Langchain production reddit. ```python. I learnt a lot from this! I used to do my agents the old fashion way, but this is really really neat. If it doesn't, switch to LlamaIndex ;) 2. Sci. Langraph is for agents that you need to have partial control over. Also mermaid when you want to describe db relations. 5 Pro & Flash APIs. thank you. I have dozens of ideas for really cool apps but I was wondering if someone already used langchain for production. I have build Openai based chatbot that uses Langchain agents - wiki, dolphin, etc. It’s important that the text chunks are clear and Hey everyone, I am a Comp. I'm trying to build a RAG using Ollama, Pyspark. What is the real difference and tradeoffs when choosing to use ChatGPT Functions instead of the ReAct agents from Langchain?… Bert gives you an embedding for each token, not one embedding for the entire piece of text. Just wrote and article on two underestimated (and mostly unknown) features of Langchain to create completely configurable chains while still being production ready. MembersOnline. r/OpenAI. Apart from using FastAPI for asynchronous requests, how to achieve streaming within this framework? Also what are some low cost options for the entire dev stack, what other things should I take into Go to LangChain r/LangChain LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. It’s free on their website. It basically does all of the following for you right out-of-the-box: Sets up local Chroma DB. ADMIN MOD. LlamaIndex supports all these features out of the box. OpenAI API seems to be the only reliable API service available. Any experienced tips welcome. Here is how you deal with this kind of application using LLMs (whether you use langchain or not): Your document is cut in manageable chunks (typically ~1000 tokens) 16 votes, 18 comments. I'm using llamaindex for a multilingual database retriever system and using claude as the provider. OTP verification using langchain. 105 upvotes · 41 comments. Autogen is for is just for building mutiagent apps only. I’d love to conenct to learn from your experiences as I have not seen it be used in productive systems yet! 1. Feel Free to join and grow this community LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. A piece of cake! 2. It works pretty well, but I figured we could maybe improve the performance with some finetuning (for example, the No-code langchain apps on production using langflow & langchain-serve. Hi, My use case is embedding documents into vector store and querying on them. It…. Also supports shared memory between ‘tools’ (also off prompt). • 4 mo. Converting a streamlit based Langchain app with ChromaDB into a production ready app with Next js for a better UI. They are separate projects, and can be used independently. I am surprised to see many posts like this one, or this one, expressing negative sentiments about LangChain and in particular the agreement about the negativity in the comment section. Set the Stage: Clone the chat-langchain repo and set it up as per the guidelines. Is Langchain the right framework for my usecase? I have ~2700 pdf documents, which are transcripts of Danish political conversations. ago. I'm currently building and deploying AI and GenAI applications for a living, and I've utilized Langchain in numerous projects. There is a LiteLLM wrapper in LangChain Implementing conversational capability goes beyond adding Langchain memory, it's just bit enough to maintain a conversation when coupled with RAG. Not really. The ‘prompt stack’ it creates is accessible programmatically so the developer still has control. if someone here had worked or having same issue, please let me know. In fact, all of those modules are considered legacy now. Wise_Housing5427. from delta import *. Autogen is more high-level. The best way to determine if RAG is returning correct results is to actually look at what text your search is returning. Hello guys, Just wrote a new blog post explaining Langchain LCEL in a easier manner: link. Checkpoints seem to be the way to go for managing history for graph-based agents, proclaimed to be advantageous for conversational agents, as history is maintained. Any alternative on how we can do this without using langchain ? Hello langchain folks! I am pretty new to langchain and I am impressed by its fast growing popularity the last couple of months. I already have some LangGraph agents in production and working pretty good, clients are happy Nobody's responded to this post yet. I haven't used LangChain recently, but given that the above are standard production RAG strategies, I'm sure it will offer some level of support for them too. I primarily work with Langchain and have embedded all my data-sources into a FAISS vector database (I tried ChromaDB, but found better results with FAISS LangChain's tools/agents vs OpenAI's Function Calling. Here's the link to the article: Getting Started with Langchain: A Beginner's Guide to Building LLM-Powered Applications. 