From 4357609ccfc4c2fbb5ff747c339a52c9d84cc1d2 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Wed, 30 Apr 2025 00:20:36 +0300 Subject: [PATCH 01/23] add server with basic tools --- .gitignore | 174 +++++++ .python-version | 1 + pyproject.toml | 12 + src/mcp-server-mariadb-vector/__init__.py | 0 src/mcp-server-mariadb-vector/server.py | 279 +++++++++++ uv.lock | 553 ++++++++++++++++++++++ 6 files changed, 1019 insertions(+) create mode 100644 .gitignore create mode 100644 .python-version create mode 100644 pyproject.toml create mode 100644 src/mcp-server-mariadb-vector/__init__.py create mode 100644 src/mcp-server-mariadb-vector/server.py create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..1800114 --- /dev/null +++ b/.gitignore @@ -0,0 +1,174 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc \ No newline at end of file diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..c8cfe39 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.10 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..37763c6 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,12 @@ +[project] +name = "mcp-server-mariadb-vector" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "mcp[cli]>=1.6.0", + "mariadb==1.1.12", + "openai", + "pydantic", +] \ No newline at end of file diff --git a/src/mcp-server-mariadb-vector/__init__.py b/src/mcp-server-mariadb-vector/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mcp-server-mariadb-vector/server.py b/src/mcp-server-mariadb-vector/server.py new file mode 100644 index 0000000..386fdfd --- /dev/null +++ b/src/mcp-server-mariadb-vector/server.py @@ -0,0 +1,279 @@ +import json +from abc import ABC, abstractmethod +from enum import Enum +from collections.abc import AsyncIterator +from typing import List, Optional +from contextlib import asynccontextmanager +from dataclasses import dataclass + +import mariadb +from openai import OpenAI +from mcp.server.fastmcp import FastMCP, Context +from pydantic import Field +from pydantic_settings import BaseSettings + + +class EmbeddingProviderType(Enum): + OPENAI = "openai" + # TODO: Add other providers here + # SENTENCE_TRANSFORMERS = "sentence-transformers" + + +class DatabaseSettings(BaseSettings): + host: str = Field(default="127.0.0.1", validation_alias="MARIADB_HOST") + port: int = Field(default=3306, validation_alias="MARIADB_PORT") + user: str = Field(validation_alias="MARIADB_USER") + password: str = Field(validation_alias="MARIADB_PASSWORD") + database: str = Field(default="mcp", validation_alias="MARIADB_DATABASE") + + +class EmbeddingSettings(BaseSettings): + provider: EmbeddingProviderType = Field( + default=EmbeddingProviderType.OPENAI, validation_alias="EMBEDDING_PROVIDER" + ) + model: str = Field( + default="text-embedding-3-small", validation_alias="EMBEDDING_MODEL" + ) + openai_api_key: Optional[str] = Field( + default=None, validation_alias="OPENAI_API_KEY" + ) + + +class EmbeddingProvider(ABC): + """Abstract base class for embedding providers.""" + + @abstractmethod + def embed_documents(self, documents: List[str]) -> List[List[float]]: + """Embed a list of documents into vectors.""" + pass + + @abstractmethod + def embed_query(self, query: str) -> List[float]: + """Embed a query into a vector.""" + pass + + @abstractmethod + def length_of_embedding(self, model: str) -> int: + """Get the length of the embedding for a model.""" + pass + + +class OpenAIEmbeddingProvider(EmbeddingProvider): + """ + OpenAI implementation of the embedding provider. + :param model_name: The name of the OpenAI model to use. + """ + + def __init__(self, model: str, api_key: str): + self.model = model + self.client = OpenAI(api_key=api_key) + + def embed_documents(self, documents: List[str]) -> List[List[float]]: + """Embed a list of documents into vectors.""" + embeddings = [ + self.client.embeddings.create( + model=self.model, + input=document, + ) + .data[0] + .embedding + for document in documents + ] + return embeddings + + def embed_query(self, query: str) -> List[float]: + """Embed a query into a vector.""" + embedding = self.client.embeddings.create( + model=self.model, + input=query, + ) + return embedding.data[0].embedding + + def length_of_embedding(self) -> int: + """Get the length of the embedding for a model.""" + if self.model == "text-embedding-3-small": + return 1536 + elif self.model == "text-embedding-3-large": + return 3072 + else: + raise ValueError(f"Unknown embedding model: {self.model}") + + +def create_embedding_provider(settings: EmbeddingSettings) -> EmbeddingProvider: + """ + Create an embedding provider based on the specified type. + :param settings: The settings for the embedding provider. + :return: An instance of the specified embedding provider. + """ + if settings.provider == EmbeddingProviderType.OPENAI: + return OpenAIEmbeddingProvider(settings.model, settings.openai_api_key) + + else: + raise ValueError(f"Unsupported embedding provider: {settings.provider}") + + +@dataclass +class AppContext: + conn: mariadb.Connection + + +@asynccontextmanager +async def app_lifespan(server: FastMCP) -> AsyncIterator[AppContext]: + conn = mariadb.connect( + host=DatabaseSettings().host, + port=DatabaseSettings().port, + user=DatabaseSettings().user, + password=DatabaseSettings().password, + database=DatabaseSettings().database, + ) + try: + yield AppContext(conn=conn) + finally: + conn.close() + + +mcp = FastMCP( + "Mariadb Vector", + lifespan=app_lifespan, + dependencies=["mariadb", "openai", "pydantic", "pydantic-settings"], +) + + +embedding_provider = create_embedding_provider(EmbeddingSettings()) + + +@mcp.tool() +def mariadb_create_vector_store( + ctx: Context, + vector_store_name: str = "vector_store", + distance_function: str = "euclidean", +) -> str: + """Create a vector store in MariaDB. + + Args: + vector_store_name: The name of the vector store. Default is "vector_store". + embedding_model: The name of the embedding model to use. Default is "openai/text-embedding-3-small". + distance_function: The distance function to use. Options: 'euclidean', 'cosine'. Default is "euclidean". + """ + + embedding_length = embedding_provider.length_of_embedding() + + schema_query = f""" + CREATE TABLE `{vector_store_name}` ( + id BIGINT UNSIGNED PRIMARY KEY AUTO_INCREMENT, + document LONGTEXT NOT NULL, + embedding VECTOR({embedding_length}) NOT NULL, + metadata JSON NOT NULL, + VECTOR INDEX (embedding) DISTANCE={distance_function} + ); + """ + + try: + conn = ctx.request_context.lifespan_context.conn + with conn.cursor() as cursor: + cursor.execute(schema_query) + except mariadb.Error as e: + return f"Error creating vector store `{vector_store_name}`: {e}" + + return f"Vector store `{vector_store_name}` created successfully." + + +@mcp.tool() +def mariadb_list_vector_stores(ctx: Context) -> str: + """List all vector stores in a MariaDB database.""" + try: + conn = ctx.request_context.lifespan_context.conn + with conn.cursor() as cursor: + cursor.execute("SHOW TABLES") + tables = [table[0] for table in cursor] + except mariadb.Error as e: + return f"Error listing vector stores: {e}" + + return "Vector stores: " + ", ".join(tables) + + +@mcp.tool() +def mariadb_insert_documents( + ctx: Context, + vector_store_name: str = "vector_store", + documents: List[str] = [], + metadata: List[dict] = [], +) -> str: + """Insert a document into a vector store. + + Args: + vector_store_name: The name of the vector store. + documents: The documents to insert. + embedding_model: The embedding model to use. + metadata: The metadata of the documents. + """ + + embeddings = embedding_provider.embed_documents(documents) + + metadata_json = [json.dumps(metadata) for metadata in metadata] + + insert_query = f""" + INSERT INTO `{vector_store_name}` (document, embedding, metadata) VALUES (%s, VEC_FromText(%s), %s); + """ + try: + conn = ctx.request_context.lifespan_context.conn + with conn.cursor() as cursor: + cursor.executemany( + insert_query, list(zip(documents, embeddings, metadata_json)) + ) + except mariadb.Error as e: + return f"Error inserting documents`{vector_store_name}`: {e}" + + return f"Documents inserted into `{vector_store_name}` successfully." + + +@mcp.tool() +def mariadb_vector_search( + ctx: Context, query: str, vector_store_name: str = "vector_store", k: int = 5 +) -> str: + """Search a vector store for the most similar documents to a query. + + Args: + query: The query to search for. + vector_store_name: The name of the vector store to search. + k: The number of results to return. Default is 5. + """ + + embedding = embedding_provider.embed_query(query) + + search_query = f""" + SELECT + document, + metadata, + VEC_DISTANCE_EUCLIDEAN(embedding, VEC_FromText(%s)) AS distance + FROM `{vector_store_name}` + ORDER BY distance ASC + LIMIT %s; + """ + + try: + conn = ctx.request_context.lifespan_context.conn + with conn.cursor(buffered=True) as cursor: + cursor.execute( + search_query, + (str(embedding), k), + ) + rows = cursor.fetchall() + except mariadb.Error as e: + return f"Error searching vector store`{vector_store_name}`: {e}" + + if not rows: + return "No similar context found." + + return "\n\n".join( + f"Document: {row[0]}\nMetadata: {json.loads(row[1])}\nDistance: {row[2]}" + for row in rows + ) + + +def main(): + mcp.run(transport="stdio") + + +if __name__ == "__main__": + main() diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..6f598af --- /dev/null +++ b/uv.lock @@ -0,0 +1,553 @@ +version = 1 +requires-python = ">=3.10" + +[[package]] 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mcp_server_mariadb_vector}/server.py (93%) diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..15fccb3 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,38 @@ +# Use official slim Python 3.10 image +FROM python:3.10-slim + +# Install system dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + gcc \ + python3-dev \ + openssl \ + curl \ + ca-certificates \ + gnupg \ + build-essential && \ + rm -rf /var/lib/apt/lists/* + +# Set up MariaDB repository and install client libraries +RUN curl -LsSO https://r.mariadb.com/downloads/mariadb_repo_setup && \ + echo "c4a0f3dade02c51a6a28ca3609a13d7a0f8910cccbb90935a2f218454d3a914a mariadb_repo_setup" | sha256sum -c - && \ + chmod +x mariadb_repo_setup && \ + ./mariadb_repo_setup --mariadb-server-version="mariadb-11.7" && \ + rm mariadb_repo_setup && \ + apt-get update && \ + apt-get install -y --no-install-recommends \ + libmariadb3 \ + libmariadb-dev && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* + +# Set working directory +WORKDIR /app + +# Install uv package manager +RUN pip install --no-cache-dir uv + +# Install project dependencies + + + +CMD ["sleep", "infinity"] diff --git a/src/mcp-server-mariadb-vector/__init__.py b/README.md similarity index 100% rename from src/mcp-server-mariadb-vector/__init__.py rename to README.md diff --git a/pyproject.toml b/pyproject.toml index 37763c6..f249d88 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "mcp-server-mariadb-vector" version = "0.1.0" -description = "Add your description here" +description = "MCP server for MariaDB vector" readme = "README.md" requires-python = ">=3.10" dependencies = [ @@ -9,4 +9,11 @@ dependencies = [ "mariadb==1.1.12", "openai", "pydantic", -] \ No newline at end of file +] + +[project.scripts] +mcp-server-mariadb-vector = "mcp_server_mariadb_vector.server:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" \ No newline at end of file diff --git a/src/mcp_server_mariadb_vector/__init__.py b/src/mcp_server_mariadb_vector/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mcp-server-mariadb-vector/server.py b/src/mcp_server_mariadb_vector/server.py similarity index 93% rename from src/mcp-server-mariadb-vector/server.py rename to src/mcp_server_mariadb_vector/server.py index 386fdfd..4ade5e8 100644 --- a/src/mcp-server-mariadb-vector/server.py +++ b/src/mcp_server_mariadb_vector/server.py @@ -15,7 +15,6 @@ class EmbeddingProviderType(Enum): OPENAI = "openai" - # TODO: Add other providers here # SENTENCE_TRANSFORMERS = "sentence-transformers" @@ -61,7 +60,8 @@ def length_of_embedding(self, model: str) -> int: class OpenAIEmbeddingProvider(EmbeddingProvider): """ OpenAI implementation of the embedding provider. - :param model_name: The name of the OpenAI model to use. + Args: + model_name: The name of the OpenAI model to use. """ def __init__(self, model: str, api_key: str): @@ -102,8 +102,10 @@ def length_of_embedding(self) -> int: def create_embedding_provider(settings: EmbeddingSettings) -> EmbeddingProvider: """ Create an embedding provider based on the specified type. - :param settings: The settings for the embedding provider. - :return: An instance of the specified embedding provider. + Args: + settings: The settings for the embedding provider. + Returns: + An instance of the specified embedding provider. """ if settings.provider == EmbeddingProviderType.OPENAI: return OpenAIEmbeddingProvider(settings.model, settings.openai_api_key) @@ -159,7 +161,7 @@ def mariadb_create_vector_store( embedding_length = embedding_provider.length_of_embedding() schema_query = f""" - CREATE TABLE `{vector_store_name}` ( + CREATE TABLE `{DatabaseSettings().database}`.`{vector_store_name}` ( id BIGINT UNSIGNED PRIMARY KEY AUTO_INCREMENT, document LONGTEXT NOT NULL, embedding VECTOR({embedding_length}) NOT NULL, @@ -184,7 +186,7 @@ def mariadb_list_vector_stores(ctx: Context) -> str: try: conn = ctx.request_context.lifespan_context.conn with conn.cursor() as cursor: - cursor.execute("SHOW TABLES") + cursor.execute(f"SHOW TABLES IN {DatabaseSettings().database}") tables = [table[0] for table in cursor] except mariadb.Error as e: return f"Error listing vector stores: {e}" @@ -213,7 +215,7 @@ def mariadb_insert_documents( metadata_json = [json.dumps(metadata) for metadata in metadata] insert_query = f""" - INSERT INTO `{vector_store_name}` (document, embedding, metadata) VALUES (%s, VEC_FromText(%s), %s); + INSERT INTO `{DatabaseSettings().database}`.`{vector_store_name}` (document, embedding, metadata) VALUES (%s, VEC_FromText(%s), %s); """ try: conn = ctx.request_context.lifespan_context.conn @@ -246,7 +248,7 @@ def mariadb_vector_search( document, metadata, VEC_DISTANCE_EUCLIDEAN(embedding, VEC_FromText(%s)) AS distance - FROM `{vector_store_name}` + FROM `{DatabaseSettings().database}`.`{vector_store_name}` ORDER BY distance ASC LIMIT %s; """ diff --git a/uv.lock b/uv.lock index 6f598af..e9e964e 100644 --- a/uv.lock +++ b/uv.lock @@ -246,7 +246,7 @@ cli = [ [[package]] name = "mcp-server-mariadb-vector" version = "0.1.0" -source = { virtual = "." } +source = { editable = "." } dependencies = [ { name = "mariadb" }, { name = "mcp", extra = ["cli"] }, @@ -273,7 +273,7 @@ wheels = [ [[package]] name = "openai" -version = "1.76.0" +version = "1.76.2" source = { registry = 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9c55993ebd407edf9d2568b0af26bc14a4705171 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Wed, 30 Apr 2025 13:38:06 +0300 Subject: [PATCH 04/23] add delete vector store tool --- src/mcp_server_mariadb_vector/server.py | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/src/mcp_server_mariadb_vector/server.py b/src/mcp_server_mariadb_vector/server.py index 32430d9..a3b3f0a 100644 --- a/src/mcp_server_mariadb_vector/server.py +++ b/src/mcp_server_mariadb_vector/server.py @@ -154,8 +154,7 @@ def mariadb_create_vector_store( """Create a vector store in MariaDB. Args: - vector_store_name: The name of the vector store. Default is "vector_store". - embedding_model: The name of the embedding model to use. Default is "openai/text-embedding-3-small". + vector_store_name: The name of the vector store to create. Default is "vector_store". distance_function: The distance function to use. Options: 'euclidean', 'cosine'. Default is "euclidean". """ @@ -195,6 +194,25 @@ def mariadb_list_vector_stores(ctx: Context) -> str: return "Vector stores: " + ", ".join(tables) +@mcp.tool() +def mariadb_delete_vector_store(ctx: Context, vector_store_name: str) -> str: + """Delete a vector store in MariaDB. + + Args: + vector_store_name: The name of the vector store to delete. + """ + try: + conn = ctx.request_context.lifespan_context.conn + with conn.cursor() as cursor: + cursor.execute( + f"DROP TABLE `{DatabaseSettings().database}`.`{vector_store_name}`" + ) + except mariadb.Error as e: + return f"Error deleting vector store `{vector_store_name}`: {e}" + + return f"Vector store `{vector_store_name}` deleted successfully." + + @mcp.tool() def mariadb_insert_documents( ctx: Context, From 5f13099e55f859459d3a51b11ea436baa37ede68 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Fri, 2 May 2025 23:43:22 +0300 Subject: [PATCH 05/23] refactor server to make it more modular --- src/mcp_server_mariadb_vector/app_context.py | 33 +++ .../embeddings/base.py | 27 +++ .../embeddings/factory.py | 20 ++ .../embeddings/openai.py | 48 ++++ src/mcp_server_mariadb_vector/server.py | 215 ++++-------------- src/mcp_server_mariadb_vector/settings.py | 22 ++ 6 files changed, 194 insertions(+), 171 deletions(-) create mode 100644 src/mcp_server_mariadb_vector/app_context.py create mode 100644 src/mcp_server_mariadb_vector/embeddings/base.py create mode 100644 src/mcp_server_mariadb_vector/embeddings/factory.py create mode 100644 src/mcp_server_mariadb_vector/embeddings/openai.py create mode 100644 src/mcp_server_mariadb_vector/settings.py diff --git a/src/mcp_server_mariadb_vector/app_context.py b/src/mcp_server_mariadb_vector/app_context.py new file mode 100644 index 0000000..95d809e --- /dev/null +++ b/src/mcp_server_mariadb_vector/app_context.py @@ -0,0 +1,33 @@ +from contextlib import asynccontextmanager +from dataclasses import dataclass +from typing import AsyncIterator + +import mariadb +from mcp.server.fastmcp import FastMCP + +from mcp_server_mariadb_vector.settings import DatabaseSettings + + +@dataclass +class AppContext: + conn: mariadb.Connection + + +@asynccontextmanager +async def app_lifespan(server: FastMCP) -> AsyncIterator[AppContext]: + """Open a MariaDB connection for the duration of the FastMCP session.""" + + cfg = DatabaseSettings() + conn = mariadb.connect( + host=cfg.host, + port=cfg.port, + user=cfg.user, + password=cfg.password, + database=cfg.database, + ) + conn.autocommit = True + + try: + yield AppContext(conn=conn) + finally: + conn.close() diff --git a/src/mcp_server_mariadb_vector/embeddings/base.py b/src/mcp_server_mariadb_vector/embeddings/base.py new file mode 100644 index 0000000..0f5bccf --- /dev/null +++ b/src/mcp_server_mariadb_vector/embeddings/base.py @@ -0,0 +1,27 @@ +from abc import ABC, abstractmethod +from enum import Enum +from typing import List + + +class EmbeddingProviderType(Enum): + OPENAI = "openai" + # SENTENCE_TRANSFORMERS = "sentence-transformers" + + +class EmbeddingProvider(ABC): + """Abstract base class for embedding providers.""" + + @abstractmethod + def embed_documents(self, documents: List[str]) -> List[List[float]]: + """Embed a list of documents into vectors.""" + pass + + @abstractmethod + def embed_query(self, query: str) -> List[float]: + """Embed a query into a vector.""" + pass + + @abstractmethod + def length_of_embedding(self, model: str) -> int: + """Get the length of the embedding for a given model.""" + pass diff --git a/src/mcp_server_mariadb_vector/embeddings/factory.py b/src/mcp_server_mariadb_vector/embeddings/factory.py new file mode 100644 index 0000000..1bdaebe --- /dev/null +++ b/src/mcp_server_mariadb_vector/embeddings/factory.py @@ -0,0 +1,20 @@ +from mcp_server_mariadb_vector.embeddings.base import ( + EmbeddingProvider, + EmbeddingProviderType, +) +from mcp_server_mariadb_vector.embeddings.openai import OpenAIEmbeddingProvider +from mcp_server_mariadb_vector.settings import EmbeddingSettings + + +def create_embedding_provider(settings: EmbeddingSettings) -> EmbeddingProvider: + """ + Create an instance of the specified embedding provider. + + Args: + settings: The settings for the embedding provider. + """ + if settings.provider == EmbeddingProviderType.OPENAI: + return OpenAIEmbeddingProvider(settings.model, settings.openai_api_key) + + else: + raise ValueError(f"Unsupported embedding provider: {settings.provider}") diff --git a/src/mcp_server_mariadb_vector/embeddings/openai.py b/src/mcp_server_mariadb_vector/embeddings/openai.py new file mode 100644 index 0000000..e6e1a64 --- /dev/null +++ b/src/mcp_server_mariadb_vector/embeddings/openai.py @@ -0,0 +1,48 @@ +from typing import List + +from openai import OpenAI + +from mcp_server_mariadb_vector.embeddings.base import EmbeddingProvider + + +class OpenAIEmbeddingProvider(EmbeddingProvider): + """ + OpenAI implementation of the embedding provider. + + Args: + model: The name of the OpenAI model to use. + """ + + def __init__(self, model: str, api_key: str): + self.model = model + self.client = OpenAI(api_key=api_key) + + def embed_documents(self, documents: List[str]) -> List[List[float]]: + """Embed a list of documents into vectors.""" + embeddings = [ + self.client.embeddings.create( + model=self.model, + input=document, + ) + .data[0] + .embedding + for document in documents + ] + return embeddings + + def embed_query(self, query: str) -> List[float]: + """Embed a query into a vector.""" + embedding = self.client.embeddings.create( + model=self.model, + input=query, + ) + return embedding.data[0].embedding + + def length_of_embedding(self) -> int: + """Get the length of the embedding for a model.""" + if self.model == "text-embedding-3-small": + return 1536 + elif self.model == "text-embedding-3-large": + return 3072 + else: + raise ValueError(f"Unknown embedding model: {self.model}") diff --git a/src/mcp_server_mariadb_vector/server.py b/src/mcp_server_mariadb_vector/server.py index a3b3f0a..9e918f5 100644 --- a/src/mcp_server_mariadb_vector/server.py +++ b/src/mcp_server_mariadb_vector/server.py @@ -1,138 +1,12 @@ import json -from abc import ABC, abstractmethod -from enum import Enum -from collections.abc import AsyncIterator -from typing import List, Optional -from contextlib import asynccontextmanager -from dataclasses import dataclass +from typing import List, Literal, Annotated import mariadb -from openai import OpenAI from mcp.server.fastmcp import FastMCP, Context from pydantic import Field -from pydantic_settings import BaseSettings - - -class EmbeddingProviderType(Enum): - OPENAI = "openai" - # SENTENCE_TRANSFORMERS = "sentence-transformers" - - -class DatabaseSettings(BaseSettings): - host: str = Field(default="127.0.0.1", validation_alias="MARIADB_HOST") - port: int = Field(default=3306, validation_alias="MARIADB_PORT") - user: str = Field(validation_alias="MARIADB_USER") - password: str = Field(validation_alias="MARIADB_PASSWORD") - database: str = Field(default="mcp", validation_alias="MARIADB_DATABASE") - - -class EmbeddingSettings(BaseSettings): - provider: EmbeddingProviderType = Field( - default=EmbeddingProviderType.OPENAI, validation_alias="EMBEDDING_PROVIDER" - ) - model: str = Field( - default="text-embedding-3-small", validation_alias="EMBEDDING_MODEL" - ) - openai_api_key: Optional[str] = Field( - default=None, validation_alias="OPENAI_API_KEY" - ) - - -class EmbeddingProvider(ABC): - """Abstract base class for embedding providers.""" - - @abstractmethod - def embed_documents(self, documents: List[str]) -> List[List[float]]: - """Embed a list of documents into vectors.""" - pass - - @abstractmethod - def embed_query(self, query: str) -> List[float]: - """Embed a query into a vector.""" - pass - - @abstractmethod - def length_of_embedding(self, model: str) -> int: - """Get the length of the embedding for a model.""" - pass - - -class OpenAIEmbeddingProvider(EmbeddingProvider): - """ - OpenAI implementation of the embedding provider. - Args: - model_name: The name of the OpenAI model to use. - """ - - def __init__(self, model: str, api_key: str): - self.model = model - self.client = OpenAI(api_key=api_key) - - def embed_documents(self, documents: List[str]) -> List[List[float]]: - """Embed a list of documents into vectors.""" - embeddings = [ - self.client.embeddings.create( - model=self.model, - input=document, - ) - .data[0] - .embedding - for document in documents - ] - return embeddings - - def embed_query(self, query: str) -> List[float]: - """Embed a query into a vector.""" - embedding = self.client.embeddings.create( - model=self.model, - input=query, - ) - return embedding.data[0].embedding - - def length_of_embedding(self) -> int: - """Get the length of the embedding for a model.""" - if self.model == "text-embedding-3-small": - return 1536 - elif self.model == "text-embedding-3-large": - return 3072 - else: - raise ValueError(f"Unknown embedding model: {self.model}") - - -def create_embedding_provider(settings: EmbeddingSettings) -> EmbeddingProvider: - """ - Create an embedding provider based on the specified type. - Args: - settings: The settings for the embedding provider. - Returns: - An instance of the specified embedding provider. - """ - if settings.provider == EmbeddingProviderType.OPENAI: - return OpenAIEmbeddingProvider(settings.model, settings.openai_api_key) - - else: - raise ValueError(f"Unsupported embedding provider: {settings.provider}") - - -@dataclass -class AppContext: - conn: mariadb.Connection - - -@asynccontextmanager -async def app_lifespan(server: FastMCP) -> AsyncIterator[AppContext]: - conn = mariadb.connect( - host=DatabaseSettings().host, - port=DatabaseSettings().port, - user=DatabaseSettings().user, - password=DatabaseSettings().password, - database=DatabaseSettings().database, - ) - conn.autocommit = True - try: - yield AppContext(conn=conn) - finally: - conn.close() +from mcp_server_mariadb_vector.settings import EmbeddingSettings +from mcp_server_mariadb_vector.embeddings.factory import create_embedding_provider +from mcp_server_mariadb_vector.app_context import app_lifespan mcp = FastMCP( @@ -148,26 +22,27 @@ async def app_lifespan(server: FastMCP) -> AsyncIterator[AppContext]: @mcp.tool() def mariadb_create_vector_store( ctx: Context, - vector_store_name: str = "vector_store", - distance_function: str = "euclidean", + vector_store_name: Annotated[ + str, + Field(description="The name of the vector store to create"), + ], + distance_function: Annotated[ + Literal["euclidean", "cosine"], + Field(description="The distance function to use."), + ] = "euclidean", ) -> str: - """Create a vector store in MariaDB. - - Args: - vector_store_name: The name of the vector store to create. Default is "vector_store". - distance_function: The distance function to use. Options: 'euclidean', 'cosine'. Default is "euclidean". - """ + """Create a vector store in the MariaDB database.""" embedding_length = embedding_provider.length_of_embedding() schema_query = f""" - CREATE TABLE `{DatabaseSettings().database}`.`{vector_store_name}` ( + CREATE TABLE `{vector_store_name}` ( id BIGINT UNSIGNED PRIMARY KEY AUTO_INCREMENT, document LONGTEXT NOT NULL, embedding VECTOR({embedding_length}) NOT NULL, metadata JSON NOT NULL, VECTOR INDEX (embedding) DISTANCE={distance_function} - ); + ) """ try: @@ -186,7 +61,7 @@ def mariadb_list_vector_stores(ctx: Context) -> str: try: conn = ctx.request_context.lifespan_context.conn with conn.cursor() as cursor: - cursor.execute(f"SHOW TABLES IN {DatabaseSettings().database}") + cursor.execute("SHOW TABLES") tables = [table[0] for table in cursor] except mariadb.Error as e: return f"Error listing vector stores: {e}" @@ -195,18 +70,18 @@ def mariadb_list_vector_stores(ctx: Context) -> str: @mcp.tool() -def mariadb_delete_vector_store(ctx: Context, vector_store_name: str) -> str: - """Delete a vector store in MariaDB. +def mariadb_delete_vector_store( + ctx: Context, + vector_store_name: Annotated[ + str, Field(description="The name of the vector store to delete.") + ], +) -> str: + """Delete a vector store in the MariaDB database.""" - Args: - vector_store_name: The name of the vector store to delete. - """ try: conn = ctx.request_context.lifespan_context.conn with conn.cursor() as cursor: - cursor.execute( - f"DROP TABLE `{DatabaseSettings().database}`.