `handle_rest_api_request` function. I can see that the provider information is available in `api_config_result`. Let me update the call:

<replace_in_file>
<path>src/dolphin_logger/server.py</path>
<diff>
------- SEARCH
        return handle_rest_api_request(
            method=request.method,
            url=downstream_url,
            headers=downstream_headers,
            data_bytes=data_to_send_final_bytes,
            is_stream=is_stream,
            original_request_json_data=original_request_json
        )
=======
        return handle_rest_api_request(
            method=request.method,
            url=downstream_url,
            headers=downstream_headers,
            data_bytes=data_to_send_final_bytes,
            is_stream=is_stream,
            original_request_json_data=original_request_json,
            provider=api_config_result.get("provider", "openai")
        )
+++++++ REPLACE
</diff>
</replace_in_file>

[User]
[replace_in_file for 'src/dolphin_logger/server.py'] Result:

The content was successfully saved to src/dolphin_logger/server.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/server.py">
import os
import json
from flask import Flask, request, Response, jsonify
from flask_cors import CORS

# Imports from our refactored modules
from .config import load_config, get_config_path, get_logs_dir
from .core_proxy import get_target_api_config, handle_anthropic_sdk_request, handle_rest_api_request
# logging_utils are used by core_proxy, not directly by server.py usually.

app = Flask(__name__)

# Enable CORS with explicit configuration
CORS(app, resources={r"/*": {"origins": "*", "allow_headers": ["Content-Type", "Authorization"]}})

# Global for model config, loaded when server starts
MODEL_CONFIG: list = []

# Handle preflight OPTIONS requests explicitly
@app.route('/', defaults={'path': ''}, methods=['OPTIONS'])
@app.route('/<path:path>', methods=['OPTIONS'])
def handle_options(path: str | None = None): # path can be None if accessing '/'
    resp = app.make_default_options_response()
    return resp

@app.route('/health', methods=['GET'])
def health_check():
    """Provides a health check endpoint for the server."""
    global MODEL_CONFIG # Ensure we are referring to the global
    if MODEL_CONFIG:
        response_data = {"status": "ok", "message": "Server is healthy, configuration loaded."}
        status_code = 200
    else:
        response_data = {"status": "error", "message": "Server is running, but configuration might have issues (e.g., no models loaded)."}
        status_code = 500

    resp = jsonify(response_data)
    resp.status_code = status_code
    return resp

@app.route('/', defaults={'path': ''}, methods=['GET', 'POST', 'PUT', 'DELETE'])
@app.route('/<path:path>', methods=['GET', 'POST', 'PUT', 'DELETE'])
def proxy(path: str | None = None): # path can be None
    global MODEL_CONFIG # Ensure we are referring to the global

    # Check if client is trying to use HTTPS on HTTP server
    if request.headers.get('X-Forwarded-Proto') == 'https' or \
       request.url.startswith('https://') or \
       request.headers.get('X-Forwarded-Ssl') == 'on':
        print("\n" + "="*60)
        print("🚨 HTTPS CONNECTION DETECTED!")
        print("="*60)
        print("You probably want to change https://localhost to http://localhost")
        print("Dolphin Logger runs on HTTP, not HTTPS.")
        print("Update your client configuration to use:")
        print(f"   http://localhost:{request.environ.get('SERVER_PORT', '5001')}")
        print("="*60 + "\n")
        return jsonify({
            "error": "HTTPS not supported. Use http://localhost instead of https://localhost",
            "suggestion": f"Change your client configuration to: http://localhost:{request.environ.get('SERVER_PORT', '5001')}"
        }), 400

    # Handle /v1/models endpoint directly here
    # Ensure path is not None before calling endswith
    if path and (path.endswith('/models') or path.endswith('/models/')):
        models_response = []
        if MODEL_CONFIG:
            for model_config_entry in MODEL_CONFIG:
                model_id = model_config_entry.get("model")
                provider = model_config_entry.get("provider", "unknown")
                if model_id:
                    models_response.append({
                        "id": model_id, "object": "model", "created": 1686935002,
                        "owned_by": provider, "provider": provider,
                        "provider_model": model_config_entry.get("providerModel", "")
                    })

        resp_data = {"data": models_response, "object": "list"}
        return jsonify(resp_data)

    # --- Main Proxy Logic ---
    raw_data_bytes = request.get_data()

    try:
        decoded_data_str = raw_data_bytes.decode('utf-8') if raw_data_bytes else "{}"
        original_request_json = json.loads(decoded_data_str) if decoded_data_str else {}
    except json.JSONDecodeError:
        return jsonify({"error": "Invalid JSON in request body"}), 400

    if request.method != 'POST' or 'model' not in original_request_json:
        error_msg_detail = "POST request JSON body must include a 'model' field for this endpoint." \
                           if request.method == 'POST' else \
                           "This endpoint primarily proxies POST requests with a 'model' field."
        return jsonify({"error": error_msg_detail}), 400

    requested_model_id = original_request_json.get('model')
    print(f"Requested model ID: {requested_model_id}")

    api_config_result = get_target_api_config(requested_model_id, MODEL_CONFIG)

    if api_config_result.get("error"):
        error_msg = api_config_result["error"]
        print(f"Error getting API config: {error_msg}")
        return jsonify({"error": error_msg}), 400

    target_api_url = api_config_result["target_api_url"]
    target_api_key = api_config_result["target_api_key"]
    target_model_for_provider = api_config_result["target_model"]

    json_data_for_downstream_call = original_request_json.copy()
    if target_model_for_provider != requested_model_id:
        json_data_for_downstream_call['model'] = target_model_for_provider

    data_to_send_final_bytes = json.dumps(json_data_for_downstream_call).encode('utf-8') if json_data_for_downstream_call else raw_data_bytes
    is_stream = original_request_json.get('stream', False)

    if target_api_url == "anthropic_sdk":
        return handle_anthropic_sdk_request(
            json_data_for_sdk=json_data_for_downstream_call,
            target_model=target_model_for_provider,
            target_api_key=target_api_key,
            is_stream=is_stream,
            original_request_json_data=original_request_json
        )
    else:
        # Handle URL construction more intelligently
        # If the target_api_url already contains a path (like /private/chat/completions),
        # don't append the original request path
        if path and path.startswith('v1/'):
            # For requests to /v1/chat/completions, extract just the endpoint part
            endpoint_part = path[3:]  # Remove 'v1/' prefix
            downstream_url = f"{target_api_url.rstrip('/')}/{endpoint_part}"
        else:
            # For other paths, use them as-is
            downstream_path_segment = path.lstrip('/') if path else ''
            downstream_url = f"{target_api_url.rstrip('/')}/{downstream_path_segment}"
        print(f"Proxying REST request to: {downstream_url}")

        downstream_headers = {
            k: v for k, v in request.headers.items()
            if k.lower() not in ['host', 'authorization', 'content-length', 'connection', 'user-agent']
        }
        downstream_headers['Host'] = target_api_url.split('//')[-1].split('/')[0]
        if target_api_key:
            downstream_headers['Authorization'] = f'Bearer {target_api_key}'

        return handle_rest_api_request(
            method=request.method,
            url=downstream_url,
            headers=downstream_headers,
            data_bytes=data_to_send_final_bytes,
            is_stream=is_stream,
            original_request_json_data=original_request_json,
            provider=api_config_result.get("provider", "openai")
        )

def run_server_main(port=None): # Renamed to avoid conflict with any 'main' in cli.py if imported directly
    """Sets up and runs the Flask development server."""
    global MODEL_CONFIG
    try:
        config_data = load_config()
        MODEL_CONFIG = config_data.get('models', [])

        # Enhanced startup status summary
        print("\n" + "="*50)
        print("🐬 Dolphin Logger Server Starting")
        print("="*50)

        if MODEL_CONFIG:
            print(f"✅ Loaded {len(MODEL_CONFIG)} model(s):")
            for model in MODEL_CONFIG:
                model_name = model.get('model', 'unknown')
                provider = model.get('provider', 'unknown')
                provider_model = model.get('providerModel', 'unknown')
                print(f"   • {model_name} ({provider}: {provider_model})")
        else:
            print("⚠  Warning: No models found in configuration.")

    except Exception as e:
        print(f"❌ Critical Error loading config: {e}. Server might not function correctly.")
        MODEL_CONFIG = []

    # Port priority: CLI argument > environment variable > default (5001)
    if port is None:
        port = int(os.environ.get('PORT', 5001))

    base_url = f"http://localhost:{port}"

    print(f"\n📍 Server Details:")
    print(f"   • URL: {base_url}")
    print(f"   • Health Check: {base_url}/health")
    print(f"   • Models Endpoint: {base_url}/v1/models")
    print(f"   • Chat Endpoint: {base_url}/v1/chat/completions")
    print(f"\n📁 File Locations:")
    print(f"   • Configuration: {get_config_path()}")
    print(f"   • Logs Directory: {get_logs_dir()}")
    print(f"\n🚀 Server ready! Use Ctrl+C to stop.")
    print("="*50 + "\n")

    app.run(host='0.0.0.0', port=port, debug=False)

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/server.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/server.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:08:49 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now let me run a quick test to ensure the refactored code doesn't have any import issues:

<execute_command>
<command>cd src && python -c "from dolphin_logger.core_proxy import get_target_api_config, handle_anthropic_sdk_request, handle_rest_api_request; print('✅ All imports successful')"</command>
<requires_approval>false</requires_approval>
</execute_command>

[User]
[execute_command for 'cd src && python -c "from dolphin_logger.core_proxy import get_target_api_config, handle_anthropic_sdk_request, handle_rest_api_request; print('✅ All imports successful')"'] Result:

The user denied this operation.

