Pravāha - Your Local Perplexity-Inspired Search Engine

Introduction:

Pravāha is your personal AI search assistant, bringing the power of a local search engine right to your fingertips. Inspired by Perplexity.ai, Pravāha lets you explore the fascinating world of Retrieval Augmented Generation (RAG) by combining cutting-edge search technology with the intelligence of Large Language Models (LLMs).

Goal:

Our mission is to empower users with a local, customizable, and insightful search experience. Pravāha aims to replicate the core functionality of popular search assistants like Perplexity, allowing you to delve deeper into information retrieval and language models.

Explore the World of RAG:

With Pravāha, you can witness firsthand how search engines and LLMs work in harmony. By understanding the underlying mechanisms of RAG, you can gain valuable insights into the future of information retrieval and AI-powered search.

Key Features:

• Streamlit Web Interface: Easy-to-use web application for interacting with Pravāha.
• Advanced Reranking: Utilizes the answerdotai/reranker library with FlashRank for improved result relevance.
• Hybrid Search: Combines BM25 and semantic search for comprehensive results.
• LLM Integration: Leverages OpenAI's language models through LiteLLM for intelligent responses.

In Progress & Future Enhancements:

Pravāha is constantly evolving! We are actively working on exciting new features, including:

• Streaming Responses: Experience real-time search results as they are generated. ✅
• Expanded Search Tool Support: Integrate with more search providers for a wider range of sources. ✅
• Persistent Storage with DuckDB: Store and manage your search history and data locally. ✅
• Agentic RAG with LangGraph: Explore the potential of agentic search and knowledge graphs.
• Customizable RAG Templates: Create and share your own RAG templates (YAML) for unique search experiences.
• UI Improvements: Enhance the user interface for a more intuitive and engaging experience.
• PRO Mode: Unlock advanced features and settings for power users.()

Name Inspiration:

The name Pravāha (प्रवाह) is derived from Sanskrit, meaning "flow." This reflects the project's goal of creating a seamless flow of information and responses, allowing users to navigate through queries and context effortlessly. Just as a river flows smoothly, Pravāha aims to provide a fluid and intuitive search experience.

System Architecture:

flowchart TD
    A[Query] --> B[Search Engine]
    B --> C[Retrieval Engine]
    C --> D[LLM]
    D --> E[Response]

    subgraph Search Engine
        B1[Tavily Search API]
        B2[Web Scraping]
    end

    subgraph Retrieval Engine
        C1[Chunking]
        C2[BM25 & Semantic Search]
        C3[Reranker]
    end

    subgraph LLM
        D1[LiteLLM OpenAI]
    end

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Getting Started

1. Run with Docker (Recommended): If you prefer not to install dependencies manually, you can use Docker:

   docker pull jayshaha5696/pravah
   docker run -p 8501:8501 -v $(pwd):/app pravaha

2. Clone the repository (if you want to install manually):

   git clone https://github.com/jayshah5696/pravaha.git
   cd pravaha

3. Install dependencies:

   • Using pip:

     pip install -r requirements.txt

   • Using Poetry:

     poetry install
     poetry shell

4. Set up environment variables: Create a .env file in the root directory and add your API keys. Depending on the models you wish to use, include the following keys:

   TVLY_API_KEY=your_tavily_api_key
   OPENAI_API_KEY=your_openai_api_key
   COHERE_API_KEY=your_cohere_api_key  # Add this if you want to use Cohere models and reranker
   ANTHROPIC_API_KEY=your_anthropic_api_key  # Add this if you want to use Anthropic models
   JINA_API_KEY=your_jina_api_key  # Add this if you want to use Jina reader api
   BRAVE_API_KEY=your_brave_api_key  # Add this if you want to use Brave search engine

   LangSmith Configuration: If you want to enable tracing with LangSmith, add the following environment variables:

   LANGCHAIN_API_KEY=your_langsmith_api_key  # Required for LangSmith API access
   LANGCHAIN_PROJECT=your_project_name  # Optional: Specify the project name for organizing traces
   LANGCHAIN_ENDPOINT=https://api.langsmith.com  # Optional: Specify the LangSmith API endpoint

5. Set up DuckDB: DuckDB is used for storing chat history. Install DuckDB using the following commands based on your operating system:

   • macOS:

     brew install duckdb

   • Linux:

     sudo apt-get install duckdb

   • Windows:

     winget install DuckDB.cli

   For more installation options, visit the DuckDB Installation Guide.

6. Run the Streamlit application:

   streamlit run app.py

File Overview

app.py

Implements the Streamlit web interface for interacting with Pravāha.

prompts.py

Generates prompt templates using Jinja2 for the RAG system.

main.py

Handles the main application logic, including user input, search queries, and displaying results.

search.py

Implements the search functionality using the Tavily API and web scraping.

retrieval.py

Manages text chunking, embedding, and retrieval using BM25 and FAISS. Includes reranking functionality using FlashRank.

Contributing

We welcome contributions to Pravāha! Please feel free to submit issues, feature requests, or pull requests to help improve the project.

docker buildx version
docker buildx inspect --bootstrap
docker buildx create --use
docker buildx build --platform linux/arm64,linux/amd64,linux/amd64/v2,linux/ppc64le,linux/s390x,linux/386,linux/arm/v7,linux/arm/v6 -t jayshaha5696/pravah:latest --push .

License

This project is licensed under the MIT License - see the LICENSE file for details or visit MIT License.

This project is licensed under the MIT License - see the LICENSE file for details or visit MIT License.