mananjain02/llm-custom-data
In this project, I've implemented LLMs on custom data, using the power of RAG and Langchain.
This tool helps you create a smart chatbot that can answer questions based on your own PDF documents. You input your PDF files into a designated folder, and the tool processes them to build a searchable knowledge base. The output is a chatbot ready to provide answers drawn directly from your documents. This is ideal for anyone who needs to quickly get answers from a collection of their own specific information, like researchers, legal professionals, or educators.
No commits in the last 6 months.
Use this if you need to quickly build a Q&A system that can accurately pull information from your specific PDF files.
Not ideal if you need to analyze diverse data types beyond PDFs or require complex analytical capabilities beyond direct question answering.
Stars
7
Forks
2
Language
Python
License
—
Category
Last pushed
Oct 11, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/mananjain02/llm-custom-data"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
run-llama/llama_index
LlamaIndex is the leading document agent and OCR platform
emarco177/documentation-helper
Reference implementation of a RAG-based documentation helper using LangChain, Pinecone, and Tavily..
janus-llm/janus-llm
Leveraging LLMs for modernization through intelligent chunking, iterative prompting and...
JetXu-LLM/llama-github
Llama-github is an open-source Python library that empowers LLM Chatbots, AI Agents, and...
Vasallo94/ObsidianRAG
RAG system to query your Obsidian notes using LangGraph and local LLMs (Ollama)