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Nora Reidy
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* docs: consolidate MongoDB documentation improvements Combines previously closed MongoDB docs PRs (#6847, #6845, #6721, #6626, #6760, #6759) into a single PR per the updated contribution guidelines requiring a linked open issue. RagTool only supports chromadb and qdrant as vector store backends; MongoDB Atlas is covered separately by MongoDBVectorSearchTool, which is already listed in the database-data tools overview, so the RagTool data-sources bullet was dropped rather than reintroduced. Fixes #8096 * revert gemini change * ai review feedback * memory wording * translations
72 lines
2.6 KiB
Plaintext
72 lines
2.6 KiB
Plaintext
---
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title: "Overview"
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description: "Connect to databases, vector stores, and data warehouses for comprehensive data access"
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icon: "face-smile"
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mode: "wide"
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---
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These tools enable your agents to interact with various database systems, from traditional SQL databases to modern vector stores and data warehouses.
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## **Available Tools**
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<CardGroup cols={2}>
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<Card title="MySQL Tool" icon="database" href="/en/tools/database-data/mysqltool">
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Connect to and query MySQL databases with SQL operations.
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</Card>
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<Card title="PostgreSQL Search" icon="elephant" href="/en/tools/database-data/pgsearchtool">
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Search and query PostgreSQL databases efficiently.
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</Card>
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<Card title="Snowflake Search" icon="snowflake" href="/en/tools/database-data/snowflakesearchtool">
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Access Snowflake data warehouse for analytics and reporting.
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</Card>
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<Card title="NL2SQL Tool" icon="language" href="/en/tools/database-data/nl2sqltool">
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Convert natural language queries to SQL statements automatically.
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</Card>
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<Card title="Qdrant Vector Search" icon="vector-square" href="/en/tools/database-data/qdrantvectorsearchtool">
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Search vector embeddings using Qdrant vector database.
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</Card>
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<Card title="Weaviate Vector Search" icon="network-wired" href="/en/tools/database-data/weaviatevectorsearchtool">
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Perform semantic search with Weaviate vector database.
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</Card>
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<Card title="MongoDB Vector Search" icon="leaf" href="/en/tools/database-data/mongodbvectorsearchtool">
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Vector similarity search on MongoDB Atlas with indexing helpers.
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</Card>
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<Card title="SingleStore Search" icon="database" href="/en/tools/database-data/singlestoresearchtool">
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Safe SELECT/SHOW queries on SingleStore with pooling and validation.
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</Card>
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</CardGroup>
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## **Common Use Cases**
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- **Data Analysis**: Query databases for business intelligence and reporting
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- **Vector Search**: Find similar content using semantic embeddings
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- **ETL Operations**: Extract, transform, and load data between systems
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- **Real-time Analytics**: Access live data for decision making
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```python
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from crewai_tools import MySQLTool, MongoDBVectorSearchTool, QdrantVectorSearchTool, NL2SQLTool
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# Create database tools
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mysql_db = MySQLTool()
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mongodb_vector_search = MongoDBVectorSearchTool(
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connection_string="<MONGODB_URI>",
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database_name="<database>",
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collection_name="<collection>",
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)
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qdrant_vector_search = QdrantVectorSearchTool()
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nl_to_sql = NL2SQLTool()
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# Add to your agent
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agent = Agent(
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role="Data Analyst",
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tools=[mysql_db, mongodb_vector_search, qdrant_vector_search, nl_to_sql],
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goal="Extract insights from various data sources"
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)
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