harshalmore31
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# Qdrant RAG Integration
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This example demonstrates how to integrate Qdrant vector database with Swarms agents for Retrieval-Augmented Generation (RAG). Qdrant is a high-performance vector database that enables agents to store, index, and retrieve documents using semantic similarity search for enhanced context and more accurate responses.
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## Prerequisites
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- Python 3.7+
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- OpenAI API key
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- Swarms library
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- Qdrant client and swarms-memory
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## Installation
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```bash
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pip install qdrant-client fastembed swarms-memory litellm
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```
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> **Note**: The `litellm` package is required for using LiteLLM provider models like OpenAI, Azure, Cohere, etc.
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## Tutorial Steps
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### Step 1: Install Swarms
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First, install the latest version of Swarms:
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```bash
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pip3 install -U swarms
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```
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### Step 2: Environment Setup
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Set up your environment variables in a `.env` file:
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```plaintext
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OPENAI_API_KEY="your-api-key-here"
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QDRANT_URL="https://your-cluster.qdrant.io"
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QDRANT_API_KEY="your-api-key"
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WORKSPACE_DIR="agent_workspace"
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```
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### Step 3: Choose Deployment
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Select your Qdrant deployment option:
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- **In-memory**: For testing and development (data is not persisted)
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- **Local server**: For production deployments with persistent storage
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- **Qdrant Cloud**: Managed cloud service (recommended for production)
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### Step 4: Configure Database
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Set up the vector database wrapper with your preferred embedding model and collection settings
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### Step 5: Add Documents
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Load documents using individual or batch processing methods
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### Step 6: Create Agent
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Initialize your agent with RAG capabilities and start querying
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## Code
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### Basic Setup with Individual Document Processing
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```python
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from qdrant_client import QdrantClient, models
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from swarms import Agent
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from swarms_memory import QdrantDB
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import os
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# Client Configuration Options
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# Option 1: In-memory (testing only - data is NOT persisted)
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# ":memory:" creates a temporary in-memory database that's lost when program ends
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client = QdrantClient(":memory:")
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# Option 2: Local Qdrant Server
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# Requires: docker run -p 6333:6333 qdrant/qdrant
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# client = QdrantClient(host="localhost", port=6333)
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# Option 3: Qdrant Cloud (recommended for production)
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# Get credentials from https://cloud.qdrant.io
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# client = QdrantClient(
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# url=os.getenv("QDRANT_URL"), # e.g., "https://xyz-abc.eu-central.aws.cloud.qdrant.io"
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# api_key=os.getenv("QDRANT_API_KEY") # Your Qdrant Cloud API key
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# )
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# Create vector database wrapper
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rag_db = QdrantDB(
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client=client,
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embedding_model="text-embedding-3-small",
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collection_name="knowledge_base",
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distance=models.Distance.COSINE,
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n_results=3
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)
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# Add documents to the knowledge base
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documents = [
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"Qdrant is a vector database optimized for similarity search and AI applications.",
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"RAG combines retrieval and generation for more accurate AI responses.",
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"Vector embeddings enable semantic search across documents.",
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"The swarms framework supports multiple memory backends including Qdrant."
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]
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# Method 1: Add documents individually
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for doc in documents:
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rag_db.add(doc)
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# Create agent with RAG capabilities
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agent = Agent(
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agent_name="RAG-Agent",
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agent_description="Agent with Qdrant-powered RAG for enhanced knowledge retrieval",
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model_name="gpt-4o",
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max_loops=1,
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dynamic_temperature_enabled=True,
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long_term_memory=rag_db
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)
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# Query with RAG
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try:
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response = agent.run("What is Qdrant and how does it relate to RAG?")
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print(response)
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except Exception as e:
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print(f"Error during query: {e}")
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# Handle error appropriately
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```
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### Advanced Setup with Batch Processing and Metadata
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```python
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from qdrant_client import QdrantClient, models
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from swarms import Agent
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from swarms_memory import QdrantDB
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import os
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# Initialize client (using in-memory for this example)
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client = QdrantClient(":memory:")
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# Create vector database wrapper
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rag_db = QdrantDB(
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client=client,
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embedding_model="text-embedding-3-small",
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collection_name="advanced_knowledge_base",
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distance=models.Distance.COSINE,
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n_results=3
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)
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# Method 2: Batch add documents (more efficient for large datasets)
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# Example with metadata
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documents_with_metadata = [
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"Machine learning is a subset of artificial intelligence.",
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"Deep learning uses neural networks with multiple layers.",
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"Natural language processing enables computers to understand human language.",
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"Computer vision allows machines to interpret visual information.",
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"Reinforcement learning learns through interaction with an environment."
