5 个仓库
Awesome GitHub RepositoriesVector Store Orchestrators
Systems that manage indexing strategies and retrieval logic across one or more vector databases.
Distinct from Vector Stores: Distinct from Vector Stores: focuses on the orchestration and management of retrieval strategies rather than the storage itself.
Explore 5 awesome GitHub repositories matching data & databases · Vector Store Orchestrators. Refine with filters or upvote what's useful.
Awesome Vector Store Orchestrators GitHub Repositories
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- meta-llama/llama-stack
meta-llama/llama-stack
8,417在 GitHub 上查看Llama-stack 是一个标准化的编排栈和生成式 AI API 网关。它提供了一个统一的通信层和一致的接口，用于部署、管理和与各种大语言模型提供商及部署进行交互。 该系统充当代理（agent）框架，管理工具执行和版本化的技能包，以自动化复杂任务。它包括一个批处理系统，用于通过离线处理处理大量异步请求，以及一个用于存储和搜索文档以实现检索增强生成（RAG）的向量数据库接口。 该栈涵盖了高级功能，包括 AI 代理编排、模型部署以及模型 API 的标准化，从而允许在不重写应用程序代码的情况下切换提供商。
Ships a standardized interface to manage indexing and retrieval logic across vector stores to enable retrieval augmented generation.
Python - chonkie-inc/chonkie
chonkie-inc/chonkie
4,170在 GitHub 上查看Chonkie 是一个专为检索增强生成 (RAG) 流水线设计的文本分块库。它充当语义文本分割器和 RAG 数据摄取流水线，将原始文本转换为嵌入片段，以便存储在向量数据库中。 该项目通过专门的分割策略脱颖而出，包括用于保留源代码逻辑边界的基于 AST 的代码分割器，以及使用嵌入模型根据语义确定边界的语义文本分割器。它还提供了一个向量数据库摄取器，用于自动化生成嵌入并将其导出到各种存储中。 该库涵盖了广泛的功能，包括通过 OCR 和 Markdown 提取进行文档解析，多种分割方法（如基于 Token 计数和分层分割），以及通过可重用流水线进行工作流编排。它支持多种向量存储集成，包括 Qdrant、Milvus、Weaviate 和 Elasticsearch，以及将数据导出为 JSON 和 Hugging Face 数据集。 用户可以通过命令行界面执行这些操作，或将系统部署为容器化的 API 服务。
Manages the process of embedding text segments and exporting them to various managed vector databases.
Pythonaichonkiechunker - bragai/brag-langchain
bragai/bRAG-langchain
4,028在 GitHub 上查看bRAG-langchain is a framework for building retrieval augmented generation pipelines using LangChain to connect documents with language models. It functions as a vector store orchestrator that manages document indexing and retrieval strategies to improve context accuracy. The system implements an advanced retrieval pipeline featuring a semantic query router that directs natural language inputs to specific data sources or prompts. It includes a metadata filtering engine that translates natural language queries into structured schemas to narrow search results. The project covers hybrid search o
Indexes documents and manages retrieval strategies across vector databases to improve context accuracy.
Jupyter Notebookagentic-ragaichatbot - datawhalechina/all-in-rag
datawhalechina/all-in-rag
3,989在 GitHub 上查看This project is a retrieval augmented generation framework designed to build pipelines that connect unstructured data and knowledge graphs with large language models. It functions as a vector database orchestrator for indexing text and multimodal content, as well as a system for translating natural language queries into structured database commands. The framework integrates a hybrid retrieval engine that combines dense vector search with sparse keyword matching to increase the precision of retrieved contexts. It further enhances reasoning and relationship mapping through a graph-augmented ret
Orchestrates the indexing of text and multimodal content into vector databases for high-performance retrieval.
Pythonaideepseekembedding - 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
Parses raw text into chunks and stores them in a remote vector database.
Pythonagentsaichatgpt
