Python: Align Redis context sample guide with current APIs

python/samples/02-agents/context_providers/redis/README.md

@@ -10,7 +10,7 @@ This folder contains an example demonstrating how to use the Redis context provi
 |------|-------------|
 | [`azure_redis_conversation.py`](azure_redis_conversation.py) | Demonstrates conversation persistence with RedisHistoryProvider and Azure Redis with Azure AD (Entra ID) authentication using credential provider. |
 | [`redis_basics.py`](redis_basics.py) | Shows standalone provider usage and agent integration. Demonstrates writing messages to Redis, retrieving context via full‑text or hybrid vector search, and persisting preferences across threads. Also includes a simple tool example whose outputs are remembered. |
-| [`redis_conversation.py`](redis_conversation.py) | Simple example showing conversation persistence with RedisContextProvider using traditional connection string authentication. |
+| [`redis_conversation.py`](redis_conversation.py) | Shows context retrieval across conversation turns with RedisContextProvider using a Redis connection URL and OpenAI embeddings. For complete conversation history persistence, use RedisHistoryProvider. |
 | [`redis_sessions.py`](redis_sessions.py) | Demonstrates memory scoping strategies. Includes: (1) global memory scope with `application_id`, `agent_id`, and `user_id` shared across operations; (2) hybrid vector search using a custom OpenAI vectorizer for richer context retrieval; and (3) multiple agents with isolated memory via different `agent_id` values. |
 
 
@@ -21,7 +21,7 @@ This folder contains an example demonstrating how to use the Redis context provi
 1. A running Redis with RediSearch (Redis Stack or a managed service)
 2. Python environment with Agent Framework Redis extra installed
 3. Microsoft Foundry project endpoint and Azure OpenAI Responses deployment
-4. Optional: OpenAI API key if using vector embeddings
+4. OpenAI API key for the OpenAI-vectorizer examples (`redis_basics.py`, `redis_conversation.py`, and the hybrid-search and multiple-agents scenarios in `redis_sessions.py`)
 
 ### Install the package
 
@@ -53,22 +53,22 @@ See quickstart: `https://learn.microsoft.com/azure/redis/quickstart-create-manag
 
 - `FOUNDRY_PROJECT_ENDPOINT` (required): Microsoft Foundry project endpoint for `FoundryChatClient`
 - `FOUNDRY_MODEL` (required): Foundry model deployment name
-- `OPENAI_API_KEY` (optional): Required only if you set `vectorizer_choice="openai"` to enable hybrid search.
+- `OPENAI_API_KEY`: Required by the examples that instantiate `OpenAITextVectorizer` for hybrid search.
 
 ### Provider configuration highlights
 
 The provider supports both full‑text only and hybrid vector search:
 
-- Set `vectorizer_choice` to `"openai"` or `"hf"` to enable embeddings and hybrid search.
-- When using a vectorizer, also set `vector_field_name` (e.g., `"vector"`).
+- Pass a RedisVL vectorizer instance, such as `OpenAITextVectorizer`, through `redis_vectorizer` and set `vector_field_name` (e.g., `"vector"`) to enable hybrid search.
+- Omit `redis_vectorizer` and `vector_field_name` for full-text-only retrieval, as in the global-scope scenario in `redis_sessions.py`.
 - Partition fields for scoping memory: `application_id`, `agent_id`, `user_id`.
-- Index management: `index_name`, `overwrite_redis_index`, `drop_redis_index`.
+- Index configuration: `index_name`, `prefix`, and `overwrite_index`.
 
 ## What the example does
 
 `redis_basics.py` walks through three scenarios:
 
-1. Standalone provider usage: adds messages and retrieves context via `invoking`.
+1. Standalone provider usage: stores messages via `after_run` and retrieves context via `before_run`.
 2. Agent integration: teaches the agent a preference and verifies it is remembered across turns.
 3. Agent + tool: calls a sample tool (flight search) and then asks the agent to recall details remembered from the tool output.
 
@@ -85,7 +85,7 @@ export FOUNDRY_PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/pr
 export FOUNDRY_MODEL="<deployment-name>"
 ```
 
-3) (Optional) Set your OpenAI key if using embeddings:
+3) Set your OpenAI key for `redis_basics.py` and the other OpenAI-vectorizer examples:
 
 ```bash
 export OPENAI_API_KEY="<your key>"
@@ -108,12 +108,12 @@ You should see the agent responses and, when using embeddings, context retrieved
 
 ### Hybrid vector search (optional)
 
-- Enable by setting `vectorizer_choice` to `"openai"` (requires `OPENAI_API_KEY`) or `"hf"` (offline model).
-- Provide `vector_field_name` (e.g., `"vector"`); other vector settings have sensible defaults.
+- Pass a vectorizer as `redis_vectorizer` and provide `vector_field_name` (e.g., `"vector"`). The examples use `OpenAITextVectorizer`, which requires `OPENAI_API_KEY`.
+- `vector_algorithm` and `vector_distance_metric` configure the vector index; the examples use `"hnsw"` and `"cosine"`.
 
 ### Index lifecycle controls
 
-- `overwrite_redis_index` and `drop_redis_index` help recreate indexes during iteration.
+- Set `overwrite_index=True` to recreate an existing index during initialization. The examples clean up their indexes with `await provider.redis_index.delete()`.
 
 ## Troubleshooting