mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Eduard van Valkenburg
aab621f5eb
* Fix tool normalization and provider samples - restore callable/single-tool normalization paths and unset tool-choice behavior\n- consolidate and expand chat/provider samples (OpenAI/Azure/Anthropic/Ollama/Bedrock)\n- migrate Bedrock lazy import surface to agent_framework.amazon and move provider samples Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * small fix in sample * Finalize provider, samples, and core cleanup Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix CopilotTool passthrough in agent Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix link --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
109 lines
3.6 KiB
Python
109 lines
3.6 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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"""Redis Context Provider: Basic usage and agent integration
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This example demonstrates how to use the Redis context provider to persist
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conversational details. Pass it as a constructor argument to create_agent.
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Requirements:
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- A Redis instance with RediSearch enabled (e.g., Redis Stack)
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- agent-framework with the Redis extra installed: pip install "agent-framework-redis"
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- Optionally an OpenAI API key if enabling embeddings for hybrid search
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Run:
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python redis_conversation.py
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"""
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import asyncio
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import os
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from agent_framework.azure import AzureOpenAIResponsesClient
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from agent_framework.redis import RedisContextProvider
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from azure.identity import AzureCliCredential
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from redisvl.extensions.cache.embeddings import EmbeddingsCache
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from redisvl.utils.vectorize import OpenAITextVectorizer
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async def main() -> None:
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"""Walk through provider and chat message store usage.
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Helpful debugging (uncomment when iterating):
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- print(await provider.redis_index.info())
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- print(await provider.search_all())
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"""
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vectorizer = OpenAITextVectorizer(
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model="text-embedding-ada-002",
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api_config={"api_key": os.getenv("OPENAI_API_KEY")},
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cache=EmbeddingsCache(name="openai_embeddings_cache", redis_url="redis://localhost:6379"),
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)
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provider = RedisContextProvider(
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source_id="redis_context",
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redis_url="redis://localhost:6379",
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index_name="redis_conversation",
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prefix="redis_conversation",
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application_id="matrix_of_kermits",
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agent_id="agent_kermit",
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user_id="kermit",
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redis_vectorizer=vectorizer,
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vector_field_name="vector",
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vector_algorithm="hnsw",
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vector_distance_metric="cosine",
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)
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# Create chat client for the agent
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client = AzureOpenAIResponsesClient(
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project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
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deployment_name=os.environ["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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# Create agent wired to the Redis context provider. The provider automatically
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# persists conversational details and surfaces relevant context on each turn.
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agent = client.as_agent(
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name="MemoryEnhancedAssistant",
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instructions=(
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"You are a helpful assistant. Personalize replies using provided context. "
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"Before answering, always check for stored context"
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),
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tools=[],
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context_providers=[provider],
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)
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# Teach a user preference; the agent writes this to the provider's memory
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query = "Remember that I enjoy gumbo"
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result = await agent.run(query)
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print("User: ", query)
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print("Agent: ", result)
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# Ask the agent to recall the stored preference; it should retrieve from memory
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query = "What do I enjoy?"
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result = await agent.run(query)
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print("User: ", query)
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print("Agent: ", result)
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query = "What did I say to you just now?"
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result = await agent.run(query)
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print("User: ", query)
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print("Agent: ", result)
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query = "Remember that I have a meeting at 3pm tomorro"
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result = await agent.run(query)
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print("User: ", query)
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print("Agent: ", result)
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query = "Tulips are red"
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result = await agent.run(query)
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print("User: ", query)
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print("Agent: ", result)
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query = "What was the first thing I said to you this conversation?"
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result = await agent.run(query)
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print("User: ", query)
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print("Agent: ", result)
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# Drop / delete the provider index in Redis
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await provider.redis_index.delete()
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if __name__ == "__main__":
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asyncio.run(main())
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