Langchain agent without openai reddit. from crewai import Agent, Task, Crew, Process.

Langchain agent without openai reddit tools allows the llm to do stuff that it cannot do or suck at e. I'm specifically interested in low-memory LLMs. Actually I was using langchain before, for my projects. Trying to make an agent that's robust enough to run on a 7b model with langchain is a fool's errand. Langchain abstracts things that don't need to be abstracted because it's easy to make API calls and langchain isn't stable enough to use in production. llms import AzureOpenAI llm = AzureOpenAI(deployment_name="your_deployment_name", model_name="text-davinci-002") 156 votes, 123 comments. calculator, access a sql database and do sql statements while users ask questions about the db data in natural language, answer questions past it’s sept 2021 training data by googling the answer. Reading the documentation, it seems that the recommended Agent for Claude is the XML Agent. Here's an example. However, the open-source LLMs I used and agents I built with LangChain wrapper didn’t produce consistent, production-ready results. Members Online. There are various language models that can be used to embed a sentence/paragraph into a vector. sorta looks like wikipedia racing. If True, only new keys generated by this chain will be returned. 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). I was trying to solve a problem which I didn't have but the hype made me believe otherwise. #5 not to my knowledge, but you can look into langchain agents. However, It's important to realize that Langchain's prompt engineering has been developed and tested against OpenAI's models and fine tuning / alignment data. This is, of course, done by LangChain by saving the entire chat history you've had so far, and resending that entire history to the LLM API every time a new input is sent to the LLM. If you must, don’t go beyond template prompts and that too, you could replicate with an f string I had very poor results with any of these LangChain built-in agents, especially with GPT-3. They mostly see LangChain as a shelf of ready-to-use apllication such as RAG and simple Agents. Watchers. OpenAI's mission is to ensure that artificial general intelligence benefits all of humanity. Let's use that! Design and early versions First version. However, we are integrating tools and we are thinking to use langchain agents for that. return_only_outputs (bool) – Whether to return only outputs in the response. agent_toolkits import SQLDatabaseToolkit from langchain. json_agent_executor = create_json_agent(llm=OpenAI(temperature=0), toolkit=json_toolkit, bitcoins are issued and managed without any central authority whatsoever: there is no government, company, or bank `create_openai_functions_agent`: the value is a single instance of type: `langchain_core. I highly recommend Assistants API Explore Langchain's capabilities without needing an OpenAI API key, focusing on its features and functionalities. ai or runpod for later fine-tuning and cloud hosting. The second tool queries the database and returns in a pydantic format Skip to main content. It said something like CSV agent could not be installed because it was not compatible with the version of langchain. [To be clear, it does not use LangChain]. The same model works fine when I use it for normal text generation outside Langchain. However this documentation is referring to Claude 2 instead of Build an Agent. 302 stars. We use heavily OpenAI LLM to take decisions. The agent can also do a simple discounted cash flow valuation. I might be asking the wrong question. The first article provides an overview of how to utilize the new OpenAI's Assistants and Vision APIs with Laravel. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. Combining LangChain agents and GPT Index is absurdly powerful and impressive LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. os. What do you think? I looked into how to properly do that, but a lot of sources tend to use openai or have it running completely locally, so it's not necessarily applicable. /r/MCAT is a place for MCAT practice, questions, discussion, advice, social networking, news, study tips and more. I'm trying to process different documents that have anywhere from 200 tokens to 2k tokens and consistently getting 20 Langchain and LCEL are both flexible and unify the interfaces with the LLMs. Once you've done this set the OPENAI_API_KEY environment variable: OpenAI is an AI research and deployment company. If I'm 2. Trying to use non-OpenAI models, but it seems like there's no equivalent to the get_openai_callback() function for other models, but the docs say Skip to main content. Also, Langchain’s main capability allows you to “chain” together operations. from langchain_openai import ChatOpenAI. By themselves, language models can't take actions - they just output text. In my case, I have integrated langchain into a production system before GPTs came out. Stars. chains import LLMChain from langchain. update: www. For example an analytics agent that can run report APIs, or a retrieval agent that has access to internal knowledge bases. like read emails, messages, access smart devices with HomeAssistant etc. A 5-minute visual guide. The memory contains all the conversions or previously generated values. Agreed. I tried reading and understanding the “WebGPT: Browser-assisted question I tried to create a sarcastic AI chatbot that can mock the user with Ollama and Langchain, and I want to be able to change the LLM running in Ollama without changing my Langchain logic. IMO, it accurately reflects my experience. 