deep seek

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Basic Information

DeepSeek is an experimental architecture and reference implementation for an LLM-powered, internet-scale retrieval engine designed to collect comprehensive lists of entities and enrich them rather than produce a single canonical answer. The repository demonstrates a multi-step research agent pipeline that breaks a user query into a plan, searches the web with both keyword and neural search, extracts entities from found content using tokenized boundaries, and then enriches each entity with additional columns defined by the planner. The project includes a demo UI showing large tabular outputs with confidence scores, development instructions to run a local dev server, an examples file with raw example data, and notes about required API keys and cost considerations for running real queries.

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App Details

Features
A four-step pipeline: Plan, Search, Extract, Enrich, implemented as a multi-step research agent. Uses both keyword search and neural search provided by Exa for broad and targeted content discovery. Extraction uses a token-boundary technique that inserts special tokens between sentences (using winkNLP sentence splitting) to let the LLM indicate start and end ranges for efficient entity extraction. Enrichment populates table columns for each entity and associates a confidence score per cell to flag low-confidence values. Includes a demo web UI to inspect large results, example queries in an examples.ts file, terminal logging for running agents, and straightforward dev commands and environment variable requirements for Anthropic and Exa API keys.
Use Cases
DeepSeek is helpful when a user or researcher needs a comprehensive, structured inventory of entities and associated metadata rather than a short answer. It automates discovery across many sources, extracts discrete entity records, and enriches each record with relevant columns and confidence estimates so users can filter, inspect, and prioritize results. The architecture is designed to be token efficient during extraction and to reveal where data is uncertain by highlighting low-confidence cells. The repo provides a runnable demo and example data to explore the approach, notes about runtime cost and API keys, and avenues for extending ranking, entity resolution, deeper browsing, and streaming population of results.

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