{"data":{"node":{"slug":"nirdiamant-rag-techniques","name":"RAG_Techniques","tagline":"Showcases advanced Retrieval-Augmented Generation (RAG) techniques with detailed notebook tutorials.","github_url":"https://github.com/NirDiamant/RAG_Techniques","owner":"NirDiamant","repo":"RAG_Techniques","owner_avatar_url":"https://avatars.githubusercontent.com/u/28316913?v=4","primary_language":"Jupyter Notebook","stars":29525,"forks":3611,"topics":["agentic-rag","ai","embeddings","generative-ai","gpt","langchain","llama-index","llm","llms","machine-learning","nlp","openai","python","rag","retrieval-augmented-generation","semantic-search","tutorials","vector-database"],"archived":false,"github_pushed_at":"2026-09-15T12:51:09+00:00","maintenance_label":"Very active","stars_delta_30d":449,"url":"https://www.graphcanon.com/tools/nirdiamant-rag-techniques","markdown_url":"https://www.graphcanon.com/tools/nirdiamant-rag-techniques.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nirdiamant-rag-techniques","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nirdiamant-rag-techniques"},"categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"ai","name":"ai"},{"slug":"embeddings","name":"embeddings"},{"slug":"information-retrieval","name":"information retrieval"},{"slug":"llm","name":"llm"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"natural-language-processing","name":"natural language processing"},{"slug":"nlp","name":"nlp"},{"slug":"rag","name":"rag"}],"edges":[{"type":"related","direction":"out","explanation":"Both PageIndex and RAG_Techniques involve document indexing techniques for retrieval-augmented generation, making them adjacent projects in the AI tools landscape.","successor_context":null,"tool":{"slug":"vectifyai-pageindex","name":"PageIndex","tagline":"Document Index for Vectorless, Reasoning-based RAG","github_url":"https://github.com/VectifyAI/PageIndex","owner":"VectifyAI","repo":"PageIndex","owner_avatar_url":"https://avatars.githubusercontent.com/u/133959746?v=4","primary_language":"Python","stars":35705,"forks":3148,"topics":["agentic-ai","agents","ai","ai-agents","context-engineering","information-retrieval","llm","rag","reasoning","retrieval","retrieval-augmented-generation","vector-database"],"archived":false,"github_pushed_at":"2026-09-18T06:32:29+00:00","maintenance_label":"Very active","stars_delta_30d":501,"url":"https://www.graphcanon.com/tools/vectifyai-pageindex","markdown_url":"https://www.graphcanon.com/tools/vectifyai-pageindex.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/vectifyai-pageindex","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=vectifyai-pageindex"}},{"type":"integrates_with","direction":"out","explanation":"'RAG_Techniques' offers advanced techniques that can be implemented in the 'Kotaemon', an open-source RAG-based tool.","successor_context":null,"tool":{"slug":"cinnamon-kotaemon","name":"kotaemon","tagline":"An open-source RAG-based tool for chatting with your documents.","github_url":"https://github.com/Cinnamon/kotaemon","owner":"Cinnamon","repo":"kotaemon","owner_avatar_url":"https://avatars.githubusercontent.com/u/3265185?v=4","primary_language":"Python","stars":25701,"forks":2141,"topics":["chatbot","llms","open-source","rag"],"archived":false,"github_pushed_at":"2026-07-14T16:53:59+00:00","maintenance_label":"Steady","stars_delta_30d":138,"url":"https://www.graphcanon.com/tools/cinnamon-kotaemon","markdown_url":"https://www.graphcanon.com/tools/cinnamon-kotaemon.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/cinnamon-kotaemon","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=cinnamon-kotaemon"}},{"type":"integrates_with","direction":"out","explanation":"RAG_Techniques provides a repository of notebooks for implementing Retrieval-Augmented Generation (RAG) techniques which require handling large amounts of context data; headroom compresses this contextual information, thereby enabling RAG systems to operate more efficiently within the token constraints of language models.","successor_context":null,"tool":{"slug":"headroomlabs-ai-headroom","name":"headroom","tagline":"Compress tool outputs, logs, files, and RAG chunks before they reach the LLM.","github_url":"https://github.com/headroomlabs-ai/headroom","owner":"headroomlabs-ai","repo":"headroom","owner_avatar_url":"https://avatars.githubusercontent.com/u/294291659?v=4","primary_language":"Python","stars":72850,"forks":5600,"topics":["agent","ai","anthropic","claude-code","compression","context-engineering","context-window","cursor","fastapi","langchain","llm","mcp","openai","prompt-engineering","proxy","python","rag","token-optimization","tokens","typescript"],"archived":false,"github_pushed_at":"2026-09-17T23:57:28+00:00","maintenance_label":"Very active","stars_delta_30d":6380,"url":"https://www.graphcanon.com/tools/headroomlabs-ai-headroom","markdown_url":"https://www.graphcanon.com/tools/headroomlabs-ai-headroom.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/headroomlabs-ai-headroom","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=headroomlabs-ai-headroom"}},{"type":"related","direction":"out","explanation":"Both RAG_Techniques and Minds delegate tasks or handle complex queries, suggesting a similar applicational scope in AI.","successor_context":null,"tool":{"slug":"mindsdb-minds","name":"mindshub","tagline":"Get real work done with AI. Easily swap models while keeping your builds intact.","github_url":"https://github.com/mindsdb/mindshub","owner":"mindsdb","repo":"mindshub","owner_avatar_url":"https://avatars.githubusercontent.com/u/31035808?v=4","primary_language":"Makefile","stars":39754,"forks":6241,"topics":["agents","ai","anton","artificial-inteligence","claude","claude-cowork","codex","cowork","deepseek","glm","hermes","hermes-agent","kimi","mcp","mindsdb"],"archived":false,"github_pushed_at":"2026-09-16T21:37:33+00:00","maintenance_label":"Very active","stars_delta_30d":258,"url":"https://www.graphcanon.com/tools/mindsdb-minds","markdown_url":"https://www.graphcanon.com/tools/mindsdb-minds.