The user provided the following feedback:
<feedback>
here is an example properly calling google provider, please adapt it to the google provider:

# To run this code you need to install the following dependencies:
# pip install google-genai

import base64
import os
from google import genai
from google.genai import types

def generate():
    client = genai.Client(
        api_key=os.environ.get("GEMINI_API_KEY"),
    )

    model = "gemini-2.5-pro-preview-05-06"
    contents = [
        types.Content(
            role="user",
            parts=[
                types.Part.from_text(text="hello"),
            ],
        ),
        types.Content(
            role="model",
            parts=[
                types.Part.from_text(text="message1"),
                types.Part.from_text(text="message2")
            ],
        ),
        types.Content(
            role="user",
            parts=[
                types.Part.from_text(text="""INSERT_INPUT_HERE"""),
            ],
        ),
    ]
    generate_content_config = types.GenerateContentConfig(
        safety_settings=[
            types.SafetySetting(
                category="HARM_CATEGORY_HARASSMENT",
                threshold="BLOCK_NONE",  # Block none
            ),
            types.SafetySetting(
                category="HARM_CATEGORY_HATE_SPEECH",
                threshold="BLOCK_NONE",  # Block none
            ),
            types.SafetySetting(
                category="HARM_CATEGORY_SEXUALLY_EXPLICIT",
                threshold="BLOCK_NONE",  # Block none
            ),
            types.SafetySetting(
                category="HARM_CATEGORY_DANGEROUS_CONTENT",
                threshold="BLOCK_NONE",  # Block none
            ),
        ],
        response_mime_type="text/plain",
    )

    for chunk in client.models.generate_content_stream(
        model=model,
        contents=contents,
        config=generate_content_config,
    ):
        print(chunk.text, end="")

if __name__ == "__main__":
    generate()
</feedback>

<environment_details>
# VSCode Visible Files
src/dolphin_logger/server.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/server.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:15:24 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Thank you for providing the Google provider example! I can see that Google uses a different SDK approach similar to Anthropic, rather than just REST API calls. Let me update the Google provider to use the actual Google GenAI SDK:

<replace_in_file>
<path>src/dolphin_logger/providers/google.py</path>
<diff>
------- SEARCH
"""
Google provider implementation for Dolphin Logger.
Handles Google AI APIs (Gemini, etc.).
"""

import json
import requests
from flask import Response, stream_with_context, jsonify

from ..logging_utils import get_current_log_file, _should_log_request, log_lock

def handle_google_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to Google AI APIs.

    Args:
        method: HTTP method for the request
        url: Target API URL
        headers: Request headers
        data_bytes: Request body as bytes
        is_stream: Whether this is a streaming request
        original_request_json_data: The original client request for logging

    Returns:
        Flask Response object
    """
    print(f"DEBUG - Sending Google API request: {method} {url}")

    try:
        api_response = requests.request(
            method=method, url=url, headers=headers, data=data_bytes,
            stream=is_stream, timeout=300
        )
        print(f"DEBUG - Google API Response status: {api_response.status_code}")

        api_response.raise_for_status()

        log_entry = {'request': original_request_json_data, 'response': None}

        if is_stream:
            def generate_google_stream_response():
                response_content_parts = []
                try:
                    for line in api_response.iter_lines():
                        if line:
                            yield line + b'\n\n'
                            # For logging, accumulate content from delta if possible
                            if line.startswith(b'data: '):
                                line_data_str = line.decode('utf-8', errors='replace')[6:]
                                if line_data_str.strip() != '[DONE]':
                                    try:
                                        parsed_chunk = json.loads(line_data_str)
                                        # Google AI might have different response structure
                                        # This is a placeholder that can be adapted based on actual API
                                        choices = parsed_chunk.get('choices', [])
                                        if choices and isinstance(choices, list) and len(choices) > 0:
                                            delta = choices[0].get('delta', {})
                                            if delta and isinstance(delta, dict):
                                                delta_content = delta.get('content', '')
                                                if delta_content and isinstance(delta_content, str):
                                                    response_content_parts.append(delta_content)
                                    except (json.JSONDecodeError, KeyError, IndexError, TypeError):
                                        pass
                finally:
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_google_stream_response()),
                          content_type=api_response.headers.get('Content-Type', 'text/event-stream'))
            # Propagate relevant headers from upstream response
            for hdr_key in ['Cache-Control', 'Content-Type', 'Transfer-Encoding', 'Date', 'Server']:
                if hdr_key in api_response.headers:
                    resp.headers[hdr_key] = api_response.headers[hdr_key]
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else:
            complete_response_text = ''
            try:
                response_json = api_response.json()
                # Google AI might have different response structure
                # This is a placeholder that can be adapted based on actual API
                choices = response_json.get('choices', [])
                if choices and isinstance(choices, list) and len(choices) > 0:
                    message = choices[0].get('message', {})
                    if message and isinstance(message, dict):
                        complete_response_text = message.get('content', '')
                if not complete_response_text and isinstance(response_json, dict):
                    complete_response_text = response_json
                log_entry['response'] = complete_response_text
            except json.JSONDecodeError:
                print("Warning: Google API Response was not JSON. Logging raw text.")
                complete_response_text = api_response.text
                log_entry['response'] = complete_response_text

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps(log_entry) + '\n')

            # Return the raw response content from the target API to the client
            resp = Response(api_response.content,
                          content_type=api_response.headers.get('Content-Type', 'application/json'),
                          status=api_response.status_code)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except requests.exceptions.RequestException as e:
        print(f"ERROR DETAILS (Google API - RequestException): Type: {type(e).__name__}, Message: {str(e)}, URL: {url}")
        error_content_type = 'application/json'
        error_status = 502  # Bad Gateway, typically for network issues with upstream
        error_body_msg = f"Error connecting to upstream API: {str(e)}"
        error_body = {"error": {"message": error_body_msg, "type": type(e).__name__}}

        if e.response is not None:
            print(f"  Upstream response status code: {e.response.status_code}")
            error_status = e.response.status_code
            error_content_type = e.response.headers.get('Content-Type', 'application/json')
            try:
                error_body = e.response.json()
            except json.JSONDecodeError:
                error_body = {"error": {"message": e.response.text, "type": "upstream_error"}}

        resp = jsonify(error_body)
        resp.status_code = error_status
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e:
        print(f"UNEXPECTED GOOGLE API HANDLER ERROR: Type: {type(e).__name__}, Message: {str(e)}")
        resp = jsonify({"error": {"message": "An unexpected error occurred in the Google API handler.", "type": type(e).__name__}})
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
=======
"""
Google provider implementation for Dolphin Logger.
Handles Google AI APIs (Gemini, etc.) using the Google GenAI SDK.
"""

import json
import uuid
from datetime import datetime
from flask import Response, stream_with_context, jsonify

from ..logging_utils import get_current_log_file, _should_log_request, log_lock

def handle_google_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to Google AI APIs using the Google GenAI SDK.

    Args:
        json_data_for_sdk: The request data formatted for the Google GenAI SDK
        target_model: The target model name
        target_api_key: The API key for Google GenAI
        is_stream: Whether this is a streaming request
        original_request_json_data: The original client request for logging

    Returns:
        Flask Response object
    """
    print(f"DEBUG - Using Google GenAI SDK for request with model: {target_model}")

    try:
        # Import Google GenAI SDK
        try:
            from google import genai
            from google.genai import types
        except ImportError:
            error_msg = "Google GenAI SDK not installed. Please install with: pip install google-genai"
            print(f"ERROR: {error_msg}")
            resp = jsonify({"error": {"message": error_msg, "type": "import_error"}})
            resp.status_code = 500
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

        if not target_api_key:
            raise ValueError("Google API key is missing in the configuration for the requested model.")

        # Initialize Google GenAI client
        client = genai.Client(api_key=target_api_key)

        # Convert OpenAI-format messages to Google GenAI format
        original_messages = json_data_for_sdk.get('messages', [])
        contents = []

        for message in original_messages:
            role = message.get('role', 'user')
            content = message.get('content', '')

            # Map OpenAI roles to Google GenAI roles
            if role == 'system':
                # Google GenAI doesn't have system role, so we'll prepend it to the first user message
                # or create a user message if none exists
                if contents and contents[-1].role == 'user':
                    # Prepend to last user message
                    existing_text = contents[-1].parts[0].text if contents[-1].parts else ""
                    new_text = f"{content}\n\n{existing_text}"
                    contents[-1] = types.Content(
                        role="user",
                        parts=[types.Part.from_text(text=new_text)]
                    )
                else:
                    # Create new user message with system content
                    contents.append(types.Content(
                        role="user",
                        parts=[types.Part.from_text(text=content)]
                    ))
            elif role == 'assistant':
                contents.append(types.Content(
                    role="model",
                    parts=[types.Part.from_text(text=content)]
                ))
            else:  # user or other roles
                contents.append(types.Content(
                    role="user",
                    parts=[types.Part.from_text(text=content)]
                ))

        # Configure safety settings to be permissive (similar to the example)
        generate_content_config = types.GenerateContentConfig(
            safety_settings=[
                types.SafetySetting(
                    category="HARM_CATEGORY_HARASSMENT",
                    threshold="BLOCK_NONE",
                ),
                types.SafetySetting(
                    category="HARM_CATEGORY_HATE_SPEECH",
                    threshold="BLOCK_NONE",
                ),
                types.SafetySetting(
                    category="HARM_CATEGORY_SEXUALLY_EXPLICIT",
                    threshold="BLOCK_NONE",
                ),
                types.SafetySetting(
                    category="HARM_CATEGORY_DANGEROUS_CONTENT",
                    threshold="BLOCK_NONE",
                ),
            ],
            response_mime_type="text/plain",
        )

        if is_stream:
            print(f"DEBUG - Creating streaming request to Google GenAI API: model={target_model}")

            def generate_google_stream_response():
                response_content_parts = []
                log_entry = {'request': original_request_json_data, 'response': None}
                try:
                    for chunk in client.models.generate_content_stream(
                        model=target_model,
                        contents=contents,
                        config=generate_content_config,
                    ):
                        if hasattr(chunk, 'text') and chunk.text:
                            delta_content = chunk.text
                            response_content_parts.append(delta_content)

                            # Convert to OpenAI-compatible streaming format
                            openai_compatible_chunk = {
                                "choices": [{"delta": {"content": delta_content}, "index": 0, "finish_reason": None}],
                                "id": f"chatcmpl-google-{uuid.uuid4()}",
                                "model": target_model,
                                "object": "chat.completion.chunk",
                                "created": int(datetime.now().timestamp())
                            }
                            yield f"data: {json.dumps(openai_compatible_chunk)}\n\n".encode('utf-8')

