Upload 2_rag_skeleton.py

2_rag_skeleton.py

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+from langchain_openai import ChatOpenAI
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain.memory import ConversationBufferMemory
+from langchain_core.prompts import PromptTemplate
+
+# Access the OpenAI API key from the environment
+open_ai_key = os.getenv("OPENAI_API_KEY")
+
+llm = ChatOpenAI(api_key=open_ai_key)
+
+template = """Use the following pieces of information to answer the user's question.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+Context: {context}
+Question: {question}
+
+Only return the helpful answer below and nothing else.
+Helpful answer:
+"""
+
+prompt = PromptTemplate(template=template, input_variables=["context", "question"])
+
+
+# Load and process the PDF
+loader = PyPDFLoader(pdf_file.name)
+pdf_data = loader.load()
+
+# Split the text into chunks
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+docs = text_splitter.split_documents(pdf_data)
+
+# Create a Chroma vector store
+embeddings = HuggingFaceEmbeddings(model_name="embaas/sentence-transformers-multilingual-e5-base")
+db = Chroma.from_documents(docs, embeddings)
+
+# Initialize message history for conversation
+message_history = ChatMessageHistory()
+
+# Memory for conversational context
+memory = ConversationBufferMemory(
+ memory_key="chat_history",
+ output_key="answer",
+ chat_memory=message_history,
+ return_messages=True,
+ )
+
+# Create a chain that uses the Chroma vector store
+chain = ConversationalRetrievalChain.from_llm(
+ llm=llm,
+ chain_type="stuff",
+ retriever=db.as_retriever(),
+ memory=memory,
+ return_source_documents=False,
+ combine_docs_chain_kwargs={'prompt': prompt}
+ )
+
+# Process the question
+res = chain({"question": question})
+answer = res["answer"]
+