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]
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metadata = [
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{"category": "AI", "difficulty": "beginner", "topic": "overview"},
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{"category": "ML", "difficulty": "intermediate", "topic": "neural_networks"},
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{"category": "NLP", "difficulty": "intermediate", "topic": "language"},
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{"category": "CV", "difficulty": "advanced", "topic": "vision"},
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{"category": "RL", "difficulty": "advanced", "topic": "learning"}
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]
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# Batch add with metadata
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doc_ids = rag_db.batch_add(documents_with_metadata, metadata=metadata, batch_size=3)
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print(f"Added {len(doc_ids)} documents in batch")
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# Query with metadata return
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results_with_metadata = rag_db.query(
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"What is artificial intelligence?",
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n_results=3,
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return_metadata=True
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)
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for i, result in enumerate(results_with_metadata):
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print(f"\nResult {i+1}:")
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print(f" Document: {result['document']}")
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print(f" Category: {result['category']}")
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print(f" Difficulty: {result['difficulty']}")
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print(f" Topic: {result['topic']}")
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print(f" Score: {result['score']:.4f}")
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# Create agent with RAG capabilities
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agent = Agent(
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agent_name="Advanced-RAG-Agent",
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agent_description="Advanced agent with metadata-enhanced RAG capabilities",
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model_name="gpt-4o",
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max_loops=1,
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dynamic_temperature_enabled=True,
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long_term_memory=rag_db
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)
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# Query with enhanced context
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response = agent.run("Explain the relationship between machine learning and artificial intelligence")
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print(response)
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```
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## Production Setup
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### Setting up Qdrant Cloud
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1. Sign up at [cloud.qdrant.io](https://cloud.qdrant.io)
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2. Create a cluster
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3. Get your cluster URL and API key
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4. Set environment variables:
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```bash
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export QDRANT_URL="https://your-cluster.eu-central.aws.cloud.qdrant.io"
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export QDRANT_API_KEY="your-api-key-here"
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```
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### Running Local Qdrant Server
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```bash
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# Docker
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docker run -p 6333:6333 qdrant/qdrant
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# Docker Compose
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version: '3.7'
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services:
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qdrant:
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image: qdrant/qdrant
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ports:
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- "6333:6333"
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volumes:
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- ./qdrant_storage:/qdrant/storage
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```
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### Production Configuration Example
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```python
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from qdrant_client import QdrantClient, models
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from swarms_memory import QdrantDB
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import os
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import logging
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# Setup logging for production monitoring
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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try:
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# Connect to Qdrant server with proper error handling
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client = QdrantClient(
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host=os.getenv("QDRANT_HOST", "localhost"),
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port=int(os.getenv("QDRANT_PORT", "6333")),
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api_key=os.getenv("QDRANT_API_KEY"), # Use environment variable
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timeout=30 # 30 second timeout
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)
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# Production RAG configuration with enhanced settings
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rag_db = QdrantDB(
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client=client,
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embedding_model="text-embedding-3-large", # Higher quality embeddings
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collection_name="production_knowledge",
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distance=models.Distance.COSINE,
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n_results=10,
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api_key=os.getenv("OPENAI_API_KEY") # Secure API key handling
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)
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logger.info("Successfully initialized production RAG database")
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except Exception as e:
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logger.error(f"Failed to initialize RAG database: {e}")
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raise
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```
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## Configuration Options
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### Distance Metrics
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| Metric | Description | Best For |
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|--------|-------------|----------|
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| **COSINE** | Cosine similarity (default) | Normalized embeddings, text similarity |
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| **EUCLIDEAN** | Euclidean distance | Absolute distance measurements |
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| **DOT** | Dot product | Maximum inner product search |
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### Embedding Model Options
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#### LiteLLM Provider Models (Recommended)
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| Model | Provider | Dimensions | Description |
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|-------|----------|------------|-------------|
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| `text-embedding-3-small` | OpenAI | 1536 | Efficient, cost-effective |
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| `text-embedding-3-large` | OpenAI | 3072 | Best quality |
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| `azure/your-deployment` | Azure | Variable | Azure OpenAI embeddings |
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| `cohere/embed-english-v3.0` | Cohere | 1024 | Advanced language understanding |
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| `voyage/voyage-3-large` | Voyage AI | 1024 | High-quality embeddings |
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#### SentenceTransformer Models
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| Model | Dimensions | Description |
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|-------|------------|-------------|
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| `all-MiniLM-L6-v2` | 384 | Fast, general-purpose |
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| `all-mpnet-base-v2` | 768 | Higher quality |
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| `all-roberta-large-v1` | 1024 | Best quality |
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#### Usage Example
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```python
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# OpenAI embeddings (default example)
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rag_db = QdrantDB(
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client=client,
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embedding_model="text-embedding-3-small",
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collection_name="openai_collection"
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)
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```
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> **Note**: QdrantDB supports all LiteLLM provider models (Azure, Cohere, Voyage AI, etc.), SentenceTransformer models, and custom embedding functions. See the embedding model options table above for the complete list.
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## Use Cases
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### Document Q&A System
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Create an intelligent document question-answering system:
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```python
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# Load company documents into Qdrant
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company_documents = [
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"Company policy on remote work allows flexible scheduling with core hours 10 AM - 3 PM.",
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"API documentation: Use POST /api/v1/users to create new user accounts.",
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"Product specifications: Our software supports Windows, Mac, and Linux platforms."
