Langchain agent without openai reddit from langchain_core. And in my opinion, for those using OpenAI's models, it's definitely the better option right If you’re looking to implement cached datastores for user convo’s or biz specific knowledge or Yes, it is indeed possible to use the SemanticChunker in the LangChain framework with a different language model and set of embedders. I am trying to switch to Open source LLM for this chatbot, has anyone used Langchain with LM studio? I was facing some issues using open source LLM from LM Studio for this task. schema module), and use it to create a System Message (this is what chat models use to give context to the LLM. Honestly, it's not hard to create custom classes in langchain via encapsulation and overriding whatever method or methods I need to be different for my purposes. OpenAI is an AI research and deployment company. The MCAT (Medical College Admission Test) is offered by the AAMC and is a required exam for admission to medical schools in the USA and Canada. Sorry I am new to langchain. I mean that you're missing the case for LangChain if you try and develop an app that's LLM agnostic without it or some other library. I’m prototyping one now using a GPT and when it stops being stupid it gives really reliable SQL queries even without metadata on my db, only the tables and relations. Has anyone had success using Langchain agents powered by an LLM other than the ones I've played around with OpenAI's Function Calling and I've found it a lot faster and easier to use than the tools and agent options provided by LangChain. Then you add it to the agent’s Does anyone knows how to add systemMessage to openai-functions agnet declared like No agent framework, Langchain or some other framework, is production ready unless your OpenAI or Microsoft (cost). I use and develop the StreamLit/Langchain so much more because everything is just easier to develop and faster to manage and deploy. Exploring alternatives to OpenAI within the LangChain ecosystem opens up numerous Our application makes heavy use of AI agents for performing different types of tasks, such as Was writing some code that wanted to print the model string for a model without having a specific model. /r/MCAT is a place for MCAT practice, questions, discussion, advice, social networking, news, study tips and more. Unfortunately, BaseChatModel does not have a model property. e. 5 your better off using chains or other deterministic workflows. agents import load_tools, AgentExecutor, initialize_agent. However all my agents are created using the function create_openai_tools_agent(). LangChain has a fairly decent async implementation which is wonderful when you switch from OpenAI to AzureOpenAI. Hi folks, it seems to me that the current sentiment around AI agents is very negative as in that they're useless but I don't Get the Reddit app Scan this I've also tried passing a list tools to an agent without the decorator using this method just in case it helped for some reason, messages, access smart devices with HomeAssistant etc. I thought it would be good to have a thread detailing peoples experiences with those alternatives? I was using the LangChain python library and got slightly bamboozled by the number of abstractions. from Does anyone know if there is a way to slow the number of times langchain agent calls OpenAI? Perhaps a parameter you can send. However, we are integrating tools and we are thinking to use langchain agents for that. Any alternative on how we can do this without using langchain ? We are using a conversational chain in an agent with OpenAI functions as tools. The new releases from openai had me convinced to drop langchain, but then the concern of being locked in to a single LLM provider scared me too much to change course away from Langchain. model_id = "microsoft/Phi-3-mini-4k-instruct" tokenizer = AutoTokenizer. We are an unofficial community. I myself tried generating the answers by manually querying the DB, but the answer are like to the point, ie when the Agent thing worked for me, which was very rarely, it gave the answer more like a conversational manner whereas when I used Langchain to make an query and then run it on the DB manually myself, I got the answer which was just the fact. You could also just append the sql code as a string/json to the output itself to return it in the typical agent from langchain_huggingface import HuggingFacePipeline. I've tried many models ranging from 7B to 30B in langchain and found that none can perform tasks. OpenAI realized they didn't have a moat, so they tried to wall the garden by making the ecosystem more valuable with closed Plugins. I'm specifically interested in low-memory LLMs. We already did a project with langchain agents before and it was very easy for us to use their agents. (we're trying to fix this in LangChain as well - revamping the architecture to split out integrations, having langchain-core as a separate thing). The actual function call requires all parameters, but I want the agent to recognize it should call foo, even if the I have an application that is currently based on 3 agents using LangChain and GPT4-turbo. Their implementation of agents are also fairly easy and robust, with a lot of tools you can integrate into an agent and seamless usage between them, unlike ChatGPT with plugins. It works with local/open and remote/proprietary LLMs. I don't think any other agent frameworks give you the same level of controllability We've also tried to learn from LangChain, and conciously keep LangGraph very low level and free of integrations. The LangChain framework is designed to be flexible and modular, allowing you to swap out There are various language models that can be used to embed a You can define agents with optional tools and vector-db, assign them tasks, and have them collaborate via messages: this is a “conversational programming” paradigm. . However, the open-source LLMs I used and agents I built with LangChain wrapper didn’t produce consistent, production-ready results. Then still return the sql output like normal. I tried reading and understanding the “WebGPT: Browser-assisted question-answering with human feedback” paper but I get lost. Tried the set of alternatives used in my code at present, 16 votes, 37 comments. We then use OpenAI Function Agent + Zep for memory and for the Vector Database. In the end, I built an agent without LangChain, using the OpenAI client, Python coroutines for async flow, and FastAPI for the web It allowed us to git rid of a lot of technical debt accumulated over the previous months of sub-classing different langchain agents. i was doing some testing and manage to use a langchain pdf chat bot with the oobabooga-api, all run locally in my gpu. The ChatGPT Plugins cannot be used outside of ChatGPT. Open-source AI Voice Agent with OpenAI Discussion How Apple Uses ML To Recognize People (Without Photos Leaving Your iPhone). We use heavily OpenAI LLM to take decisions. And because whatever OpenAI is using to store their assistant knowledge base sucks or at least it’s hard to get the agent to actually use it without extra prompts. LangChain gives you one standard interface for many use cases. OpenAI makes ChatGPT, GPT-4, and DALL·E 3. The #1 social media platform for MCAT advice. In this example you find where sql_code is defined or created in the tool run, then send it to the run manager. Hosted on GCP Kubernetes. 