Update ingest.py

ingest.py

@@ -1,30 +1,32 @@
-import streamlit as st
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import SentenceTransformerEmbeddings
-from langchain.vectorstores import Chroma
+from langchain.document_loaders import PyPDFLoader, DirectoryLoader, PDFMinerLoader 
+from langchain.text_splitter import RecursiveCharacterTextSplitter 
+from langchain.embeddings import SentenceTransformerEmbeddings 
+from langchain.vectorstores import Chroma 
+import os 
 from constants import CHROMA_SETTINGS
-persist_directory = "db"
 
+persist_directory = "db"
 
 def main():
-    st.title("PDF Processor")
-    uploaded_file = st.file_uploader("Upload a PDF file")
-    if uploaded_file is not None:
-        st.write("Processing PDF...")
-        loader = PyPDFLoader(uploaded_file.read())
-        documents = loader.load()
-        st.write("Splitting into chunks")
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
-        texts = text_splitter.split_documents(documents)
-        st.write("Loading sentence transformers model")
-        embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
-        st.write("Creating embeddings. This may take some time...")
-        db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
-        db.persist()
-        db = None
-        st.success("Ingestion complete! You can now run privateGPT.py to query your documents")
+    for root, dirs, files in os.walk("docs"):
+        for file in files:
+            if file.endswith(".pdf"):
+                print(file)
+                loader = PyPDFLoader(os.path.join(root, file))
+    documents = loader.load()
+    print("splitting into chunks")
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+    texts = text_splitter.split_documents(documents)
+    #create embeddings here
+    print("Loading sentence transformers model")
+    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+    #create vector store here
+    print(f"Creating embeddings. May take some minutes...")
+    db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
+    db.persist()
+    db=None 
 
+    print(f"Ingestion complete! You can now run privateGPT.py to query your documents")
 
 if __name__ == "__main__":
-    main()
+    main()