                    # Send final chunk
                    final_chunk = {
                        "choices": [{"delta": {}, "index": 0, "finish_reason": "stop"}],
                        "id": f"chatcmpl-google-{uuid.uuid4()}",
                        "model": target_model,
                        "object": "chat.completion.chunk",
                        "created": int(datetime.now().timestamp())
                    }
                    yield f"data: {json.dumps(final_chunk)}\n\n".encode('utf-8')
                    yield b"data: [DONE]\n\n"
                finally:
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_google_stream_response()), content_type='text/event-stream')
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else:
            print(f"DEBUG - Creating non-streaming request to Google GenAI API: model={target_model}")

            # For non-streaming, we need to collect all the content
            response_parts = []
            for chunk in client.models.generate_content_stream(
                model=target_model,
                contents=contents,
                config=generate_content_config,
            ):
                if hasattr(chunk, 'text') and chunk.text:
                    response_parts.append(chunk.text)

            response_content = "".join(response_parts)

            # Convert to OpenAI-compatible format
            response_data_converted = {
                "id": f"chatcmpl-google-{uuid.uuid4()}",
                "object": "chat.completion",
                "created": int(datetime.now().timestamp()),
                "model": target_model,
                "choices": [{"index": 0, "message": {"role": "assistant", "content": response_content}, "finish_reason": "stop"}],
                "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}  # Google GenAI doesn't provide token counts in the same way
            }

            print(f"DEBUG - Response from Google GenAI API received. Preview: {response_content[:100]}...")

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps({'request': original_request_json_data, 'response': response_content}) + '\n')

            resp = jsonify(response_data_converted)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except Exception as e:
        print(f"ERROR DETAILS (Google GenAI SDK): Type: {type(e).__name__}, Message: {str(e)}")
        if original_request_json_data:
            print(f"  Full request data (original for logging): {json.dumps(original_request_json_data)}")
        error_body = {"error": {"message": str(e), "type": type(e).__name__}}
        resp = jsonify(error_body)
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
+++++++ REPLACE
</diff>
</replace_in_file>

[User]
[replace_in_file for 'src/dolphin_logger/providers/google.py'] Result:

The content was successfully saved to src/dolphin_logger/providers/google.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/providers/google.py">
"""
Google provider implementation for Dolphin Logger.
Handles Google AI APIs (Gemini, etc.) using the Google GenAI SDK.
"""

import json
import uuid
from datetime import datetime
from flask import Response, stream_with_context, jsonify

from ..logging_utils import get_current_log_file, _should_log_request, log_lock

def handle_google_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to Google AI APIs using the Google GenAI SDK.

    Args:
        json_data_for_sdk: The request data formatted for the Google GenAI SDK
        target_model: The target model name
        target_api_key: The API key for Google GenAI
        is_stream: Whether this is a streaming request
        original_request_json_data: The original client request for logging

    Returns:
        Flask Response object
    """
    print(f"DEBUG - Using Google GenAI SDK for request with model: {target_model}")

    try:
        # Import Google GenAI SDK
        try:
            from google import genai
            from google.genai import types
        except ImportError:
            error_msg = "Google GenAI SDK not installed. Please install with: pip install google-genai"
            print(f"ERROR: {error_msg}")
            resp = jsonify({"error": {"message": error_msg, "type": "import_error"}})
            resp.status_code = 500
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

        if not target_api_key:
            raise ValueError("Google API key is missing in the configuration for the requested model.")

        # Initialize Google GenAI client
        client = genai.Client(api_key=target_api_key)

        # Convert OpenAI-format messages to Google GenAI format
        original_messages = json_data_for_sdk.get('messages', [])
        contents = []

        for message in original_messages:
            role = message.get('role', 'user')
            content = message.get('content', '')

            # Map OpenAI roles to Google GenAI roles
            if role == 'system':
                # Google GenAI doesn't have system role, so we'll prepend it to the first user message
                # or create a user message if none exists
                if contents and contents[-1].role == 'user':
                    # Prepend to last user message
                    existing_text = contents[-1].parts[0].text if contents[-1].parts else ""
                    new_text = f"{content}\n\n{existing_text}"
                    contents[-1] = types.Content(
                        role="user",
                        parts=[types.Part.from_text(text=new_text)]
                    )
                else:
                    # Create new user message with system content
                    contents.append(types.Content(
                        role="user",
                        parts=[types.Part.from_text(text=content)]
                    ))
            elif role == 'assistant':
                contents.append(types.Content(
                    role="model",
                    parts=[types.Part.from_text(text=content)]
                ))
            else:  # user or other roles
                contents.append(types.Content(
                    role="user",
                    parts=[types.Part.from_text(text=content)]
                ))

        # Configure safety settings to be permissive (similar to the example)
        generate_content_config = types.GenerateContentConfig(
            safety_settings=[
                types.SafetySetting(
                    category="HARM_CATEGORY_HARASSMENT",
                    threshold="BLOCK_NONE",
                ),
                types.SafetySetting(
                    category="HARM_CATEGORY_HATE_SPEECH",
                    threshold="BLOCK_NONE",
                ),
                types.SafetySetting(
                    category="HARM_CATEGORY_SEXUALLY_EXPLICIT",
                    threshold="BLOCK_NONE",
                ),
                types.SafetySetting(
                    category="HARM_CATEGORY_DANGEROUS_CONTENT",
                    threshold="BLOCK_NONE",
                ),
            ],
            response_mime_type="text/plain",
        )

        if is_stream:
            print(f"DEBUG - Creating streaming request to Google GenAI API: model={target_model}")

            def generate_google_stream_response():
                response_content_parts = []
                log_entry = {'request': original_request_json_data, 'response': None}
                try:
                    for chunk in client.models.generate_content_stream(
                        model=target_model,
                        contents=contents,
                        config=generate_content_config,
                    ):
                        if hasattr(chunk, 'text') and chunk.text:
                            delta_content = chunk.text
                            response_content_parts.append(delta_content)

                            # Convert to OpenAI-compatible streaming format
                            openai_compatible_chunk = {
                                "choices": [{"delta": {"content": delta_content}, "index": 0, "finish_reason": None}],
                                "id": f"chatcmpl-google-{uuid.uuid4()}",
                                "model": target_model,
                                "object": "chat.completion.chunk",
                                "created": int(datetime.now().timestamp())
                            }
                            yield f"data: {json.dumps(openai_compatible_chunk)}\n\n".encode('utf-8')

                    # Send final chunk
                    final_chunk = {
                        "choices": [{"delta": {}, "index": 0, "finish_reason": "stop"}],
                        "id": f"chatcmpl-google-{uuid.uuid4()}",
                        "model": target_model,
                        "object": "chat.completion.chunk",
                        "created": int(datetime.now().timestamp())
                    }
                    yield f"data: {json.dumps(final_chunk)}\n\n".encode('utf-8')
                    yield b"data: [DONE]\n\n"
                finally:
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_google_stream_response()), content_type='text/event-stream')
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else:
            print(f"DEBUG - Creating non-streaming request to Google GenAI API: model={target_model}")

            # For non-streaming, we need to collect all the content
            response_parts = []
            for chunk in client.models.generate_content_stream(
                model=target_model,
                contents=contents,
                config=generate_content_config,
            ):
                if hasattr(chunk, 'text') and chunk.text:
                    response_parts.append(chunk.text)

            response_content = "".join(response_parts)

            # Convert to OpenAI-compatible format
            response_data_converted = {
                "id": f"chatcmpl-google-{uuid.uuid4()}",
                "object": "chat.completion",
                "created": int(datetime.now().timestamp()),
                "model": target_model,
                "choices": [{"index": 0, "message": {"role": "assistant", "content": response_content}, "finish_reason": "stop"}],
                "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}  # Google GenAI doesn't provide token counts in the same way
            }

            print(f"DEBUG - Response from Google GenAI API received. Preview: {response_content[:100]}...")

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps({'request': original_request_json_data, 'response': response_content}) + '\n')

            resp = jsonify(response_data_converted)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except Exception as e:
        print(f"ERROR DETAILS (Google GenAI SDK): Type: {type(e).__name__}, Message: {str(e)}")
        if original_request_json_data:
            print(f"  Full request data (original for logging): {json.dumps(original_request_json_data)}")
        error_body = {"error": {"message": str(e), "type": type(e).__name__}}
        resp = jsonify(error_body)
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/providers/google.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/server.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:16:26 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I need to update the core_proxy.py to handle Google providers that use the SDK approach (similar to Anthropic) rather than REST API calls. Let me update the configuration logic:

<replace_in_file>
<path>src/dolphin_logger/core_proxy.py</path>
<diff>
------- SEARCH
            elif provider == "anthropic":
                return {
                    "target_api_url": "anthropic_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            else: # OpenAI-compatible
                if not api_base:
                    # Ensure a specific error message for missing apiBase for OpenAI-like providers
                    return {"error": f"apiBase not configured for OpenAI-compatible model '{requested_model_id}'"}
                return {
                    "target_api_url": api_base,
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
=======
            elif provider == "anthropic":
                return {
                    "target_api_url": "anthropic_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "google":
                return {
                    "target_api_url": "google_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            else: # OpenAI-compatible
                if not api_base:
                    # Ensure a specific error message for missing apiBase for OpenAI-like providers
                    return {"error": f"apiBase not configured for OpenAI-compatible model '{requested_model_id}'"}
                return {
                    "target_api_url": api_base,
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
+++++++ REPLACE
</diff>
</replace_in_file>

[User]
[replace_in_file for 'src/dolphin_logger/core_proxy.py'] Result:

The content was successfully saved to src/dolphin_logger/core_proxy.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/core_proxy.py">
import json
from flask import Response

from .providers.anthropic import handle_anthropic_request
from .providers.openai import handle_openai_request
from .providers.google import handle_google_request
# MODEL_CONFIG is typically loaded in server.py and passed to get_target_api_config.

def get_target_api_config(requested_model_id: str, model_config_list: list) -> dict:
    """
    Determines the target API URL, key, and model based on the requested model ID
    and the server's model configuration.

