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LangChain is just too much, personal solutions are great, until you need to compare metrics or methodologies of prompt generation. Then the onus is on these n-p
by reallymental 3y ago
LangChain is just too much, personal solutions are great, until you need to compare metrics or methodologies of prompt generation. Then the onus is on these n-parties who are sharing their resources to ensure that all of them used the same templates, they were generated the same way, with the only diff being the models these prompts were run on.
So maybe a simpler library like Microsoft's Guidance (https://github.com/microsoft/guidance https://github.com/microsoft/guidance)? It does this really well.
Also LangChain has a lot of integrations, that just pop up as soon as new API for anything LLM pops up, so that helps with new user onboarding as well.
- msp26 3y agoI wouldn't rely on guidance. I've been waiting for them to approve a commit for something as basic as token counts for OAI models for weeks now. It's just not actively developed enough for serious use cases.
- reallymental 3y agoI wouldn't count anything out as of yet, the trend is usually that people move towards simpler tools that are also widely used by others. Kind of a chicken-and-egg problem (to be widely used, it helps to be simple, but not all simple-to-use products make it to being widely used). TensorFlow vs PyTorch anyone? Now, which will be more likely to be widely adopted? No one knows, just keep using both, learning both linguistics and hopefully one of them gets enough traction and all your effort put into building something with these two doesn't go to waste, fingers crossed.
- msp26 3y agoI do have some guidance code (running on a more universal wrapper I made for LLMs) sitting in my prompts directory. I'm not going to throw it out but I don't see the point in maintaining it.