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/mindsdb-minds","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=mindsdb-minds"}},{"type":"depends_on","direction":"out","explanation":"'RAGAI_Catalyst' provides a framework and tools which can be used to implement the advanced RAG techniques detailed in 'RAG_Techniques'.","successor_context":null,"tool":{"slug":"raga-ai-hub-ragaai-catalyst","name":"RagaAI-Catalyst","tagline":"Python SDK for AI agent observability and evaluation","github_url":"https://github.com/raga-ai-hub/RagaAI-Catalyst","owner":"raga-ai-hub","repo":"RagaAI-Catalyst","owner_avatar_url":"https://avatars.githubusercontent.com/u/161833182?v=4","primary_language":"Python","stars":16162,"forks":3567,"topics":["agentic-ai","agentic-ai-development","agentneo","agents","ai-agent-monitoring","ai-application-debugging","ai-evaluation-tools","ai-performance-optimization","ai-tool-interaction-monitoring","llm-testing","llm-tracing","llmops"],"archived":false,"github_pushed_at":"2026-02-11T14:43:33+00:00","maintenance_label":"Slowing","stars_delta_30d":14,"url":"https://www.graphcanon.com/tools/raga-ai-hub-ragaai-catalyst","markdown_url":"https://www.graphcanon.com/tools/raga-ai-hub-ragaai-catalyst.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/raga-ai-hub-ragaai-catalyst","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=raga-ai-hub-ragaai-catalyst"}},{"type":"related","direction":"out","explanation":"Both RAG_Techniques and the Prompt-Engineering-Guide provide extensive resources on guiding AI systems, making them adjacent in educational and practical contexts.","successor_context":null,"tool":{"slug":"dair-ai-prompt-engineering-guide","name":"Prompt-Engineering-Guide","tagline":"Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents","github_url":"https://github.com/dair-ai/Prompt-Engineering-Guide","owner":"dair-ai","repo":"Prompt-Engineering-Guide","owner_avatar_url":"https://avatars.githubusercontent.com/u/30384625?v=4","primary_language":"MDX","stars":77531,"forks":8518,"topics":["agent","agents","ai-agents","chatgpt","deep-learning","generative-ai","language-model","llms","openai","prompt-engineering","rag"],"archived":false,"github_pushed_at":"2026-03-11T20:09:13+00:00","maintenance_label":"Slowing","stars_delta_30d":829,"url":"https://www.graphcanon.com/tools/dair-ai-prompt-engineering-guide","markdown_url":"https://www.graphcanon.com/tools/dair-ai-prompt-engineering-guide.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/dair-ai-prompt-engineering-guide","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=dair-ai-prompt-engineering-guide"}},{"type":"integrates_with","direction":"out","explanation":"RAG_Techniques integrates with transformers by utilizing its pre-trained models for enhancing retrieval-augmented generation processes within over 42 runnable notebooks that cover various RAG techniques, thus providing advanced capabilities for text generation tasks.","successor_context":null,"tool":{"slug":"huggingface-transformers","name":"transformers","tagline":"🤗 Transformers: state-of-the-art machine learning models for text, vision, audio, and multimodal models","github_url":"https://github.com/huggingface/transformers","owner":"huggingface","repo":"transformers","owner_avatar_url":"https://avatars.githubusercontent.com/u/25720743?v=4","primary_language":"Python","stars":166279,"forks":34619,"topics":["audio","deep-learning","deepseek","gemma","glm","hacktoberfest","llm","machine-learning","model-hub","natural-language-processing","nlp","pretrained-models","python","pytorch","pytorch-transformers","qwen","speech-recognition","transformer","vlm"],"archived":false,"github_pushed_at":"2026-09-18T08:16:29+00:00","maintenance_label":"Very active","stars_delta_30d":2158,"url":"https://www.graphcanon.com/tools/huggingface-transformers","markdown_url":"https://www.graphcanon.com/tools/huggingface-transformers.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/huggingface-transformers","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=huggingface-transformers"}},{"type":"related","direction":"out","explanation":null,"successor_context":null,"tool":{"slug":"langflow-ai-langflow","name":"langflow","tagline":"A tool for building and deploying AI-powered agents and workflows","github_url":"https://github.com/langflow-ai/langflow","owner":"langflow-ai","repo":"langflow","owner_avatar_url":"https://avatars.githubusercontent.com/u/85702467?v=4","primary_language":"Python","stars":154963,"forks":10109,"topics":["agents","chatgpt","generative-ai","large-language-models","multiagent","react-flow"],"archived":false,"github_pushed_at":"2026-09-18T02:17:58+00:00","maintenance_label":"Very active","stars_delta_30d":2219,"url":"https://www.graphcanon.com/tools/langflow-ai-langflow","markdown_url":"https://www.graphcanon.com/tools/langflow-ai-langflow.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/langflow-ai-langflow","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=langflow-ai-langflow"}},{"type":"successor","direction":"out","explanation":"Both repositories are related to the development and implementation of GenAI agents with detailed tutorials, but 'GenAI_Agents' is more comprehensive covering both development and implementation aspects.","successor_context":{"status":"coexists","reason":"Provides complementary resources focused on more practical application."