Update document_chat.py

document_chat.py

@@ -7,42 +7,48 @@ from langchain.chains import ConversationalRetrievalChain
 from langchain.memory import ConversationBufferMemory
 from langchain.llms import HuggingFaceHub
 
-#Constants
+# Constants
 CHROMA_DB_PATH = "chroma_db"
-SENTENCE_TRANSFORMER_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
-LLM_Model = "HuggingFaceH4/zephyr-7b-beta"
+SENTENCE_TRANSFORMER_MODEL = "sentence-transformers/all-MiniLM-L6-v2"  # Corrected model name
+LLM_MODEL = "HuggingFaceH4/zephyr-7b-beta"  # Free chatbot model from Hugging Face
 
-#Initialize vector store
+# Initialize vector store
 def initialize_vector_store():
-    embeddings = HuggingFaceEmbeddings(model_name = SENTENCE_TRANSFORMER_MODEL)
-    vector_store = Chroma(persist_directory = CHROMA_DB_PATH, embedding_fnction = embeddings)
+    """Initialize or load ChromaDB vector store"""
+    embeddings = HuggingFaceEmbeddings(model_name=SENTENCE_TRANSFORMER_MODEL)
+    vector_store = Chroma(persist_directory=CHROMA_DB_PATH, embedding_function=embeddings)
     return vector_store
+
 vector_store = initialize_vector_store()
+
 def ingest_pdf(pdf_path):
+    """Processes a PDF, splits text, and stores embeddings in ChromaDB."""
     loader = PyMuPDFLoader(pdf_path)
     documents = loader.load()
 
-    #split text into smaller chunks
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 100)
-    splitdocs = text_splitter.split_documents(documents)
+    # Split text into smaller chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+    split_docs = text_splitter.split_documents(documents)
 
-    #store in vector db
-    vector_store.add_documents(splitdocs)
+    # Store in vector database
+    vector_store.add_documents(split_docs)
     vector_store.persist()
 
 def process_query_with_memory(query, chat_history=[]):
+    """Retrieves relevant document chunks and generates a conversational response."""
     retriever = vector_store.as_retriever()
 
-    #Initialize chat memory
-    memory = ConversationalBufferMemory(memory_key = "chat_history", return_messages = True)
+    # Initialize chat memory
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
 
-    #Load a free hugging face model
-    llm = HuggingFaceHub(repo_id = LLM_Model, model_kwargs = {"max_new_tokens": 500})
+    # Load a free Hugging Face model
+    llm = HuggingFaceHub(repo_id=LLM_MODEL, model_kwargs={"max_new_tokens": 500})
 
-    #Create a conversational retrieval chain
+    # Create a conversational retrieval chain
     qa_chain = ConversationalRetrievalChain(
-        llm = llm,
-        retriever = retriever,
-        memory = memory)
-    return qa_chain.run({"question":query, "chat_history": chat_history})
+        llm=llm,
+        retriever=retriever,
+        memory=memory
+    )
 
+    return qa_chain.run({"question": query, "chat_history": chat_history})