Langchain csv agent with memory reddit. Sep 25, 2023 路 Langchain CSV_agent馃 Hello, From your code, it seems like you're trying to use the ConversationBufferMemory to store the chat history and then use it in your CSV agent. answering questions on the basis of documents, websites, repositories etc. However, it appears that you're not actually using the memory_x object that you've created anywhere in your code. Continous conversation between agents Hi all! I recently discovered crewai and have been building various agents to hold a conversation in which I set the crew process as sequential. Sometimes starts hallucinating. Thank you! How can I speed up an analytic chatbot that's based on Langchain (with agents and tools) and Streamlit and disable its intermediate steps? I created an analytic chatbot using Langchain (with tools and agents) for the backend and Streamlit for the frontend. 馃殌 To create a zero-shot react agent in LangChain with the ability of a csv_agent embedded inside, you would need to create a csv_agent as a BaseTool and include it in the tools sequence when creating the react agent. But lately, when running the agent I been running with the token limit error: This model's maximum context length is 4097 tokens. Anyone knows of an optimal solution to speed up langchain when using with vector db (pinecone in this case). Since , csv_agent () does not support memory at the moment , how… What practical applications for langchain based agents have you been having success with? Of particular interests, what foundational models have you been seeing perform best as agents? Learn to build AI agents with LangChain and LangGraph. 5-turbo as llm. agents import create_csv_agen Jun 5, 2024 路 To include conversation history in the create_csv_agent function, you can use the ConversationBufferMemory class and pass it as a parameter to the agent. create_csv_agent(llm: LanguageModelLike, path: str | IOBase | List[str | IOBase], pandas_kwargs: dict | None = None, **kwargs: Any) → AgentExecutor [source] # Create pandas dataframe agent by loading csv to a dataframe. I got good results using OpenAI and Langchain. js (so the Javascript library) that uses a CSV with soccer info to answer questions. We already did a project with langchain agents before and it was very easy for us to use their agents. The bare-simplest option is to just rate-limit your requests to something more reasonable using a sleep () or delayed scheduler or something similar. Is this… I was trying to test out I have encountered difficulties while attempting to implement custom table operations. However, I realised that that after the agents complete their tasks, the conversation activity stops. agents. Message Memory in Agent backed by a database This notebook goes over adding memory to an Agent where the memory uses an external message store. I have prepared 100 Python sample programs and stored them in a JSON/CSV file. I'm liking it so far. Besides the number of rows, there are many columns. Was planning to integrate my Langchain agents in autogen but with Langgraph I have the capability that I needed. chains import ConversationChain, LLMChain from Problem while using CSV agent. Specific questions, for example "How many goals did Haaland score?" get answered properly, since it searches info about Haaland in the CSV (I'm embedding the CSV and storing the vectors in Pinecone). It said something like CSV agent could not be installed because it was not compatible with the version of langchain. This notebook goes over adding memory to an Agent. It's weird because I remember using the same file and now I can't run the agent. I've been using langchain's csv_agent to ask questions about my csv files or to make request to the agent. What I meant by Tools are utilities designed to be called by a model: their inputs are designed to be generated by models, and their outputs are designed to be passed back to models. The project would not involve agents (yet) but will have both long and short term memory and other custom components. Observability, lineage: All multi-agent chats are logged, and lineage of messages is tracked. Right now, i've managed to create a sort of router agent, which decides which agent to pick based on the text in the conversation. Chase Since the pace of everything is moving so fast, I wanted to know what objects in the Langchain library I could rely on sticking around with the next round of updates. It's also Feb 7, 2024 路 馃 Hey @652994331, great to see you diving into LangChain again! Always a pleasure to help out a familiar face. Reading the documentation, it seems that the recommended Agent for Claude is the XML Agent. More complex modifications create_csv_agent # langchain_experimental. I have used embedding techniques just like the normal docs but I don't think this work well for structured data. I have gotten to this final product where I get a specific response schema back and I'd like to use it to provide an answer, along with an embedded plot that is related to said answer. Considering that OpenAI will just improve and build upon this new feature, is there really a reason anymore to stick with LangChain? I’m working on a solution for a client who needs an agent to pull data from a CSV