How to Fix LLM Hallucinations Using RAG Techniques

steps

1. Ingest and Preprocess Data:

   • Use datasets, such as Wikipedia from Hugging Face, for creating your knowledge base.
   • Split long passages into manageable chunks.
   • Use tokenization to meet tokenizer limits for your chosen LLM.
   pythonCopy

   from langchain.text_splitter import CharacterTextSplitter
   from langchain.llms import OpenAI
   import tiktoken
   
   # Load and tokenize text data
   dataset = ["This is a long piece of text that needs to be split into chunks."]
   splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=20)
   chunks = splitter.split_text(dataset[0])
   print(chunks)  # Output: Smaller text chunks

2. Embed the Data:

   • Create vector embeddings for each text chunk using OpenAI's embedding models (e.g., text-embedding-ada-002).
   pythonCopy

   from langchain.embeddings import OpenAIEmbeddings
   
   # Initialize embeddings
   embedding_model = OpenAIEmbeddings()
   embeddings = embedding_model.embed_documents(chunks)
   print(len(embeddings[0]))  # Verify embedding dimensionality

3. Set Up a Vector Database:

   • Use a vector database like Pinecone to store and query embeddings.
   pythonCopy

   import pinecone
   
   # Initialize Pinecone
   pinecone.init(api_key="YOUR_API_KEY", environment="us-west1-gcp")
   index_name = "rag-index"
   
   if index_name not in pinecone.list_indexes():
       pinecone.create_index(index_name, dimension=1536)
   
   index = pinecone.Index(index_name)
   index.upsert([(f"id-{i}", vector) for i, vector in enumerate(embeddings)])

4. Integrate Retrieval with LangChain:

   • Query the vector database for relevant embeddings during runtime.
   pythonCopy

   from langchain.vectorstores import Pinecone
   from langchain.docstore.document import Document
   
   # Load the Pinecone index into LangChain
   vectorstore = Pinecone(index, embedding_model.embed_query, "text")
   
   # Query the database
   query = "Tell me about Benito Mussolini."
   results = vectorstore.similarity_search(query, k=3)
   print([result.page_content for result in results])

5. Fine-Tune Retrieval with a Language Model:

   • Use retrieved data as context in LLM prompts through LangChain's RetrievalQA component.
   pythonCopy

   from langchain.chains import RetrievalQA
   
   llm = OpenAI(model="gpt-3.5-turbo", temperature=0)
   qa_chain = RetrievalQA(llm=llm, retriever=vectorstore.as_retriever())
   
   # Query with retrieval augmentation
   answer = qa_chain.run(query)
   print(answer)

6. Add Citation Generation:

   • Use LangChain's RetrievalQAWithSourcesChain to include source citations in the LLM output.
   pythonCopy

   from langchain.chains import RetrievalQAWithSourcesChain
   
   qa_with_sources = RetrievalQAWithSourcesChain(llm=llm, retriever=vectorstore.as_retriever())
   answer = qa_with_sources.run(query)
   print("Answer:", answer["answer"])
   print("Sources:", answer["sources"])