LLM App Project Introduction
Pathway's LLM App, or Large Language Model Apps, provide an innovative and efficient solution for deploying artificial intelligence applications that offer high-accuracy Retrieval-Augmented Generation (RAG) at scale. These apps utilize the most up-to-date knowledge available from various data sources, ensuring that they operate with the latest information.
Key Features
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Integration with Data Sources: The LLM apps connect and synchronize with a range of data sources, including file systems, Google Drive, Sharepoint, S3, Kafka, PostgreSQL, and real-time data APIs. This comprehensive integration ensures that any data addition, deletion, or update is promptly reflected in the AI applications.
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Built-in Data Indexing: The apps come equipped with built-in data indexing that allows for in-memory vector search, hybrid search, and full-text search, all integrated with a caching system. This enables efficient and quick data retrieval without the need for extensive infrastructure setup.
Application Templates
Pathway provides a set of application templates designed to handle millions of document pages. Users can select a template based on their specific requirements, whether they prioritize simplicity or high accuracy. The templates are flexible, allowing users to customize certain aspects, such as adding new data sources or modifying the Vector Index to a Hybrid Index with minimal effort.
Here are some of the notable application templates:
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Question-Answering RAG App: This template offers a full end-to-end question-answering pipeline using a GPT model to respond to queries about documents stored in various formats like PDF or DOCX.
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Live Document Indexing: This pipeline provides real-time indexing of documents from connected sources, acting as a vector store service. It's adaptable for use with different frontends or as a backend for applications like Langchain or Llamaindex.
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Multimodal RAG Pipeline with GPT4o: This template is ideal for extracting data from complex documents, such as financial reports, and keeps the information updated as documents change.
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Unstructured-to-SQL Pipeline: This app transforms unstructured data into SQL, loading it into a PostgreSQL table and enabling natural language queries on the data.
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Alerting System for Google Drive: Provides notifications when responses to queries change due to updates in the Google Docs folder, highlighting any significant changes in data.
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Adaptive RAG App: Utilizes Adaptive RAG technology to lower token costs while maintaining accuracy.
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Private RAG App: A fully private version allowing local deployment without external dependencies.
Operation and Flexibility
The LLM apps are easy to run as Docker containers, offering an HTTP API for frontend connection. Some templates also include a Streamlit UI for easy testing and demonstrations. The apps operate using the Pathway framework, a standalone Python library with a built-in Rust engine, ensuring seamless data synchronization and API request handling. This framework simplifies application logic and eliminates the need for separate module integration.
Getting Started
Each application template includes a README file with detailed instructions on running the app. Additionally, Pathway provides more ready-to-run code templates on their website for developers looking to explore further.
Visual Highlights and Support
The project includes visual demonstrations of its capabilities, such as real-time data extraction and organization, and automated knowledge mining and alerting systems. For support or troubleshooting, users are encouraged to report issues on the project’s issue tracker or join the Pathway Discord server for direct assistance.
Contributors are welcome, and detailed guidance is available for those new to contributing to GitHub projects, encouraging a collaborative and open development environment.
The Pathway team supports this project, offering a wide range of AI application solutions, continually maintained to meet user needs.