Seems. amazing solution for advanced rag
@avikumart_
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LLM Research Development Gaining Momentum in India
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Really awesome to see LLM research happening in india
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Data Science Course: Build Your ML Career Portfolio
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Are you looking to take a DATA SCIENCE course and build a career in the data and ML domains? Then, the AI learning hub course is the way to go. Check out the link below to sign up for the amazing course and get hands-on projects to build a portfolio.
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Portfolio concerns as market declines amid sector challenges
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All hopes on him now otherwise portfolio gonna go down further
It's all red everywhere -
Vectorize: Essential Tool for RAG Application Development
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I find Vectorize go to tools that ease the process of developing RAG-based applications without any hassle. It also allows to deploy your pipeline to production to run RAG applications for users. take a look below post for the documentation of vectorize.
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Vectorize RAG Evaluation Documentation Now Available
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Check out the documentation for RAG evaluation below https://
docs.vectorize.io/getting-starte
d/rag-evaluation-quick-start
… So what are you waiting for? Sign up on Vectorize using the below link for FREE and upgrade as per your needs -
Vectorize Real-Time Vector Storage and Search Index Monitoring
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That's not it! Vectorize also allows to monitor querying, and vector storage from your sources in real-time – Never worry about stale vector search indexes again – Vectorize can be configured to immediately update changes in your unstructured data sources as soon as they occur
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Vectorize RAG Capabilities Advanced Query Processing Features
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Vectorize also offers advanced RAG capabilities for more complex queries and application requirements. – Automatically vectorize the user query – Provides built-in re-ranking of results – Returns the metadata of retrieval context with relevancy scores and cosine similarity
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RAG Pipeline Builder Connects Cloud Data to Vector Databases
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RAG pipeline builder allowed me to quickly connect my cloud-based data storage to connect with embeddings and vectordb. – It populates vector search indexes with unstructured data from documents, SaaS platforms, knowledge bases, etc. – Automatically syncs data with vectordb.