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344 lines
10 KiB
Plaintext
344 lines
10 KiB
Plaintext
---
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title: 'Qdrant Vector Search Tool'
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description: 'Semantic search capabilities for CrewAI agents using Qdrant vector database'
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icon: vector-square
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mode: "wide"
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---
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## Overview
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The Qdrant Vector Search Tool enables semantic search capabilities in your CrewAI agents by leveraging [Qdrant](https://qdrant.tech/), a vector similarity search engine. This tool allows your agents to search through documents stored in a Qdrant collection using semantic similarity.
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## Installation
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Install the required packages:
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```bash
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uv add qdrant-client
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```
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## Basic Usage
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Here's a minimal example of how to use the tool:
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```python
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from crewai import Agent
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from crewai_tools import QdrantVectorSearchTool, QdrantConfig
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# Initialize the tool with QdrantConfig
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qdrant_tool = QdrantVectorSearchTool(
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qdrant_config=QdrantConfig(
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qdrant_url="your_qdrant_url",
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qdrant_api_key="your_qdrant_api_key",
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collection_name="your_collection"
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)
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)
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# Create an agent that uses the tool
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agent = Agent(
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role="Research Assistant",
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goal="Find relevant information in documents",
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tools=[qdrant_tool]
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)
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# The tool will automatically use OpenAI embeddings
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# and return the 3 most relevant results with scores > 0.35
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```
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## Complete Working Example
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Here's a complete example showing how to:
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1. Extract text from a PDF
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2. Generate embeddings using OpenAI
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3. Store in Qdrant
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4. Create a CrewAI agentic RAG workflow for semantic search
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```python
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import os
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import uuid
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import pdfplumber
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from openai import OpenAI
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from dotenv import load_dotenv
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from crewai import Agent, Task, Crew, Process, LLM
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from crewai_tools import QdrantVectorSearchTool
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from qdrant_client import QdrantClient
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from qdrant_client.models import PointStruct, Distance, VectorParams
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# Load environment variables
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load_dotenv()
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# Initialize OpenAI client
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client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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# Extract text from PDF
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def extract_text_from_pdf(pdf_path):
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text = []
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with pdfplumber.open(pdf_path) as pdf:
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for page in pdf.pages:
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page_text = page.extract_text()
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if page_text:
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text.append(page_text.strip())
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return text
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# Generate OpenAI embeddings
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def get_openai_embedding(text):
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response = client.embeddings.create(
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input=text,
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model="text-embedding-3-large"
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)
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return response.data[0].embedding
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# Store text and embeddings in Qdrant
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def load_pdf_to_qdrant(pdf_path, qdrant, collection_name):
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# Extract text from PDF
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text_chunks = extract_text_from_pdf(pdf_path)
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# Create Qdrant collection
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if qdrant.collection_exists(collection_name):
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qdrant.delete_collection(collection_name)
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qdrant.create_collection(
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collection_name=collection_name,
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vectors_config=VectorParams(size=3072, distance=Distance.COSINE)
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)
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# Store embeddings
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points = []
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for chunk in text_chunks:
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embedding = get_openai_embedding(chunk)
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points.append(PointStruct(
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id=str(uuid.uuid4()),
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vector=embedding,
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payload={"text": chunk}
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))
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qdrant.upsert(collection_name=collection_name, points=points)
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# Initialize Qdrant client and load data
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qdrant = QdrantClient(
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url=os.getenv("QDRANT_URL"),
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api_key=os.getenv("QDRANT_API_KEY")
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)
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collection_name = "example_collection"
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pdf_path = "path/to/your/document.pdf"
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load_pdf_to_qdrant(pdf_path, qdrant, collection_name)
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# Initialize Qdrant search tool
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from crewai_tools import QdrantConfig
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qdrant_tool = QdrantVectorSearchTool(
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qdrant_config=QdrantConfig(
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qdrant_url=os.getenv("QDRANT_URL"),
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qdrant_api_key=os.getenv("QDRANT_API_KEY"),
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collection_name=collection_name,
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limit=3,
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score_threshold=0.35
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)
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)
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# Create CrewAI agents
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search_agent = Agent(
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role="Senior Semantic Search Agent",
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goal="Find and analyze documents based on semantic search",
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backstory="""You are an expert research assistant who can find relevant
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information using semantic search in a Qdrant database.""",
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tools=[qdrant_tool],
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verbose=True
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)
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answer_agent = Agent(
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role="Senior Answer Assistant",
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goal="Generate answers to questions based on the context provided",
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backstory="""You are an expert answer assistant who can generate
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answers to questions based on the context provided.""",
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tools=[qdrant_tool],
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verbose=True
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)
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# Define tasks
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search_task = Task(
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description="""Search for relevant documents about the {query}.