    Args:
        requested_model_id (str): The model ID from the client's request.
        model_config_list (list): The MODEL_CONFIG list (passed from server.py).

    Returns:
        dict: A dictionary containing 'target_api_url', 'target_api_key',
              'target_model', 'provider', and 'error' (if any).
    """
    for model_config in model_config_list:
        if model_config.get("model") == requested_model_id:
            provider = model_config.get("provider")
            target_model = model_config.get("providerModel", requested_model_id)
            api_key = model_config.get("apiKey")
            api_base = model_config.get("apiBase")

            if provider == "ollama":
                return {
                    "target_api_url": api_base or "http://localhost:11434/v1",
                    "target_api_key": "",
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "anthropic":
                return {
                    "target_api_url": "anthropic_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "google":
                return {
                    "target_api_url": "google_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            else: # OpenAI-compatible
                if not api_base:
                    # Ensure a specific error message for missing apiBase for OpenAI-like providers
                    return {"error": f"apiBase not configured for OpenAI-compatible model '{requested_model_id}'"}
                return {
                    "target_api_url": api_base,
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }

    if not model_config_list: # Check if the list itself is empty
        return {"error": f"No models configured. Cannot process request for model '{requested_model_id}'."}
    return {"error": f"Model '{requested_model_id}' not found in configured models."}

def handle_anthropic_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Anthropic SDK.
    Delegates to the Anthropic provider.
    """
    return handle_anthropic_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )

def handle_rest_api_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict,
    provider: str = "openai"
) -> Response:
    """
    Handles requests to REST APIs by routing to the appropriate provider.
    Delegates to the specific provider based on the provider parameter.
    """
    # Route to the appropriate provider
    if provider == "google":
        return handle_google_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
    else:
        # Default to OpenAI provider for OpenAI-compatible APIs (including Ollama)
        return handle_openai_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/core_proxy.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/server.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:18:44 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I need to add a new function to handle Google SDK requests and update the server.py to handle the "google_sdk" special marker. Let me add the Google SDK handler:

<replace_in_file>
<path>src/dolphin_logger/core_proxy.py</path>
<diff>
------- SEARCH
def handle_anthropic_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Anthropic SDK.
    Delegates to the Anthropic provider.
    """
    return handle_anthropic_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )
=======
def handle_anthropic_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Anthropic SDK.
    Delegates to the Anthropic provider.
    """
    return handle_anthropic_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )

def handle_google_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Google GenAI SDK.
    Delegates to the Google provider.
    """
    return handle_google_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )
+++++++ REPLACE
</diff>
</replace_in_file>

[User]
[replace_in_file for 'src/dolphin_logger/core_proxy.py'] Result:

The content was successfully saved to src/dolphin_logger/core_proxy.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/core_proxy.py">
import json
from flask import Response

from .providers.anthropic import handle_anthropic_request
from .providers.openai import handle_openai_request
from .providers.google import handle_google_request
# MODEL_CONFIG is typically loaded in server.py and passed to get_target_api_config.

def get_target_api_config(requested_model_id: str, model_config_list: list) -> dict:
    """
    Determines the target API URL, key, and model based on the requested model ID
    and the server's model configuration.

    Args:
        requested_model_id (str): The model ID from the client's request.
        model_config_list (list): The MODEL_CONFIG list (passed from server.py).

    Returns:
        dict: A dictionary containing 'target_api_url', 'target_api_key',
              'target_model', 'provider', and 'error' (if any).
    """
    for model_config in model_config_list:
        if model_config.get("model") == requested_model_id:
            provider = model_config.get("provider")
            target_model = model_config.get("providerModel", requested_model_id)
            api_key = model_config.get("apiKey")
            api_base = model_config.get("apiBase")

            if provider == "ollama":
                return {
                    "target_api_url": api_base or "http://localhost:11434/v1",
                    "target_api_key": "",
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "anthropic":
                return {
                    "target_api_url": "anthropic_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "google":
                return {
                    "target_api_url": "google_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            else: # OpenAI-compatible
                if not api_base:
                    # Ensure a specific error message for missing apiBase for OpenAI-like providers
                    return {"error": f"apiBase not configured for OpenAI-compatible model '{requested_model_id}'"}
                return {
                    "target_api_url": api_base,
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }

    if not model_config_list: # Check if the list itself is empty
        return {"error": f"No models configured. Cannot process request for model '{requested_model_id}'."}
    return {"error": f"Model '{requested_model_id}' not found in configured models."}

def handle_anthropic_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Anthropic SDK.
    Delegates to the Anthropic provider.
    """
    return handle_anthropic_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )

def handle_google_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Google GenAI SDK.
    Delegates to the Google provider.
    """
    return handle_google_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )

def handle_rest_api_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict,
    provider: str = "openai"
) -> Response:
    """
    Handles requests to REST APIs by routing to the appropriate provider.
    Delegates to the specific provider based on the provider parameter.
    """
    # Route to the appropriate provider
    if provider == "google":
        return handle_google_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
    else:
        # Default to OpenAI provider for OpenAI-compatible APIs (including Ollama)
        return handle_openai_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/core_proxy.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/server.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:19:00 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I need to update the server.py to import and handle the new Google SDK handler:

<replace_in_file>
<path>src/dolphin_logger/server.py</path>
<diff>
------- SEARCH
from .core_proxy import get_target_api_config, handle_anthropic_sdk_request, handle_rest_api_request
=======
from .core_proxy import get_target_api_config, handle_anthropic_sdk_request, handle_google_sdk_request, handle_rest_api_request
+++++++ REPLACE
</diff>
</replace_in_file>

[User]
[replace_in_file for 'src/dolphin_logger/server.py'] Result:

The content was successfully saved to src/dolphin_logger/server.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/server.py">
import os
import json
from flask import Flask, request, Response, jsonify
from flask_cors import CORS

# Imports from our refactored modules
from .config import load_config, get_config_path, get_logs_dir
from .core_proxy import get_target_api_config, handle_anthropic_sdk_request, handle_google_sdk_request, handle_rest_api_request
# logging_utils are used by core_proxy, not directly by server.py usually.

app = Flask(__name__)

# Enable CORS with explicit configuration
CORS(app, resources={r"/*": {"origins": "*", "allow_headers": ["Content-Type", "Authorization"]}})

# Global for model config, loaded when server starts
MODEL_CONFIG: list = []

# Handle preflight OPTIONS requests explicitly
@app.route('/', defaults={'path': ''}, methods=['OPTIONS'])
@app.route('/<path:path>', methods=['OPTIONS'])
def handle_options(path: str | None = None): # path can be None if accessing '/'
    resp = app.make_default_options_response()
    return resp

@app.route('/health', methods=['GET'])
def health_check():
    """Provides a health check endpoint for the server."""
    global MODEL_CONFIG # Ensure we are referring to the global
    if MODEL_CONFIG:
        response_data = {"status": "ok", "message": "Server is healthy, configuration loaded."}
        status_code = 200
    else:
        response_data = {"status": "error", "message": "Server is running, but configuration might have issues (e.g., no models loaded)."}
        status_code = 500

    resp = jsonify(response_data)
    resp.status_code = status_code
    return resp

@app.route('/', defaults={'path': ''}, methods=['GET', 'POST', 'PUT', 'DELETE'])
@app.route('/<path:path>', methods=['GET', 'POST', 'PUT', 'DELETE'])
def proxy(path: str | None = None): # path can be None
    global MODEL_CONFIG # Ensure we are referring to the global

    # Check if client is trying to use HTTPS on HTTP server
    if request.headers.get('X-Forwarded-Proto') == 'https' or \
       request.url.startswith('https://') or \
       request.headers.get('X-Forwarded-Ssl') == 'on':
        print("\n" + "="*60)
        print("🚨 HTTPS CONNECTION DETECTED!")
        print("="*60)
        print("You probably want to change https://localhost to http://localhost")
        print("Dolphin Logger runs on HTTP, not HTTPS.")
        print("Update your client configuration to use:")
        print(f"   http://localhost:{request.environ.get('SERVER_PORT', '5001')}")
        print("="*60 + "\n")
        return jsonify({
            "error": "HTTPS not supported. Use http://localhost instead of https://localhost",
            "suggestion": f"Change your client configuration to: http://localhost:{request.environ.get('SERVER_PORT', '5001')}"
        }), 400

    # Handle /v1/models endpoint directly here
    # Ensure path is not None before calling endswith
    if path and (path.endswith('/models') or path.endswith('/models/')):
        models_response = []
        if MODEL_CONFIG:
            for model_config_entry in MODEL_CONFIG:
                model_id = model_config_entry.get("model")
                provider = model_config_entry.get("provider", "unknown")
                if model_id:
                    models_response.append({
                        "id": model_id, "object": "model", "created": 1686935002,
                        "owned_by": provider, "provider": provider,
                        "provider_model": model_config_entry.get("providerModel", "")
                    })

        resp_data = {"data": models_response, "object": "list"}
        return jsonify(resp_data)