file, which contains information about a provider’s location, services, categories, phone numbers, and addresses. Hi folks, it seems to me that the current sentiment around AI agents is very negative as in that they're useless but I don't… Langchain seems pretty messed up. Can someone suggest me how can I plot charts using agents. One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. Currently nudging towards LangChain because of the extensive documentation and popularity of the project. I'm trying to process different documents that have anywhere from 200 tokens to 2k tokens and consistently getting 20 seconds response time from langchain. Sep 27, 2023 路 馃 Hello, To create a chain in LangChain that utilizes the create_csv_agent() function and memory, you would first need to import the necessary modules and classes. In my first approach I actually tried to create a Llama2 agent with Langchain Tools with one tool being the retriever for the vector database but I could not make Llama2 use them. 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 Memory in Agent In order to add a memory with an external message store to an agent we are going Does Langchain’s `create_csv_agent` and `create_pandas_dataframe_agent` functions work with non-OpenAl LLMs Hey guys, have a question hoping if anyone knows the answer and can help. I tried searching for what's the difference between chain and agent without getting a clear answer to it. Any suggestions? Hello All, I am trying to create a conversation chatbot that can converse on csv/excel file. I'm new to Langchain and I made a chatbot using Next. 5 though. Productionization Other specialized agents include SQLChatAgent, Neo4jChatAgent, TableChatAgent (csv, etc). These are applications that can answer questions about specific source information. Basically, this test shows that this function can’t remember from previous conversation but fortunately LangChain package Does langchain support it out of the box with configuration, or is this something that needs to be done on my own? It seems that loading several langchain agents takes quite a bit of time which means the client would have to wait quite a bit if I recretead the agent for every request. Langchain/semantic kernel = Allow flow control and agents/planners. It can recover from errors by running a generated query, catching the traceback and regenerating it As of the v0. Better to use pandas agent by langchain. I am wondering if embeddings are required for a file like this, I have it working using csv_agent, it creates the pandas query and filters the data. Here's how you can modify your code to achieve this: Initialize the ConversationBufferMemory: This will store the conversation history. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source components and third-party integrations. In this case, we save all memories scoped to a configurable user_id, which lets the bot learn a user's preferences across Upcoming Changes to Langchain Memory and Agents in LCEL: Twitter Thread with Langchain CEO H. But I’m not able to place a memory in my agent. I have added some context to the prompt so that it properly understands the data dataset. create_csv_agent function can’t memorize our conversation. I hope that users can ask questions using the chatbot and get relevant responses (rather than directly displaying sample programs). How to add memory to chatbots A key feature of chatbots is their ability to use the content of previous conversational turns as context. . It likely performs better with advanced commercial LLMs like GPT4o. Evyerthing seems to be working "well", except for the fact that the LLM thinks that there are only 4 rows in the data, when in fact there are 200 rows. Nov 15, 2024 路 A step by step guide to building a user friendly CSV query tool with langchain, ollama and gradio. I'm using create_csv_agent to get a csv parsing agent to analyze and return a list of items that meets the criteria. Hello developers, I have been working in LangChain for about 3 months. I have developed a conversational Agent in text based data and was really… If you are using csv or Excel which contain sales figures or if you are trying to do data analysis operations. Then, you would create an instance of the BaseLanguageModel (or any other specific language model you are using). The above, but trimming old messages to reduce the amount of distracting information the model has to deal with. I agree that langchain overcomplicate a lot of things, but understanding how it works is a great learning. ). prompt import PromptTemplate from langchain. Tried to do the same locally with csv loader, chroma and langchain and results (Q&A on the same dataset and GPT model - gpt4) were poor. Say I want it to We would like to show you a description here but the site won’t allow us. 5-turbo LLM had inconsistent outcomes. Hi Everyone, I am using Langchain with GPT4All to analyze a CSV using the CSVLoader package. memory import ConversationBufferMemory from langchain. When i print the memory it actually contains the chat history but it is not passed to the model or agent with the input text. NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. (OpenAI’s gpt’s also won’t search some specific sites, like UpToDate