Framework RAG para Python
For framework de Python para construir pipelines de RAG, the strongest matches are openai/chatgpt-retrieval-plugin (This repository is a comprehensive retrieval-augmented generation pipeline in), cinnamon/kotaemon (Kotaemon is a Python orchestration framework explicitly built for) and infiniflow/ragflow (RagFlow is a comprehensive Python-based RAG platform that integrates). run-llama/llama_index and timescale/pgai round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
Explora los mejores frameworks de RAG en Python. Compara las librerías mejor valoradas para construir pipelines de recuperación, clasificadas por actividad para encontrar la opción ideal.
Framework RAG para Python
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- openai/chatgpt-retrieval-plugin
openai/chatgpt-retrieval-plugin
21,192Ver en GitHubThis project is a retrieval-augmented generation pipeline designed for building custom ChatGPT plugins that allow language models to query private or professional documents. It implements a full retrieval workflow, from processing and indexing document chunks to retrieving relevant context for natural language queries. The system distinguishes itself through a hybrid retrieval approach that combines dense vector embeddings with sparse keyword matching, further refined by a two-stage semantic re-ranking process. It includes specialized data privacy tools for screening personally identifiable i
This repository is a comprehensive retrieval-augmented generation pipeline in Python, offering hybrid search, re-ranking, vector storage, and document processing—exactly the kind of RAG framework you need for building applications, albeit with a focus on ChatGPT plugins.
PythonDocument Chunking StrategiesHybrid SearchRetrieval Re-ranking - cinnamon/kotaemon
Cinnamon/kotaemon
25,139Ver en GitHubKotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q
Kotaemon is a Python orchestration framework explicitly built for modular RAG workflows with document processing, LLM integration, and multi-step reasoning, making it a comprehensive match for a RAG framework.
PythonDocument Chunking StrategiesDocument RerankersDocument Retrieval Strategies - infiniflow/ragflow
infiniflow/ragflow
82,922Ver en GitHubThis project is a comprehensive retrieval-augmented generation platform designed for building, managing, and deploying knowledge-based AI applications. It provides a unified environment for organizing datasets, configuring conversational chat assistants, and developing autonomous agents that execute multi-step reasoning workflows. By integrating document intelligence with advanced retrieval pipelines, the platform enables the creation of grounded, verifiable responses supported by traceable citations. The platform distinguishes itself through deep document understanding and sophisticated know
RagFlow is a comprehensive Python-based RAG platform that integrates document parsing, vector retrieval, LLM integration, and agentic workflows, precisely matching the request for a framework to build RAG pipelines with the listed capabilities.
PythonDocument Chunking StrategiesPython SDKs - run-llama/llama_index
run-llama/llama_index
50,306Ver en GitHubLlamaIndex is a comprehensive development framework designed to connect private or external data sources to large language models. It functions as a data-centric toolkit that enables the construction of retrieval-augmented generation systems, allowing developers to build applications that provide context-aware answers based on specific organizational information. The project distinguishes itself through a robust agentic orchestration engine that supports the creation of autonomous agents capable of multi-step reasoning, memory management, and complex tool execution. Beyond simple retrieval, i
LlamaIndex is a full-featured Python framework designed explicitly for building retrieval-augmented generation systems, covering document parsing, vector storage, LLM integration, embedding generation, re-ranking, and evaluation just as you need.
PythonRetrieval Re-ranking - timescale/pgai
timescale/pgai
5,802Ver en GitHubpgai is a PostgreSQL AI toolkit and framework designed to integrate large language models and vector embeddings directly into a database. It serves as a bridge for executing machine learning model requests and performing text-to-SQL translations within standard database queries. The project provides an automated vector embedding pipeline that handles the loading, parsing, and chunking of text from tables and unstructured documents. This system utilizes a background worker to synchronize embeddings automatically as source data changes and includes specialized tools for building retrieval-augme
pgai is a RAG framework built directly into PostgreSQL, handling vector embeddings, document chunking, and LLM integration, so it fits the category—but it is implemented as a database extension (PL/pgSQL) rather than as a standalone Python library, which is what you explicitly requested.