5 as Skip to main content. The problem with langchain is that it is not plug and play with different models. I want to be able to really understand how I can create an agent without using Langchain. I've tried using `JsonSpec`, `JsonToolkit`, and `create_json_agent` but I was able to apply this approach on a single JSON file, not multiple. Validation Agent: Ensures the questions generated by the Questioning Agent are of high quality. In fact, in one of their github issues they If you are using open source LLMs or any other models which are not as good as OpenAI models, then agent execution might end up in CoT confusion and hallucinations leading to provide inaccurate results. Hi Reddit! Today is LangChain's first birthday and it's been incredibly exciting to see how far LLM app development has come in that time–and how much more there is to go OpenAI is an AI research and deployment company. input_keys except for inputs that will be set by the chain’s memory. environ["OPENAI_API_KEY"] = "MY_KEY" The initial thought process to choose langchain was that it would be easier to switch gpt with other models (in future). I don’t know you, but when I build an LLM app for a client LangChain is always more of a hassle to get started than just OpenAI is an AI research and deployment company. ToolAgentAction` instances Their example of the `execute_tools(data)` expects the first case. You constantly have to wrap it in wrappers. In the end, I built an agent without LangChain, using the OpenAI client, Python coroutines for async flow, and FastAPI for the web OpenAi is one possible model you use inside langchain. First of all these LLM guidance system without doubt are a glimpse into the future of Ai programming. If you know what you're doing sometimes langchain works against you. This agent chain is able to pull information from Reddit and use these posts to respond to subsequent input. To even make things worse, now I see that it will be harder to switch the models than using those models without langchain. We expressly designed this framework to simplify building applications, using an agent-oriented approach from the start. zod-to-openai-tool - a npm package for creating openai tools (function calling) using the new assistants/chat endpoints upvotes · comment r/laravel Trying to use non-OpenAI models, but it seems like there's no equivalent to the get_openai_callback() function for other models, but the docs say it's only usable for OpenAI. • ⁠elegant multi-agent communication orchestration • ⁠natively defined tools as well as OpenAI Fn-calling, both via Langroid ToolMessage class (define your structures/fn AND the handler methods via pydantic classes) • ⁠Just released: Full OpenAI Assistants API support in a new OpenAIAssistant subclass of the ChatAgent I'm pretty sure gpt already knows these subjects but I want the information 100% correct without any hallucinations. Log In / Sign Up; First, I'm a bit of a neophyte to LangChain, and I cannot say I have a minimum of 5 years experience with LangChain and local LLMs - like Skip to main content Open menu Open navigation Go to Reddit Home . I have an application that is currently based on 3 agents using LangChain and GPT4-turbo. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. One solution for this is you can switch to OpenAI models. OpenAI is an AI research and deployment company. Langchain is a good concept but poorly executed. 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. I’d like to share a chain of thoughts. I’ve been using both of these a lot lately. With Bitcoin, you can be your own bank. 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. As such, it is more resistant to wild inflation and corrupt banks. Memory in Agent. I'd like to test Claude 3 in this context. Has anyone had success using Langchain agents powered by an LLM other than the ones from OpenAI? I've specifically been working on understanding the differences between using 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. In this example, we adapt existing code from the docs, and use ChatOpenAI to create an agent chain with memory. Open menu Open navigation Go to Reddit Home. Readme License. OpenAi does have other models/services that are not language like image and audio. Bite the bullet, and use OpenAI or some As someone who’s been developing my own AI applications without Langchain or Python, I 2nd the motions above. I was working on using gpt4all's open AI-like API backend to see if I could start phasing out the actual openai API to some extent. I've seen that a lot of the guides and tutorials are using Jupyter Notebooks, which are awesome, and some (fewer) are using JS/TS. OpenAI have changed their models many times, without affecting our community. com to sign up to OpenAI and generate an API key. I don’t see OpenAI doing this. Log In / Sign Up; Advertise on Reddit; Shop Collectible Avatars; Get the Reddit app Not going to lie, I’ve had a bit of an obsession with Language model guidance tools such as Microsoft's Guidance and LangChain. I will say tho that using langchain for RAG and agentic programs has had the best results for me. Reply reply PrivateUser010 • Could you give me some numbers on the tokens or expense. openai. 