zylon-ai/private-gpt

Features

• Retrieval Augmented Generation Engines - A backend service that processes local documents to provide context-aware conversational responses using both local and cloud-based language models.
• Local Model Runtimes - Run language models locally using specific configuration profiles to manage model parameters, context window sizes, and hardware-specific settings for private, offline processing.
• Privacy-First AI Backends - A modular service architecture designed to run language models and document processing entirely on local infrastructure for data security.
• Text Generation Services - Generate text completions from a prompt by incorporating ingested document context and system instructions to provide relevant and accurate output for the user in real time.
• Retrieval-Augmented Generation Pipelines - Processes documents into vector embeddings and stores them to provide relevant context for language model completion requests.
• Context-Aware Chat Interfaces - Generate conversational responses by automatically retrieving relevant document context and applying prompt engineering to execute completion models for high-level interactions.
• Document Retrieval - Retrieve relevant text segments from stored documents based on a search query while optionally filtering by document identifier and including surrounding context for each search result.
• Private Retrieval Augmented Generation - Building secure, local-first applications that answer questions based on private document collections without sending sensitive data to external cloud providers.
• Document Ingestion Pipelines - Manage document ingestion by automatically parsing, splitting, extracting metadata, and generating embeddings for storage within a retrieval augmented generation pipeline.
• Reranking Strategies - Improve retrieval accuracy by pre-selecting the most relevant documents from an initial set before passing them to the generation process for final answer construction.
• Document Intelligence Pipelines - Automating the ingestion, parsing, and vectorization of diverse file formats to enable semantic search and intelligent analysis across internal knowledge bases.
• Document Ingestion Pipelines - A data processing workflow that extracts text from diverse file formats and converts them into searchable vector representations for retrieval.
• Vector Database Orchestrators - A management layer that handles document ingestion, text chunking, and vector embedding storage across various database providers for semantic search.
• Local Language Model Hosting - Running large language models on private hardware to maintain full control over data privacy, security, and infrastructure costs.
• Contextual Retrieval Services - Retrieve relevant text chunks from ingested documents based on a specific query to facilitate custom retrieval and generation logic for specialized tasks.
• File Ingestion Services - Extract text chunks and metadata from files and store them to provide searchable context for subsequent chat and completion requests within the system.
• Local Document Ingestion - Ingest local folders of documents into the system for querying with options to watch for file changes and log processing results.
• Supported File Formats - Process a wide range of document types including text, office documents, images, and code with automatic fallback to plain text for unsupported formats.
• Document Ingestion Pipelines - Parses raw files into structured text chunks and metadata to enable efficient semantic search and retrieval during query execution.
• Vector Database Abstractions - Uses a modular interface layer to support multiple storage backends like local disk, PostgreSQL, or specialized vector databases.
• Application Configuration Managers - Configure the application by selecting language model, embedding, and vector store providers, and manage dependencies using environment-specific profiles.
• Text Ingestion Services - Convert raw text into a searchable document by processing its chunks and retrieving a unique identifier for filtering future completion requests during the retrieval process.
• External Model Integrations - Configure the application to use external cloud-based language models by defining specific profiles with API keys, base URLs, and model identifiers.
• Local Infrastructure Setups - Run the application entirely on local infrastructure by selecting local language model, embedding, and vector store providers and downloading necessary model files.
• Text Embedding Generators - Generate vector representations of text strings to enable consumption by machine learning models and various analytical algorithms for advanced search or classification tasks.
• Streaming Response Architectures - Streams generated text tokens from the language model to the user interface in real time to reduce perceived latency.
• Enterprise Vector Database Integrations - Connecting language models to scalable, production-grade vector storage backends to manage large-scale document retrieval and contextual information processing.
• Vector Database Integrations - Configure a vector store by specifying connection details like host, port, and authentication keys within the application settings file.
• Chroma Integrations - Configure a vector store by installing the required dependencies and enabling the database in the application settings file for local disk-based storage.
• PostgreSQL Vector Stores - Configure a PostgreSQL vector store by installing the required dependencies and providing database connection credentials and schema details in the application settings file.
• Text Embedding Generators - Generate vector embeddings for arbitrary text input to support custom pipeline implementations and advanced search or analysis workflows across different data sources.
• Vector Databases - Configure a vector store by installing the required dependencies and providing server connection details and security settings in the application settings file.
• Chat Completion Services - Generate conversational text by processing message history and document context while streaming the output to the user in real time to ensure immediate and relevant feedback.
• Multi-Provider Model Integrations - Connects to both local and cloud-based language models through a unified interface to balance privacy and computational performance.
• Hardware Profile Deployments - Deploy the service using various hardware profiles including CPU-only or GPU-accelerated configurations to match specific system capabilities and performance requirements.
• Execution Modes - Select between query, search, and chat modes to control how the system uses ingested documents and conversation history to generate responses.
• Reranking Retrieval Logics - Refines the initial set of retrieved document chunks using a secondary scoring pass to improve the accuracy of generated answers.
• Execution Profiles - Execute the application using environment-specific profiles to manage local or cloud-based model inference, including support for various GPU-accelerated configurations.
• Document Deletion Operations - Remove a previously stored document from the system by providing its unique identifier to the deletion endpoint for permanent removal from the search index.
• Document Retrieval Interfaces - Retrieve a list of all stored documents including their unique identifiers and metadata to enable precise filtering of context for chat or completion requests.
• Document Deletion APIs - Delete specific documents from storage by sending a request to the ingestion API endpoint designed for document removal.
• Text Summarization Services - Summarize provided text using a language model with options to include ingested document context, custom instructions, and streaming responses for real-time output.
• System Prompt Configurations - Configure the system prompt for the language model to define specific roles, expertise, or response criteria for chat interactions.