Cinnamon/kotaemon

Features

• Agentic Reasoning Frameworks - Provides an agentic framework for decomposing complex queries through iterative reasoning and tool-calling.
• LLM Application Orchestration - Chains language models, prompt templates, and external tools into complex, multi-step reasoning and data processing pipelines.
• Grounded Answer Generation - Generates grounded answers supported by direct citations and source verification from retrieved documents.
• Question Answering Systems - Provides an interactive system for retrieving and citing specific evidence from documents to generate grounded answers.
• Reasoning Chains - Sequences multiple reasoning units into complex workflows with conditional logic for multi-step problem solving.
• Reasoning Pipelines - Combine prompt templates, language models, and post-processing functions to transform input data into structured outputs.
• Retrieval-Augmented Generation Frameworks - Constructs modular workflows that combine document indexing, hybrid search, and language models to generate context-aware responses.
• Conversational Retrieval - Enables interactive chat sessions that retrieve and cite specific evidence from documents to provide grounded answers.
• Autonomous Agents - Constructs autonomous agents by configuring language models, prompt templates, and tool sets.
• Agentic Reasoning Frameworks - Implements agentic reasoning loops and tool-use logic to solve complex, multi-hop queries.
• Embedding Generators - Generates vector representations of text using local or remote models to enable semantic search.
• Hybrid Search Systems - Combines vector and full-text search with re-ranking to retrieve relevant information for question answering.
• Document Question Answering Pipelines - Extracts text, tables, and figures from multi-modal documents to support complex, grounded question answering.
• RAG Frameworks - Provides a modular platform for building document-based question answering systems using LLMs and custom retrieval workflows.
• Retrieval Orchestration - Orchestrates hybrid retrieval strategies to combine vector and keyword search for improved context accuracy.
• Language Model Integrations - Connects to various language model providers and local runtimes for document-based question answering.
• Chain of Thought Implementations - Executes sequential prompt-based steps to break down complex reasoning tasks into manageable parts.
• Chat Model Integrations - Integrates various chat models through unified interfaces to enable conversational document-based question answering.
• Tool Calling - Enables autonomous agents to select and execute external tools during conversational interactions.
• Document Collections - Provides centralized management for document collections to support efficient data organization and retrieval.
• Semantic Parsing Tools - Extracts text, tables, and visual elements from complex documents using multi-modal parsing.
• Modular Pipeline Orchestrators - Structures complex data workflows into independent, modular components with caching and logging.
• Large Language Model Configurations - Provides configuration settings to connect and manage local or cloud-based language and embedding models.
• Private Document Retrieval - Indexes and queries diverse file formats to provide grounded, cited answers from private document collections.
• Local Language Model Execution - Loads and executes large language models locally on hardware to enable offline document processing and reasoning.
• Model Provider Configurations - Centralizes the configuration and authentication of external language and embedding model providers for document processing tasks.
• Reasoning Workflows - Deploys specialized agent architectures to execute sequential or distributive reasoning steps.
• Retrieval Strategies - Combines vector similarity, full-text search, and re-ranking to improve retrieval accuracy.
• Sequential Orchestration - Chains prompts, models, and post-processors into sequential pipelines for automated document processing.
• Document Parsing Pipelines - Supports multi-modal parsing of diverse file formats to extract text, tables, and visual elements.
• Vector Document Indexing - Automates the indexing of documents into vector databases to support real-time search and retrieval.
• Vector Memory Stores - Performs similarity searches against stored embeddings to retrieve relevant context for agentic reasoning.
• Build Pipeline Extensions - Allows building modular processing workflows by chaining reusable components.
• Component Composition Patterns - Enables modular pipeline composition by linking reusable processing units into flexible workflows.
• Web Chat Interfaces - Provides a web-based interface for interactive document querying and conversational AI interactions.
• Model Provider Integrations - Provides a unified interface to connect local or cloud-based model services for document analysis and retrieval.
• Citation Management Systems - Provides detailed source references and highlights to verify the accuracy of generated answers.
• Document Indexing - Retrieves relevant document context from indexed data for use in analytical tasks.
• External Model Connectors - Configures connections to external language model endpoints to generate text responses for document-based pipelines.
• Document Chunking Strategies - Segments large documents into manageable chunks to optimize retrieval accuracy for question answering.
• Language Model Response Generators - Processes chat history through language models to generate conversational responses based on provided context.
• LLM Application Platforms - Provides an integrated platform for building, testing, and deploying AI-powered applications and workflows.
• Prompt Templates - Defines reusable prompt structures with dynamic placeholders for language model inputs.
• Knowledge Retrieval - Clean and customizable UI for document-based chatting.
• RAG and Data Pipelines - RAG-based tool for chatting with local documents.
• Retrieval Augmented Generation - Clean, customizable UI for document-based chat.
• RAG Frameworks - Open-source document Q&A tool with multi-modal support and agentic reasoning.
• Collaborative Chat Sessions - Supports collaborative chat sessions and shared document access for multiple users.
• Document Parsing Services - Extracts text and structured content from various file formats using cloud-based analysis services.
• Data Ingestion - Ingests and parses diverse file formats into structured text for downstream processing and AI consumption.