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]
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for doc in company_documents:
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rag_db.add(doc)
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# Agent can now answer questions using the documents
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agent = Agent(
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agent_name="Company-DocQA-Agent",
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agent_description="Intelligent document Q&A system for company information",
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model_name="gpt-4o",
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long_term_memory=rag_db
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)
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answer = agent.run("What is the company policy on remote work?")
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print(answer)
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```
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### Knowledge Base Management
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Build a comprehensive knowledge management system:
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```python
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class KnowledgeBaseAgent:
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def __init__(self):
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self.client = QdrantClient(":memory:")
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self.rag_db = QdrantDB(
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client=self.client,
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embedding_model="text-embedding-3-small",
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collection_name="knowledge_base",
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n_results=5
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)
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self.agent = Agent(
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agent_name="KB-Management-Agent",
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agent_description="Knowledge base management and retrieval system",
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model_name="gpt-4o",
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long_term_memory=self.rag_db
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)
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def add_knowledge(self, text: str, metadata: dict = None):
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"""Add new knowledge to the base"""
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if metadata:
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return self.rag_db.batch_add([text], metadata=[metadata])
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return self.rag_db.add(text)
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def query(self, question: str):
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"""Query the knowledge base"""
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return self.agent.run(question)
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def bulk_import(self, documents: list, metadata_list: list = None):
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"""Import multiple documents efficiently"""
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return self.rag_db.batch_add(documents, metadata=metadata_list, batch_size=50)
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# Usage
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kb = KnowledgeBaseAgent()
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kb.add_knowledge("Python is a high-level programming language.", {"category": "programming"})
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kb.add_knowledge("Qdrant is optimized for vector similarity search.", {"category": "databases"})
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result = kb.query("What programming languages are mentioned?")
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print(result)
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```
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## Best Practices
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### Document Processing Strategy
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| Practice | Recommendation | Details |
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|----------|----------------|---------|
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| **Chunking** | 200-500 tokens | Split large documents into optimal chunks for retrieval |
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| **Overlap** | 20-50 tokens | Maintain context between consecutive chunks |
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| **Preprocessing** | Clean & normalize | Remove noise and standardize text format |
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### Collection Organization
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| Practice | Recommendation | Details |
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|----------|----------------|---------|
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| **Separation** | Type-based collections | Use separate collections for docs, policies, code, etc. |
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| **Naming** | Consistent conventions | Follow clear, descriptive naming patterns |
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| **Lifecycle** | Update strategies | Plan for document versioning and updates |
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### Embedding Model Selection
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| Environment | Recommended Model | Use Case |
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|-------------|-------------------|----------|
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| **Development** | `all-MiniLM-L6-v2` | Fast iteration and testing |
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| **Production** | `text-embedding-3-small/large` | High-quality production deployment |
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| **Specialized** | Domain-specific models | Industry or domain-focused applications |
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### Performance Optimization
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| Setting | Recommendation | Rationale |
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|---------|----------------|-----------|
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| **Retrieval Count** | Start with 3-5 results | Balance relevance with performance |
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| **Batch Operations** | Use `batch_add()` | Efficient bulk document processing |
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| **Metadata** | Strategic storage | Enable filtering and enhanced context |
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### Production Deployment
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| Component | Best Practice | Implementation |
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|-----------|---------------|----------------|
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| **Storage** | Persistent server | Use Qdrant Cloud or self-hosted server |
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| **Error Handling** | Robust mechanisms | Implement retry logic and graceful failures |
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| **Monitoring** | Performance tracking | Monitor metrics and embedding quality |
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## Performance Tips
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- **Development**: Use in-memory mode for rapid prototyping and testing
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- **Production**: Deploy dedicated Qdrant server with appropriate resource allocation
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- **Scalability**: Use batch operations for adding multiple documents efficiently
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- **Memory Management**: Monitor memory usage with large document collections
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- **API Usage**: Consider rate limits when using cloud-based embedding services
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- **Caching**: Implement caching strategies for frequently accessed documents
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## Customization
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You can modify the system configuration to create specialized RAG agents for different use cases:
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| Use Case | Configuration | Description |
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|----------|---------------|-------------|
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| **Technical Documentation** | High n_results (10-15), precise embeddings | Comprehensive technical Q&A |
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| **Customer Support** | Fast embeddings, metadata filtering | Quick response with categorization |
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| **Research Assistant** | Large embedding model, broad retrieval | Deep analysis and synthesis |
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| **Code Documentation** | Code-specific embeddings, semantic chunking | Programming-focused assistance |
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## Related Resources
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- [Qdrant Documentation](https://qdrant.tech/documentation/)
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- [Swarms Memory GitHub Repository](https://github.com/The-Swarm-Corporation/swarms-memory)
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- [Agent Documentation](../agents/new_agent.md)
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- [OpenAI Embeddings Guide](https://platform.openai.com/docs/guides/embeddings)
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- [Vector Database Concepts](https://qdrant.tech/documentation/concepts/)
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@ -0,0 +1,38 @@
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from swarms import Agent
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# SYSTEM_PROMPT = (
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# "You are an expert system for generating immersive, location-based augmented reality (AR) experiences. "
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# "Given an input image, your task is to thoroughly analyze the scene and identify every point of interest (POI), "
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# "including landmarks, objects, architectural features, signage, and any elements relevant to the location or context. "
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# "For each POI you detect, provide a clear annotation that includes:\n"
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# "- A concise label or title for the POI\n"
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# "- A detailed description explaining its significance, historical or cultural context, or practical information\n"
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# "- Any relevant facts, trivia, or actionable insights that would enhance a user's AR experience\n"
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# "Present your output as a structured list, with each POI clearly separated. "
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# "Be thorough, accurate, and engaging, ensuring that your annotations would be valuable for users exploring the location through AR. "
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# "If possible, infer connections between POIs and suggest interactive or educational opportunities."
