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Introduction to Jar3d.MD

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Introduction to Jar3d

Problem Statement

The goal was to develop an AI agent capable of leveraging the full potential of both proprietary and open-source models for research-intensive tasks.

What is Jar3d?

Jar3d is a versatile research agent that combines chain-of-reasoning, Meta-Prompting, and Agentic RAG techniques.

  • It features integrations with popular providers and open-source models, allowing for 100% local operation given sufficient hardware resources.
  • Research is conducted via the SERPER API giving the agent access to google search, and shopping with plans to extend this to include other services.

Use Cases & Applications

  • Long-running research tasks, writing literature reviews, newsletters, sourcing products etc.
  • Potential adaptation for use with internal company documents, requiring no internet access.
  • Can function as a research assistant or a local version of services like Perplexity.

For setup instructions, please refer to the Setup Jar3d guide.

Prompt Engineering

Jar3d utilizes two powerful prompts written entirely in Markdown:

  1. Jar3d Meta-Prompt
  2. Jar3d Requirements Prompt

Both prompts incorporate adaptations of the Chain of Reasoning technique.

Jar3d Architecture

The Jar3d architecture incorporates aspects of Meta-Prompting, Agentic RAG, and an adaptation of Chain of Reasoning.

graph TD
    A[Jar3d] -->|Gathers requirements| B[MetaExpert]
    B -->|Uses chain of reasoning| C{Router}
    C -->|Tool needed| D[Tool Expert]
    C -->|No tool needed| E[Non-Tool Expert]
    D -->|Internet research & RAG| F[Result]
    E -->|Writer or Planner| G[Result]
    F --> B
    G --> B
    B --> C
    C -->|Final output| I[Deliver to User]
    C -->|Needs more detail| B
    
    subgraph "Jar3d Process"
    J[Start] --> K[Gather Requirements]
    K --> L{Requirements Adequate?}
    L -->|No| K
    L -->|Yes| M[Pass on Requirements]
    end
    
    A -.-> J
    M -.-> B
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Jar3d's Retrieval Mechanism for Internet Research

This system employs a sophisticated retrieval mechanism for conducting internet research. The process involves several steps, utilizing various tools and techniques to ensure comprehensive and relevant results.

1. Web Page Discovery

  • Utilizes the SERPER tool to find relevant web pages.
  • Employs an LLM-executed search algorithm, expressed in natural language.
  • Each iteration of the algorithm generates a search query for SERPER.
  • SERPER returns a search engine results page (SERP).
  • Another LLM call selects the most appropriate URL from the SERP.
  • This process is repeated a predetermined number of times to compile a list of URLs for in-depth research.

2. Content Extraction and Chunking

  • Employs LLM Sherpa as a document ingestor.
  • Intelligently chunks the content from each URL in the compiled list.
  • Results in a corpus of chunked text across all accumulated URLs.

3. Text Embedding

  • Embeds the chunked text using a locally hosted model from FastEmbed.
  • Indexes embeddings in an in-memory FAISS vector store.

4. Similarity Search

  • Performs retrieval using a similarity search over the FAISS vector store.
  • Utilizes cosine similarity between indexed embeddings and the meta-prompt (written by the meta-agent).
  • Retrieves the most relevant information based on this similarity measure.

5. Re-ranking

  • Leverages FlashRank as a locally hosted re-ranking service.
  • FlashRank uses cross-encoders for more accurate assessment of document relevance to the query.

6. Final Selection

  • Selects a designated percentile of the highest-scoring documents from the re-ranked results.
  • Passes this final set of retrieved documents to the meta-agent for further processing or analysis.