local-rag-researcher-deepseek

# 🚀 **Local RAG Researcher with DeepSeek R1 & Langgraph**

### 👉 **[Learn How to Build a Local RAG Researcher – Step-by-Step Guide Inside!](https://dev.to/kaymen99/build-your-own-local-rag-researcher-with-deepseek-r1-11m) 🚀**

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.  

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  <img src="https://github.com/user-attachments/assets/5dc34341-3a2f-461c-b66d-46b134fe5bd9" alt="Demo of Local RAG Researcher with LangGraph & DeepSeek">
</div>

## **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