|
||||
# "Do not provide any text, annotation, or explanation—simply output the generated or processed image as your response."
|
||||
# )
|
||||
|
||||
|
||||
SYSTEM_PROMPT = (
|
||||
"You are a location-based AR experience generator. Highlight points of interest in this image and annotate relevant information about it. "
|
||||
"Return the image only."
|
||||
)
|
||||
|
||||
agent = Agent(
|
||||
agent_name="Tactical-Strategist-Agent",
|
||||
agent_description="Agent specialized in tactical strategy, scenario analysis, and actionable recommendations for complex situations.",
|
||||
model_name="gemini/gemini-2.5-flash-image-preview",
|
||||
dynamic_temperature_enabled=True,
|
||||
max_loops=1,
|
||||
dynamic_context_window=True,
|
||||
retry_interval=1,
|
||||
)
|
||||
|
||||
out = agent.run(
|
||||
task=f"{SYSTEM_PROMPT} \n\n Annotate all the tallest buildings in the image",
|
||||
img="hk.jpg",
|
||||
)
|
||||
|
||||
print(out)
|
||||
|
After Width: | Height: | Size: 151 KiB |
@ -0,0 +1,97 @@
|
||||
"""
|
||||
Agent with Qdrant RAG (Retrieval-Augmented Generation)
|
||||
|
||||
This example demonstrates using Qdrant as a vector database for RAG operations,
|
||||
allowing agents to store and retrieve documents for enhanced context.
|
||||
"""
|
||||
|
||||
from qdrant_client import QdrantClient, models
|
||||
from swarms import Agent
|
||||
from swarms_memory import QdrantDB
|
||||
|
||||
|
||||
# Initialize Qdrant client
|
||||
# Option 1: In-memory (for testing/development - data is not persisted)
|
||||
# client = QdrantClient(":memory:")
|
||||
|
||||
# Option 2: Local Qdrant server
|
||||
# client = QdrantClient(host="localhost", port=6333)
|
||||
|
||||
# Option 3: Qdrant Cloud (recommended for production)
|
||||
import os
|
||||
client = QdrantClient(
|
||||
url=os.getenv("QDRANT_URL", "https://your-cluster.qdrant.io"),
|
||||
api_key=os.getenv("QDRANT_API_KEY", "your-api-key")
|
||||
)
|
||||
|
||||
# Create QdrantDB wrapper for RAG operations
|
||||
rag_db = QdrantDB(
|
||||
client=client,
|
||||
embedding_model="text-embedding-3-small",
|
||||
collection_name="knowledge_base",
|
||||
distance=models.Distance.COSINE,
|
||||
n_results=3
|
||||
)
|
||||
|
||||
# Add documents to the knowledge base
|
||||
documents = [
|
||||
"Qdrant is a vector database optimized for similarity search and AI applications.",
|
||||
"RAG combines retrieval and generation for more accurate AI responses.",
|
||||
"Vector embeddings enable semantic search across documents.",
|
||||
"The swarms framework supports multiple memory backends including Qdrant."
|
||||
]
|
||||
|
||||
# Method 1: Add documents individually
|
||||
for doc in documents:
|
||||
rag_db.add(doc)