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. But with the docs it’s hard to swap the models without significant refactor and testing :( Reply reply hwchase17 • How so? What errors do you run into? Reply reply More replies. I've been experimenting with combining LangChain agents with OpenAI's recently announced support for function calling. This. ). Essentially, I would like to take in any question and return back an answer from llmchain using a vectordb or using a dataframe via the agent. For my situation I use openai functions to route a question to 4 different sub agents, where each one has specific tools and tasks. Also there is something like agent_executor so there are many terms that I am not sure which one is responsible about my customization. Head to https://platform. " It seems like you were already moving in that direction, so I'm not sure if that prompt tweak will help at all. We are an unofficial community. Langchain makes it fairly easy to do context augmented retrieval (i. The way I confirmed that the main reason for long connection time is by setting time. I'm sure they went through dozens of iterations of each prompt to get the output right. Will post a separate post about this since this is such an obvious usecase and it really doesn't work very well with off-the-shelf Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. Entering new AgentExecutor chain If you have used tools or custom tools then scratchpad is where the tools descriptions are loaded for the agent to understand and use them properly. Log In / Sign Up; Advertise on Check out this tutorial from the Data Professor and explore the use of LangChain Agents. Reply reply I’m building an agent with custom tools with Langchain and wanna know how to use different llms within it. TakedownGPT - LangChain agents with OpenAI function calling. An examples code to make langchain agents without openai API key (Google Gemini), Completely free unlimited and open source, run it yourself on website. Overall, I think your customer chatbot would benefit from augmented search instead of purely relying on a large chat context especially if you want to go off of a large customer support knowledge base (your question #2) Have been looking into the feasibility of operating llama-2 with agents through a feature similar to OpenAI's function calling. He uses the pandas DataFrame Agent, that lets you work with pandas DataFrame by simply asking questions. Even if by some miracle you get it answering in a way that an agent can parse, and even if you wrap it all up in custom regex so that it stops on the right token, it Dumb question: For web apps, are people using the JS version of LangChain, or are they calling an API to a Python-based service? I'm totally new to this, but have a couple of projects I want to try to build. Even agents are simply loops with a carefully crafted prompt that gives you whatever you need I am trying to use GPTQ models via Langchain. 34 forks. If I combine multiple json files into a single file and try the above approach, it's not able to find the answer. Disclaimer: The agent is not intended as financial advice. The agent can compute financial metrics like owner earnings, return on equity, and return on invested capital. Parameters:. What is important is understanding it’s shortcomings and limitations as well as the techniques the community has created to overcome these limitations. Or check it out in the app stores Does anyone know if there is a way to slow the number of times langchain agent calls OpenAI? Perhaps a parameter you can send. custom OpenAI is an AI research and deployment company. 5. ADMIN MOD ChatGPT without the size limits: upload any pdf and apply any prompt to it . How Apple Uses ML To Recognize People (Without Photos Leaving Your iPhone). Have been looking into the feasibility of operating llama-2 with agents through a feature similar to OpenAI's function calling. 3. . " The query should be passed to a router type object which should trigger the image agent. Same haha OpenAI + string formatting and you can already do 90% of what langchain does, without the black box aspect The langchain agent currently fetches results from tools and runs another round of LLM on the tool’s results which changes the format (json for instance) and sometimes worsen the results before sending it as “final answer LOL. Reply reply Jl_btdipsbro • Yeahhhright again, it’s mainly about being able to chat with PDFs in airplane mode essentially. For my current project I simply use the basic OpenAI api and control the inter-agent conversations through simple application specific control code. It says: "You might know the answer without running any code, but you should still run the code to get the answer. time() at start and end of the db = SQLDatabase. I have a second app on StreamLit with Langchain and pay $0. We'll need a rather complicated agent workflow, in fact, multiple ones. When I use the Langchain Agent it feels like a black box. The issue I ran into with assistant API from OpenAI is that it’s super slow. 