`{vector_store_name}`" - ) + cursor.execute(f"DROP TABLE `{vector_store_name}`") except mariadb.Error as e: return f"Error deleting vector store `{vector_store_name}`: {e}" @@ -216,25 +91,24 @@ def mariadb_delete_vector_store(ctx: Context, vector_store_name: str) -> str: @mcp.tool() def mariadb_insert_documents( ctx: Context, - vector_store_name: str = "vector_store", - documents: List[str] = [], - metadata: List[dict] = [], + vector_store_name: Annotated[ + str, Field(description="The name of the vector store to insert documents into.") + ], + documents: Annotated[ + List[str], Field(description="The documents to insert into the vector store.") + ], + metadata: Annotated[ + List[dict], Field(description="The metadata of the documents to insert.") + ], ) -> str: - """Insert a document into a vector store. - - Args: - vector_store_name: The name of the vector store. - documents: The documents to insert. - embedding_model: The embedding model to use. - metadata: The metadata of the documents. - """ + """Insert a document into a vector store.""" embeddings = embedding_provider.embed_documents(documents) metadata_json = [json.dumps(metadata) for metadata in metadata] insert_query = f""" - INSERT INTO `{DatabaseSettings().database}`.`{vector_store_name}` (document, embedding, metadata) VALUES (%s, VEC_FromText(%s), %s); + INSERT INTO `{vector_store_name}` (document, embedding, metadata) VALUES (%s, VEC_FromText(%s), %s) """ try: conn = ctx.request_context.lifespan_context.conn @@ -250,15 +124,14 @@ def mariadb_insert_documents( @mcp.tool() def mariadb_vector_search( - ctx: Context, query: str, vector_store_name: str = "vector_store", k: int = 5 + ctx: Context, + query: Annotated[str, Field(description="The query to search for.")], + vector_store_name: Annotated[ + str, Field(description="The name of the vector store to search.") + ], + k: Annotated[int, Field(gt=0, description="The number of results to return.")] = 5, ) -> str: - """Search a vector store for the most similar documents to a query. - - Args: - query: The query to search for. - vector_store_name: The name of the vector store to search. - k: The number of results to return. Default is 5. - """ + """Search a vector store for the most similar documents to a query.""" embedding = embedding_provider.embed_query(query) @@ -267,9 +140,9 @@ def mariadb_vector_search( document, metadata, VEC_DISTANCE_EUCLIDEAN(embedding, VEC_FromText(%s)) AS distance - FROM `{DatabaseSettings().database}`.`{vector_store_name}` + FROM `{vector_store_name}` ORDER BY distance ASC - LIMIT %s; + LIMIT %s """ try: diff --git a/src/mcp_server_mariadb_vector/settings.py b/src/mcp_server_mariadb_vector/settings.py new file mode 100644 index 0000000..1ab613c --- /dev/null +++ b/src/mcp_server_mariadb_vector/settings.py @@ -0,0 +1,22 @@ +from typing import Optional + +from pydantic import Field +from pydantic_settings import BaseSettings + +from mcp_server_mariadb_vector.embeddings.base import EmbeddingProviderType + + +class DatabaseSettings(BaseSettings): + host: str = Field(default="127.0.0.1", alias="MARIADB_HOST") + port: int = Field(default=3306, alias="MARIADB_PORT") + user: str = Field(..., alias="MARIADB_USER") + password: str = Field(..., alias="MARIADB_PASSWORD") + database: str = Field(default="mcp", alias="MARIADB_DATABASE") + + +class EmbeddingSettings(BaseSettings): + provider: EmbeddingProviderType = Field( + default=EmbeddingProviderType.OPENAI, alias="EMBEDDING_PROVIDER" + ) + model: str = Field(default="text-embedding-3-small", alias="EMBEDDING_MODEL") + openai_api_key: Optional[str] = Field(default=None, alias="OPENAI_API_KEY") From f127808925e523bfcf5b305563f033cba368684a Mon Sep 17 00:00:00 2001 From: David Ramos Date: Sat, 3 May 2025 15:19:11 +0300 Subject: [PATCH 06/23] changed to FastMCP --- pyproject.toml | 1 + .../embeddings/base.py | 10 +- .../embeddings/openai.py | 18 ++-- src/mcp_server_mariadb_vector/server.py | 10 +- uv.lock | 92 +++++++++++++++++++ 5 files changed, 112 insertions(+), 19 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f249d88..b2cb526 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,6 +9,7 @@ dependencies = [ "mariadb==1.1.12", "openai", "pydantic", + "fastmcp>=2.2.6", ] [project.scripts] diff --git a/src/mcp_server_mariadb_vector/embeddings/base.py b/src/mcp_server_mariadb_vector/embeddings/base.py index 0f5bccf..2bae3d5 100644 --- a/src/mcp_server_mariadb_vector/embeddings/base.py +++ b/src/mcp_server_mariadb_vector/embeddings/base.py @@ -11,6 +11,11 @@ class EmbeddingProviderType(Enum): class EmbeddingProvider(ABC): """Abstract base class for embedding providers.""" + @abstractmethod + def length_of_embedding(self) -> int: + """Get the length of the embedding for a given model.""" + pass + @abstractmethod def embed_documents(self, documents: List[str]) -> List[List[float]]: """Embed a list of documents into vectors.""" @@ -20,8 +25,3 @@ def embed_documents(self, documents: List[str]) -> List[List[float]]: def embed_query(self, query: str) -> List[float]: """Embed a query into a vector.""" pass - - @abstractmethod - def length_of_embedding(self, model: str) -> int: - """Get the length of the embedding for a given model.""" - pass diff --git a/src/mcp_server_mariadb_vector/embeddings/openai.py b/src/mcp_server_mariadb_vector/embeddings/openai.py index e6e1a64..03f5b07 100644 --- a/src/mcp_server_mariadb_vector/embeddings/openai.py +++ b/src/mcp_server_mariadb_vector/embeddings/openai.py @@ -17,6 +17,15 @@ def __init__(self, model: str, api_key: str): self.model = model self.client = OpenAI(api_key=api_key) + def length_of_embedding(self) -> int: + """Get the length of the embedding for a given model.""" + if self.model == "text-embedding-3-small": + return 1536 + elif self.model == "text-embedding-3-large": + return 3072 + else: + raise ValueError(f"Unknown embedding model: {self.model}") + def embed_documents(self, documents: List[str]) -> List[List[float]]: """Embed a list of documents into vectors.""" embeddings = [ @@ -37,12 +46,3 @@ def embed_query(self, query: str) -> List[float]: input=query, ) return embedding.data[0].embedding - - def length_of_embedding(self) -> int: - """Get the length of the embedding for a model.""" - if self.model == "text-embedding-3-small": - return 1536 - elif self.model == "text-embedding-3-large": - return 3072 - else: - raise ValueError(f"Unknown embedding model: {self.model}") diff --git a/src/mcp_server_mariadb_vector/server.py b/src/mcp_server_mariadb_vector/server.py index 9e918f5..cb4b5ee 100644 --- a/src/mcp_server_mariadb_vector/server.py +++ b/src/mcp_server_mariadb_vector/server.py @@ -1,13 +1,13 @@ import json -from typing import List, Literal, Annotated +from typing import Annotated, List, Literal import mariadb -from mcp.server.fastmcp import FastMCP, Context +from fastmcp import Context, FastMCP from pydantic import Field -from mcp_server_mariadb_vector.settings import EmbeddingSettings -from mcp_server_mariadb_vector.embeddings.factory import create_embedding_provider -from mcp_server_mariadb_vector.app_context import app_lifespan +from mcp_server_mariadb_vector.app_context import app_lifespan +from mcp_server_mariadb_vector.embeddings.factory import create_embedding_provider +from mcp_server_mariadb_vector.settings import EmbeddingSettings mcp = FastMCP( "Mariadb Vector", diff --git a/uv.lock b/uv.lock index e9e964e..9257167 100644 --- a/uv.lock +++ b/uv.lock @@ -73,6 +73,25 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/02/cc/b7e31358aac6ed1ef2bb790a9746ac2c69bcb3c8588b41616914eb106eaf/exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b", size = 16453 }, ] +[[package]] +name = "fastmcp" +version = "2.2.6" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "exceptiongroup" }, + { name = "httpx" }, + { name = "mcp" }, + { name = "openapi-pydantic" }, + { name = "python-dotenv" }, + { name = "rich" }, + { name = "typer" }, + { name = "websockets" }, +] +sdist = { url = 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"https://files.pythonhosted.org/packages/fa/a8/5b41e0da817d64113292ab1f8247140aac61cbf6cfd085d6a0fa77f4984f/websockets-15.0.1-py3-none-any.whl", hash = "sha256:f7a866fbc1e97b5c617ee4116daaa09b722101d4a3c170c787450ba409f9736f", size = 169743 }, +] From 7d32d35c691360eeec711dbdafb19bab92355532 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Sat, 3 May 2025 16:26:44 +0300 Subject: [PATCH 07/23] add test for mcp tools with mock embeddings --- pyproject.toml | 7 +- tests/test_server.py | 162 +++++++++++++++++++++++++++++++++++++++++++ uv.lock | 90 ++++++++++++++++++++++++ 3 files changed, 258 insertions(+), 1 deletion(-) create mode 100644 tests/test_server.py diff --git a/pyproject.toml b/pyproject.toml index b2cb526..12af790 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,6 +10,8 @@ dependencies = [ "openai", "pydantic", "fastmcp>=2.2.6", + "pytest>=8.3.5", + "pytest-asyncio>=0.26.0", ] [project.scripts] @@ -17,4 +19,7 @@ mcp-server-mariadb-vector = "mcp_server_mariadb_vector.server:main" [build-system] requires = ["hatchling"] -build-backend = "hatchling.build" \ No newline at end of file +build-backend = "hatchling.build" + +[tool.pytest.ini_options] +asyncio_mode = "auto" diff --git a/tests/test_server.py b/tests/test_server.py new file mode 100644 index 0000000..3a0c04a --- /dev/null +++ b/tests/test_server.py @@ -0,0 +1,162 @@ +import mariadb +import pytest +from fastmcp import Client, FastMCP + +import mcp_server_mariadb_vector.server