mirror of
https://github.com/google/adk-docs.git
synced 2026-09-14 16:16:59 +08:00
Kristopher Overholt
149 lines
5.4 KiB
Markdown
149 lines
5.4 KiB
Markdown
---
|
|
catalog_title: Qdrant
|
|
catalog_description: Store and retrieve information using semantic vector search
|
|
catalog_icon: /integrations/assets/qdrant.png
|
|
catalog_tags: ["data","mcp"]
|
|
---
|
|
|
|
# Qdrant MCP tool for ADK
|
|
|
|
<div class="language-support-tag">
|
|
<span class="lst-supported">Supported in ADK</span><span class="lst-python">Python</span><span class="lst-typescript">TypeScript</span>
|
|
</div>
|
|
|
|
The [Qdrant MCP Server](https://github.com/qdrant/mcp-server-qdrant) connects
|
|
your ADK agent to [Qdrant](https://qdrant.tech/), an open-source vector search engine. This integration gives your agent the ability to store and
|
|
retrieve information using semantic search.
|
|
|
|
## Use cases
|
|
|
|
- **Semantic Memory for Agents**: Store conversation context, facts, or learned
|
|
information that agents can retrieve later using natural language queries.
|
|
|
|
- **Code Repository Search**: Build a searchable index of code snippets,
|
|
documentation, and implementation patterns that can be queried semantically.
|
|
|
|
- **Knowledge Base Retrieval**: Create a retrieval-augmented generation (RAG)
|
|
system by storing documents and retrieving relevant context for responses.
|
|
|
|
## Prerequisites
|
|
|
|
- A running Qdrant instance. You can:
|
|
- Use [Qdrant Cloud](https://cloud.qdrant.io/) (managed service)
|
|
- Run locally with Docker: `docker run -p 6333:6333 qdrant/qdrant`
|
|
- (Optional) A Qdrant API key for authentication
|
|
|
|
## Use with agent
|
|
|
|
=== "Python"
|
|
|
|
=== "Local MCP Server"
|
|
|
|
```python
|
|
from google.adk.agents import Agent
|
|
from google.adk.tools.mcp_tool import McpToolset
|
|
from google.adk.tools.mcp_tool.mcp_session_manager import StdioConnectionParams
|
|
from mcp import StdioServerParameters
|
|
|
|
QDRANT_URL = "http://localhost:6333" # Or your Qdrant Cloud URL
|
|
COLLECTION_NAME = "my_collection"
|
|
# QDRANT_API_KEY = "YOUR_QDRANT_API_KEY"
|
|
|
|
root_agent = Agent(
|
|
model="gemini-flash-latest",
|
|
name="qdrant_agent",
|
|
instruction="Help users store and retrieve information using semantic search",
|
|
tools=[
|
|
McpToolset(
|
|
connection_params=StdioConnectionParams(
|
|
server_params=StdioServerParameters(
|
|
command="uvx",
|
|
args=["mcp-server-qdrant"],
|
|
env={
|
|
"QDRANT_URL": QDRANT_URL,
|
|
"COLLECTION_NAME": COLLECTION_NAME,
|
|
# "QDRANT_API_KEY": QDRANT_API_KEY,
|
|
}
|
|
),
|
|
timeout=30,
|
|
),
|
|
)
|
|
],
|
|
)
|
|
```
|
|
|
|
=== "TypeScript"
|
|
|
|
=== "Local MCP Server"
|
|
|
|
```typescript
|
|
import { LlmAgent, MCPToolset } from "@google/adk";
|
|
|
|
const QDRANT_URL = "http://localhost:6333"; // Or your Qdrant Cloud URL
|
|
const COLLECTION_NAME = "my_collection";
|
|
// const QDRANT_API_KEY = "YOUR_QDRANT_API_KEY";
|
|
|
|
const rootAgent = new LlmAgent({
|
|
model: "gemini-flash-latest",
|
|
name: "qdrant_agent",
|
|
instruction: "Help users store and retrieve information using semantic search",
|
|
tools: [
|
|
new MCPToolset({
|
|
type: "StdioConnectionParams",
|
|
serverParams: {
|
|
command: "uvx",
|
|
args: ["mcp-server-qdrant"],
|
|
env: {
|
|
QDRANT_URL: QDRANT_URL,
|
|
COLLECTION_NAME: COLLECTION_NAME,
|
|
// QDRANT_API_KEY: QDRANT_API_KEY,
|
|
},
|
|
},
|
|
}),
|
|
],
|
|
});
|
|
|
|
export { rootAgent };
|
|
```
|
|
|
|
## Available tools
|
|
|
|
Tool | Description
|
|
---- | -----------
|
|
`qdrant-store` | Store information in Qdrant with optional metadata
|
|
`qdrant-find` | Search for relevant information using natural language queries
|
|
|
|
## Configuration
|
|
|
|
The Qdrant MCP server can be configured using environment variables:
|
|
|
|
Variable | Description | Default
|
|
-------- | ----------- | -------
|
|
`QDRANT_URL` | URL of the Qdrant server | `None` (required)
|
|
`QDRANT_API_KEY` | API key for Qdrant Cloud authentication | `None`
|
|
`COLLECTION_NAME` | Name of the collection to use | `None`
|
|
`QDRANT_LOCAL_PATH` | Path for local persistent storage (alternative to URL) | `None`
|
|
`EMBEDDING_MODEL` | Embedding model to use | `sentence-transformers/all-MiniLM-L6-v2`
|
|
`EMBEDDING_PROVIDER` | Provider for embeddings (`fastembed` or `ollama`) | `fastembed`
|
|
`TOOL_STORE_DESCRIPTION` | Custom description for the store tool | Default description
|
|
`TOOL_FIND_DESCRIPTION` | Custom description for the find tool | Default description
|
|
|
|
### Custom tool descriptions
|
|
|
|
You can customize the tool descriptions to guide the agent's behavior:
|
|
|
|
```python
|
|
env={
|
|
"QDRANT_URL": "http://localhost:6333",
|
|
"COLLECTION_NAME": "code-snippets",
|
|
"TOOL_STORE_DESCRIPTION": "Store code snippets with descriptions. The 'information' parameter should contain a description of what the code does, while the actual code should be in 'metadata.code'.",
|
|
"TOOL_FIND_DESCRIPTION": "Search for relevant code snippets using natural language. Describe the functionality you're looking for.",
|
|
}
|
|
```
|
|
|
|
## Additional resources
|
|
|
|
- [Qdrant MCP Server Repository](https://github.com/qdrant/mcp-server-qdrant)
|
|
- [Qdrant Documentation](https://qdrant.tech/documentation/)
|
|
- [Qdrant Cloud](https://cloud.qdrant.io/)
|