Blog

Open AI announcement and RAG

LMs are trained on static/historical data. RAG is used to connect to latest data, propriety data 

you can build RAG functionality or use OPEN AI to build RAG.

Read more info at LLM Overview 

OPEN AI Assistant

OpenAI Assistants is a tool within the OpenAI API that enables developers to build and integrate AI assistants into their applications. It empowers developers to create customized assistants that can:

Key Features:

• Leverage powerful language models: Assistants can access OpenAI's large language models, including text-davinci-003, to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

• Access tools and knowledge: Assistants can utilize various tools to enhance their capabilities:

  • Code Interpreter: Execute code in different programming languages and return the results.

  • Knowledge Retrieval: Retrieve relevant information from external documents or databases to provide more comprehensive responses.

  • Function Calling: Trigger custom-built functions to integrate with external services or perform specific tasks.

• 
  

• Personalize with instructions: Developers can fine-tune Assistants' behavior and responses by providing specific instructions, tailoring them to unique use cases and brand voices.

• Manage conversations with threads: Assistants can engage in multi-turn conversations, maintaining context and history through a feature called Threads.

How API Developers Use It:

1. Create an Assistant: Define its custom instructions and select the desired language model.

2. Enable tools: Choose which tools (Code Interpreter, Retrieval, Function Calling) to integrate.

3. Create a Thread: Initiate a conversation when a user interacts with the application.

4. Add Messages: Add user queries or prompts to the Thread.

5. Run the Assistant: Trigger the Assistant to process the Thread and generate responses using relevant tools and knowledge.

Benefits for API Developers:

• Streamline AI integration: Simplifies the process of building AI-powered features into applications.

• Enhance user experiences: Creates more engaging and interactive experiences through natural language interactions.

• Expand application capabilities: Unlocks new possibilities for tasks like content generation, translation, code execution, knowledge retrieval, and more.

• Customize for specific needs: Tailor Assistants to align with unique brand voices and use cases.