qdrant-mcp
qdrant-mcp
用于 Qdrant 文档摄入和语义搜索的 MCP 服务器。
概述
qdrant-mcp 提供的工具可以:
将本地文档摄入到 Qdrant 集合中
使用 OpenAI 生成嵌入
运行带有可选元数据过滤的向量搜索
Related MCP server: mcp-server-qdrant
功能
ingest_documents通过 MarkItDown 将
docx、pptx和pdf等文件转换为 Markdown使用
chunk_size和overlap_ratio将内容拆分为块使用 OpenAI Embeddings(默认为
text-embedding-3-small)对块进行嵌入将块文本和元数据插入/更新到 Qdrant
search_documents使用相同的嵌入 API 对查询文本进行嵌入
从 Qdrant 检索前
k个匹配项支持按
category和path进行过滤
要求
Python 3.11+
uvQdrant(例如
http://localhost:6333)OPENAI_API_KEY
设置
uv sync在 Codex CLI 中运行
[mcp_servers.qdrant-mcp]
command = "uv"
args = ["run", "qdrant-mcp"]
cwd = "/sandbox/qdrant-mcp"
env = {
OPENAI_API_KEY = "sk-...",
QDRANT_URL = "http://127.0.0.1:6333",
QDRANT_API_KEY = "QDRANT_API_KEY",
QDRANT_COLLECTION = "codex_collection",
CHUNK_HEADER_MODEL = "gpt-5.4-mini"
}测试
在 .env 中设置 OPENAI_API_KEY、QDRANT_URL 和 QDRANT_API_KEY,然后运行:
uv run python -m unittest tests/integration/test_qdrant_integration.pyMCP 工具
ingest_documents
参数:
paths: list[str]category: strchunk_size: int = 1200overlap_ratio: float = 0.15embedding_model: str = "text-embedding-3-small"chunk_header_mode: Literal["enabled", "disabled"] = "enabled"
返回:
collectionembedding_modelingested_filesingested_pointsfailed_files
search_documents
参数:
query: strtop_k: int = 5category: str | None = Nonepath: str | None = Noneembedding_model: str = "text-embedding-3-small"
返回:
collectionembedding_modelquerycountresults(score,path,category,chunk_index,text)
delete_documents_by_path
参数:
path: strcategory: str | None = None
返回:
collectionpathcategorystatusoperation_id
list_category
参数:
limit: int = 100
返回:
collectioncountcategories
list_path
参数:
category: strlimit: int = 1000
返回:
collectioncategorycountpaths
注意事项
如果目标集合不存在,它会在首次摄入时自动创建。
如果
category和path的负载索引不存在,它们会在摄入过程中创建。默认情况下,摄入过程会在每个块前添加一个生成的
Chunk-Header(最大 64 字符),该头部派生自前 4096 字节。当
chunk_header_mode为enabled(默认值:gpt-5.4-mini)时,Chunk-Header 模型从CHUNK_HEADER_MODEL读取。集合名称仅通过
QDRANT_COLLECTION配置(而非通过 MCP 工具参数)。使用
text-embedding-3-small时,向量大小为1536。
许可证
参见 LICENSE。
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