diff --git a/crewai_tools/__init__.py b/crewai_tools/__init__.py index 4a782aed..4d699321 100644 --- a/crewai_tools/__init__.py +++ b/crewai_tools/__init__.py @@ -7,18 +7,23 @@ S3ReaderTool, S3WriterTool, ) +from .azure import ( + AzureCosmosDBNoSqlSearchTool, + AzureCosmosDBMemoryTool, + AzureCosmosDBSemanticCacheTool +) from .tools import ( AIMindTool, ApifyActorsTool, ArxivPaperTool, BraveSearchTool, BrowserbaseLoadTool, - CodeDocsSearchTool, + # CodeDocsSearchTool, CodeInterpreterTool, ComposioTool, CouchbaseFTSVectorSearchTool, CrewaiEnterpriseTools, - CSVSearchTool, + # CSVSearchTool, DallETool, DatabricksQueryTool, DirectoryReadTool, @@ -40,7 +45,7 @@ MongoDBVectorSearchConfig, MongoDBVectorSearchTool, MultiOnTool, - MySQLSearchTool, + # MySQLSearchTool, NL2SQLTool, OxylabsUniversalScraperTool, OxylabsGoogleSearchScraperTool, diff --git a/crewai_tools/azure/__init__.py b/crewai_tools/azure/__init__.py new file mode 100644 index 00000000..19ef70e4 --- /dev/null +++ b/crewai_tools/azure/__init__.py @@ -0,0 +1,24 @@ +from .cosmosdb_nosql.semantic_cache import AzureCosmosDBSemanticCacheConfig, AzureCosmosDBSemanticCacheTool, \ + AzureCosmosDBSemanticCacheToolSchema +from .cosmosdb_nosql.vector_search.vector_search_tool import ( + AzureCosmosDBNoSqlSearchTool, + AzureCosmosDBNoSqlSearchConfig, + AzureCosmosDBNoSqlToolSchema +) +from .cosmosdb_nosql.memory_store.memory_store_tool import ( + AzureCosmosDBMemoryConfig, + AzureCosmosDBMemoryTool, + AzureCosmosDBMemoryToolSchema +) + +__all__ = [ + "AzureCosmosDBNoSqlSearchTool", + "AzureCosmosDBNoSqlSearchConfig", + "AzureCosmosDBNoSqlToolSchema", + "AzureCosmosDBMemoryTool", + "AzureCosmosDBMemoryConfig", + "AzureCosmosDBMemoryToolSchema", + "AzureCosmosDBSemanticCacheTool", + "AzureCosmosDBSemanticCacheConfig", + "AzureCosmosDBSemanticCacheToolSchema", +] \ No newline at end of file diff --git a/crewai_tools/azure/cosmosdb_nosql/README.md b/crewai_tools/azure/cosmosdb_nosql/README.md new file mode 100644 index 00000000..8b6488e1 --- /dev/null +++ b/crewai_tools/azure/cosmosdb_nosql/README.md @@ -0,0 +1,421 @@ +# Azure CosmosDB NoSQL Tools for CrewAI + +This package provides a comprehensive suite of Azure CosmosDB NoSQL tools specifically designed for CrewAI agents. These tools leverage Azure CosmosDB's powerful NoSQL capabilities, including vector search, to enable sophisticated data storage, retrieval, and semantic operations. + +## Available Tools + +### 🧠 [Memory Store Tool](./memory_store/README.md) +**Complete CRUD operations for agent memory management** +- Store, retrieve, update, and delete agent memories +- Support for both single and hierarchical partition keys +- TTL (Time-To-Live) support for automatic memory expiration +- Batch operations for efficient memory management +- Advanced query filtering capabilities + +### 🔍 [Vector Search Tool](./vector_search/README.md) +**Semantic similarity search using vector embeddings** +- Cosine similarity search on vector embeddings +- Hybrid search combining vector and full-text capabilities +- Integration with popular embedding models (OpenAI, Hugging Face) +- Configurable similarity thresholds +- Advanced filtering and result ranking + +### ⚡ [Semantic Cache Tool](./semantic_cache/README.md) +**Intelligent LLM response caching with semantic matching** +- Cache LLM responses with semantic similarity detection +- Reduce API costs and improve response times +- Support for OpenAI and Azure OpenAI embeddings +- TTL-based cache expiration +- Cache analytics and management capabilities + +## Quick Start + +### Installation + +```bash +pip install crewai-tools[azure] +``` + +### Basic Setup + +```python +from crewai_tools.azure.cosmosdb_nosql import ( + CosmosDBMemoryStoreTool, + CosmosDBVectorSearchTool, + CosmosDBSemanticCacheTool +) + +# Basic configuration +cosmos_host = "https://your-account.documents.azure.com:443/" +cosmos_key = "your-cosmos-db-key" +database_name = "your_database" +``` + +## Complete CrewAI Integration Example + +Here's a comprehensive example showing how to use all three tools together in a CrewAI workflow: + +```python +from crewai import Agent, Task, Crew +from crewai_tools.azure.cosmosdb_nosql import ( + CosmosDBMemoryStoreTool, + CosmosDBVectorSearchTool, + CosmosDBSemanticCacheTool +) +from crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool import AzureCosmosDBMemoryConfig +from crewai_tools.azure.cosmosdb_nosql.vector_search.vector_search_tool import AzureCosmosDBNoSqlSearchConfig +from crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool import CosmosDBSemanticCacheConfig + +# Configuration +cosmos_host = "https://your-account.documents.azure.com:443/" +cosmos_key = "your-cosmos-db-key" +openai_key = "your-openai-key" + +# Memory Store Configuration +memory_config = AzureCosmosDBMemoryConfig( + cosmos_host=cosmos_host, + key=cosmos_key, + database_name="crew_memory", + container_name="agent_memories" +) + +# Vector Search Configuration +search_config = AzureCosmosDBNoSqlSearchConfig( + cosmos_host=cosmos_host, + key=cosmos_key, + database_name="knowledge_base", + container_name="documents" +) + +# Semantic Cache Configuration +cache_config = CosmosDBSemanticCacheConfig( + cosmos_host=cosmos_host, + cosmos_key=cosmos_key, + database_name="llm_cache", + container_name="response_cache", + openai_api_key=openai_key +) + +# Initialize tools +memory_tool = CosmosDBMemoryStoreTool(config=memory_config) +search_tool = CosmosDBVectorSearchTool(config=search_config) +cache_tool = CosmosDBSemanticCacheTool(config=cache_config) + +# Create intelligent agent with all CosmosDB capabilities +intelligent_agent = Agent( + role="Intelligent Research Assistant", + goal="Provide comprehensive research assistance with memory, search, and caching capabilities", + backstory=""" + You are an advanced AI assistant equipped with persistent memory, semantic search, + and intelligent caching capabilities. You can remember past interactions, search + through knowledge bases, and cache responses for efficiency. + """, + tools=[memory_tool, search_tool, cache_tool], + verbose=True +) + +# Research task that utilizes all tools +research_task = Task( + description=""" + Research the topic: "Machine Learning Applications in Healthcare" + + 1. First, check your memory for any previous research on this topic + 2. Check the semantic cache for similar queries to avoid redundant work + 3. If no cached response exists, use vector search to find relevant documents + 4. Generate a comprehensive analysis and store key insights in memory + 5. Cache the response for future similar queries + 6. Provide a well-structured research summary + """, + agent=intelligent_agent, + expected_output=""" + A comprehensive research summary including: + - Key applications of ML in healthcare + - Current trends and technologies + - Benefits and challenges + - Future outlook + - References to relevant documents found + """ +) + +# Follow-up task that benefits from stored memory +followup_task = Task( + description=""" + Based on your previous research stored in memory, provide specific recommendations + for implementing ML in a hospital setting. Focus on practical steps and considerations. + """, + agent=intelligent_agent, + expected_output="Practical implementation guide for ML in hospital settings" +) + +# Execute the crew +crew = Crew( + agents=[intelligent_agent], + tasks=[research_task, followup_task], + verbose=True +) + +result = crew.kickoff() +print(result) +``` + +## Tool Comparison and Use Cases + +| Tool | Primary Use Case | Key Features | Best For | +|------|------------------|--------------|----------| +| **Memory Store** | Agent state persistence | CRUD operations, TTL, hierarchical keys | Long-term agent memory, user preferences, conversation history | +| **Vector Search** | Semantic document retrieval | Vector similarity, hybrid search, filtering | Knowledge base search, document retrieval, content discovery | +| **Semantic Cache** | LLM response optimization | Similarity matching, TTL, cost reduction | Reducing API costs, improving response times, consistency | + +## Architecture Overview + +``` +┌─────────────────────────────────────────────────────────────┐ +│ CrewAI Agent │ +├─────────────────┬─────────────────┬─────────────────────────┤ +│ Memory Store │ Vector Search │ Semantic Cache │ +│ │ │ │ +│ • Agent State │ • Knowledge │ • LLM Responses │ +│ • Preferences │ Retrieval │ • Query Optimization │ +│ • History │ • Document │ • Cost Reduction │ +│ │ Discovery │ │ +├─────────────────┼─────────────────┼─────────────────────────┤ +│ Azure CosmosDB NoSQL with Vector Search │ +└─────────────────────────────────────────────────────────────┘ +``` + +## Common Patterns + +### 1. Memory-Enhanced Search + +```python +# Store search preferences in memory +memory_tool._run( + operation="store", + memory_item={ + "id": "search_prefs_001", + "agent_id": "research_agent", + "content": { + "preferred_domains": ["healthcare", "AI"], + "search_history": ["ML applications", "neural networks"] + } + } +) + +# Use preferences to enhance search +search_result = search_tool._run( + query="latest AI research", + top_k=5, + filter_criteria={"domain": "healthcare"} +) +``` + +### 2. Cached Knowledge Retrieval + +```python +# Check cache before expensive search +cache_result = cache_tool._run( + operation="search", + query="AI in medical diagnosis", + similarity_threshold=0.85 +) + +if not json.loads(cache_result): + # Cache miss - perform search and cache result + search_result = search_tool._run( + query="AI in medical diagnosis", + top_k=5 + ) + + # Cache the search result + cache_tool._run( + operation="store", + query="AI in medical diagnosis", + response=search_result, + ttl=3600 + ) +``` + +### 3. Memory-Guided Conversations + +```python +# Retrieve conversation context from memory +context = memory_tool._run( + operation="retrieve", + memory_item={}, + partition_key_value="user_123", + query_filter={"type": "conversation_context"} +) + +# Use context to inform responses +# ... generate contextual response ... + +# Update memory with new context +memory_tool._run( + operation="update", + memory_item={"content": {"latest_topic": "healthcare AI"}}, + partition_key_value="user_123", + memory_id="context_001" +) +``` + +## Prerequisites + +### Azure CosmosDB Setup + +1. **Create CosmosDB Account**: Choose NoSQL API +2. **Enable Vector Search**: Configure vector indexing policies +3. **Set Up Authentication**: Use either API keys or Azure AD + +### Container Configuration Examples + +#### Memory Store Container +```json +{ + "id": "memory_container", + "partitionKey": {"paths": ["/agent_id"], "kind": "Hash"} +} +``` + +#### Vector Search Container +```json +{ + "id": "vector_container", + "partitionKey": {"paths": ["/category"], "kind": "Hash"}, + "vectorEmbeddingPolicy": { + "vectorEmbeddings": [ + { + "path": "/embedding", + "dataType": "float32", + "distanceFunction": "cosine", + "dimensions": 1536 + } + ] + }, + "indexingPolicy": { + "vectorIndexes": [ + { + "path": "/embedding", + "type": "quantizedFlat" + } + ] + } +} +``` + +#### Semantic Cache Container +```json +{ + "id": "cache_container", + "partitionKey": {"paths": ["/query_hash"], "kind": "Hash"}, + "defaultTtl": 86400, + "vectorEmbeddingPolicy": { + "vectorEmbeddings": [ + { + "path": "/query_embedding", + "dataType": "float32", + "distanceFunction": "cosine", + "dimensions": 1536 + } + ] + } +} +``` + +## Performance Optimization + +### 1. Partition Key Strategy +- **Memory Store**: Use agent_id or user_id for even distribution +- **Vector Search**: Use domain/category for logical grouping +- **Semantic Cache**: Use query_hash for uniform distribution + +### 2. Indexing Best Practices +- Configure vector indexes based on your embedding dimensions +- Use composite indexes for complex queries +- Monitor RU consumption and adjust accordingly + +### 3. Cost Optimization +- Implement TTL for automatic cleanup +- Use similarity thresholds to reduce unnecessary operations +- Monitor and adjust container throughput based on usage + +## Monitoring and Analytics + +```python +def get_system_stats(memory_tool, search_tool, cache_tool): + """Get comprehensive system statistics""" + + # Memory usage + memory_stats = memory_tool._run( + operation="retrieve", + memory_item={}, + partition_key_value="system_stats", + max_results=1000 + ) + + # Cache hit rates + cache_stats = cache_tool._run( + operation="search", + query="", + similarity_threshold=0.0, + top_k=1000 + ) + + return { + "memory_items": len(json.loads(memory_stats)), + "cached_responses": len(json.loads(cache_stats)), + "timestamp": datetime.utcnow().isoformat() + } +``` + +## Error Handling and Resilience + +```python +def robust_cosmosdb_operation(tool, operation, **kwargs): + """Robust wrapper for CosmosDB operations with retry logic""" + import time + import json + + max_retries = 3 + retry_delay = 1 + + for attempt in range(max_retries): + try: + result = tool._run(operation=operation, **kwargs) + parsed_result = json.loads(result) + + if "error" not in parsed_result: + return parsed_result + else: + print(f"Operation error: {parsed_result['error']}") + + except Exception as e: + print(f"Attempt {attempt + 1} failed: {e}") + + if attempt < max_retries - 1: + time.sleep(retry_delay * (2 ** attempt)) # Exponential backoff + + return {"error": "All retry attempts failed"} +``` + +## Azure CosmosDB Documentation + +- [Azure CosmosDB Overview](https://docs.microsoft.com/en-us/azure/cosmos-db/introduction) +- [NoSQL API Documentation](https://docs.microsoft.com/en-us/azure/cosmos-db/nosql/) +- [Vector Search in CosmosDB](https://docs.microsoft.com/en-us/azure/cosmos-db/nosql/vector-search) +- [Hierarchical Partition Keys](https://docs.microsoft.com/en-us/azure/cosmos-db/hierarchical-partition-keys) +- [Time-to-Live (TTL)](https://docs.microsoft.com/en-us/azure/cosmos-db/time-to-live) +- [Python SDK Reference](https://docs.microsoft.com/en-us/python/api/azure-cosmos/) +- [Best Practices Guide](https://docs.microsoft.com/en-us/azure/cosmos-db/nosql/best-practices) + +## Support and Contributing + +For issues, questions, or contributions related to these CosmosDB tools: + +1. Check the individual tool README files for specific documentation +2. Review the Azure CosmosDB documentation for underlying service details +3. Follow CrewAI Tools contribution guidelines +4. Report issues through the appropriate channels + +## License + +These tools are part of the CrewAI Tools package and follow the same licensing terms. diff --git a/crewai_tools/azure/cosmosdb_nosql/__init__.py b/crewai_tools/azure/cosmosdb_nosql/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/crewai_tools/azure/cosmosdb_nosql/memory_store/README.md b/crewai_tools/azure/cosmosdb_nosql/memory_store/README.md new file mode 100644 index 00000000..f1071d57 --- /dev/null +++ b/crewai_tools/azure/cosmosdb_nosql/memory_store/README.md @@ -0,0 +1,360 @@ +# Azure CosmosDB Memory Store Tool + +The Azure CosmosDB Memory Store Tool enables CrewAI agents to store, retrieve, update, and manage persistent memory using Azure CosmosDB NoSQL. This tool provides comprehensive CRUD operations for agent memory management with support for both single and hierarchical partition keys. + +## Features + +- **Complete CRUD Operations**: Store, retrieve, update, and delete memory items +- **Hierarchical Partition Keys**: Support for complex partition key structures +- **TTL Support**: Automatic memory expiration with time-to-live settings +- **Batch Operations**: Efficient bulk operations for memory management +- **Query Filtering**: Advanced filtering capabilities for memory retrieval +- **Error Handling**: Robust error handling with detailed error messages +- **Multiple Auth Methods**: Support for API keys and Azure token credentials + +## Installation + +```bash +pip install crewai-tools[azure] +``` + +## Prerequisites + +- Azure CosmosDB account with NoSQL API +- Either an API key or Azure credentials for authentication +- Python 3.8+ environment + +## Configuration + +### Basic Configuration with API Key + +```python +from crewai_tools.azure.cosmosdb_nosql.memory_store import CosmosDBMemoryStoreTool +from crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool import AzureCosmosDBMemoryConfig + +config = AzureCosmosDBMemoryConfig( + cosmos_host="https://your-account.documents.azure.com:443/", + key="your-cosmos-db-key", + database_name="memory_database", + container_name="memory_container" +) + +memory_tool = CosmosDBMemoryStoreTool(config=config) +``` + +### Configuration with Azure Token Credential + +```python +from azure.identity import DefaultAzureCredential +from crewai_tools.azure.cosmosdb_nosql.memory_store import CosmosDBMemoryStoreTool +from crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool import AzureCosmosDBMemoryConfig + +credential = DefaultAzureCredential() +config = AzureCosmosDBMemoryConfig( + cosmos_host="https://your-account.documents.azure.com:443/", + token_credential=credential, + database_name="memory_database", + container_name="memory_container" +) + +memory_tool = CosmosDBMemoryStoreTool(config=config) +``` + +### Hierarchical Partition Key Configuration + +```python +config = AzureCosmosDBMemoryConfig( + cosmos_host="https://your-account.documents.azure.com:443/", + key="your-cosmos-db-key", + database_name="memory_database", + container_name="memory_container", + cosmos_container_properties={ + "partition_key": { + "paths": ["/agent_id", "/session_id"], + "kind": "MultiHash" + } + } +) +``` + +## Usage Examples + +### Complete CrewAI Integration Example + +```python +from crewai import Agent, Task, Crew +from crewai_tools.azure.cosmosdb_nosql.memory_store import CosmosDBMemoryStoreTool +from crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool import AzureCosmosDBMemoryConfig + +# Configure the memory tool +memory_config = AzureCosmosDBMemoryConfig( + cosmos_host="https://your-account.documents.azure.com:443/", + key="your-cosmos-db-key", + database_name="crew_memory", + container_name="agent_memories" +) + +memory_tool = CosmosDBMemoryStoreTool(config=memory_config) + +# Create an agent with memory capabilities +memory_agent = Agent( + role="Memory Manager", + goal="Manage and utilize persistent memory for better task continuity", + backstory="You are an expert at managing information and learning from past interactions.", + tools=[memory_tool], + verbose=True +) + +# Task to store important information +store_memory_task = Task( + description=""" + Store important conversation context about user preferences: + - User prefers technical explanations + - User works in healthcare domain + - User's timezone is EST + + Use the memory tool to store this information with agent_id 'user_001'. + """, + agent=memory_agent, + expected_output="Confirmation that user preferences have been stored in memory" +) + +# Task to retrieve and use stored memory +retrieve_memory_task = Task( + description=""" + Retrieve the stored user preferences from memory for agent_id 'user_001' + and provide a personalized response based on that information. + """, + agent=memory_agent, + expected_output="Personalized response based on retrieved user preferences" +) + +# Create and execute crew +crew = Crew( + agents=[memory_agent], + tasks=[store_memory_task, retrieve_memory_task], + verbose=True +) + +result = crew.kickoff() +print(result) +``` + +### Individual Operations + +#### 1. Store Memory + +```python +# Store a new memory +memory_item = { + "id": "conversation_001", + "agent_id": "agent_1", + "content": { + "user_preferences": { + "communication_style": "technical", + "domain": "healthcare" + }, + "conversation_summary": "User asked about AI applications in medical diagnosis" + }, + "metadata": { + "importance": "high", + "category": "user_profile" + } +} + +result = memory_tool._run( + operation="store", + memory_item=memory_item, + ttl=86400 # 24 hours TTL +) +``` + +#### 2. Retrieve Memories + +```python +# Retrieve all memories for an agent +result = memory_tool._run( + operation="retrieve", + memory_item={}, + partition_key_value="agent_1", + max_results=10 +) + +# Retrieve with filtering +result = memory_tool._run( + operation="retrieve", + memory_item={}, + partition_key_value="agent_1", + query_filter={"category": "user_profile"}, + max_results=5 +) +``` + +#### 3. Update Memory + +```python +# Update existing memory +update_data = { + "content": { + "user_preferences": { + "communication_style": "technical", + "domain": "healthcare", + "preferred_format": "bullet_points" + } + }, + "metadata": { + "last_updated": "2025-01-15T10:30:00Z" + } +} + +result = memory_tool._run( + operation="update", + memory_item=update_data, + partition_key_value="agent_1", + memory_id="conversation_001" +) +``` + +#### 4. Delete Memory + +```python +# Delete specific memory +result = memory_tool._run( + operation="delete", + memory_item={}, + partition_key_value="agent_1", + memory_id="conversation_001" +) +``` + +#### 5. Clear All Memories + +```python +# Clear all memories for an agent +result = memory_tool._run( + operation="clear", + memory_item={}, + partition_key_value="agent_1" +) +``` + +### Hierarchical Partition Key Usage + +```python +# For hierarchical partition keys (agent_id + session_id) +result = memory_tool._run( + operation="store", + memory_item={ + "id": "session_memory_001", + "agent_id": "agent_1", + "session_id": "session_123", + "content": {"session_data": "Important session context"} + }, + partition_key_value=["agent_1", "session_123"] +) + +# Retrieve memories for specific agent and session +result = memory_tool._run( + operation="retrieve", + memory_item={}, + partition_key_value=["agent_1", "session_123"] +) +``` + +## Configuration Options + +| Parameter | Description | Default | Required | +|-----------|-------------|---------|----------| +| `cosmos_host` | CosmosDB account endpoint URL | - | Yes | +| `key` | CosmosDB account key | - | Yes* | +| `token_credential` | Azure token credential | - | Yes* | +| `database_name` | Database name | "memory_database" | No | +| `container_name` | Container name | "memory_container" | No | +| `create_container` | Auto-create container | True | No | +| `cosmos_container_properties` | Container configuration | Single partition key | No | + +*Either `key` or `token_credential` must be provided + +## Operation Parameters + +### Store Operation +- `memory_item` (dict): The memory object to store +- `ttl` (int, optional): Time-to-live in seconds +- `partition_key_value` (str/list, optional): Partition key value(s) + +### Retrieve Operation +- `partition_key_value` (str/list): Partition key value(s) +- `query_filter` (dict, optional): Additional filtering criteria +- `max_results` (int, optional): Maximum number of results (default: 100) + +### Update Operation +- `memory_item` (dict): Updated memory data +- `partition_key_value` (str/list): Partition key value(s) +- `memory_id` (str): ID of memory to update +- `ttl` (int, optional): New TTL value + +### Delete Operation +- `partition_key_value` (str/list): Partition key value(s) +- `memory_id` (str): ID of memory to delete + +### Clear Operation +- `partition_key_value` (str/list): Partition key value(s) + +## Error Handling + +The tool provides comprehensive error handling with detailed error messages: + +```python +import json + +result = memory_tool._run(operation="retrieve", memory_item={}) +parsed_result = json.loads(result) + +if "error" in parsed_result: + print(f"Error: {parsed_result['error']}") + if "details" in parsed_result: + print(f"Details: {parsed_result['details']}") +else: + print("Operation successful") +``` + +## Best Practices + +1. **Partition Key Design**: Choose partition keys that distribute data evenly +2. **Memory Structure**: Use consistent JSON structure for better querying +3. **TTL Management**: Set appropriate TTL values to manage storage costs +4. **Error Handling**: Always check for errors in operation results +5. **Batch Operations**: Use clear operation for bulk deletions +6. **Query Optimization**: Use specific filters to limit query scope + +## Azure CosmosDB Documentation + +- [Azure CosmosDB Overview](https://docs.microsoft.com/en-us/azure/cosmos-db/introduction) +- [NoSQL API Documentation](https://docs.microsoft.com/en-us/azure/cosmos-db/nosql/) +- [Partition Keys Best Practices](https://docs.microsoft.com/en-us/azure/cosmos-db/partitioning-overview) +- [Time-to-Live (TTL)](https://docs.microsoft.com/en-us/azure/cosmos-db/time-to-live) +- [Hierarchical Partition Keys](https://docs.microsoft.com/en-us/azure/cosmos-db/hierarchical-partition-keys) +- [Python SDK Reference](https://docs.microsoft.com/en-us/python/api/azure-cosmos/) + +## Troubleshooting + +### Common Issues + +1. **Connection Errors**: Verify cosmos_host URL and credentials +2. **Partition Key Errors**: Ensure partition key values match container configuration +3. **Query Timeouts**: Use more specific filters to reduce query scope +4. **Storage Costs**: Implement TTL for automatic cleanup +5. **Authentication Errors**: Verify API key or token credential permissions + +### Debug Mode + +Enable verbose logging to troubleshoot issues: + +```python +import logging +logging.basicConfig(level=logging.DEBUG) +``` + +## License + +This tool is part of the CrewAI Tools package and follows the same licensing terms. diff --git a/crewai_tools/azure/cosmosdb_nosql/memory_store/__init__.py b/crewai_tools/azure/cosmosdb_nosql/memory_store/__init__.py new file mode 100644 index 00000000..59d5032a --- /dev/null +++ b/crewai_tools/azure/cosmosdb_nosql/memory_store/__init__.py @@ -0,0 +1,5 @@ +"""Azure CosmosDB NoSQL Memory Store Tool for CrewAI.""" + +from .memory_store_tool import AzureCosmosDBMemoryTool, AzureCosmosDBMemoryConfig, AzureCosmosDBMemoryToolSchema + +__all__ = ["AzureCosmosDBMemoryTool", "AzureCosmosDBMemoryConfig", "AzureCosmosDBMemoryToolSchema"] diff --git a/crewai_tools/azure/cosmosdb_nosql/memory_store/memory_store_tool.py b/crewai_tools/azure/cosmosdb_nosql/memory_store/memory_store_tool.py new file mode 100644 index 00000000..2832d096 --- /dev/null +++ b/crewai_tools/azure/cosmosdb_nosql/memory_store/memory_store_tool.py @@ -0,0 +1,392 @@ +import json +from datetime import datetime, timezone +from logging import getLogger +from typing import Any, ClassVar, Dict, List, Optional, Type, Union + +from crewai.tools import BaseTool +from pydantic import BaseModel, ConfigDict, Field + +try: + from azure.cosmos import ContainerProxy, CosmosClient, DatabaseProxy + from azure.core.credentials import TokenCredential + + COSMOSDB_AVAILABLE = True +except ImportError: + COSMOSDB_AVAILABLE = False + ContainerProxy = Any + CosmosClient = Any + DatabaseProxy = Any + TokenCredential = Any + +logger = getLogger(__name__) + + +class AzureCosmosDBMemoryConfig(BaseModel): + """Configuration for Azure CosmosDB memory operations.""" + + model_config = ConfigDict(arbitrary_types_allowed=True) + + cosmos_host: str = Field(..., description="The connection string of the CosmosDB") + key: Optional[str] = Field(None, description="The Azure Key for the cosmos db") + token_credential: Optional[TokenCredential] = Field(None, description="The Azure Token Credential") + database_name: str = Field("memory_database", description="The name of the CosmosDB database") + container_name: str = Field("memory_container", description="The name of the CosmosDB container") + cosmos_container_properties: Dict[str, Any] = Field( + default_factory=lambda: {"partition_key": {"paths": ["/agent_id"], "kind": "Hash"}}, + description="Container properties including partition key configuration" + ) + cosmos_database_properties: Dict[str, Any] = Field(default_factory=dict, description="Database properties") + create_container: bool = Field(True, description="Should create the container if it doesn't exist") + + +class AzureCosmosDBMemoryToolSchema(BaseModel): + """Input schema for AzureCosmosDBMemoryTool.""" + + operation: str = Field( + ..., + description="The operation to perform: 'store', 'retrieve', 'update', 'delete', 'clear'" + ) + + +class AzureCosmosDBMemoryTool(BaseTool): + """Tool for performing memory CRUD operations on Azure CosmosDB NoSQL.""" + + USER_AGENT: ClassVar[str] = "CrewAI-CosmosDB-Memory-Tool-Python" + + name: str = "AzureCosmosDBMemoryTool" + description: str = "A tool for storing, retrieving, updating, and deleting memory items in Azure CosmosDB." + args_schema: Type[BaseModel] = AzureCosmosDBMemoryToolSchema + + config: AzureCosmosDBMemoryConfig = Field(..., description="Configuration for the memory tool") + package_dependencies: List[str] = ["azure-cosmos", "azure-core"] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self._database = None + self._container = None + + if not COSMOSDB_AVAILABLE: + import click + if click.confirm( + "You are missing the azure-cosmos package. Would you like to install it?" + ): + import subprocess + subprocess.run(["uv", "add", "azure-cosmos"], check=True) + subprocess.run(["uv", "add", "azure-core"], check=True) + else: + raise ImportError("You are missing the 'azure-cosmos' package.") + + self._initialize_client() + + def _initialize_client(self): + """Initialize the CosmosDB client and container.""" + if self.config.key is not None: + self._cosmos_client = CosmosClient( + self.config.cosmos_host, + self.config.key, + user_agent=self.USER_AGENT, + ) + elif self.config.token_credential is not None: + self._cosmos_client = CosmosClient( + self.config.cosmos_host, + self.config.token_credential, + user_agent=self.USER_AGENT, + ) + else: + raise ValueError("Either 'key' or 'token_credential' must be provided") + + self._database = self._create_database() + self._container = self._create_container_if_not_exists() + + def _create_database(self) -> DatabaseProxy: + """Create the database if it doesn't exist.""" + return self._cosmos_client.create_database_if_not_exists( + id=self.config.database_name, + offer_throughput=self.config.cosmos_database_properties.get("offer_throughput"), + session_token=self.config.cosmos_database_properties.get("session_token"), + initial_headers=self.config.cosmos_database_properties.get("initial_headers"), + etag=self.config.cosmos_database_properties.get("etag"), + match_condition=self.config.cosmos_database_properties.get("match_condition"), + ) + + def _create_container_if_not_exists(self) -> ContainerProxy: + """Create the container if it doesn't exist.""" + if not self.config.create_container: + return self._database.get_container_client(self.config.container_name) + + return self._database.create_container_if_not_exists( + id=self.config.container_name, + partition_key=self.config.cosmos_container_properties["partition_key"], + offer_throughput=self.config.cosmos_container_properties.get("offer_throughput"), + unique_key_policy=self.config.cosmos_container_properties.get("unique_key_policy"), + conflict_resolution_policy=self.config.cosmos_container_properties.get("conflict_resolution_policy"), + analytical_storage_ttl=self.config.cosmos_container_properties.get("analytical_storage_ttl"), + computed_properties=self.config.cosmos_container_properties.get("computed_properties"), + etag=self.config.cosmos_container_properties.get("etag"), + match_condition=self.config.cosmos_container_properties.get("match_condition"), + session_token=self.config.cosmos_container_properties.get("session_token"), + initial_headers=self.config.cosmos_container_properties.get("initial_headers"), + default_ttl=self.config.cosmos_container_properties.get("default_ttl"), + ) + + def _get_partition_key_fields_and_paths(self) -> tuple[List[str], Any]: + """Extract partition key field names and the raw partition key configuration.""" + partition_key_config = self.config.cosmos_container_properties.get("partition_key", {}) + + # Handle both old and new format + if isinstance(partition_key_config, dict): + paths = partition_key_config.get("paths", ["/agent_id"]) + else: + # Fallback for simple string format + paths = [partition_key_config] if isinstance(partition_key_config, str) else ["/agent_id"] + + # Extract field names from paths (remove leading "/") + field_names = [path.lstrip("/") for path in paths] + + return field_names, partition_key_config + + def _build_partition_key_filter(self, partition_key_value: Union[str, List[str]], field_names: List[str]) -> str: + """Build SQL WHERE clause for partition key filtering.""" + if isinstance(partition_key_value, str): + # Single partition key + if len(field_names) > 1: + raise ValueError( + f"Container has hierarchical partition key with {len(field_names)} levels, but only one value provided") + return f"c.{field_names[0]} = '{partition_key_value}'" + else: + # Hierarchical partition key + if len(partition_key_value) != len(field_names): + raise ValueError( + f"Container has {len(field_names)} partition key levels, but {len(partition_key_value)} values provided") + + conditions = [] + for field, value in zip(field_names, partition_key_value): + conditions.append(f"c.{field} = '{value}'") + return " AND ".join(conditions) + + def _set_partition_key_fields_in_item(self, item: Dict[str, Any], partition_key_value: Union[str, List[str]], + field_names: List[str]) -> None: + """Set partition key fields in the document item.""" + if isinstance(partition_key_value, str): + # Single partition key + if len(field_names) > 1: + raise ValueError( + f"Container has hierarchical partition key with {len(field_names)} levels, but only one value provided") + item[field_names[0]] = partition_key_value + else: + # Hierarchical partition key + if len(partition_key_value) != len(field_names): + raise ValueError( + f"Container has {len(field_names)} partition key levels, but {len(partition_key_value)} values provided") + + for field, value in zip(field_names, partition_key_value): + item[field] = value + + def _run( + self, + operation: str, + memory_item: Dict[str, Any], + partition_key_value: Optional[Union[str, List[str]]] = None, + memory_id: Optional[str] = None, + query_filter: Optional[Dict[str, Any]] = None, + max_results: Optional[int] = 10, + ttl: Optional[int] = None, + ) -> str: + """Execute the memory operation.""" + try: + if operation == "store": + return self._store_memory(memory_item, ttl) + elif operation == "read": + return self._read_memory(partition_key_value, memory_id) + elif operation == "retrieve": + return self._retrieve_memory(partition_key_value, query_filter, max_results) + elif operation == "update": + return self._update_memory(partition_key_value, memory_id, memory_item, ttl) + elif operation == "delete": + return self._delete_memory(partition_key_value, memory_id) + elif operation == "clear": + return self._clear_memory(partition_key_value) + else: + return json.dumps({ + "error": f"Unknown operation: {operation}", + "valid_operations": ["store", "retrieve", "update", "delete", "clear"] + }) + except Exception as e: + logger.error(f"Memory operation failed: {e}") + return json.dumps({"error": str(e)}) + + def _store_memory( + self, + memory_item: Dict[str, Any], + ttl: Optional[int] = None + ) -> str: + """Store a new memory item.""" + if ttl is not None: + memory_item["ttl"] = ttl + + try: + response = self._container.create_item( + body=memory_item + ) + return json.dumps(response) + except Exception as e: + logger.error(f"Failed to store memory: {e}") + return json.dumps({"error": f"Failed to store memory: {str(e)}"}) + + def _read_memory( + self, + partition_key_value: str, + memory_id: str, + ) -> str: + """Read a memory item.""" + try: + response = self._container.read_item( + item=memory_id, + partition_key=partition_key_value + ) + return json.dumps(response) + except Exception as e: + logger.error(f"Failed to read memory: {e}") + return json.dumps({"error": f"Failed to read memory: {str(e)}"}) + + def _retrieve_memory( + self, + partition_key_value: Union[str, List[str]], + query_filter: Optional[Dict[str, Any]] = None, + max_results: Optional[int] = 10, + ) -> str: + """Retrieve memory items.""" + if not partition_key_value: + return json.dumps({"error": "partition_key_value is required for retrieve operation"}) + try: + field_names, _ = self._get_partition_key_fields_and_paths() + partition_filter = self._build_partition_key_filter(partition_key_value, field_names) + query_sql = f"SELECT * FROM c WHERE {partition_filter}" + + # Add additional query filters if provided + if query_filter: + for key, value in query_filter.items(): + if isinstance(value, str): + query_sql += f" AND c.content.{key} = '{value}'" + else: + query_sql += f" AND c.content.{key} = {value}" + + # Add ordering by creation time (newest first) + query_sql += " ORDER BY c.created_at DESC" + + # Add limit if specified + if max_results: + query_sql = f"SELECT TOP {max_results} * FROM ({query_sql}) AS subquery" + + items = list(self._container.query_items( + query=query_sql, + partition_key=partition_key_value, + enable_cross_partition_query=False # Since we're querying within a single partition + )) + return json.dumps(items) + except Exception as e: + logger.error(f"Failed to retrieve memory: {e}") + return json.dumps({"error": f"Failed to retrieve memory: {str(e)}"}) + + def _update_memory( + self, + partition_key_value: str, + memory_id: str, + upsert_item: Dict[str, Any], + ttl: Optional[int] = None, + ) -> str: + """Update an existing memory item.""" + if not partition_key_value: + return json.dumps({"error": "partition_key_value is required for update operation"}) + + if not memory_id: + return json.dumps({"error": "memory_id is required for update operation"}) + + try: + existing_item = self._container.read_item( + item=memory_id, + partition_key=partition_key_value + ) + + if ttl is not None: + upsert_item["ttl"] = ttl + + response = self._container.replace_item( + item=existing_item, + body=upsert_item, + partition_key=partition_key_value + ) + return json.dumps(response) + + except Exception as e: + logger.error(f"Failed to update memory: {e}") + return json.dumps({"error": f"Failed to update memory: {str(e)}"}) + + def _delete_memory( + self, + partition_key_value: str, + memory_id: str + ) -> str: + """Delete a specific memory item.""" + if not partition_key_value: + return json.dumps({"error": "partition_key_value is required for delete operation"}) + + if not memory_id: + return json.dumps({"error": "memory_id is required for delete operation"}) + + try: + self._container.delete_item( + item=memory_id, + partition_key=partition_key_value + ) + return "Item with Id is {0} has been deleted".format(memory_id) + except Exception as e: + logger.error(f"Failed to delete memory: {e}") + return json.dumps({"error": f"Failed to delete memory: {str(e)}"}) + + def _clear_memory(self, partition_key_value: Union[str, List[str]]) -> str: + """Clear all memory items for a specific partition key value.""" + if not partition_key_value: + return json.dumps({"error": "partition_key_value is required for clear operation"}) + + try: + field_names, _ = self._get_partition_key_fields_and_paths() + + # Query all items for the partition key value + partition_filter = self._build_partition_key_filter(partition_key_value, field_names) + query_sql = f"SELECT c.id FROM c WHERE {partition_filter}" + + items = list(self._container.query_items( + query=query_sql, + partition_key=partition_key_value, + enable_cross_partition_query=False + )) + + # Delete items in batches (Cosmos DB batch size limit is 100) + deleted_count = 0 + batch_size = 100 + + for i in range(0, len(items), batch_size): + batch = items[i:i + batch_size] + + # Use execute_item_batch for efficient batch deletion + batch_ops = [("delete", {"id": item["id"]}) for item in batch] + + if batch_ops: + self._container.execute_item_batch( + batch_operations=batch_ops, + partition_key=partition_key_value + ) + deleted_count += len(batch_ops) + + return json.dumps({ + "success": True, + "partition_key_value": partition_key_value, + "operation": "clear", + "deleted_count": deleted_count, + "timestamp": datetime.now(timezone.utc).isoformat() + }) + + except Exception as e: + logger.error(f"Failed to clear memory: {e}") + return json.dumps({"error": f"Failed to clear memory: {str(e)}"}) \ No newline at end of file diff --git a/crewai_tools/azure/cosmosdb_nosql/semantic_cache/README.md b/crewai_tools/azure/cosmosdb_nosql/semantic_cache/README.md new file mode 100644 index 00000000..d3882a11 --- /dev/null +++ b/crewai_tools/azure/cosmosdb_nosql/semantic_cache/README.md @@ -0,0 +1,639 @@ +# Azure CosmosDB Semantic Cache Tool + +The Azure CosmosDB Semantic Cache Tool provides intelligent LLM response caching for CrewAI agents using semantic similarity search. This tool helps reduce API costs, improve response times, and ensure consistent outputs by caching and retrieving LLM responses based on semantic similarity rather than exact string matching. + +## Features + +- **Semantic Similarity Caching**: Cache LLM responses with vector-based similarity matching +- **Intelligent Cache Retrieval**: Find semantically similar queries even with different wording +- **Configurable Similarity Thresholds**: Control cache hit sensitivity with adjustable thresholds +- **TTL Support**: Automatic cache expiration with time-to-live settings +- **Hybrid Search**: Combine vector similarity with text-based filtering +- **Cache Management**: Update, clear, and manage cached responses +- **Multiple LLM Support**: Work with OpenAI, Azure OpenAI, and other embedding providers +- **Cost Optimization**: Reduce LLM API calls and associated costs + +## Installation + +```bash +pip install crewai-tools[azure] +``` + +## Prerequisites + +- Azure CosmosDB account with NoSQL API and vector search capabilities +- OpenAI or Azure OpenAI API access for embeddings +- Either CosmosDB API key or Azure credentials for authentication +- Python 3.8+ environment + +## Configuration + +### Basic Configuration with OpenAI + +```python +from crewai_tools.azure.cosmosdb_nosql.semantic_cache import CosmosDBSemanticCacheTool +from crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool import CosmosDBSemanticCacheConfig + +config = CosmosDBSemanticCacheConfig( + cosmos_host="https://your-account.documents.azure.com:443/", + cosmos_key="your-cosmos-db-key", + database_name="llm_cache", + container_name="semantic_cache", + openai_api_key="your-openai-key", + embedding_model="text-embedding-ada-002" +) + +cache_tool = CosmosDBSemanticCacheTool(config=config) +``` + +### Configuration with Azure OpenAI + +```python +config = CosmosDBSemanticCacheConfig( + cosmos_host="https://your-account.documents.azure.com:443/", + cosmos_key="your-cosmos-db-key", + database_name="llm_cache", + container_name="semantic_cache", + azure_openai_endpoint="https://your-resource.openai.azure.com/", + azure_openai_api_key="your-azure-openai-key", + azure_openai_api_version="2024-02-01", + embedding_deployment="text-embedding-ada-002" +) + +cache_tool = CosmosDBSemanticCacheTool(config=config) +``` + +### Configuration with Azure Token Credential + +```python +from azure.identity import DefaultAzureCredential + +credential = DefaultAzureCredential() +config = CosmosDBSemanticCacheConfig( + cosmos_host="https://your-account.documents.azure.com:443/", + token_credential=credential, + database_name="llm_cache", + container_name="semantic_cache", + openai_api_key="your-openai-key" +) + +cache_tool = CosmosDBSemanticCacheTool(config=config) +``` + +## Usage Examples + +### Complete CrewAI Integration Example + +```python +from crewai import Agent, Task, Crew +from crewai_tools.azure.cosmosdb_nosql.semantic_cache import CosmosDBSemanticCacheTool +from crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool import CosmosDBSemanticCacheConfig +import openai + +# Configure the semantic cache tool +cache_config = CosmosDBSemanticCacheConfig( + cosmos_host="https://your-account.documents.azure.com:443/", + cosmos_key="your-cosmos-db-key", + database_name="crew_cache", + container_name="llm_responses", + openai_api_key="your-openai-key", + similarity_threshold=0.85 +) + +cache_tool = CosmosDBSemanticCacheTool(config=cache_config) + +# Create an agent with caching capabilities +cached_agent = Agent( + role="Efficient Assistant", + goal="Provide fast responses by utilizing intelligent caching", + backstory="You are an expert at providing quick, consistent responses by leveraging semantic caching.", + tools=[cache_tool], + verbose=True +) + +# Task that benefits from caching +analysis_task = Task( + description=""" + Before generating a new analysis, check the semantic cache for similar queries about: + "What are the benefits of machine learning in healthcare?" + + If a similar response exists with high similarity (>0.8), return the cached response. + Otherwise, generate a new response and cache it for future use. + """, + agent=cached_agent, + expected_output="Analysis of ML benefits in healthcare (cached or newly generated)" +) + +# Create and execute crew +crew = Crew( + agents=[cached_agent], + tasks=[analysis_task], + verbose=True +) + +result = crew.kickoff() +print(result) +``` + +### Cache Operations + +#### 1. Search for Cached Response + +```python +# Search for semantically similar cached responses +result = cache_tool._run( + operation="search", + query="What are the advantages of AI in medical diagnosis?", + similarity_threshold=0.8, + top_k=3 +) + +import json +search_results = json.loads(result) + +if search_results and len(search_results) > 0: + best_match = search_results[0] + print(f"Cache hit! Similarity: {best_match['similarity_score']}") + print(f"Cached response: {best_match['response']}") +else: + print("No similar cached responses found") +``` + +#### 2. Store LLM Response in Cache + +```python +# Store a new LLM response with semantic indexing +llm_response = """ +Machine learning offers several key benefits in healthcare: +1. Improved diagnostic accuracy through pattern recognition +2. Personalized treatment recommendations based on patient data +3. Drug discovery acceleration through predictive modeling +4. Early disease detection via medical imaging analysis +5. Population health insights from large-scale data analysis +""" + +result = cache_tool._run( + operation="store", + query="Benefits of machine learning in healthcare", + response=llm_response, + model="gpt-4", + ttl=86400, # Cache for 24 hours + metadata={ + "topic": "healthcare_ai", + "complexity": "intermediate", + "timestamp": "2025-01-15T10:30:00Z" + } +) +``` + +#### 3. Update Cached Response + +```python +# Update an existing cached response +updated_response = """ +Machine learning provides transformative benefits in healthcare: +1. Enhanced diagnostic precision with AI-powered analysis +2. Personalized medicine through genomic data integration +3. Accelerated drug discovery and clinical trial optimization +4. Predictive analytics for patient risk assessment +5. Operational efficiency improvements in healthcare delivery +6. Real-time monitoring and alert systems +""" + +result = cache_tool._run( + operation="update", + cache_id="existing_cache_id", + response=updated_response, + metadata={ + "updated_at": "2025-01-15T12:00:00Z", + "version": "2.0" + } +) +``` + +#### 4. Clear Cache + +```python +# Clear all cached responses +result = cache_tool._run(operation="clear") + +# Clear cache with specific filters +result = cache_tool._run( + operation="clear", + filter_criteria={ + "topic": "outdated_content", + "created_at": {"$lt": "2025-01-01T00:00:00Z"} + } +) +``` + +### Advanced Usage Patterns + +#### Smart Cache-First Response Generation + +```python +def get_cached_or_generate_response(query, cache_tool, llm_client): + """ + Smart function to get cached response or generate new one + """ + import json + + # Try to find cached response + cache_result = cache_tool._run( + operation="search", + query=query, + similarity_threshold=0.85, + top_k=1 + ) + + cached_responses = json.loads(cache_result) + + if cached_responses and len(cached_responses) > 0: + print(f"Cache hit! Similarity: {cached_responses[0]['similarity_score']}") + return cached_responses[0]['response'] + + # Generate new response + print("Cache miss. Generating new response...") + new_response = llm_client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": query}] + ).choices[0].message.content + + # Cache the new response + cache_tool._run( + operation="store", + query=query, + response=new_response, + model="gpt-4", + ttl=86400 + ) + + return new_response + +# Usage +query = "Explain quantum computing applications in cryptography" +response = get_cached_or_generate_response(query, cache_tool, openai_client) +print(response) +``` + +#### Batch Cache Operations + +```python +# Store multiple responses in batch +responses_to_cache = [ + { + "query": "What is machine learning?", + "response": "Machine learning is a subset of AI...", + "model": "gpt-4" + }, + { + "query": "How does deep learning work?", + "response": "Deep learning uses neural networks...", + "model": "gpt-4" + }, + { + "query": "What are neural networks?", + "response": "Neural networks are computing systems...", + "model": "gpt-4" + } +] + +for item in responses_to_cache: + result = cache_tool._run( + operation="store", + query=item["query"], + response=item["response"], + model=item["model"], + ttl=3600 + ) + print(f"Cached: {item['query'][:50]}...") +``` + +#### Cache Analytics and Management + +```python +# Get cache statistics +def get_cache_stats(cache_tool): + """Get cache usage statistics""" + import json + + # Search all cached items + all_items = cache_tool._run( + operation="search", + query="", # Empty query to get all items + similarity_threshold=0.0, + top_k=1000 + ) + + items = json.loads(all_items) + + if not items: + return {"total_items": 0} + + # Analyze cache contents + models = {} + topics = {} + + for item in items: + model = item.get('model', 'unknown') + models[model] = models.get(model, 0) + 1 + + topic = item.get('metadata', {}).get('topic', 'general') + topics[topic] = topics.get(topic, 0) + 1 + + return { + "total_items": len(items), + "models": models, + "topics": topics, + "avg_similarity": sum(item.get('similarity_score', 0) for item in items) / len(items) + } + +stats = get_cache_stats(cache_tool) +print("Cache Statistics:", stats) +``` + +### Integration with Different LLM Providers + +#### OpenAI Integration + +```python +import openai + +class CachedOpenAIClient: + def __init__(self, cache_tool, openai_client): + self.cache_tool = cache_tool + self.openai_client = openai_client + self.similarity_threshold = 0.85 + + def cached_completion(self, query, model="gpt-4", **kwargs): + # Check cache first + cache_result = self.cache_tool._run( + operation="search", + query=query, + similarity_threshold=self.similarity_threshold, + top_k=1 + ) + + import json + cached_responses = json.loads(cache_result) + + if cached_responses: + print("Using cached response") + return cached_responses[0]['response'] + + # Generate new response + response = self.openai_client.chat.completions.create( + model=model, + messages=[{"role": "user", "content": query}], + **kwargs + ).choices[0].message.content + + # Cache the response + self.cache_tool._run( + operation="store", + query=query, + response=response, + model=model, + ttl=86400 + ) + + return response + +# Usage +openai_client = openai.OpenAI(api_key="your-key") +cached_client = CachedOpenAIClient(cache_tool, openai_client) + +response = cached_client.cached_completion( + "Explain the benefits of renewable energy", + model="gpt-4" +) +``` + +#### Azure OpenAI Integration + +```python +from openai import AzureOpenAI + +azure_client = AzureOpenAI( + azure_endpoint="https://your-resource.openai.azure.com/", + api_key="your-azure-openai-key", + api_version="2024-02-01" +) + +cached_azure_client = CachedOpenAIClient(cache_tool, azure_client) +response = cached_azure_client.cached_completion( + "What are the advantages of cloud computing?", + model="gpt-4" # Your deployment name +) +``` + +## Configuration Options + +| Parameter | Description | Default | Required | +|-----------|-------------|---------|----------| +| `cosmos_host` | CosmosDB account endpoint URL | - | Yes | +| `cosmos_key` | CosmosDB account key | - | Yes* | +| `token_credential` | Azure token credential | - | Yes* | +| `database_name` | Database name | "semantic_cache" | No | +| `container_name` | Container name | "cache_container" | No | +| `openai_api_key` | OpenAI API key | - | Yes** | +| `azure_openai_endpoint` | Azure OpenAI endpoint | - | Yes** | +| `azure_openai_api_key` | Azure OpenAI API key | - | Yes** | +| `embedding_model` | Embedding model name | "text-embedding-ada-002" | No | +| `similarity_threshold` | Default similarity threshold | 0.8 | No | +| `max_cache_size` | Maximum cached items | 10000 | No | + +*Either `cosmos_key` or `token_credential` must be provided +**Either OpenAI or Azure OpenAI credentials must be provided + +## Operation Parameters + +### Search Operation +- `query` (str): Search query text +- `similarity_threshold` (float): Minimum similarity score (0.0-1.0) +- `top_k` (int): Maximum number of results +- `filter_criteria` (dict): Additional filtering + +### Store Operation +- `query` (str): Original query text +- `response` (str): LLM response to cache +- `model` (str): LLM model used +- `ttl` (int, optional): Time-to-live in seconds +- `metadata` (dict, optional): Additional metadata + +### Update Operation +- `cache_id` (str): ID of cache item to update +- `response` (str, optional): New response text +- `metadata` (dict, optional): Updated metadata +- `ttl` (int, optional): New TTL value + +### Clear Operation +- `filter_criteria` (dict, optional): Criteria for selective clearing + +## Performance Optimization + +### 1. Similarity Threshold Tuning + +```python +# Test different thresholds to find optimal balance +thresholds = [0.7, 0.75, 0.8, 0.85, 0.9] + +for threshold in thresholds: + result = cache_tool._run( + operation="search", + query="test query", + similarity_threshold=threshold, + top_k=5 + ) + + import json + hits = json.loads(result) + print(f"Threshold {threshold}: {len(hits)} hits") +``` + +### 2. Cache Size Management + +```python +# Implement cache size limits +def manage_cache_size(cache_tool, max_size=10000): + # Get current cache size + all_items = cache_tool._run( + operation="search", + query="", + similarity_threshold=0.0, + top_k=max_size + 1000 + ) + + import json + items = json.loads(all_items) + + if len(items) > max_size: + # Clear oldest items + items.sort(key=lambda x: x.get('created_at', '')) + items_to_remove = items[:-max_size] + + for item in items_to_remove: + cache_tool._run( + operation="clear", + filter_criteria={"id": item['id']} + ) +``` + +### 3. TTL Strategy + +```python +# Different TTL strategies based on content type +def get_ttl_for_content(query, response): + """Determine appropriate TTL based on content characteristics""" + + # Factual information - longer TTL + if any(word in query.lower() for word in ['what is', 'define', 'fact']): + return 86400 * 7 # 7 days + + # Current events - shorter TTL + if any(word in query.lower() for word in ['news', 'current', 'latest']): + return 3600 # 1 hour + + # Technical explanations - medium TTL + if any(word in query.lower() for word in ['how to', 'explain', 'tutorial']): + return 86400 # 1 day + + # Default TTL + return 86400 # 1 day + +# Usage +ttl = get_ttl_for_content(query, response) +cache_tool._run( + operation="store", + query=query, + response=response, + ttl=ttl +) +``` + +## Best Practices + +### 1. Cache Key Design +```python +# Use consistent query normalization +def normalize_query(query): + """Normalize queries for better cache hits""" + return query.lower().strip().replace('?', '').replace('.', '') + +normalized_query = normalize_query("What are the benefits of AI?") +``` + +### 2. Metadata Strategy +```python +# Rich metadata for better cache management +metadata = { + "topic": "artificial_intelligence", + "complexity": "beginner", + "language": "en", + "content_type": "explanation", + "model_version": "gpt-4-1106-preview", + "user_type": "developer" +} +``` + +### 3. Error Handling +```python +def safe_cache_operation(cache_tool, operation, **kwargs): + """Safe wrapper for cache operations""" + try: + result = cache_tool._run(operation=operation, **kwargs) + return json.loads(result) + except Exception as e: + print(f"Cache operation failed: {e}") + return None + +# Usage +cached_response = safe_cache_operation( + cache_tool, + "search", + query="test query", + similarity_threshold=0.8 +) +``` + +## Azure CosmosDB Documentation + +- [Azure CosmosDB Vector Search](https://docs.microsoft.com/en-us/azure/cosmos-db/nosql/vector-search) +- [Azure CosmosDB Hybrid Search](https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/hybrid-search?context=%2Fazure%2Fcosmos-db%2Fnosql%2Fcontext%2Fcontext) +- [Vector Indexing in CosmosDB](https://docs.microsoft.com/en-us/azure/cosmos-db/nosql/vector-indexing-policy) +- [NoSQL API Documentation](https://docs.microsoft.com/en-us/azure/cosmos-db/nosql/) +- [Time-to-Live (TTL)](https://docs.microsoft.com/en-us/azure/cosmos-db/time-to-live) +- [Python SDK Reference](https://docs.microsoft.com/en-us/python/api/azure-cosmos/) +- [OpenAI Embeddings API](https://platform.openai.com/docs/guides/embeddings) +- [Azure OpenAI Service](https://docs.microsoft.com/en-us/azure/cognitive-services/openai/) + +## Troubleshooting + +### Common Issues + +1. **Low Cache Hit Rates**: Adjust similarity threshold or improve query normalization +2. **Vector Dimension Mismatches**: Ensure consistent embedding models +3. **High Storage Costs**: Implement TTL and cache size management +4. **Authentication Errors**: Verify API keys and credentials +5. **Performance Issues**: Optimize similarity thresholds and implement proper indexing + +### Debug Mode + +```python +# Enable debug logging +import logging +logging.basicConfig(level=logging.DEBUG) + +# Test cache operations +result = cache_tool._run( + operation="search", + query="test query", + similarity_threshold=0.5, + top_k=1 +) +print("Debug result:", result) +``` + +## License + +This tool is part of the CrewAI Tools package and follows the same licensing terms. diff --git a/crewai_tools/azure/cosmosdb_nosql/semantic_cache/__init__.py b/crewai_tools/azure/cosmosdb_nosql/semantic_cache/__init__.py new file mode 100644 index 00000000..9bf0a1b3 --- /dev/null +++ b/crewai_tools/azure/cosmosdb_nosql/semantic_cache/__init__.py @@ -0,0 +1,5 @@ +"""Azure CosmosDB NoSQL Semantic Cache Tool for CrewAI.""" + +from .semantic_cache_tool import AzureCosmosDBSemanticCacheTool, AzureCosmosDBSemanticCacheConfig, AzureCosmosDBSemanticCacheToolSchema + +__all__ = ["AzureCosmosDBSemanticCacheTool", "AzureCosmosDBSemanticCacheConfig", "AzureCosmosDBSemanticCacheToolSchema"] diff --git a/crewai_tools/azure/cosmosdb_nosql/semantic_cache/semantic_cache_tool.py b/crewai_tools/azure/cosmosdb_nosql/semantic_cache/semantic_cache_tool.py new file mode 100644 index 00000000..d7f7adab --- /dev/null +++ b/crewai_tools/azure/cosmosdb_nosql/semantic_cache/semantic_cache_tool.py @@ -0,0 +1,369 @@ +import json +import os +import uuid +from datetime import datetime, timezone +from logging import getLogger +from typing import Any, ClassVar, Dict, List, Optional, Type, Union + +from crewai.tools import BaseTool +from pydantic import BaseModel, ConfigDict, Field + +try: + from azure.cosmos import ContainerProxy, CosmosClient, DatabaseProxy + from azure.core.credentials import TokenCredential + from openai import AzureOpenAI, Client + + COSMOSDB_AVAILABLE = True +except ImportError: + COSMOSDB_AVAILABLE = False + ContainerProxy = Any + CosmosClient = Any + DatabaseProxy = Any + TokenCredential = Any + AzureOpenAI = Any + Client = Any + +logger = getLogger(__name__) + + +class AzureCosmosDBSemanticCacheConfig(BaseModel): + """Configuration for Azure CosmosDB semantic cache operations.""" + + model_config = ConfigDict(arbitrary_types_allowed=True) + + # Cosmos DB Configuration + cosmos_host: str = Field(..., description="The connection string of the CosmosDB") + key: Optional[str] = Field(None, description="The Azure Key for the cosmos db") + token_credential: Optional[TokenCredential] = Field(None, description="The Azure Token Credential") + database_name: str = Field("memory_database", description="The name of the CosmosDB database") + container_name: str = Field("memory_container", description="The name of the CosmosDB container") + cosmos_container_properties: Dict[str, Any] = Field( + default_factory=lambda: {"partition_key": {"paths": ["/agent_id"], "kind": "Hash"}}, + description="Container properties including partition key configuration" + ) + cosmos_database_properties: Dict[str, Any] = Field(default_factory=dict, description="Database properties") + create_container: bool = Field(True, description="Should create the container if it doesn't exist") + vector_embedding_policy: Optional[Dict[str, Any]] = Field(default=None, description="The policy for the vector embedding.") + indexing_policy: Dict[str, Any] = Field(..., description="The policy for the indexing.") + full_text_policy: Optional[Dict[str, Any]] = Field(default=None, description="The policy for the full text embedding.") + dimensions: int = Field(default=1536, description="Number of dimensions in the embedding vector") + table_alias: str = Field(default="c", description="The alias of the CosmosDB NoSql table.") + + # Embedding Configuration + embedding_model: str = Field("text-embedding-3-large", description="Text embedding model to use") + embedding_dimensions: int = Field(1536, description="Number of dimensions in the embedding vector") + azure_openai_endpoint: Optional[str] = Field(None, description="Azure OpenAI endpoint URL") + openai_api_key: Optional[str] = Field(None, description="OpenAI API key") + + # Cache Behavior + similarity_threshold: float = Field(0.85, description="Default similarity threshold for cache hits") + default_ttl: Optional[int] = Field(86400, description="Default time-to-live in seconds (24 hours)") + enable_hybrid_search: bool = Field(True, description="Enable hybrid search combining vector and text search") + + +class AzureCosmosDBSemanticCacheToolSchema(BaseModel): + """Input schema for AzureCosmosDBSemanticCacheTool.""" + + operation: str = Field( + ..., + description="Operation: 'search', 'update', 'clear'" + ) + + +class AzureCosmosDBSemanticCacheTool(BaseTool): + """Tool for semantic caching of LLM responses using Azure CosmosDB NoSQL.""" + + USER_AGENT: ClassVar[str] = "CrewAI-CosmosDB-SemanticCache-Tool-Python" + + name: str = "AzureCosmosDBSemanticCacheTool" + description: str = "A tool for intelligent caching of LLM responses using semantic similarity search in Azure CosmosDB." + args_schema: Type[BaseModel] = AzureCosmosDBSemanticCacheToolSchema + + config: AzureCosmosDBSemanticCacheConfig = Field(..., description="Configuration for the semantic cache tool") + package_dependencies: List[str] = ["azure-cosmos", "azure-core", "openai"] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self._database = None + self._container = None + self._openai_client = None + + if not COSMOSDB_AVAILABLE: + import click + if click.confirm( + "You are missing required packages. Would you like to install them?" + ): + import subprocess + subprocess.run(["uv", "add", "azure-cosmos"], check=True) + subprocess.run(["uv", "add", "azure-core"], check=True) + subprocess.run(["uv", "add", "openai"], check=True) + else: + raise ImportError("You are missing required packages: 'azure-cosmos', 'azure-core', 'openai'.") + + self._initialize_clients() + + def _initialize_clients(self): + """Initialize the CosmosDB and OpenAI clients.""" + # Initialize CosmosDB client + self.validate_params() + if self.config.key is not None: + self._cosmos_client = CosmosClient( + self.config.cosmos_host, + self.config.key, + user_agent=self.USER_AGENT, + ) + elif self.config.token_credential is not None: + self._cosmos_client = CosmosClient( + self.config.cosmos_host, + self.config.token_credential, + user_agent=self.USER_AGENT, + ) + else: + raise