Langchain agent without openai reddit. but nowdays i started directly using openai sdks.

Langchain agent without openai reddit Let's use that! Design and early versions First version. I was trying to solve a problem which I didn't have but the hype made me believe otherwise. The langchain is failing to perform a Hey all, i've seen some mention's around here briefly about comparing LangChain's tooling (or even just building out your retrieval models yourself by removing abstractions) to the current state of assistant's API (w/ v2) Im going to be scrutinized for this but lang chain is a garbage framework. Setting up a chain with a prototyped library that can suffer from a prompt injection attack (Yes, I know LangChain has something builtin to mitigate it, I've read the source code) is just asking for trouble in this early stage of the process. I replaced my old project with LangChain ReAct Agent tools with new OpenAI Functions and gotten better results. To run the example, add your reddit API access information and also get an OpenAI key from the OpenAI API. utilities. Questions: Q1. jira import JiraAPIWrapper from langchain_google_genai import ChatGoogleGenerativeAI from langchain_openai import ChatOpenAI from langchain import hub llm=ChatOpenAI(temperature=0) jira=JiraAPIWrapper() toolkit=JiraToolkit. agent_toolkits import create_csv_agrnt I didn't read whole text you have posted , just noticed that CSV agent part , OpenAI is an AI research and deployment company. llms import OpenAI from langchain import PromptTemplate, LLMChain openai_api_key = "your_api_key_here" prompt_template = "Once upon a time, in a faraway land, there was a {subject}. I have a second app on StreamLit with Langchain and pay $0. js will lag too far behind Langchain (python) and that I'll regret focusing on langchain js. I’m working in python, but I figure JS would be similar. invoke({"content": "What's the weather in SF today divided by 2. It almost always fails with local models. I was able to solve for it by doing something that looks a lot like the new StructuredChat agent, so I’ll probably switch so subclassing that; I’m also excited about the output parser wjth retries. prompts import MessagesPlaceholder from langchain. 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. We are an unofficial community. The main reason I dropped langchain is that it's based on hopium and voodoo "prompt engineering" that kinda sometimes maybe works with OpenAI stuff. You could also just append the sql code as a string/json to the output itself to return it in the typical agent I replaced my old project with LangChain ReAct Agent tools with new OpenAI Functions and gotten better results The reasoning part got faster (maybe just because of improvements to gpt-3. 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. Build a real application without LangChain, after several week or month of development, have a look at the question I am running the following in a Jupyter Notebook: from langchain. 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. If you’re in enterprise, you’re probably using Azure OpenAI, Vertex AI or Bedrock. I tried searching for what's the difference between chain and agent without getting a clear answer to it. With the big dogs openly focusing on Agent orchestration, function calling, and internal RAG integration it seems like this is inevitable. Also my agents have spontaneously broken due to a langchain change. After having worked with Anthropic API and Gemini 1. I am new to building AI agents (robotics background) and I was curious to learn about the most common workflows you guys use. 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). Langchain only deals with the language models. I was under the impression that Langchain works with all Chat Models that OpenAI offers via the API. I'm also a bit hesitant/frustrated with Python in general, which makes Langchain. 5 Pro & Flash APIs. 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. Agents, by those who bash them, often really mean "Super Agents" or drop in human replacements. environ["OPENAI_API_KEY"]="sk-xxxxxxxx" agent_executor = create_python_agent( 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. We will be making use of I had my qualms with langchain, but I think the openai updates has mostly made langchain obselete, for me. e. Then still return the sql output like normal. There are a lot of agents and parsers written in LangChain that you would never use and that number just keeps on increasing. Damn, gpt4 is cool but like it’s kind of dumb that it can’t store any memory for like long term use. Also there is something like agent_executor so there are many terms that I am not sure which one is responsible about my customization. 7"}) The content property of the object that is being passed into the invoke function has the user prompt - what we are sending to the assistant. 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. 