},"tool":{"slug":"nirdiamant-genai-agents","name":"GenAI_Agents","tagline":"50+ tutorials and implementations for Generative AI Agent techniques","github_url":"https://github.com/NirDiamant/GenAI_Agents","owner":"NirDiamant","repo":"GenAI_Agents","owner_avatar_url":"https://avatars.githubusercontent.com/u/28316913?v=4","primary_language":"Jupyter Notebook","stars":23814,"forks":3999,"topics":["agentic-ai","agents","ai","ai-agents","autonomous-agents","genai","generative-ai","langchain","langgraph","llm","llms","machine-learning","mcp","multi-agent","multi-agent-systems","openai","python","rag","tutorials"],"archived":false,"github_pushed_at":"2026-08-15T00:52:09+00:00","maintenance_label":"Very active","stars_delta_30d":529,"url":"https://www.graphcanon.com/tools/nirdiamant-genai-agents","markdown_url":"https://www.graphcanon.com/tools/nirdiamant-genai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nirdiamant-genai-agents","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nirdiamant-genai-agents"}},{"type":"related","direction":"out","explanation":null,"successor_context":null,"tool":{"slug":"chatchat-space-langchain-chatchat","name":"Langchain-Chatchat","tagline":"Local knowledge-based LLM RAG and Agent app with Langchain","github_url":"https://github.com/chatchat-space/Langchain-Chatchat","owner":"chatchat-space","repo":"Langchain-Chatchat","owner_avatar_url":"https://avatars.githubusercontent.com/u/139558948?v=4","primary_language":"Python","stars":38644,"forks":6265,"topics":["chatbot","chatchat","chatglm","chatgpt","embedding","faiss","fastchat","gpt","knowledge-base","langchain","langchain-chatglm","llama","llm","milvus","ollama","qwen","rag","retrieval-augmented-generation","streamlit","xinference"],"archived":false,"github_pushed_at":"2025-11-10T09:27:42+00:00","maintenance_label":"Slowing","stars_delta_30d":122,"url":"https://www.graphcanon.com/tools/chatchat-space-langchain-chatchat","markdown_url":"https://www.graphcanon.com/tools/chatchat-space-langchain-chatchat.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/chatchat-space-langchain-chatchat","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=chatchat-space-langchain-chatchat"}},{"type":"integrates_with","direction":"out","explanation":"'Semantic_Kernel' can be used to easily integrate and deploy advanced RAG systems detailed in 'RAG_Techniques'.","successor_context":null,"tool":{"slug":"microsoft-semantic-kernel","name":"semantic-kernel","tagline":"Integrate cutting-edge LLM technology quickly and easily into your apps","github_url":"https://github.com/microsoft/semantic-kernel","owner":"microsoft","repo":"semantic-kernel","owner_avatar_url":"https://avatars.githubusercontent.com/u/6154722?v=4","primary_language":"C#","stars":28575,"forks":4774,"topics":["ai","artificial-intelligence","llm","openai","sdk"],"archived":false,"github_pushed_at":"2026-09-18T01:35:01+00:00","maintenance_label":"Very active","stars_delta_30d":148,"url":"https://www.graphcanon.com/tools/microsoft-semantic-kernel","markdown_url":"https://www.graphcanon.com/tools/microsoft-semantic-kernel.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/microsoft-semantic-kernel","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=microsoft-semantic-kernel"}},{"type":"related","direction":"out","explanation":"RAG_Techniques and ragflow both deal with Retrieval-Augmented Generation techniques, making them adjacent in the AI development space.","successor_context":null,"tool":{"slug":"infiniflow-ragflow","name":"ragflow","tagline":"Retrieval-Augmented Generation (RAG) engine with agent capabilities","github_url":"https://github.com/infiniflow/ragflow","owner":"infiniflow","repo":"ragflow","owner_avatar_url":"https://avatars.githubusercontent.com/u/69962740?v=4","primary_language":"Go","stars":90923,"forks":10767,"topics":["agent-harness","agentic-ai","agentic-nagive","agentic-retrieval","agentic-search","ai","ai-agents","context-engine","context-engineering","context-management","harness-engineering","knowledge-compilation","rag","retrieval-augmented-generation","search-harness"],"archived":false,"github_pushed_at":"2026-09-18T07:56:25+00:00","maintenance_label":"Very active","stars_delta_30d":4382,"url":"https://www.graphcanon.com/tools/infiniflow-ragflow","markdown_url":"https://www.graphcanon.com/tools/infiniflow-ragflow.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/infiniflow-ragflow","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=infiniflow-ragflow"}},{"type":"integrates_with","direction":"out","explanation":"'RAG_Techniques' provides tutorials and notebooks that can be integrated with ready-to-run cloud templates from 'LLM_APP' to build RAG systems.","successor_context":null,"tool":{"slug":"pathwaycom-llm-app","name":"llm-app","tagline":"Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data","github_url":"https://github.com/pathwaycom/llm-app","owner":"pathwaycom","repo":"llm-app","owner_avatar_url":"https://avatars.githubusercontent.com/u/25750857?v=4","primary_language":"Jupyter Notebook","stars":58920,"forks":1498,"topics":["chatbot","hugging-face","llm","llm-local","llm-prompting","llm-security","llmops","machine-learning","open-ai","pathway","rag","real-time","retrieval-augmented-generation","vector-database","vector-index"],"archived":false,"github_pushed_at":"2026-07-05T17:59:07+00:00","maintenance_label":"Steady","stars_delta_30d":-117,"url":"https://www.graphcanon.com/tools/pathwaycom-llm-app","markdown_url":"https://www.graphcanon.com/tools/pathwaycom-llm-app.