as server +from mcp_server_mariadb_vector.app_context import app_lifespan +from mcp_server_mariadb_vector.settings import DatabaseSettings + + +class MockEmbeddingProvider: + def __init__(self): + self.embed_documents_called = 0 + self.embed_query_called = 0 + + def length_of_embedding(self): + return 3 + + def embed_documents(self, documents): + self.embed_documents_called += 1 + print(f"Mock embed_documents called with {len(documents)} documents") + return [[1.0, 0.0, 0.0] for _ in documents] + + def embed_query(self, query): + self.embed_query_called += 1 + print(f"Mock embed_query called with query: '{query}'") + return [1.0, 0.0, 0.0] + + +@pytest.fixture +def db_settings(): + return DatabaseSettings() + + +@pytest.fixture +def db_connection(db_settings): + conn = mariadb.connect( + host=db_settings.host, + port=db_settings.port, + user=db_settings.user, + password=db_settings.password, + database=db_settings.database, + ) + conn.autocommit = True + yield conn + conn.close() + + +@pytest.fixture +def clean_database(db_connection): + """Clean up any test tables before and after tests.""" + with db_connection.cursor() as cursor: + cursor.execute("SHOW TABLES") + tables = [table[0] for table in cursor] + for table in tables: + if table.startswith("test_"): + cursor.execute(f"DROP TABLE `{table}`") + yield + with db_connection.cursor() as cursor: + cursor.execute("SHOW TABLES") + tables = [table[0] for table in cursor] + for table in tables: + if table.startswith("test_"): + cursor.execute(f"DROP TABLE `{table}`") + + +@pytest.fixture +def mock_embedding_provider(): + return MockEmbeddingProvider() + + +@pytest.fixture +async def mcp_test_server(mock_embedding_provider): + """Set up MCP server with mocked embedding provider and regular app lifespan.""" + + server.embedding_provider = mock_embedding_provider + + test_server = FastMCP("TestServer", lifespan=app_lifespan) + + # FastMCP specific: import server with "test" prefix so all tools appear as test_* + await test_server.import_server("test", server.mcp) + print("Server imported successfully") + return test_server + + +async def test_create_and_delete_vector_store( + mcp_test_server, clean_database, db_connection +): + # FastMCP specific: use Client to call tools in-memory + async with Client(mcp_test_server) as client: + result = await client.call_tool( + "test_mariadb_create_vector_store", + {"vector_store_name": "test_store", "distance_function": "euclidean"}, + ) + assert "Vector store `test_store` created successfully" in str(result[0]) + + with db_connection.cursor() as cursor: + cursor.execute("SHOW TABLES LIKE 'test_store'") + assert cursor.fetchone() is not None + + result = await client.call_tool( + "test_mariadb_delete_vector_store", {"vector_store_name": "test_store"} + ) + assert "Vector store `test_store` deleted successfully" in str(result[0]) + + with db_connection.cursor() as cursor: + cursor.execute("SHOW TABLES LIKE 'test_store'") + assert cursor.fetchone() is None + + +async def test_list_vector_stores(mcp_test_server, clean_database, db_connection): + async with Client(mcp_test_server) as client: + await client.call_tool( + "test_mariadb_create_vector_store", + {"vector_store_name": "test_store1", "distance_function": "euclidean"}, + ) + await client.call_tool( + "test_mariadb_create_vector_store", + {"vector_store_name": "test_store2", "distance_function": "euclidean"}, + ) + + result = await client.call_tool("test_mariadb_list_vector_stores", {}) + assert "test_store1" in str(result[0]) + assert "test_store2" in str(result[0]) + + +async def test_insert_and_search_documents( + mcp_test_server, clean_database, db_connection, mock_embedding_provider +): + documents = ["This is a test document", "This is another test document"] + metadata = [{"type": "test1"}, {"type": "test2"}] + + async with Client(mcp_test_server) as client: + await client.call_tool( + "test_mariadb_create_vector_store", + {"vector_store_name": "test_store", "distance_function": "euclidean"}, + ) + + result = await client.call_tool( + "test_mariadb_insert_documents", + { + "vector_store_name": "test_store", + "documents": documents, + "metadata": metadata, + }, + ) + assert "Documents inserted into `test_store` successfully" in str(result[0]) + assert mock_embedding_provider.embed_documents_called > 0, ( + "Mock embed_documents was not called" + ) + + result = await client.call_tool( + "test_mariadb_vector_search", + {"query": "test document", "vector_store_name": "test_store", "k": 2}, + ) + + assert "This is a test document" in str(result[0]) + assert "This is another test document" in str(result[0]) + assert "'type': 'test1'" in str(result[0]) + assert "'type': 'test2'" in str(result[0]) + assert mock_embedding_provider.embed_query_called > 0, ( + "Mock embed_query was not called" + ) diff --git a/uv.lock b/uv.lock index 9257167..04ea950 100644 --- a/uv.lock +++ b/uv.lock @@ -147,6 +147,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442 }, ] +[[package]] +name = "iniconfig" +version = "2.1.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/f2/97/ebf4da567aa6827c909642694d71c9fcf53e5b504f2d96afea02718862f3/iniconfig-2.1.0.tar.gz", hash = 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size = 14257 }, +] + [[package]] name = "tqdm" version = "4.67.1" From 115694c44565377b8fd5a6e311ff0497dc3cb046 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Sun, 4 May 2025 09:54:25 +0300 Subject: [PATCH 08/23] add working Dockerfile --- Dockerfile | 16 +++++++++------- src/mcp_server_mariadb_vector/server.py | 25 ++++++++++++++++++++++++- 2 files changed, 33 insertions(+), 8 deletions(-) diff --git a/Dockerfile b/Dockerfile index 15fccb3..39f7b97 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,3 @@ -# Use official slim Python 3.10 image FROM python:3.10-slim # Install system dependencies @@ -12,7 +11,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ build-essential && \ rm -rf /var/lib/apt/lists/* -# Set up MariaDB repository and install client libraries +# Set up MariaDB's Python connector dependencies RUN curl -LsSO https://r.mariadb.com/downloads/mariadb_repo_setup && \ echo "c4a0f3dade02c51a6a28ca3609a13d7a0f8910cccbb90935a2f218454d3a914a mariadb_repo_setup" | sha256sum -c - && \ chmod +x mariadb_repo_setup && \ @@ -25,14 +24,17 @@ RUN curl -LsSO https://r.mariadb.com/downloads/mariadb_repo_setup && \ apt-get clean && \ rm -rf /var/lib/apt/lists/* -# Set working directory -WORKDIR /app - # Install uv package manager RUN pip install --no-cache-dir uv -# Install project dependencies +WORKDIR /app + +# Copy project files +COPY . /app +# Install project dependencies +RUN uv sync +EXPOSE 8000 -CMD ["sleep", "infinity"] +CMD ["uv", "run", "mcp-server-mariadb-vector", "--host", "0.0.0.0", "--transport", "sse"] diff --git a/src/mcp_server_mariadb_vector/server.py b/src/mcp_server_mariadb_vector/server.py index cb4b5ee..713ca36 100644 --- a/src/mcp_server_mariadb_vector/server.py +++ b/src/mcp_server_mariadb_vector/server.py @@ -1,3 +1,4 @@ +import argparse import json from typing import Annotated, List, Literal @@ -166,7 +167,29 @@ def mariadb_vector_search( def main(): - mcp.run(transport="stdio") + parser = argparse.ArgumentParser() + parser.add_argument( + "--transport", + choices=["stdio", "sse"], + default="stdio", + ) + parser.add_argument( + "--host", + type=str, + default="127.0.0.1", + ) + parser.add_argument( + "--port", + type=int, + default=8000, + ) + + args = parser.parse_args() + + if args.transport == "sse": + mcp.run(transport=args.transport, host=args.host, port=args.port) + else: + mcp.run(transport=args.transport) if __name__ == "__main__": From 2b5be7b91e972e9c6931802b3e4a83f06ebb8af9 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Sun, 4 May 2025 10:23:28 +0300 Subject: [PATCH 09/23] add testing to github actions --- .github/workflows/test.yml | 74 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 74 insertions(+) create mode 100644 .github/workflows/test.yml diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 0000000..7d05c40 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,74 @@ +name: Run Tests + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + test: + runs-on: ubuntu-24.04 + + services: + mariadb: + image: mariadb:11.7 + env: + MARIADB_USER: root + MARIADB_PASSWORD: Password123! + MARIADB_DATABASE: test + + ports: + - 3306:3306 + options: >- + --health-cmd="mysqladmin ping" + --health-interval=10s + --health-timeout=5s + --health-retries=3 + + steps: + - uses: actions/checkout@v4 + + - name: Install system dependencies + run: | + curl -LsSO https://r.mariadb.com/downloads/mariadb_repo_setup + echo "c4a0f3dade02c51a6a28ca3609a13d7a0f8910cccbb90935a2f218454d3a914a mariadb_repo_setup" | sha256sum -c - + chmod +x mariadb_repo_setup + sudo ./mariadb_repo_setup --mariadb-server-version="mariadb-11.7" + sudo apt-get update + sudo apt-get install -y --no-install-recommends \ + build-essential \ + python3-dev \ + libmariadb3 \ + libmariadb-dev \ + pkg-config + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: ".python-version" + + - name: Enable caching + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + version: "0.5.14" + + - name: Install dependencies + run: | + uv sync + + - name: Run tests + run: | + uv run pytest tests/test_server.py -v + env: + # Environment variables for database + MARIADB_HOST: 127.0.0.1 + MARIADB_PORT: 3306 + MARIADB_USER: root + MARIADB_PASSWORD: Password123! + MARIADB_DATABASE: test From fbbf2318ff1a56a21586465f445fd8963ea46643 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Sun, 4 May 2025 10:29:50 +0300 Subject: [PATCH 10/23] fix db setup in github action --- .github/workflows/test.