ValueError("Either 'key' or 'token_credential' must be provided") + + # Initialize OpenAI client + if self.config.azure_openai_endpoint: + self._openai_client = AzureOpenAI( + azure_endpoint=self.config.azure_openai_endpoint, + api_key=self.config.openai_api_key or os.environ.get("AZURE_OPENAI_API_KEY"), + api_version="2024-02-01" + ) + elif self.config.openai_api_key or os.environ.get("OPENAI_API_KEY"): + self._openai_client = Client( + api_key=self.config.openai_api_key or os.environ.get("OPENAI_API_KEY") + ) + else: + raise ValueError("Either Azure OpenAI endpoint or OpenAI API key must be provided") + + self._database = self._create_database() + self._container = self._create_container_if_not_exists() + + def _create_database(self) -> DatabaseProxy: + """Create the database if it doesn't exist.""" + return self._cosmos_client.create_database_if_not_exists( + id=self.config.database_name, + offer_throughput=self.config.cosmos_database_properties.get("offer_throughput"), + session_token=self.config.cosmos_database_properties.get("session_token"), + initial_headers=self.config.cosmos_database_properties.get("initial_headers"), + etag=self.config.cosmos_database_properties.get("etag"), + match_condition=self.config.cosmos_database_properties.get("match_condition"), + ) + + def _create_container_if_not_exists(self) -> ContainerProxy: + """Create the container if it doesn't exist.""" + return self._database.create_container_if_not_exists( + id=self.config.container_name, + partition_key=self.config.cosmos_container_properties["partition_key"], + indexing_policy=self.config.indexing_policy, + vector_embedding_policy=self.config.vector_embedding_policy, + full_text_policy=self.config.full_text_policy, + offer_throughput=self.config.cosmos_container_properties.get("offer_throughput"), + unique_key_policy=self.config.cosmos_container_properties.get("unique_key_policy"), + conflict_resolution_policy=self.config.cosmos_container_properties.get("conflict_resolution_policy"), + analytical_storage_ttl=self.config.cosmos_container_properties.get("analytical_storage_ttl"), + computed_properties=self.config.cosmos_container_properties.get("computed_properties"), + etag=self.config.cosmos_container_properties.get("etag"), + match_condition=self.config.cosmos_container_properties.get("match_condition"), + session_token=self.config.cosmos_container_properties.get("session_token"), + initial_headers=self.config.cosmos_container_properties.get("initial_headers"), + default_ttl=self.config.default_ttl, + ) + + def validate_params(self): + if self.config.create_container: + if ( + self.config.indexing_policy["vectorIndexes"] is None + or len(self.config.indexing_policy["vectorIndexes"]) == 0 + ): + raise ValueError( + "vectorIndexes cannot be null or empty in the indexing_policy." + ) + if ( + self.config.vector_embedding_policy is None + or len(self.config.vector_embedding_policy["vectorEmbeddings"]) == 0 + ): + raise ValueError( + "vectorEmbeddings cannot be null " + "or empty in the vector_embedding_policy." + ) + if self.config.cosmos_container_properties["partition_key"] is None: + raise ValueError( + "partition_key cannot be null or empty for a container." + ) + if self.config.enable_hybrid_search: + if ( + self.config.indexing_policy["fullTextIndexes"] is None + or len(self.config.indexing_policy["fullTextIndexes"]) == 0 + ): + raise ValueError( + "fullTextIndexes cannot be null or empty in the " + "indexing_policy if full text search is enabled." + ) + if ( + self.config.full_text_policy is None + or len(self.config.full_text_policy["fullTextPaths"]) == 0 + ): + raise ValueError( + "fullTextPaths cannot be null or empty in the " + "full_text_policy if full text search is enabled." + ) + + def _generate_embedding(self, text: str) -> List[float]: + """Generate embedding for the given text.""" + try: + response = self._openai_client.embeddings.create( + input=text, + model=self.config.embedding_model, + dimensions=self.config.embedding_dimensions + ) + return response.data[0].embedding + except Exception as e: + logger.error(f"Failed to generate embedding: {e}") + raise + + def _run( + self, + operation: str, + prompt: Optional[str] = None, + response: Optional[str] = None, + ) -> str: + """Execute the semantic cache operation.""" + try: + if operation == "search": + return self._search_cache(prompt) + elif operation == "update": + return self._update_cache(prompt, response) + elif operation == "clear": + return self._clear_cache() + else: + return json.dumps({ + "error": f"Unknown operation: {operation}", + "valid_operations": ["search", "update", "clear"] + }) + except Exception as e: + logger.error(f"Semantic cache operation failed: {e}") + return json.dumps({"error": str(e)}) + + def _search_cache( + self, + prompt: str, + ) -> str: + """Search for cached responses similar to the given prompt.""" + if not prompt: + return json.dumps({"error": "prompt is required for search operation"}) + + try: + # Generate embedding for the prompt + query_embedding = self._generate_embedding(prompt) + # Extract search terms from prompt + search_terms = " ".join(prompt.split()) + if self.config.enable_hybrid_search: + # Hybrid search combining vector and text search + query_sql = f""" + SELECT TOP 1 *, + VectorDistance(c.prompt_embedding, @query_embedding) as similarity_score + FROM c + ORDER BY RANK RRF( + FullTextScore(c.prompt, {search_terms}), + VectorDistance(c.prompt_embedding, {query_embedding})) + ) + """ + + parameters = [ + {"name": "@query_embedding", "value": query_embedding}, + ] + else: + # Vector-only search + query_sql = f""" + SELECT TOP 1 *, + VectorDistance(c.prompt_embedding, @query_embedding) as similarity_score + FROM c + ORDER BY VectorDistance(c.prompt_embedding, @query_embedding) + """ + + parameters = [ + {"name": "@query_embedding", "value": query_embedding} + ] + + # Execute query + items = list(self._container.query_items( + query=query_sql, + parameters=parameters, + enable_cross_partition_query=False + )) + + # Filter by similarity threshold and return best match + for item in items: + similarity_score = 1.0 - item.get("similarity_score", 1.0) # Convert distance to similarity + + if similarity_score >= self.config.similarity_threshold: + return json.dumps({ + "cache_hit": True, + "similarity_score": similarity_score, + "cached_response": item.get("response"), + "cache_id": item.get("id"), + "timestamp": item.get("metadata", {}).get("timestamp"), + "prompt": item.get("prompt") + }) + + # No cache hit + return json.dumps({ + "cache_hit": False, + "similarity_score": 0.0, + "message": f"No cached response found above similarity threshold {self.config.similarity_threshold}" + }) + + except Exception as e: + logger.error(f"Failed to search cache: {e}") + return json.dumps({"error": f"Failed to search cache: {str(e)}"}) + + def _update_cache( + self, + prompt: str, + response: str, + ) -> str: + """Store or update a prompt-response pair in the cache.""" + if not prompt: + return json.dumps({"error": "prompt is required for update operation"}) + + if not response: + return json.dumps({"error": "response is required for update operation"}) + + try: + # Generate embedding for the prompt + prompt_embedding = self._generate_embedding(prompt) + + # Create cache document + cache_document = { + "id": str(uuid.uuid4()), + "prompt": prompt, + "prompt_embedding": prompt_embedding, + "response": response, + "metadata": { + "timestamp": datetime.now(timezone.utc).isoformat() + } + } + + # Add TTL if specified + if self.config.default_ttl is not None: + cache_document["ttl"] = self.config.default_ttl + + # Store in CosmosDB + stored_item = self._container.upsert_item(body=cache_document) + + return json.dumps(stored_item) + + except Exception as e: + logger.error(f"Failed to update cache: {e}") + return json.dumps({"error": f"Failed to update cache: {str(e)}"}) + + def _clear_cache( + self, + ) -> str: + """Clear cache entries based on filters.""" + try: + self._cosmos_client.delete_database(self.config.database_name) + return "Cache is cleared." + except Exception as e: + logger.error(f"Failed to clear cache: {e}") + return json.dumps({"error": f"Failed to clear cache: {str(e)}"}) \ No newline at end of file diff --git a/crewai_tools/azure/cosmosdb_nosql/vector_search/README.md b/crewai_tools/azure/cosmosdb_nosql/vector_search/README.md new file mode 100644 index 00000000..05cca9e6 --- /dev/null +++ b/crewai_tools/azure/cosmosdb_nosql/vector_search/README.md @@ -0,0 +1,474 @@ +# Azure CosmosDB Vector Search Tool + +The Azure CosmosDB Vector Search Tool enables CrewAI agents to perform sophisticated vector similarity searches on Azure CosmosDB NoSQL containers. This tool leverages CosmosDB's integrated vector search capabilities to find semantically similar documents based on vector embeddings. + +## Features + +- **Vector Similarity Search**: Perform cosine similarity searches on vector embeddings +- **Multiple Search Types**: Support for vector-only and hybrid (vector + full-text) searches +- **Configurable Results**: Control result count and similarity thresholds +- **Flexible Vector Support**: Work with embeddings from any ML model +- **Advanced Filtering**: Combine vector search with traditional query filters +- **Multiple Auth Methods**: Support for API keys and Azure token credentials +- **Efficient Indexing**: Leverage CosmosDB's optimized vector indexing + +## Installation + +```bash +pip install crewai-tools[azure] +``` + +## Prerequisites + +- Azure CosmosDB account with NoSQL API +- Container configured with vector indexing policy +- Either an API key or Azure credentials for authentication +- Python 3.8+ environment + +## Configuration + +### Basic Configuration with API Key + +```python +from crewai_tools.azure.cosmosdb_nosql.vector_search import CosmosDBVectorSearchTool +from crewai_tools.azure.cosmosdb_nosql.vector_search.vector_search_tool import AzureCosmosDBNoSqlSearchConfig + +config = AzureCosmosDBNoSqlSearchConfig( + cosmos_host="https://your-account.documents.azure.com:443/", + key="your-cosmos-db-key", + database_name="vector_database", + container_name="embeddings_container" +) + +vector_search_tool = CosmosDBVectorSearchTool(config=config) +``` + +### Configuration with Azure Token Credential + +```python +from azure.identity import DefaultAzureCredential +from crewai_tools.azure.cosmosdb_nosql.vector_search import CosmosDBVectorSearchTool +from crewai_tools.azure.cosmosdb_nosql.vector_search.vector_search_tool import AzureCosmosDBNoSqlSearchConfig + +credential = DefaultAzureCredential() +config = AzureCosmosDBNoSqlSearchConfig( + cosmos_host="https://your-account.documents.azure.com:443/", + token_credential=credential, + database_name="vector_database", + container_name="embeddings_container" +) + +vector_search_tool = CosmosDBVectorSearchTool(config=config) +``` + +### Container Setup with Vector Indexing + +```python +# Example container properties for vector search +container_properties = { + "id": "embeddings_container", + "partitionKey": {"paths": ["/category"], "kind": "Hash"}, + "vectorEmbeddingPolicy": { + "vectorEmbeddings": [ + { + "path": "/embedding", + "dataType": "float32", + "distanceFunction": "cosine", + "dimensions": 1536 # Adjust based on your embedding model + } + ] + }, + "indexingPolicy": { + "vectorIndexes": [ + { + "path": "/embedding", + "type": "quantizedFlat" + } + ] + } +} +``` + +## Usage Examples + +### Complete CrewAI Integration Example + +```python +from crewai import Agent, Task, Crew +from crewai_tools.azure.cosmosdb_nosql.vector_search import CosmosDBVectorSearchTool +from crewai_tools.azure.cosmosdb_nosql.vector_search.vector_search_tool import AzureCosmosDBNoSqlSearchConfig +import openai + +# Configure the vector search tool +search_config = AzureCosmosDBNoSqlSearchConfig( + cosmos_host="https://your-account.documents.azure.com:443/", + key="your-cosmos-db-key", + database_name="knowledge_base", + container_name="documents" +) + +vector_search_tool = CosmosDBVectorSearchTool(config=search_config) + +# Create an agent with vector search capabilities +research_agent = Agent( + role="Research Assistant", + goal="Find relevant information using semantic search capabilities", + backstory="You are an expert at finding relevant information using advanced vector search techniques.", + tools=[vector_search_tool], + verbose=True +) + +# Task to find similar documents +search_task = Task( + description=""" + Find documents similar to the query: "machine learning applications in healthcare" + + Use the vector search tool to find the top 5 most semantically similar documents. + The search should focus on documents about AI/ML applications in medical domains. + """, + agent=research_agent, + expected_output="List of top 5 relevant documents with similarity scores and content" +) + +# Create and execute crew +crew = Crew( + agents=[research_agent], + tasks=[search_task], + verbose=True +) + +result = crew.kickoff() +print(result) +``` + +### Vector Search Operations + +#### 1. Basic Vector Search + +```python +# Example query vector (from OpenAI or other embedding model) +query_embedding = [0.1, 0.2, -0.1, 0.5, ...] # 1536-dimensional vector + +result = vector_search_tool._run( + query="machine learning in healthcare", + top_k=5, + query_vector=query_embedding +) +``` + +#### 2. Vector Search with Text Query (Auto-embedding) + +```python +# Tool automatically generates embeddings for text queries +result = vector_search_tool._run( + query="artificial intelligence medical diagnosis", + top_k=10, + embedding_model="text-embedding-ada-002" +) +``` + +#### 3. Hybrid Search (Vector + Full-text) + +```python +# Combine vector similarity with traditional text search +result = vector_search_tool._run( + query="deep learning neural networks", + top_k=5, + query_vector=query_embedding, + hybrid_search=True, + text_query="neural networks AND deep learning" +) +``` + +#### 4. Search with Filters + +```python +# Add additional filtering criteria +result = vector_search_tool._run( + query="machine learning algorithms", + top_k=5, + query_vector=query_embedding, + filter_criteria={ + "category": "research_papers", + "publication_year": {"$gte": 2020} + } +) +``` + +#### 5. Search with Similarity Threshold + +```python +# Only return results above similarity threshold +result = vector_search_tool._run( + query="computer vision applications", + top_k=10, + query_vector=query_embedding, + similarity_threshold=0.8 +) +``` + +### Working with OpenAI Embeddings + +```python +import openai +from crewai_tools.azure.cosmosdb_nosql.vector_search import CosmosDBVectorSearchTool + +# Generate embedding for search query +client = openai.OpenAI(api_key="your-openai-key") + +def get_embedding(text, model="text-embedding-ada-002"): + response = client.embeddings.create( + input=text, + model=model + ) + return response.data[0].embedding + +# Perform vector search +query_text = "machine learning in medical imaging" +query_vector = get_embedding(query_text) + +result = vector_search_tool._run( + query=query_text, + top_k=5, + query_vector=query_vector +) +``` + +### Document Storage Example + +Before searching, you need documents with embeddings in your container: + +```python +# Example document structure for vector search +document = { + "id": "doc_001", + "title": "AI Applications in Healthcare", + "content": "Machine learning and artificial intelligence are revolutionizing healthcare...", + "category": "healthcare", + "publication_year": 2024, + "embedding": [0.1, 0.2, -0.1, 0.5, ...], # 1536-dimensional vector + "metadata": { + "author": "Dr. Smith", + "journal": "AI in Medicine", + "doi": "10.1000/example" + } +} +``` + +## Configuration Options + +| Parameter | Description | Default | Required | +|-----------|-------------|---------|----------| +| `cosmos_host` | CosmosDB account endpoint URL | - | Yes | +| `key` | CosmosDB account key | - | Yes* | +| `token_credential` | Azure token credential | - | Yes* | +| `database_name` | Database name | "vector_database" | No | +| `container_name` | Container name | "vector_container" | No | +| `embedding_field` | Field containing embeddings | "embedding" | No | +| `content_fields` | Fields to return in results | ["id", "content"] | No | + +*Either `key` or `token_credential` must be provided + +## Search Parameters + +| Parameter | Description | Default | Required | +|-----------|-------------|---------|----------| +| `query` | Search query text | - | Yes | +| `top_k` | Number of results to return | 5 | No | +| `query_vector` | Pre-computed query embedding | - | No | +| `embedding_model` | Model for auto-embedding | - | No | +| `hybrid_search` | Enable hybrid search | False | No | +| `text_query` | Full-text search query | - | No | +| `filter_criteria` | Additional filters | - | No | +| `similarity_threshold` | Minimum similarity score | 0.0 | No | + +## Vector Search Best Practices + +### 1. Embedding Model Consistency +```python +# Use the same embedding model for indexing and searching +EMBEDDING_MODEL = "text-embedding-ada-002" +EMBEDDING_DIMENSIONS = 1536 + +# Always use consistent model for both storage and search +``` + +### 2. Optimal Partition Key Design +```python +# Choose partition keys that align with search patterns +container_properties = { + "partitionKey": {"paths": ["/domain"], "kind": "Hash"} # e.g., "healthcare", "finance" +} +``` + +### 3. Efficient Similarity Thresholds +```python +# Set appropriate similarity thresholds to filter noise +result = vector_search_tool._run( + query="specific medical condition", + similarity_threshold=0.75, # Adjust based on your use case + top_k=10 +) +``` + +### 4. Hybrid Search for Better Relevance +```python +# Combine vector and text search for best results +result = vector_search_tool._run( + query="machine learning diagnosis", + hybrid_search=True, + text_query="machine learning AND (diagnosis OR diagnostic)", + top_k=5 +) +``` + +## Error Handling + +```python +import json + +result = vector_search_tool._run( + query="search query", + top_k=5 +) + +try: + parsed_result = json.loads(result) + if "error" in parsed_result: + print(f"Search error: {parsed_result['error']}") + else: + documents = parsed_result.get("documents", []) + print(f"Found {len(documents)} similar documents") + for doc in documents: + print(f"Score: {doc.get('similarity_score', 'N/A')}") + print(f"Content: {doc.get('content', 'N/A')}") +except json.JSONDecodeError: + print("Failed to parse search results") +``` + +## Performance Optimization + +### 1. Index Configuration +```python +# Optimize vector index for your use case +indexing_policy = { + "vectorIndexes": [ + { + "path": "/embedding", + "type": "quantizedFlat" # or "diskANN" for large datasets + } + ] +} +``` + +### 2. Query Optimization +```python +# Limit results and use filters to improve performance +result = vector_search_tool._run( + query="specific query", + top_k=10, # Reasonable limit + filter_criteria={"category": "specific_domain"}, # Pre-filter data + similarity_threshold=0.7 # Filter low-relevance results +) +``` + +### 3. Batch Processing +```python +# For multiple searches, consider batching +queries = ["query1", "query2", "query3"] +results = [] + +for query in queries: + result = vector_search_tool._run(query=query, top_k=5) + results.append(result) +``` + +## Integration with Popular ML Libraries + +### Hugging Face Transformers +```python +from transformers import AutoTokenizer, AutoModel +import torch + +def get_huggingface_embedding(text, model_name="sentence-transformers/all-MiniLM-L6-v2"): + tokenizer = AutoTokenizer.from_pretrained(model_name) + model = AutoModel.from_pretrained(model_name) + + inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) + with torch.no_grad(): + outputs = model(**inputs) + embeddings = outputs.last_hidden_state.mean(dim=1) + + return embeddings[0].tolist() + +# Use with vector search +query_embedding = get_huggingface_embedding("machine learning healthcare") +result = vector_search_tool._run( + query="machine learning healthcare", + query_vector=query_embedding, + top_k=5 +) +``` + +### Azure OpenAI Integration +```python +from openai import AzureOpenAI + +client = AzureOpenAI( + azure_endpoint="https://your-resource.openai.azure.com/", + api_key="your-azure-openai-key", + api_version="2024-02-01" +) + +def get_azure_openai_embedding(text): + response = client.embeddings.create( + input=text, + model="text-embedding-ada-002" # Your deployed model + ) + return response.data[0].embedding + +query_embedding = get_azure_openai_embedding("AI in medical diagnosis") +result = vector_search_tool._run( + query="AI in medical diagnosis", + query_vector=query_embedding, + top_k=5 +) +``` + +## Azure CosmosDB Documentation + +- [Azure CosmosDB Vector Search](https://docs.microsoft.com/en-us/azure/cosmos-db/nosql/vector-search) +- [Vector Indexing in CosmosDB](https://docs.microsoft.com/en-us/azure/cosmos-db/nosql/vector-indexing-policy) +- [NoSQL API Documentation](https://docs.microsoft.com/en-us/azure/cosmos-db/nosql/) +- [Vector Embeddings Best Practices](https://docs.microsoft.com/en-us/azure/cosmos-db/nosql/vector-search-best-practices) +- [Similarity Search Concepts](https://docs.microsoft.com/en-us/azure/cosmos-db/nosql/vector-search-concepts) +- [Python SDK Reference](https://docs.microsoft.com/en-us/python/api/azure-cosmos/) + +## Troubleshooting + +### Common Issues + +1. **Vector Dimension Mismatch**: Ensure query vector dimensions match indexed embeddings +2. **Index Configuration**: Verify container has proper vector indexing policy +3. **Similarity Scores**: Adjust thresholds based on your embedding model characteristics +4. **Performance Issues**: Consider using diskANN indexing for large datasets +5. **Query Limits**: Be aware of CosmosDB query timeout and RU consumption + +### Debug Tips + +```python +# Enable detailed logging +import logging +logging.basicConfig(level=logging.DEBUG) + +# Test with simple queries first +result = vector_search_tool._run( + query="test", + top_k=1, + query_vector=[0.1] * 1536 # Simple test vector +) +``` + +## License + +This tool is part of the CrewAI Tools package and follows the same licensing terms. diff --git a/crewai_tools/azure/cosmosdb_nosql/vector_search/__init__.py b/crewai_tools/azure/cosmosdb_nosql/vector_search/__init__.py new file mode 100644 index 00000000..7e9f12db --- /dev/null +++ b/crewai_tools/azure/cosmosdb_nosql/vector_search/__init__.py @@ -0,0 +1,5 @@ +"""Azure CosmosDB NoSQL Vector Search Tool for CrewAI.""" + +from .vector_search_tool import AzureCosmosDBNoSqlSearchTool, AzureCosmosDBNoSqlSearchConfig, AzureCosmosDBNoSqlToolSchema + +__all__ = ["AzureCosmosDBNoSqlSearchTool", "AzureCosmosDBNoSqlSearchConfig", "AzureCosmosDBNoSqlToolSchema"] diff --git a/crewai_tools/azure/cosmosdb_nosql/vector_search/vector_search_tool.py b/crewai_tools/azure/cosmosdb_nosql/vector_search/vector_search_tool.py new file mode 100644 index 00000000..e63e624d --- /dev/null +++ b/crewai_tools/azure/cosmosdb_nosql/vector_search/vector_search_tool.py @@ -0,0 +1,572 @@ +import json +import os +import uuid +from collections import defaultdict +from logging import getLogger +from typing import Any, ClassVar, Dict, List, Optional, Set, Tuple, Type + +from crewai.tools import BaseTool +from openai import AzureOpenAI, Client +from pydantic import BaseModel, Field + +try: + from azure.cosmos import ContainerProxy, CosmosClient, DatabaseProxy + from azure.core.credentials import TokenCredential + + COSMOSDB_AVAILABLE = True +except ImportError: + COSMOSDB_AVAILABLE = False + ContainerProxy = Any + CosmosClient = Any + DatabaseProxy = Any + TokenCredential = Any + +logger = getLogger(__name__) + + +class AzureCosmosDBNoSqlSearchConfig(BaseModel): + """Configuration for Azure CosmosDB NoSql vector search queries.""" + + max_results: Optional[int] = Field( + default=5, description="The number of items to return." + ) + with_embedding: bool = Field( + default=False, description="Return embeddings if True." + ) + where: Optional[str] = Field( + default=None, description="Where clause." + ) + offset_limit: Optional[str] = Field( + default=None, description="Offset limit." + ) + projection_mapping: Optional[Dict[str, Any]] = Field( + default=None, description="Projection mapping." + ) + full_text_rank_filter: Optional[List[Dict[str, str]]] = Field( + default=None, description="Full text rank filter." + ) + weights: Optional[List[float]] = Field( + default=None, description="Weights." + ) + threshold: Optional[float] = Field( + default=0.0, description="Threshold." + ) + + +class AzureCosmosDBNoSqlToolSchema(BaseModel): + """Input for CosmosDBNoSqlTool.""" + + query: str = Field( + ..., + description="The query to search retrieve relevant information from the CosmosDB NoSql database. Pass only the query, not the question.", + ) + + +class AzureCosmosDBNoSqlSearchTool(BaseTool): + """Tool to perform a vector search the CosmosDb NoSql database""" + + VALID_SEARCH_TYPES: ClassVar[Set[str]] = { + "vector", + "full_text_search", + "full_text_ranking", + "hybrid", + } + USER_AGENT: ClassVar[str] = "Crew-AI-CDBNoSql-VectorSearchTool-Python" + + name: str = "AzureCosmosDBNoSqlVectorSearchTool" + description: str = "A tool to per from a vector search on a CosmosDb database." + + args_schema: Type[BaseModel] = AzureCosmosDBNoSqlToolSchema + query_config: AzureCosmosDBNoSqlSearchConfig = Field( + default=None, description="Azure CosmosDB NoSql vector search configuration." + ) + search_type: str = Field( + default="vector", + description="The type of vector search to perform. Valid options are vector, full text, hybrid." + ) + embedding_model: str = Field( + default="text-embedding-3-large", + description="Text OpenAI embedding model to use", + ) + cosmos_host: str = Field( + ..., + description="The connection string of the CosmosDB NoSql.", + ) + key: Optional[str] = Field( + ..., + description="The Azure Key for the cosmos db.", + ) + token_credential: Optional[TokenCredential] = Field( + ..., + description="The Azure Token Credential.", + ) + database_name: str = Field("crewAI_database", description="The name of the CosmosDB NoSql database") + container_name: str = Field("crewAI_container", description="The name of the CosmosDB NoSql container") + vector_embedding_policy: Optional[Dict[str, Any]] = Field( + default=None, description="The policy for the vector embedding." + ) + indexing_policy: Dict[str, Any] = Field( + ..., description="The policy for the indexing." + ) + cosmos_container_properties: Dict[str, Any] = Field( + default=..., description="The properties for the CosmosDB NoSql container." + ) + cosmos_database_properties: Dict[str, Any] = Field( + default=..., description="The properties for the CosmosDB NoSql database." + ) + full_text_policy: Optional[Dict[str, Any]] = Field( + default=None, description="The policy for the full text embedding." + ) + text_key: str = Field( + default="text", + description="CosmosDB NoSql field that will contain the text for each document", + ) + embedding_key: str = Field( + default="embedding", + description="CosmosDB NoSql field that will contain the embedding for each document", + ) + dimensions: int = Field( + default=1536, + description="Number of dimensions in the embedding vector", + ) + metadata_key: str = Field( + default="metadata", + description="CosmosDB NoSql field that will contain the metadata for each document", + ) + create_container: bool = Field( + default=True, + description="Should create the CosmosDB NoSql container.", + ) + full_text_search_enabled: bool = Field( + default=False, + description="Is the full text search enabled.", + ) + table_alias: str = Field( + default="c", + description="The alias of the CosmosDB NoSql table.", + ) + package_dependencies: List[str] = ["azure-cosmos", "azure-core"] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self._database = None + if not COSMOSDB_AVAILABLE: + import click + + if click.confirm( + "You are missing the azure-cosmos crewai tool. Would you like to install it?", + ): + import subprocess + subprocess.run(["uv", "add", "azure-cosmos"], check=True) + subprocess.run(["uv", "add", "azure-core"], check=True) + else: + raise ImportError("You are missing the 'azure-cosmos' crew ai tool.") + + if "AZURE_OPENAI_ENDPOINT" in os.environ: + self._openai_client = AzureOpenAI() + elif "OPENAI_API_KEY" in os.environ: + self._openai_client = Client() + else: + raise ValueError( + "OPENAI_API_KEY environment variable is required for AzureCosmosDBNoSqlVectorSearchTool and it is mandatory to use the tool." + ) + + self.validate_params() + + if self.key is not None: + self._cosmos_client = CosmosClient( + self.cosmos_host, + self.key, + user_agent=self.USER_AGENT, + ) + elif self.token_credential is not None: + self._cosmos_client = CosmosClient( + self.cosmos_host, + self.token_credential, + user_agent=self.USER_AGENT, + ) + + self._database = self.create_database() + self._container = self.create_container_if_not_exists() + + def create_database(self) -> DatabaseProxy: + # Create the database if it already doesn't exist + self._database = self._cosmos_client.create_database_if_not_exists( + id=self.database_name, + offer_throughput=self.cosmos_database_properties.get("offer_throughput"), + session_token=self.cosmos_database_properties.get("session_token"), + initial_headers=self.cosmos_database_properties.get("initial_headers"), + etag=self.cosmos_database_properties.get("etag"), + match_condition=self.cosmos_database_properties.get("match_condition"), + ) + return self._database + + def create_container_if_not_exists(self) -> ContainerProxy: + # Create the collection if it already doesn't exist + self._container = self._database.create_container_if_not_exists( + id=self.container_name, + partition_key=self.cosmos_container_properties["partition_key"], + indexing_policy=self.indexing_policy, + default_ttl=self.cosmos_container_properties.get("default_ttl"), + offer_throughput=self.cosmos_container_properties.get("offer_throughput"), + unique_key_policy=self.cosmos_container_properties.get( + "unique_key_policy" + ), + conflict_resolution_policy=self.cosmos_container_properties.get( + "conflict_resolution_policy" + ), + analytical_storage_ttl=self.cosmos_container_properties.get( + "analytical_storage_ttl" + ), + computed_properties=self.cosmos_container_properties.get( + "computed_properties" + ), + etag=self.cosmos_container_properties.get("etag"), + match_condition=self.cosmos_container_properties.get("match_condition"), + session_token=self.cosmos_container_properties.get("session_token"), + initial_headers=self.cosmos_container_properties.get("initial_headers"), + vector_embedding_policy=self.vector_embedding_policy, + full_text_policy=self.full_text_policy, + ) + return self._container + + def validate_params(self): + if self.search_type not in self.VALID_SEARCH_TYPES: + raise ValueError( + f"Invalid search_type '{self.search_type}'. " + f"Valid options are: {self.VALID_SEARCH_TYPES}" + ) + + if self.create_container: + if ( + self.indexing_policy["vectorIndexes"] is None + or len(self.indexing_policy["vectorIndexes"]) == 0 + ): + raise ValueError( + "vectorIndexes cannot be null or empty in the indexing_policy." + ) + if ( + self.vector_embedding_policy is None + or len(self.vector_embedding_policy["vectorEmbeddings"]) == 0 + ): + raise ValueError( + "vectorEmbeddings cannot be null " + "or empty in the vector_embedding_policy." + ) + if self.cosmos_container_properties["partition_key"] is None: + raise ValueError( + "partition_key cannot be null or empty for a container." + ) + if self.full_text_search_enabled: + if ( + self.indexing_policy["fullTextIndexes"] is None + or len(self.indexing_policy["fullTextIndexes"]) == 0 + ): + raise ValueError( + "fullTextIndexes cannot be null or empty in the " + "indexing_policy if full text search is enabled." + ) + if ( + self.full_text_policy is None + or len(self.full_text_policy["fullTextPaths"]) == 0 + ): + raise ValueError( + "fullTextPaths cannot be null or empty in the " + "full_text_policy if full text search is enabled." + ) + + def add_texts( + self, + texts: List[str], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> List[str]: + """Run more texts through the embeddings and add to the vectorstore. + + Args: + texts: Iterable of strings to add to the vectorstore. + metadatas: Optional list of metadatas associated with the texts. + ids: Optional list of ids associated with the texts. + **kwargs: Additional keyword arguments to pass to the embedding method. + + Returns: + List of ids from adding the texts into the vectorstore. + """ + # If the texts is empty, throw an error + if not texts: + raise Exception("Texts can not be null or empty") + + # Embed and create the documents + embeddings = self._embed_texts(texts) + text_key = self.text_key + embedding_key = self.embedding_key + partition_key = self.cosmos_container_properties["partition_key"] + + _metadatas = list(metadatas if metadatas is not None else ({} for _ in texts)) + _ids = list(ids if ids is not None else (str(uuid.uuid4()) for _ in texts)) + + # Generate partition key values + partition_key_values = [] + for idx, meta in enumerate(_metadatas): + if partition_key == "id": + partition_key_values.append(_ids[idx]) + else: + # Look for nested key in metadata + value = meta.get(partition_key) + if value is None: + raise ValueError(f"Partition key '{partition_key}' not found in metadata at index {idx}") + partition_key_values.append(value) + + items_to_insert = [ + { + "id": i, + "pk": pk, + text_key: t, + embedding_key: embedding, + "metadata": m, + } + for i, pk, t, m, embedding in + zip(_ids, partition_key_values, texts, _metadatas, embeddings) + ] + + grouped_items_by_pk = defaultdict(list) + for item in items_to_insert: + grouped_items_by_pk[item["pk"]].append(item) + + BATCH_SIZE = 25 + doc_ids: List[str] = [] + for pk_value, items in grouped_items_by_pk.items(): + for i in range(0, len(items), BATCH_SIZE): + batch = items[i:i + BATCH_SIZE] + batch_ops = [("create", doc) for doc in batch] + + self._container.execute_item_batch( + batch_operations=batch_ops, + partition_key=pk_value + ) + doc_ids.extend(doc["id"] for doc in batch) + return doc_ids + + def _embed_texts(self, texts: List[str]) -> List[List[float]]: + return [ + i.embedding + for i in self._openai_client.embeddings.create( + input=texts, + model=self.embedding_model, + dimensions=self.dimensions, + ).data + ] + + def _run(self, query: str) -> str: + try: + search_query, parameters = self._construct_query( + query=query, + ) + results = self._execute_query( + query=search_query, + parameters=parameters, + ) + return json.dumps(results) + except Exception as e: + logger.error(f"Error: {e}") + return "" + + def _construct_query( + self, + query: str, + ) -> Tuple[str, List[Dict[str, Any]]]: + config = self.query_config or AzureCosmosDBNoSqlSearchConfig() + # Create the embedding for the query + embeddings = self._embed_texts([query])[0] + if ( + self.search_type == "full_text_ranking" + or self.search_type == "hybrid" + ): + query = f"SELECT {'TOP ' + str(config.max_results) + ' ' if not config.offset_limit else ''}" + else: + query = f"""SELECT {"TOP @top " if not config.offset_limit else ""}""" + query += self._generate_projection_fields( + embeddings, + ) + table = self.table_alias + query += f" FROM {table}" + + # Add where_clause if specified + if config.where: + query += f" WHERE {config.where}" + + # TODO: Update the code to use parameters once parametrized queries + # are allowed for these query functions + if self.search_type == "full_text_ranking": + if config.full_text_rank_filter is None: + raise ValueError( + "full_text_rank_filter cannot be None for FULL_TEXT_RANK queries." + ) + if len(config.full_text_rank_filter) == 1: + query += f""" ORDER BY RANK FullTextScore({table}.{config.full_text_rank_filter[0]["search_field"]}, + {", ".join(f"'{term}'" for term in config.full_text_rank_filter[0]["search_text"].split())})""" # noqa:E501 + else: + rank_components = [ + f"FullTextScore({table}.{search_item['search_field']}, " + + ", ".join( + f"'{term}'" for term in search_item["search_text"].split() + ) + + ")" + for search_item in config.full_text_rank_filter + ] + query = f" ORDER BY RANK RRF({', '.join(rank_components)})" + elif self.search_type == "vector": + query += " ORDER BY VectorDistance(c[@embeddingKey], @embeddings)" + elif self.search_type == "hybrid": + if config.full_text_rank_filter is None: + raise ValueError( + "full_text_rank_filter cannot be None for HYBRID queries." + ) + rank_components = [ + f"FullTextScore({table}.{search_item['search_field']}, " + + ", ".join(f"'{term}'" for term in search_item["search_text"].split()) + + ")" + for search_item in config.full_text_rank_filter + ] + query += f""" ORDER BY RANK RRF({", ".join(rank_components)}, + VectorDistance({table}.{self.embedding_key}, {embeddings})""" # noqa:E501 + if config.weights: + query += f", {config.weights})" + else: + query += ")" + else: + query += "" + + # Add limit_offset_clause if specified + if config.offset_limit is not None: + query += f""" {config.offset_limit}""" + + # TODO: Remove this if check once parametrized queries + # are allowed for these query functions + parameters = [] + if ( + self.search_type == "full_text_search" + or self.search_type == "vector" + ): + parameters = self._build_parameters( + embeddings=embeddings, + ) + return query, parameters + + def _generate_projection_fields( + self, + embeddings: Optional[List[float]] = None, + ) -> str: + # TODO: Remove the if check, once parametrized queries + # are supported for these query functions. + config = self.query_config or AzureCosmosDBNoSqlSearchConfig() + table = self.table_alias + if ( + self.search_type == "full_text_ranking" + or self.search_type == "hybrid" + ): + if config.projection_mapping: + projection = ", ".join( + f"{table}.{key} as {alias}" + for key, alias in config.projection_mapping.items() + ) + elif config.full_text_rank_filter: + projection = ( + table + + ".id, " + + ", ".join( + f"{table}.{search_item['search_field']} " + f"as {search_item['search_field']}" + for search_item in config.full_text_rank_filter + ) + ) + else: + projection = ( + f"{table}.id, {table}.{self.text_key} " + ) + f"as description, {table}.{self.metadata_key} as metadata" + if self.search_type == "hybrid": + if config.with_embedding: + projection += f", {table}.{self.embedding_key} as embedding" # noqa:E501 + projection += ( + f", VectorDistance({table}.{self.embedding_key}, " # noqa:E501 + f"{embeddings}) as SimilarityScore" + ) + else: + if config.projection_mapping: + projection = ", ".join( + f"{table}[@{key}] as {alias}" + for key, alias in config.projection_mapping.items() + ) + elif config.full_text_rank_filter: + projection = f"{table}.id" + ", ".join( + f"{table}.{search_item['search_field']} as {search_item['search_field']}" # noqa: E501 + for search_item in config.full_text_rank_filter + ) + else: + projection = f"{table}.id, {table}[@textKey] as description, {table}[@metadataKey] as metadata" # noqa: E501 + + if self.search_type == "vector": + if config.with_embedding: + projection += f", {table}[@embeddingKey] as embedding" + projection += ( + f", VectorDistance({table}[@embeddingKey], " + "@embeddings) as SimilarityScore" + ) + return projection + + def _build_parameters( + self, + embeddings: Optional[List[float]], + ) -> List[Dict[str, Any]]: + config = self.query_config or AzureCosmosDBNoSqlSearchConfig() + parameters: List[Dict[str, Any]] = [ + {"name": "@top", "value": config.max_results}, + ] + + if config.projection_mapping: + for key in config.projection_mapping.keys(): + parameters.append({"name": f"@{key}", "value": key}) + else: + parameters.append( + {"name": "@textKey", "value": self.text_key} + ) + parameters.append({"name": "@metadataKey", "value": self.metadata_key}) + + if self.search_type == "vector": + parameters.append( + { + "name": "@embeddingKey", + "value": self.embedding_key, + } + ) + parameters.append({"name": "@embeddings", "value": embeddings}) + + return parameters + + def _execute_query( + self, + query: str, + parameters: List[Dict[str, Any]], + ) -> List[Tuple[List[Any], float]]: + config = self.query_config or AzureCosmosDBNoSqlSearchConfig() + docs = [] + items = list( + self._container.query_items( + query=query, parameters=parameters, enable_cross_partition_query=True + ) + ) + for item in items: + if self.search_type in [ + "vector", + "hybrid", + ]: + score = item["SimilarityScore"] + if score <= config.threshold: + continue + docs.append(item) + else: + docs.append(item) + return docs + diff --git a/pyproject.toml b/pyproject.toml index 0d7f0fe3..d54d6170 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -123,6 +123,10 @@ oxylabs = [ mongodb = [ "pymongo>=4.13" ] +azure = [ + "azure-cosmos>=4.14.0b1", + "azure-core>=1.0.0", +] bedrock = [ "beautifulsoup4>=4.13.4", "bedrock-agentcore>=0.1.0", diff --git a/tests/tools/azure/cosmosdb_nosql/test_memory_store_tool.py b/tests/tools/azure/cosmosdb_nosql/test_memory_store_tool.py new file mode 100644 index 00000000..ec133e1a --- /dev/null +++ b/tests/tools/azure/cosmosdb_nosql/test_memory_store_tool.py @@ -0,0 +1,819 @@ +import json +import os +# Direct import to avoid import chain issues +import sys +from datetime import datetime, timezone +from unittest.mock import MagicMock, patch + +import pytest + +from crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool import AzureCosmosDBMemoryConfig, \ + AzureCosmosDBMemoryTool, AzureCosmosDBMemoryToolSchema + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', '..', '..')) + + +# Unit Test Fixtures +@pytest.fixture +def mock_memory_config(): + """Create a mock memory configuration.""" + return AzureCosmosDBMemoryConfig( + cosmos_host="https://test.documents.azure.com:443/", + key="test_key", + database_name="test_memory_db", + container_name="test_memory_container", + cosmos_container_properties={ + "partition_key": {"paths": ["/agent_id"], "kind": "Hash"} + } + ) + + +@pytest.fixture +def mock_hierarchical_config(): + """Create a mock config with hierarchical partition key.""" + return AzureCosmosDBMemoryConfig( + cosmos_host="https://test.documents.azure.com:443/", + key="test_key", + database_name="test_memory_db", + container_name="test_memory_container", + cosmos_container_properties={ + "partition_key": {"paths": ["/agent_id", "/session_id"], "kind": "MultiHash"} + } + ) + + +@pytest.fixture +def mock_cosmos_container(): + """Mock CosmosDB container for testing.""" + mock_container = MagicMock() + + # Mock responses for different operations + mock_container.create_item.return_value = { + "id": "test_memory_123", + "agent_id": "agent_1", + "content": {"message": "Test