DeepSeek: Revolutionizing Internet-Scale Retrieval
Introduction
DeepSeek introduces a novel experimental architecture designed specifically for internet-scale retrieval, powered by a large language model (LLM). Unlike traditional research agents that serve as answer engines, DeepSeek functions as a retrieval engine. This distinction marks a significant paradigm shift in how information is gathered and presented from the internet.
Key Differences
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Answer Engine: This type of engine filters through multiple sources to pinpoint a singular correct answer, producing a research report as its final result. Examples in this category include Perplexity and GPT-Researcher.
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Retrieval Engine: Rather than seeking a single answer, this engine amasses a comprehensive list of entities, resulting in a detailed table with enriched columns. This approach, spearheaded by DeepSeek, is relatively unexplored.
How It Works
DeepSeek's functionality manifests in the production of an extensive table filled with entities—a result so comprehensive that capturing its full scale in a single screenshot is implausible. For a given query, DeepSeek examined 356 sources to retrieve and enrich 94 records. Confidence scores are also generated, helping identify the reliability of data within the table cells. Cells with low confidence are flagged for potential review.
Getting Started
Installation
To experiment with DeepSeek, you'll need to install a package manager such as npm, yarn, pnpm, or bun. Follow the installation instructions and run the development server using:
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev
After setup, navigate your browser to http://localhost:3000 to start exploring. Though real-time queries aren't active due to cost constraints, users can inspect pre-built examples displaying both the architecture's strengths and weaknesses.
Environment Setup
You'll require API keys from Anthropic and Exa. Store these keys in a .env file like so:
ANTHROPIC_KEY="anthropic_api_key"
EXA_KEY="exa_api_key"
Architecture Overview
DeepSeek operates through a multi-step pipeline categorized under flow engineering. Each user query is processed through four crucial steps:
- Plan: Develops an outline based on the user query, identifying entity types and relevant data columns.
- Search: Utilizes standard keyword and neural search methods provided by Exa to find relevant content.
- Extract: Applies a LLM technique to efficiently extract entities and associated data using special tokens.
- Enrich: Places an emphasis on enriching data columns for each entity, enhancing the retrieval details.
Future Enhancements
There are numerous future directions for DeepSeek:
- Introducing sorting/ranking mechanisms for retrieved entities.
- Improving entity resolution to avoid duplicates.
- Bolstering source verification during enrichment processes.
- Facilitating deeper browsing of web sources to improve content accuracy.
- Implementing real-time streaming data to enhance user interface interactivity.
For collaboration or discussion, reach out via email at [email protected] or on Twitter.
DeepSeek is committed to merging robust technology with retrieval efficiency, setting new standards in data acquisition and utilization.