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 7d05c40..c6b53ae 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -14,8 +14,7 @@ jobs: mariadb: image: mariadb:11.7 env: - MARIADB_USER: root - MARIADB_PASSWORD: Password123! + MARIADB_ROOT_PASSWORD: Password123! MARIADB_DATABASE: test ports: From aa6f43a66e79110f2203087a0063b210be3eda7c Mon Sep 17 00:00:00 2001 From: David Ramos Date: Sun, 4 May 2025 10:42:15 +0300 Subject: [PATCH 11/23] tweak db setup in github action --- .github/workflows/test.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index c6b53ae..02257ea 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -20,10 +20,10 @@ jobs: ports: - 3306:3306 options: >- - --health-cmd="mysqladmin ping" - --health-interval=10s - --health-timeout=5s - --health-retries=3 + --health-cmd="mysqladmin ping --silent" + --health-interval=20s + --health-timeout=10s + --health-retries=5 steps: - uses: actions/checkout@v4 From dd25a4a9a6b0e490ab9a951ac0d39c356e3a5ace Mon Sep 17 00:00:00 2001 From: David Ramos Date: Sun, 4 May 2025 10:54:01 +0300 Subject: [PATCH 12/23] fix db and python setup in github action --- .github/workflows/test.yml | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 02257ea..345296a 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -19,11 +19,6 @@ jobs: ports: - 3306:3306 - options: >- - --health-cmd="mysqladmin ping --silent" - --health-interval=20s - --health-timeout=10s - --health-retries=5 steps: - uses: actions/checkout@v4 @@ -45,7 +40,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v5 with: - python-version: ".python-version" + python-version-file: ".python-version" - name: Enable caching uses: astral-sh/setup-uv@v5 From dd873c6d840c834fbd3e37926f95c575b9188a71 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Sun, 4 May 2025 11:13:47 +0300 Subject: [PATCH 13/23] added test embedding provider to simplify tests --- .github/workflows/test.yml | 3 +- .../embeddings/base.py | 1 + .../embeddings/factory.py | 4 ++- .../embeddings/test.py | 18 ++++++++++ tests/test_server.py | 36 ++----------------- 5 files changed, 26 insertions(+), 36 deletions(-) create mode 100644 src/mcp_server_mariadb_vector/embeddings/test.py diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 345296a..829dfb9 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -60,9 +60,10 @@ jobs: run: | uv run pytest tests/test_server.py -v env: - # Environment variables for database + # Environment variables for MCP server MARIADB_HOST: 127.0.0.1 MARIADB_PORT: 3306 MARIADB_USER: root MARIADB_PASSWORD: Password123! MARIADB_DATABASE: test + EMBEDDING_PROVIDER: test diff --git a/src/mcp_server_mariadb_vector/embeddings/base.py b/src/mcp_server_mariadb_vector/embeddings/base.py index 2bae3d5..e15983a 100644 --- a/src/mcp_server_mariadb_vector/embeddings/base.py +++ b/src/mcp_server_mariadb_vector/embeddings/base.py @@ -5,6 +5,7 @@ class EmbeddingProviderType(Enum): OPENAI = "openai" + TEST = "test" # SENTENCE_TRANSFORMERS = "sentence-transformers" diff --git a/src/mcp_server_mariadb_vector/embeddings/factory.py b/src/mcp_server_mariadb_vector/embeddings/factory.py index 1bdaebe..ec0e2e1 100644 --- a/src/mcp_server_mariadb_vector/embeddings/factory.py +++ b/src/mcp_server_mariadb_vector/embeddings/factory.py @@ -3,6 +3,7 @@ EmbeddingProviderType, ) from mcp_server_mariadb_vector.embeddings.openai import OpenAIEmbeddingProvider +from mcp_server_mariadb_vector.embeddings.test import TestEmbeddingProvider from mcp_server_mariadb_vector.settings import EmbeddingSettings @@ -15,6 +16,7 @@ def create_embedding_provider(settings: EmbeddingSettings) -> EmbeddingProvider: """ if settings.provider == EmbeddingProviderType.OPENAI: return OpenAIEmbeddingProvider(settings.model, settings.openai_api_key) - + elif settings.provider == EmbeddingProviderType.TEST: + return TestEmbeddingProvider() else: raise ValueError(f"Unsupported embedding provider: {settings.provider}") diff --git a/src/mcp_server_mariadb_vector/embeddings/test.py b/src/mcp_server_mariadb_vector/embeddings/test.py new file mode 100644 index 0000000..75a0c88 --- /dev/null +++ b/src/mcp_server_mariadb_vector/embeddings/test.py @@ -0,0 +1,18 @@ +from typing import List + +from mcp_server_mariadb_vector.embeddings.base import EmbeddingProvider + + +class TestEmbeddingProvider(EmbeddingProvider): + """ + Embedding provider for testing. + """ + + def length_of_embedding(self) -> int: + return 3 + + def embed_documents(self, documents: List[str]) -> List[List[float]]: + return [[0.1, 0.2, 0.3]] * len(documents) + + def embed_query(self, query: str) -> List[float]: + return [0.1, 0.2, 0.3] diff --git a/tests/test_server.py b/tests/test_server.py index 3a0c04a..2a66a78 100644 --- a/tests/test_server.py +++ b/tests/test_server.py @@ -7,25 +7,6 @@ from mcp_server_mariadb_vector.settings import DatabaseSettings -class MockEmbeddingProvider: - def __init__(self): - self.embed_documents_called = 0 - self.embed_query_called = 0 - - def length_of_embedding(self): - return 3 - - def embed_documents(self, documents): - self.embed_documents_called += 1 - print(f"Mock embed_documents called with {len(documents)} documents") - return [[1.0, 0.0, 0.0] for _ in documents] - - def embed_query(self, query): - self.embed_query_called += 1 - print(f"Mock embed_query called with query: '{query}'") - return [1.0, 0.0, 0.0] - - @pytest.fixture def db_settings(): return DatabaseSettings() @@ -64,16 +45,9 @@ def clean_database(db_connection): @pytest.fixture -def mock_embedding_provider(): - return MockEmbeddingProvider() - - -@pytest.fixture -async def mcp_test_server(mock_embedding_provider): +async def mcp_test_server(): """Set up MCP server with mocked embedding provider and regular app lifespan.""" - server.embedding_provider = mock_embedding_provider - test_server = FastMCP("TestServer", lifespan=app_lifespan) # FastMCP specific: import server with "test" prefix so all tools appear as test_* @@ -124,7 +98,7 @@ async def test_list_vector_stores(mcp_test_server, clean_database, db_connection async def test_insert_and_search_documents( - mcp_test_server, clean_database, db_connection, mock_embedding_provider + mcp_test_server, clean_database, db_connection ): documents = ["This is a test document", "This is another test document"] metadata = [{"type": "test1"}, {"type": "test2"}] @@ -144,9 +118,6 @@ async def test_insert_and_search_documents( }, ) assert "Documents inserted into `test_store` successfully" in str(result[0]) - assert mock_embedding_provider.embed_documents_called > 0, ( - "Mock embed_documents was not called" - ) result = await client.call_tool( "test_mariadb_vector_search", @@ -157,6 +128,3 @@ async def test_insert_and_search_documents( assert "This is another test document" in str(result[0]) assert "'type': 'test1'" in str(result[0]) assert "'type': 'test2'" in str(result[0]) - assert mock_embedding_provider.embed_query_called > 0, ( - "Mock embed_query was not called" - ) From d448437359be5ee9f8c7de5d416e76d7e3cbd9cf Mon Sep 17 00:00:00 2001 From: David Ramos Date: Sun, 4 May 2025 16:40:41 +0300 Subject: [PATCH 14/23] change Docker CMD args --- Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index 39f7b97..a5ab5e3 100644 --- a/Dockerfile +++ b/Dockerfile @@ -37,4 +37,4 @@ RUN uv sync EXPOSE 8000 -CMD ["uv", "run", "mcp-server-mariadb-vector", "--host", "0.0.0.0", "--transport", "sse"] +CMD ["uv", "run", "mcp-server-mariadb-vector", "--transport", "sse", "--host", "0.0.0.0"] From f961e736e5b6a86716915a480a02310a2a872852 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Sun, 4 May 2025 16:41:23 +0300 Subject: [PATCH 15/23] change tool name for clarity --- src/mcp_server_mariadb_vector/server.py | 2 +- tests/test_server.