    # --- Main Proxy Logic ---
    raw_data_bytes = request.get_data()

    try:
        decoded_data_str = raw_data_bytes.decode('utf-8') if raw_data_bytes else "{}"
        original_request_json = json.loads(decoded_data_str) if decoded_data_str else {}
    except json.JSONDecodeError:
        return jsonify({"error": "Invalid JSON in request body"}), 400

    if request.method != 'POST' or 'model' not in original_request_json:
        error_msg_detail = "POST request JSON body must include a 'model' field for this endpoint." \
                           if request.method == 'POST' else \
                           "This endpoint primarily proxies POST requests with a 'model' field."
        return jsonify({"error": error_msg_detail}), 400

    requested_model_id = original_request_json.get('model')
    print(f"Requested model ID: {requested_model_id}")

    api_config_result = get_target_api_config(requested_model_id, MODEL_CONFIG)

    if api_config_result.get("error"):
        error_msg = api_config_result["error"]
        print(f"Error getting API config: {error_msg}")
        return jsonify({"error": error_msg}), 400

    target_api_url = api_config_result["target_api_url"]
    target_api_key = api_config_result["target_api_key"]
    target_model_for_provider = api_config_result["target_model"]

    json_data_for_downstream_call = original_request_json.copy()
    if target_model_for_provider != requested_model_id:
        json_data_for_downstream_call['model'] = target_model_for_provider

    data_to_send_final_bytes = json.dumps(json_data_for_downstream_call).encode('utf-8') if json_data_for_downstream_call else raw_data_bytes
    is_stream = original_request_json.get('stream', False)

    if target_api_url == "anthropic_sdk":
        return handle_anthropic_sdk_request(
            json_data_for_sdk=json_data_for_downstream_call,
            target_model=target_model_for_provider,
            target_api_key=target_api_key,
            is_stream=is_stream,
            original_request_json_data=original_request_json
        )
    else:
        # Handle URL construction more intelligently
        # If the target_api_url already contains a path (like /private/chat/completions),
        # don't append the original request path
        if path and path.startswith('v1/'):
            # For requests to /v1/chat/completions, extract just the endpoint part
            endpoint_part = path[3:]  # Remove 'v1/' prefix
            downstream_url = f"{target_api_url.rstrip('/')}/{endpoint_part}"
        else:
            # For other paths, use them as-is
            downstream_path_segment = path.lstrip('/') if path else ''
            downstream_url = f"{target_api_url.rstrip('/')}/{downstream_path_segment}"
        print(f"Proxying REST request to: {downstream_url}")

        downstream_headers = {
            k: v for k, v in request.headers.items()
            if k.lower() not in ['host', 'authorization', 'content-length', 'connection', 'user-agent']
        }
        downstream_headers['Host'] = target_api_url.split('//')[-1].split('/')[0]
        if target_api_key:
            downstream_headers['Authorization'] = f'Bearer {target_api_key}'

        return handle_rest_api_request(
            method=request.method,
            url=downstream_url,
            headers=downstream_headers,
            data_bytes=data_to_send_final_bytes,
            is_stream=is_stream,
            original_request_json_data=original_request_json,
            provider=api_config_result.get("provider", "openai")
        )

def run_server_main(port=None): # Renamed to avoid conflict with any 'main' in cli.py if imported directly
    """Sets up and runs the Flask development server."""
    global MODEL_CONFIG
    try:
        config_data = load_config()
        MODEL_CONFIG = config_data.get('models', [])

        # Enhanced startup status summary
        print("\n" + "="*50)
        print("🐬 Dolphin Logger Server Starting")
        print("="*50)

        if MODEL_CONFIG:
            print(f"✅ Loaded {len(MODEL_CONFIG)} model(s):")
            for model in MODEL_CONFIG:
                model_name = model.get('model', 'unknown')
                provider = model.get('provider', 'unknown')
                provider_model = model.get('providerModel', 'unknown')
                print(f"   • {model_name} ({provider}: {provider_model})")
        else:
            print("⚠  Warning: No models found in configuration.")

    except Exception as e:
        print(f"❌ Critical Error loading config: {e}. Server might not function correctly.")
        MODEL_CONFIG = []

    # Port priority: CLI argument > environment variable > default (5001)
    if port is None:
        port = int(os.environ.get('PORT', 5001))

    base_url = f"http://localhost:{port}"

    print(f"\n📍 Server Details:")
    print(f"   • URL: {base_url}")
    print(f"   • Health Check: {base_url}/health")
    print(f"   • Models Endpoint: {base_url}/v1/models")
    print(f"   • Chat Endpoint: {base_url}/v1/chat/completions")
    print(f"\n📁 File Locations:")
    print(f"   • Configuration: {get_config_path()}")
    print(f"   • Logs Directory: {get_logs_dir()}")
    print(f"\n🚀 Server ready! Use Ctrl+C to stop.")
    print("="*50 + "\n")

    app.run(host='0.0.0.0', port=port, debug=False)

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/server.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/server.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:19:28 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I need to add the Google SDK handling to the server.py logic:

<replace_in_file>
<path>src/dolphin_logger/server.py</path>
<diff>
------- SEARCH
    if target_api_url == "anthropic_sdk":
        return handle_anthropic_sdk_request(
            json_data_for_sdk=json_data_for_downstream_call,
            target_model=target_model_for_provider,
            target_api_key=target_api_key,
            is_stream=is_stream,
            original_request_json_data=original_request_json
        )
    else:
=======
    if target_api_url == "anthropic_sdk":
        return handle_anthropic_sdk_request(
            json_data_for_sdk=json_data_for_downstream_call,
            target_model=target_model_for_provider,
            target_api_key=target_api_key,
            is_stream=is_stream,
            original_request_json_data=original_request_json
        )
    elif target_api_url == "google_sdk":
        return handle_google_sdk_request(
            json_data_for_sdk=json_data_for_downstream_call,
            target_model=target_model_for_provider,
            target_api_key=target_api_key,
            is_stream=is_stream,
            original_request_json_data=original_request_json
        )
    else:
+++++++ REPLACE
</diff>
</replace_in_file>

[User]
[replace_in_file for 'src/dolphin_logger/server.py'] Result:

The content was successfully saved to src/dolphin_logger/server.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/server.py">
import os
import json
from flask import Flask, request, Response, jsonify
from flask_cors import CORS

# Imports from our refactored modules
from .config import load_config, get_config_path, get_logs_dir
from .core_proxy import get_target_api_config, handle_anthropic_sdk_request, handle_google_sdk_request, handle_rest_api_request
# logging_utils are used by core_proxy, not directly by server.py usually.

app = Flask(__name__)

# Enable CORS with explicit configuration
CORS(app, resources={r"/*": {"origins": "*", "allow_headers": ["Content-Type", "Authorization"]}})

# Global for model config, loaded when server starts
MODEL_CONFIG: list = []

# Handle preflight OPTIONS requests explicitly
@app.route('/', defaults={'path': ''}, methods=['OPTIONS'])
@app.route('/<path:path>', methods=['OPTIONS'])
def handle_options(path: str | None = None): # path can be None if accessing '/'
    resp = app.make_default_options_response()
    return resp

@app.route('/health', methods=['GET'])
def health_check():
    """Provides a health check endpoint for the server."""
    global MODEL_CONFIG # Ensure we are referring to the global
    if MODEL_CONFIG:
        response_data = {"status": "ok", "message": "Server is healthy, configuration loaded."}
        status_code = 200
    else:
        response_data = {"status": "error", "message": "Server is running, but configuration might have issues (e.g., no models loaded)."}
        status_code = 500

    resp = jsonify(response_data)
    resp.status_code = status_code
    return resp

@app.route('/', defaults={'path': ''}, methods=['GET', 'POST', 'PUT', 'DELETE'])
@app.route('/<path:path>', methods=['GET', 'POST', 'PUT', 'DELETE'])
def proxy(path: str | None = None): # path can be None
    global MODEL_CONFIG # Ensure we are referring to the global

    # Check if client is trying to use HTTPS on HTTP server
    if request.headers.get('X-Forwarded-Proto') == 'https' or \
       request.url.startswith('https://') or \
       request.headers.get('X-Forwarded-Ssl') == 'on':
        print("\n" + "="*60)
        print("🚨 HTTPS CONNECTION DETECTED!")
        print("="*60)
        print("You probably want to change https://localhost to http://localhost")
        print("Dolphin Logger runs on HTTP, not HTTPS.")
        print("Update your client configuration to use:")
        print(f"   http://localhost:{request.environ.get('SERVER_PORT', '5001')}")
        print("="*60 + "\n")
        return jsonify({
            "error": "HTTPS not supported. Use http://localhost instead of https://localhost",
            "suggestion": f"Change your client configuration to: http://localhost:{request.environ.get('SERVER_PORT', '5001')}"
        }), 400

    # Handle /v1/models endpoint directly here
    # Ensure path is not None before calling endswith
    if path and (path.endswith('/models') or path.endswith('/models/')):
        models_response = []
        if MODEL_CONFIG:
            for model_config_entry in MODEL_CONFIG:
                model_id = model_config_entry.get("model")
                provider = model_config_entry.get("provider", "unknown")
                if model_id:
                    models_response.append({
                        "id": model_id, "object": "model", "created": 1686935002,
                        "owned_by": provider, "provider": provider,
                        "provider_model": model_config_entry.get("providerModel", "")
                    })

        resp_data = {"data": models_response, "object": "list"}
        return jsonify(resp_data)

    # --- Main Proxy Logic ---
    raw_data_bytes = request.get_data()

    try:
        decoded_data_str = raw_data_bytes.decode('utf-8') if raw_data_bytes else "{}"
        original_request_json = json.loads(decoded_data_str) if decoded_data_str else {}
    except json.JSONDecodeError:
        return jsonify({"error": "Invalid JSON in request body"}), 400

    if request.method != 'POST' or 'model' not in original_request_json:
        error_msg_detail = "POST request JSON body must include a 'model' field for this endpoint." \
                           if request.method == 'POST' else \
                           "This endpoint primarily proxies POST requests with a 'model' field."
        return jsonify({"error": error_msg_detail}), 400

    requested_model_id = original_request_json.get('model')
    print(f"Requested model ID: {requested_model_id}")

    api_config_result = get_target_api_config(requested_model_id, MODEL_CONFIG)

    if api_config_result.get("error"):
        error_msg = api_config_result["error"]
        print(f"Error getting API config: {error_msg}")
        return jsonify({"error": error_msg}), 400

    target_api_url = api_config_result["target_api_url"]
    target_api_key = api_config_result["target_api_key"]
    target_model_for_provider = api_config_result["target_model"]

    json_data_for_downstream_call = original_request_json.copy()
    if target_model_for_provider != requested_model_id:
        json_data_for_downstream_call['model'] = target_model_for_provider

    data_to_send_final_bytes = json.dumps(json_data_for_downstream_call).encode('utf-8') if json_data_for_downstream_call else raw_data_bytes
    is_stream = original_request_json.get('stream', False)