• Document Retrieval Interfaces - Provides interfaces for querying document and vector stores to retrieve relevant information for question answering.
• Vector Database Integrations - Integrates with existing vector database implementations to perform document indexing and similarity searching.
• Document Stores - Configures storage backends for managing full-text and vector-based document indices.
• Embedding Service Integrations - Connects to remote embedding services to generate numerical representations of documents for semantic search.
• Full Text Search - Executes keyword-based full-text search against stored document collections.
• Search and Indexing - Integrates document storage with full-text search capabilities to enable efficient information discovery.
• Vector Embedding Indexes - Persists document embeddings in vector databases to enable efficient semantic similarity search.
• External Tool Integrations - Defines custom tools with input validation to extend agent capabilities with external data sources.
• Conversation State Managers - Tracks interaction history and manages conversational context to ensure stateful, multi-turn dialogue.
• Document Rerankers - Filters retrieved content using language models to retain only the most pertinent information.
• Evidence Extraction Tools - Isolates and retrieves specific document segments that support generated answers to ensure verifiable citations.
• Inference Endpoint Integrations - Connects to compatible API endpoints to utilize custom or hosted language models for document processing.
• Local Embedding Generators - Generates vector embeddings locally to enable semantic search without relying on external API dependencies.
• Model Integration Configurations - Manages programmatic access to configured language and embedding models within custom pipelines.
• Document Layout Analysis - Parses complex documents to extract text, tables, and visual elements for deep analysis.
• Vector Databases - Manages the storage and querying of high-dimensional vector embeddings to enable efficient semantic search.
• Vector Embeddings - Converts text into numerical vector representations using cloud-based models to enable semantic search and document retrieval.
• Office Document Parsers - Parses common office document formats like PDF, Word, and Excel into structured text nodes for indexing.
• Web Content Scrapers - Parses live web information into structured formats for use as external context in pipelines.
• Document and Unstructured Extraction - Extracts text content from various unstructured file formats including office documents, images, and emails.
• Indexing and Search - Allows customization of indexing and search logic through extensible base classes.
• Real-Time Data Streaming - Streams incremental results from reasoning pipelines to the user interface in real-time.
• Vector Collection Management - Performs administrative operations to add, delete, or remove entire collections of embeddings from storage.
• Tool Wrappers - Exposes internal system components as executable tools for autonomous agents.
• Web Search Integrations - Fetches real-time information from the internet to provide up-to-date context for question answering.
• Private Data Hosting - Supports deployment of private, isolated application instances using predefined templates.
• Retrieval Configuration Interfaces - Provides interfaces to adjust retrieval and generation settings for customized system behavior.
• Event-Driven Architectures - Implements event-driven processing to deliver incremental pipeline results in real-time.
• Indexing Pipeline Frameworks - Provides frameworks for extending indexing and reasoning pipelines to meet specific requirements.
• Custom Component Extensions - Enables the creation of modular indexing and reasoning components for dynamic loading.
• LLM Response Streaming - Streams generated model output in real-time chunks to provide immediate feedback during long-running interactions.
• Conditional Execution Flows - Routes pipeline execution based on dynamic evaluation of conditions to handle branching logic.
• Context-Aware Retrieval - Consolidates retrieved document chunks and media into structured context strings for language models.
• External Service Integrations - Integrates third-party indexing and transformation tools into the native pipeline architecture.
• Knowledge Retrieval Tools - Enables agents to retrieve external information by querying the Wikipedia API.
• Model Credential Managers - Manages administrative credentials for external data sources and language model providers.
• Document Transformation Pipelines - Provides pipelines for programmatically splitting, filtering, and enriching document collections.
• Local Document Ingestion - Imports files from local directories with support for recursive scanning and specialized extraction logic.
• Schema-Driven Data Normalizers - Standardizes heterogeneous data sources into consistent structures to ensure schema uniformity across indexing and reasoning components.
• Document Retrieval Strategies - Sorts and filters retrieved documents to improve the accuracy of information retrieval.
• File Upload Management - Handles the upload and storage of user-provided files for subsequent indexing and question-answering interactions.
• Local Data Persistence - Persists document collections and indexes to local storage to ensure data availability across system restarts.
• PDF Parsers - Parses PDF files into structured text with spatial coordinates and page metadata.
• Result Streaming APIs - Streams incremental processing results to provide immediate feedback during long-running tasks.
• Relevance Ranking Engines - Ranks retrieved documents numerically using language models to improve the relevance of context.
• Search Result Filtering - Filters search results using language models to remove irrelevant documents before generation.
• Runtime Configuration Interfaces - Renders user-defined settings in the interface for runtime adjustment of pipeline parameters.
• Web Content Ingestion Tools - Converts HTML and MHTML web content into structured document objects for processing.
• Infrastructure Configuration - Defines operational parameters for managing the deployment environment and infrastructure.
• Pipeline Setting Exposers - Generates user interface controls automatically from custom pipeline configuration parameters.
• Response Streaming Interfaces - Delivers incremental text responses from language models to the user interface in real-time.

Star history