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Your final answer should include:
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- The relevant information found
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- The similarity scores of the results
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- The metadata of the relevant documents""",
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agent=search_agent
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)
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answer_task = Task(
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description="""Given the context and metadata of relevant documents,
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generate a final answer based on the context.""",
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agent=answer_agent
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)
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# Run CrewAI workflow
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crew = Crew(
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agents=[search_agent, answer_agent],
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tasks=[search_task, answer_task],
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process=Process.sequential,
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verbose=True
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)
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result = crew.kickoff(
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inputs={"query": "What is the role of X in the document?"}
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)
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print(result)
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```
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## Tool Parameters
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### Required Parameters
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- `qdrant_config` (QdrantConfig): Configuration object containing all Qdrant settings
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### QdrantConfig Parameters
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- `qdrant_url` (str): The URL of your Qdrant server
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- `qdrant_api_key` (str, optional): API key for authentication with Qdrant
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- `collection_name` (str): Name of the Qdrant collection to search
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- `limit` (int): Maximum number of results to return (default: 3)
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- `score_threshold` (float): Minimum similarity score threshold (default: 0.35)
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- `filter` (Any, optional): Qdrant Filter instance for advanced filtering (default: None)
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### Optional Tool Parameters
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- `custom_embedding_fn` (Callable[[str], list[float]]): Custom function for text vectorization
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- `qdrant_package` (str): Base package path for Qdrant (default: "qdrant_client")
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- `client` (Any): Pre-initialized Qdrant client (optional)
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## Advanced Filtering
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The QdrantVectorSearchTool supports powerful filtering capabilities to refine your search results:
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### Dynamic Filtering
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Use `filter_by` and `filter_value` parameters in your search to filter results on-the-fly:
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```python
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# Agent will use these parameters when calling the tool
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# The tool schema accepts filter_by and filter_value
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# Example: search with category filter
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# Results will be filtered where category == "technology"
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```
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### Preset Filters with QdrantConfig
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For complex filtering, use Qdrant Filter instances in your configuration:
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```python
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from qdrant_client.http import models as qmodels
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from crewai_tools import QdrantVectorSearchTool, QdrantConfig
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# Create a filter for specific conditions
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preset_filter = qmodels.Filter(
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must=[
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qmodels.FieldCondition(
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key="category",
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match=qmodels.MatchValue(value="research")
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),
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qmodels.FieldCondition(
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key="year",
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match=qmodels.MatchValue(value=2024)
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)
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]
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)
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# Initialize tool with preset filter
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qdrant_tool = QdrantVectorSearchTool(
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qdrant_config=QdrantConfig(
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qdrant_url="your_url",
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qdrant_api_key="your_key",
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collection_name="your_collection",
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filter=preset_filter # Preset filter applied to all searches
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)
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)
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```
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### Combining Filters
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The tool automatically combines preset filters from `QdrantConfig` with dynamic filters from `filter_by` and `filter_value`:
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```python
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# If QdrantConfig has a preset filter for category="research"
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# And the search uses filter_by="year", filter_value=2024
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# Both filters will be combined (AND logic)
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```
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## Search Parameters
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The tool accepts these parameters in its schema:
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- `query` (str): The search query to find similar documents
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- `filter_by` (str, optional): Metadata field to filter on
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- `filter_value` (Any, optional): Value to filter by
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## Return Format
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The tool returns results in JSON format:
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```json
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[
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{
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"metadata": {
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// Any metadata stored with the document
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},
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"context": "The actual text content of the document",
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"distance": 0.95 // Similarity score
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}
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]
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```
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## Default Embedding
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By default, the tool uses OpenAI's `text-embedding-3-large` model for vectorization. This requires:
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- OpenAI API key set in environment: `OPENAI_API_KEY`
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## Custom Embeddings
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Instead of using the default embedding model, you might want to use your own embedding function in cases where you:
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1. Want to use a different embedding model (e.g., Cohere, HuggingFace, Ollama models)
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2. Need to reduce costs by using open-source embedding models
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3. Have specific requirements for vector dimensions or embedding quality
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4. Want to use domain-specific embeddings (e.g., for medical or legal text)
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Here's an example using a HuggingFace model:
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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# Load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
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model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
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def custom_embeddings(text: str) -> list[float]:
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# Tokenize and get model outputs
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inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
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outputs = model(**inputs)
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# Use mean pooling to get text embedding
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embeddings = outputs.last_hidden_state.mean(dim=1)
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# Convert to list of floats and return
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return embeddings[0].tolist()
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# Use custom embeddings with the tool
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from crewai_tools import QdrantConfig
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tool = QdrantVectorSearchTool(
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qdrant_config=QdrantConfig(
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qdrant_url="your_url",
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qdrant_api_key="your_key",
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collection_name="your_collection"
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),
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custom_embedding_fn=custom_embeddings # Pass your custom function
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)
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```
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## Error Handling
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The tool handles these specific errors:
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- Raises ImportError if `qdrant-client` is not installed (with option to auto-install)
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- Raises ValueError if `QDRANT_URL` is not set
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- Prompts to install `qdrant-client` if missing using `uv add qdrant-client`
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## Environment Variables
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Required environment variables:
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```bash
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export QDRANT_URL="your_qdrant_url" # If not provided in constructor
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export QDRANT_API_KEY="your_api_key" # If not provided in constructor
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export OPENAI_API_KEY="your_openai_key" # If using default embeddings
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