for example but I can still get UpToDate searches for gpt4 by using Langchain with search tool). Initial trials with chroma embeddings and a gpt-3. - There are so many ways of creating, for instance, an Does Langchain’s `create_csv_agent` and `create_pandas_dataframe_agent` functions work with non-OpenAl LLMs Hey guys, have a question hoping if anyone knows the answer and can help. It works with GPT-3. And in my opinion, for those using OpenAI's models, it's definitely the better option right now. Also, you can use try-blocks to prevent crashes on the error, and catch I tested a csv upload and Q&A to web gpt-4 and worked like a charm. The CSV data is relatively big. Each sample program has hundreds of lines of code and related descriptions. path (Union[str, IOBase Agents as a practical solution Hello there, What practical applications for langchain based agents have you been having success with? Of particular interests, what foundational models have you been seeing perform best as agents? What size of datasets do you have it reasoning through? I’ve been building agents and chains for the past year. This results in a larger prompt and a slower performance. 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 have to run ourselves. We use heavily OpenAI LLM to take decisions. We're launching with Langchain integration. - The documentation is subpar compared to what one can expect from a tool that can be used in production. The main advantages of using the SQL Agent are: It can answer questions based on the databases' schema as well as on the databases' content (like describing a specific table). chat_models import ChatOpenAI from langchain. However, switching to gpt-4-1106-preview and adjusting the chroma retriever kwargs “k” from 4 to 8 I have an application that is currently based on 3 agents using LangChain and GPT4-turbo. Previous conversations are saved but agent is not able to relate. So, I'm doing a project on chat with CSV files, as the name user can ask question in natural language and the CSV agent is suppose to generate a pandas code, run it and get the answer in response. In the end, I realized that LangChain hadn't done anything but get in the way, while I had implemented everything useful myself. Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. However, we are integrating tools and we are thinking to use langchain agents for that. Each record consists of one or more fields, separated by commas. Does Langchain’s `create_csv_agent` and `create_pandas_dataframe_agent` functions work with non-OpenAl LLMs Hey guys, have a question hoping if anyone knows the answer and can help. e. Is there a way to train CSV agents? Hello, as a mobile app developer, I started to work on AI. CSV Agent # This notebook shows how to use agents to interact with a csv. Does the size of the csv files inputted to the agent have an impact on the costs incurred? In other words I installed langchain [All] and the OpenAI import seemed to work. This is a simple way to let an agent persist important information to reuse later. 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. Hope you're ready to dive back into the world of code with another intriguing question! 馃槉 Based on the code you've provided, it seems like you're using the ConversationBufferWindowMemory correctly. The agent handles the questions fine and I can see the correct results printed out in its Observations. Parameters: llm (LanguageModelLike) – Language model to use for the agent. just to combine two of its features. I suspect i need to create better embeddings with chroma or any vector db. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. sql import SQLDatabaseChain from langchain. agents import create_csv_agent from langch… I'm trying to build a CSV Agent that holds memory of the previous conversations. This notebook shows how to use agents to interact with a Pandas DataFrame. I have tested it, and it seems to work but the only thing is that my Oct 28, 2023 路 Figure 2. I developed a simple agent which is able to answer simple queries like , how many rows in dataframe, list all transaction realated to xyz, etc. Obviously for such an application there should be memory with the agent. However, the open-source LLMs I used and agents I built with LangChain wrapper didn’t produce consistent, production-ready results. 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). Access Google's Generative AI models, including the Gemini family, directly via the Gemini API or experiment rapidly using Google AI Studio. This class is designed to manage a conversation's memory within a limited-size window. However, I am facing several issues Spent several hours fighting with LangChain, debugging its internals, etc. I'm using Ada-v2 for embeddings & gpt-3. But when the csv structure is different it seems to fail. However, I'm not sure how to get the router agent to know when to pick the next agent based on the conversation memory. But there is a problem: Questions other than the data I provide are also answered. Here's an example of how you might do this: We would like to show you a description here but the site won’t allow us. This repo provides a simple example of a ReAct-style agent with a tool to save memories. I created