PLpgSQLEmbedding GenerationResult RerankingVector Similarity Search - zylon-ai/private-gpt
zylon-ai/private-gpt
57,278Ver en GitHubThis project is a privacy-first backend service designed to facilitate retrieval-augmented generation by processing local documents into searchable vector representations. It provides a modular architecture that allows users to ingest diverse file formats, manage document metadata, and perform semantic searches to provide context-aware responses for chat and completion requests. The system distinguishes itself through a database-agnostic abstraction layer that supports various storage backends, ranging from local disk storage to enterprise-grade vector databases. It offers flexible deployment
This is a privacy-first, Python-based backend service for RAG that ingests local documents, manages vector representations, and provides semantic search for context-aware chat responses, making it a solid fit for building RAG applications despite missing some advanced ranking/evaluation features out of the box.
PythonReranking Retrieval LogicsReranking StrategiesVector Databases - hkuds/lightrag
HKUDS/LightRAG
36,651Ver en GitHubLightRAG is a graph-based retrieval framework designed to build retrieval-augmented generation pipelines. It structures unstructured text into knowledge graphs, enabling multi-hop reasoning and complex query synthesis across large document collections. By integrating dense vector embeddings with structured knowledge graphs, the system facilitates both similarity-based and relationship-aware information retrieval. The framework distinguishes itself through a dual-level retrieval strategy that combines low-level keyword matching with high-level semantic graph traversal to capture both specific
LightRAG is a Python-based RAG framework that builds knowledge graphs from documents and supports dense vector search, multi-hop reasoning, hybrid retrieval, and LLM integration, directly matching your need for a comprehensive RAG pipeline builder.
PythonKnowledge Graph Retrieval SystemsRetrieval Augmented Generation PipelinesGraph Reasoning Systems - langchain-ai/langchain
langchain-ai/langchain
139,458Ver en GitHubLangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing
LangChain is a comprehensive Python orchestration framework that directly supports building RAG systems with built-in document loaders and splitters, vector store integrations, embedding models, LLM connectors, query rewriting, re-ranking, and evaluation tools—covering all the required features for a RAG pipeline.
PythonAgent Orchestration FrameworksLLM Application OrchestrationLLM Integration Layers - chatchat-space/langchain-chatchat
chatchat-space/Langchain-Chatchat
38,211Ver en GitHubLangchain-Chatchat is a system for building retrieval-augmented generation applications and autonomous AI agents. It integrates a knowledge base management system and an agent framework to enable language models to interact with private documents and execute multi-step tasks through external tools. The platform supports local deployment of language models on private infrastructure to operate without an internet connection. It includes a multimodal AI platform that combines vision models for image analysis with text-to-image generation capabilities. The system provides a web-based conversatio
Langchain-Chatchat is a full-featured RAG system built on LangChain, offering a Python-based framework with knowledge base management, vector storage (FAISS, Milvus), LLM integration, document parsing, and embedding generation, directly matching the visitor's need for a RAG development tool.
PythonKnowledge Base RetrievalLocal Model DeploymentAI Agent Orchestrators - deepset-ai/haystack
deepset-ai/haystack
24,253Ver en GitHubHaystack is an orchestration framework designed for building complex search and generative AI pipelines. It functions as an agentic workflow engine, enabling the construction of automated sequences that allow AI agents to perform multi-step reasoning and data analysis. The framework utilizes a modular, component-based architecture that connects processing steps into directed acyclic graphs. By employing a provider-agnostic integration layer, it decouples core logic from specific external AI services and vector databases, allowing for the flexible exchange of underlying technologies. This desi
Haystack is a Python-based orchestration framework purpose-built for building RAG pipelines, offering modular components for vector storage, LLM integration, document chunking, embedding generation, and hybrid search, so it directly matches your search.
MDXAgentic Workflow EnginesPipeline Orchestration FrameworksModular Pipeline Orchestration - 1517005260/graph-rag-agent
1517005260/graph-rag-agent
2,240Ver en GitHubThis project is a comprehensive framework for constructing, managing, and evaluating knowledge graphs through multi-agent reasoning and deep search capabilities. It provides an end-to-end pipeline that ingests multi-format documents, extracts entities and relationships based on configurable schemas, and maintains structured knowledge bases to support evidence-based retrieval. The system distinguishes itself through its multi-agent orchestration, which decomposes complex queries into parallel research steps and synthesizes long-form reports. It leverages advanced graph-based techniques, includ
A Python framework that integrates GraphRAG, LightRAG, and Neo4j for knowledge graph construction and search, with DeepSearch reasoning and a built-in evaluation suite—covering the core RAG pipeline, vector/graph retrieval, LLM integration, and evaluation you need.
