Langchain-RAG-Chatbot is an advanced AI chatbot that utilizes Retrieval-Augmented Generation (RAG) with LangChain to provide highly relevant, context-aware responses. By combining large language models (LLMs) with external knowledge retrieval, the chatbot can answer domain-specific queries, support users with documentation, and maintain coherent multi-turn conversations.
RAG is an architecture that combines traditional language models with a retrieval system. Instead of relying solely on the LLM's training data, the model retrieves relevant information from external sources (like documents, databases, or APIs) at runtime. This enables the chatbot to:
LangChain is a framework for developing applications powered by language models. It offers out-of-the-box tools for chaining LLM prompts with retrieval, memory, and more.
Here’s a high-level flow of a typical RAG-based chatbot using LangChain:
User Query → Retriever → Relevant Documents → LLM (with context) → Response
Step-by-step:
Below is a simplified example of how you might implement a RAG chatbot using LangChain in Python.
from langchain.text_splitter import CharacterTextSplitter from langchain.document_loaders import TextLoader from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import FAISS # Load and split your documents loader = TextLoader("data/knowledge_base.txt") documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50) docs = text_splitter.split_documents(documents) # Create embeddings and build the vector store embeddings = OpenAIEmbeddings() vectorstore = FAISS.from_documents(docs, embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
from langchain.llms import OpenAI llm = OpenAI(temperature=0)
from langchain.chains import RetrievalQA rag_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", # "stuff" is a simple chain that puts all context into the prompt retriever=retriever, return_source_documents=True, )
query = "Explain how vector search improves chatbot accuracy." result = rag_chain({"query": query}) print("Answer:", result["result"]) print("Sources:", [doc.metadata for doc in result["source_documents"]])
ConversationBufferMemory from LangChain to maintain chat history.from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationalRetrievalChain memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) conversational_rag = ConversationalRetrievalChain.from_llm( llm, retriever=retriever, memory=memory, ) # Example chat response = conversational_rag({"question": "What is RAG in chatbots?"}) print(response["answer"])
Langchain-RAG-chatbot/
├── data/ # Knowledge base and document storage
├── src/ # Source code for the chatbot
├── requirements.txt # Python dependencies
├── app.py # Main application entry point
└── README.md
Clone the Repository
git clone https://github.com/erenyeager101/Langchain-RAG-chatbot.git cd Langchain-RAG-chatbot
Install Requirements
pip install -r requirements.txt
Configure Environment
data/ directory or configure your document loader.Run the Chatbot
python app.py
Contributions are welcome! Please open issues or submit pull requests for improvements or new features.
This project is licensed under the MIT License.