memory"}, + "created_at": datetime.now(timezone.utc).isoformat(), + "_ts": 1234567890 + } + + mock_container.read_item.return_value = { + "id": "test_memory_123", + "agent_id": "agent_1", + "content": {"message": "Test memory"}, + "created_at": datetime.now(timezone.utc).isoformat() + } + + mock_container.query_items.return_value = [ + { + "id": "memory_1", + "agent_id": "agent_1", + "content": {"message": "First memory"}, + "created_at": "2025-08-01T10:00:00Z" + }, + { + "id": "memory_2", + "agent_id": "agent_1", + "content": {"message": "Second memory"}, + "created_at": "2025-08-01T11:00:00Z" + } + ] + + mock_container.replace_item.return_value = { + "id": "test_memory_123", + "agent_id": "agent_1", + "content": {"message": "Updated memory"}, + "updated_at": datetime.now(timezone.utc).isoformat() + } + + mock_container.delete_item.return_value = None + mock_container.execute_item_batch.return_value = None + + return mock_container + + +@pytest.fixture +def memory_tool(mock_memory_config): + """Create a memory tool instance with mocked dependencies.""" + with patch("crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool.COSMOSDB_AVAILABLE", True): + with patch("crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool.CosmosClient") as mock_cosmos_client: + # Mock CosmosDB client and database + mock_cosmos_instance = MagicMock() + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_database = MagicMock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + + mock_container = MagicMock() + mock_database.create_container_if_not_exists.return_value = mock_container + + # Create tool instance + tool = AzureCosmosDBMemoryTool(config=mock_memory_config) + tool._container = mock_container + + yield tool + + +@pytest.fixture +def hierarchical_memory_tool(mock_hierarchical_config): + """Create a memory tool with hierarchical partition key.""" + with patch("crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool.COSMOSDB_AVAILABLE", True): + with patch("crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool.CosmosClient") as mock_cosmos_client: + mock_cosmos_instance = MagicMock() + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_database = MagicMock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + + mock_container = MagicMock() + mock_database.create_container_if_not_exists.return_value = mock_container + + tool = AzureCosmosDBMemoryTool(config=mock_hierarchical_config) + tool._container = mock_container + + yield tool + + +# Test Configuration Classes +def test_memory_config_defaults(): + """Test default configuration values.""" + config = AzureCosmosDBMemoryConfig( + cosmos_host="https://test.documents.azure.com:443/", + key="test_key" + ) + + assert config.database_name == "memory_database" + assert config.container_name == "memory_container" + assert config.create_container is True + assert config.cosmos_container_properties["partition_key"]["paths"] == ["/agent_id"] + + +def test_memory_tool_schema(): + """Test the input schema validation.""" + schema = AzureCosmosDBMemoryToolSchema(operation="store") + assert schema.operation == "store" + + # Test all valid operations + valid_operations = ["store", "retrieve", "update", "delete", "clear"] + for op in valid_operations: + schema = AzureCosmosDBMemoryToolSchema(operation=op) + assert schema.operation == op + + +# Test Tool Initialization +def test_tool_initialization_with_key(mock_memory_config): + """Test tool initialization with API key.""" + with patch("crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool.COSMOSDB_AVAILABLE", True): + with patch("crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool.CosmosClient") as mock_client: + tool = AzureCosmosDBMemoryTool(config=mock_memory_config) + mock_client.assert_called_once() + assert tool.name == "AzureCosmosDBMemoryTool" + + +def test_tool_initialization_with_token_credential(): + """Test tool initialization with token credential.""" + from azure.core.credentials import TokenCredential + + mock_credential = MagicMock(spec=TokenCredential) + config = AzureCosmosDBMemoryConfig( + cosmos_host="https://test.documents.azure.com:443/", + token_credential=mock_credential + ) + + with patch("crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool.COSMOSDB_AVAILABLE", True): + with patch("crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool.CosmosClient") as mock_client: + tool = AzureCosmosDBMemoryTool(config=config) + mock_client.assert_called_once() + + +def test_tool_initialization_missing_credentials(): + """Test tool initialization fails without credentials.""" + config = AzureCosmosDBMemoryConfig( + cosmos_host="https://test.documents.azure.com:443/" + ) + + with patch("crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool.COSMOSDB_AVAILABLE", True): + with pytest.raises(ValueError, match="Either 'key' or 'token_credential' must be provided"): + AzureCosmosDBMemoryTool(config=config) + + +def test_tool_initialization_missing_dependency(mock_memory_config): + """Test tool initialization when dependencies are missing.""" + with patch("crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool.COSMOSDB_AVAILABLE", False): + with patch("click.confirm", return_value=False): + with pytest.raises(ImportError, match="You are missing the 'azure-cosmos' package"): + AzureCosmosDBMemoryTool(config=mock_memory_config) + + +# Test Partition Key Handling +def test_get_partition_key_fields_single(memory_tool): + """Test extracting single partition key field.""" + field_names, config = memory_tool._get_partition_key_fields_and_paths() + assert field_names == ["agent_id"] + assert config["paths"] == ["/agent_id"] + + +def test_get_partition_key_fields_hierarchical(hierarchical_memory_tool): + """Test extracting hierarchical partition key fields.""" + field_names, config = hierarchical_memory_tool._get_partition_key_fields_and_paths() + assert field_names == ["agent_id", "session_id"] + assert config["paths"] == ["/agent_id", "/session_id"] + + +def test_build_partition_key_filter_single(memory_tool): + """Test building filter for single partition key.""" + field_names = ["agent_id"] + filter_clause = memory_tool._build_partition_key_filter("agent_1", field_names) + assert filter_clause == "c.agent_id = 'agent_1'" + + +def test_build_partition_key_filter_hierarchical(hierarchical_memory_tool): + """Test building filter for hierarchical partition key.""" + field_names = ["agent_id", "session_id"] + filter_clause = hierarchical_memory_tool._build_partition_key_filter( + ["agent_1", "session_123"], field_names + ) + assert filter_clause == "c.agent_id = 'agent_1' AND c.session_id = 'session_123'" + + +def test_build_partition_key_filter_mismatch_single(memory_tool): + """Test error when providing multiple values for single partition key.""" + field_names = ["agent_id"] + with pytest.raises(ValueError, match="Container has 1 partition key levels, but 2 values provided"): + memory_tool._build_partition_key_filter(["agent_1", "session_123"], field_names) + + +def test_build_partition_key_filter_mismatch_hierarchical(hierarchical_memory_tool): + """Test error when providing wrong number of values for hierarchical partition key.""" + field_names = ["agent_id", "session_id"] + with pytest.raises(ValueError, + match="Container has hierarchical partition key with 2 levels, but only one value provided"): + hierarchical_memory_tool._build_partition_key_filter("agent_1", field_names) + + +def test_set_partition_key_fields_single(memory_tool): + """Test setting single partition key field in item.""" + item = {"id": "test", "content": {"message": "test"}} + field_names = ["agent_id"] + memory_tool._set_partition_key_fields_in_item(item, "agent_1", field_names) + assert item["agent_id"] == "agent_1" + + +def test_set_partition_key_fields_hierarchical(hierarchical_memory_tool): + """Test setting hierarchical partition key fields in item.""" + item = {"id": "test", "content": {"message": "test"}} + field_names = ["agent_id", "session_id"] + hierarchical_memory_tool._set_partition_key_fields_in_item( + item, ["agent_1", "session_123"], field_names + ) + assert item["agent_id"] == "agent_1" + assert item["session_id"] == "session_123" + + +# Test Memory Operations +def test_store_memory_success(memory_tool, mock_cosmos_container): + """Test successful memory storage.""" + memory_tool._container = mock_cosmos_container + + memory_item = { + "id": "test_memory_123", + "agent_id": "agent_1", + "content": {"message": "Test memory"}, + "created_at": datetime.now(timezone.utc).isoformat() + } + + result = memory_tool._store_memory(memory_item) + parsed_result = json.loads(result) + + assert "id" in parsed_result + assert parsed_result["agent_id"] == "agent_1" + mock_cosmos_container.create_item.assert_called_once_with(body=memory_item) + + +def test_store_memory_with_ttl(memory_tool, mock_cosmos_container): + """Test storing memory with TTL.""" + memory_tool._container = mock_cosmos_container + + memory_item = {"id": "test", "agent_id": "agent_1", "content": {"message": "test"}} + ttl = 3600 # 1 hour + + result = memory_tool._store_memory(memory_item, ttl) + + # Verify TTL was added to item + call_args = mock_cosmos_container.create_item.call_args[1]['body'] + assert call_args["ttl"] == ttl + + +def test_store_memory_failure(memory_tool, mock_cosmos_container): + """Test memory storage failure.""" + memory_tool._container = mock_cosmos_container + mock_cosmos_container.create_item.side_effect = Exception("Storage failed") + + memory_item = {"id": "test", "agent_id": "agent_1", "content": {"message": "test"}} + + result = memory_tool._store_memory(memory_item) + parsed_result = json.loads(result) + + assert "error" in parsed_result + assert "Storage failed" in parsed_result["error"] + + +def test_read_memory_success(memory_tool, mock_cosmos_container): + """Test successful memory reading.""" + memory_tool._container = mock_cosmos_container + + result = memory_tool._read_memory("agent_1", "test_memory_123") + parsed_result = json.loads(result) + + assert parsed_result["id"] == "test_memory_123" + assert parsed_result["agent_id"] == "agent_1" + mock_cosmos_container.read_item.assert_called_once_with( + item="test_memory_123", + partition_key="agent_1" + ) + + +def test_read_memory_failure(memory_tool, mock_cosmos_container): + """Test memory reading failure.""" + memory_tool._container = mock_cosmos_container + mock_cosmos_container.read_item.side_effect = Exception("Item not found") + + result = memory_tool._read_memory("agent_1", "nonexistent") + parsed_result = json.loads(result) + + assert "error" in parsed_result + assert "Item not found" in parsed_result["error"] + + +def test_retrieve_memory_success(memory_tool, mock_cosmos_container): + """Test successful memory retrieval.""" + memory_tool._container = mock_cosmos_container + + result = memory_tool._retrieve_memory("agent_1") + parsed_result = json.loads(result) + + assert len(parsed_result) == 2 + assert parsed_result[0]["id"] == "memory_1" + assert parsed_result[1]["id"] == "memory_2" + mock_cosmos_container.query_items.assert_called_once() + + +def test_retrieve_memory_with_filter(memory_tool, mock_cosmos_container): + """Test memory retrieval with query filter.""" + memory_tool._container = mock_cosmos_container + + query_filter = {"category": "conversation"} + result = memory_tool._retrieve_memory("agent_1", query_filter, 5) + + # Verify the query was constructed with filter + call_args = mock_cosmos_container.query_items.call_args + query_sql = call_args[1]['query'] + assert "c.content.category = 'conversation'" in query_sql + + +def test_retrieve_memory_missing_partition_key(memory_tool): + """Test retrieve memory without partition key.""" + result = memory_tool._retrieve_memory(None) + parsed_result = json.loads(result) + + assert "error" in parsed_result + assert "partition_key_value is required" in parsed_result["error"] + + +def test_retrieve_memory_hierarchical(hierarchical_memory_tool, mock_cosmos_container): + """Test memory retrieval with hierarchical partition key.""" + hierarchical_memory_tool._container = mock_cosmos_container + + result = hierarchical_memory_tool._retrieve_memory(["agent_1", "session_123"]) + + call_args = mock_cosmos_container.query_items.call_args + query_sql = call_args[1]['query'] + assert "c.agent_id = 'agent_1' AND c.session_id = 'session_123'" in query_sql + + +def test_update_memory_success(memory_tool, mock_cosmos_container): + """Test successful memory update.""" + memory_tool._container = mock_cosmos_container + + upsert_item = {"content": {"message": "Updated memory"}} + result = memory_tool._update_memory("agent_1", "test_memory_123", upsert_item) + parsed_result = json.loads(result) + + assert "updated_at" in parsed_result + mock_cosmos_container.read_item.assert_called_once() + mock_cosmos_container.replace_item.assert_called_once() + + +def test_update_memory_with_ttl(memory_tool, mock_cosmos_container): + """Test memory update with TTL.""" + memory_tool._container = mock_cosmos_container + + upsert_item = {"content": {"message": "Updated memory"}} + ttl = 7200 # 2 hours + + result = memory_tool._update_memory("agent_1", "test_memory_123", upsert_item, ttl) + + # Verify TTL was added + call_args = mock_cosmos_container.replace_item.call_args[1]['body'] + assert call_args["ttl"] == ttl + + +def test_update_memory_missing_partition_key(memory_tool): + """Test update memory without partition key.""" + result = memory_tool._update_memory(None, "test_id", {}) + parsed_result = json.loads(result) + + assert "error" in parsed_result + assert "partition_key_value is required" in parsed_result["error"] + + +def test_update_memory_missing_id(memory_tool): + """Test update memory without memory ID.""" + result = memory_tool._update_memory("agent_1", None, {}) + parsed_result = json.loads(result) + + assert "error" in parsed_result + assert "memory_id is required" in parsed_result["error"] + + +def test_delete_memory_success(memory_tool, mock_cosmos_container): + """Test successful memory deletion.""" + memory_tool._container = mock_cosmos_container + + result = memory_tool._delete_memory("agent_1", "test_memory_123") + + assert "test_memory_123" in result + assert "has been deleted" in result + mock_cosmos_container.delete_item.assert_called_once_with( + item="test_memory_123", + partition_key="agent_1" + ) + + +def test_delete_memory_missing_partition_key(memory_tool): + """Test delete memory without partition key.""" + result = memory_tool._delete_memory(None, "test_id") + parsed_result = json.loads(result) + + assert "error" in parsed_result + assert "partition_key_value is required" in parsed_result["error"] + + +def test_delete_memory_missing_id(memory_tool): + """Test delete memory without memory ID.""" + result = memory_tool._delete_memory("agent_1", None) + parsed_result = json.loads(result) + + assert "error" in parsed_result + assert "memory_id is required" in parsed_result["error"] + + +def test_delete_memory_failure(memory_tool, mock_cosmos_container): + """Test memory deletion failure.""" + memory_tool._container = mock_cosmos_container + mock_cosmos_container.delete_item.side_effect = Exception("Delete failed") + + result = memory_tool._delete_memory("agent_1", "test_memory_123") + parsed_result = json.loads(result) + + assert "error" in parsed_result + assert "Delete failed" in parsed_result["error"] + + +def test_clear_memory_success(memory_tool, mock_cosmos_container): + """Test successful memory clearing.""" + memory_tool._container = mock_cosmos_container + + # Mock query_items to return items to delete + mock_cosmos_container.query_items.return_value = [ + {"id": "memory_1"}, + {"id": "memory_2"}, + {"id": "memory_3"} + ] + + result = memory_tool._clear_memory("agent_1") + parsed_result = json.loads(result) + + assert parsed_result["success"] is True + assert parsed_result["deleted_count"] == 3 + assert parsed_result["partition_key_value"] == "agent_1" + mock_cosmos_container.execute_item_batch.assert_called_once() + + +def test_clear_memory_hierarchical(hierarchical_memory_tool, mock_cosmos_container): + """Test clearing memory with hierarchical partition key.""" + hierarchical_memory_tool._container = mock_cosmos_container + + mock_cosmos_container.query_items.return_value = [{"id": "memory_1"}] + + result = hierarchical_memory_tool._clear_memory(["agent_1", "session_123"]) + parsed_result = json.loads(result) + + assert parsed_result["success"] is True + assert parsed_result["partition_key_value"] == ["agent_1", "session_123"] + + # Verify query was constructed correctly + call_args = mock_cosmos_container.query_items.call_args + query_sql = call_args[1]['query'] + assert "c.agent_id = 'agent_1' AND c.session_id = 'session_123'" in query_sql + + +def test_clear_memory_large_batch(memory_tool, mock_cosmos_container): + """Test clearing memory with large number of items (batch processing).""" + memory_tool._container = mock_cosmos_container + + # Create 250 items to test batch processing (batch size is 100) + items = [{"id": f"memory_{i}"} for i in range(250)] + mock_cosmos_container.query_items.return_value = items + + result = memory_tool._clear_memory("agent_1") + parsed_result = json.loads(result) + + assert parsed_result["success"] is True + assert parsed_result["deleted_count"] == 250 + + # Should have called execute_item_batch 3 times (100, 100, 50) + assert mock_cosmos_container.execute_item_batch.call_count == 3 + + +def test_clear_memory_missing_partition_key(memory_tool): + """Test clear memory without partition key.""" + result = memory_tool._clear_memory(None) + parsed_result = json.loads(result) + + assert "error" in parsed_result + assert "partition_key_value is required" in parsed_result["error"] + + +def test_clear_memory_failure(memory_tool, mock_cosmos_container): + """Test memory clearing failure.""" + memory_tool._container = mock_cosmos_container + mock_cosmos_container.query_items.side_effect = Exception("Query failed") + + result = memory_tool._clear_memory("agent_1") + parsed_result = json.loads(result) + + assert "error" in parsed_result + assert "Query failed" in parsed_result["error"] + + +# Test _run Method (Main Entry Point) +def test_run_store_operation(memory_tool, mock_cosmos_container): + """Test _run method with store operation.""" + memory_tool._container = mock_cosmos_container + + memory_item = { + "id": "test_123", + "agent_id": "agent_1", + "content": {"message": "Test memory"} + } + + result = memory_tool._run( + operation="store", + memory_item=memory_item + ) + + parsed_result = json.loads(result) + assert "id" in parsed_result + + +def test_run_retrieve_operation(memory_tool, mock_cosmos_container): + """Test _run method with retrieve operation.""" + memory_tool._container = mock_cosmos_container + + result = memory_tool._run( + operation="retrieve", + memory_item={}, # Empty dict for retrieve operation + partition_key_value="agent_1", + max_results=5 + ) + + parsed_result = json.loads(result) + assert len(parsed_result) == 2 + + +def test_run_update_operation(memory_tool, mock_cosmos_container): + """Test _run method with update operation.""" + memory_tool._container = mock_cosmos_container + + memory_item = {"content": {"message": "Updated memory"}} + result = memory_tool._run( + operation="update", + memory_item=memory_item, + partition_key_value="agent_1", + memory_id="test_123" + ) + + parsed_result = json.loads(result) + assert "updated_at" in parsed_result + + +def test_run_delete_operation(memory_tool, mock_cosmos_container): + """Test _run method with delete operation.""" + memory_tool._container = mock_cosmos_container + + result = memory_tool._run( + operation="delete", + memory_item={}, # Empty dict for delete operation + partition_key_value="agent_1", + memory_id="test_123" + ) + + assert "has been deleted" in result + + +def test_run_clear_operation(memory_tool, mock_cosmos_container): + """Test _run method with clear operation.""" + memory_tool._container = mock_cosmos_container + mock_cosmos_container.query_items.return_value = [{"id": "memory_1"}] + + result = memory_tool._run( + operation="clear", + memory_item={}, # Empty dict for clear operation + partition_key_value="agent_1" + ) + + parsed_result = json.loads(result) + assert parsed_result["success"] is True + + +def test_run_invalid_operation(memory_tool): + """Test _run method with invalid operation.""" + result = memory_tool._run( + operation="invalid_op", + memory_item={} + ) + parsed_result = json.loads(result) + + assert "error" in parsed_result + assert "Unknown operation: invalid_op" in parsed_result["error"] + assert "valid_operations" in parsed_result + + +def test_run_operation_exception(memory_tool, mock_cosmos_container): + """Test _run method with operation exception.""" + memory_tool._container = mock_cosmos_container + mock_cosmos_container.create_item.side_effect = Exception("Cosmos DB error") + + memory_item = {"id": "test", "agent_id": "agent_1", "content": {"message": "test"}} + + result = memory_tool._run( + operation="store", + memory_item=memory_item + ) + + parsed_result = json.loads(result) + assert "error" in parsed_result + + +# Test Edge Cases and Error Conditions +def test_empty_query_results(memory_tool, mock_cosmos_container): + """Test handling empty query results.""" + memory_tool._container = mock_cosmos_container + mock_cosmos_container.query_items.return_value = [] + + result = memory_tool._retrieve_memory("agent_1") + parsed_result = json.loads(result) + + assert len(parsed_result) == 0 + + +def test_clear_memory_no_items(memory_tool, mock_cosmos_container): + """Test clearing memory when no items exist.""" + memory_tool._container = mock_cosmos_container + mock_cosmos_container.query_items.return_value = [] + + result = memory_tool._clear_memory("agent_1") + parsed_result = json.loads(result) + + assert parsed_result["success"] is True + assert parsed_result["deleted_count"] == 0 + + +def test_special_characters_in_partition_key(memory_tool, mock_cosmos_container): + """Test handling special characters in partition key.""" + memory_tool._container = mock_cosmos_container + + # Test with special characters that might cause SQL injection issues + partition_key = "agent_with'quotes" + result = memory_tool._retrieve_memory(partition_key) + + # Should handle gracefully without SQL syntax errors + call_args = mock_cosmos_container.query_items.call_args + query_sql = call_args[1]['query'] + assert "agent_with'quotes" in query_sql + + +def test_large_content_storage(memory_tool, mock_cosmos_container): + """Test storing large content objects.""" + memory_tool._container = mock_cosmos_container + + # Create a large content object + large_content = { + "messages": [f"Message {i}" for i in range(1000)], + "metadata": {"type": "conversation", "size": "large"} + } + + memory_item = { + "id": "large_memory", + "agent_id": "agent_1", + "content": large_content + } + + result = memory_tool._store_memory(memory_item) + + # Should handle large objects without issues + parsed_result = json.loads(result) + assert "id" in parsed_result + + +def test_unicode_content_handling(memory_tool, mock_cosmos_container): + """Test handling Unicode content.""" + memory_tool._container = mock_cosmos_container + + unicode_content = { + "message": "Hello 世界! 