Local RAG researcher agent built using Langgraph, DeepSeek R1 and Ollama

🚀 Local RAG Researcher with DeepSeek R1 & Langgraph

👉 Learn How to Build a Local RAG Researcher – Step-by-Step Guide Inside! 🚀

I built a local adaptive RAG research agent using LangGraph and a local DeepSeek R1 model running on Ollama. This agent act like a deep researcher, designed to gather, analyze, and summarize information based on user instructions.

How It Works

1. Generating Research Queries – The agent takes user input and formulates relevant research questions to find the most useful information.

2. Retrieving Documents – It searches a local Chroma database to pull relevant documents related to the query.

3. Evaluating Relevance – Each document is checked against the original query to ensure it contains meaningful and accurate information.

4. Expanding Search if Needed – If the retrieved documents are not sufficient or relevant, the agent can search the web for additional sources.

5. Summarizing Findings – After gathering all necessary information, the agent processes the data and extracts key insights.

6. Final Report Generation – The summarized findings are sent to a writer agent, which structures the information into a detailed and well-formatted report based on a predefined format.

This system allows for an efficient and adaptive research process, ensuring high-quality and relevant outputs while minimizing unnecessary or low-value data.

Key Features

• Dynamic Search Through Local Documents – Efficiently retrieves relevant information from your internal documents.
• Advanced Insight Extraction – Leverages the reasoning power of DeepSeek R1 model to evaluate, a