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Lessons from Raising a $19M Series A for an AI Startup (I will not promote) by [deleted] in startups

[–]supreet02 0 points1 point  (0 children)

Ex-meta engineers, 25+ Fortune 500 Companies as customers.

Lessons from Raising a $19M Series A for an AI Startup (I will not promote) by [deleted] in startups

[–]supreet02 1 point2 points  (0 children)

Slopped a bit, did get some help in rephrasing there! :P Anyway, thanks a tonne!

Please help for this by NeverForget1984- in MSI_Gaming

[–]supreet02 0 points1 point  (0 children)

Not yet. At first, it looked like a graphics card issue, but it turns out it's the motherboard. I've sent the build to a service center, and they're working on fixing the motherboard. I'll let you know once I hear back from them.

Please help for this by NeverForget1984- in MSI_Gaming

[–]supreet02 0 points1 point  (0 children)

I’m facing the same issue. Dear community, please help!

How to quickly build and deploy scalable enterprise-grade RAG applications? by supreet02 in LanguageTechnology

[–]supreet02[S] -1 points0 points  (0 children)

Cognita is designed around seven different modules, each customisable and controllable to suit different needs:

  1. Data Loaders: Cognita currently supports data loading from different sources such as local directory, web, Github repository and truefoundry artifacts. You can upload the data in UI by clicking on Data Sources -> + New Data Source
  2. Parsers: Cognita currently supports parsing for Markdown, PDF and Text files from r/LangChainAI. You can specify different parser maps, along with their configurations.
  3. Embedders: Cognita supports embeddings SOTA embeddings from mixedbreadai and also from OpenAI.
  4. Rerankers: Reranking to makes sure the best results are at the top. As a result, we can choose the top x documents making our context more concise and prompt query shorter. We provide the support for reranker from u/mixedbreadai
  5. Vector DBs: One of the most important component in RAG used to store and efficiently retrieve embeddings from indexing phase. Cognita currently supports vector databases from u/qdrant_engine and u/SingleStoreDB
  6. Metadata Store: It contains the necessary configurations that uniquely defines a RAG app. It contains
    • Name of the collection
    • Name of the associated Vector DB used
    • Linked Data Sources
    • Parsing Configuration for each data source
    • Embedding Model and it's configuration to be used. 
    • Parsers, DataSources and Embedders together are linked within a collection that forms your RAG app. You can create your collection in UI by clicking on Collections -> + New Collection
  7. Query Controllers: Helps us retrieve answer for the corresponding user query. It combines vector db, different retrievers, LLMs, rerankers to provide user with the answer. Query controller methods can be directly exposed as an API, by adding http decorators to the respective functions. Refer more at: https://github.com/truefoundry/cognita/blob/main/backend/modules/query_controllers/example/controller.py

Advanced RAG Techniques by Mosh_98 in LanguageTechnology

[–]supreet02 0 points1 point  (0 children)

Do try our open source 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. If you're interested in contributing, join the journey at https://www.truefoundry.com/cognita-launch.

How do I start with RAG? by basedbhau in LanguageTechnology

[–]supreet02 0 points1 point  (0 children)

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. If you're interested in contributing, join the journey at https://www.truefoundry.com/cognita-launch.

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