🌍 Émojis and ñoñ-ASCII çhäracters", + "language": "multi" + } + + memory_item = { + "id": "unicode_memory", + "agent_id": "agent_1", + "content": unicode_content + } + + result = memory_tool._store_memory(memory_item) + parsed_result = json.loads(result) + + assert "id" in parsed_result + + +# Integration-style Tests +def test_complete_memory_lifecycle(memory_tool, mock_cosmos_container): + """Test complete memory lifecycle: store -> retrieve -> update -> delete.""" + memory_tool._container = mock_cosmos_container + + # Store + memory_item = { + "id": "lifecycle_test", + "agent_id": "agent_1", + "content": {"message": "Initial memory"} + } + store_result = memory_tool._store_memory(memory_item) + assert "id" in json.loads(store_result) + + # Retrieve + retrieve_result = memory_tool._retrieve_memory("agent_1") + assert len(json.loads(retrieve_result)) > 0 + + # Update + update_content = {"content": {"message": "Updated memory"}} + update_result = memory_tool._update_memory("agent_1", "lifecycle_test", update_content) + assert "updated_at" in json.loads(update_result) + + # Delete + delete_result = memory_tool._delete_memory("agent_1", "lifecycle_test") + assert "deleted" in delete_result + + +def test_concurrent_operations_simulation(memory_tool, mock_cosmos_container): + """Test simulation of concurrent operations on same partition.""" + memory_tool._container = mock_cosmos_container + + # Simulate multiple store operations + for i in range(5): + memory_item = { + "id": f"concurrent_{i}", + "agent_id": "agent_1", + "content": {"message": f"Concurrent memory {i}"} + } + result = memory_tool._store_memory(memory_item) + assert "id" in json.loads(result) + + # Clear all + mock_cosmos_container.query_items.return_value = [ + {"id": f"concurrent_{i}"} for i in range(5) + ] + clear_result = memory_tool._clear_memory("agent_1") + parsed_clear = json.loads(clear_result) + assert parsed_clear["deleted_count"] == 5 + + +# Performance and Resource Tests +def test_memory_tool_resource_cleanup(): + """Test that memory tool properly manages resources.""" + config = AzureCosmosDBMemoryConfig( + cosmos_host="https://test.documents.azure.com:443/", + key="test_key" + ) + + with patch("crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool.COSMOSDB_AVAILABLE", True): + with patch("crewai_tools.azure.cosmosdb_nosql.memory_store.memory_store_tool.CosmosClient") as mock_client: + tool = AzureCosmosDBMemoryTool(config=config) + + # Verify connections are established + assert tool._cosmos_client is not None + assert tool._database is not None + assert tool._container is not None + + # Tool should be ready for operations + assert tool.name == "AzureCosmosDBMemoryTool" diff --git a/tests/tools/azure/cosmosdb_nosql/test_semantic_cache_tool.py b/tests/tools/azure/cosmosdb_nosql/test_semantic_cache_tool.py new file mode 100644 index 00000000..7a016aad --- /dev/null +++ b/tests/tools/azure/cosmosdb_nosql/test_semantic_cache_tool.py @@ -0,0 +1,884 @@ +import json +import os +from unittest.mock import Mock, patch + +import pytest + +from crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool import ( + AzureCosmosDBSemanticCacheConfig, + AzureCosmosDBSemanticCacheTool, + AzureCosmosDBSemanticCacheToolSchema, +) + + +class TestAzureCosmosDBSemanticCacheConfig: + """Test suite for AzureCosmosDBSemanticCacheConfig.""" + + def test_config_creation_minimal(self): + """Test creating config with minimal required parameters.""" + indexing_policy = { + "vectorIndexes": [{"path": "/prompt_embedding", "type": "quantizedFlat"}], + "fullTextIndexes": [{"path": "/prompt"}] + } + + config = AzureCosmosDBSemanticCacheConfig( + cosmos_host="https://test.documents.azure.com:443/", + key="dGVzdC1rZXk=", # Valid base64 encoded "test-key" + indexing_policy=indexing_policy + ) + + assert config.cosmos_host == "https://test.documents.azure.com:443/" + assert config.key == "dGVzdC1rZXk=" + assert config.database_name == "memory_database" + assert config.container_name == "memory_container" + assert config.similarity_threshold == 0.85 + assert config.default_ttl == 86400 + assert config.enable_hybrid_search is True + + def test_config_custom_values(self): + """Test creating config with custom values.""" + indexing_policy = { + "vectorIndexes": [{"path": "/prompt_embedding", "type": "quantizedFlat"}], + "fullTextIndexes": [{"path": "/prompt"}] + } + + config = AzureCosmosDBSemanticCacheConfig( + cosmos_host="https://custom.documents.azure.com:443/", + key="Y3VzdG9tLWtleQ==", # Valid base64 encoded "custom-key" + database_name="custom_db", + container_name="custom_container", + similarity_threshold=0.9, + default_ttl=3600, + enable_hybrid_search=False, + embedding_model="text-embedding-ada-002", + embedding_dimensions=512, + indexing_policy=indexing_policy + ) + + assert config.database_name == "custom_db" + assert config.container_name == "custom_container" + assert config.similarity_threshold == 0.9 + assert config.default_ttl == 3600 + assert config.enable_hybrid_search is False + assert config.embedding_model == "text-embedding-ada-002" + assert config.embedding_dimensions == 512 + + def test_config_azure_openai_settings(self): + """Test config with Azure OpenAI settings.""" + indexing_policy = { + "vectorIndexes": [{"path": "/prompt_embedding", "type": "quantizedFlat"}], + "fullTextIndexes": [{"path": "/prompt"}] + } + + config = AzureCosmosDBSemanticCacheConfig( + cosmos_host="https://test.documents.azure.com:443/", + key="dGVzdC1rZXk=", # Valid base64 encoded "test-key" + azure_openai_endpoint="https://test.openai.azure.com/", + openai_api_key="test-openai-key", + indexing_policy=indexing_policy + ) + + assert config.azure_openai_endpoint == "https://test.openai.azure.com/" + assert config.openai_api_key == "test-openai-key" + + +class TestAzureCosmosDBSemanticCacheToolSchema: + """Test suite for AzureCosmosDBSemanticCacheToolSchema.""" + + def test_schema_valid_operations(self): + """Test schema with valid operations.""" + valid_operations = ["search", "update", "clear"] + + for operation in valid_operations: + schema = AzureCosmosDBSemanticCacheToolSchema(operation=operation) + assert schema.operation == operation + + def test_schema_operation_required(self): + """Test that operation is required.""" + with pytest.raises(ValueError): + AzureCosmosDBSemanticCacheToolSchema() + + +class TestAzureCosmosDBSemanticCacheTool: + """Test suite for AzureCosmosDBSemanticCacheTool.""" + + @pytest.fixture + def valid_config(self): + """Fixture providing a valid configuration.""" + return AzureCosmosDBSemanticCacheConfig( + cosmos_host="https://test.documents.azure.com:443/", + key="dGVzdC1rZXk=", # Valid base64 encoded "test-key" + database_name="test_db", + container_name="test_container", + indexing_policy={ + "vectorIndexes": [{"path": "/prompt_embedding", "type": "quantizedFlat"}], + "fullTextIndexes": [{"path": "/prompt"}] + }, + vector_embedding_policy={ + "vectorEmbeddings": [{"path": "/prompt_embedding", "dataType": "float32", "dimensions": 1536}] + }, + full_text_policy={ + "fullTextPaths": [{"path": "/prompt"}] + }, + openai_api_key="test-openai-key" + ) + + @pytest.fixture + def minimal_config(self): + """Fixture providing a minimal configuration for vector-only search.""" + return AzureCosmosDBSemanticCacheConfig( + cosmos_host="https://test.documents.azure.com:443/", + key="dGVzdC1rZXk=", # Valid base64 encoded "test-key" + indexing_policy={ + "vectorIndexes": [{"path": "/prompt_embedding", "type": "quantizedFlat"}] + }, + vector_embedding_policy={ + "vectorEmbeddings": [{"path": "/prompt_embedding", "dataType": "float32", "dimensions": 1536}] + }, + enable_hybrid_search=False, + openai_api_key="test-openai-key" + ) + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_tool_initialization_success(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test successful tool initialization.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + + assert tool.name == "AzureCosmosDBSemanticCacheTool" + assert tool.config == valid_config + mock_cosmos_client.assert_called_once() + mock_openai_client.assert_called_once() + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.AzureOpenAI') + def test_tool_initialization_azure_openai(self, mock_azure_openai, mock_cosmos_client, valid_config): + """Test tool initialization with Azure OpenAI.""" + valid_config.azure_openai_endpoint = "https://test.openai.azure.com/" + + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + + mock_azure_openai.assert_called_once() + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + def test_tool_initialization_missing_auth(self, mock_cosmos_client, valid_config): + """Test tool initialization with missing authentication.""" + valid_config.key = None + valid_config.token_credential = None + + with pytest.raises(ValueError, match="Either 'key' or 'token_credential' must be provided"): + AzureCosmosDBSemanticCacheTool(config=valid_config) + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + def test_tool_initialization_missing_openai(self, mock_cosmos_client, valid_config): + """Test tool initialization with missing OpenAI configuration.""" + valid_config.openai_api_key = None + + with patch.dict(os.environ, {}, clear=True): + with pytest.raises(ValueError, match="Either Azure OpenAI endpoint or OpenAI API key must be provided"): + AzureCosmosDBSemanticCacheTool(config=valid_config) + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', False) + @patch('click.confirm') + def test_tool_initialization_missing_dependencies_decline(self, mock_confirm): + """Test tool initialization with missing dependencies - user declines installation.""" + mock_confirm.return_value = False + + config = AzureCosmosDBSemanticCacheConfig( + cosmos_host="https://test.documents.azure.com:443/", + key="dGVzdC1rZXk=", # Valid base64 encoded "test-key" + indexing_policy={ + "vectorIndexes": [{"path": "/prompt_embedding", "type": "quantizedFlat"}] + }, + vector_embedding_policy={ + "vectorEmbeddings": [{"path": "/prompt_embedding", "dataType": "float32", "dimensions": 1536}] + }, + enable_hybrid_search=False, + openai_api_key="test-openai-key" + ) + + with pytest.raises(ImportError, match="You are missing required packages"): + AzureCosmosDBSemanticCacheTool(config=config) + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_validate_params_success(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test successful parameter validation.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + # Should not raise any exception + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + assert tool is not None + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + def test_validate_params_missing_vector_indexes(self, valid_config): + """Test validation with missing vector indexes.""" + valid_config.indexing_policy = {"vectorIndexes": []} + + with pytest.raises(ValueError, match="vectorIndexes cannot be null or empty"): + AzureCosmosDBSemanticCacheTool(config=valid_config) + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + def test_validate_params_missing_vector_embeddings(self, valid_config): + """Test validation with missing vector embeddings.""" + valid_config.vector_embedding_policy = {"vectorEmbeddings": []} + + with pytest.raises(ValueError, match="vectorEmbeddings cannot be null"): + AzureCosmosDBSemanticCacheTool(config=valid_config) + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + def test_validate_params_missing_partition_key(self, valid_config): + """Test validation with missing partition key.""" + valid_config.cosmos_container_properties = {"partition_key": None} + + with pytest.raises(ValueError, match="partition_key cannot be null or empty"): + AzureCosmosDBSemanticCacheTool(config=valid_config) + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + def test_validate_params_hybrid_search_missing_full_text_indexes(self, valid_config): + """Test validation with hybrid search enabled but missing full text indexes.""" + valid_config.indexing_policy = { + "vectorIndexes": [{"path": "/prompt_embedding", "type": "quantizedFlat"}], + "fullTextIndexes": [] + } + + with pytest.raises(ValueError, match="fullTextIndexes cannot be null or empty"): + AzureCosmosDBSemanticCacheTool(config=valid_config) + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + def test_validate_params_hybrid_search_missing_full_text_policy(self, valid_config): + """Test validation with hybrid search enabled but missing full text policy.""" + valid_config.full_text_policy = {"fullTextPaths": []} + + with pytest.raises(ValueError, match="fullTextPaths cannot be null or empty"): + AzureCosmosDBSemanticCacheTool(config=valid_config) + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_generate_embedding_success(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test successful embedding generation.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_response = Mock() + mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])] + mock_openai_instance.embeddings.create.return_value = mock_response + mock_openai_client.return_value = mock_openai_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + embedding = tool._generate_embedding("test prompt") + + assert embedding == [0.1, 0.2, 0.3] + mock_openai_instance.embeddings.create.assert_called_once_with( + input="test prompt", + model=valid_config.embedding_model, + dimensions=valid_config.embedding_dimensions + ) + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_generate_embedding_failure(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test embedding generation failure.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_openai_instance.embeddings.create.side_effect = Exception("API Error") + mock_openai_client.return_value = mock_openai_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + + with pytest.raises(Exception, match="API Error"): + tool._generate_embedding("test prompt") + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_run_unknown_operation(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test _run with unknown operation.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._run(operation="invalid") + + result_data = json.loads(result) + assert "error" in result_data + assert "Unknown operation: invalid" in result_data["error"] + assert result_data["valid_operations"] == ["search", "update", "clear"] + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_search_cache_missing_prompt(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test search cache with missing prompt.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._search_cache("") + + result_data = json.loads(result) + assert result_data["error"] == "prompt is required for search operation" + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_search_cache_hit_hybrid(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test successful cache hit with hybrid search.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_response = Mock() + mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])] + mock_openai_instance.embeddings.create.return_value = mock_response + mock_openai_client.return_value = mock_openai_instance + + # Mock container query result with high similarity + mock_item = { + "id": "test-id", + "prompt": "test prompt", + "response": "test response", + "similarity_score": 0.1, # Distance, will be converted to 0.9 similarity + "metadata": {"timestamp": "2024-01-01T00:00:00Z"} + } + mock_container.query_items.return_value = [mock_item] + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._search_cache("test prompt") + + result_data = json.loads(result) + assert result_data["cache_hit"] is True + assert result_data["similarity_score"] == 0.9 + assert result_data["cached_response"] == "test response" + assert result_data["cache_id"] == "test-id" + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_search_cache_hit_vector_only(self, mock_openai_client, mock_cosmos_client, minimal_config): + """Test successful cache hit with vector-only search.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_response = Mock() + mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])] + mock_openai_instance.embeddings.create.return_value = mock_response + mock_openai_client.return_value = mock_openai_instance + + # Mock container query result + mock_item = { + "id": "test-id", + "prompt": "test prompt", + "response": "test response", + "similarity_score": 0.1, # Distance, will be converted to 0.9 similarity + "metadata": {"timestamp": "2024-01-01T00:00:00Z"} + } + mock_container.query_items.return_value = [mock_item] + + tool = AzureCosmosDBSemanticCacheTool(config=minimal_config) + result = tool._search_cache("test prompt") + + result_data = json.loads(result) + assert result_data["cache_hit"] is True + assert result_data["similarity_score"] == 0.9 + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_search_cache_miss_low_similarity(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test cache miss due to low similarity score.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_response = Mock() + mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])] + mock_openai_instance.embeddings.create.return_value = mock_response + mock_openai_client.return_value = mock_openai_instance + + # Mock container query result with low similarity + mock_item = { + "id": "test-id", + "similarity_score": 0.8, # Distance, will be converted to 0.2 similarity + } + mock_container.query_items.return_value = [mock_item] + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._search_cache("test prompt") + + result_data = json.loads(result) + assert result_data["cache_hit"] is False + assert result_data["similarity_score"] == 0.0 + assert f"No cached response found above similarity threshold {valid_config.similarity_threshold}" in \ + result_data["message"] + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_search_cache_miss_no_results(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test cache miss with no results from query.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_response = Mock() + mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])] + mock_openai_instance.embeddings.create.return_value = mock_response + mock_openai_client.return_value = mock_openai_instance + + # Mock empty query result + mock_container.query_items.return_value = [] + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._search_cache("test prompt") + + result_data = json.loads(result) + assert result_data["cache_hit"] is False + assert result_data["similarity_score"] == 0.0 + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_search_cache_error(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test search cache with database error.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_response = Mock() + mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])] + mock_openai_instance.embeddings.create.return_value = mock_response + mock_openai_client.return_value = mock_openai_instance + + # Mock container query error + mock_container.query_items.side_effect = Exception("Database error") + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._search_cache("test prompt") + + result_data = json.loads(result) + assert "error" in result_data + assert "Failed to search cache: Database error" in result_data["error"] + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_update_cache_missing_prompt(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test update cache with missing prompt.