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MCP App
The MCP application is a service that combines RAG and web search tools, using OpenAI embedding vector storage, PostgreSQL as the database, and PGVector as the vector storage, supporting knowledge retrieval and document addition functions.
rating : 2.5 points
downloads : 9.1K
What is the MCP server?
The MCP server is an application that combines retrieval-augmented generation (RAG) and web search tools. It allows large language models (LLMs) to not only retrieve knowledge from the vector database but also add new documents, thereby expanding the scope of knowledge it uses.How to use the MCP server?
After installing the dependencies, start the server and configure it to start using. It supports multiple query methods, including direct questions and adding documents.Applicable scenarios
It is suitable for enterprises and individual users who need to expand the knowledge scope of LLM, especially for application scenarios that deal with complex business problems or require real-time information updates.Main features
Retrieval-augmented generation (RAG) integration
Through RAG technology, LLM can efficiently retrieve relevant information from the vector database.
Web search engine
It has a built-in web search function that can quickly obtain the latest external information.
Document management
It supports adding, managing, and querying custom documents.
Advantages
Powerful knowledge expansion ability
Supports multiple query methods
Easy to integrate into existing systems
Limitations
High requirements for hardware resources
Initial configuration may be complex
How to use
Install dependencies
Ensure that all necessary dependencies are installed, such as SQLAlchemy, PGVector, etc.
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Start the server
Run the following command to start the MCP server.
Usage examples
Case 1: Basic query
Users can send query requests to the MCP server through simple commands.
Case 2: Add documents
Users can easily add custom documents to the database.
Frequently asked questions
Does the MCP server support multi-language queries?
How to solve the performance bottleneck problem?
Related resources
Official documentation
Detailed guide on using the MCP server.
GitHub code repository
Open-source code repository containing source code and examples.
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