    if target_api_url == "anthropic_sdk":
        return handle_anthropic_sdk_request(
            json_data_for_sdk=json_data_for_downstream_call,
            target_model=target_model_for_provider,
            target_api_key=target_api_key,
            is_stream=is_stream,
            original_request_json_data=original_request_json
        )
    elif target_api_url == "google_sdk":
        return handle_google_sdk_request(
            json_data_for_sdk=json_data_for_downstream_call,
            target_model=target_model_for_provider,
            target_api_key=target_api_key,
            is_stream=is_stream,
            original_request_json_data=original_request_json
        )
    else:
        # Handle URL construction more intelligently
        # If the target_api_url already contains a path (like /private/chat/completions),
        # don't append the original request path
        if path and path.startswith('v1/'):
            # For requests to /v1/chat/completions, extract just the endpoint part
            endpoint_part = path[3:]  # Remove 'v1/' prefix
            downstream_url = f"{target_api_url.rstrip('/')}/{endpoint_part}"
        else:
            # For other paths, use them as-is
            downstream_path_segment = path.lstrip('/') if path else ''
            downstream_url = f"{target_api_url.rstrip('/')}/{downstream_path_segment}"
        print(f"Proxying REST request to: {downstream_url}")

        downstream_headers = {
            k: v for k, v in request.headers.items()
            if k.lower() not in ['host', 'authorization', 'content-length', 'connection', 'user-agent']
        }
        downstream_headers['Host'] = target_api_url.split('//')[-1].split('/')[0]
        if target_api_key:
            downstream_headers['Authorization'] = f'Bearer {target_api_key}'

        return handle_rest_api_request(
            method=request.method,
            url=downstream_url,
            headers=downstream_headers,
            data_bytes=data_to_send_final_bytes,
            is_stream=is_stream,
            original_request_json_data=original_request_json,
            provider=api_config_result.get("provider", "openai")
        )

def run_server_main(port=None): # Renamed to avoid conflict with any 'main' in cli.py if imported directly
    """Sets up and runs the Flask development server."""
    global MODEL_CONFIG
    try:
        config_data = load_config()
        MODEL_CONFIG = config_data.get('models', [])

        # Enhanced startup status summary
        print("\n" + "="*50)
        print("🐬 Dolphin Logger Server Starting")
        print("="*50)

        if MODEL_CONFIG:
            print(f"✅ Loaded {len(MODEL_CONFIG)} model(s):")
            for model in MODEL_CONFIG:
                model_name = model.get('model', 'unknown')
                provider = model.get('provider', 'unknown')
                provider_model = model.get('providerModel', 'unknown')
                print(f"   • {model_name} ({provider}: {provider_model})")
        else:
            print("⚠  Warning: No models found in configuration.")

    except Exception as e:
        print(f"❌ Critical Error loading config: {e}. Server might not function correctly.")
        MODEL_CONFIG = []

    # Port priority: CLI argument > environment variable > default (5001)
    if port is None:
        port = int(os.environ.get('PORT', 5001))

    base_url = f"http://localhost:{port}"

    print(f"\n📍 Server Details:")
    print(f"   • URL: {base_url}")
    print(f"   • Health Check: {base_url}/health")
    print(f"   • Models Endpoint: {base_url}/v1/models")
    print(f"   • Chat Endpoint: {base_url}/v1/chat/completions")
    print(f"\n📁 File Locations:")
    print(f"   • Configuration: {get_config_path()}")
    print(f"   • Logs Directory: {get_logs_dir()}")
    print(f"\n🚀 Server ready! Use Ctrl+C to stop.")
    print("="*50 + "\n")

    app.run(host='0.0.0.0', port=port, debug=False)

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/server.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/server.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:19:48 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Perfect! Now let me create a simple test to verify that the refactored code doesn't have any basic import issues:

<execute_command>
<command>cd src && python -c "
try:
    from dolphin_logger import providers
    from dolphin_logger.providers import anthropic, openai, google
    from dolphin_logger.core_proxy import get_target_api_config, handle_anthropic_sdk_request, handle_google_sdk_request, handle_rest_api_request
    print('✅ All imports successful!')
    print('✅ Provider modules: anthropic, openai, google')
    print('✅ Core proxy functions available')
    print('✅ Refactoring completed successfully!')
except Exception as e:
    print(f'❌ Import error: {e}')
    import traceback
    traceback.print_exc()
"</command>
<requires_approval>false</requires_approval>
</execute_command>

[User]
[TASK RESUMPTION] This task was interrupted just now. It may or may not be complete, so please reassess the task context. Be aware that the project state may have changed since then. The current working directory is now '/Volumes/SB-XTM5/git/dolphin-logger'. If the task has not been completed, retry the last step before interruption and proceed with completing the task.

Note: If you previously attempted a tool use that the user did not provide a result for, you should assume the tool use was not successful and assess whether you should retry. If the last tool was a browser_action, the browser has been closed and you must launch a new browser if needed.

New instructions for task continuation:
<user_message>
cd src && python -c "                                                             (base)
                                                         try:
                                                             from dolphin_logger import providers
                                                             from dolphin_logger.providers import anthropic, openai, google
                                                             from dolphin_logger.core_proxy import get_target_api_config, handle_anthropic_sdk_req
uest, handle_google_sdk_request, handle_rest_api_request
                                                             print('✅ All imports successful!')
                                                             print('✅ Provider modules: anthropic, openai, google')
                                                             print('✅ Core proxy functions available')
                                                             print('✅ Refactoring completed successfully!')
                                                         except Exception as e:
                                                             print(f'❌ Import error: {e}')
                                                             import traceback
                                                             traceback.print_exc()
                                                         "
✅ All imports successful!
✅ Provider modules: anthropic, openai, google
✅ Core proxy functions available
✅ Refactoring completed successfully!
</user_message>

<environment_details>
# VSCode Visible Files
src/dolphin_logger/server.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/server.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:20:46 AM (America/New_York, UTC-4:00)

# Current Working Directory (/Volumes/SB-XTM5/git/dolphin-logger) Files
.gitignore
chat-logger.png
config.json.example
pyproject.toml
README.md
src/
src/dolphin_logger/
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/config.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/logging_utils.py
src/dolphin_logger/main.py
src/dolphin_logger/server.py
src/dolphin_logger/upload.py
src/dolphin_logger/providers/
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger.egg-info/
tests/
tests/__init__.py
tests/test_cli.py
tests/test_config.py
tests/test_core_proxy.py
tests/test_functional.py
tests/test_logging_utils.py
tests/test_server.py
tests/test_upload.py

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Excellent! The import test shows that the refactoring has been completed successfully. Let me provide a summary of what was accomplished and then mark the task as complete.

<attempt_completion>
<result>
Successfully redesigned the provider system in dolphin-logger to follow the dolphin-mcp pattern with each provider in its own file. Here's what was accomplished:

## Provider Structure Created:
- **`src/dolphin_logger/providers/`** - New providers directory
- **`src/dolphin_logger/providers/__init__.py`** - Provider package initialization
- **`src/dolphin_logger/providers/anthropic.py`** - Anthropic provider with SDK integration
- **`src/dolphin_logger/providers/openai.py`** - OpenAI provider for OpenAI-compatible APIs (including Ollama)
- **`src/dolphin_logger/providers/google.py`** - Google GenAI provider with proper SDK integration

## Key Features:
1. **Modular Design**: Each provider is cleanly separated into its own module
2. **SDK Integration**: Anthropic and Google providers use their respective SDKs, while OpenAI provider handles REST APIs
3. **Consistent Interface**: All providers follow the same function signature pattern
4. **Proper Routing**: Core proxy routes requests to appropriate providers based on configuration
5. **Backward Compatibility**: Existing functionality preserved while improving code organization

## Provider Capabilities:
- **Anthropic**: Full SDK integration with streaming and non-streaming support
- **OpenAI**: REST API handling for OpenAI, Ollama, and other OpenAI-compatible services
- **Google**: Google GenAI SDK integration with proper message format conversion and safety settings

## Architecture Improvements:
- Cleaner separation of concerns
- Easier to add new providers
- Better maintainability and testability
- Follows dolphin-mcp's proven design pattern

The refactoring maintains all existing functionality while providing a much cleaner, more maintainable codebase that will be easier to extend with additional providers in the future.
</result>
</attempt_completion>

[User]
[attempt_completion] Result:

The user has provided feedback on the results. Consider their input to continue the task, and then attempt completion again.
<feedback>
but you forgot to add ollama provider
</feedback>

<environment_details>
# VSCode Visible Files
src/dolphin_logger/server.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/server.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:21:42 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
You're absolutely right! I need to add a dedicated Ollama provider. Currently, Ollama is being handled by the OpenAI provider since it's OpenAI-compatible, but it should have its own provider file for consistency and potential future enhancements. Let me create the Ollama provider:

<write_to_file>
<path>src/dolphin_logger/providers/ollama.py</path>
<content>
"""
Ollama provider implementation for Dolphin Logger.
Handles Ollama API requests using REST API calls.
"""

import json
import requests
from flask import Response, stream_with_context, jsonify

from ..logging_utils import get_current_log_file, _should_log_request, log_lock

def handle_ollama_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to Ollama APIs.

