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c71ce8df61
# What does this PR do? - Migrates the remaining documentation sections to the new documentation format <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan - Partial migration <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* -->
68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient
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vector_db_id = "my_demo_vector_db"
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client = LlamaStackClient(base_url="http://localhost:8321")
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models = client.models.list()
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# Select the first LLM and first embedding models
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model_id = next(m for m in models if m.model_type == "llm").identifier
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embedding_model_id = (
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em := next(m for m in models if m.model_type == "embedding")
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).identifier
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embedding_dimension = em.metadata["embedding_dimension"]
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vector_db = client.vector_dbs.register(
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vector_db_id=vector_db_id,
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embedding_model=embedding_model_id,
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embedding_dimension=embedding_dimension,
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provider_id="faiss",
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)
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vector_db_id = vector_db.identifier
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source = "https://www.paulgraham.com/greatwork.html"
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print("rag_tool> Ingesting document:", source)
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document = RAGDocument(
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document_id="document_1",
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content=source,
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mime_type="text/html",
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metadata={},
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)
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client.tool_runtime.rag_tool.insert(
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documents=[document],
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vector_db_id=vector_db_id,
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chunk_size_in_tokens=100,
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)
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agent = Agent(
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client,
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model=model_id,
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instructions="You are a helpful assistant",
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tools=[
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{
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"name": "builtin::rag/knowledge_search",
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"args": {"vector_db_ids": [vector_db_id]},
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}
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],
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)
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prompt = "How do you do great work?"
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print("prompt>", prompt)
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use_stream = True
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response = agent.create_turn(
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messages=[{"role": "user", "content": prompt}],
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session_id=agent.create_session("rag_session"),
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stream=use_stream,
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)
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# Only call `AgentEventLogger().log(response)` for streaming responses.
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if use_stream:
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for log in AgentEventLogger().log(response):
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log.print()
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else:
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print(response)
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