Chroma MCP
Chroma is an open-source vector database that provides memory support for Python and JavaScript LLM applications, supporting multiple client types and document operations.
rating : 3 points
downloads : 675
What is the MCP Server?
The MCP server is an open-source tool built on Chroma that enhances the capabilities of LLMs (Large Language Models) by providing context data to AI models. It supports multiple data retrieval methods, such as vector search, full-text search, and metadata filtering.How to Use the MCP Server?
You can start using the MCP server in just a few simple steps. First, install the necessary dependencies. Then, choose the appropriate client type according to your needs (such as cloud client, HTTP client, or local client). Finally, configure the connection and run queries.Application Scenarios
The MCP server is well-suited for application scenarios that require enhanced LLM memory capabilities, such as chatbots, knowledge base management, and personalized recommendation systems.Main Features
Flexible Client TypesSupports multiple client types, including cloud clients, HTTP clients, persistent clients, and temporary clients.
Powerful Data Manipulation CapabilitiesSupports creating, modifying, and deleting collections, as well as advanced data querying and filtering functions.
Embedding Function SupportComes with multiple built-in embedding functions, such as default, Cohere, OpenAI, etc., for easy and quick integration.
Advantages and Limitations
Advantages
Open source and free to use
Supports multiple embedding functions and query methods
Easy to integrate into existing systems
Limitations
Performance may be limited for large-scale datasets
Requires a certain technical background for efficient configuration
How to Use
Install the MCP Server
Download and install the MCP server and its dependencies.
Configure the Client
Choose the client type according to your needs and configure the connection parameters.
Run Queries
Execute queries to obtain the desired results.
Usage Examples
Example 1: Add Documents to a CollectionAdd documents to a collection and perform queries.
Example 2: Query DocumentsRetrieve documents based on query conditions.
Frequently Asked Questions
How to choose the appropriate client type?
Does it support custom embedding functions?
Related Resources
Official Documentation
Detailed usage guides and technical documentation.
GitHub Repository
The project's source code and contribution guidelines.
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