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. However this documentation is referring to Claude 2 instead of Hi LangChain community, I am trying to create a chatbot that excel not only in calling functions but also in having communications based on memory and on its prior knowledge to an extent. This is the agents final reply: As an AI developed by OpenAI, I'm unable to directly modify files or execute code, including applying changes to API specifications or saving files. The reasoning part got faster (maybe just because of improvements to gpt-3. agent_toolkits import create_python_agent from langchain. The problem with langchain is that it is not plug and play with different models. To utilize LangChain without an OpenAI API key, developers can leverage As someone who’s been developing my own AI applications without Langchain or Python, I 2nd the motions above. Community dedicated to building cool things without needing to be a developer. I tried reading and understanding the “WebGPT: Browser-assisted question-answering with human feedback” paper but I get lost. The project was a LangChain agent Hi, When I try to embed documents with openAI embeddings, I get a very paradoxical error: Retrying I am working with a Dev on a project and he explained to me that no all OpenAI Chat Models are supported by Langchain, especially not the newer ones (e. ai or autogen. For my situation I use openai functions to route a question to 4 different sub agents, where each one has specific tools and tasks. 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. agent_toolkits. View community ranking In the Top 10% of largest communities on Reddit. I have build Openai based chatbot that uses Langchain agents - wiki, dolphin, etc. Sure, you can approximate a GPT using the API. toolkit import JiraToolkit from langchain_community. Reddit is an American social news aggregation, content rating, and discussion website. Langchain executes multiple prompts one after the other. LangGraph is more flexible than crew. Incorrect import of OpenAI: If you're using Azure OpenAI, you should use the AzureOpenAI class instead of OpenAI. PineCone is fast and easy to use, without the hassle of a lot of maintenance, but we might consider other alternatives as the data scales, to reduce costs. Using one agent to solve a complex problem is not the optimum solution. Make a Reddit Application and initialize the Unity is the ultimate entertainment development platform. llms. Stars. 28 votes, 41 comments. the "function-calling" concept in openai obiviates the need for from langchain. Reply reply Top 4% Rank by size . The platform aims to provide an easy way to create, upload, and manage these tools, giving you the power to extend your AI assistant's capabilities with ease. The only other thing to learn from the OpenAi API is config like temperature and repeating scores Couple of minutes. I'm specifically interested in low-memory LLMs. I use and develop the StreamLit/Langchain so much more because everything is just easier to develop and faster to manage and deploy. Entering new AgentExecutor chain Get the Reddit app Scan this QR code to download the app now. Different purposes aside, and for all its popularity, LangChain has 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. tools. 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. Deploy them across mobile, desktop, VR/AR, consoles or the Web and connect with people globally. schema module), and use it to create a System Message (this is what chat I have an application that is currently based on 3 agents using LangChain and GPT4-turbo. Or check it out in the app stores Optional from uuid import UUID from langchain_community. 2. Im not using langchain 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 is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. (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 can definitely see its use case, but at this point I would rather just use xagent anyway. source library for many things that could be swapped in and out of your LLMOps loop -> maybe focusing on prompts and agents. The first article provides an overview of how to utilize the new OpenAI's Assistants and Vision APIs with Laravel. 5), and the tool-picking part is more accurate. The image agent should run its object detection algorithm to detect cars (and red cars). 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. Any alternative on how we can do this without using langchain ? However with Azure OpenAI, you can use the GPT 3. You can train and tune an LLM and give it memory to create an agent. The #1 social media platform for MCAT advice. Post reviews of your current and past hosts, post questions to the community regarding your needs, or simply offer help to your fellow redditors. LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners. - The documentation is subpar compared to what one can expect from a tool that can be used in production. I'd like to test Claude 3 in this context. agents import Tool, load_tools, initialize_agent, create_csv_agent, AgentType from langchain. One solution for this is you can switch to OpenAI models. (Update when i am free) Resources. like read emails, messages, access smart devices with HomeAssistant etc. OpenAi does have other models/services that are not language like image and audio. I highly recommend Assistants API 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. So, I've created a blog. The biggest issue with LangChain is the Arbitrary Code Execution IMHO. [To be clear, it does not use LangChain]. In my case, I have integrated langchain into a production system before GPTs came out. From shared hosting to bare metal servers, and everything in between. Any help is appreciated, and let me know if I'm missing details. So when I use langchain with my own data, I still saw it uses GPT Api. So it's very relevant even for people who never use actual OpenAI models or services. View community ranking In the Top 5% of largest communities on Reddit. 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 It is because the term "Agent" is still being defined. In this example you find where sql_code is defined or created in the tool run, then send it to the run manager. 