|
||||
|
||||
# Method 2: Batch add documents (more efficient for large datasets)
|
||||
# Example with metadata
|
||||
# documents_with_metadata = [
|
||||
# "Machine learning is a subset of artificial intelligence.",
|
||||
# "Deep learning uses neural networks with multiple layers.",
|
||||
# "Natural language processing enables computers to understand human language.",
|
||||
# "Computer vision allows machines to interpret visual information.",
|
||||
# "Reinforcement learning learns through interaction with an environment."
|
||||
# ]
|
||||
#
|
||||
# metadata = [
|
||||
# {"category": "AI", "difficulty": "beginner", "topic": "overview"},
|
||||
# {"category": "ML", "difficulty": "intermediate", "topic": "neural_networks"},
|
||||
# {"category": "NLP", "difficulty": "intermediate", "topic": "language"},
|
||||
# {"category": "CV", "difficulty": "advanced", "topic": "vision"},
|
||||
# {"category": "RL", "difficulty": "advanced", "topic": "learning"}
|
||||
# ]
|
||||
#
|
||||
# # Batch add with metadata
|
||||
# doc_ids = rag_db.batch_add(documents_with_metadata, metadata=metadata, batch_size=3)
|
||||
# print(f"Added {len(doc_ids)} documents in batch")
|
||||
#
|
||||
# # Query with metadata return
|
||||
# results_with_metadata = rag_db.query(
|
||||
# "What is artificial intelligence?",
|
||||
# n_results=3,
|
||||
# return_metadata=True
|
||||
# )
|
||||
#
|
||||
# for i, result in enumerate(results_with_metadata):
|
||||
# print(f"\nResult {i+1}:")
|
||||
# print(f" Document: {result['document']}")
|
||||
# print(f" Category: {result['category']}")
|
||||
# print(f" Difficulty: {result['difficulty']}")
|
||||
# print(f" Topic: {result['topic']}")
|
||||
# print(f" Score: {result['score']:.4f}")
|
||||
|
||||
# Create agent with RAG capabilities
|
||||
agent = Agent(
|
||||
agent_name="RAG-Agent",
|
||||
agent_description="Agent with Qdrant-powered RAG for enhanced knowledge retrieval",
|
||||
model_name="gpt-4o",
|
||||
max_loops=1,
|
||||
dynamic_temperature_enabled=True,
|
||||
long_term_memory=rag_db
|
||||
)
|
||||
|
||||
# Query with RAG
|
||||
response = agent.run("What is Qdrant and how does it relate to RAG?")
|
||||
print(response)
|
||||
@ -0,0 +1,20 @@
|
||||
from swarms import Agent
|
||||
from swarms_tools import exa_search
|
||||
|
||||
# Initialize the agent
|
||||
agent = Agent(
|
||||
agent_name="Quantitative-Trading-Agent",
|
||||
agent_description="Advanced quantitative trading and algorithmic analysis agent",
|
||||
model_name="claude-sonnet-4-20250514",
|
||||
dynamic_temperature_enabled=True,
|
||||
max_loops=1,
|
||||
tools=[exa_search],
|
||||
dynamic_context_window=True,
|
||||
streaming_on=False,
|
||||
)
|
||||
|
||||
out = agent.run(
|
||||
task="What are the best top 3 etfs for gold coverage?"
|
||||
)
|
||||
|
||||
print(out)
|
||||
@ -0,0 +1,411 @@
|
||||
"""Testing all the parameters and methods of the reasoning agent router
|
||||
- Parameters: description, model_name, system_prompt, max_loops, swarm_type, num_samples, output_types, num_knowledge_items, memory_capacity, eval, random_models_on, majority_voting_prompt, reasoning_model_name
|
||||
- Methods: select_swarm(), run (task: str, img: Optional[List[str]] = None, **kwargs), batched_run (tasks: List[str], imgs: Optional[List[List[str]]] = None, **kwargs)
|
||||
"""
|
||||
import time
|
||||
from swarms.agents import ReasoningAgentRouter
|
||||
from swarms.structs.agent import Agent
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
class TestReport:
|
||||
def __init__(self):
|
||||
self.results = []
|
||||
self.start_time = None
|
||||
self.end_time = None
|
||||
|
||||
def start(self):
|
||||
self.start_time = datetime.now()
|
||||
|
||||
def end(self):
|
||||
self.end_time = datetime.now()
|
||||
|
||||
def add_result(self, test_name, passed, message="", duration=0):
|
||||
self.results.append(
|
||||
{
|
||||
"test_name": test_name,
|
||||
"passed": passed,
|
||||
"message": message,
|
||||
"duration": duration,
|
||||
}
|
||||
)
|
||||
|
||||
def generate_report(self):
|
||||
total_tests = len(self.results)
|
||||
passed_tests = sum(1 for r in self.results if r["passed"])
|
||||
failed_tests = total_tests - passed_tests
|
||||
duration = (
|
||||
(self.end_time - self.start_time).total_seconds()
|
||||
if self.start_time and self.end_time
|
||||
else 0
|
||||
)
|
||||
|
||||
report_lines = []
|
||||
report_lines.append("=" * 60)
|
||||
report_lines.append("REASONING AGENT ROUTER TEST SUITE REPORT")
|
||||
report_lines.append("=" * 60)
|
||||
if self.start_time:
|
||||
report_lines.append(f"Test Run Started: {self.start_time.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
if self.end_time:
|
||||
report_lines.append(f"Test Run Ended: {self.end_time.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
report_lines.append(f"Duration: {duration:.2f} seconds")
|
||||
report_lines.append(f"Total Tests: {total_tests}")
|
||||
report_lines.append(f"Passed: {passed_tests}")
|
||||
report_lines.append(f"Failed: {failed_tests}")
|
||||
report_lines.append("")
|
||||
|
||||
for idx, result in enumerate(self.results, 1):
|
||||
status = "PASS" if result["passed"] else "FAIL"
|
||||
line = f"{idx:02d}. [{status}] {result['test_name']} ({result['duration']:.2f}s)"
|
||||
if result["message"]:
|
||||
line += f" - {result['message']}"
|
||||
report_lines.append(line)
|
||||
|
||||
report_lines.append("=" * 60)
|
||||
return "\n".join(report_lines)
|
||||
|
||||
# INSERT_YOUR_CODE
|
||||
# Default parameters for ReasoningAgentRouter, can be overridden in each test
|
||||
DEFAULT_AGENT_NAME = "reasoning-agent"
|
||||
DEFAULT_DESCRIPTION = "A reasoning agent that can answer questions and help with tasks."
