[my original post was a brain dump of various notes about the new Assistants API and vector database RAG style chat augmentation. I asked GPT4 to translate my notes and thoughts into a more structured format, which you’ll find below.]
Curious if I’m thinking of this correctly, or if Assistants/Files (messages/threads) is not comparable to vector database style RAG generation?
Certainly, the diagram you’ve provided outlines the traditional RAG workflow, where a query is processed through an embedding model to retrieve context from a vector database, which is then fed into a Large Language Model (LLM) like GPT to generate a response, potentially evaluated against a reference response.
Here is a proposed workflow that leverages the new Assistant’s API capabilities, analogous to the RAG method:
1. Query Reception:
- The user submits a query to the Assistant.
2. Preprocessing and Embedding:
- The Assistant’s API processes the query and uses its internal mechanisms (potentially an embedding model, though specifics may not be publicly disclosed) to understand the context of the query.
- The API then identifies key concepts within the query that need additional context for a more accurate response.
3. Retrieval from Files/Threads:
- The API searches through attached files or threads linked to the Assistant or the specific conversation. These files are pre-indexed and stored, analogous to a vector database, but the content is in raw text form or in some structured format like JSON, CSV, etc.
- It retrieves the relevant content based on the query’s context, much like the retrieval of context vectors in the traditional RAG workflow.
4. Response Generation:
- With the relevant context in hand, the Assistant’s API leverages its LLM (like GPT) to formulate a response. The LLM uses the provided context to inform its generation, ensuring the response is accurate and relevant to the user’s query.
- The response may be a direct answer, additional questions for clarification, or a more complex response that incorporates data from the retrieved documents.
5. Quality Scoring (Optional):
- If there is a mechanism within the Assistant’s API to evaluate the quality of the response (for example, through some form of scoring or feedback system), it can be applied here.
- The API could potentially adjust future responses based on continuous learning from user interactions and feedback, improving over time.
6. Delivery of Response:
- The generated response is then delivered to the user.
- The system logs the interaction, which could be used for future reference or to further train the model on user-specific queries and information needs.
7. Reference Response (Optional):
- If there is a known good response (a reference response), it could be used to compare the quality of the generated response. This comparison could be manual or automated depending on the system capabilities.
- User feedback on the response quality could be incorporated into the system, helping to refine the process for future queries.
Here’s a simplified flow diagram for this workflow:
User Query -> Assistant's API -> Preprocessing/Embedding -> Retrieve from Files/Threads -> Generate Response -> Deliver Response | v Quality Scoring (Optional)
This workflow retains the essence of the RAG architecture while leveraging the new functionalities offered by the Assistant’s API, such as the ability to reference external data sources more seamlessly and to have ongoing threads that maintain context over the course of multiple interactions.