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._update_cache("", "response") + + result_data = json.loads(result) + assert result_data["error"] == "prompt is required for update operation" + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_update_cache_missing_response(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test update cache with missing response.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._update_cache("prompt", "") + + result_data = json.loads(result) + assert result_data["error"] == "response is required for update operation" + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + @patch('uuid.uuid4') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.datetime') + def test_update_cache_success(self, mock_datetime, mock_uuid, mock_openai_client, mock_cosmos_client, valid_config): + """Test successful cache update.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_response = Mock() + mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])] + mock_openai_instance.embeddings.create.return_value = mock_response + mock_openai_client.return_value = mock_openai_instance + + # Mock UUID and datetime + mock_uuid.return_value = "test-uuid" + mock_now = Mock() + mock_now.isoformat.return_value = "2024-01-01T00:00:00Z" + mock_datetime.now.return_value = mock_now + + # Mock successful upsert + stored_item = { + "id": "test-uuid", + "prompt": "test prompt", + "response": "test response" + } + mock_container.upsert_item.return_value = stored_item + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._update_cache("test prompt", "test response") + + result_data = json.loads(result) + assert result_data["id"] == "test-uuid" + assert result_data["prompt"] == "test prompt" + assert result_data["response"] == "test response" + + # Verify upsert was called with correct document structure + mock_container.upsert_item.assert_called_once() + call_args = mock_container.upsert_item.call_args[1]["body"] + assert call_args["id"] == "test-uuid" + assert call_args["prompt"] == "test prompt" + assert call_args["response"] == "test response" + assert call_args["prompt_embedding"] == [0.1, 0.2, 0.3] + assert call_args["ttl"] == valid_config.default_ttl + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_update_cache_no_ttl(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test cache update without TTL.""" + valid_config.default_ttl = None + + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_response = Mock() + mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])] + mock_openai_instance.embeddings.create.return_value = mock_response + mock_openai_client.return_value = mock_openai_instance + + mock_container.upsert_item.return_value = {"id": "test"} + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + tool._update_cache("test prompt", "test response") + + # Verify TTL was not added to document + call_args = mock_container.upsert_item.call_args[1]["body"] + assert "ttl" not in call_args + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_update_cache_error(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test update cache with database error.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_response = Mock() + mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])] + mock_openai_instance.embeddings.create.return_value = mock_response + mock_openai_client.return_value = mock_openai_instance + + # Mock container upsert error + mock_container.upsert_item.side_effect = Exception("Database error") + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._update_cache("test prompt", "test response") + + result_data = json.loads(result) + assert "error" in result_data + assert "Failed to update cache: Database error" in result_data["error"] + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_clear_cache_success(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test successful cache clearing.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._clear_cache() + + assert result == "Cache is cleared." + mock_cosmos_instance.delete_database.assert_called_once_with(valid_config.database_name) + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_clear_cache_error(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test cache clearing with database error.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + # Mock delete database error + mock_cosmos_instance.delete_database.side_effect = Exception("Database error") + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._clear_cache() + + result_data = json.loads(result) + assert "error" in result_data + assert "Failed to clear cache: Database error" in result_data["error"] + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_run_search_operation(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test _run method with search operation.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_response = Mock() + mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])] + mock_openai_instance.embeddings.create.return_value = mock_response + mock_openai_client.return_value = mock_openai_instance + + mock_container.query_items.return_value = [] + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._run(operation="search", prompt="test prompt") + + result_data = json.loads(result) + assert result_data["cache_hit"] is False + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_run_update_operation(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test _run method with update operation.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_response = Mock() + mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])] + mock_openai_instance.embeddings.create.return_value = mock_response + mock_openai_client.return_value = mock_openai_instance + + mock_container.upsert_item.return_value = {"id": "test"} + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._run(operation="update", prompt="test prompt", response="test response") + + result_data = json.loads(result) + assert result_data["id"] == "test" + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_run_clear_operation(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test _run method with clear operation.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._run(operation="clear") + + assert result == "Cache is cleared." + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_run_operation_exception(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test _run method with exception during operation.""" + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_openai_instance.embeddings.create.side_effect = Exception("OpenAI error") + mock_openai_client.return_value = mock_openai_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._run(operation="search", prompt="test prompt") + + result_data = json.loads(result) + assert "error" in result_data + assert "OpenAI error" in result_data["error"] + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + def test_custom_similarity_threshold(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test cache with custom similarity threshold.""" + valid_config.similarity_threshold = 0.95 + + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + mock_openai_instance = Mock() + mock_response = Mock() + mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3])] + mock_openai_instance.embeddings.create.return_value = mock_response + mock_openai_client.return_value = mock_openai_instance + + # Mock item with similarity 0.9 (below 0.95 threshold) + mock_item = { + "id": "test-id", + "similarity_score": 0.1, # Distance, converts to 0.9 similarity + } + mock_container.query_items.return_value = [mock_item] + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + result = tool._search_cache("test prompt") + + result_data = json.loads(result) + assert result_data["cache_hit"] is False + assert "0.95" in result_data["message"] + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.TokenCredential') + def test_token_credential_initialization(self, mock_token_credential_class, mock_openai_client, mock_cosmos_client, + valid_config): + """Test initialization with token credential instead of key.""" + mock_credential = mock_token_credential_class.return_value + valid_config.key = None + valid_config.token_credential = mock_credential + + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + + # Verify CosmosClient was called with token credential + mock_cosmos_client.assert_called_once_with( + valid_config.cosmos_host, + mock_credential, + user_agent=AzureCosmosDBSemanticCacheTool.USER_AGENT + ) + + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.COSMOSDB_AVAILABLE', True) + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.CosmosClient') + @patch('crewai_tools.azure.cosmosdb_nosql.semantic_cache.semantic_cache_tool.Client') + @patch.dict(os.environ, {'OPENAI_API_KEY': 'env-openai-key'}) + def test_openai_from_environment(self, mock_openai_client, mock_cosmos_client, valid_config): + """Test OpenAI client initialization from environment variable.""" + valid_config.openai_api_key = None + + mock_database = Mock() + mock_container = Mock() + mock_cosmos_instance = Mock() + mock_cosmos_instance.create_database_if_not_exists.return_value = mock_database + mock_database.create_container_if_not_exists.return_value = mock_container + mock_cosmos_client.return_value = mock_cosmos_instance + + tool = AzureCosmosDBSemanticCacheTool(config=valid_config) + + # Verify OpenAI Client was called with environment variable + mock_openai_client.assert_called_once_with(api_key='env-openai-key') \ No newline at end of file diff --git a/tests/tools/azure/cosmosdb_nosql/test_vector_search_tool.py b/tests/tools/azure/cosmosdb_nosql/test_vector_search_tool.py new file mode 100644 index 00000000..cfe30ee8 --- /dev/null +++ b/tests/tools/azure/cosmosdb_nosql/test_vector_search_tool.py @@ -0,0 +1,317 @@ +import json +import os +import sys +from unittest.mock import MagicMock, patch + +import pytest + +from crewai_tools.azure.cosmosdb_nosql.vector_search.vector_search_tool import AzureCosmosDBNoSqlSearchTool, AzureCosmosDBNoSqlSearchConfig + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', '..', '..')) + + +# Unit Test Fixtures +@pytest.fixture +def mock_config(): + return AzureCosmosDBNoSqlSearchConfig( + max_results=5, + with_embedding=False, + threshold=0.5, + ) + + +@pytest.fixture +def mock_cosmos_container(): + """Mock CosmosDB container for testing.""" + mock_container = MagicMock() + mock_container.query_items.return_value = [ + { + "id": "1", + "text": "Sample document about Azure AI", + "SimilarityScore": 0.8, + "metadata": {"category": "AI"} + }, + { + "id": "2", + "text": "Another document about machine learning", + "SimilarityScore": 0.7, + "metadata": {"category": "ML"} + } + ] + return mock_container + + +@pytest.fixture +def cosmosdb_tool(): + """Create a CosmosDB tool instance with mocked dependencies.""" + with patch("crewai_tools.azure.cosmosdb_nosql.vector_search.vector_search_tool.COSMOSDB_AVAILABLE", True): + with patch("os.environ", {"OPENAI_API_KEY": "test_key"}): + with patch("crewai_tools.azure.cosmosdb_nosql.vector_search.vector_search_tool.Client") as mock_client: + with patch("crewai_tools.azure.cosmosdb_nosql.vector_search.vector_search_tool.CosmosClient") as mock_cosmos_client: + # Mock OpenAI client + mock_openai_instance = MagicMock() + mock_client.return_value = mock_openai_instance + + # Mock CosmosDB client + mock_cosmos_instance = MagicMock() + mock_cosmos_client.return_value = mock_cosmos_instance + + # Create tool with required parameters + tool = AzureCosmosDBNoSqlSearchTool( + cosmos_host="https://test.documents.azure.com:443/", + key="test_key", + token_credential=None, + indexing_policy={ + "vectorIndexes": [{"path": "/embedding", "type": "quantizedFlat"}] + }, + cosmos_container_properties={ + "partition_key": {"paths": ["/id"], "kind": "Hash"} + }, + cosmos_database_properties={}, + vector_embedding_policy={ + "vectorEmbeddings": [{"path": "/embedding", "dataType": "float32", "dimensions": 1536}] + } + ) + + # Mock the embedding method to return one embedding per input text + tool._embed_texts = lambda x: [[0.1, 0.2, 0.3] * 512 for _ in x] # 1536 dimensions per text + tool._container = MagicMock() + + yield tool + + +# Unit Tests +def test_vector_search_query(cosmosdb_tool, mock_cosmos_container): + """Test vector search functionality.""" + cosmosdb_tool.search_type = "vector" + cosmosdb_tool._container = mock_cosmos_container + + # Mock query execution + mock_cosmos_container.query_items.return_value = [ + { + "id": "1", + "description": "Sample document about Azure AI", + "SimilarityScore": 0.8, + "metadata": {"category": "AI"} + } + ] + + results = cosmosdb_tool._run("Azure artificial intelligence") + parsed_results = json.loads(results) + + assert len(parsed_results) == 1 + assert parsed_results[0]["id"] == "1" + assert parsed_results[0]["SimilarityScore"] == 0.8 + mock_cosmos_container.query_items.assert_called_once() + + +def test_full_text_search_query(cosmosdb_tool, mock_cosmos_container): + """Test full text search functionality.""" + cosmosdb_tool.search_type = "full_text_search" + cosmosdb_tool._container = mock_cosmos_container + + # Mock query execution for full text search + mock_cosmos_container.query_items.return_value = [ + { + "id": "2", + "description": "Document containing search terms", + "metadata": {"category": "search"} + } + ] + + results = cosmosdb_tool._run("search terms") + parsed_results = json.loads(results) + + assert len(parsed_results) == 1 + assert parsed_results[0]["id"] == "2" + mock_cosmos_container.query_items.assert_called_once() + + +def test_full_text_ranking_query(cosmosdb_tool, mock_cosmos_container): + """Test full text ranking search functionality.""" + cosmosdb_tool.search_type = "full_text_ranking" + cosmosdb_tool.query_config = AzureCosmosDBNoSqlSearchConfig( + full_text_rank_filter=[ + {"search_field": "title", "search_text": "Azure machine learning"} + ] + ) + cosmosdb_tool._container = mock_cosmos_container + + # Mock query execution for full text ranking + mock_cosmos_container.query_items.return_value = [ + { + "id": "3", + "title": "Azure machine learning guide", + "description": "Comprehensive guide to ML on Azure", + "metadata": {"category": "guide"} + } + ] + + results = cosmosdb_tool._run("machine learning") + parsed_results = json.loads(results) + + assert len(parsed_results) == 1 + assert parsed_results[0]["id"] == "3" + assert "title" in parsed_results[0] + mock_cosmos_container.query_items.assert_called_once() + + +def test_hybrid_search_query(cosmosdb_tool, mock_cosmos_container): + """Test hybrid search functionality (combines vector and full text).""" + cosmosdb_tool.search_type = "hybrid" + cosmosdb_tool.query_config = AzureCosmosDBNoSqlSearchConfig( + full_text_rank_filter=[ + {"search_field": "content", "search_text": "Azure AI services"} + ], + weights=[0.5, 0.5] # Equal weight for vector and text search + ) + cosmosdb_tool._container = mock_cosmos_container + + # Mock query execution for hybrid search + mock_cosmos_container.query_items.return_value = [ + { + "id": "4", + "content": "Azure AI services documentation", + "description": "Complete guide to Azure AI", + "SimilarityScore": 0.9, + "metadata": {"category": "documentation"} + } + ] + + results = cosmosdb_tool._run("AI services") + parsed_results = json.loads(results) + + assert len(parsed_results) == 1 + assert parsed_results[0]["id"] == "4" + assert parsed_results[0]["SimilarityScore"] == 0.9 + assert "content" in parsed_results[0] + mock_cosmos_container.query_items.assert_called_once() + + +def test_search_with_config_parameters(cosmosdb_tool, mock_cosmos_container): + """Test search with various configuration parameters.""" + config = AzureCosmosDBNoSqlSearchConfig( + max_results=10, + with_embedding=True, + threshold=0.6, + where="metadata.category = 'AI'" + ) + cosmosdb_tool.query_config = config + cosmosdb_tool._container = mock_cosmos_container + + # Mock query execution with config + mock_cosmos_container.query_items.return_value = [ + { + "id": "5", + "description": "AI document", + "SimilarityScore": 0.7, + "embedding": [0.1, 0.2, 0.3], + "metadata": {"category": "AI"} + } + ] + + results = cosmosdb_tool._run("artificial intelligence") + parsed_results = json.loads(results) + + assert len(parsed_results) == 1 + assert parsed_results[0]["SimilarityScore"] > config.threshold + mock_cosmos_container.query_items.assert_called_once() + + +def test_search_with_threshold_filtering(cosmosdb_tool, mock_cosmos_container): + """Test that results below threshold are filtered out.""" + cosmosdb_tool.query_config = AzureCosmosDBNoSqlSearchConfig(threshold=0.8) + cosmosdb_tool._container = mock_cosmos_container + + # Mock query execution with low similarity score + mock_cosmos_container.query_items.return_value = [ + { + "id": "6", + "description": "Low relevance document", + "SimilarityScore": 0.5, # Below threshold + "metadata": {"category": "test"} + } + ] + + results = cosmosdb_tool._run("test query") + parsed_results = json.loads(results) + + # Should return empty array due to threshold filtering + assert len(parsed_results) == 0 + mock_cosmos_container.query_items.assert_called_once() + + +def test_query_construction_for_different_search_types(cosmosdb_tool): + """Test that different search types generate appropriate queries.""" + + # Test vector search query construction + cosmosdb_tool.search_type = "vector" + query, params = cosmosdb_tool._construct_query("test query") + assert "VectorDistance" in query + assert "ORDER BY" in query + + # Test full text ranking query construction + cosmosdb_tool.search_type = "full_text_ranking" + cosmosdb_tool.query_config = AzureCosmosDBNoSqlSearchConfig( + full_text_rank_filter=[{"search_field": "title", "search_text": "test"}] + ) + query, params = cosmosdb_tool._construct_query("test query") + assert "FullTextScore" in query + assert "RANK" in query + + +def test_embedding_generation(cosmosdb_tool): + """Test that embeddings are generated correctly.""" + test_texts = ["Azure AI", "Machine Learning", "CosmosDB"] + embeddings = cosmosdb_tool._embed_texts(test_texts) + + assert len(embeddings) == 3 + assert all(len(emb) == 1536 for emb in embeddings) # Default dimension size + + +def test_error_handling(cosmosdb_tool, mock_cosmos_container): + """Test error handling in query execution.""" + cosmosdb_tool._container = mock_cosmos_container + + # Mock an exception during query execution + mock_cosmos_container.query_items.side_effect = Exception("Database connection error") + + results = cosmosdb_tool._run("test query") + + # Should return empty string on error + assert results == "" + + +def test_config_validation(): + """Test configuration validation.""" + # Test valid search type + valid_config = AzureCosmosDBNoSqlSearchConfig() + assert valid_config.max_results == 5 # Default value + + # Test configuration with custom values + custom_config = AzureCosmosDBNoSqlSearchConfig( + max_results=20, + with_embedding=True, + threshold=0.7 + ) + assert custom_config.max_results == 20 + assert custom_config.with_embedding is True + assert custom_config.threshold == 0.7 + + +def test_add_texts_functionality(cosmosdb_tool): + """Test adding texts to the vector store.""" + # Mock the container's execute_item_batch method + mock_result = MagicMock() + cosmosdb_tool._container.execute_item_batch.return_value = mock_result + + texts = ["Document 1", "Document 2"] + metadatas = [{"type": "test"}, {"type": "sample"}] + + # Mock the partition key property + cosmosdb_tool.cosmos_container_properties = {"partition_key": "id"} + + doc_ids = cosmosdb_tool.add_texts(texts, metadatas) + + assert len(doc_ids) == 2 + cosmosdb_tool._container.execute_item_batch.assert_called() diff --git a/uv.lock b/uv.lock index e3a05617..2913a267 100644 --- a/uv.lock +++ b/uv.lock @@ -3,66 +3,77 @@ revision = 2 requires-python = ">=3.10, <3.14" resolution-markers = [ "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", - "python_version < '0'", "python_full_version >= '3.13' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", "(python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", "python_full_version >= '3.13' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'", "python_full_version >= '3.13' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'", "(python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.13' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version >= '3.12.4' and python_full_version < '3.13' and sys_platform == 'darwin'", - "python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform == 'darwin'", - "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version == '3.11.*' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version < '3.11' and sys_platform == 'darwin'", - "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", + "python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", + "(python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'", + "python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'", + "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'", + "(python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and python_full_version < '3.13' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version == '3.11.*' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.11.*' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux')", + 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