Have you ever wished a chatbot could be more than just pre-programmed responses? This blog post dives into building a Retrieval-Augmented Generation (RAG) chatbot, a powerful tool that leverages external knowledge to provide informative and engaging conversations.
What the RAG?
Retrieval Augmented Generation (RAG) systems combine large language models (LLMs) with external knowledge sources to enhance the accuracy and informativeness of responses.
Imagine a librarian and a conversationalist working together. The librarian (think: document storage) provides relevant information, while the conversationalist (think: large language model) crafts a natural-sounding response. That’s the essence of a RAG chatbot!
HOW DOES IT WORK?
Here’s a simplified breakdown of the magic behind RAG chatbots:
Knowledge Storage: We start by feeding the chatbot documents like articles, FAQs, or manuals. These documents are then processed and converted into a format the computer understands.
Understanding Your Questions: When you ask a question, the chatbot analyzes your words to find similar concepts within its knowledge base.
Combining Knowledge and Conversation: The chatbot retrieves the most relevant documents and uses a powerful language model to craft a response that incorporates the retrieved information.
Technologies Used
Cloud Services:
Amazon Web Services (AWS) S3 for storing documents.
Amazon OpenSearch for storing vector embeddings.
Libraries:
boto3: Interacting with AWS services.
langchain: Building RAG pipelines.
chainlit: Building the chat interface.
opensearchpy: Interacting with OpenSearch.
Machine Learning Models:
Pre-trained embedding model (amazon.titan-embed-text-v1).
Large language model (Anthropic Claude.v2)
Benefits of RAG Chatbots
More informative responses: RAG chatbots can access and leverage a vast amount of information, leading to richer and more informative responses.
Improved accuracy: By using external knowledge sources, RAG chatbots can provide more accurate and relevant answers to your questions.
Natural conversation: The combination of retrieved information and language models allows for a more natural and engaging conversation compared to traditional chatbots.
Conclusion
By combining these technologies, you’ve built a powerful RAG chatbot that can access and leverage information from external documents to provide informative responses to user queries. This demonstrates your expertise in cloud services, NLP libraries, and building intelligent chat applications.
This blog post references code snippets from two Python files (index.py and chat.py) in the GitHub repository https://github.com/edtech-masters/edtech-chat-bot.
Let’s explore what these files do in simpler terms:
index.py: This file prepares the documents for the chatbot. It connects to a cloud storage service (like Amazon S3) to access documents, processes them, and creates a special kind of index that helps the chatbot find relevant information quickly.
- chat.py: This file handles the conversation. It connects to the knowledge base (where the processed documents are stored) and the language model. When you ask a question, this file uses the knowledge base to find relevant information and the language model to craft a response that combines the retrieved information with its own conversational skills.
index.py
Purpose – This file processes documents and creates embeddings for efficient retrieval.
1. Connect to an S3 bucket and access documents
The following code connects to an S3 bucket using the boto3 library to access documents.
2. Load and Split Documents
It checks the file type (text or PDF) and uses appropriate loaders (TextLoader or PyPDFLoader) to load the content of the documents. The text is then split into smaller chunks using RecursiveCharacterTextSplitter for efficient processing during embedding creation.
3. Create embeddings and store in the vector database
BedrockEmbeddings are generated using the amazon.titan-embed-text-v1 (Amazon Titan Text Embeddings) model, an embedding model for text embedding provided by AWS.
chat.py
Purpose – This file handles user interaction and utilizes the pre-built vector store and LLM for chat functionality.
1. Connect to the vector store and build the retrieval chain
This code snippet defines functions for handling chat start and chat messages using the chainlit library.
The on_chat_start function is triggered when the chat starts.
It retrieves environment variables for connecting to the OpenSearch vector store and creates instances of BedrockEmbeddings and OpenSearchVectorSearch for handling embeddings and interacting with the vector store.
Furthermore, chat prompt is defined using langchain-ai/retrieval-qa-chat and an LLM model (anthropic.claude-v2) for generating text responses.
The core functionality lies in creating two chains:
retrieval_chain: This chain retrieves the most relevant documents from the vector store based on the user’s message using the create_retrieval_chain function.
chain: This chain combines the retrieved documents with the LLM response using the create_stuff_documents_chain function.
2. Store session state
The chain is then stored in the session_state for later use.
3. Respond to the User Query
The on_message function is triggered when the user sends a message.
It retrieves the chain from the session_state.
It uses the chain to process the user’s message and generate a response using the LLM and retrieved documents.
Finally, the generated response is sent back to the user using cl.message.
