Create llm.py

llm.py

@@ -0,0 +1,37 @@
+import langchain
+from langchain import HuggingFaceHub
+from langchain.embeddings import HuggingFaceHubEmbeddings
+from langchain.document_loaders import PyPDFLoader
+from langchain.vectorstores import FAISS
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chains.question_answering import load_qa_chain
+
+def llm_conv(filename):
+    document_loader = PyPDFLoader(filename)
+    chunks = document_loader.load_and_split()
+    embeddings = HuggingFaceHubEmbeddings()
+    db = FAISS.from_documents(chunks, embeddings)
+    return db, chunks
+
+def similarity(filename, repo_id, model_kwargs, query):
+    db, chunks = llm_conv(filename)
+    docs = db.similarity_search(query)
+    chain = load_qa_chain(
+        HuggingFaceHub(
+            repo_id=repo_id,
+            model_kwargs=model_kwargs
+        ),
+        chain_type="stuff"
+    )
+    question = f"""
+    Answer the question based on the context, if you don't know then output "Out of Context".
+    Context: \n {chunks[0].page_content} \n
+    Question: \n {query} \n
+    Answer:
+    """
+    result = chain.run(
+        input_documents=docs,
+        question=question
+    )
+    return result
+