|
||||
DEFAULT_MODEL_NAME = "gpt-4o-mini"
|
||||
DEFAULT_SYSTEM_PROMPT = "You are a helpful assistant that can answer questions and help with tasks."
|
||||
DEFAULT_MAX_LOOPS = 1
|
||||
DEFAULT_SWARM_TYPE = "self-consistency"
|
||||
DEFAULT_NUM_SAMPLES = 3
|
||||
DEFAULT_EVAL = False
|
||||
DEFAULT_RANDOM_MODELS_ON = False
|
||||
DEFAULT_MAJORITY_VOTING_PROMPT = None
|
||||
|
||||
def test_agents_swarm(
|
||||
agent_name=DEFAULT_AGENT_NAME,
|
||||
description=DEFAULT_DESCRIPTION,
|
||||
model_name=DEFAULT_MODEL_NAME,
|
||||
system_prompt=DEFAULT_SYSTEM_PROMPT,
|
||||
max_loops=DEFAULT_MAX_LOOPS,
|
||||
swarm_type=DEFAULT_SWARM_TYPE,
|
||||
num_samples=DEFAULT_NUM_SAMPLES,
|
||||
eval=DEFAULT_EVAL,
|
||||
random_models_on=DEFAULT_RANDOM_MODELS_ON,
|
||||
majority_voting_prompt=DEFAULT_MAJORITY_VOTING_PROMPT,
|
||||
):
|
||||
reasoning_agent_router = ReasoningAgentRouter(
|
||||
agent_name=agent_name,
|
||||
description=description,
|
||||
model_name=model_name,
|
||||
system_prompt=system_prompt,
|
||||
max_loops=max_loops,
|
||||
swarm_type=swarm_type,
|
||||
num_samples=num_samples,
|
||||
eval=eval,
|
||||
random_models_on=random_models_on,
|
||||
majority_voting_prompt=majority_voting_prompt,
|
||||
)
|
||||
|
||||
result = reasoning_agent_router.run(
|
||||
"What is the best possible financial strategy to maximize returns but minimize risk? Give a list of etfs to invest in and the percentage of the portfolio to allocate to each etf."
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
"""
|
||||
PARAMETERS TESTING
|
||||
"""
|
||||
|
||||
def test_router_description(report):
|
||||
"""Test ReasoningAgentRouter with custom description (only change description param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
result = test_agents_swarm(description="Test description for router")
|
||||
# Check if the description was set correctly
|
||||
router = ReasoningAgentRouter(description="Test description for router")
|
||||
if router.description == "Test description for router":
|
||||
report.add_result("Parameter: description", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: description", False, message=f"Expected description 'Test description for router', got '{router.description}'", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: description", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_model_name(report):
|
||||
"""Test ReasoningAgentRouter with custom model_name (only change model_name param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
result = test_agents_swarm(model_name="gpt-4")
|
||||
router = ReasoningAgentRouter(model_name="gpt-4")
|
||||
if router.model_name == "gpt-4":
|
||||
report.add_result("Parameter: model_name", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: model_name", False, message=f"Expected model_name 'gpt-4', got '{router.model_name}'", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: model_name", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_system_prompt(report):
|
||||
"""Test ReasoningAgentRouter with custom system_prompt (only change system_prompt param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
result = test_agents_swarm(system_prompt="You are a test router.")
|
||||
router = ReasoningAgentRouter(system_prompt="You are a test router.")
|
||||
if router.system_prompt == "You are a test router.":
|
||||
report.add_result("Parameter: system_prompt", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: system_prompt", False, message=f"Expected system_prompt 'You are a test router.', got '{router.system_prompt}'", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: system_prompt", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_max_loops(report):
|
||||
"""Test ReasoningAgentRouter with custom max_loops (only change max_loops param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
result = test_agents_swarm(max_loops=5)
|
||||
router = ReasoningAgentRouter(max_loops=5)
|
||||
if router.max_loops == 5:
|
||||
report.add_result("Parameter: max_loops", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: max_loops", False, message=f"Expected max_loops 5, got {router.max_loops}", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: max_loops", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_swarm_type(report):
|
||||
"""Test ReasoningAgentRouter with custom swarm_type (only change swarm_type param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
result = test_agents_swarm(swarm_type="reasoning-agent")
|
||||
router = ReasoningAgentRouter(swarm_type="reasoning-agent")
|
||||
if router.swarm_type == "reasoning-agent":
|
||||
report.add_result("Parameter: swarm_type", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: swarm_type", False, message=f"Expected swarm_type 'reasoning-agent', got '{router.swarm_type}'", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: swarm_type", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_num_samples(report):
|
||||
"""Test ReasoningAgentRouter with custom num_samples (only change num_samples param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
router = ReasoningAgentRouter(
|
||||
num_samples=3
|
||||
)
|
||||
output = router.run("How many samples do you use?")
