Removing openai from requirements

requirements.txt

@@ -11,4 +11,3 @@ pymupdf
 wandb
 chainlit
 huggingface_hub
-openai

utils.py

@@ -23,8 +23,11 @@ def chunk_documents(docs, tiktoken_len):
         chunk_overlap = 0,
         length_function = tiktoken_len,
     )
+
     split_chunks = text_splitter.split_documents(docs)
+
     print('len(split_chunks) =', len(split_chunks))
+
     return split_chunks
 
 
@@ -32,22 +35,31 @@ def create_raqa_chain_from_docs():
     # Load the documents from a PDF file using PyMuPDFLoader
     # docs = PyMuPDFLoader("data/c7318154-f6ae-4866-89fa-f0c589f2ee3d.pdf").load()
     docs = PyMuPDFLoader("https://d18rn0p25nwr6d.cloudfront.net/CIK-0001326801/c7318154-f6ae-4866-89fa-f0c589f2ee3d.pdf").load()
+
+    # Print the number of loaded documents
     print("Loaded", len(docs), "documents")
+
+    # Print the first document
     print(docs[0])
 
-    # Chunk documents, load embedding model, create vectorstore and retriever
+    # Split the documents into chunks based on their length
     split_chunks = chunk_documents(docs, tiktoken_len)
+
+    # Create an instance of the OpenAIEmbeddings model for text embeddings
     embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
+
+    # Create a Qdrant vector store from the split chunks
     qdrant_vectorstore = Qdrant.from_documents(
         split_chunks,
         embedding_model,
         location=":memory:",
         collection_name="Meta 10-k Filings",
     )
+
+    # Create a retriever from the Qdrant vector store
     qdrant_retriever = qdrant_vectorstore.as_retriever()
 
     # Define the RAG prompt template
-    openai_chat_model = ChatOpenAI(model="gpt-3.5-turbo")
     RAG_PROMPT = """
     CONTEXT:
     {context}
@@ -57,9 +69,14 @@ def create_raqa_chain_from_docs():
 
     Use the provided context to answer the provided user query. Only use the provided context to answer the query. If you do not know the answer, respond with "I don't know".
     """
+
+    # Create a ChatPromptTemplate instance from the RAG prompt template
     rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
 
-    # Define the RAQA chain
+    # Create an instance of the ChatOpenAI model
+    openai_chat_model = ChatOpenAI(model="gpt-3.5-turbo")
+
+    # Define the retrieval augmented QA chain
     retrieval_augmented_qa_chain = (
         {"context": itemgetter("question") | qdrant_retriever, "question": itemgetter("question")}
         | RunnablePassthrough.assign(context=itemgetter("context"))