6. Agent Interaction Finally define the chat logic. Once the user prompt is passed to the agent, it plans and takes actions. It first queries the vector database. If no relevant results are returned, it falls back to DuckDuckGo and responds with the final output.
@sumanth_077
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Loading Knowledge Base and Initializing RAG Agent for PDF Processing
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5. Next, Loading the Knowledge & Agent When a PDF is processed, we instantiate PDFKnowledgeBase with the file path & our Milvus vector_db. Then we pass it into the above "get_rag_agent" method to initialize a fully ready-to-chat agent and then store it as a session variable.
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Building a PDF RAG Agent with Agno and GPT-4o-mini
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4. Next, let’s build the PDF RAG Agent The get_rag_agent function sets up the Agno Agent with gpt-4o-mini, PDFKnowledgeBase, and the DuckDuckGo tool for web search. This design allows the agent to try PDF knowledge base first and fall back to websearch if needed.
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Setup Milvus VectorDB for RAG Agent Semantic Search
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3. Setup vectorDB First, connect to Milvus to store and search vector embeddings. Agno simplifies this with an abstraction for the Milvus client. Provide the collection name, embedding model, and server info to create the collection, enabling semantic search in our RAG Agent.
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Installation de Milvus : Base de données vectorielle open-source
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2. Install Milvus We use Milvus, an open-source vector database, to store document embeddings. Milvus supports flexible deployments via Kubernetes, Docker, or Lite mode via pip install. Let's run the "standalone_embed. sh" script to launch Milvus in Docker standalone mode.
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Browser Use: AI Agents Web Integration with Python
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Make websites accessible for AI agents! Browser Use is the easiest way to connect your AI agents to the web with just a few lines of Python code. 100% Open Source
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SambaNova’s RDUs: 10x Faster AI Hardware for Modern Models
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GPUs are not built for modern AI needs. @SambaNovaAI created RDUs, a new type of hardware that runs AI 10x faster. Their stack is open, so you can bring your own models! They are also the official launch partner of Llama 4. Read more here:
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Assembling Agents and Tasks into a Crew
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6. Assembling the Crew Finally, combine agents & tasks into a Crew:
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Defining Tasks for Stock Analysis and Report Generation
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5. Defining the Tasks Next, the tasks: Analysis Task: Instructs the Analyst agent to use the stock_data_tool, analyze metrics (price, 52wk, P/E etc.), and output findings. Report Task: Tells the Writer agent to structure the analysis into a formatted report.
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Creating Stock Analyst and Report Writer Agents with Llama-4-Maverick
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4. Creating the Agents We define two agents, both powered by Llama-4-Maverick from Sambanova: – Stock Analyst: Role: Wall Street Analyst. Goal: Analyze {symbol} using the YFinance tool for live data. – Report Writer: Role: Report Specialist. Goal: Craft a professional report.