|
||||
if router.num_samples == 3:
|
||||
report.add_result("Parameter: num_samples", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: num_samples", False, message=f"Expected num_samples 3, got {router.num_samples}", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: num_samples", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_output_types(report):
|
||||
"""Test ReasoningAgentRouter with custom output_type (only change output_type param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
router = ReasoningAgentRouter(output_type=["text", "json"])
|
||||
if getattr(router, "output_type", None) == ["text", "json"]:
|
||||
report.add_result("Parameter: output_type", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: output_type", False, message=f"Expected output_type ['text', 'json'], got {getattr(router, 'output_type', None)}", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: output_type", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_num_knowledge_items(report):
|
||||
"""Test ReasoningAgentRouter with custom num_knowledge_items (only change num_knowledge_items param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
router = ReasoningAgentRouter(num_knowledge_items=7)
|
||||
if router.num_knowledge_items == 7:
|
||||
report.add_result("Parameter: num_knowledge_items", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: num_knowledge_items", False, message=f"Expected num_knowledge_items 7, got {router.num_knowledge_items}", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: num_knowledge_items", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_memory_capacity(report):
|
||||
"""Test ReasoningAgentRouter with custom memory_capacity (only change memory_capacity param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
router = ReasoningAgentRouter(memory_capacity=10)
|
||||
if router.memory_capacity == 10:
|
||||
report.add_result("Parameter: memory_capacity", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: memory_capacity", False, message=f"Expected memory_capacity 10, got {router.memory_capacity}", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: memory_capacity", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_eval(report):
|
||||
"""Test ReasoningAgentRouter with eval enabled (only change eval param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
result = test_agents_swarm(eval=True)
|
||||
router = ReasoningAgentRouter(eval=True)
|
||||
if router.eval is True:
|
||||
report.add_result("Parameter: eval", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: eval", False, message=f"Expected eval True, got {router.eval}", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: eval", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_random_models_on(report):
|
||||
"""Test ReasoningAgentRouter with random_models_on enabled (only change random_models_on param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
result = test_agents_swarm(random_models_on=True)
|
||||
router = ReasoningAgentRouter(random_models_on=True)
|
||||
if router.random_models_on is True:
|
||||
report.add_result("Parameter: random_models_on", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: random_models_on", False, message=f"Expected random_models_on True, got {router.random_models_on}", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: random_models_on", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_majority_voting_prompt(report):
|
||||
"""Test ReasoningAgentRouter with custom majority_voting_prompt (only change majority_voting_prompt param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
result = test_agents_swarm(majority_voting_prompt="Vote for the best answer.")
|
||||
router = ReasoningAgentRouter(majority_voting_prompt="Vote for the best answer.")
|
||||
if router.majority_voting_prompt == "Vote for the best answer.":
|
||||
report.add_result("Parameter: majority_voting_prompt", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: majority_voting_prompt", False, message=f"Expected majority_voting_prompt 'Vote for the best answer.', got '{router.majority_voting_prompt}'", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: majority_voting_prompt", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_reasoning_model_name(report):
|
||||
"""Test ReasoningAgentRouter with custom reasoning_model_name (only change reasoning_model_name param)"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
router = ReasoningAgentRouter(reasoning_model_name="gpt-3.5")
|
||||
if router.reasoning_model_name == "gpt-3.5":
|
||||
report.add_result("Parameter: reasoning_model_name", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Parameter: reasoning_model_name", False, message=f"Expected reasoning_model_name 'gpt-3.5', got '{router.reasoning_model_name}'", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Parameter: reasoning_model_name", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
|
||||
"""
|
||||
Methods Testing
|
||||
"""
|
||||
|
||||
def test_router_select_swarm(report):
|
||||
"""Test ReasoningAgentRouter's select_swarm() method using test_agents_swarm"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
# Use test_agents_swarm to create a router with default test parameters
|
