embedchain/embedchain

Features

• Long-term Memory Stores - Provides a persistent long-term memory store that extracts facts from interactions to maintain a knowledge base across sessions.
• AI Memory Layers - Provides a persistent storage layer for AI agents to remember user preferences and interaction history.
• Fact Extraction Pipelines - Ships a pipeline that extracts confirmed facts from unstructured interactions to build a permanent knowledge base.
• Agent Memory Managers - Acts as a comprehensive agent memory manager providing persistent storage for user preferences and interaction history.
• Context-Aware Retrieval - Implements context-aware retrieval by combining temporal ranking and multi-level state management to optimize AI agent responses.
• RAG Pipelines - Functions as a RAG pipeline that orchestrates vector and keyword search to provide relevant context for LLMs.
• Context Partitioning - Partitions context into distinct user, session, and agent layers to maintain state across interaction scales.
• Multi-Level Memory Management - Manages hierarchical memory layers across user, session, and agent tiers to maintain interaction context.
• Fact Extraction Pipelines - Provides a pipeline to process unstructured interactions and isolate confirmed facts as permanent long-term memory entries.
• Hybrid Search Engines - Integrates vector-based semantic retrieval with keyword-based indexing for finding relevant stored information.
• Hybrid Vector-Keyword Indexing - Combines semantic vector embeddings with inverted keyword indices to improve information recall.
• Information Retrieval - Implements a retrieval mechanism to query stored information based on semantic and keyword context.
• Stateful Weight Management - Maintains stateful weight management of memories based on temporal timestamps.
• Retrieval Re-ranking - Applies temporal weighting to re-rank retrieved information, distinguishing current states from historical data.
• Signal Pipelines - Uses signal-based processing stages to aggregate semantic and keyword data for context retrieval.
• Entity Relationships - Provides configurations for defining associations between entities to enable retrieval of complex associated information.
• Temporal - Utilizes temporal tracking to rank retrieved data and distinguish current states from past events.
• Knowledge Graph Retrieval - Utilizes a knowledge graph for entity-relationship retrieval to connect associated data points for AI agents.
• Multi-Signal Retrieval Aggregators - Aggregates semantic, keyword, and entity signals to surface the most relevant context for given queries.
• Graph Linking Systems - Implements a graph-based system to link related information nodes across memory entries for faster retrieval.
• Agent Frameworks - Simplifies the creation of RAG-optimized AI bots.
• Application Development - Framework for creating bots over custom datasets.
• Development Frameworks - Framework for building bots over custom datasets.
• Development Platforms - Simplifies creating custom bots over specific datasets.
• Infrastructure and Utilities - Simplifies creating bots over custom datasets.
• Prompt Engineering Resources - Framework for creating ChatGPT-like bots over custom datasets.
• Prompting Frameworks - Tool for creating custom chatbots over specific datasets.
• RAG Frameworks - Universal memory layer for AI agents with a simple RAG API.
• Templates and Boilerplates - Framework for creating LLM bots over datasets.

Star history