a CSV agent with Langchain and I want it to provide information about my CSV data. As title suggests, i want to add memory to vreate_csv_agent so that it remembers past conversations and queries from the subset of data it provided in the past in case the user prompts for it? Apr 26, 2023 路 I am trying to add ConversationBufferMemory to the create_csv_agent method. To use the ConversationBufferMemory with your agent, you need to pass it as an argument when creating the Mar 4, 2024 路 Conversational memory in csv agentHey there @Raghulkannan14! Fancy seeing you here again. Is there a "chunk . After that, you would call the create_csv_agent() function with the language model instance, the path to your CSV The token limit is per-minute. want something like autogen System Info working on ubantu 22. It is mostly optimized for question answering. LangGraph: LangGraph looks interesting. 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. For the agents I found Langgraph is a great solution, you have total control of the fluxes, structure, tokens memory etc. What are the alternatives to langchain agents ? Working on a product that is on production . Both of them from what I've seen from code snippets allow you to define pieces of code that either call LLM online (or local?) with certain configuration (max tokens, temperature etc etc) and allow building agents by giving these pieces of code a description, then it's up to 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. Hi! I am trying to use the LangChain's Pandas agent for performing customer level conversations. This is often the best starting point for individual developers. This state management can take several forms, including: Simply stuffing previous messages into a chat model prompt. Whereas in the latter it is common to generate text that can be searched against a vector database, the approach for structured data is often for the LLM to write and execute queries in a DSL, such as SQL. Jun 4, 2024 路 I retrieve a ConversationBufferMemory instance from the user session and pass it to create_csv_agent. Despite this, the memory state does not appear to be maintained across subsequent calls to get_message. It needs to be told the ID everytime. For csv-like files I've usually gone the route of making a text to SQL agent so a user query gets translated into a SQL query that can return data by the LLM. Sep 11, 2024 路 Although I have tested the application and it works, but we want to pass external memory, We can use ZeroShotAgent with memory but it's deprecated and we're suggest to use create_react_agent. Hello everyone. 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. . It's simple enough to just plug it in and get robust memory and retrieval yet offers plenty of extension and customization options. it will give correct answers plus do prompt finetuning to explain the structure of workbook to llm. But create_react_agent does not have an option to pass memory. I had also to manually increase the context size implemented by the interface between Langchain and GPT4All. Can someone please help me figure out how I can use memory with create_react_agent? OpeningMarsupial7229 Large CSV files with llama Hello everyone I'm trying do an usecase where I can chat with CSV files,my CSV files is of 100k rows and 56 columns when I'm creating an CSV agent it is failing beacause of input token limit is exceeded and allowed limit is 4096,how do approach this problem please help 5 4 Share Add a Comment Sort by: I'm trying to create a conversational chatbot, using multiple agents who specialise in certain sections of the conversation. I’m using create_pandas_dataframe_agent (using OpenAI Function as agent type) with a df . However all my agents are created using the function create_openai_tools_agent(). I am a beginner in this field. Nov 7, 2024 路 LangChain’s CSV Agent simplifies the process of querying and analyzing tabular data, offering a seamless interface between natural language and structured data formats like CSV files. Agents select and use Tools and Toolkits for actions. The problem starts when I ask general What are the benefits of using Langchain compared to just applying the code that is within the OpenAIs documentation? 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. The langchain-google-genai package provides the LangChain integration for these models. memory import ConversationBufferWindowMemory from langchain. However this documentation is referring to Claude 2 instead of Claude 3. 