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| import chainlit as cl | |
| from operator import itemgetter | |
| from langchain_openai import ChatOpenAI, OpenAIEmbeddings | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.prompts import ChatPromptTemplate | |
| from langchain.schema import StrOutputParser | |
| from langchain_community.document_loaders import PyMuPDFLoader | |
| from langchain_community.vectorstores import Qdrant | |
| # Constants (you can adjust these as per your environment) | |
| # DATA LOADER | |
| DATA_LINK1 = "https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf" | |
| DATA_LINK2 = "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf" | |
| # CHUNKING CONFIGS | |
| CHUNK_SIZE = 500 | |
| CHUNK_OVERLAP = 50 | |
| # RETRIEVER CONFIGS | |
| COLLECTION_NAME = "AI Bill of Rights" | |
| EMBEDDING_MODEL = "text-embedding-3-small" | |
| # FINAL RAG CONFIGS | |
| QA_MODEL = "gpt-4o" | |
| RAG_PROMPT = """\ | |
| Given a provided context and question, you must answer the question based only on context. | |
| If you cannot answer the question based on the context - you must say "I don't know". | |
| Context: {context} | |
| Question: {question} | |
| """ | |
| # Function to chunk documents | |
| def chunk_documents(unchunked_documents, chunk_size, chunk_overlap): | |
| text_splitter = RecursiveCharacterTextSplitter( | |
| chunk_size=chunk_size, | |
| chunk_overlap=chunk_overlap, | |
| ) | |
| return text_splitter.split_documents(unchunked_documents) | |
| # Function to build retriever | |
| def build_retriever(chunked_documents, embeddings, collection_name): | |
| vectorstore = Qdrant.from_documents( | |
| documents=chunked_documents, | |
| embedding=embeddings, | |
| location=":memory:", # Storing in-memory for demonstration | |
| collection_name=collection_name, | |
| ) | |
| retriever = vectorstore.as_retriever() | |
| return retriever | |
| # Load documents and prepare retriever | |
| rag_documents_1 = PyMuPDFLoader(file_path=DATA_LINK1).load() | |
| rag_documents_2 = PyMuPDFLoader(file_path=DATA_LINK2).load() | |
| chunked_rag_documents = chunk_documents(rag_documents_1, CHUNK_SIZE, CHUNK_OVERLAP) + \ | |
| chunk_documents(rag_documents_2, CHUNK_SIZE, CHUNK_OVERLAP) | |
| async def on_chat_start(): | |
| embeddings = OpenAIEmbeddings(model=EMBEDDING_MODEL) | |
| retriever = build_retriever(chunked_rag_documents, embeddings, COLLECTION_NAME) | |
| rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT) | |
| qa_llm = ChatOpenAI(model=QA_MODEL) | |
| rag_chain = ( | |
| {"context": itemgetter("question") | retriever, "question": itemgetter("question")} | |
| | rag_prompt | qa_llm | StrOutputParser() | |
| ) | |
| cl.user_session.set("chain", rag_chain) | |
| # Chainlit app | |
| async def main(message): | |
| chain = cl.user_session.get("chain") | |
| result = chain.invoke({"question" : message.content}) | |
| await cl.Message( | |
| content=result, # Extract the response from the chain | |
| author="AI" | |
| ).send() | |