Building a fully local research assistant from scratch with Ollama
AI Summary
Summary of Video Transcript
- Lance BL Chain demonstrates an agent built for automated research and summarization using local LLMs (Language Models), which are free to use.
- The agent operates in Langra Studio, an environment for testing and working with agents.
- The process involves:
- Importing a local LLM.
- Setting the number of research iterations.
- Adding a research topic.
- The agent generates a search query, conducts web research, and summarizes the findings.
- It reflects on the summary, identifies gaps, asks follow-up questions, and repeats the process for the set number of iterations.
- The final output is a markdown document with a summary, notes, and sources.
- The motivation for this tool is based on a survey indicating research and summarization as the top use case for agents.
- The approach is inspired by iterative retrieval of documents and generating answers, suitable for local models due to system limitations.
- For choosing models, Lance suggests using Local Llama, Twitter, and the Hugging Face Local LLM leaderboard.
- The agent is built using Langra, which utilizes the concept of state to preserve information over the agent’s lifetime.
- The agent’s process includes:
- Generating a search query using a local LLM.
- Performing web research using Tav search (up to 1,000 free requests).
- Summarizing sources and reflecting on the summary to identify knowledge gaps.
- Deciding whether to continue research or finalize based on the number of completed iterations.
- The agent is tested in a notebook environment, and the process is visualized in Langra Studio.
- The README file provides instructions for setting up the agent, including obtaining a model via AMA pull and using Tav for web research.
- The agent is open source and can be configured for any local model.
Detailed Instructions and URLs
- No specific CLI commands, website URLs, or detailed instructions were provided in the transcript.