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. Get the Reddit app Scan this QR code to download the app now OpenAI from langchain. schema module), and use it to create a System Message (this is what chat LangChain seems very OpenAI-centric. The gist: it feels like there are layers and layers of abstractions on top of a relatively simple end-result: the prompt and context, which are just text. Consequently, the results returned by the agents can vary as the APIs or underlying models evolve. Hey everyone, I've been working on a multi-agent system using OpenAI's GPT-4o model, but I'm running into performance issues. The execution time is longer than I'd like, even though I've set max_iter to 2 for each agent. View community ranking In the Top 10% of largest communities on Reddit. I replaced my old project with LangChain ReAct Agent tools with new OpenAI Functions and gotten better results. However all my agents are created using the function create_openai_tools_agent(). Get the Reddit app Scan this QR code to download the app now. Expand user menu Open settings menu. inputs (Dict[str, Any] | Any) – Dictionary of inputs, or single input if chain expects only one param. But that’s something i can write on my own like deepcoder’s sdk. Credentials . 2 watching. Does it send the question (code snippet) to OpenAi? Or does some stuff using OpenAi Gpt on my data and question (code snippet) stays local? Confused here. but i have user groq its fast but i need solution for gpt-4o or openAI api only !. It worked somewhat with GPT-4 but that would be really slow (and really expensive). Need to know it's affordable or not. It is super impressive, the main difference I've seen with Haystack's history in comparison to langchain is the tight coupling with LLMs and generative applications. 5), and the tool-picking part is more accurate. and have the better results that you'll get from reasoning. input (Any) – The input to the Runnable. Here's how you can do it: Here's how you can do it: from langchain. Forks. LangGraph is more flexible than crew. the "function-calling" concept in openai obiviates the need for More than that and you want to start grouping them, or using sub-agents with a router. Have had very little success through prompting so far :( Just wondering if anyone had a different experience or if we might If you have used tools or custom tools then scratchpad is where the tools descriptions are loaded for the agent to understand and use them properly. Here's how a separate Wikipedia agent chain works: It uses agent based modeling, basically asking itself a series of questions until it gets to the right answer. For a simple example of how this agent works, check out the colab notebook here. The tools are being used in a sequential way. We already did a project with langchain agents before and it was very easy for us to use their agents. Web GPT4 Vs Assistants GPT4 Vs langchain CSV_agent, regarding asking questions over structured data. 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. Log In / Sign Up; This is how I set up the agent for my blog. They might have some off the shelf ones. LangGraph: LangGraph looks interesting. Before, the agent would get confused if I gave it more than 3 I am using azure SQL server. Two types of agents are provided: HfAgent, which uses inference endpoints for open-source models, and OpenAiAgent, which uses OpenAI's proprietary models. Should contain all inputs specified in Chain. agents. The problem with pandas/CSV agents is that, there is no advantage of explaining interested in this, can you point to the code where such an override was implemented I did find some blogs on various ways to implement pipes [1] but they don't seem as concise, the reverse or `__ror__` seems a bit of a hack overriding such a method. Since you asked about possible alternatives, I’ll mention I want to be able to really understand how I can create an agent without using Langchain. If langchain can improve their documentation and consistency of APIs with important features exposed as parameters I'll go back to them. But I don’t They are not used to heavy frameworks such as Langchain. . Our software offers cutting-edge crypto trading bots without coding for advanced traders, empowering traders with I'm working on a conversational agent (with buffer memory), and want to be able to add a prompt or system message to give it a persona + some context. Crew: Manages the agents and tasks, executing them hierarchically. No, LangChain does not upload all data to OpenAI. Much less flexibility than the above frameworks, but total control over exactly what I need. r/OpenAI A chip A close button. I am creating a huggingface pipeline object and passing that as the LLM instead of OpenAI. Langroid is multi-agent LLM framework, and we wanted to make it very easy for developers to build apps they can showcase on a WebAPP UI (and even deploy to cloud) rather than the default