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/pathwaycom-llm-app","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=pathwaycom-llm-app"}},{"type":"related","direction":"out","explanation":"RAG_Techniques and quivr both involve the integration of retrieval and generation techniques within AI applications, making them related but not directly integrating or succeeding one another.","successor_context":null,"tool":{"slug":"quivrhq-quivr","name":"quivr","tagline":"Opiniated RAG for integrating GenAI in your apps","github_url":"https://github.com/The-Vibe-Company/quivr","owner":"The-Vibe-Company","repo":"quivr","owner_avatar_url":"https://avatars.githubusercontent.com/u/252322352?v=4","primary_language":"Python","stars":39533,"forks":3731,"topics":["ai","api","chatbot","chatgpt","database","docker","framework","frontend","groq","html","javascript","llm","openai","postgresql","privacy","rag","react","security","typescript","vector"],"archived":false,"github_pushed_at":"2026-08-31T18:02:45+00:00","maintenance_label":"Active","stars_delta_30d":132,"url":"https://www.graphcanon.com/tools/quivrhq-quivr","markdown_url":"https://www.graphcanon.com/tools/quivrhq-quivr.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/quivrhq-quivr","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=quivrhq-quivr"}},{"type":"related","direction":"out","explanation":null,"successor_context":null,"tool":{"slug":"hiyouga-llamafactory","name":"LlamaFactory","tagline":"Unified Efficient Fine-Tuning of 100+ LLMs & VLMs","github_url":"https://github.com/hiyouga/LlamaFactory","owner":"hiyouga","repo":"LlamaFactory","owner_avatar_url":"https://avatars.githubusercontent.com/u/16256802?v=4","primary_language":"Python","stars":74849,"forks":9170,"topics":["agent","ai","deepseek","fine-tuning","gemma","gpt","instruction-tuning","large-language-models","llama","llama3","llm","lora","moe","nlp","peft","qlora","quantization","qwen","rlhf","transformers"],"archived":false,"github_pushed_at":"2026-09-14T08:09:48+00:00","maintenance_label":"Very active","stars_delta_30d":717,"url":"https://www.graphcanon.com/tools/hiyouga-llamafactory","markdown_url":"https://www.graphcanon.com/tools/hiyouga-llamafactory.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/hiyouga-llamafactory","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=hiyouga-llamafactory"}},{"type":"integrates_with","direction":"in","explanation":"GraphRAG could integrate various RAG techniques from this repository to enhance its own Retrieval-Augmented Generation capabilities.","successor_context":null,"tool":{"slug":"microsoft-graphrag","name":"graphrag","tagline":"A modular graph-based Retrieval-Augmented Generation (RAG) system","github_url":"https://github.com/microsoft/graphrag","owner":"microsoft","repo":"graphrag","owner_avatar_url":"https://avatars.githubusercontent.com/u/6154722?v=4","primary_language":"Python","stars":36025,"forks":3796,"topics":["gpt","gpt-4","gpt4","graphrag","llm","llms","rag"],"archived":false,"github_pushed_at":"2026-09-16T17:15:35+00:00","maintenance_label":"Very active","stars_delta_30d":506,"url":"https://www.graphcanon.com/tools/microsoft-graphrag","markdown_url":"https://www.graphcanon.com/tools/microsoft-graphrag.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/microsoft-graphrag","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=microsoft-graphrag"}},{"type":"related","direction":"in","explanation":"Both explore generative AI systems but focus on retrieval-augmented generation techniques in the form of a technique guide and application listings.","successor_context":null,"tool":{"slug":"steven2358-awesome-generative-ai","name":"awesome-generative-ai","tagline":"A curated list of modern Generative Artificial Intelligence projects and services","github_url":"https://github.com/steven2358/awesome-generative-ai","owner":"steven2358","repo":"awesome-generative-ai","owner_avatar_url":"https://avatars.githubusercontent.com/u/164072?v=4","primary_language":null,"stars":12651,"forks":2126,"topics":["ai","artificial-intelligence","awesome","awesome-list","generative-ai","generative-art","large-language-models","llm"],"archived":false,"github_pushed_at":"2026-09-16T21:39:51+00:00","maintenance_label":"Very active","stars_delta_30d":150,"url":"https://www.graphcanon.com/tools/steven2358-awesome-generative-ai","markdown_url":"https://www.graphcanon.com/tools/steven2358-awesome-generative-ai.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/steven2358-awesome-generative-ai","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=steven2358-awesome-generative-ai"}},{"type":"integrates_with","direction":"in","explanation":"RAG_Techniques focuses on retrieval-augmented generation systems, and easy-dataset is useful for creating datasets that can enrich the RAG system's knowledge base.","successor_context":null,"tool":{"slug":"conardli-easy-dataset","name":"easy-dataset","tagline":"A powerful tool for creating datasets for LLM fine-tuning, RAG, and evaluation","github_url":"https://github.com/ConardLi/easy-dataset","owner":"ConardLi","repo":"easy-dataset","owner_avatar_url":"https://avatars.githubusercontent.com/u/30708545?v=4","primary_language":"JavaScript","stars":14792,"forks":1523,"topics":["dataset","fine-tuning","javascript","llm","rag"],"archived":false,"github_pushed_at":"2026-05-01T15:03:32+00:00","maintenance_label":"Slowing","stars_delta_30d":125,"url":"https://www.graphcanon.com/tools/conardli-easy-dataset","markdown_url":"https://www.graphcanon.com/tools/conardli-easy-dataset.