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Python: Fix Redis samples for session migration and configurable REDIS_URL (#4060)

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* fix: update Redis samples for session migration and configurable REDIS_URL

- Replace hardcoded redis://localhost:6379 with configurable REDIS_URL env var
- Fix SessionContext usage: use input_messages kwarg instead of removed extend_messages
- Remove obsolete scope_to_per_operation_thread_id parameter
- Remove stale commented-out overwrite_redis_index/drop_redis_index params

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Remove docker commands from comments to avoid security scan flags

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Giles Odigwe
2026-02-18 20:24:38 -08:00
committed by GitHub
Unverified
parent 396807ab17
commit 6fd50464b0
3 changed files with 27 additions and 21 deletions
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python/samples/02-agents/context_providers/redis/redis_basics.py
+11 -9
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@@ -37,6 +37,10 @@ from azure.identity import AzureCliCredential
from redisvl.extensions.cache.embeddings import EmbeddingsCache
from redisvl.utils.vectorize import OpenAITextVectorizer
# 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")
# NOTE: approval_mode="never_require" is for sample brevity.
# Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py
@@ -121,14 +125,14 @@ async def main() -> None:
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://localhost:6379"),
cache=EmbeddingsCache(name="openai_embeddings_cache", redis_url=REDIS_URL),
)
# The provider manages persistence and retrieval. application_id/agent_id/user_id
# scope data for multi-tenant separation; thread_id (set later) narrows to a
# specific conversation.
provider = RedisContextProvider(
source_id="redis_context",
redis_url="redis://localhost:6379",
redis_url=REDIS_URL,
index_name="redis_basics",
application_id="matrix_of_kermits",
agent_id="agent_kermit",
@@ -151,16 +155,14 @@ async def main() -> None:
from agent_framework import AgentSession, SessionContext
session = AgentSession(session_id="runA")
context = SessionContext()
context.extend_messages("input", messages)
context = SessionContext(input_messages=messages)
state = session.state
# Store messages via after_run
await provider.after_run(agent=None, session=session, context=context, state=state)
# Retrieve relevant memories via before_run
query_context = SessionContext()
query_context.extend_messages("input", [Message("system", ["B: Assistant Message"])])
query_context = SessionContext(input_messages=[Message("system", ["B: Assistant Message"])])
await provider.before_run(agent=None, session=session, context=query_context, state=state)
# Inspect retrieved memories that would be injected into instructions
@@ -179,12 +181,12 @@ async def main() -> None:
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://localhost:6379"),
cache=EmbeddingsCache(name="openai_embeddings_cache", redis_url=REDIS_URL),
)
# Recreate a clean index so the next scenario starts fresh
provider = RedisContextProvider(
source_id="redis_context",
redis_url="redis://localhost:6379",
redis_url=REDIS_URL,
index_name="redis_basics_2",
prefix="context_2",
application_id="matrix_of_kermits",
@@ -232,7 +234,7 @@ async def main() -> None:
# Text-only provider (full-text search only). Omits vectorizer and related params.
provider = RedisContextProvider(
source_id="redis_context",
redis_url="redis://localhost:6379",
redis_url=REDIS_URL,
index_name="redis_basics_3",
prefix="context_3",
application_id="matrix_of_kermits",
python/samples/02-agents/context_providers/redis/redis_conversation.py
+6 -2
View File
@@ -23,6 +23,10 @@ from azure.identity import AzureCliCredential
from redisvl.extensions.cache.embeddings import EmbeddingsCache
from redisvl.utils.vectorize import OpenAITextVectorizer
# 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.
@@ -34,12 +38,12 @@ async def main() -> None:
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://localhost:6379"),
cache=EmbeddingsCache(name="openai_embeddings_cache", redis_url=REDIS_URL),
)
provider = RedisContextProvider(
source_id="redis_context",
redis_url="redis://localhost:6379",
redis_url=REDIS_URL,
index_name="redis_conversation",
prefix="redis_conversation",
application_id="matrix_of_kermits",
python/samples/02-agents/context_providers/redis/redis_sessions.py
+10 -10
View File
@@ -35,6 +35,10 @@ from azure.identity import AzureCliCredential
from redisvl.extensions.cache.embeddings import EmbeddingsCache
from redisvl.utils.vectorize import OpenAITextVectorizer
# 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")
# Please set OPENAI_API_KEY to use the OpenAI vectorizer.
# For chat responses, also set AZURE_AI_PROJECT_ENDPOINT and AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME.
@@ -57,12 +61,11 @@ async def example_global_thread_scope() -> None:
provider = RedisContextProvider(
source_id="redis_context",
redis_url="redis://localhost:6379",
redis_url=REDIS_URL,
index_name="redis_threads_global",
application_id="threads_demo_app",
agent_id="threads_demo_agent",
user_id="threads_demo_user",
scope_to_per_operation_thread_id=False, # Share memories across all sessions
)
agent = client.as_agent(
@@ -106,19 +109,16 @@ async def example_per_operation_thread_scope() -> None:
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://localhost:6379"),
cache=EmbeddingsCache(name="openai_embeddings_cache", redis_url=REDIS_URL),
)
provider = RedisContextProvider(
source_id="redis_context",
redis_url="redis://localhost:6379",
redis_url=REDIS_URL,
index_name="redis_threads_dynamic",
# overwrite_redis_index=True,
# drop_redis_index=True,
application_id="threads_demo_app",
agent_id="threads_demo_agent",
user_id="threads_demo_user",
scope_to_per_operation_thread_id=True, # Isolate memories per session
redis_vectorizer=vectorizer,
vector_field_name="vector",
vector_algorithm="hnsw",
@@ -172,12 +172,12 @@ async def example_multiple_agents() -> None:
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://localhost:6379"),
cache=EmbeddingsCache(name="openai_embeddings_cache", redis_url=REDIS_URL),
)
personal_provider = RedisContextProvider(
source_id="redis_context",
redis_url="redis://localhost:6379",
redis_url=REDIS_URL,
index_name="redis_threads_agents",
application_id="threads_demo_app",
agent_id="agent_personal",
@@ -196,7 +196,7 @@ async def example_multiple_agents() -> None:
work_provider = RedisContextProvider(
source_id="redis_context",
redis_url="redis://localhost:6379",
redis_url=REDIS_URL,
index_name="redis_threads_agents",
application_id="threads_demo_app",
agent_id="agent_work",