command-line interface. Langchain routing between dataframe agent and llmchain Hello all - I am curious to know if it is possible to route between a dataframe agent and the standard llmchain depending on the question. Here's a brief overview of my setup: So it's very relevant even for people who never use actual OpenAI models or services. Currently on gpt-3. Reply reply steampunk_ant • I have to disagree, because those terms brought me here from google :) Reply reply pussifricker1337 • Same Reply reply More replies More replies More replies More replies `create_openai_functions_agent`: the value is a single instance of type: `langchain_core. MIT license Activity. Might be worth a shot, though! So, I've created a blog. They do affect us by changing their API with as little warning and documentation as they did. And I'm a huge fan of libraries and frameworks and whatever makes your life easier but I found langchain to, well, not do that. When able, I hope to write mostly about Laravel and other related topics. What I mean by "stateful" is that the the hidden state of the transformer is preserved. 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). llms import AzureOpenAI llm = OpenAI is an AI research and deployment company. The reasoning part got faster (maybe just because of improvements to gpt-3. Please share. from crewai import Agent, Task, Crew, Process. I'm pretty sure gpt already knows these subjects but I want the information 100% correct without any hallucinations. Here's a sample LangChain agent based React-Act modeling on Wikipedia/TMDB: Log. 5 turbo via Api and on waiting list for gpt-4 api, wondering on difference for this use case I think it's partly a documentation issue, but I think the design is a bit confusing as well. from_uri(conn_str) and then calculate it. The main challenge was Parameters. They want to throw it away and The prompt for the Python agent explicitly tells the LLM to always use the Python tool. Planning on checking it out. Combining LangChain agents and GPT Index is absurdly powerful and impressive though. 0. This notebook goes over adding memory to an Agent. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them: Memory in LLMChain; Custom Agents; In order to add a memory to an agent we are going to perform the following steps: We are going to create an LLMChain In this video, I will show you how to interact with your data using LangChain without the need for OpenAI apis, for absolutely free. wrotescan. Because, Langchain is unnecessarily complex Lack of proper documentation Only advantage i see with langchain is the ability to switch to different llms. Log In / Sign Up; Advertise Incorrect import of OpenAI: If you're using Azure OpenAI, you should use the AzureOpenAI class instead of OpenAI. The datasets are open source and can be used to evaluate common chains and agents. However, when I try to query CSV/dataframes with this, the speed is abysmally slow and the code gets stuck. The agent is for informational Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. It also results in MUCH LESS expense per run while still getting the output I need. No default will be assigned until the API is stabilized. Say you wrote a program without langchain that uses GPT3. 🍎📱 Transformers Agent is an experimental API, meaning it is subject to change at any point. As a demo I've put together an app that allows SecOps teams to autonomously find the domain 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. AutoGen is similar, it abstracts away easy API calls, hides everything behind abstraction and doesn't make it clear how to deeply customize an agent. Last I explored, I only had access to 3. This is for my day job, so actual serious work that will go to production soon. 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 Working on a product that is on production . ai or autogen. Incorrect import of OpenAI: If you're using Azure OpenAI, you should use the AzureOpenAI class instead of OpenAI. g. Langroid is a multi-agent LLM framework from ex-CMU and UW Madison researchers: GitHub - langroid/langroid: Harness LLMs with Multi-Agent Programming. Users should use v2. Since OpenAI came out with function calling I'm not sure I need chains. Langchain is a bit new to me: - current code: template = "Question: {question}\n\nAnswer:" If we could have better fine-tuned models without the "OpenAI infection" in the training (fine-tuning) data that would OpenAI is an AI research and deployment company. Im not using langchain However with Azure OpenAI, you can use the GPT 3. Any model OpenAI is an AI research and deployment company. Langchain execution agent too slow Anyone knows of an optimal solution to speed up langchain when using with vector db (pinecone in this case). I still may use langchain as a utility library for all its integrations, but without using the chains functionality due to the added complexity. With the big dogs openly focusing on Agent orchestration, function calling, and internal RAG integration it seems like this is inevitable. OpenAI makes ChatGPT, GPT-4, and DALL·E 3. Questions: Q1. agent_types import AgentType from dotenv import load_dotenv import os load_dotenv() openai_api_key = Actually I was using langchain before, for my projects. you can even create your own custom tool. Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. I plan to explore it more in the future. From the start, we knew it was impossible to do it using a "one prompt, one agent" solution. 1st example: hierarchical planning agent . The image agent should run its object detection algorithm to detect cars (and red cars). Agents, by those who promote them, are units of abstraction used to break a big problem into multiple small problems. 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). After executing actions, the results can be fed back into the LLM to determine whether more actions OpenAI is an AI research and deployment company. So I thought since Groq is ultra fast and rolled out the new tool calling feature, I’d give it a shot. In this example, we'll consider an approach called hierarchical planning, common in robotics and appearing in recent works for LLMs X robotics. There are some custom agent tutorials but still they are not very easy to understand and I am not sure if this a situation to use custom agent or customize openai functions type agent. Moreover, `create_json_agent` it's using Q&A agent not the chatting agent. So when I use langchain with my own data, I still saw it uses GPT Api. 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. Question Was Langchain went very early into agents and has assembled a truly impressive variety of features there by now. It wasn't the easiest library to use, and it doesn't have much documentation that is up to date with their rapidly changing code base. but nowdays i started directly using openai sdks. So the memory is what you provide, the agent_scratchpad is where the tools are loaded for the intermediate steps. Edit: Actually screw it, I'm just gonna use the api for each provider instead, seems way more straightforward and less of a hassle. AgentActionMessageLog`. e. The text agent should take input from the image agent and "summarize" them (whatever that means). `create_openai_tools_agent`, the value is a list containing `langchain. To utilize LangChain without an OpenAI API key, developers can leverage Depending on your project, some of these open-source alternatives might be better suited to your needs or could be used in conjunction with LangChain. However if you have a more complex LLM-based system, especially a multi-agent one, there's more work involved. r/LangChain A chip A close button. Ready to support Here’s a recent discussion (one of many) responding to a question about using LangChain in production, in the r/LocalLLama forum: Reddit - Dive into anything. Members Online • aicharades. After making somewhat complex applications, even for OAI, my realization is it’s faster to just write an optimized prompt than use this framework. 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. config (Optional[RunnableConfig]) – The config to use for the Runnable. jpthoma2 • I know you see all the criticism but just want to say thanks for your attention and dedication to Langchain and constantly being on the lookout for ways to improve. From what I've found so far, I'd like to use a Llama2 model, LangChain for pdf ingestion, and I'm deciding whether or not I could use together. look it up. So it's very relevant even for people who never use actual OpenAI models or services. 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. I was curious how people are generally feeling now about the autonomous agents that are being developed. 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. I was looking into conversational retrieval agents from Langchain (linked below), but it seems they only work with OpenAI models. 4. Any alternative on how we can do this without using langchain ? It is because the term "Agent" is still being defined. More than that and you want to start grouping them, or using sub-agents with a router. (we're trying to fix this in LangChain as well - revamping the architecture to split out integrations, having langchain-core as a separate thing). I tried reading and understanding the “WebGPT: Browser-assisted question-answering with human feedback” paper but I get lost. But when they need to implement something more specific, they don't want to really understand how LangChain works under the hood to extend its functionality. Ready to support ollama. it’s based on the mrkl paper. To access OpenAI models you'll need to create an OpenAI account, get an API key, and install the langchain-openai integration package. But I think in 2024 we will see the foundation models capable of Langchain type results and granularity. Ie, your full time dev or customer service replacement. (Update when i am free) Resources. agents and tools. Agents, by those who bash them, often really mean "Super Agents" or drop in human replacements. Don’t get me wrong. And in my opinion, for those using OpenAI's models, it's definitely the better option right OpenAI Functions is a separate fine-tuned model from OpenAI that you can send a list of functions and description to and get back which one to use based on your string query. Or check it out in the app stores 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, but it didn't. A big use case for LangChain is creating agents. We will be making use of I installed langchain[All] and the OpenAI import seemed to work. The #1 social media platform for MCAT advice. Langchain went very early into agents and has assembled a truly impressive variety of features there by now. The problem is every LLM seems to have a different preference for the instruction format, and the response will be awful if I don't comply with that format. Get app Get the Reddit app Log In Log in to Reddit. LangChain has its own Community space on Hugging Face, called LangChainDatasets, where users can upload datasets. 