33 forks. Langchain seems pretty messed up. jira. You can use this to control the agent. Actually I was using langchain before, for my projects. prompts import PromptTemplate from langchain. . r/LangChain Chatbot Using Langchain Agents and Llama 2 Say you wrote a program without langchain that uses GPT3. Just go on the official docs for langchain, it literally shows you exactly what code you need and gives you options to use OpenAI API or a locally hosted Ollama. Langchain makes it fairly easy to do context augmented retrieval (i. This group focuses on using AI tools like ChatGPT, OpenAI API, and other automated code generators for Ai programming & prompt engineering. Pros: Open-source AI Voice Agent with OpenAI Welcome to r/LearnJapanese, *the* hub on Reddit for learners of the Japanese Language. The prompt for the Python agent explicitly tells the LLM to always use the Python tool. At the time I was wrong, there were many issues with the project and I found out I was better off removing and replacing LangChain with my own implementations. Or check it out in the app stores Building an AI driven travel agent with Langchain and OpenAI I recently did an experiment to create a AI driven travel agent. 5 as a language model, chroma for your vector store, and you wrote some code for splitting your text docs. i was doing some testing and manage to use a langchain pdf chat bot with the oobabooga-api, all run locally in my gpu. Can't find this info in the docs. 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. This. 28. But I think in 2024 we will see the foundation models capable of Langchain type results and granularity. The whole app comes crashing down, recovering states is a pain, etc. Two types of agents are provided: HfAgent, which uses inference endpoints for open-source models, and OpenAiAgent, which uses OpenAI's proprietary models. From the start, we knew it was impossible to do it using a "one prompt, one agent" solution. llms import AzureOpenAI llm = AzureOpenAI(deployment_name="your_deployment_name", model_name="text-davinci-002") Please replace "your_deployment_name" with your own deployment name[1]. OpenAI storage costs are insane: $0. 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. vectorstores import FAISS from langchain. This loader fetches the text from the Posts of Subreddits or Reddit users, using the praw Python package. g. I built a langchain agent to solve math problems. Readme License. Since you asked about possible alternatives, I’ll mention There are various language models that can be used to embed a sentence/paragraph into a vector. We'll need a rather complicated agent workflow, in fact, multiple ones. 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 Langchain execution agent too slow Anyone knows of an optimal solution to speed up langchain when using with vector db (pinecone in this case). It was time for a change "import openai" by "from langchain import PromptTemplate, OpenAI, LLMChain". To even make things worse, now I see that it will be harder to switch the models than using those models without langchain. agents and tools. I will say tho that using langchain for RAG and agentic programs has had the best results for me. I gave it a bunch of examples in the prompt template about how to solve the math problem and send it to the LLM for prompt engineering. OpenAI API Complete Guide: With Practical Examples in Python (paid) Free ChatGPT Course: Use The OpenAI API to Code 5 Projects. Watchers. LangGraph: LangGraph looks interesting. 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. If I need some functionality that langchain has, I'll use its utilities module and the above function-calling library. agents import AgentFinish from langchain_openai import ChatOpenAI from langchain. The LLM-agent can act like a domain-expert for whatever domain you've trained and equipped it for. 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. Look for SystemMessage (in python it’s in langchain. Langchain and LCEL are both flexible and unify the interfaces with the LLMs. 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. Working on a product that is on production . After executing actions, the results can be fed back into the LLM to determine whether more actions I was trying to test out I have encountered difficulties while attempting to implement custom table operations. (Gpt4 is the engine that runs chatgpt) Basically a bunch of dudes were like. Then I compiled the code with the command "python3 nom_du_fichier ". I use a tiny library that makes it super easy to interface with arbitrary python functions. A big use case for LangChain is creating agents. 5 turbo via Api and on waiting list for gpt-4 api, wondering on difference for this use case. I've tried to be as descriptive as possible. Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. 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. 