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Discussion community for DS, ML, IOT, AI, DEEP LEARNING and much more from Main Channel @kdnuggets and discuss @DatascienceChats
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https://astra.mmusic.workers.dev
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Yeah, I don't like your flooding. Casper has been muted.
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Welcome Shomurodoff to the group. Please read the pinned message for rules or before you start.
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Your data isn't the product here.
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We are helping Data Science candidates to get placed in mnc companies.
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Use LLMs. They are the best teachers
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Welcome the hashler to the group. Please read the pinned message for rules or before you start.
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🚀 JUST BUILT: A Complete Agentic RAG System 🔥
What if an AI agent could decide whether it actually needs to retrieve information before answering? 🤯
I’ve just released a complete Agentic RAG project built with:
🧠 LangGraph — intelligent routing & workflows
🔎 Qdrant — semantic/vector search 🤖 Azure OpenAI — LLM + embeddings
📊 LangSmith — tracing & observability
⚡ LangServe + FastAPI — API & deployment
💾 Conversation Memory — multi-turn conversations
📚 Source Citations + Citation Repair — grounded answers
The agent evaluates the query first:
Question → 🧠 Route → 💬 Answer Directly OR 🔎 Retrieve → ✅ Validate Citations → 💬 Final Answer
This isn’t just a basic RAG demo — it covers the complete workflow, including ingestion, chunking, retrieval, agentic routing, memory, citations, validation, and observability.
🎥 Watch the complete build: https://youtu.be/BFrgPq-23Mo
💻 Explore the complete GitHub repository: https://github.com/Vaibhav-Joshi-code/Complete-Agentic-RAG-System
🔥
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CCmax邮箱+密码+sk质保30天
Claude官Key小额大 额
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Ryos AI St.ore says hello to everyone.
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Anyone here looking for a ai engineer role or already working as a AI engineer. I need your guidance
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If you guys are in AI engineering or transitioning then system design becomes very important for you. Bee good at it!
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can anyone recommend good resource for pytorch
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🚀 JUST BUILT: A Complete Agentic RAG System 🔥
What if an AI agent could decide whether it actually needs to retrieve information before answering? 🤯
I’ve just released a complete Agentic RAG project built with:
🧠 LangGraph — intelligent routing & workflows
🔎 Qdrant — semantic/vector search 🤖 Azure OpenAI — LLM + embeddings
📊 LangSmith — tracing & observability
⚡ LangServe + FastAPI — API & deployment
💾 Conversation Memory — multi-turn conversations
📚 Source Citations + Citation Repair — grounded answers
The agent evaluates the query first:
Question → 🧠 Route → 💬 Answer Directly OR 🔎 Retrieve → ✅ Validate Citations → 💬 Final Answer
This isn’t just a basic RAG demo — it covers the complete workflow, including ingestion, chunking, retrieval, agentic routing, memory, citations, validation, and observability.
🎥 Watch the complete build: https://youtu.be/BFrgPq-23Mo
💻 Explore the complete GitHub repository: https://github.com/Vaibhav-Joshi-code/Complete-Agentic-RAG-System
🔥