3 release of LangChain, we recommend that LangChain users take advantage of LangGraph persistence to incorporate memory into new LangChain applications. prompts. These applications use a technique known as Retrieval Augmented Generation, or RAG. I'd like to test Claude 3 in this context. I have used pandas agent as well csv agent which performed for most of the csv. The langchain is failing to perform a… Just as the docs point out the issues arise when it begins to perform data analysis/science tasks: NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them: I actually like langchain, it makes agents and tools easy and it handles API upgrades and LLM changeover well. I am not sure how to approach this. And for crying out loud, just feed the source to gpt4 and have it write your langchain agents. Hi there, I am currently preparing a programming assistant for software. My code is as follows: from langchain. I ve been using langchain, autogen, crew ai and langgraph. Hi Following are the libraries I use for my chatbot: import os import json import yaml from langchain import SQLDatabase from langchain_experimental. In this guide we'll go over the basic ways to create a Q&A system over tabular data I’m also having some trouble with extracting proper answers related to a csv file, Are you using csv agent or pandas agent? I also hear a lot of that LLMs are not good with tabular data :/ Does Langchain’s `create_csv_agent` and `create_pandas_dataframe_agent` functions work with non-OpenAl LLMs Hey guys, have a question hoping if anyone knows the answer and can help. Agent and Tools: LangChain’s unified interface for adding tools and building agents is great. I was looking into conversational retrieval agents from Langchain (linked below), but it seems they only work with OpenAI models. base. To make it more clever, you can use the tiktoken package to keep a running total of tokens spent in the past minute before you decide to send. It maintains a Sep 21, 2023 路 0 i have this lines to create the Langchain csv agent with the memory or a chat history added to itiwan to make the agent have access to the user questions and the responses and consider them in the actions but the agent doesn't recognize the memory at all here is my code >> After hundreds of hours struggling to find solutions to real-world problems with AI such as making API requests to custom API so that the LLMs have data to base their answers or even real-time voice enable support agents, I have come to this conclusion: Langchain tools are pointless and extremely convoluted, do not waste your time with them! All agents are a pre-prompt that makes whatever Hii, I am trying to develop a data analysis agent, and using langchain CSV agent with local llm mistral through Ollama. If your code is already relying on RunnableWithMessageHistory or BaseChatMessageHistory, you do not need to make any changes. - The discord community is pretty inactive honestly so many unclosed queries still in the chat. Zep allows developers to focus on developing their AI apps, rather than building memory persistence, search, and enrichment infrastructure. I had a hard time finding information about how to make a local LLM Agent with advanced RAG and Memory. The agent can store, retrieve, and use memories to enhance its interactions with users. We would like to show you a description here but the site won’t allow us. Gave up on LangChain and had a cleaner, more extensible implementation in two hours. Ultimate goal is to built a chatbot which can query database and have a memory of previous conversations. Each line of the file is a data record. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support. Built a CSV Question and Answering using Langchain, OpenAI and Streamlit : r/LangChain r/LangChain Current search is within r/LangChain Remove r/LangChain filter and expand search to all of Reddit 16 votes, 37 comments. agent_toolkits. 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 am building a web application to query data from CSV files and tried importing csv langchain agent using this command, which was taken from langchain documentation: Enabling a LLM system to query structured data can be qualitatively different from unstructured text data. csv. , not a large text file) Introduction LangChain is a framework for developing applications powered by large language models (LLMs). Hey guys, so I've been creating an agent that went from a SQL to Python/CSV agent (I kept getting errors from the db so gave up on that). Jan 30, 2024 路 Description i want to build RAG which has memory ,& it can use agents to communicate with other tools the current langchain tool has very basic RAG features. But when I create the typical ConversationBufferMemory() and pass the memory=memory but the agent has no memory whatsoever. Apr 26, 2023 路 I am trying to add ConversationBufferMemory to the create_csv_agent method. I'ts been the method that brings me the best results. Create autonomous workflows using memory, tools, and LLM orchestration. 04 machine 3 What's the best way to chunk, store and, query extremely large datasets where the data is in a CSV/SQL type format (item by item basis with name, description, etc. This template uses a csv agent with tools (Python REPL) and memory (vectorstore) for interaction (question-answering) with text data. What should be my prompt? If you're looking for something robust and don't want to have to build out what it really takes from the building blocks LangChain gives you check out Zep. You can check out the following notebooks for some examples on using Zep's Memory and Retriever functionality: A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Any alternative on how we can do this without using langchain Langchain makes it fairly easy to do context augmented retrieval (i. This tutorial shows how to implement an agent with long-term memory capabilities using LangGraph. oulfrl hznhuz gvnupt mgrasq ubvlrmes dqvaj qnbm jdojaui jywxsep ycevk