5-turbo” with “gpt-4-1106-preview”. Working on a product that is on production . 8K subscribers in the LangChain community. My scope is kinda small as I am aiming to host it locally in WAMP/XAMP or AWS server to be used as demonstration of the app. Saving it return ConversationalRetrievalChain. Add your thoughts and get the conversation going. There's too much effort in making everything work with every model leading to it being non-optimal for any one specific model. I saw tons of videos of really amazing things you can do with this abstraction library. It's a beginner's guide to building Language Model (LLM) powered applications using Langchain. In terms of what to run in production, I am a GCP engineer, so using VertexAI services with custom prompts & maybe a finetuned model would at minimum be more scalable for production use. TheInternetShill. OpenAI's mission is to ensure that artificial general intelligence benefits all of humanity. LangChain is an open-source development framework for LLM applications. I was asked to try out Pinecone as vector store instead of Azure Search. There was a good tutorial video from James Briggs about NeMo Guardrails, he said it's good because it saves some of those API calls, as it doesn't need to call an LLM just to make a decision of what tool to use, and so is faster and cheaper. The data is not confidential. They offer many batteries-included, pre-built application modules that plug in with your data or configuration. The article provides a comprehensive walkthrough, making it a great resource for anyone interested in AI and language models. Given the abundance of tools being developed nowadays, I conducted research but only found refining the tool descriptions as a potential solution. 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. Prompt Perfection: Modify the prompt (example below) and ensure your LangChain And Vector Databases in Production. I think SBERT takes the average of the econd last or last layer of token embeddings from BERT to create a sentence embedding. Add a Comment. You can swap out LLM choice with a single parameter. What is the best vectorstore to selfhost your vector indexes? I know that Pinecone is the easiest, but on the free tier they delete your indexes after 7 days. I am also curious about this. I'm unable to call desired otp verification tool. Let's dicsuss this sub's negative feelings towards LangChain. Langchain is a Python/Javascript interface for chatgpt that allows users to do some really interesting things (similar to plugins for chatgpt). Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to its underlying VectorStore. We can auto switch you to the best one. What is the best chunk size and overlap for such a situation. Get the Reddit app Scan this QR code to download the app now A production app using Langchain LangChain is an open-source framework and developer toolkit that Go to LangChain r/LangChain LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. 5 api, it seems as though one can use the 22K subscribers in the LangChain community. I am working with transcription of a video. Not only that, but there is the ability to move forward or go backward in the history as well, to cover up errors, or go back in time. Hi! Appreciate your review. (langchain has an example with like a political debate, but this would just scale that from 3 to however many chunks) Each of the “data personas” would each gives their answer and a score from 1-100 for their determined relevance to the context. When you use it, you construct a lot of things that llamaIndex does automatically. Currently, I am using Chroma DB in production as a vector database. But you can still run local models via an openai mock api, like fastchat. I try to keep it simple while going over important points so you can get started in no time. I am trying to switch to Open source LLM for this chatbot, has…. I'm not able to store the user otp to use in a particular tool. With Anthropic - I am unable to add credits to their console, even after multiple mails to the customer support I have received no resolution. Langchain for production. And just as important as ensuring it’s returning correct results is how you structure your vector store. However, the agent struggles to select suitable tools for the task consistently. I used several of Langchain's retrievers, like the MultiVectorRetriever, a BM25 retriever and even tried pooling everything together with an One complaint I've read is that Langchain is inefficient, expensive and slow, so not good for production. Using the same, we've launched langflow deployment on Jina AI Cloud with just one command. I'd like to build a simple Q&A app over a list of docs (let's say 100 pages). I have set everything up, using a stuff RetrievalQA chain with Azure OpenAI. I would define an SOP (standard operating procedure) for how How