Merge pull request #200 from crewAIInc/feat/add-qdrant-tools

enable qdrant as vector search tool for crew agents

src/crewai_tools/__init__.py

@@ -30,6 +30,7 @@ from .tools import (
     PatronusPredefinedCriteriaEvalTool,
     PDFSearchTool,
     PGSearchTool,
+    QdrantVectorSearchTool,
     RagTool,
     ScrapeElementFromWebsiteTool,
     ScrapegraphScrapeTool,

src/crewai_tools/tools/__init__.py

@@ -35,6 +35,7 @@ from .patronus_eval_tool import (
 )
 from .pdf_search_tool.pdf_search_tool import PDFSearchTool
 from .pg_seach_tool.pg_search_tool import PGSearchTool
+from .qdrant_vector_search_tool.qdrant_search_tool import QdrantVectorSearchTool
 from .rag.rag_tool import RagTool
 from .scrape_element_from_website.scrape_element_from_website import (
     ScrapeElementFromWebsiteTool,

src/crewai_tools/tools/qdrant_vector_search_tool/README.md

@@ -0,0 +1,49 @@
+# QdrantVectorSearchTool
+
+## Description
+
+This tool is specifically crafted for conducting semantic searches within docs within a Qdrant vector database. Use this tool to find semantically similar docs to a given query.
+
+Qdrant is a vector database that is used to store and query vector embeddings. You can follow their docs here: https://qdrant.tech/documentation/
+
+## Installation
+
+Install the crewai_tools package by executing the following command in your terminal:
+
+```shell
+uv pip install 'crewai[tools] qdrant-client openai'
+```
+
+## Example
+
+To utilize the QdrantVectorSearchTool for different use cases, follow these examples: Default model is openai.
+
+```python
+from crewai_tools import QdrantVectorSearchTool
+
+# To enable the tool to search any website the agent comes across or learns about during its operation
+tool = QdrantVectorSearchTool(
+    collection_name="example_collections",
+    limit=3,
+    qdrant_url="https://your-qdrant-cluster-url.com",
+    qdrant_api_key="your-qdrant-api-key",
+)
+
+
+# Adding the tool to an agent
+rag_agent = Agent(
+    name="rag_agent",
+    role="You are a helpful assistant that can answer questions with the help of the QdrantVectorSearchTool. Retrieve the most relevant docs from the Qdrant database.",
+    llm="gpt-4o-mini",
+    tools=[tool],
+)
+```
+
+## Arguments
+
+- `collection_name` : The name of the collection to search within. (Required)
+- `qdrant_url` : The URL of the Qdrant cluster. (Required)
+- `qdrant_api_key` : The API key for the Qdrant cluster. (Required)
+- `limit` : The number of results to return. (Optional)
+- `vectorizer` : The vectorizer to use. (Optional)
+