5 as a language model, chroma for your vector store, and you wrote some code for splitting your text docs. I’m working in python, but I figure JS would be similar. Essentially, I wanted to use Langchain's ChatOpenAI(), but switch the OPENAI_BASE_URL, and put something random in for the key. Setup . Members Online • jim_andr . 5 turbo model in your own infrastructure, not on OpenAI, Microsoft doesnt share your logs with anyone, they are there for you and only for your as a company, and then on top of that you add langchat to create a Private ChatGPT with (RAG)or without your own data. Learn how to build an app for answering questions on a pandas DataFrame created from a user-uploaded CSV file in four steps: Get an OpenAI API key 25 votes, 123 comments. While this was the original design purpose of langchain, for Haystack I wanted to know if the chain method and ChatOpenAI from langchain_openai supports gpt4o image inputs and if there are any guides out there showing Skip to main content. Execute the chain. I realized that the time it takes to create the Here is a thread from Hacker News with lots of discussion on real world frustrations w/Langchain. Here's how you can do it: from langchain. Note: you can of course use open source models without using OpenAI's API. answering questions on the basis of documents, websites, repositories etc. version (Literal['v1', 'v2']) – The version of the schema to use either v2 or v1. Pros: A user inputs a search query like "Describe the red cars in this image. I use and develop the StreamLit/Langchain so much more because everything is just easier to develop and faster to manage and deploy. Look for SystemMessage (in python it’s in langchain. Langchain only deals with the language models. View community ranking In the Top 5% of largest communities on Reddit. agents import create_sql_agent from langchain. If you are restricted to use only open source then sure use Langchain until open source matures and rip it out once it does if u value flexibility and simplicity. The only other thing to learn from the OpenAi API is config like temperature and repeating scores Couple of minutes. To run the example, add your reddit API access information and also get an OpenAI key from the OpenAI API. It's not An examples code to make langchain agents without openai API key (Google Gemini), Completely free unlimited and open source, run it yourself on website. i was doing some testing and manage to use a langchain pdf chat bot with the oobabooga-api, all run locally in my gpu. Short Answer: Use GPTs if you don't want to handle tooling yourself with local LLMs and have a reason for it. output_parsers. Open-source AI Voice Agent with OpenAI Welcome to r/LearnJapanese, *the* hub on Reddit for learners of the Japanese Language. v1 is for backwards compatibility and will be deprecated in 0. com is now I have worked with LangChain a good bit now and one of the main benefits is that it makes LLM APIs stateful. embeddings = OpenAIEmbeddings() db = Skip to main content. So i tried to install langchain expiremental because the csv agent works for this one but for some reason after I installed the OpenAI import was greyed out again I'm Harrison Chase, CEO and cofounder of LangChain–an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. Has anyone successfully used LM Studio with Langchain agents? If so 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. A place for members of r/LangChain to chat with each other I’ll answer this too - it’s not necessary to intimately understand the underlying architecture or training of the LLM to build on top. Now let's say a week later you want the same program to use a local Llama language model, faiss for vectors, and a want to split PDF docs instead of text docs. I see you did the pdf stuff using langchain, that's cool. Here's a snippet of my code for context: import os. We see you and it IS appreciated While LangChain claims it’s pluggable, most OpenAI prompts don’t just work for the other two. Unlike traditional currencies such as dollars, bitcoins are issued and managed without any central authority whatsoever: there is no government, company, or bank in charge of Bitcoin. Different purposes aside, and for all its popularity, LangChain has I have build Openai based chatbot that uses Langchain agents - wiki, dolphin, etc. Once you've done this set the OPENAI_API_KEY environment variable: I have an agent with two tools. Cobbled together the same exact thing with plain openai and chromadb in like an hour. awladxdw wrsaehsn cypdcnj faoteeaw foqch irjrb cirhcl nkvly uyb bzeyka