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-# Vector store e2e
-
-Live end-to-end coverage for litellm managed vector stores, driven through a
-running proxy exactly the way a user would. The first (and currently only)
-backend is Milvus / Zilliz Cloud
-
-The suite stands up a real store and proves the whole path in one flow: it creates
-a Milvus collection, embeds and inserts five known documents, registers the
-collection as a managed vector store on the proxy, searches it over the
-OpenAI-compatible route, and uses it as retrieval context in a chat completion.
-One seeded document holds a made-up access code that appears nowhere else, so a
-chat answer that echoes the code can only have come from the store being searched
-and its content injected as context; that is the retrieval proof
-
-## What it covers
-
-- search returns the seeded documents in the OpenAI `vector_store.search_results.page` shape
-- `limit` caps the number of results
-- a Milvus `filter` expression restricts results to specific primary ids
-- chat completions with `vector_store_ids` retrieve context and the answer echoes the seeded code
-- a store pointing at a non-existent collection fails cleanly (non-2xx) instead of hanging or 5xx-crashing
-
-## Credentials
-
-Three credentials, nothing else to tune. Set them in the environment (or `.env`)
-of the test process (and the proxy needs its own working provider keys)
-
-| Variable | Notes |
-| --- | --- |
-| `MILVUS_API_BASE` | Zilliz Cloud cluster public endpoint, e.g. `https://in03-xxxx.serverless.gcp-us-west1.cloud.zilliz.com`. For self-hosted Milvus, the base URL of the REST server |
-| `MILVUS_API_KEY` | Zilliz Cloud API key. For self-hosted Milvus, a `user:password` token |
-| `OPENAI_API_KEY` | Used to embed the seed documents, and on the proxy to embed queries at search time and to back the chat model the suite registers |
-
-Everything else is fixed in `milvus_client.py`: the embedding model
-(`text-embedding-3-small`, 1536 dims) and the chat model. Rather than depend on
-whatever chat model the proxy has, the suite registers its own deployment through
-`POST /model/new` and deletes it on teardown, so the chat test is deterministic
-
-The proxy must be reachable (see `tests/e2e/e2e_config.py` for `LITELLM_PROXY_URL`
-/ `LITELLM_MASTER_KEY`), connected to a database (managed vector store
-registration persists to the store registry), and started with
-`STORE_MODEL_IN_DB=True` (so the chat model can be registered). Without model
-storage, only the chat test skips; the search tests still run
-
-Absent any of the required credentials, or a live proxy, the whole suite skips;
-it never silently passes
-
-## Running
-
-```bash
-cd tests/e2e
-uv run --python 3.12 pytest vector-stores/ -v
-```
-
-The e2e harness uses `type` alias syntax, so Python 3.12+ is required