src/crewai_tools/tools/qdrant_vector_search_tool/qdrant_search_tool.py

@@ -0,0 +1,181 @@
+import json
+import os
+from typing import Any, Optional, Type
+
+
+try:
+    from qdrant_client import QdrantClient
+    from qdrant_client.http.models import Filter, FieldCondition, MatchValue
+
+    QDRANT_AVAILABLE = True
+except ImportError:
+    QDRANT_AVAILABLE = False
+    QdrantClient = Any  # type placeholder
+    Filter = Any
+    FieldCondition = Any
+    MatchValue = Any
+
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+
+class QdrantToolSchema(BaseModel):
+    """Input for QdrantTool."""
+
+    query: str = Field(
+        ...,
+        description="The query to search retrieve relevant information from the Qdrant database. Pass only the query, not the question.",
+    )
+    filter_by: Optional[str] = Field(
+        default=None,
+        description="Filter by properties. Pass only the properties, not the question.",
+    )
+    filter_value: Optional[str] = Field(
+        default=None,
+        description="Filter by value. Pass only the value, not the question.",
+    )
+
+
+class QdrantVectorSearchTool(BaseTool):
+    """Tool to query and filter results from a Qdrant database.
+
+    This tool enables vector similarity search on internal documents stored in Qdrant,
+    with optional filtering capabilities.
+
+    Attributes:
+        client: Configured QdrantClient instance
+        collection_name: Name of the Qdrant collection to search
+        limit: Maximum number of results to return
+        score_threshold: Minimum similarity score threshold
+        qdrant_url: Qdrant server URL
+        qdrant_api_key: Authentication key for Qdrant
+    """
+
+    model_config = {"arbitrary_types_allowed": True}
+    client: QdrantClient = None
+    name: str = "QdrantVectorSearchTool"
+    description: str = "A tool to search the Qdrant database for relevant information on internal documents."
+    args_schema: Type[BaseModel] = QdrantToolSchema
+    query: Optional[str] = None
+    filter_by: Optional[str] = None
+    filter_value: Optional[str] = None
+    collection_name: Optional[str] = None
+    limit: Optional[int] = Field(default=3)
+    score_threshold: float = Field(default=0.35)
+    qdrant_url: str = Field(
+        ...,
+        description="The URL of the Qdrant server",
+    )
+    qdrant_api_key: str = Field(
+        ...,
+        description="The API key for the Qdrant server",
+    )
+    custom_embedding_fn: Optional[callable] = Field(
+        default=None,
+        description="A custom embedding function to use for vectorization. If not provided, the default model will be used.",
+    )
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        if QDRANT_AVAILABLE:
+            self.client = QdrantClient(
+                url=self.qdrant_url,
+                api_key=self.qdrant_api_key,
+            )
+        else:
+            import click
+
+            if click.confirm(
+                "The 'qdrant-client' package is required to use the QdrantVectorSearchTool. "
+                "Would you like to install it?"
+            ):
+                import subprocess
+
+                subprocess.run(["uv", "add", "qdrant-client"], check=True)
+            else:
+                raise ImportError(
+                    "The 'qdrant-client' package is required to use the QdrantVectorSearchTool. "
+                    "Please install it with: uv add qdrant-client"
+                )
+
+    def _run(
+        self,
+        query: str,
+        filter_by: Optional[str] = None,
+        filter_value: Optional[str] = None,
+    ) -> str:
+        """Execute vector similarity search on Qdrant.
+
+        Args:
+            query: Search query to vectorize and match
+            filter_by: Optional metadata field to filter on
+            filter_value: Optional value to filter by
+
+        Returns:
+            JSON string containing search results with metadata and scores
+
+        Raises:
+            ImportError: If qdrant-client is not installed
+            ValueError: If Qdrant credentials are missing
+        """
+
+        if not self.qdrant_url:
+            raise ValueError("QDRANT_URL is not set")
+
+        # Create filter if filter parameters are provided
+        search_filter = None
+        if filter_by and filter_value:
+            search_filter = Filter(
+                must=[
+                    FieldCondition(key=filter_by, match=MatchValue(value=filter_value))
+                ]
+            )
+
+        # Search in Qdrant using the built-in query method
+        query_vector = (
+            self._vectorize_query(query)
+            if not self.custom_embedding_fn
+            else self.custom_embedding_fn(query)
+        )
+        search_results = self.client.query_points(
+            collection_name=self.collection_name,
+            query=query_vector,
+            query_filter=search_filter,
+            limit=self.limit,
+            score_threshold=self.score_threshold,
+        )
+
+        # Format results similar to storage implementation
+        results = []
+        # Extract the list of ScoredPoint objects from the tuple
+        for point in search_results:
+            result = {
+                "metadata": point[1][0].payload.get("metadata", {}),
+                "context": point[1][0].payload.get("text", ""),
+                "distance": point[1][0].score,
+            }
+            results.append(result)
+
+        return json.dumps(results, indent=2)
+
+    def _vectorize_query(self, query: str) -> list[float]:
+        """Default vectorization function with openai.
+
+        Args:
+            query (str): The query to vectorize
+
+        Returns:
+            list[float]: The vectorized query
+        """
+        import openai
+
+        client = openai.Client(api_key=os.getenv("OPENAI_API_KEY"))
+        embedding = (
+            client.embeddings.create(
+                input=[query],
+                model="text-embedding-3-small",
+            )
+            .data[0]
+            .embedding
+        )
+        return embedding