agent-search 0.0.3

AgentSearch: An open source framework and dataset for webscale local search.

AgentSearch [ΨΦ]: A Comprehensive Agent-First Framework and Dataset for Webscale Search

AgentSearch is a powerful agent-first search engine which enables you to run a webscale search engine locally or to connect via remote API. It's ideal for both Large Language Models (LLMs) and human users, providing access to over one billion high-quality embeddings from diverse sources like Creative Commons, Arxiv, Wikipedia, and Project Gutenberg.

Features of AgentSearch

• Customizable: Allows uploading of local data or tailoring of provided datasets to meet specific needs.
• Offline Support: Facilitates operation in a completely offline environment. Download the full dataset here.
• API Endpoint: Offers fully managed access through a dedicated API for seamless integration into various workflows.

Quickstart Guide for AgentSearch

Install the AgentSearch Client

pip install agent-search

Perform a Search

• To perform a search with the hosted AgentSearch API, follow these steps:

1. Register for a free API key at SciPhi.
2. Use the following command to run a search query:

   export SCIPHI_API_KEY=MY_SCIPHI_API_KEY
   python -m agent_search.scripts.run_search run --query="What is Fermat's last theorem?"

This will output results like the following:

1. URL: https://en.wikipedia.org/wiki/Wiles%27s%20proof%20of%20Fermat%27s%20Last%20Theorem (Score: 0.85)
--------------------------------------------------
Title: Wiles's proof of Fermat's Last Theorem
Text:
is a proof by British mathematician Andrew Wiles of a special case of the modularity theorem for elliptic curves ... Response Continues ...

For further information, you may refer to the documentation.

Generate a RAG response

• To generate a rag response with the hosted AgentSearch API:

# Install SciPhi's synthesizer package -
pip install sciphi-synthesizer

# Setup environment
export SCIPHI_API_KEY=MY_SCIPHI_API_KEY
# Use the SciPhi `SearchAgent` for LLM RAG w/ AgentSearch
python -m agent_search.scripts.run_rag run --query="What is Fermat's last theorem?"

export OPENAI_API_KEY=MY_OPENAI_KEY
# Use OpenAI `gpt-3.5-turbo` for LLM generation
python -m agent_search.scripts.run_rag run --query="What is Fermat's last theorem?" --llm_provider_name=openai --llm_model_name=gpt-3.5-turbo

Resulting output:

{"summary": "\nFermat's Last Theorem is a significant result in number theory, stating that for any natural number n greater than 2, there are no solutions to the equation \\(a^n + b^n = c^n\\) where \\(a\\), \\(b\\), and \\(c\\) are positive integers [5]. The theorem was first proposed by Pierre de Fermat in the margins of his copy of Diophantus's \"Arithmetica\" in the 17th century, but it remained unproved for over three centuries [8]. The first case of the theorem to be proven was by Fermat himself for \\(n = 4\\), using a method of infinite descent [9]. Leonhard Euler later provided a proof for the case \\(n = 3\\), although his initial proof contained errors that were later corrected [9].\n\nThe theorem was finally proven in its entirety in 1995 by British mathematician Andrew Wiles, using sophisticated mathematical tools and techniques that were not available during Fermat's lifetime [10]. This breakthrough marked the end of a long period of mathematical speculation and the resolution of a major historical puzzle in mathematics [10]. The proof of Fermat's Last Theorem has been hailed as one of the most significant achievements in the history of mathematics, demonstrating the power of modern mathematical methods and the persistence of mathematical inquiry over centuries [10].\n\n", "other_queries": ["Details of Fermat's Last Theorem proof", "Historical impact of Fermat's Last Theorem", "Contributions of Andrew Wiles to mathematics", "Techniques used in the proof of Fermat's Last Theorem", "Evolution of number theory post-Fermat's Last Theorem"]}

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Community & Support

• Engage with the community on Discord.
• For tailored inquiries or feedback, please email us.

Local Setup and Initialization

For self-hosting, follow the steps below.

Prerequisites

Ensure Docker and Postgres are installed on your system.

• Download Docker here.
• Download Postgres here.

Steps:

1. Launch Postgres Database:

   • Start the Postgres service on your system:

     # Command to start Postgres, adjust based on your system's configuration
     sudo service postgresql start
     

   • This step ensures that the Postgres database is running and ready to be populated.

2. Relational Database Population:

   • Command to populate the Postgres database:

     python -m agent_search.scripts.populate_postgres_from_hf run
     

   • This script populates a postgres database with the parameters from config.ini, adaptable to custom datasets. For help directly istalling the full 4TB postgres database, contact our team.

3. Start Qdrant Service with Docker:

   • Run the Qdrant service in Docker:

     docker run -p 6333:6333 -p 6334:6334 \
         -v $(pwd)/qdrant_storage:/qdrant/storage:z \
         qdrant/qdrant
     

   • For Qdrant installation guidance, see Qdrant Documentation.

4. Vector Database Population:

   • Populate the Vector database:

     python -m agent_search.scripts.populate_qdrant_from_postgres run --delete_existing=True
     

   • This step prepares a qdrant database with the parameters from config.ini. For direct installation assistance, contact our team.

5. Run the Server:

   • Launch the AgentSearch server:

     python -m agent_search.app.server
     

Additional Notes

• Execute all commands from the root directory of the AgentSearch project.
• Replace query in the run command with your search query.
• Check back soon for our User Guide.