From 66f18b3cd40eb0a48bf41292775e1180e7202979 Mon Sep 17 00:00:00 2001 From: Brian Henson Date: Sat, 5 Sep 2026 07:26:40 -0600 Subject: [PATCH] feat: add OpenAI-compatible embedding provider Adds `EMBEDDING_PROVIDER=openai`, which embeds through any service exposing an OpenAI-compatible `/v1/embeddings` endpoint. This covers OpenAI itself as well as locally hosted servers such as Ollama, vLLM and LM Studio. The provider is additive: FastEmbed remains the default and its behaviour is unchanged. No existing server code is refactored. - `EMBEDDING_BASE_URL` points at the endpoint, so one provider serves every OpenAI-compatible backend rather than one provider per vendor. - `EMBEDDING_API_KEY` falls back to `OPENAI_API_KEY` and then to a placeholder, because local servers do not check it but the client refuses to start without one. - `EMBEDDING_VECTOR_SIZE` is detected from the API when unset, since the dimensionality is not otherwise discoverable for arbitrary models. - `EMBEDDING_QUERY_PREFIX` / `EMBEDDING_DOCUMENT_PREFIX` support asymmetric models that expect an instruction prefix on queries but not on documents. Tests are mocked and require no network access. Co-Authored-By: Claude Opus 5 (1M context) Claude-Session: https://claude.ai/code/session_01EnVZkTXryuPFcRqe8R9XAa --- README.md | 33 +++- pyproject.toml | 1 + src/mcp_server_qdrant/embeddings/factory.py | 12 ++ src/mcp_server_qdrant/embeddings/openai.py | 76 ++++++++ src/mcp_server_qdrant/embeddings/types.py | 1 + src/mcp_server_qdrant/settings.py | 12 ++ tests/test_openai_provider.py | 109 ++++++++++++ tests/test_settings.py | 33 ++++ uv.lock | 181 +++++++++++++++++++- 9 files changed, 452 insertions(+), 6 deletions(-) create mode 100644 src/mcp_server_qdrant/embeddings/openai.py create mode 100644 tests/test_openai_provider.py diff --git a/README.md b/README.md index aec03945..40ce89c1 100644 --- a/README.md +++ b/README.md @@ -47,13 +47,24 @@ Configuration is done via environment variables. The only command-line argument | `QDRANT_API_KEY` | API key for the Qdrant server | None | | `COLLECTION_NAME` | Name of the default collection to use. | None | | `QDRANT_LOCAL_PATH` | Path to the local Qdrant database (alternative to `QDRANT_URL`) | None | -| `EMBEDDING_PROVIDER` | Embedding provider to use (currently only "fastembed" is supported) | `fastembed` | +| `EMBEDDING_PROVIDER` | Embedding provider to use (`fastembed` or `openai`) | `fastembed` | | `EMBEDDING_MODEL` | Name of the embedding model to use | `sentence-transformers/all-MiniLM-L6-v2` | | `TOOL_STORE_DESCRIPTION` | Custom description for the store tool | See default in [`settings.py`](src/mcp_server_qdrant/settings.py) | | `TOOL_FIND_DESCRIPTION` | Custom description for the find tool | See default in [`settings.py`](src/mcp_server_qdrant/settings.py) | | `QDRANT_SEARCH_LIMIT` | Maximum number of results to return from search | `10` | | `QDRANT_READ_ONLY` | Enable read-only mode (disables `qdrant-store` tool) | `false` | +The following variables only apply when `EMBEDDING_PROVIDER` is set to `openai`: + +| Name | Description | Default Value | +|-----------------------------|---------------------------------------------------------------------------------|--------------------------------| +| `EMBEDDING_BASE_URL` | Base URL of the OpenAI-compatible API, including the `/v1` suffix | OpenAI's own API | +| `EMBEDDING_API_KEY` | API key for the embeddings API. Falls back to `OPENAI_API_KEY` | A placeholder, for local servers that do not check it | +| `EMBEDDING_VECTOR_SIZE` | Dimensionality of the embeddings. Detected from the API when not set | None | +| `EMBEDDING_VECTOR_NAME` | Name of the vector in the Qdrant collection | Derived from `EMBEDDING_MODEL` | +| `EMBEDDING_QUERY_PREFIX` | Prefix prepended to every query before embedding it | Empty | +| `EMBEDDING_DOCUMENT_PREFIX` | Prefix prepended to every document before embedding it | Empty | + ### FastMCP Environment Variables Since `mcp-server-qdrant` is based on FastMCP, it also supports all the FastMCP environment variables. The most @@ -181,8 +192,24 @@ For local Qdrant mode: This MCP server will automatically create a collection with the specified name if it doesn't exist. -By default, the server will use the `sentence-transformers/all-MiniLM-L6-v2` embedding model to encode memories. -For the time being, only [FastEmbed](https://qdrant.github.io/fastembed/) models are supported. +By default, the server will use the `sentence-transformers/all-MiniLM-L6-v2` embedding model to encode memories, +running locally through [FastEmbed](https://qdrant.github.io/fastembed/). + +Alternatively, setting `EMBEDDING_PROVIDER=openai` calls out to any service exposing an OpenAI-compatible +`/v1/embeddings` endpoint. This covers OpenAI itself as well as locally hosted servers such as +[Ollama](https://ollama.com/), [vLLM](https://docs.vllm.ai/) or [LM Studio](https://lmstudio.ai/): + +```bash +QDRANT_URL="http://localhost:6333" \ +COLLECTION_NAME="my-collection" \ +EMBEDDING_PROVIDER="openai" \ +EMBEDDING_BASE_URL="http://localhost:11434/v1" \ +EMBEDDING_MODEL="nomic-embed-text" \ +uvx mcp-server-qdrant +``` + +Models that distinguish queries from documents can be configured with `EMBEDDING_QUERY_PREFIX` and +`EMBEDDING_DOCUMENT_PREFIX`. ## Support for other tools diff --git a/pyproject.toml b/pyproject.toml index 296e831d..de6018be 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,6 +10,7 @@ dependencies = [ "qdrant-client>=1.12.0", "pydantic>=2.10.6,<2.12.0", "fastmcp==2.7.0", + "openai>=1.60.0", ] [build-system] diff --git a/src/mcp_server_qdrant/embeddings/factory.py b/src/mcp_server_qdrant/embeddings/factory.py index efac851c..442eaf21 100644 --- a/src/mcp_server_qdrant/embeddings/factory.py +++ b/src/mcp_server_qdrant/embeddings/factory.py @@ -13,5 +13,17 @@ def create_embedding_provider(settings: EmbeddingProviderSettings) -> EmbeddingP from mcp_server_qdrant.embeddings.fastembed import FastEmbedProvider return FastEmbedProvider(settings.model_name) + elif settings.provider_type == EmbeddingProviderType.OPENAI: + from mcp_server_qdrant.embeddings.openai import OpenAIProvider + + return OpenAIProvider( + settings.model_name, + base_url=settings.base_url, + api_key=settings.api_key, + vector_size=settings.vector_size, + vector_name=settings.vector_name, + query_prefix=settings.query_prefix, + document_prefix=settings.document_prefix, + ) else: raise ValueError(f"Unsupported embedding provider: {settings.provider_type}") diff --git a/src/mcp_server_qdrant/embeddings/openai.py b/src/mcp_server_qdrant/embeddings/openai.py new file mode 100644 index 00000000..14479b54 --- /dev/null +++ b/src/mcp_server_qdrant/embeddings/openai.py @@ -0,0 +1,76 @@ +import os +import re + +from openai import AsyncOpenAI, OpenAI + +from mcp_server_qdrant.embeddings.base import EmbeddingProvider + + +class OpenAIProvider(EmbeddingProvider): + """ + OpenAI-compatible implementation of the embedding provider. + Works with the OpenAI API and with any server exposing an `/v1/embeddings` endpoint. + :param model_name: The name of the embedding model to use. + :param base_url: The base URL of the API, including the `/v1` suffix. + :param api_key: The API key to use. Falls back to `OPENAI_API_KEY`, then to a placeholder, + as local servers typically do not check it. + :param vector_size: The dimensionality of the embeddings. Detected from the API if not provided. + :param vector_name: The name of the vector in the Qdrant collection. + :param query_prefix: A string prepended to every query before embedding it. + :param document_prefix: A string prepended to every document before embedding it. + """ + + def __init__( + self, + model_name: str, + base_url: str | None = None, + api_key: str | None = None, + vector_size: int | None = None, + vector_name: str | None = None, + query_prefix: str = "", + document_prefix: str = "", + ): + self.model_name = model_name + self.base_url = base_url + self.api_key = api_key or os.environ.get("OPENAI_API_KEY") or "unset" + self.query_prefix = query_prefix + self.document_prefix = document_prefix + self._vector_size = vector_size + self._vector_name = vector_name + self._client = AsyncOpenAI(base_url=base_url, api_key=self.api_key) + + async def embed_documents(self, documents: list[str]) -> list[list[float]]: + """Embed a list of documents into vectors.""" + response = await self._client.embeddings.create( + model=self.model_name, + input=[f"{self.document_prefix}{document}" for document in documents], + ) + return [item.embedding for item in sorted(response.data, key=lambda d: d.index)] + + async def embed_query(self, query: str) -> list[float]: + """Embed a query into a vector.""" + response = await self._client.embeddings.create( + model=self.model_name, + input=[f"{self.query_prefix}{query}"], + ) + return response.data[0].embedding + + def get_vector_name(self) -> str: + """Return the name of the vector for the Qdrant collection.""" + if self._vector_name is None: + model_name = self.model_name.split("/")[-1].lower() + self._vector_name = "openai-" + re.sub( + r"[^a-z0-9]+", "-", model_name + ).strip("-") + return self._vector_name + + def get_vector_size(self) -> int: + """ + Get the size of the vector for the Qdrant collection. + The API does not advertise it, so an unconfigured size is detected by embedding a probe document. + """ + if self._vector_size is None: + client = OpenAI(base_url=self.base_url, api_key=self.api_key) + response = client.embeddings.create(model=self.model_name, input=["probe"]) + self._vector_size = len(response.data[0].embedding) + return self._vector_size diff --git a/src/mcp_server_qdrant/embeddings/types.py b/src/mcp_server_qdrant/embeddings/types.py index 5290703d..484acaa3 100644 --- a/src/mcp_server_qdrant/embeddings/types.py +++ b/src/mcp_server_qdrant/embeddings/types.py @@ -3,3 +3,4 @@ class EmbeddingProviderType(Enum): FASTEMBED = "fastembed" + OPENAI = "openai" diff --git a/src/mcp_server_qdrant/settings.py b/src/mcp_server_qdrant/settings.py index e48c10d1..104464a5 100644 --- a/src/mcp_server_qdrant/settings.py +++ b/src/mcp_server_qdrant/settings.py @@ -46,6 +46,18 @@ class EmbeddingProviderSettings(BaseSettings): default="sentence-transformers/all-MiniLM-L6-v2", validation_alias="EMBEDDING_MODEL", ) + base_url: str | None = Field(default=None, validation_alias="EMBEDDING_BASE_URL") + api_key: str | None = Field(default=None, validation_alias="EMBEDDING_API_KEY") + vector_size: int | None = Field( + default=None, validation_alias="EMBEDDING_VECTOR_SIZE" + ) + vector_name: str | None = Field( + default=None, validation_alias="EMBEDDING_VECTOR_NAME" + ) + query_prefix: str = Field(default="", validation_alias="EMBEDDING_QUERY_PREFIX") + document_prefix: str = Field( + default="", validation_alias="EMBEDDING_DOCUMENT_PREFIX" + ) class FilterableField(BaseModel): diff --git a/tests/test_openai_provider.py b/tests/test_openai_provider.py new file mode 100644 index 00000000..e26cee2b --- /dev/null +++ b/tests/test_openai_provider.py @@ -0,0 +1,109 @@ +from types import SimpleNamespace + +import pytest + +from mcp_server_qdrant.embeddings.openai import OpenAIProvider + + +class FakeEmbeddings: + """Records the payloads it receives and replays canned embeddings.""" + + def __init__(self, vectors: list[list[float]]): + self._vectors = vectors + self.calls: list[dict] = [] + + async def create(self, *, model: str, input: list[str]): + self.calls.append({"model": model, "input": input}) + return SimpleNamespace( + data=[ + SimpleNamespace(index=index, embedding=vector) + for index, vector in enumerate(self._vectors[: len(input)]) + ] + ) + + +def make_provider(vectors: list[list[float]], **kwargs) -> OpenAIProvider: + provider = OpenAIProvider("test-model", base_url="http://localhost/v1", **kwargs) + provider._client = SimpleNamespace(embeddings=FakeEmbeddings(vectors)) + return provider + + +@pytest.mark.asyncio +class TestOpenAIProvider: + async def test_embed_documents(self): + """Documents are embedded in a single request, preserving input order.""" + provider = make_provider([[1.0, 0.0], [0.0, 1.0]]) + + embeddings = await provider.embed_documents(["first", "second"]) + + assert embeddings == [[1.0, 0.0], [0.0, 1.0]] + assert