Chatgpt-Pgvector

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chatgpt-pgvector is a starter app for building domain-specific ChatGPT applications using gpt3.5-turbo. It leverages embeddings and vector search to provide more accurate and trustworthy answers.

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At a glance

Pricing

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Free tier

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API

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Skill level

Technical

About

What is chatgpt-pgvector?

chatgpt-pgvector is a starter application designed for developing domain-specific ChatGPT applications. It utilizes the gpt3.5-turbo model and integrates embeddings with vector search capabilities. This approach allows the application to overcome the limitations of general-purpose ChatGPT by incorporating specialized domain knowledge. A key feature is its ability to cite sources, which enhances the accuracy and trustworthiness of the generated answers, making it suitable for applications requiring precise, context-aware information.

Best used for

Building domain-specific ChatGPT applications that require accurate, trustworthy, and source-cited answers.

Common actions

Build custom AI apps

Improve AI accuracy

Integrate domain knowledge

Develop with GPT-3.5

Enhance search with vectors

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Capabilities

Key features

Domain-specific ChatGPT
GPT-3.5-turbo integration
Embeddings and vector search
Cites sources
Accurate, trustworthy answers

Target Audience

DevelopersAI engineersData scientists

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

Does chatgpt-pgvector have a free tier or trial period for development?

As a starter application, chatgpt-pgvector itself doesn't have a direct pricing model. However, you would incur costs for using the underlying GPT-3.5-turbo API and potentially for hosting the PostgreSQL database with pgvector, which may offer free tiers or trials depending on the provider.

What kind of data can I use to train my domain-specific ChatGPT with chatgpt-pgvector?

You can use any text-based data relevant to your specific domain. This could include documentation, articles, reports, internal knowledge bases, or any other textual information that you want your ChatGPT application to be knowledgeable about.

How does chatgpt-pgvector ensure the accuracy and trustworthiness of its answers?

It achieves this by integrating embeddings with vector search, allowing it to retrieve and utilize specific domain knowledge. Crucially, it cites its sources, enabling users to verify the information and understand the origin of the generated answers.

Can chatgpt-pgvector be integrated with other large language models besides GPT-3.5-turbo?

While chatgpt-pgvector is designed with GPT-3.5-turbo in mind, its architecture for embeddings and vector search is flexible. Adapting it to work with other LLMs would likely require modifications to the API calls and potentially the embedding generation process.

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