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/mcp_server_mariadb_vector/server.py b/src/mcp_server_mariadb_vector/server.py index 713ca36..bcef27a 100644 --- a/src/mcp_server_mariadb_vector/server.py +++ b/src/mcp_server_mariadb_vector/server.py @@ -124,7 +124,7 @@ def mariadb_insert_documents( @mcp.tool() -def mariadb_vector_search( +def mariadb_search_vector_store( ctx: Context, query: Annotated[str, Field(description="The query to search for.")], vector_store_name: Annotated[ diff --git a/tests/test_server.py b/tests/test_server.py index 2a66a78..6e690e6 100644 --- a/tests/test_server.py +++ b/tests/test_server.py @@ -120,7 +120,7 @@ async def test_insert_and_search_documents( assert "Documents inserted into `test_store` successfully" in str(result[0]) result = await client.call_tool( - "test_mariadb_vector_search", + "test_mariadb_search_vector_store", {"query": "test document", "vector_store_name": "test_store", "k": 2}, ) From 01e9f799640052a634bc6f83d00db89a5eaa77ff Mon Sep 17 00:00:00 2001 From: David Ramos Date: Sun, 4 May 2025 16:42:05 +0300 Subject: [PATCH 16/23] add proper README --- README.md | 135 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 135 insertions(+) diff --git a/README.md b/README.md index e69de29..35cb3d7 100644 --- a/README.md +++ b/README.md @@ -0,0 +1,135 @@ +# mcp-server-mariadb-vector + +The MariaDB Vector MCP server provides tools that LLM agents can use to interact with a MariaDB Vector database, providing users with a natural language interface to store and interact with their data. Thanks to the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction), this server is compatible with any MCP client, including those provided by applications like Claude Desktop and Cursor/Windsurf, as well as LLM Agent frameworks like LangGraph and PydanticAI. + +Using the MariaDB Vector MCP server, users can for example: + +- Provide context from a knowledge-base to their conversations with LLM agents +- Store and query their conversations with LLM agents + +## Features + +- **Vector Store Management** + + - Create and delete vector stores in a MariaDB database + - List all vector stores in a MariaDB database + +- **Document Management** + + - Add documents with optional metadata to a vector store + - Query a vector store using semantic search + +- **Embedding Provider** + + - Use OpenAI's embedding models to embed documents + +## MCP Tools + +- `mariadb_create_vector_store`: Create a vector store in a MariaDB database +- `mariadb_delete_vector_store`: Delete a vector store in a MariaDB database +- `mariadb_list_vector_stores`: List all vector stores in a MariaDB database +- `mariadb_insert_documents`: Add documents with optional metadata to a vector store +- `mariadb_search_vector_store`: Query a vector store using semantic search + +## Setup + +First clone the repository: + +```bash +git clone https://github.com/DavidRamosSal/mcp-server-mariadb-vector.git +``` + +There are two ways to run the MariaDB Vector MCP server: as a Python package using uv or as a Docker container building it from the Dockerfile. + +### Requirements for running the server using uv + +- MariaDB Connector/C - [installation instructions](https://mariadb.com/docs/server/connect/programming-languages/c/install) +- uv - [installation instructions](https://docs.astral.sh/uv/#installation) + +### Requirements for running the server as a Docker container + +- Docker - [installation instructions](https://docs.docker.com/get-docker/) + +### Configuration + +The server needs to be configured with the following environment variables: + +| Name | Description | Default Value | +| -------------------- | ---------------------------------------- | ------------------------ | +| `MARIADB_HOST` | host of the running MariaDB database | `127.0.0.1` | +| `MARIADB_PORT` | port of the running MariaDB database | `3306` | +| `MARIADB_USER` | user of the running MariaDB database | None | +| `MARIADB_PASSWORD` | password of the running MariaDB database | None | +| `MARIADB_DATABASE` | name of the running MariaDB database | `mcp` | +| `EMBEDDING_PROVIDER` | provider of the embedding models | `openai` | +| `EMBEDDING_MODEL` | model of the embedding provider | `text-embedding-3-small` | +| `OPENAI_API_KEY` | API key for OpenAI's embedding models | None | + +### Running the server using uv + +Using uv, you can add a `.env` file to the root of the cloned repository with the environment variables and run the server with the following command: + +```bash +uv run --dir path/to/mcp-server-mariadb-vector/ --env-file path/to/mcp-server-mariadb-vector/.env mcp_server_mariadb_vector +``` + +The dependencies will be installed automatically. An optional `--transport` argument can be added to specify the transport protocol to use. The default value is `stdio`. + +### Running the server as a Docker container + +Build the Docker container from the root directory of the cloned repository by running the following command: + +```bash +docker build -t mcp-server-mariadb-vector . +``` + +Then run the container: + +```bash +docker run -p 8000:8000 \ + --add-host host.docker.internal:host-gateway \ + -e MARIADB_HOST="host.docker.internal" \ + -e MARIADB_PORT="port" \ + -e MARIADB_USER="user" \ + -e MARIADB_PASSWORD="password" \ + -e MARIADB_DATABASE="database" \ + -e EMBEDDING_PROVIDER="openai" \ + -e EMBEDDING_MODEL="embedding-model" \ + -e OPENAI_API_KEY="your-openai-api-key" \ + mcp-server-mariadb-vector +``` + +The server will be available at `http://localhost:8000/sse`, using the SSE transport protocol. Make sure to leave `MARIADB_HOST` set to `host.docker.internal` if you are running the MariaDB database as a Docker container on your host machine. + +### Integration with Claude Desktop | Cursor | Windsurf + +Claude Desktop, Cursor and Windsurf can run and connect to the server automatically using stdio transport. To do so, add the following to your configuration file (`claude_desktop_config.json` for Claude Desktop, `mcp.json` for Cursor or `mcp_config.json` for Windsurf): + +```json +{ + "mcpServers": { + "mariadb-vector": { + "command": "uv", + "args": [ + "run", + "--directory", + "path/to/mcp-server-mariadb-vector/", + "--env-file", + "path/to/mcp-server-mariadb-vector/.env", + "mcp-server-mariadb-vector" + ] + } + } +} +``` + +Alternatively, Cursor and Windsurf can connect to the an already running server on your host machine (e.g. if you are running the server as a Docker container) using SSE transport. To do so, add the following to the corresponding configuration file: + +```json + "mcpServers": { + "mariadb-vector": { + "url": "http://127.0.0.1:8000/sse" + } + } +} +``` From 09c17a56810d5369ce0fdb77405945b5e4c0625d Mon Sep 17 00:00:00 2001 From: David Ramos Date: Sun, 4 May 2025 17:24:02 +0300 Subject: [PATCH 17/23] add schema check --- src/mcp_server_mariadb_vector/server.py | 63 ++++++++++++++++++++++++- 1 file changed, 62 insertions(+), 1 deletion(-) diff --git a/src/mcp_server_mariadb_vector/server.py b/src/mcp_server_mariadb_vector/server.py index bcef27a..d0162b7 100644 --- a/src/mcp_server_mariadb_vector/server.py +++ b/src/mcp_server_mariadb_vector/server.py @@ -56,6 +56,62 @@ def mariadb_create_vector_store( return f"Vector store `{vector_store_name}` created successfully." +def is_vector_store(conn, table: str, embedding_length: int) -> bool: + """ + True if `table` has the right schema, with vectors of the correct length, and a VECTOR index. + """ + + with conn.cursor(dictionary=True) as cur: + # check columns + cur.execute(f"SHOW COLUMNS FROM `{table}`") + rows = {r["Field"]: r for r in cur} + + if set(rows) != {"id", "document", "embedding", "metadata"}: + return False + + # id + id_type = rows["id"]["Type"].lower() + if id_type != "bigint(20) unsigned": + return False + if ( + rows["id"]["Null"] != "NO" + or rows["id"]["Key"] != "PRI" + or "auto_increment" not in rows["id"]["Extra"].lower() + ): + return False + + # document + if ( + rows["document"]["Type"].lower() != "longtext" + or rows["document"]["Null"] != "NO" + ): + return False + + # embedding + if ( + rows["embedding"]["Type"].lower() != f"vector({embedding_length})" + or rows["embedding"]["Null"] != "NO" + ): + return False + + # metadata + if ( + rows["metadata"]["Type"].lower() != "longtext" + or rows["metadata"]["Null"] != "NO" + ): + return False + + # check vector index + cur.execute(f""" + SHOW INDEX FROM `{table}` + WHERE Index_type = 'VECTOR' AND Column_name = 'embedding' + """) + if cur.fetchone() is None: + return False + + return True + + @mcp.tool() def mariadb_list_vector_stores(ctx: Context) -> str: """List all vector stores in a MariaDB database.""" @@ -67,7 +123,12 @@ def mariadb_list_vector_stores(ctx: Context) -> str: except mariadb.Error as e: return f"Error listing vector stores: {e}" - return "Vector stores: " + ", ".join(tables) + embedding_length = embedding_provider.length_of_embedding() + vector_stores = [ + table for table in tables if is_vector_store(conn, table, embedding_length) + ] + + return "Vector stores: " + ", ".join(vector_stores) @mcp.tool() From a158b9ad357a358a5bee05f8de8d883de6b1d331 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Mon, 5 May 2025 11:19:54 +0300 Subject: [PATCH 18/23] remove default database name --- src/mcp_server_mariadb_vector/settings.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mcp_server_mariadb_vector/settings.py b/src/mcp_server_mariadb_vector/settings.py index 1ab613c..505d88a 100644 --- a/src/mcp_server_mariadb_vector/settings.py +++ b/src/mcp_server_mariadb_vector/settings.py @@ -11,7 +11,7 @@ class DatabaseSettings(BaseSettings): port: int = Field(default=3306, alias="MARIADB_PORT") user: str = Field(..., alias="MARIADB_USER") password: str = Field(..., alias="MARIADB_PASSWORD") - database: str = Field(default="mcp", alias="MARIADB_DATABASE") + database: str = Field(..., alias="MARIADB_DATABASE") class EmbeddingSettings(BaseSettings): From 56cdf96d59239e817b47c8f1ce24fc2b97869b7e Mon Sep 17 00:00:00 2001 From: David Ramos Date: Mon, 5 May 2025 11:20:20 +0300 Subject: [PATCH 19/23] fix and update readme --- README.md | 22 ++++++++++++++-------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 35cb3d7..155a896 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # mcp-server-mariadb-vector -The MariaDB Vector MCP server provides tools that LLM agents can use to interact with a MariaDB Vector database, providing users with a natural language interface to store and interact with their data. Thanks to the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction), this server is compatible with any MCP client, including those provided by applications like Claude Desktop and Cursor/Windsurf, as well as LLM Agent frameworks like LangGraph and PydanticAI. +The MariaDB Vector MCP server provides tools