GoMate

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GoMate User Guide

Applicable people

Developer

data scientist

Businesses that need to efficiently process large amounts of text data

Usage scenarios

Answer user questions quickly and accurately in a Q&A system

Analyze user interests and recommend relevant documents in content recommendation systems

Quickly find the information you need through retrieval and generation technology in enterprise knowledge management

Product features

Document parsing: Use the TextParser module to parse documents and extract key content

Vector storage: Convert document content into vector form for storage through the VectorStore module

Embedding model: Use embedding models such as BgeEmbedding to convert text into vector representations

Question Query: Use embedding models to perform vector queries based on user questions to find the most relevant documents

Text generation: Use generative models such as GLMChat to generate answers based on the retrieved document content.

Document update: Supports dynamically adding documents to update the model's retrieval and generation capabilities

Tutorial

1. Install the Python environment and dependent libraries required by GoMate

2. Prepare text data and use the TextParser module for document parsing

3. Use the VectorStore module to store the parsed document in vector form

4. Select or train an appropriate embedding model, such as BgeEmbedding

5. Use the embedding model to perform vector queries based on the questions entered by the user.

6. Combine the retrieved document content and use the generative model to generate answers or output

7. Adjust model parameters based on feedback and optimize output results