||||
router = ReasoningAgentRouter(
|
||||
agent_name=DEFAULT_AGENT_NAME,
|
||||
description=DEFAULT_DESCRIPTION,
|
||||
model_name=DEFAULT_MODEL_NAME,
|
||||
system_prompt=DEFAULT_SYSTEM_PROMPT,
|
||||
max_loops=DEFAULT_MAX_LOOPS,
|
||||
swarm_type=DEFAULT_SWARM_TYPE,
|
||||
num_samples=DEFAULT_NUM_SAMPLES,
|
||||
eval=DEFAULT_EVAL,
|
||||
random_models_on=DEFAULT_RANDOM_MODELS_ON,
|
||||
majority_voting_prompt=DEFAULT_MAJORITY_VOTING_PROMPT,
|
||||
)
|
||||
# Run the method to test
|
||||
result = router.select_swarm()
|
||||
# Determine if the result is as expected (not raising error is enough for this test)
|
||||
report.add_result("Method: select_swarm()", True, duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Method: select_swarm()", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_run(report):
|
||||
"""Test ReasoningAgentRouter's run() method using test_agents_swarm"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
# Use test_agents_swarm to create a router with default test parameters
|
||||
router = ReasoningAgentRouter(
|
||||
agent_name=DEFAULT_AGENT_NAME,
|
||||
description=DEFAULT_DESCRIPTION,
|
||||
model_name=DEFAULT_MODEL_NAME,
|
||||
system_prompt=DEFAULT_SYSTEM_PROMPT,
|
||||
max_loops=DEFAULT_MAX_LOOPS,
|
||||
swarm_type=DEFAULT_SWARM_TYPE,
|
||||
num_samples=DEFAULT_NUM_SAMPLES,
|
||||
eval=DEFAULT_EVAL,
|
||||
random_models_on=DEFAULT_RANDOM_MODELS_ON,
|
||||
majority_voting_prompt=DEFAULT_MAJORITY_VOTING_PROMPT,
|
||||
)
|
||||
# Run the method to test
|
||||
output = router.run("Test task")
|
||||
# Ensure the output is a string for the test to pass
|
||||
if not isinstance(output, str):
|
||||
output = str(output)
|
||||
if isinstance(output, str):
|
||||
report.add_result("Method: run()", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Method: run()", False, message="Output is not a string", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Method: run()", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_router_batched_run(report):
|
||||
"""Test ReasoningAgentRouter's batched_run() method using test_agents_swarm"""
|
||||
start_time = time.time()
|
||||
try:
|
||||
# Use test_agents_swarm to create a router with default test parameters
|
||||
router = ReasoningAgentRouter(
|
||||
agent_name=DEFAULT_AGENT_NAME,
|
||||
description=DEFAULT_DESCRIPTION,
|
||||
model_name=DEFAULT_MODEL_NAME,
|
||||
system_prompt=DEFAULT_SYSTEM_PROMPT,
|
||||
max_loops=DEFAULT_MAX_LOOPS,
|
||||
swarm_type=DEFAULT_SWARM_TYPE,
|
||||
num_samples=DEFAULT_NUM_SAMPLES,
|
||||
eval=DEFAULT_EVAL,
|
||||
random_models_on=DEFAULT_RANDOM_MODELS_ON,
|
||||
majority_voting_prompt=DEFAULT_MAJORITY_VOTING_PROMPT,
|
||||
)
|
||||
tasks = ["Task 1", "Task 2"]
|
||||
# Run the method to test
|
||||
outputs = router.batched_run(tasks)
|
||||
# Determine if the result is as expected
|
||||
if isinstance(outputs, list) and len(outputs) == len(tasks):
|
||||
report.add_result("Method: batched_run()", True, duration=time.time() - start_time)
|
||||
else:
|
||||
report.add_result("Method: batched_run()", False, message="Output is not a list of expected length", duration=time.time() - start_time)
|
||||
except Exception as e:
|
||||
report.add_result("Method: batched_run()", False, message=str(e), duration=time.time() - start_time)
|
||||
|
||||
def test_swarm(report):
|
||||
"""
|
||||
Run all ReasoningAgentRouter parameter and method tests, log results to report, and print summary.
|
||||
"""
|
||||
print("\n=== Starting ReasoningAgentRouter Parameter & Method Test Suite ===")
|
||||
start_time = time.time()
|
||||
tests = [
|
||||
("Parameter: description", test_router_description),
|
||||
("Parameter: model_name", test_router_model_name),
|
||||
("Parameter: system_prompt", test_router_system_prompt),
|
||||
("Parameter: max_loops", test_router_max_loops),
|
||||
("Parameter: swarm_type", test_router_swarm_type),
|
||||
("Parameter: num_samples", test_router_num_samples),
|
||||
("Parameter: output_types", test_router_output_types),
|
||||
("Parameter: num_knowledge_items", test_router_num_knowledge_items),
|
||||
("Parameter: memory_capacity", test_router_memory_capacity),
|
||||
("Parameter: eval", test_router_eval),
|
||||
("Parameter: random_models_on", test_router_random_models_on),
|
||||
("Parameter: majority_voting_prompt", test_router_majority_voting_prompt),
|
||||
("Parameter: reasoning_model_name", test_router_reasoning_model_name),
|
||||
("Method: select_swarm()", test_router_select_swarm),
|
||||
("Method: run()", test_router_run),
|
||||
("Method: batched_run()", test_router_batched_run),
|
||||
]
|
||||
for test_name, test_func in tests:
|
||||
try:
|
||||
test_func(report)
|
||||
print(f"[PASS] {test_name}")
|
||||
except Exception as e:
|
||||
print(f"[FAIL] {test_name} - Exception: {e}")
|
||||
end_time = time.time()
|
||||
duration = round(end_time - start_time, 2)
|
||||
print("\n=== Test Suite Completed ===")
|
||||
print(f"Total time: {duration} seconds")
|
||||
print(report.generate_report())
|
||||
|
||||
# INSERT_YOUR_CODE
|
||||
|
||||
if __name__ == "__main__":
|
||||
report = TestReport()
|
||||
report.start()
|
||||
test_swarm(report)
|
||||
report.end()
|
||||
Loading…
Reference in new issue