    Args:
        method: HTTP method for the request
        url: Target API URL
        headers: Request headers
        data_bytes: Request body as bytes
        is_stream: Whether this is a streaming request
        original_request_json_data: The original client request for logging

    Returns:
        Flask Response object
    """
    print(f"DEBUG - Sending Ollama API request: {method} {url}")

    try:
        api_response = requests.request(
            method=method, url=url, headers=headers, data=data_bytes,
            stream=is_stream, timeout=300
        )
        print(f"DEBUG - Ollama API Response status: {api_response.status_code}")

        api_response.raise_for_status()

        log_entry = {'request': original_request_json_data, 'response': None}

        if is_stream:
            def generate_ollama_stream_response():
                response_content_parts = []
                try:
                    for line in api_response.iter_lines():
                        if line:
                            yield line + b'\n\n'
                            # For logging, accumulate content from delta if possible
                            if line.startswith(b'data: '):
                                line_data_str = line.decode('utf-8', errors='replace')[6:]
                                if line_data_str.strip() != '[DONE]':
                                    try:
                                        parsed_chunk = json.loads(line_data_str)
                                        choices = parsed_chunk.get('choices', [])
                                        if choices and isinstance(choices, list) and len(choices) > 0:
                                            delta = choices[0].get('delta', {})
                                            if delta and isinstance(delta, dict):
                                                delta_content = delta.get('content', '')
                                                if delta_content and isinstance(delta_content, str):
                                                    response_content_parts.append(delta_content)
                                    except (json.JSONDecodeError, KeyError, IndexError, TypeError):
                                        pass
                finally:
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_ollama_stream_response()),
                          content_type=api_response.headers.get('Content-Type', 'text/event-stream'))
            # Propagate relevant headers from upstream response
            for hdr_key in ['Cache-Control', 'Content-Type', 'Transfer-Encoding', 'Date', 'Server']:
                if hdr_key in api_response.headers:
                    resp.headers[hdr_key] = api_response.headers[hdr_key]
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else:
            complete_response_text = ''
            try:
                response_json = api_response.json()
                choices = response_json.get('choices', [])
                if choices and isinstance(choices, list) and len(choices) > 0:
                    message = choices[0].get('message', {})
                    if message and isinstance(message, dict):
                        complete_response_text = message.get('content', '')
                if not complete_response_text and isinstance(response_json, dict):
                    complete_response_text = response_json
                log_entry['response'] = complete_response_text
            except json.JSONDecodeError:
                print("Warning: Ollama API Response was not JSON. Logging raw text.")
                complete_response_text = api_response.text
                log_entry['response'] = complete_response_text

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps(log_entry) + '\n')

            # Return the raw response content from the target API to the client
            resp = Response(api_response.content,
                          content_type=api_response.headers.get('Content-Type', 'application/json'),
                          status=api_response.status_code)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except requests.exceptions.RequestException as e:
        print(f"ERROR DETAILS (Ollama API - RequestException): Type: {type(e).__name__}, Message: {str(e)}, URL: {url}")
        error_content_type = 'application/json'
        error_status = 502  # Bad Gateway, typically for network issues with upstream
        error_body_msg = f"Error connecting to upstream API: {str(e)}"
        error_body = {"error": {"message": error_body_msg, "type": type(e).__name__}}

        if e.response is not None:
            print(f"  Upstream response status code: {e.response.status_code}")
            error_status = e.response.status_code
            error_content_type = e.response.headers.get('Content-Type', 'application/json')
            try:
                error_body = e.response.json()
            except json.JSONDecodeError:
                error_body = {"error": {"message": e.response.text, "type": "upstream_error"}}

        resp = jsonify(error_body)
        resp.status_code = error_status
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e:
        print(f"UNEXPECTED OLLAMA API HANDLER ERROR: Type: {type(e).__name__}, Message: {str(e)}")
        resp = jsonify({"error": {"message": "An unexpected error occurred in the Ollama API handler.", "type": type(e).__name__}})
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
</content>
</write_to_file>

[User]
[write_to_file for 'src/dolphin_logger/providers/ollama.py'] Result:

The content was successfully saved to src/dolphin_logger/providers/ollama.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/providers/ollama.py">
"""
Ollama provider implementation for Dolphin Logger.
Handles Ollama API requests using REST API calls.
"""

import json
import requests
from flask import Response, stream_with_context, jsonify

from ..logging_utils import get_current_log_file, _should_log_request, log_lock

def handle_ollama_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to Ollama APIs.

    Args:
        method: HTTP method for the request
        url: Target API URL
        headers: Request headers
        data_bytes: Request body as bytes
        is_stream: Whether this is a streaming request
        original_request_json_data: The original client request for logging

    Returns:
        Flask Response object
    """
    print(f"DEBUG - Sending Ollama API request: {method} {url}")

    try:
        api_response = requests.request(
            method=method, url=url, headers=headers, data=data_bytes,
            stream=is_stream, timeout=300
        )
        print(f"DEBUG - Ollama API Response status: {api_response.status_code}")

        api_response.raise_for_status()

        log_entry = {'request': original_request_json_data, 'response': None}

        if is_stream:
            def generate_ollama_stream_response():
                response_content_parts = []
                try:
                    for line in api_response.iter_lines():
                        if line:
                            yield line + b'\n\n'
                            # For logging, accumulate content from delta if possible
                            if line.startswith(b'data: '):
                                line_data_str = line.decode('utf-8', errors='replace')[6:]
                                if line_data_str.strip() != '[DONE]':
                                    try:
                                        parsed_chunk = json.loads(line_data_str)
                                        choices = parsed_chunk.get('choices', [])
                                        if choices and isinstance(choices, list) and len(choices) > 0:
                                            delta = choices[0].get('delta', {})
                                            if delta and isinstance(delta, dict):
                                                delta_content = delta.get('content', '')
                                                if delta_content and isinstance(delta_content, str):
                                                    response_content_parts.append(delta_content)
                                    except (json.JSONDecodeError, KeyError, IndexError, TypeError):
                                        pass
                finally:
                    if _should_log_request(original_request_json_data):
                        log_entry['response'] = "".join(response_content_parts)
                        log_file_path = get_current_log_file()
                        with log_lock:
                            with open(log_file_path, 'a') as log_file:
                                log_file.write(json.dumps(log_entry) + '\n')

            resp = Response(stream_with_context(generate_ollama_stream_response()),
                          content_type=api_response.headers.get('Content-Type', 'text/event-stream'))
            # Propagate relevant headers from upstream response
            for hdr_key in ['Cache-Control', 'Content-Type', 'Transfer-Encoding', 'Date', 'Server']:
                if hdr_key in api_response.headers:
                    resp.headers[hdr_key] = api_response.headers[hdr_key]
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp
        else:
            complete_response_text = ''
            try:
                response_json = api_response.json()
                choices = response_json.get('choices', [])
                if choices and isinstance(choices, list) and len(choices) > 0:
                    message = choices[0].get('message', {})
                    if message and isinstance(message, dict):
                        complete_response_text = message.get('content', '')
                if not complete_response_text and isinstance(response_json, dict):
                    complete_response_text = response_json
                log_entry['response'] = complete_response_text
            except json.JSONDecodeError:
                print("Warning: Ollama API Response was not JSON. Logging raw text.")
                complete_response_text = api_response.text
                log_entry['response'] = complete_response_text

            if _should_log_request(original_request_json_data):
                log_file_path = get_current_log_file()
                with log_lock:
                    with open(log_file_path, 'a') as log_file:
                        log_file.write(json.dumps(log_entry) + '\n')

            # Return the raw response content from the target API to the client
            resp = Response(api_response.content,
                          content_type=api_response.headers.get('Content-Type', 'application/json'),
                          status=api_response.status_code)
            resp.headers['Access-Control-Allow-Origin'] = '*'
            return resp

    except requests.exceptions.RequestException as e:
        print(f"ERROR DETAILS (Ollama API - RequestException): Type: {type(e).__name__}, Message: {str(e)}, URL: {url}")
        error_content_type = 'application/json'
        error_status = 502  # Bad Gateway, typically for network issues with upstream
        error_body_msg = f"Error connecting to upstream API: {str(e)}"
        error_body = {"error": {"message": error_body_msg, "type": type(e).__name__}}

        if e.response is not None:
            print(f"  Upstream response status code: {e.response.status_code}")
            error_status = e.response.status_code
            error_content_type = e.response.headers.get('Content-Type', 'application/json')
            try:
                error_body = e.response.json()
            except json.JSONDecodeError:
                error_body = {"error": {"message": e.response.text, "type": "upstream_error"}}

        resp = jsonify(error_body)
        resp.status_code = error_status
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp
    except Exception as e:
        print(f"UNEXPECTED OLLAMA API HANDLER ERROR: Type: {type(e).__name__}, Message: {str(e)}")
        resp = jsonify({"error": {"message": "An unexpected error occurred in the Ollama API handler.", "type": type(e).__name__}})
        resp.status_code = 500
        resp.headers['Access-Control-Allow-Origin'] = '*'
        return resp

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/providers/ollama.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/server.py
src/dolphin_logger/providers/ollama.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:22:20 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I need to update the providers `__init__.py` to include ollama:

<replace_in_file>
<path>src/dolphin_logger/providers/__init__.py</path>
<diff>
------- SEARCH
"""
Provider-specific implementations for different LLM services.
"""

__all__ = ["openai", "anthropic", "google"]
=======
"""
Provider-specific implementations for different LLM services.
"""

__all__ = ["openai", "anthropic", "google", "ollama"]
+++++++ REPLACE
</diff>
</replace_in_file>

[User]
[replace_in_file for 'src/dolphin_logger/providers/__init__.py'] Result:

The content was successfully saved to src/dolphin_logger/providers/__init__.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/providers/__init__.py">
"""
Provider-specific implementations for different LLM services.
"""

__all__ = ["openai", "anthropic", "google", "ollama"]

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/providers/__init__.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/server.py
src/dolphin_logger/providers/ollama.py
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:22:34 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I need to update the core_proxy.py to import and use the ollama provider:

<replace_in_file>
<path>src/dolphin_logger/core_proxy.py</path>
<diff>
------- SEARCH
from .providers.anthropic import handle_anthropic_request
from .providers.openai import handle_openai_request
from .providers.google import handle_google_request
=======
from .providers.anthropic import handle_anthropic_request
from .providers.openai import handle_openai_request
from .providers.google import handle_google_request
from .providers.ollama import handle_ollama_request
+++++++ REPLACE
</diff>
</replace_in_file>

[User]
[replace_in_file for 'src/dolphin_logger/core_proxy.py'] Result:

The content was successfully saved to src/dolphin_logger/core_proxy.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/core_proxy.py">
import json
from flask import Response

from .providers.anthropic import handle_anthropic_request
from .providers.openai import handle_openai_request
from .providers.google import handle_google_request
from .providers.ollama import handle_ollama_request
# MODEL_CONFIG is typically loaded in server.py and passed to get_target_api_config.

def get_target_api_config(requested_model_id: str, model_config_list: list) -> dict:
    """
    Determines the target API URL, key, and model based on the requested model ID
    and the server's model configuration.

