Remembering the Past: How Langchain RAG Can Enhance Chat History and Source Retrieval
Imagine this: You're having a conversation with a chatbot about a complex topic, like the history of quantum computing. You ask a series of questions, each building on the previous one. Now, wouldn't it be great if the chatbot could actually remember your previous questions and the information it retrieved from its knowledge base?
This is where Langchain's Retrieval-Augmented Generation (RAG) and chat history management come into play. By combining these powerful features, you can create conversational AI experiences that are not only engaging but also remarkably informative and context-aware.
Scenario:
Let's say you want to build a chatbot that answers questions about a specific scientific paper. You can use Langchain to integrate the paper as a knowledge base and retrieve relevant information based on user queries. But how can you ensure the chatbot remembers what it's learned from the paper during the conversation?
Original Code (Simplified):
from langchain.chains import RetrievalQA
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI
# Load the scientific paper
with open("scientific_paper.txt", "r") as f:
text = f.read()
# Create embeddings and vector store
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_texts([text], embeddings)
# Create the RetrievalQA chain
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type="stuff",
retriever=vectorstore.as_retriever(),
)
# User query
user_query = "What are the main findings of the paper?"
# Get response
response = qa(user_query)
print(response["answer"])
Adding Chat History and Source Return:
This code snippet demonstrates a basic RAG setup, but it lacks chat history management and source retrieval. To enhance the conversational experience, we can incorporate these features.
1. Chat History Management:
Langchain offers different approaches to managing chat history:
- Simple List: Maintain a list of previous user questions and chatbot responses.
- Memory Class: Use a dedicated memory class to store conversation context, including user queries, bot responses, and relevant documents.
2. Source Retrieval:
- Retrieve Source Documents: Use Langchain's retrieval capabilities to identify and return the relevant source documents from the knowledge base.
- Highlight Relevant Passages: Use text highlighting techniques to highlight the specific parts of the source documents that are relevant to the user's query.
Benefits of Combining Chat History and Source Retrieval:
- Improved Contextual Understanding: The chatbot can better understand the flow of the conversation and provide more relevant responses based on previous exchanges.
- Increased Transparency: Users can see the source documents used by the chatbot, making the responses more reliable and trustworthy.
- Enhanced Learning: By providing access to source materials, users can learn more about the topic and gain a deeper understanding of the information presented.
Example Code:
from langchain.chains import RetrievalQA
from langchain.chains.question_answering import load_qa_chain
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI
from langchain.memory import ConversationBufferMemory
# Load the scientific paper
with open("scientific_paper.txt", "r") as f:
text = f.read()
# Create embeddings and vector store
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_texts([text], embeddings)
# Create a memory class
memory = ConversationBufferMemory()
# Create the RetrievalQA chain
qa = load_qa_chain(
llm=OpenAI(),
chain_type="stuff",
retriever=vectorstore.as_retriever(),
memory=memory
)
# User query
user_query = "What are the main findings of the paper?"
# Get response
response = qa(user_query)
print(response["answer"])
print(f"\nSources: {response['source']}")
Conclusion:
By incorporating chat history management and source retrieval into your Langchain RAG applications, you can create conversational AI experiences that are more engaging, informative, and trustworthy. This approach provides a powerful way to leverage knowledge bases effectively and make complex information accessible to a wider audience.
Further Resources:
