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LLM Rag Chatbot With Langchain

A Python question-answering chatbot application that uses LangChain retrieval-augmented generation, a Streamlit web interface and a GGML-quantized Llama 2 7B model to answer questions from external documents. Its documented design targets CPU inference and describes deployment on AWS.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

LLM Rag Chatbot With Langchain — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it as a reference when designing a question-answering application that retrieves context from external documents before generating an answer.

Who it is for

  • Developers building a document-grounded question-answering chatbot.
  • Data practitioners exploring a CPU-based RAG example with a web interface.
  • Readers studying document chunking, embeddings, vector retrieval and model decoding settings.

Documented uses

  • Retrieve relevant passages from external documents in response to a question.
  • Combine retrieved passages and a query as context for LLM answer generation.
  • Explore a Streamlit chatbot and deployment-oriented AWS project structure.
Before you choose

Requirements & limits

  • The example uses a GGML-quantized Llama 2 7B model and is described as CPU-capable.
  • The README describes chunking and embedding documents, storing vectors, and retrieving similar passages with FAISS.
  • The repository includes Docker and Kubernetes deployment materials as well as an application and PDF dataset directory.
  • The README notes that sampling settings can increase output variability and the risk of incorrect responses.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Understand the chatbot’s RAG answer flow

Trace how external documents are used to provide context for a chatbot answer.

Before you start

  • External documents to use as knowledge sources.
  • A document embedding model with a maximum sequence length.
  • A vector database and a similarity-search method; the README describes FAISS and notes cosine similarity for its model.
  • The project’s quantized Llama-2-7B model and LangChain-based chatbot.

Steps

  1. Split each knowledge document into chunks and embed the chunks, taking the embedding model’s maximum sequence length into account.

  2. Store the chunk embeddings in a vector database.

  3. Embed a user query with the same model and retrieve the top-k closest chunks using similarity search.

  4. Combine the retrieved content into context, add the query to form a prompt, and send it to the LLM to generate an answer.

Expected outcome

The LLM generates an answer using context retrieved from external documents.

Guide limitations

  • The README describes the RAG workflow but does not provide implementation commands or configuration values for building the index or running the chatbot.
  • Retrieval quality depends on choices such as the distance metric and search method; the README says its particular model works well with cosine similarity.
  • The README notes that document embedding must respect the embedding model’s maximum sequence length.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
LLM Rag Chatbot With LangchainRetrieve relevant passages from external documents in response to a question.Cloud, Self-hosted, Web
Rag Powered AI Assistant Transforming Data Retrieval And Analysis Across The Web And PdfsRetrieve relevant passages from web and PDF content to answer a question.Web
LangChain in Supabase Edge FunctionsInitialize and start a Supabase project locally.Cloud, Server
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
amajji/llm-rag-chatbot-with-langchain · Official homepage title · retained in its source language
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