    Args:
        requested_model_id (str): The model ID from the client's request.
        model_config_list (list): The MODEL_CONFIG list (passed from server.py).

    Returns:
        dict: A dictionary containing 'target_api_url', 'target_api_key',
              'target_model', 'provider', and 'error' (if any).
    """
    for model_config in model_config_list:
        if model_config.get("model") == requested_model_id:
            provider = model_config.get("provider")
            target_model = model_config.get("providerModel", requested_model_id)
            api_key = model_config.get("apiKey")
            api_base = model_config.get("apiBase")

            if provider == "ollama":
                return {
                    "target_api_url": api_base or "http://localhost:11434/v1",
                    "target_api_key": "",
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "anthropic":
                return {
                    "target_api_url": "anthropic_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "google":
                return {
                    "target_api_url": "google_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            else: # OpenAI-compatible
                if not api_base:
                    # Ensure a specific error message for missing apiBase for OpenAI-like providers
                    return {"error": f"apiBase not configured for OpenAI-compatible model '{requested_model_id}'"}
                return {
                    "target_api_url": api_base,
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }

    if not model_config_list: # Check if the list itself is empty
        return {"error": f"No models configured. Cannot process request for model '{requested_model_id}'."}
    return {"error": f"Model '{requested_model_id}' not found in configured models."}

def handle_anthropic_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Anthropic SDK.
    Delegates to the Anthropic provider.
    """
    return handle_anthropic_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )

def handle_google_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Google GenAI SDK.
    Delegates to the Google provider.
    """
    return handle_google_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )

def handle_rest_api_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict,
    provider: str = "openai"
) -> Response:
    """
    Handles requests to REST APIs by routing to the appropriate provider.
    Delegates to the specific provider based on the provider parameter.
    """
    # Route to the appropriate provider
    if provider == "google":
        return handle_google_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
    else:
        # Default to OpenAI provider for OpenAI-compatible APIs (including Ollama)
        return handle_openai_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/core_proxy.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/server.py
src/dolphin_logger/providers/ollama.py
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:23:02 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now I need to update the routing logic in `handle_rest_api_request` to use the dedicated Ollama provider:

<replace_in_file>
<path>src/dolphin_logger/core_proxy.py</path>
<diff>
------- SEARCH
def handle_rest_api_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict,
    provider: str = "openai"
) -> Response:
    """
    Handles requests to REST APIs by routing to the appropriate provider.
    Delegates to the specific provider based on the provider parameter.
    """
    # Route to the appropriate provider
    if provider == "google":
        return handle_google_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
    else:
        # Default to OpenAI provider for OpenAI-compatible APIs (including Ollama)
        return handle_openai_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
=======
def handle_rest_api_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict,
    provider: str = "openai"
) -> Response:
    """
    Handles requests to REST APIs by routing to the appropriate provider.
    Delegates to the specific provider based on the provider parameter.
    """
    # Route to the appropriate provider
    if provider == "google":
        return handle_google_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
    elif provider == "ollama":
        return handle_ollama_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
    else:
        # Default to OpenAI provider for OpenAI-compatible APIs
        return handle_openai_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
+++++++ REPLACE
</diff>
</replace_in_file>

[User]
[replace_in_file for 'src/dolphin_logger/core_proxy.py'] Result:

The content was successfully saved to src/dolphin_logger/core_proxy.py.

Here is the full, updated content of the file that was saved:

<final_file_content path="src/dolphin_logger/core_proxy.py">
import json
from flask import Response

from .providers.anthropic import handle_anthropic_request
from .providers.openai import handle_openai_request
from .providers.google import handle_google_request
from .providers.ollama import handle_ollama_request
# MODEL_CONFIG is typically loaded in server.py and passed to get_target_api_config.

def get_target_api_config(requested_model_id: str, model_config_list: list) -> dict:
    """
    Determines the target API URL, key, and model based on the requested model ID
    and the server's model configuration.

    Args:
        requested_model_id (str): The model ID from the client's request.
        model_config_list (list): The MODEL_CONFIG list (passed from server.py).

    Returns:
        dict: A dictionary containing 'target_api_url', 'target_api_key',
              'target_model', 'provider', and 'error' (if any).
    """
    for model_config in model_config_list:
        if model_config.get("model") == requested_model_id:
            provider = model_config.get("provider")
            target_model = model_config.get("providerModel", requested_model_id)
            api_key = model_config.get("apiKey")
            api_base = model_config.get("apiBase")

            if provider == "ollama":
                return {
                    "target_api_url": api_base or "http://localhost:11434/v1",
                    "target_api_key": "",
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "anthropic":
                return {
                    "target_api_url": "anthropic_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            elif provider == "google":
                return {
                    "target_api_url": "google_sdk", # Special marker
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }
            else: # OpenAI-compatible
                if not api_base:
                    # Ensure a specific error message for missing apiBase for OpenAI-like providers
                    return {"error": f"apiBase not configured for OpenAI-compatible model '{requested_model_id}'"}
                return {
                    "target_api_url": api_base,
                    "target_api_key": api_key,
                    "target_model": target_model,
                    "provider": provider,
                    "error": None,
                }

    if not model_config_list: # Check if the list itself is empty
        return {"error": f"No models configured. Cannot process request for model '{requested_model_id}'."}
    return {"error": f"Model '{requested_model_id}' not found in configured models."}

def handle_anthropic_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Anthropic SDK.
    Delegates to the Anthropic provider.
    """
    return handle_anthropic_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )

def handle_google_sdk_request(
    json_data_for_sdk: dict,
    target_model: str,
    target_api_key: str | None,
    is_stream: bool,
    original_request_json_data: dict
) -> Response:
    """
    Handles requests to the Google GenAI SDK.
    Delegates to the Google provider.
    """
    return handle_google_request(
        json_data_for_sdk,
        target_model,
        target_api_key,
        is_stream,
        original_request_json_data
    )

def handle_rest_api_request(
    method: str,
    url: str,
    headers: dict,
    data_bytes: bytes,
    is_stream: bool,
    original_request_json_data: dict,
    provider: str = "openai"
) -> Response:
    """
    Handles requests to REST APIs by routing to the appropriate provider.
    Delegates to the specific provider based on the provider parameter.
    """
    # Route to the appropriate provider
    if provider == "google":
        return handle_google_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
    elif provider == "ollama":
        return handle_ollama_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )
    else:
        # Default to OpenAI provider for OpenAI-compatible APIs
        return handle_openai_request(
            method, url, headers, data_bytes, is_stream, original_request_json_data
        )

</final_file_content>

IMPORTANT: For any future changes to this file, use the final_file_content shown above as your reference. This content reflects the current state of the file, including any auto-formatting (e.g., if you used single quotes but the formatter converted them to double quotes). Always base your SEARCH/REPLACE operations on this final version to ensure accuracy.

<environment_details>
# VSCode Visible Files
src/dolphin_logger/core_proxy.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/server.py
src/dolphin_logger/providers/ollama.py
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:23:26 AM (America/New_York, UTC-4:00)

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>

[Assistant]
Now let me test that all imports work correctly with the addition of the Ollama provider:

<execute_command>
<command>cd src && python -c "
try:
    from dolphin_logger.providers import anthropic, openai, google, ollama
    from dolphin_logger.core_proxy import get_target_api_config, handle_anthropic_sdk_request, handle_google_sdk_request, handle_rest_api_request
    print('✅ All imports successful!')
    print('✅ Provider modules: anthropic, openai, google, ollama')
    print('✅ Core proxy functions available')
    print('✅ Refactoring completed successfully with all 4 providers!')
except Exception as e:
    print(f'❌ Import error: {e}')
    import traceback
    traceback.print_exc()
"</command>
<requires_approval>false</requires_approval>
</execute_command>

[User]
[TASK RESUMPTION] This task was interrupted just now. It may or may not be complete, so please reassess the task context. Be aware that the project state may have changed since then. The current working directory is now '/Volumes/SB-XTM5/git/dolphin-logger'. If the task has not been completed, retry the last step before interruption and proceed with completing the task.

Note: If you previously attempted a tool use that the user did not provide a result for, you should assume the tool use was not successful and assess whether you should retry. If the last tool was a browser_action, the browser has been closed and you must launch a new browser if needed.

New instructions for task continuation:
<user_message>
✅ All imports successful!
✅ Provider modules: anthropic, openai, google, ollama
✅ Core proxy functions available
✅ Refactoring completed successfully with all 4 providers!
</user_message>

<environment_details>
# VSCode Visible Files
src/dolphin_logger/core_proxy.py

# VSCode Open Tabs
src/dolphin_logger/config.json
../../../../Users/eric/.dolphin-logger/config.json
config.json.example
src/dolphin_logger/upload.py
pyproject.toml
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/server.py
src/dolphin_logger/providers/ollama.py
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py

# Current Time
6/14/2025, 8:24:20 AM (America/New_York, UTC-4:00)

# Current Working Directory (/Volumes/SB-XTM5/git/dolphin-logger) Files
.gitignore
chat-logger.png
config.json.example
pyproject.toml
README.md
src/
src/dolphin_logger/
src/dolphin_logger/__init__.py
src/dolphin_logger/cli.py
src/dolphin_logger/config.py
src/dolphin_logger/core_proxy.py
src/dolphin_logger/logging_utils.py
src/dolphin_logger/main.py
src/dolphin_logger/server.py
src/dolphin_logger/upload.py
src/dolphin_logger/providers/
src/dolphin_logger/providers/__init__.py
src/dolphin_logger/providers/anthropic.py
src/dolphin_logger/providers/google.py
src/dolphin_logger/providers/ollama.py
src/dolphin_logger/providers/openai.py
src/dolphin_logger.egg-info/
tests/
tests/__init__.py
tests/test_cli.py
tests/test_config.py
tests/test_core_proxy.py
tests/test_functional.py
tests/test_logging_utils.py
tests/test_server.py
tests/test_upload.py

# Context Window Usage
0 / 128K tokens used (0%)

# Current Mode
ACT MODE
</environment_details>