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. If we use example from your link, what if user asks - How do I use anthropic and langchain? With agents, it can utilize Anthropic tool to get information on using anthropic and langchain tool to get information on using Langchain. Sure you can leverage LangChain to create an agent that works with other models as well. There are multiple agents working behind the scenes, using OpenAI and Cortex to In this video, I will show you how to interact with your data using LangChain without the need for OpenAI apis, for absolutely free. 0. - The discord community is pretty inactive honestly so many unclosed queries still in the chat. OpenAI makes ChatGPT, GPT-4, and DALL·E 3. Reading the documentation, it seems that the recommended Agent for Claude is the XML Agent. I am using OpenAI_Multi_Functions agenttype currently and I wanna combine it with a conversational agent but I can't find anything relevant anywhere. I've been experimenting with combining LangChain agents with OpenAI's recently announced support for function calling. OpenAI is an AI research and deployment company. It's great as it is for getting things out fast right out of the box, but once you go to prod that gets a bit slow, and it also use way more tokens that it should. Langchain is not ai Langchain has nothing to do with chatgpt Langchain is a tool that makes Gpt4 and other language models more useful. I'm sure they went through dozens of iterations of each prompt to get the output right. tool import PythonREPLTool 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. embeddings import OpenAIEmbeddings The inconsistency between tool/final answer output parsing. 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 also think about using openai-function calls via langchain. Here's how you can do it: from langchain. As a demo I've put together an app that allows SecOps teams to autonomously find the domain OpenAI is an AI research and deployment company. Bite the bullet, and use OpenAI or some I want to be able to really understand how I can create an agent without using Langchain. Videos. Don’t get me wrong. the embeddings, export them, load them again, and then conduct a question-answering. The lack of recovery on json parsing failures makes it unusable. 5 your better off using chains or other deterministic workflows. OpenAI API seems to be the only reliable API service We decided to build an AI Agent for this using Langchain, Snowflake Cortex and Open AI! Check out this quick demo where I ask the agent about my Snowflake spending. callbacks import get_openai_callback from langchain_core. Use Unity to build high-quality 3D and 2D games and experiences. Tool class. I was looking into conversational retrieval agents from Langchain (linked below), but it seems they only work with OpenAI models. They mostly see LangChain as a shelf of ready-to-use apllication such as RAG and simple Agents. Agents, by those who promote them, are units of abstraction used to break a big problem into multiple small problems. 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. Agreed. Chat with OpenAI in LangChain - #5 (Again featuring James Briggs) Tool Juggler is built on top of the Langchain library, and all custom tools are instances of the langchain. For the vector db we used PineCone. Also, Langchain’s main capability allows you to “chain” together operations. If langchain can improve their documentation and consistency of APIs with important features exposed as parameters I'll go back to them. Here's a sample LangChain agent based React-Act modeling on Wikipedia/TMDB: Log. """ With legacy LangChain agents you have to pass in a prompt template. from_jira_api 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. 11. TakedownGPT - LangChain agents with OpenAI function calling. For example an analytics agent that can run report APIs, or a retrieval agent that has access to internal knowledge bases. I'm glad to say I've started to bring LangChain back into my projects. If you want to create a custom agent with pure langchain, you either inherit one, meaning that you always need to look for what function to override so it would play together (often ending up with rewriting most of the stuff anyway) - and you will need to "copy&paste" a lot of lanchain infrastructure code alongside it. While LangChain claims it’s pluggable, most OpenAI prompts don’t just work for the other two. Get the Reddit app Scan this QR code to download the app now. it’s based on the mrkl paper. 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. Please share. Short Answer: Use GPTs if you don't want to handle tooling yourself with local LLMs and have a reason for it. 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. It looks literally like the way to actually see what it's sending to openai is to use a logging http proxy? LLM Agent: Build an agent that leverages a modified version of the ReAct framework to do chain-of-thought reasoning. Reddit. They do affect us by changing their API with as little warning and documentation as they did. sorta looks like wikipedia racing. " Thanks for the reference -- I'll take a look at this tool definition. 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). agents import AgentExecutor agent_executor = AgentExecutor(agent=agent, tools=tools) agent_executor. Dumb question: For web apps, are people using the JS version of LangChain, or are they calling an API to a Python-based service? If you build it without any kind of backend, make sure you don’t embed any keys to other services like pinecone or openai in the code. 3. Or, it might be that AutoGPT leverages Langchain, I'm not sure. As others have said the libraries and field is very fast changing, so you're gonna have to get comfortable with learning yourself through documentation because any video tutorial or 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. look it up. But GPTs and LangChain agents serve entirely different markets and those other models don't actually compete with GPT-4 This is a place to discuss everything related to web and cloud hosting. I don’t see OpenAI doing this. You can override on_tool_end() callback to send anything you want to your preferred callback, such as log files, apis, etc. I’ve tried llamaindex, langchain, haystack, griptape, and I usually end up going back to langchain because it has much more functionality and keeps up with the updates. 