PythonBusiness Knowledge AgentsGraph RAG FrameworksGraph Retrieval Augmented Generation - langroid/langroid
langroid/langroid
3,894Ver en GitHubLangroid 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
Langroid is a Python multi-agent orchestration framework that explicitly includes retrieval-augmented generation, LLM integration, and vector retrieval, making it a genuine RAG pipeline builder, though its emphasis on agent coordination may mean some features like document chunking or re-ranking are not as central.
PythonLanguage Model IntegrationsVector SearchVector Similarity Search - microsoft/graphrag
microsoft/graphrag
33,792Ver en GitHubGraphRAG is a data processing pipeline and retrieval engine designed to transform unstructured text into interconnected knowledge graphs. By utilizing language models to extract entities and relationships, it builds structured representations of information that enable context-aware retrieval for downstream applications. The system distinguishes itself through hierarchical graph clustering and large-scale data synthesis, which organize massive document corpora into multi-level structures. This approach allows for both vector-based semantic searches and graph-based traversals, providing a comp
GraphRAG is a Python-based RAG pipeline that builds knowledge graphs and supports vector-based and graph-based retrieval, fitting your search for a RAG framework with LLM integration, embedding generation, and hybrid search, though it lacks explicit query rewriting and evaluation features.
PythonVector Search Engines - quivrhq/quivr
QuivrHQ/quivr
39,165Ver en GitHubQuivr is a retrieval-augmented generation platform designed to transform raw documents into searchable knowledge bases. It functions as a centralized environment where users can ingest files, index them into vector databases, and interact with language models to receive contextually relevant, data-backed responses. The platform distinguishes itself through an agentic workflow orchestrator that sequences retrieval tasks, tool execution, and model interactions to resolve complex, multi-step queries. This engine is entirely configuration-driven, allowing users to define document ingestion, chunk
Quivr is a Python-based RAG platform that ingests documents, indexes them into vector databases, and orchestrates LLM interactions with an agentic workflow — making it a genuine RAG framework, though it doesn't explicitly cover every advanced feature like query rewriting or evaluation metrics listed in your search.
PythonDocument Parsers - pathwaycom/pathway
pathwaycom/pathway
62,959Ver en GitHubPathway is a high-performance data processing framework designed for building unified batch and streaming pipelines. It functions as an orchestrator for complex data transformations, utilizing a differential dataflow engine to process updates incrementally. By treating static datasets and continuous event streams with identical logic, the platform ensures exactly-once processing semantics and consistent results across diverse data sources. The framework distinguishes itself through its specialized support for real-time artificial intelligence and retrieval-augmented generation. It features in
Pathway is a high-performance data processing framework with built-in support for building real-time RAG pipelines, including vector indexing, LLM integration, and document processing, making it a strong fit for your search.
PythonData Processing FrameworksData Stream ProcessorsDeclarative Pipeline Construction - stangirard/quiver
StanGirard/quiver
39,167Ver en GitHubQuiver is a framework for integrating retrieval augmented generation into applications. It provides a generative AI integration layer that connects large language models with vector stores to produce context-aware responses based on custom data. The project features a knowledge base pipeline that parses diverse file types into searchable embeddings and a vector database orchestrator to manage data across different storage implementations. It utilizes a provider-agnostic model interface, allowing users to switch between various external AI providers or local models through a single unified sys
Quiver is a Python-based RAG framework that provides a knowledge base pipeline for parsing documents into embeddings, a vector database orchestrator, and a provider-agnostic LLM integration layer—directly matching the core need for building retrieval-augmented generation pipelines, though it may not include advanced features like query rewriting or re-ranking out of the box.
PythonRAG FrameworksGenerative AI Integration LayersLLM Provider Integrations - microsoft/pike-rag
microsoft/PIKE-RAG
2,388Ver en GitHubPike-RAG is a framework designed for industrial-grade language model applications that require high factual accuracy and logical consistency. It functions as a platform for orchestrating multi-agent systems and implementing rationale-augmented generation, ensuring that model outputs are grounded in specialized domain knowledge rather than relying solely on internal training data. The system distinguishes itself through its ability to decompose complex, high-level queries into atomic tasks that are executed by specialized autonomous agents. By enforcing explicit logical reasoning steps before
PIKE-RAG is a Python-based RAG framework from Microsoft, specialized for domain-specific and industrial knowledge extraction and reasoning, making it a fitting candidate for building retrieval-augmented generation systems.
PythonRationale-Augmented Generation FrameworksRetrieval-Augmented Generation FrameworksMulti-Agent Coordination Systems