provider._client.embeddings.calls == [ + {"model": "test-model", "input": ["first", "second"]} + ] + + async def test_embed_query(self): + """A query is embedded on its own and returns a single vector.""" + provider = make_provider([[1.0, 0.0]]) + + embedding = await provider.embed_query("a query") + + assert embedding == [1.0, 0.0] + assert provider._client.embeddings.calls == [ + {"model": "test-model", "input": ["a query"]} + ] + + async def test_out_of_order_response_is_sorted(self): + """The API is only guaranteed to return an index, not a particular order.""" + provider = make_provider([[1.0], [2.0]]) + provider._client.embeddings.create = _reversed_create([[1.0], [2.0]]) + + assert await provider.embed_documents(["a", "b"]) == [[1.0], [2.0]] + + async def test_prefixes_are_applied(self): + """Asymmetric models need distinct query and document prefixes.""" + provider = make_provider( + [[1.0]], query_prefix="Query: ", document_prefix="Passage: " + ) + + await provider.embed_documents(["doc"]) + await provider.embed_query("qry") + + assert [call["input"] for call in provider._client.embeddings.calls] == [ + ["Passage: doc"], + ["Query: qry"], + ] + + async def test_no_prefixes_by_default(self): + """Without configuration the text is sent through untouched.""" + provider = make_provider([[1.0]]) + + await provider.embed_query("qry") + + assert provider._client.embeddings.calls[0]["input"] == ["qry"] + + +class TestOpenAIProviderVectorMetadata: + def test_vector_name_is_derived_from_the_model(self): + provider = OpenAIProvider("Qwen/Qwen3-Embedding-8B") + assert provider.get_vector_name() == "openai-qwen3-embedding-8b" + + def test_vector_name_can_be_overridden(self): + provider = OpenAIProvider("test-model", vector_name="custom") + assert provider.get_vector_name() == "custom" + + def test_configured_vector_size_is_used_without_a_request(self): + """A configured size must not trigger a probe request to the API.""" + provider = OpenAIProvider("test-model", vector_size=1536) + assert provider.get_vector_size() == 1536 + + +def _reversed_create(vectors: list[list[float]]): + async def create(*, model: str, input: list[str]): + return SimpleNamespace( + data=[ + SimpleNamespace(index=index, embedding=vector) + for index, vector in reversed(list(enumerate(vectors[: len(input)]))) + ] + ) + + return create diff --git a/tests/test_settings.py b/tests/test_settings.py index b5c5af9d..0ce65f84 100644 --- a/tests/test_settings.py +++ b/tests/test_settings.py @@ -78,6 +78,39 @@ def test_custom_values(self, monkeypatch): assert settings.provider_type == EmbeddingProviderType.FASTEMBED assert settings.model_name == "custom_model" + def test_openai_provider_values(self, monkeypatch): + """Test loading the OpenAI-compatible provider configuration.""" + monkeypatch.setenv("EMBEDDING_PROVIDER", "openai") + monkeypatch.setenv("EMBEDDING_MODEL", "text-embedding-3-small") + monkeypatch.setenv("EMBEDDING_BASE_URL", "http://localhost:11434/v1") + monkeypatch.setenv("EMBEDDING_API_KEY", "test_api_key") + monkeypatch.setenv("EMBEDDING_VECTOR_SIZE", "1536") + monkeypatch.setenv("EMBEDDING_VECTOR_NAME", "custom_vector") + monkeypatch.setenv("EMBEDDING_QUERY_PREFIX", "Query: ") + monkeypatch.setenv("EMBEDDING_DOCUMENT_PREFIX", "Passage: ") + + settings = EmbeddingProviderSettings() + + assert settings.provider_type == EmbeddingProviderType.OPENAI + assert settings.model_name == "text-embedding-3-small" + assert settings.base_url == "http://localhost:11434/v1" + assert settings.api_key == "test_api_key" + assert settings.vector_size == 1536 + assert settings.vector_name == "custom_vector" + assert settings.query_prefix == "Query: " + assert settings.document_prefix == "Passage: " + + def test_openai_settings_are_optional(self, monkeypatch): + """The OpenAI-specific settings must not affect the default provider.""" + settings = EmbeddingProviderSettings() + + assert settings.base_url is None + assert settings.api_key is None + assert settings.vector_size is None + assert settings.vector_name 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