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. 20/GB/assistant/day This means you cannot seamlessly integrate the context of one thread into another without external handling – that was my point on follow-up across Posted by u/skylight22 - 1 vote and 1 comment The initial thought process to choose langchain was that it would be easier to switch gpt with other models (in future). So far the backend is done. This agent chain is able to pull information from Reddit and use these posts to respond to subsequent input. from langchain. With LangGraph react agent executor, by default there is no prompt So, he asked me, "When I use ChatGPT Tools with the new gpt-4-1106-preview model, or ChatGPT functions with the older gpt-4-0314 model, am I talking to an AI Agent? My initial thought was yes, as LangChain has listed both models in their There’s been a bit of time now for a few alternatives to come out to langchain. Langchain moved CSV agent to experimental package , so you should import it as from langchain_experimental. 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. I want to be able to really understand how I can create an agent without using Langchain. 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. Explore Langchain's capabilities without needing an OpenAI API key, focusing on its features and functionalities. I started building internal LLM tools for my company and originally thought LangChain would be a good tool. If you don't have it in the AgentExecutor, it doesn't see previous steps. Has anyone successfully used LM Studio with Langchain agents? If so 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. from langchain_community. python. The JSON approach works great out of the box with GPT4 but breaks down with 3. Anyone know where I can find good documentation so I can really understand how to build A user inputs a search query like "Describe the red cars in this image. We already did a project with langchain agents before and it was very easy for us to use their agents. 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. I plan to explore it more in the future. 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. I'm trying to process different documents that have anywhere from 200 tokens to 2k tokens and consistently getting 20 LOL. The text agent should take input from the image agent and "summarize" them (whatever that means). and have the better results that you'll get from reasoning. " The query should be passed to a router type object which should trigger the image agent. What if I also want the agent to answer questions unrelated to Math? For example, I want it to answer history of a math theory or a mathematician. Edit: Actually screw it, I'm just gonna use the api for each provider instead, seems way more straightforward and less of a hassle. All of these types of posts ignore or gloss over a LOT. Ready to support ollama. OpenAI have changed their models many times, without affecting our community. python import PythonREPL from langchain. 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. js attractive, but I'm concerne that Langchain. They are not used to heavy frameworks such as Langchain. It only works “well enough” for OpenAI. Agents work great with GPT4, with 3. " Langroid is a multi-agent LLM framework from ex-CMU and UW Madison researchers: GitHub - langroid/langroid: Harness LLMs with Multi-Agent Programming. Created 5 GPTs today and feel like those More than that and you want to start grouping them, or using sub-agents with a router. Or check it out in the app stores   and use Langchain OpenAI LLM to interact with your OpenAI-mock server. I noticed that in the langchain documentation there was no happy medium where it's explained how to add a memory to both the AgentExecutor and the chat itself. Here's an example. agent. 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. ). No agent framework, Langchain or some other framework, is production ready unless your OpenAI or Microsoft (cost). Consequently, the results returned by the agents can vary as the APIs or underlying models evolve. However if you have a more complex LLM-based system, especially a multi-agent one, there's more work involved. you can even create your own custom tool. However, the open-source LLMs I used and agents I built with LangChain wrapper didn’t produce consistent, production-ready results. But that’s something i can write on my own like deepcoder’s sdk. A 5-minute visual guide. tools I'm trying to build a chatbot using langchain and openai's gpt which should be able to answer quantitative questions asked by users on csv files. answering questions on the basis of documents, websites, repositories etc. Thank you very much Is it possible to use memory with the OPENAI_MULTI_FUNCTIONS agent? I tried the same way as other agents but it doesn't work for me. My biggest gripe is that it’s great when working with OpenAI, but it I'm nearly positive I've read docs/seen examples where you can indeed pass multi-input tools into this type of agent, but I very well could be wrong. 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. 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. Having started playing with it in its relative infancy and watched it grow (growing pains included), I’ve come to believe langchain is really suited more to very rapid prototyping and an eclectic selection of helpers for testing different implementations. Langchain is probably the issue here, not the embeddings. Because, Langchain is unnecessarily complex Lack of proper documentation Only advantage i see with langchain is the ability to switch to different llms. Ie, your full time dev or customer service replacement. It says: "You might know the answer without running any code, but you should still run the code to get the answer. . 