docs: add Qdrant vector search tool documentation (#2184)

Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>

docs/mint.json

@@ -139,6 +139,7 @@
         "tools/nl2sqltool",
         "tools/pdfsearchtool",
         "tools/pgsearchtool",
+        "tools/qdrantvectorsearchtool",
         "tools/scrapewebsitetool",
         "tools/seleniumscrapingtool",
         "tools/spidertool",

docs/tools/qdrantvectorsearchtool.mdx

@@ -0,0 +1,271 @@
+---
+title: 'Qdrant Vector Search Tool'
+description: 'Semantic search capabilities for CrewAI agents using Qdrant vector database'
+icon: magnifying-glass-plus
+---
+
+# `QdrantVectorSearchTool`
+
+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.
+
+## Installation
+
+Install the required packages:
+
+```bash
+uv pip install 'crewai[tools] qdrant-client'
+```
+
+## Basic Usage
+
+Here's a minimal example of how to use the tool:
+
+```python
+from crewai import Agent
+from crewai_tools import QdrantVectorSearchTool
+
+# Initialize the tool
+qdrant_tool = QdrantVectorSearchTool(
+    qdrant_url="your_qdrant_url",
+    qdrant_api_key="your_qdrant_api_key",
+    collection_name="your_collection"
+)
+
+# Create an agent that uses the tool
+agent = Agent(
+    role="Research Assistant",
+    goal="Find relevant information in documents",
+    tools=[qdrant_tool]
+)
+
+# The tool will automatically use OpenAI embeddings
+# and return the 3 most relevant results with scores > 0.35
+```
+
+## Complete Working Example
+
+Here's a complete example showing how to:
+1. Extract text from a PDF
+2. Generate embeddings using OpenAI
+3. Store in Qdrant
+4. Create a CrewAI agentic RAG workflow for semantic search
+
+```python
+import os
+import uuid
+import pdfplumber
+from openai import OpenAI
+from dotenv import load_dotenv
+from crewai import Agent, Task, Crew, Process, LLM
+from crewai_tools import QdrantVectorSearchTool
+from qdrant_client import QdrantClient
+from qdrant_client.models import PointStruct, Distance, VectorParams
+
+# Load environment variables
+load_dotenv()
+
+# Initialize OpenAI client
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# Extract text from PDF
+def extract_text_from_pdf(pdf_path):
+    text = []
+    with pdfplumber.open(pdf_path) as pdf:
+        for page in pdf.pages:
+            page_text = page.extract_text()
+            if page_text:
+                text.append(page_text.strip())
+    return text
+
+# Generate OpenAI embeddings
+def get_openai_embedding(text):
+    response = client.embeddings.create(
+        input=text,
+        model="text-embedding-3-small"
+    )
+    return response.data[0].embedding
+
+# Store text and embeddings in Qdrant
+def load_pdf_to_qdrant(pdf_path, qdrant, collection_name):
+    # Extract text from PDF
+    text_chunks = extract_text_from_pdf(pdf_path)
+    
+    # Create Qdrant collection
+    if qdrant.collection_exists(collection_name):
+        qdrant.delete_collection(collection_name)
+    qdrant.create_collection(
+        collection_name=collection_name,
+        vectors_config=VectorParams(size=1536, distance=Distance.COSINE)
+    )
+
+    # Store embeddings
+    points = []
+    for chunk in text_chunks:
+        embedding = get_openai_embedding(chunk)
+        points.append(PointStruct(
+            id=str(uuid.uuid4()),
+            vector=embedding,
+            payload={"text": chunk}
+        ))
+    qdrant.upsert(collection_name=collection_name, points=points)
+
+# Initialize Qdrant client and load data
+qdrant = QdrantClient(
+    url=os.getenv("QDRANT_URL"),
+    api_key=os.getenv("QDRANT_API_KEY")
+)
+collection_name = "example_collection"
+pdf_path = "path/to/your/document.pdf"
+load_pdf_to_qdrant(pdf_path, qdrant, collection_name)
+
+# Initialize Qdrant search tool
+qdrant_tool = QdrantVectorSearchTool(
+    qdrant_url=os.getenv("QDRANT_URL"),
+    qdrant_api_key=os.getenv("QDRANT_API_KEY"),
+    collection_name=collection_name,
+    limit=3,
+    score_threshold=0.35
+)
+
+# Create CrewAI agents
+search_agent = Agent(
+    role="Senior Semantic Search Agent",
+    goal="Find and analyze documents based on semantic search",
+    backstory="""You are an expert research assistant who can find relevant 