17 votes, 16 comments. One funny story is we ran into an issue where the bot would always reply no matter what, so users would get into a "thanks, have a good day", "you too again", in an endless loop, so we had to implement a stop-code in the middleware. . ChatGPT seems to be the only zero shot agent capable of producing the correct Action, Action Input, Observation loop. But I think the value of langchain is mainly on local. Reading the documentation, it seems that the recommended Agent for Claude is the XML Agent. I'd like to test Claude 3 in this context. It forces you to use a common set of inputs/outputs for all your steps, which means future changes are much simpler and more modular. But in this jungle, how can you find some working stacks that uses OpenAI, LangChain and whatever else? Lets say I want an agent/bot that: * knows about my local workspace (git repo) but knows about it in REAL TIME * the agent or a sibling agent has access to all the latest documentation, say for example React Native I have build Openai based chatbot that uses Langchain agents - wiki, dolphin, etc. For the models I modified This. Hi all, I read in a thread about some frustrations in production and a few people chimed in with alternatives to LangChain that I wasn't aware of. Have people tried using other frameworks for local LLMs? Is so, what do you recommend? In particular I have trouble getting LangChain to work with quantized Vicuna (4-bit GPTQ). from_pretrained(model_id) model = AutoModelForCausalLM. using this main code langchain-ask-pdf-local with the webui class in oobaboogas-webui-langchain_agent this is the result (100% not my code, i just copy and pasted it) PDFChat_Oobabooga. And I found all the examples are OpenAI. I'm working on a conversational agent Look for SystemMessage (in python it’s in langchain. I don’t see OpenAI doing this. I want to be able to really understand how I can create an agent without using Langchain. from langchain. I’m working with the gpt-4 model using azure OpenAI and get rate limit exceeded based on my subscription. Please share. You can override on_tool_end() callback to send anything you want to your preferred callback, such as log files, apis, etc. answering questions on the basis of documents, websites, repositories etc. I plan to explore it more in the future. However this documentation is referring to Claude 2 instead of Agreed. As a demo I've put together an app that allows SecOps teams to autonomously find the domain registrar for malicious / copyright infringing websites and draft a custom takedown request. I have built an open-source AI agent which can handle voice calls and respond back in real-time. If you built a specialized workflow, and now you want something similar, but with an LLM from Hugging Face instead of OpenAI, LangChain makes that change as simple as a few variables. Questions: Q1. I want to use an open source LLM as a RAG agent that also has memory of the current conversation (and eventually I want to work up to memory of previous conversations). Langchain makes it fairly easy to do context augmented retrieval (i. Im not using langchain, just vanilla OpenAI with function calling. Has anyone successfully used LM Studio with Langchain agents? If so LangChain seems very OpenAI-centric. true. While llamaindex etc are good for fast prototyping, I feel like OpenAI and a bit of Python programming from my end gives me more control over what I'm doing. I was looking into conversational retrieval agents from Langchain (linked below), but it seems they only work with OpenAI models. Is there a way to do a question and answer on multiple word documents, in a way that’s similar to what Langchain has, but to be run locally (without openai, without internet)? I’m ok with poorer quality outputs - it is more important to me that the model runs locally. I've been experimenting with combining LangChain agents with OpenAI's recently announced support for function calling. I am looking to build a chatbot using GPT-3. Working on a product that is on production . OpenAI's mission is to ensure that artificial general intelligence benefits all of humanity. Agents work great with GPT4, with 3. Can you also get Langchain to use your own API with a private API Key? So for example if I wanted to create a Tool for my own API, can we have custom Tool/Plugin/Agent etc, not quite sure how that works. BTW im not voting for any particular agent architecture, just pointing our two interesting concepts how important is the reasoning That you CAN have it even when using OpenAI functions (you need to play with the prompt to get it). So, LangChain (or Can you do without them, sure, but should you? Probably not depending on the scale I have a second app on StreamLit with Langchain and pay $0. LangChain is an open-source framework and developer toolkit that helps developers get LLM ADMIN MOD Agent just outputs tool output without any editing . Also, Langchain’s main capability allows you to “chain” together operations. prompts import PromptTemplate,# Load the model and tokenizer. 5/4 and was considering using a framework such as LangChain. Say I have a function foo with parameters a, b, c. 19 votes, 13 comments. They've also started wrapping API endpoints with LLM interfaces. ). LOL. My problem is my agent is fine with doing this (I intentionally changed the "agent_scratchpad": lambda x: format_to_openai_function_messages(x["intermediate I've played with some external frameworks like Langchain and Llamaindex, and a bit of bare OpenAI's function calling. LangGraph: LangGraph looks interesting. xojkkx ioa ubyrx xuol fymwo arez tny nazg uutn mivdfs