mirror of
https://github.com/Shubhamsaboo/awesome-llm-apps.git
synced 2026-03-08 23:13:56 -05:00
9bc6394fae
- Replaced PDFUrlKnowledgeBase with Knowledge class for improved document management. - Updated to use AgentOS for the application interface instead of Playground. - Adjusted README to reflect changes in the interface and knowledge base loading process. - Specified minimum version for the 'agno' package in requirements.txt.
43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
# Import necessary libraries
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from agno.agent import Agent
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from agno.models.ollama import Ollama
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from agno.knowledge.knowledge import Knowledge
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from agno.vectordb.qdrant import Qdrant
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from agno.knowledge.embedder.ollama import OllamaEmbedder
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from agno.os import AgentOS
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# Define the collection name for the vector database
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collection_name = "thai-recipe-index"
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# Set up Qdrant as the vector database with the embedder
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vector_db = Qdrant(
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collection=collection_name,
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url="http://localhost:6333/",
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embedder=OllamaEmbedder()
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)
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# Define the knowledge base
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knowledge_base = Knowledge(
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vector_db=vector_db,
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)
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# Add content to the knowledge base, comment out after the first run to avoid reloading
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knowledge_base.add_content(
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url="https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
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)
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# Create the Agent using Ollama's llama3.2 model and the knowledge base
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agent = Agent(
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name="Local RAG Agent",
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model=Ollama(id="llama3.2"),
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knowledge=knowledge_base,
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)
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# UI for RAG agent
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agent_os = AgentOS(agents=[agent])
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app = agent_os.get_app()
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# Run the AgentOS app
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if __name__ == "__main__":
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agent_os.serve(app="local_rag_agent:app", reload=True)
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