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/conardli-easy-dataset","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=conardli-easy-dataset"}},{"type":"related","direction":"in","explanation":"Both repositories are related to advanced AI techniques such as Retrieval-Augmented Generation systems, but Rag Techniques focuses on enhancing and elevating these systems in contrast to the multimodal aspects covered by Awesome-Multimodal-Large-Language-Models.","successor_context":null,"tool":{"slug":"bradyfu-awesome-multimodal-large-language-models","name":"Awesome-Multimodal-Large-Language-Models","tagline":"Latest Advances on Multimodal Large Language Models","github_url":"https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models","owner":"BradyFU","repo":"Awesome-Multimodal-Large-Language-Models","owner_avatar_url":"https://avatars.githubusercontent.com/u/54254631?v=4","primary_language":null,"stars":18026,"forks":1136,"topics":["chain-of-thought","in-context-learning","instruction-following","instruction-tuning","large-language-models","large-vision-language-model","large-vision-language-models","multi-modality","multimodal-chain-of-thought","multimodal-in-context-learning","multimodal-instruction-tuning","multimodal-large-language-models","visual-instruction-tuning"],"archived":false,"github_pushed_at":"2026-09-18T03:09:18+00:00","maintenance_label":"Very active","stars_delta_30d":48,"url":"https://www.graphcanon.com/tools/bradyfu-awesome-multimodal-large-language-models","markdown_url":"https://www.graphcanon.com/tools/bradyfu-awesome-multimodal-large-language-models.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/bradyfu-awesome-multimodal-large-language-models","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=bradyfu-awesome-multimodal-large-language-models"}},{"type":"related","direction":"in","explanation":"Both focus on enhancing retrieval-augmented generation techniques and could be used in conjunction with RAG applications that require evaluation.","successor_context":null,"tool":{"slug":"open-compass-vlmevalkit","name":"VLMEvalKit","tagline":"An open-source evaluation toolkit for large vision-language models","github_url":"https://github.com/open-compass/VLMEvalKit","owner":"open-compass","repo":"VLMEvalKit","owner_avatar_url":"https://avatars.githubusercontent.com/u/143521324?v=4","primary_language":"Python","stars":4345,"forks":745,"topics":["chatgpt","claude","clip","computer-vision","evaluation","gemini","gpt","gpt-4v","gpt4","large-language-models","llava","llm","multi-modal","openai","openai-api","pytorch","qwen","vit","vqa"],"archived":false,"github_pushed_at":"2026-08-17T17:16:26+00:00","maintenance_label":"Very active","stars_delta_30d":60,"url":"https://www.graphcanon.com/tools/open-compass-vlmevalkit","markdown_url":"https://www.graphcanon.com/tools/open-compass-vlmevalkit.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/open-compass-vlmevalkit","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=open-compass-vlmevalkit"}},{"type":"related","direction":"in","explanation":"LangWatch focuses on evaluation and testing AI agents which can include systems like Retrieval-Augmented Generation (RAG), making it indirectly relevant to RAG techniques.","successor_context":null,"tool":{"slug":"langwatch-langwatch","name":"langwatch","tagline":"The platform for LLM evaluations and AI agent testing","github_url":"https://github.com/langwatch/langwatch","owner":"langwatch","repo":"langwatch","owner_avatar_url":"https://avatars.githubusercontent.com/u/146763322?v=4","primary_language":"TypeScript","stars":4814,"forks":393,"topics":["agent-testing","ai","analytics","datasets","dspy","evaluation","gpt","llm","llm-ops","llmops","low-code","observability","observability-platform","openai","prompt-engineering","simulation-testing"],"archived":false,"github_pushed_at":"2026-09-18T08:04:08+00:00","maintenance_label":"Very active","stars_delta_30d":1335,"url":"https://www.graphcanon.com/tools/langwatch-langwatch","markdown_url":"https://www.graphcanon.com/tools/langwatch-langwatch.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/langwatch-langwatch","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=langwatch-langwatch"}},{"type":"depends_on","direction":"in","explanation":"PaperQA2 employs RAG techniques to improve its question answering accuracy, thus PaperQA2 depends on advancements and understanding of RAG_Techniques.","successor_context":null,"tool":{"slug":"future-house-paper-qa","name":"paper-qa","tagline":"High accuracy RAG for answering questions from scientific documents with citations","github_url":"https://github.com/Future-House/paper-qa","owner":"Future-House","repo":"paper-qa","owner_avatar_url":"https://avatars.githubusercontent.com/u/131727825?v=4","primary_language":"Python","stars":9048,"forks":907,"topics":["ai","rag","science","search"],"archived":false,"github_pushed_at":"2026-08-12T15:33:57+00:00","maintenance_label":"Very active","stars_delta_30d":154,"url":"https://www.graphcanon.com/tools/future-house-paper-qa","markdown_url":"https://www.graphcanon.com/tools/future-house-paper-qa.