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. Or check it out in the app stores Langchain Synthetic Data without OpenAI Hello! Has anyone here ever tried to create synthetic data with Langchain using a local model different from those of OpenAI (for example, Llama)? OpenAI is an AI research and deployment company. tools allows the llm to do stuff that it cannot do or suck at e. OpenAI API, and other automated code generators for Ai programming The reason to use agents is sometimes users can ask a question which may need to use multiple tools to answer. Essentially, I wanted to use Langchain's ChatOpenAI(), but switch the OPENAI_BASE_URL, and put something random in for the key. 325, OpenAI 0. Our software offers cutting-edge crypto trading bots without coding for advanced traders, empowering traders with OpenAI is an AI research and deployment company. LangChain seems very OpenAI-centric. Currently on gpt-3. Running this on LangChain 0. from langchain_openai import ChatOpenAI model = ChatOpenAI (model = "gpt-4o") @tool def magic_function (input: int)-> int: """Applies a magic function to an input. 302 stars. I can see the prompt text, but not the function arguments (which are built from the tools provided to the create_openai_functions_agent factory method). We use heavily OpenAI LLM to take decisions. utilities import BingSearchAPIWrapper from langchain import LLMMathChain from langchain. The workflows are so fragile, and openai/others can break these wrappers very easily - even though langchain is a bit like keras as a wrapper. You constantly have to wrap it in wrappers. More posts you may like r/LangChain. 🍎📱 When I use the Langchain Agent it feels like a black box. 2. OpenAI's mission is to ensure that artificial general intelligence benefits all of humanity. By themselves, language models can't take actions - they just output text. Forks. Function calling does what I need with much less complexity. chains import RetrievalQA from langchain. In the custom agent example, it has you managing the chat history manually. With LangChain you might be installing close to 120 packages for your application. Transformers Agent is an experimental API, meaning it is subject to change at any point. An examples code to make langchain agents without openai API key (Google Gemini), Completely free unlimited and open source, run it yourself on website. In this example, we adapt existing code from the docs, and use ChatOpenAI to create an agent chain with memory. It's for anyone interested in learning, sharing, and discussing how AI can be leveraged to optimize businesses or Get the Reddit app Scan this QR code to download the app now. For example: What is the average sales for the period so and so? I was thinking of using create_csv_agent for this purpose but I had a question. When able, I hope to write mostly about Laravel and other related topics. We expressly designed this framework to simplify building applications, using an agent-oriented approach from the start. How Apple Uses ML To Recognize People (Without Photos Leaving Your iPhone). /r/MCAT is a place for MCAT practice, questions, discussion, advice, social networking, news, study tips and more. MIT license Activity. 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. I've been digging for hours. Note: you can of course use open source models without using OpenAI's API. GPT4-Turbo) when they come out. LLM Agent with History: Provide the LLM with access to previous steps in the conversation. On the other Langchain went very early into agents and has assembled a truly impressive variety of features there by now. Has anyone created a langchain and/or autogen web scraping and crawling agent that on given a key word or series of keywords could scrape the web based on certain kpis. The main challenge was 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. Members Online. agents. Build an Agent. Based on the type of your application you might end up using 10 to 15 packages if you write it without LangChain. 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. Langchain is a good concept but poorly executed. LangChain for LLM Application Development. For embeddings, I used the Instructor-XL Embedding model. but nowdays i started directly using openai sdks. 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. However all my agents are created using the function create_openai_tools_agent(). 1, Python 3. Would you know how I can do this without using openAI or Huggingface APIs ---- a full local implementation? Thank you! ``` import pickle Alternatives to OpenAI for Langchain Agents It feels to me that openai interface will become the go-to interface for all future LLM projects. However, we are integrating tools and we are thinking to use langchain agents for that. openai import OpenAI import os os. Langchain tutorials. For example, I would say help me with Tesla information and choose 5-10 kpis from a predefined list such as valuation, assets, liability, share price , number of cars sold by It allowed us to git rid of a lot of technical debt accumulated over the previous months of sub-classing different langchain agents. 2 watching. memory import ConversationBufferMemory agent_kwargs = { "extra_prompt_messages In this example, we adapt existing code from the docs, and use ChatOpenAI to create an agent chain with memory. mtfcy vghvn wtcxld jeiaqcjy zvfnk zdgtgim fmey egay ouhcep clptztq