4 个仓库
Awesome GitHub RepositoriesDocument Embedding Stores
Storing vector embeddings of document chunks in a feature store for later retrieval by similarity search.
Distinct from Vector Stores: Distinct from Vector Stores: focuses on storing embeddings of document chunks specifically, not general vector storage.
Explore 4 awesome GitHub repositories matching data & databases · Document Embedding Stores. Refine with filters or upvote what's useful.
Awesome Document Embedding Stores GitHub Repositories
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- mayooear/ai-pdf-chatbot-langchain
mayooear/ai-pdf-chatbot-langchain
16,543在 GitHub 上查看This project is a retrieval-augmented generation application designed to answer questions from uploaded PDF documents. It functions as a document question-answering engine and a streaming AI chat interface that provides responses backed by specific source citations. The system utilizes a state-machine workflow orchestrator to coordinate multi-step document ingestion and retrieval pipelines. This orchestration allows for step-by-step visualization and debugging of the process as documents are parsed and processed. The application manages the full lifecycle of document interaction, including P
Stores numerical vector embeddings of PDF text chunks to enable efficient semantic similarity searching.
TypeScriptagentsaichatbot - feast-dev/feast
feast-dev/feast
6,727在 GitHub 上查看Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma
Stores vector embeddings of document chunks in a feature store for later retrieval by similarity search.
Pythonbig-datadata-engineeringdata-quality - langroid/langroid
langroid/langroid
3,894在 GitHub 上查看Langroid is a multi-agent orchestration framework and tool integration suite designed for building complex AI applications. It serves as a multi-modal integration layer that connects diverse local and remote language models with an agentic retrieval-augmented generation system. The project distinguishes itself through a collaborative message-exchange paradigm, allowing specialized agents to delegate tasks hierarchically and coordinate via structured communication. It features an advanced state management system for conversational AI, including the ability to rewind and prune conversation hist
Ingests text and metadata into a vector store for efficient similarity searches.
Pythonagentsaichatgpt - i-am-bee/beeai-framework
i-am-bee/beeai-framework
3,304在 GitHub 上查看The BeeAI Framework is an LLM agent framework and multi-agent orchestration engine used to build autonomous agents that coordinate reasoning, tool execution, and complex workflows. It functions as a structured AI output controller and RAG integration library, providing a unified interface to manage multiple language model providers. The framework is distinguished by its implementation of the Model Context Protocol, allowing agents, tools, and models to be shared between different AI platforms and hosted as agentic tooling servers. It enables the design of collaborative agent teams through dec
Stores vector embeddings of document chunks in semantic databases for similarity-based retrieval.
Pythonagentsaiai-agent