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/future-house-paper-qa","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=future-house-paper-qa"}},{"type":"integrates_with","direction":"in","explanation":"The 'Machine-Learning-Interviews' repo covers GenAI system design including RAG techniques, indicating that it could integrate with 'RAG Techniques' for deeper insights and practical knowledge.","successor_context":null,"tool":{"slug":"alirezadir-machine-learning-interviews","name":"Machine-Learning-Interviews","tagline":"Guide for Machine Learning/AI technical interviews","github_url":"https://github.com/alirezadir/Machine-Learning-Interviews","owner":"alirezadir","repo":"Machine-Learning-Interviews","owner_avatar_url":"https://avatars.githubusercontent.com/u/5262877?v=4","primary_language":"Jupyter Notebook","stars":9694,"forks":1695,"topics":["agentic","ai","ai-agents","ai-engineering","deep-learning","interview","interview-practice","interview-preparation","interviews","machine-learning","machine-learning-algorithms","scalable-applications","system-design"],"archived":false,"github_pushed_at":"2026-09-02T07:20:33+00:00","maintenance_label":"Active","stars_delta_30d":1056,"url":"https://www.graphcanon.com/tools/alirezadir-machine-learning-interviews","markdown_url":"https://www.graphcanon.com/tools/alirezadir-machine-learning-interviews.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/alirezadir-machine-learning-interviews","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=alirezadir-machine-learning-interviews"}},{"type":"related","direction":"in","explanation":"Both deal with Retrieval-Augmented Generation techniques; R2R implements such techniques in a production-grade framework, whereas Nirdiamant-RAG-Techniques provides educational material on them.","successor_context":null,"tool":{"slug":"sciphi-ai-r2r","name":"R2R","tagline":"SoTA production-ready AI retrieval system with RESTful API","github_url":"https://github.com/SciPhi-AI/R2R","owner":"SciPhi-AI","repo":"R2R","owner_avatar_url":"https://avatars.githubusercontent.com/u/148402514?v=4","primary_language":"Python","stars":7999,"forks":647,"topics":["artificial-intelligence","large-language-models","python","question-answering","rag","retrieval-augmented-generation","retrieval-systems","search"],"archived":false,"github_pushed_at":"2025-11-07T01:02:44+00:00","maintenance_label":"Slowing","stars_delta_30d":32,"url":"https://www.graphcanon.com/tools/sciphi-ai-r2r","markdown_url":"https://www.graphcanon.com/tools/sciphi-ai-r2r.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/sciphi-ai-r2r","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=sciphi-ai-r2r"}},{"type":"integrates_with","direction":"in","explanation":"FlashRAG, as a Python toolkit for efficient RAG research, could integrate with RAG_Techniques which suggests improving and elevating RAG systems. The toolkits may share datasets or methods that improve the retrieval and generation processes.","successor_context":null,"tool":{"slug":"ruc-nlpir-flashrag","name":"FlashRAG","tagline":"A Python toolkit for efficient RAG research","github_url":"https://github.com/RUC-NLPIR/FlashRAG","owner":"RUC-NLPIR","repo":"FlashRAG","owner_avatar_url":"https://avatars.githubusercontent.com/u/139615285?v=4","primary_language":"Python","stars":3542,"forks":311,"topics":["benchmark","datasets","large-language-models","retrieval-augmented-generation"],"archived":false,"github_pushed_at":"2026-08-09T05:52:43+00:00","maintenance_label":"Active","stars_delta_30d":20,"url":"https://www.graphcanon.com/tools/ruc-nlpir-flashrag","markdown_url":"https://www.graphcanon.com/tools/ruc-nlpir-flashrag.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/ruc-nlpir-flashrag","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=ruc-nlpir-flashrag"}},{"type":"related","direction":"in","explanation":"RAPTOR and nirdiamant's RAG_Techniques both focus on retrieval-augmented generation but approach the problem differently, emphasizing recursive tree organization in RAPTOR as opposed to technique enhancement in RAG_Techniques.","successor_context":null,"tool":{"slug":"parthsarthi03-raptor","name":"raptor","tagline":"Recursive Abstractive Processing for Tree-Organized Retrieval","github_url":"https://github.com/parthsarthi03/raptor","owner":"parthsarthi03","repo":"raptor","owner_avatar_url":"https://avatars.githubusercontent.com/u/39787228?v=4","primary_language":"Python","stars":1759,"forks":235,"topics":["agents","clustering","framework","language-model","llm","machine-learning","rag","retrieval","retrieval-augmented-generation","vector-database"],"archived":false,"github_pushed_at":"2024-09-03T08:34:31+00:00","maintenance_label":"Dormant","stars_delta_30d":32,"url":"https://www.graphcanon.com/tools/parthsarthi03-raptor","markdown_url":"https://www.graphcanon.com/tools/parthsarthi03-raptor.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/parthsarthi03-raptor","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=parthsarthi03-raptor"}},{"type":"related","direction":"in","explanation":"'recipes' covers retrieval-augmented generation, which aligns with the scope of RAG_Techniques but does not directly integrate or succeed it.","successor_context":null,"tool":{"slug":"weaviate-recipes","name":"recipes","tagline":"End-to-end notebooks for using Weaviate features and integrations.","github_url":"https://github.com/weaviate/recipes","owner":"weaviate","repo":"recipes","owner_avatar_url":"https://avatars.githubusercontent.com/u/37794290?v=4","primary_language":"Jupyter Notebook","stars":947,"forks":198,"topics":["function-calling","generative-ai","llm-frameworks","python","retrieval-augmented-generation","vector-database","vector-search"],"archived":false,"github_pushed_at":"2026-09-16T22:08:32+00:00","maintenance_label":"Very active","stars_delta_30d":6,"url":"https://www.graphcanon.com/tools/weaviate-recipes","markdown_url":"https://www.graphcanon.com/tools/weaviate-recipes.