This project demonstrates the development of a RAG (Retrieval-Augmented Generation) assistant capable of answering natural language questions from a custom knowledge base. The assistant was built using the LangChain framework to orchestrate the pipeline. To ensure the project is cost-effective and runs locally, it leverages Ollama to serve the Llama3 large language model. The knowledge base is vectorized using HuggingFace sentence transformers and stored in a ChromaDB vector store. The final result is a fully functional, private, and free-to-run conversational AI that can act as an expert on the contents of any given text document.
The core of this project is a RAG pipeline implemented in Python with the LangChain library. The process follows several key stages:
Document Loading & Chunking: The knowledge base, a text file about the Python programming language, is loaded using LangChain's TextLoader. It is then split into smaller, overlapping chunks with the RecursiveCharacterTextSplitter to ensure semantic context is maintained.
Embedding & Vector Storage: Each text chunk is converted into a numerical representation (embedding) using the all-MiniLM-L6-v2 model from HuggingFace. These embeddings are stored in a local ChromaDB vector store, which allows for efficient similarity searches.
Local LLM Integration: A key decision for this project was to avoid reliance on paid APIs. To achieve this, Ollama was installed to serve the llama3 model locally. LangChain's Ollama integration was used to connect the pipeline to this local LLM.
Retrieval and Generation: When a user submits a query, the RetrievalQA chain first converts the query into an embedding. It then retrieves the most relevant document chunks from ChromaDB. Finally, these chunks and the original query are passed to the Llama3 model, which generates a coherent, context-aware answer.
The RAG assistant performed effectively, providing accurate answers based solely on the content within the sample.txt knowledge base. The use of a local LLM via Ollama proved to be a viable and responsive solution for this scale of project.
Sample Interactions:
Query 1: "What are the main uses of Python?"
Assistant Response: "Python is widely used in web development, data science, artificial intelligence, scientific computing, and automation."
Query 2: "What's new in Python 3.13?"
Assistant Response: "Python 3.13 includes a new interactive interpreter, experimental support for free-threading, and a Just-In-Time (JIT) compiler. Its error messages are also improved with color-highlighted tracebacks."
Conclusion: The project successfully meets its objective of creating a functional RAG assistant. The quality of the answers is directly dependent on the information present in the source document, highlighting the importance of a comprehensive knowledge base.