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Cognita – open-source RAG framework for modular applications

Hacker News

Cognita – open-source RAG framework for modular applications

Hey HN, exciting news! Our RAG framework, Cognita ( https://github.com/truefoundry/cognita ), born from collaborations with diverse enterprises, is now open-source. Currently, it offers seamless integrations with Qdrant and SingleStore. In recent weeks, numerous engineers have explored Cognita, providing invaluable insights and feedback. We deeply appreciate your input and encourage ongoing dialogue (share your thoughts in the comments – let's keep this ‘open source’). While RAG is undoubtedly powerful, the process of building a functional application with it can feel overwhelming. From selecting the right AI models to organizing data effectively, there's a lot to navigate. While tools like LangChain and LlamaIndex simplify prototyping, an accessible, ready-to-use open-source RAG template with modular support is still missing. That's where Cognita comes in. Key benefits of Cognita: 1. Central repository for parsers, loaders, embedders, and retrievers. 2. User-friendly UI empowers non-technical users to upload documents and engage in Q&A. 3. Fully API-driven for seamless integration with other systems. We invite you to explore Cognita and share your feedback as we refine and expand its capabilities. Interested in contributing? Join the journey at https://www.truefoundry.com/cognita-launch .

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: lua, open source, llama · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly, users · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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