are you handling memory when deploying your apps to the production environment? All the examples that Langchain gives are for persisting memory locally which won't work in a serverless (statelesss) environment, and the one solution documented for stateless applications, getmetal/motorhead , is a containerized, Rust-based service we would The goal of the r/ArtificialIntelligence is to provide a gateway to the many different facets of the Artificial Intelligence community, and to promote discussion relating to the ideas and concepts that we know of as AI. I have similar complex stuff already working, can recommend jsonschemas for defining the exact output, i combination with enum and one_of stuff. I recently are trying to implement agents following the official LangChain tutorials but it just not work : (. Long answer: The limiting factor is the LLM model context size. For a community that comes together for the LangChain package and ecosystem, there seems After having worked with Anthropic API and Gemini 1. Its usually the chief complaint amongst seasoned devs. 19K subscribers in the LangChain community. Thanks. Just look for something like 365AI. So far I have tried using this azure search open ai I'm trying to build a production-level LLM-powered app, and LangChain makes me a lot of pain; the documents are inconsistent, it passes parameters so randomly but still checks types with pydantic, so some use cases fail and some do not, and the time I spent debugging custom tools and chains is even enough for me to implement one from scratch. I'd love any feedback or suggestions on the platform, and hope this can be helpful for you all with all the new models coming out! 0. 13K subscribers in the LangChain community. post on it on Reddit It also allows to use different frameworks to build the different nodes, which allows to use, for example, instructor framework and getting structured results, it is very easy to add them in the graph, and have also langsmith to see those traces, is a pretty good integration. small document, I used only embedding and happy 99% of time. Separately it'd also be helpful to have the ability to manage states within a conversation and langgraph seems to meet the criteria. For a financial earnings report, Summarize the earnings surprises and LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. This section in their docs got me thinking about possibly using it with Salesforce Apis. I was storing each speech segment as text and speaker and timestamp as metadata to a Chroma collection. It is available for Python and…. llm=llm, memory=memory, retriever=self_query_retriever, verbose=True, return_source_documents=True. We've upgraded langchain-serve to deploy any r/FastAPI app to enhance your langchain applications' API endpoints. Which vector store in langchain supports saving an index locally so you can pull saved vectors like Pinecone? I have tried Chroma, but it does not seem to have that functionality from Using lower-level tools makes better GenAI apps: an alternative to the LangChain way. 3. LangChain's tools/agents vs OpenAI's Function Calling. So I finally have to give up hope and just use Open AI. IMHO, you can learn a lot while Langchain handles some of the details, and then you can circle back to the details later. Thanks! It depends on the context and the LLM. Sharing the code below. However, we are integrating tools and we are thinking to use langchain agents for that. I wish to use my delta table as the source for this POC. Completely unedited, each question asked for the first time. Reply. from_llm (. I'm interested in integrating external apis ( function calling) and knowledge graphs. document_loaders import PySparkDataFrameLoader. I want to create an gpt-4-based chatbot (Using something like streamlit or chainlit) that can answer (In Danish) about the pdfs. However, I am facing challenges, including delayed responses from the API and potential issues with semantic search, leading to results that do not meet our expectations. The main point of this community rather than Langchain which already exists but have to create a separate for developers , who need help to learn and need assistance. There are lots of example code. Hi there, I've got a question. For Langchain, retrieval is core because it determines the context that is fed into the final prompt to the LLM. Is quite easy to create a chatbot with langchain LCEL and using the buffer memory, but what if in production I wanna store the history of the conversation in a db and not in the RAM and retrieve it when the user restart the conversation with the chatbot? I cannot find a tuto anywhere. Langchain vs llamaindex. I dont trust the benchmarks, so I recorded my very first test run. While it's an good tool for high-level prototyping, it tends to become inadequate for specialized use cases and production deployment. student and I am using LangChain for one of my projects. In