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/weaviate-recipes","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=weaviate-recipes"}}],"neighbours":[{"slug":"datawhalechina-all-in-rag","name":"all-in-rag","tagline":"🔍 检索增强生成 (RAG) 技术全栈指南","github_url":"https://github.com/datawhalechina/all-in-rag","owner":"datawhalechina","repo":"all-in-rag","owner_avatar_url":"https://avatars.githubusercontent.com/u/46047812?v=4","primary_language":"Python","stars":10437,"forks":5170,"topics":["ai","deepseek","embedding","kimi-k2","langchain","llama-index","llm","milvus","multimodal","neo4j","python","rag"],"archived":false,"github_pushed_at":"2026-07-29T11:43:11+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/datawhalechina-all-in-rag","markdown_url":"https://www.graphcanon.com/tools/datawhalechina-all-in-rag.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/datawhalechina-all-in-rag","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=datawhalechina-all-in-rag","shared_categories":["data-retrieval","llm-frameworks"]},{"slug":"run-llama-rags","name":"rags","tagline":"Build ChatGPT over your data with natural language","github_url":"https://github.com/run-llama/rags","owner":"run-llama","repo":"rags","owner_avatar_url":"https://avatars.githubusercontent.com/u/130722866?v=4","primary_language":"Python","stars":6549,"forks":656,"topics":["agent","chatbot","chatgpt","gpts","llamaindex","llm","openai","rag","streamlit"],"archived":false,"github_pushed_at":"2024-04-05T05:36:59+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/run-llama-rags","markdown_url":"https://www.graphcanon.com/tools/run-llama-rags.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/run-llama-rags","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=run-llama-rags","shared_categories":["data-retrieval"]},{"slug":"marker-inc-korea-autorag","name":"AutoRAG","tagline":"Open-source framework for RAG evaluation and optimization via AutoML","github_url":"https://github.com/Marker-Inc-Korea/AutoRAG","owner":"Marker-Inc-Korea","repo":"AutoRAG","owner_avatar_url":"https://avatars.githubusercontent.com/u/74290595?v=4","primary_language":"TypeScript","stars":5060,"forks":434,"topics":["analysis","automl","benchmarking","document-parser","embeddings","evaluation","llm","llm-evaluation","llm-ops","open-source","ops","optimization","pipeline","python","qa","rag","rag-evaluation","retrieval-augmented-generation"],"archived":false,"github_pushed_at":"2026-09-07T07:05:41+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/marker-inc-korea-autorag","markdown_url":"https://www.graphcanon.com/tools/marker-inc-korea-autorag.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/marker-inc-korea-autorag","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=marker-inc-korea-autorag","shared_categories":["model-training"]},{"slug":"giovannipasq-agentic-rag-for-dummies","name":"agentic-rag-for-dummies","tagline":"A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents","github_url":"https://github.com/GiovanniPasq/agentic-rag-for-dummies","owner":"GiovanniPasq","repo":"agentic-rag-for-dummies","owner_avatar_url":"https://avatars.githubusercontent.com/u/33225259?v=4","primary_language":"Jupyter Notebook","stars":4188,"forks":552,"topics":["agent","agentic-ai","agentic-rag","agents","ai-agents","bm25","generative-ai","gradio","langchain","langgraph","llm","ollama","qdrant","rag","rag-agents","rag-chatbot","rag-pipeline","retrieval-augmented-generation","retrieval-augmented-generation-rag"],"archived":false,"github_pushed_at":"2026-08-30T10:19:02+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/giovannipasq-agentic-rag-for-dummies","markdown_url":"https://www.graphcanon.com/tools/giovannipasq-agentic-rag-for-dummies.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/giovannipasq-agentic-rag-for-dummies","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=giovannipasq-agentic-rag-for-dummies","shared_categories":["data-retrieval"]},{"slug":"ray-project-llm-applications","name":"llm-applications","tagline":"Comprehensive guide to building RAG-based LLM applications for production","github_url":"https://github.com/ray-project/llm-applications","owner":"ray-project","repo":"llm-applications","owner_avatar_url":"https://avatars.githubusercontent.com/u/22125274?v=4","primary_language":"Jupyter Notebook","stars":1857,"forks":258,"topics":["anyscale","fine-tuning","llama2","llms","machine-learning","openai","ray","serving"],"archived":false,"github_pushed_at":"2026-08-15T00:14:04+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/ray-project-llm-applications","markdown_url":"https://www.graphcanon.com/tools/ray-project-llm-applications.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/ray-project-llm-applications","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=ray-project-llm-applications","shared_categories":["llm-frameworks"]},{"slug":"peremartra-large-language-model-notebooks-course","name":"Large-Language-Model-Notebooks-Course","tagline":"Practical course about Large Language Models","github_url":"https://github.com/peremartra/Large-Language-Model-Notebooks-Course","owner":"peremartra","repo":"Large-Language-Model-Notebooks-Course","owner_avatar_url":"https://avatars.githubusercontent.com/u/7319142?v=4","primary_language":"Jupyter Notebook","stars":1824,"forks":450,"topics":["chatbots","fine-tuning-llm","hf","huggingface","langchain","large-language-models","peft-fine-tuning-llm","pruning","transformers","vector-database"],"archived":false,"github_pushed_at":"2026-05-28T06:32:56+00:00","maintenance_label":"Slowing","url":"https://www.graphcanon.com/tools/peremartra-large-language-model-notebooks-course","markdown_url":"https://www.graphcanon.com/tools/peremartra-large-language-model-notebooks-course.