[Feature Request] Build a tutorial for using Agent and retrieval for extracting insight from large data corpus

[codingjaguar]

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Motivation

Large public corpora like Common Crawl or Wikipedia contain vast amounts of information, but extracting actionable insights from them can be challenging. For instance, analyzing public sentiment toward a political figure involves a sophisticated, iterative exploration process. Here’s how it can be done:
1. Embedding and Storage: Process the corpus to generate embeddings for all documents and store them in a high-performance vector database like Milvus to enable efficient search.
2. Initial Retrieval: Retrieve potentially relevant articles from billions of documents using similarity-based search.
3. Article Sampling and Filtering: Sample articles related to the political figure and analyze them to identify patterns. Utilize a small LLM to refine the pool by excluding documents that are related but not useful for sentiment analysis.
4. Refined Analysis: Use a larger LLM to perform in-depth analysis on the refined set of documents. Employ a multi-agent approach, where agents in different roles (e.g., journalist, analyst, fact-checker) collaborate to provide a nuanced sentiment analysis.
5. Sentiment Evaluation: Synthesize the insights from the refined documents to derive meaningful conclusions about public sentiment.

A reference implementation like this will be very useful to demonstrate how to combine the strengths of vector search, LLM capabilities, and multi-agent collaboration to extract valuable insights from massive datasets.

Solution

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Alternatives

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Additional context

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