practice, many usecases genuinely don't need any special components indeed. Can you provide more information about the purpose In fact, all of those modules are considered legacy now. LangChain provides a bunch of things (chain logic, vectorstores, etc) and also provides different interfaces to LLMs. We are an unofficial community. LangChain is an open-source framework and developer toolkit that helps developers get LLM applications…. Langchain is doing a great job integration traditional technologies with new methods for processing. from langchain_community. Takes in data source (URL, YouTube, PDF, etc…) Makes chunks out of the data. It’s about 20 hours of detailed coursework, videos, and code snippets. Here's what I have done so far. I have a few number of documents but need to get accurate answers for the questions. I tried to do this in LangChain and it was a nightmare for my team, literally drained a week of dev time trying to do this for different filetypes. While inferencing and embedding, you won’t have any speed-up unless the vectorization, matrix multiplication and linear algebra is completely written in Mojo. 5. If you are a solo developer, or a small team, Langchain is an entire organization on your side. I have also parallelised some operations, executing multiple prompts in parallel to split the workload and increase speed (less tokens per query, faster response) Each module is about an hour long, and there’s other expertise now such as cohere and openai modules. Active_Diamond_2256. (Org wants to reduce costs), So i setup a PoC pipeline with Pinecone as vector store. Finetuning for RetrievalQA chain. The Magic Swap: In chain. you may have a lot of insightful and useful modifications in your design, but if you don't communicate what those are, you're just assuming everyone is as Start here! Tutorial. 1. Preparing model for production Hi, I have wrote a chat model based on huggingface inference through langchain, and I would like to prepare it for production. What I see it lacking is support for multi-agent systems like crewai and autogen do, but you can easily achieve those results by tinkering with the tools LC gives you. Summarize this doc as a prompt doesn't give any semantic relevance for the retriever. I think the opposite. Langchain Chatbot in production. Another great free training tool, is DeepLakes certification. First impression is that it is good, very very good for its size. IndividualExtreme561. I'm facing an issue regarding OTP verification using langchain tools. I'm interested in this conversation. I’d be interested in whether anyone here is using LangChain’s SQL Agent (or similar self-built agents with LangChain or autogen). Discussion. • 7 mo. Only local stuff like chromadb rag or local model are more painful in js. Hi all, I am relatively new to LangChain and ChatGPT, but for my work we want to use it for querying (single) documents. All of default guides say I should chunk the data and put it in VectorDB and then for every question, fetch top 3-5 similar doc chunks and pass it to LLM with the initial question to have this retrieval augmented in context learning. Using nodejs also enables stuff like webscraping with cypress. I want an LLM like GPT to be the brain of my automated workflow. We use heavily OpenAI LLM to take decisions. Has anyone performed comparative analyses on the performance of Azure Search Index Only way Mojo would speed up langchain would be if langchain was rewritten in Mojo. langchain and llamaindex are stuck in the chatbot paradigm and are particularly designed with OpenAI chatbots in mind. However, you can use LiteLLM in LangChain should you choose. LangGraph: Checkpoints vs History. No, Langchain is not trash. My data-sources are classified into about 3 specific domains, and I want my RAG bot to know the difference between these domains and query only the necessary datasets, depending on the query. Langchain expands on those capabilities, and when it doesn’t match perfectly with production grade apps, people seem to get discouraged. Award. DSPy can distill arbitrary tasks into optimized prompts or even fine-tune underneath the abstraction, it is much more legit. LangChain + LangSmith + LangServe cover most of the use cases for creating good AI apps. No-code langchain apps on production using langflow & langchain-serve. r/MachineLearning. OpenAI makes ChatGPT, GPT-4, and DALL·E 3. Far from trash, but very much an alpha product from one dude. llms import Ollama. It occasionally picks the right tool but often chooses incorrectly. I was recently introduced to Embedchain, a Python library built on top of LangChain that takes care of your RAG needs in a few lines of Python code. Initial tests: RAG with Phi-3. You should also incorporate a reranker, such as Cohere. langflow allows users to use langchain components & define Flows by Go to LangChain r/LangChain LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. Here's