Assistants API vs using LangChain (or other) library

[raviw]

I’m certain that creating an assistant via the Open AI assistant platform is not the same as fine tuning. I’m trying to understand the differences with using the LangChain (or other) library to directly talk to a model.

1. The assistant thread helps keep context so that I don’t have to keep passing the entire thread each time in a conversation? But with another library I’m responsible for passing all previous messages in a prompt as it’s stateless? Seems like one can use the ChatMemory capability to handle this with Langchain?
2. I’m assuming that the token calculations are no different. So my cost won’t be any different? I means it’s not that the assistant is doing anything differently to reduce cost.
3. The tool definitions adhere to a specific schema with the assistants api which the LLM is presumable trained to understand whereas providing them via another library may not be as effective?
4. A benefit of a library like LangChain may be to do RAG and provide few shot examples in a prompt? Or is this possible via the Assistant API?

Thanks in advance.

[anon10827405]

Both are solutions that help bootstrap an LLM by abstracting away a lot of the technical features required.

LangChain provides more steer-ability compared to Assistants, especially when it comes to RAG. It also means you can plug in other LLMs from different vendors.

Assistants are much quicker to bootstrap and manage all the background data, but at the cost of the control. Their RAG solutions are usually sufficient for new projects. The downside is that you are confined to OpenAI models only.

@raviw

The assistant thread helps keep context so that I don’t have to keep passing the entire thread each time in a conversation? But with another library I’m responsible for passing all previous messages in a prompt as it’s stateless? Seems like one can use the ChatMemory capability to handle this with Langchain?

In both cases the full message is technically sent. For Assistant’s the state is saved on OpenAI’s servers while in LangChain it is saved in a local or remote database.

@raviw

I’m assuming that the token calculations are no different. So my cost won’t be any different? I means it’s not that the assistant is doing anything differently to reduce cost.

This all depends on how you setup LangChain and your RAG database.

@raviw

The tool definitions adhere to a specific schema with the assistants api which the LLM is presumable trained to understand whereas providing them via another library may not be as effective?

They use the same JSON schema

@raviw

A benefit of a library like LangChain may be to do RAG and provide few shot examples in a prompt? Or is this possible via the Assistant API?

In both cases you have the same amount of control when it comes to prompts.

My recommendation would be to start with Assistants. Then if you find a need to go past what they offer you can look into alternative solutions. I really wouldn’t recommend LangChain for anything besides messing around.