+    information using semantic search in a Qdrant database.""",
+    tools=[qdrant_tool],
+    verbose=True
+)
+
+answer_agent = Agent(
+    role="Senior Answer Assistant",
+    goal="Generate answers to questions based on the context provided",
+    backstory="""You are an expert answer assistant who can generate 
+    answers to questions based on the context provided.""",
+    tools=[qdrant_tool],
+    verbose=True
+)
+
+# Define tasks
+search_task = Task(
+    description="""Search for relevant documents about the {query}.
+    Your final answer should include:
+    - The relevant information found
+    - The similarity scores of the results
+    - The metadata of the relevant documents""",
+    agent=search_agent
+)
+
+answer_task = Task(
+    description="""Given the context and metadata of relevant documents,
+    generate a final answer based on the context.""",
+    agent=answer_agent
+)
+
+# Run CrewAI workflow
+crew = Crew(
+    agents=[search_agent, answer_agent],
+    tasks=[search_task, answer_task],
+    process=Process.sequential,
+    verbose=True
+)
+
+result = crew.kickoff(
+    inputs={"query": "What is the role of X in the document?"}
+)
+print(result)
+```
+
+## Tool Parameters
+
+### Required Parameters
+- `qdrant_url` (str): The URL of your Qdrant server
+- `qdrant_api_key` (str): API key for authentication with Qdrant
+- `collection_name` (str): Name of the Qdrant collection to search
+
+### Optional Parameters
+- `limit` (int): Maximum number of results to return (default: 3)
+- `score_threshold` (float): Minimum similarity score threshold (default: 0.35)
+- `custom_embedding_fn` (Callable[[str], list[float]]): Custom function for text vectorization
+
+## Search Parameters
+
+The tool accepts these parameters in its schema:
+- `query` (str): The search query to find similar documents
+- `filter_by` (str, optional): Metadata field to filter on
+- `filter_value` (str, optional): Value to filter by
+
+## Return Format
+
+The tool returns results in JSON format:
+
+```json
+[
+  {
+    "metadata": {
+      // Any metadata stored with the document
+    },
+    "context": "The actual text content of the document",
+    "distance": 0.95  // Similarity score
+  }
+]
+```
+
+## Default Embedding
+
+By default, the tool uses OpenAI's `text-embedding-3-small` model for vectorization. This requires:
+- OpenAI API key set in environment: `OPENAI_API_KEY`
+
+## Custom Embeddings
+
+Instead of using the default embedding model, you might want to use your own embedding function in cases where you:
+
+1. Want to use a different embedding model (e.g., Cohere, HuggingFace, Ollama models)
+2. Need to reduce costs by using open-source embedding models
+3. Have specific requirements for vector dimensions or embedding quality
+4. Want to use domain-specific embeddings (e.g., for medical or legal text)
+
+Here's an example using a HuggingFace model:
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+
+def custom_embeddings(text: str) -> list[float]:
+    # Tokenize and get model outputs
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    outputs = model(**inputs)
+    
+    # Use mean pooling to get text embedding
+    embeddings = outputs.last_hidden_state.mean(dim=1)
+    
+    # Convert to list of floats and return
+    return embeddings[0].tolist()
+
+# Use custom embeddings with the tool
+tool = QdrantVectorSearchTool(
+    qdrant_url="your_url",
+    qdrant_api_key="your_key",
+    collection_name="your_collection",
+    custom_embedding_fn=custom_embeddings  # Pass your custom function
+)
+```
+
+## Error Handling
+
+The tool handles these specific errors:
+- Raises ImportError if `qdrant-client` is not installed (with option to auto-install)
+- Raises ValueError if `QDRANT_URL` is not set
+- Prompts to install `qdrant-client` if missing using `uv add qdrant-client`
+
+## Environment Variables
+
+Required environment variables:
+```bash
+export QDRANT_URL="your_qdrant_url"  # If not provided in constructor
+export QDRANT_API_KEY="your_api_key"  # If not provided in constructor
+export OPENAI_API_KEY="your_openai_key"  # If using default embeddings