how they compare DSPy to LangChain: LangChain and LlamaIndex are popular libraries that target high-level application development with LMs. . I developed a multi-tool agent with langchain. Langchain seems to put more thought into the features they add than LlamaIndex. If you’re new to LangChain, you’ll want to read this introductory post and then dive deeper into more advanced topics as you progress. Enjoy. I really love LCEL (feels a little like functional programing right !?) and wanted to try to explain it in a simpler way. As a result, it is easier to customize and more transparent. This is actually what I use in my own production chains. Know, when I use routers or any tool I prefer to do it with the library instructor to get structures data to modify the state or guide the next node. langflow allows users to use langchain components & define Flows by connecting several components. OpenAI is an AI research and deployment company. Thanks! i enrolled. You can of course build a RAG pipeline without langchain (pick your own component for extraction, chunking, index, retrieval), but for simple cases - just copy an example from langchain. 2. What I am not sure is how to benchmark both vector stores for performance and find limitations of both. We already did a project with langchain agents before and it was very easy for us to use their agents. If one is worried about exposing sensitive data like PPI to the gpt3. And in my opinion, for those using OpenAI's models, it's definitely the better option right now. Griptape has some unique patterns like support for ‘off-prompt’ retrieval and long running workflows. py, replace “GPT-3. ty. LiteLLM provides a single interface to a bunch of LLM providers. Hi 👋. Ended up packaging that into a pip package to solve this problem :) My production code runs 3x faster than the prototype that was using Langchain's pre-built chains, and use less than half the tokens, for much better performances. I primarily work with Langchain and have embedded all my data-sources into a FAISS vector database (I tried ChromaDB, but found better results with FAISS, lmk if you have better suggestions). Boost the number of retrieved chunks (I’m rocking search_kwargs=dict(k=20)). import pyspark. Yeah it is possible. Best Chunking Strategy for Detailed Answers. LangChain is an open-source framework and developer toolkit that helps developers get LLM applications… IMHO, to get better at RAG, you need to know what goes on under the hood. I´m using it in "production" with cloud But langchain is a great tool for this application, it has examples for every required steps. Go to LangChain r/LangChain LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. Langchain though can be a bit cumbersome to use sometimes though. Read these to understand what embeddings are used for. ) or, should I be using some part of LangChain that I don't know of that generates the filters from the query, which I can then use in my (Pinecone) retriever: filter = "parse_structured_filter_from The flaws of LangChain have been discussed often, especially r/langchain it's fine for prototyping and leaning but lousy for building production quality products. Production / complex data sources (periodic ingestion, etc): I'd start with a SaaS solution and see if you can configure the prebuilt RAG to your liking . Yeah if you want to build out more general purpose apps and say use langchain to handle conversations to llamaindex or include agents that only call certain things then langchain will work. 22K subscribers in the LangChain community. As far as I understand, you can run Python code in the newly released Mojo language for As long you are using cloud ai providers like openai etc the js version offers most python. LangChain is an open-source framework and developer toolkit that helps developers get LLM applications… a couple of bulletpoints of "here are the problems this solves that langchain doesn't" or "ways this is different from langchain" would go a long way. It is a bit more effort to get things done, but in the long term this saves time as you will want to customize things. Langraph allows to the the same as autogen with a little bit more work and it can do more. A self-querying retriever is one that, as the name suggests, has the ability to query itself. I wrote this introductory post for anyone just getting started with LangChain. LangChain abstracts away relatively simple API calls code. langchain already has a lot of adoption so you're fighting an uphill battle to begin with. r/LangChain: LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. This instructor has saberão validators to be sure the structures data is why you need. •. [D] Full causal self-attention layer in O (NlogN) computation steps and O (logN) time rather than O (N^2) computation steps and O (1) time, with a big caveat, but hope for the future. jg qf jf tz rc az gd qf ym jl