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/peremartra-large-language-model-notebooks-course","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=peremartra-large-language-model-notebooks-course","shared_categories":["model-training","llm-frameworks"]},{"slug":"deepsense-ai-ragbits","name":"ragbits","tagline":"Building blocks for rapid development of GenAI applications","github_url":"https://github.com/deepsense-ai/ragbits","owner":"deepsense-ai","repo":"ragbits","owner_avatar_url":"https://avatars.githubusercontent.com/u/14294751?v=4","primary_language":"Python","stars":1668,"forks":143,"topics":["agents","document-search","evaluation","guardrails","llms","optimization","prompts","rag","vector-stores"],"archived":false,"github_pushed_at":"2026-05-18T07:51:25+00:00","maintenance_label":"Slowing","url":"https://www.graphcanon.com/tools/deepsense-ai-ragbits","markdown_url":"https://www.graphcanon.com/tools/deepsense-ai-ragbits.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/deepsense-ai-ragbits","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=deepsense-ai-ragbits","shared_categories":["data-retrieval","llm-frameworks"]},{"slug":"andrewnguonly-lumos","name":"Lumos","tagline":"A RAG LLM co-pilot for browsing the web","github_url":"https://github.com/andrewnguonly/Lumos","owner":"andrewnguonly","repo":"Lumos","owner_avatar_url":"https://avatars.githubusercontent.com/u/7654246?v=4","primary_language":"TypeScript","stars":1513,"forks":111,"topics":["chrome-extension","langchain","langchain-js","llm","ollama","react","typescript","webpack"],"archived":false,"github_pushed_at":"2025-01-26T15:42:22+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/andrewnguonly-lumos","markdown_url":"https://www.graphcanon.com/tools/andrewnguonly-lumos.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/andrewnguonly-lumos","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=andrewnguonly-lumos","shared_categories":["data-retrieval","llm-frameworks"]},{"slug":"jxzhangjhu-awesome-llm-rag","name":"Awesome-LLM-RAG","tagline":"a curated list of advanced retrieval augmented generation (RAG) in Large Language Models","github_url":"https://github.com/jxzhangjhu/Awesome-LLM-RAG","owner":"jxzhangjhu","repo":"Awesome-LLM-RAG","owner_avatar_url":"https://avatars.githubusercontent.com/u/19910910?v=4","primary_language":null,"stars":1348,"forks":95,"topics":["embeddings","large-language-models","llm","rag","rag-embeddings","retrieval-augmented-generation","retrieval-information"],"archived":false,"github_pushed_at":"2026-07-22T03:17:18+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/jxzhangjhu-awesome-llm-rag","markdown_url":"https://www.graphcanon.com/tools/jxzhangjhu-awesome-llm-rag.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/jxzhangjhu-awesome-llm-rag","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=jxzhangjhu-awesome-llm-rag","shared_categories":["data-retrieval","llm-frameworks"]},{"slug":"superlinear-ai-raglite","name":"raglite","tagline":"Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL","github_url":"https://github.com/superlinear-ai/raglite","owner":"superlinear-ai","repo":"raglite","owner_avatar_url":"https://avatars.githubusercontent.com/u/32849250?v=4","primary_language":"Python","stars":1202,"forks":109,"topics":["chainlit","colbert","duckdb","evals","late-chunking","late-interaction","llm","markdown","pdf","pgvector","postgres","postgresql","query-adapter","rag","reranking","retrieval-augmented-generation","sqlite","vector-search"],"archived":false,"github_pushed_at":"2026-08-17T07:19:07+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/superlinear-ai-raglite","markdown_url":"https://www.graphcanon.com/tools/superlinear-ai-raglite.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/superlinear-ai-raglite","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=superlinear-ai-raglite","shared_categories":["model-training","data-retrieval"]},{"slug":"dmayboroda-minima","name":"minima","tagline":"On-premises conversational RAG with configurable containers","github_url":"https://github.com/dmayboroda/minima","owner":"dmayboroda","repo":"minima","owner_avatar_url":"https://avatars.githubusercontent.com/u/2357342?v=4","primary_language":"Python","stars":1049,"forks":105,"topics":["ai","claude","custom-gpts","docker","docker-compose","huggingface","langchain","mcp","model-context-protocol","ollama","qdrant","sentence-transformers"],"archived":false,"github_pushed_at":"2026-01-22T09:22:09+00:00","maintenance_label":"Slowing","url":"https://www.graphcanon.com/tools/dmayboroda-minima","markdown_url":"https://www.graphcanon.com/tools/dmayboroda-minima.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/dmayboroda-minima","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=dmayboroda-minima","shared_categories":["model-training","data-retrieval"]},{"slug":"raudaschl-rag-fusion","name":"rag-fusion","tagline":"multi-query generation + Reciprocal Rank Fusion for retrieval-augmented generation","github_url":"https://github.com/Raudaschl/rag-fusion","owner":"Raudaschl","repo":"rag-fusion","owner_avatar_url":"https://avatars.githubusercontent.com/u/6103284?v=4","primary_language":"Python","stars":958,"forks":114,"topics":["chromadb","information-retrieval","openai","python","rag","rag-fusion","reciprocal-rank-fusion","retrieval-augmented-generation","vector-search"],"archived":false,"github_pushed_at":"2026-04-26T18:45:05+00:00","maintenance_label":"Slowing","url":"https://www.graphcanon.com/tools/raudaschl-rag-fusion","markdown_url":"https://www.graphcanon.com/tools/raudaschl-rag-fusion.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/raudaschl-rag-fusion","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=raudaschl-rag-fusion","shared_categories":["data-retrieval"]}]}}