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Building a fully local research assistant from scratch with Ollama

2024-12-11 Science & Technology
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LangChain
LangChain
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Description

Research and summarization is one of the most popular agent use-cases identified in our State of AI Agents survey. We've seen high demand for lightweight research assistants that can be run with local LLMs for personal use. Here, we show an assistant that uses Ollama for local model access and LangGraph for agent orchestration. In the video we show how to use it with LangGraph Studio, configure it for different LLMs, and we built it from scratch. Code: https://github.com/langchain-ai/research-rabbit Video notes: https://mirror-feeling-d80.notion.site/Fully-Local-Research-Summarization-157808527b1780bfa5e5f4650630ae08?pvs=73

Top Comments (10)

@Hoxle-87 2024-12-14

Thanks for the video n demo. To me “fully local” means RAG and a local LLM

7
@DN19756 2024-12-12

I love how you explain things. Thanks.

4
@kevinz1991 2025-02-03

Awesome video thank you

1 1 replies
@getting_sleepy 2025-02-02

Love this, thank you, Lance!

1 1 replies
@user-wr4yl7tx3w 2024-12-12

another excellent video

1
@YourAI_agent 2024-12-11

Thanks man looking more such practical use case videos

1
@barts5040 2024-12-12

Thank you for this, it's a gem! Btw., what tool do you use to create such beautiful diagrams??

0
@FarizDarari 2025-11-24

Looks awesome (though not really local). Thank you.

0
@TerraMagnus 2024-12-29

If “exo” were part of your tool stack, your ability to parallelize agents could become an option. Chuck another Mac mini in the pile when it’s worth the investment.

0
@vishaldwdi 2024-12-13

How about connecting VectorDB into this loop so gap query can first run through RAG vectors then still if gap persist it'll go through tavily

0

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