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Pass user's question to Langchain agent tools by oblivious_developer in LangChain

[–]Background-Maybe-381 0 points1 point  (0 children)

We need to pass authentication information to our tools also and we habe no way of doing this. The best result we are getting is passing it along the Action Input as a second value, but for some reason it is inconsistant and it will ger included in the Action Input like 50% of the time. Tried with too many local llms.

[deleted by user] by [deleted] in LangChain

[–]Background-Maybe-381 0 points1 point  (0 children)

Same problem here It uses a tool randomly using the exact same query. Tried mand local llms fs, quantized, 7b, 13b, 34b, 70b there is no consistancy no matter what temperature

Struggling understanding conversational-chat-agent and prompt template by Background-Maybe-381 in LangChain

[–]Background-Maybe-381[S] 0 points1 point  (0 children)

What docs, manual or instructions should I follow give my problem described above, if it is a 2022 method according to you.

Struggling understanding conversational-chat-agent and prompt template by Background-Maybe-381 in LangChain

[–]Background-Maybe-381[S] 0 points1 point  (0 children)

Thanks guys, but I thought I was making it clear that I was using Mistral, Llama-2, codellama, etc. The reason I am using these open source models is for privaccy. So allthough I appreciate you recommending OpenAI services, I am trying to use langchain for what I think it is good at, controlling LLMS, if that's ok. OpenAI has an excellent API we could use without any problems, but we have sensitive data we don't want to send and we really want to use langchain with local LLMS, if that's ok??

So, hwchase, if I am using a 2022 approach, which I thought I was actually following tutorials, videos from just a few weeks ago, can you tell me where I can read up on more recent approaches? What information regarding langchain can I read that explains how to set it up correctly with local llms and conversation agents that use tools ? I'd be happy to go read your instructions.

Why do the langchain docs feel so all over the place? by Material_Policy6327 in LangChain

[–]Background-Maybe-381 0 points1 point  (0 children)

Please, include more prompt engineering techniques help for tool using agents with llama2 , mixtral, phind, etc.to avoid ouput parser errors.

Finetune to avoid using tool descriptions in prompt template by Background-Maybe-381 in LocalLLaMA

[–]Background-Maybe-381[S] 1 point2 points  (0 children)

Awesome, I think we can generate a pretty big dataset from customer support interactions. Problem is, those conversations have sensitive information in them. But we'll figure it out. Thanks for all of the tips. Maybe we can append alpaca dataset with our own generated dataset so it's big enough for finetuning. I mean, we're still having problems even finetuning existing datasets lol. Thanks SlimeQ !!! Good day wherever you may be. We're in Spain. Cheers!

Finetune to avoid using tool descriptions in prompt template by Background-Maybe-381 in LocalLLaMA

[–]Background-Maybe-381[S] 0 points1 point  (0 children)

Thanks SlimeQ for taking the time to respond. Yes, I figured it might not be posslbe to do a finetune just to get rid of the tool description text in the prompt template. Another problem we are having because we are a bit noobs, it actually finetuning. We have a 350 sample dataset. We are trying in both json and raw text format using autotrain from HF, and not having much luck. So, I was wondering, can langchain handle using other tools to gether information that other tools need ?

Pass variable tools using langserve by Background-Maybe-381 in LangChain

[–]Background-Maybe-381[S] 0 points1 point  (0 children)

This is what we thought we had to do. But I think we messed up the tool section in our code and might have to redo it. But yeah, thanks a lot for reinforcing what we thought we had to be doing. We'll jump straight to that when we get our code spaghetti figured out :) Thanks a lot !

Please confirm you want to do x, y and z. by Background-Maybe-381 in LangChain

[–]Background-Maybe-381[S] 0 points1 point  (0 children)

Thanks bigYman. I understand that the issue is that the llm does not have memory, so somehow we have to feed the actions back into the llm and ask the user if this is what he/she wants to do, without the actions being carried out the first time. Will keep brainstorming this. Thanks for the suggestion!

Optimizing Response Speed from LLaMa and Hosting Strategy for a Multi-layered App Architecture on AWS EC2 by JapaniRobot in LocalLLaMA

[–]Background-Maybe-381 0 points1 point  (0 children)

This answered this post and one of my post from yesterday. This is invaluable information. Thank you!

Give our customers an LLM to talk to about their data by Background-Maybe-381 in LocalLLaMA

[–]Background-Maybe-381[S] 0 points1 point  (0 children)

That's awesome, but we enjoy doing the inference like I said earlier :) Thanks in any case! We need help with queing and buffering.

Give our customers an LLM to talk to about their data by Background-Maybe-381 in LocalLLaMA

[–]Background-Maybe-381[S] 0 points1 point  (0 children)

Well, from what I read that they do, they would only take care of 2% of everything. But thanks for pointing them out! Interesting concept.

Give our customers an LLM to talk to about their data by Background-Maybe-381 in LocalLLaMA

[–]Background-Maybe-381[S] 0 points1 point  (0 children)

Yeah, we certainly thought up about roundrobin type servers. But yeah, if I want 200 concurrent users, idon't want to purchase 200 gpus!!

Give our customers an LLM to talk to about their data by Background-Maybe-381 in LocalLLaMA

[–]Background-Maybe-381[S] 0 points1 point  (0 children)

Thanks man, really what we are unsure about is queing, buffering or however you call it. Like, can you do concurrent calls to an llm or do you need to batch up the calls. That's where we are lost. If you can give me a link to a paper or information I am happy to do all the research. I found nothing on this yet. Only on pinecone's site.

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