chore: import upstream snapshot with attribution

python/samples/02-agents/context_providers/redis/redis_conversation.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""Redis Context Provider: Basic usage and agent integration
+
+This example demonstrates how to use the Redis context provider to persist
+conversational details. Pass it as a constructor argument to create_agent.
+
+Note: For session history persistence, see RedisHistoryProvider in the
+conversations/redis_history_provider.py sample. RedisContextProvider is for
+AI context (RAG, memories), while RedisHistoryProvider stores message history.
+
+Requirements:
+  - A Redis instance with RediSearch enabled (e.g., Redis Stack)
+  - agent-framework with the Redis extra installed: pip install "agent-framework-redis"
+  - Optionally an OpenAI API key if enabling embeddings for hybrid search
+
+Run:
+  python redis_conversation.py
+"""
+
+import asyncio
+import os
+
+from agent_framework import Agent
+from agent_framework.foundry import FoundryChatClient
+from agent_framework.redis import RedisContextProvider
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+from redisvl.extensions.cache.embeddings import EmbeddingsCache
+from redisvl.utils.vectorize import OpenAITextVectorizer
+
+load_dotenv()
+
+# Default Redis URL for local Redis Stack.
+# Override via the REDIS_URL environment variable for remote or authenticated instances.
+REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379")
+
+
+async def main() -> None:
+    """Walk through provider and chat message store usage.
+
+    Helpful debugging (uncomment when iterating):
+      - print(await provider.redis_index.info())
+      - print(await provider.search_all())
+    """
+    vectorizer = OpenAITextVectorizer(
+        model="text-embedding-ada-002",
+        api_config={"api_key": os.getenv("OPENAI_API_KEY")},
+        cache=EmbeddingsCache(name="openai_embeddings_cache", redis_url=REDIS_URL),
+    )
+
+    provider = RedisContextProvider(
+        source_id="redis_context",
+        redis_url=REDIS_URL,
+        index_name="redis_conversation",
+        prefix="redis_conversation",
+        application_id="matrix_of_kermits",
+        agent_id="agent_kermit",
+        user_id="kermit",
+        redis_vectorizer=vectorizer,
+        vector_field_name="vector",
+        vector_algorithm="hnsw",
+        vector_distance_metric="cosine",
+    )
+
+    # Create chat client for the agent
+    client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["FOUNDRY_MODEL"],
+        credential=AzureCliCredential(),
+    )
+    # Create agent wired to the Redis context provider. The provider automatically
+    # persists conversational details and surfaces relevant context on each turn.
+    agent = Agent(
+        client=client,
+        name="MemoryEnhancedAssistant",
+        instructions=(
+            "You are a helpful assistant. Personalize replies using provided context. "
+            "Before answering, always check for stored context"
+        ),
+        tools=[],
+        context_providers=[provider],
+    )
+
+    # Create a session to manage conversation state
+    session = agent.create_session()
+
+    # Teach a user preference; the agent writes this to the provider's memory
+    query = "Remember that I enjoy gumbo"
+    result = await agent.run(query, session=session)
+    print("User: ", query)
+    print("Agent: ", result)
+
+    # Ask the agent to recall the stored preference; it should retrieve from memory
+    query = "What do I enjoy?"
+    result = await agent.run(query, session=session)
+    print("User: ", query)
+    print("Agent: ", result)
+
+    query = "What did I say to you just now?"
+    result = await agent.run(query, session=session)
+    print("User: ", query)
+    print("Agent: ", result)
+
+    query = "Remember that I have a meeting at 3pm tomorro"
+    result = await agent.run(query, session=session)
+    print("User: ", query)
+    print("Agent: ", result)
+
+    query = "Tulips are red"
+    result = await agent.run(query, session=session)
+    print("User: ", query)
+    print("Agent: ", result)
+
+    query = "What was the first thing I said to you this conversation?"
+    result = await agent.run(query, session=session)
+    print("User: ", query)
+    print("Agent: ", result)
+    # Drop / delete the provider index in Redis
+    await provider.redis_index.delete()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())