that LLM agents can use to interact with MariaDB database [with vector support](https://mariadb.org/projects/mariadb-vector/), providing users with a natural language interface to store and interact with their data. Thanks to the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction), this server is compatible with any MCP client, including those provided by applications like Claude Desktop and Cursor/Windsurf, as well as LLM Agent frameworks like LangGraph and PydanticAI. Using the MariaDB Vector MCP server, users can for example: @@ -33,13 +33,19 @@ Using the MariaDB Vector MCP server, users can for example: ## Setup +> **Note:** From here on, it is assumed that you have a running MariaDB instance with vector support (version **11.7** or higher). If you don't have one, you can quickly spin up a MariaDB instance using Docker: +> +> ```bash +> docker run -p 3306:3306 --name mariadb-instance -e MARIADB_ROOT_PASSWORD=password -e MARIADB_DATABASE=database_name mariadb:11.7 +> ``` + First clone the repository: ```bash git clone https://github.com/DavidRamosSal/mcp-server-mariadb-vector.git ``` -There are two ways to run the MariaDB Vector MCP server: as a Python package using uv or as a Docker container building it from the Dockerfile. +There are two ways to run the MariaDB Vector MCP server: as a Python package using **uv** or as a **Docker container** built from the provided Dockerfile. ### Requirements for running the server using uv @@ -60,10 +66,10 @@ The server needs to be configured with the following environment variables: | `MARIADB_PORT` | port of the running MariaDB database | `3306` | | `MARIADB_USER` | user of the running MariaDB database | None | | `MARIADB_PASSWORD` | password of the running MariaDB database | None | -| `MARIADB_DATABASE` | name of the running MariaDB database | `mcp` | +| `MARIADB_DATABASE` | name of the running MariaDB database | None | | `EMBEDDING_PROVIDER` | provider of the embedding models | `openai` | | `EMBEDDING_MODEL` | model of the embedding provider | `text-embedding-3-small` | -| `OPENAI_API_KEY` | API key for OpenAI's embedding models | None | +| `OPENAI_API_KEY` | API key for OpenAI's platform | None | ### Running the server using uv @@ -83,7 +89,7 @@ Build the Docker container from the root directory of the cloned repository by r docker build -t mcp-server-mariadb-vector . ``` -Then run the container: +Then run the container (replace with your own configuration): ```bash docker run -p 8000:8000 \ @@ -99,7 +105,7 @@ docker run -p 8000:8000 \ mcp-server-mariadb-vector ``` -The server will be available at `http://localhost:8000/sse`, using the SSE transport protocol. Make sure to leave `MARIADB_HOST` set to `host.docker.internal` if you are running the MariaDB database as a Docker container on your host machine. +The server will be available at `http://localhost:8000/sse`, using the SSE transport protocol. **Make sure** to leave `MARIADB_HOST` set to `host.docker.internal` if you are running the MariaDB database as a Docker container on your host machine. ### Integration with Claude Desktop | Cursor | Windsurf @@ -123,12 +129,12 @@ Claude Desktop, Cursor and Windsurf can run and connect to the server automatica } ``` -Alternatively, Cursor and Windsurf can connect to the an already running server on your host machine (e.g. if you are running the server as a Docker container) using SSE transport. To do so, add the following to the corresponding configuration file: +Alternatively, Cursor and Windsurf can connect to an already running server on your host machine (e.g. if you are running the server as a Docker container) using SSE transport. To do so, add the following to the corresponding configuration file: ```json "mcpServers": { "mariadb-vector": { - "url": "http://127.0.0.1:8000/sse" + "url": "http://localhost:8000/sse" } } } From ec9285ccc49651ae01b6b1a742548db58ded4a55 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Mon, 5 May 2025 11:49:24 +0300 Subject: [PATCH 20/23] fix readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 155a896..0f640f9 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # mcp-server-mariadb-vector -The MariaDB Vector MCP server provides tools that LLM agents can use to interact with MariaDB database [with vector support](https://mariadb.org/projects/mariadb-vector/), providing users with a natural language interface to store and interact with their data. Thanks to the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction), this server is compatible with any MCP client, including those provided by applications like Claude Desktop and Cursor/Windsurf, as well as LLM Agent frameworks like LangGraph and PydanticAI. +The MariaDB Vector MCP server provides tools that LLM agents can use to interact with a MariaDB database [with vector support](https://mariadb.org/projects/mariadb-vector/), providing users with a natural language interface to store and interact with their data. Thanks to the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction), this server is compatible with any MCP client, including those provided by applications like Claude Desktop and Cursor/Windsurf, as well as LLM Agent frameworks like LangGraph and PydanticAI. Using the MariaDB Vector MCP server, users can for example: From 68f4c79d13498021a1f09716849299da9dbc2338 Mon Sep 17 00:00:00 2001 From: David Ramos Salamanca <33958847+DavidRamosSal@users.noreply.github.com> Date: Mon, 5 May 2025 18:57:31 +0300 Subject: [PATCH 21/23] Create LICENSE --- LICENSE | 504 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 504 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..8000a6f --- /dev/null +++ b/LICENSE @@ -0,0 +1,504 @@ + GNU LESSER GENERAL PUBLIC LICENSE + Version 2.1, February 1999 + + Copyright (C) 1991, 1999 Free Software Foundation, Inc. + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + +[This is the first released version of the Lesser GPL. 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From 67ad9aec93ee707362c2ede08da891aaea4eeb26 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Mon, 12 May 2025 10:03:24 +0300 Subject: [PATCH 22/23] change server versions to LTS branch --- .github/workflows/test.yml | 4 ++-- Dockerfile | 2 +- README.md | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 829dfb9..35d66db 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -12,7 +12,7 @@ jobs: services: mariadb: - image: mariadb:11.7 + image: mariadb:11.8 env: MARIADB_ROOT_PASSWORD: Password123! MARIADB_DATABASE: test @@ -28,7 +28,7 @@ jobs: curl -LsSO https://r.mariadb.com/downloads/mariadb_repo_setup echo "c4a0f3dade02c51a6a28ca3609a13d7a0f8910cccbb90935a2f218454d3a914a mariadb_repo_setup" | sha256sum -c - chmod +x mariadb_repo_setup - sudo ./mariadb_repo_setup --mariadb-server-version="mariadb-11.7" + sudo ./mariadb_repo_setup --mariadb-server-version="mariadb-11.8" sudo apt-get update sudo apt-get install -y --no-install-recommends \ build-essential \ diff --git a/Dockerfile b/Dockerfile index a5ab5e3..cdfc5a1 100644 --- a/Dockerfile +++ b/Dockerfile @@ -15,7 +15,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ RUN curl -LsSO https://r.mariadb.com/downloads/mariadb_repo_setup && \ echo "c4a0f3dade02c51a6a28ca3609a13d7a0f8910cccbb90935a2f218454d3a914a mariadb_repo_setup" | sha256sum -c - && \ chmod +x mariadb_repo_setup && \ - ./mariadb_repo_setup --mariadb-server-version="mariadb-11.7" && \ + ./mariadb_repo_setup --mariadb-server-version="mariadb-11.8" && \ rm mariadb_repo_setup && \ apt-get update && \ apt-get install -y --no-install-recommends \ diff --git a/README.md b/README.md index 0f640f9..cb967f4 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ Using the MariaDB Vector MCP server, users can for example: > **Note:** From here on, it is assumed that you have a running MariaDB instance with vector support (version **11.7** or higher). If you don't have one, you can quickly spin up a MariaDB instance using Docker: > > ```bash -> docker run -p 3306:3306 --name mariadb-instance -e MARIADB_ROOT_PASSWORD=password -e MARIADB_DATABASE=database_name mariadb:11.7 +> docker run -p 3306:3306 --name mariadb-instance -e MARIADB_ROOT_PASSWORD=password -e MARIADB_DATABASE=database_name mariadb:11.8 > ``` First clone the repository: From 7231353a1c1bbd07b54895078f69306ad0a72144 Mon Sep 17 00:00:00 2001 From: David Ramos Date: Mon, 12 May 2025 10:07:31 +0300 Subject: [PATCH 23/23] fix urls in readme to refer to correct repo and documentation --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index cb967f4..33f55f4 100644 --- a/README.md +++ b/README.md @@ -42,14 +42,14 @@ Using the MariaDB Vector MCP server, users can for example: First clone the repository: ```bash -git clone https://github.com/DavidRamosSal/mcp-server-mariadb-vector.git +git clone https://github.com/MariaDB/mcp mcp-server-mariadb-vector ``` There are two ways to run the MariaDB Vector MCP server: as a Python package using **uv** or as a **Docker container** built from the provided Dockerfile. ### Requirements for running the server using uv -- MariaDB Connector/C - [installation instructions](https://mariadb.com/docs/server/connect/programming-languages/c/install) +- MariaDB Connector/C - [installation instructions](https://mariadb.com/kb/en/mariadb-connector-c/) - uv - [installation instructions](https://docs.astral.sh/uv/#installation) ### Requirements for running the server as a Docker container