Ollama csv agent reddit. Their performance is not great.

  • Ollama csv agent reddit. DeepSeek-R1 is a family of open reasoning models with performance approaching that of leading models, such as O3 and Gemini 2. If i use OpenAIEmbeddings(), its, as far as i'm concerned Hii, I am trying to develop a data analysis agent, and using langchain CSV agent with local llm mistral through Ollama. cpp, and that Langchain supports Ollama, it would be great to have the capability to use it with Langflow. Output should be formatted as a simple English sentence. llm = Ollama(model="mistral") "Convert a Pandas DataFrame into a SmartDataframe from pandasai by wrapping it with SmartDataframe (data, config= {"llm": llm}), integrating advanced language model capabilities for enhanced data analysis. The OpenAI embeddeder is a class above all the currently available Ollama embedders, in terms of retrieval. Even using the cli is simple and straightforward. 1 package We would like to show you a description here but the site won’t allow us. csv. Note: Previously, to use Ollama with AutoGen you required LiteLLM. I have this big csv of data on books. " PrivateGPT lets you ingest multiple file types (including csv) into a local vector db that you can searching using any local LLM. I wanted to get faster results so I made an MLC version and it How can i create a source of truth agent with an ollama models? which are the step necessary to create a private chat bot to talk about a series of specific arguments? The ollama community on Reddit. Taxes I'd like to re-check my past 3-4 years of (UK) tax 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). - agno-agi/agno Jan 8, 2025 路 In this tutorial, we explain how to run a powerful and simple-to-use AI-agent library called smolagents that is developed by Huggingface. agent_toolkits import create_csv_agrnt I didn't read whole text you have posted , just noticed that CSV agent part , also it's better to use langchain with open ai 0. The only model i get half-way decent retrieval is the snowflake-artic-embed, and its still not that great. We will use the following approach: Run an Ubuntu app Install Ollama Load a local LLM Build the web app Ubuntu on Windows Ubuntu is Linux, but you can have it running on Windows by using the Windows Subsystem for Linux. I cannot see why and I am not sure how to Hi, I'm trying to extract the phone numbers from a 170-lines CSV text content like the following: 53,AAA,+39xxxxxxxxxx,1683028425453,0… I currently use ollama with ollama-webui (which has a look and feel like ChatGPT). Now I've seen allot of people talking about Ollama and how it lets you run llm models locally. Execute the query or tools and output result of execution. Is there any local web UI with actually decent RAG features and knowledge base handling? I think I have looked everywhere… Build an AI Agent from Libraries of Functions -- My most advanced agent framework - MikeyBeez/Ollama_Agents Ollama and Llama3 — A Streamlit App to convert your files into local Vector Stores and chat with them using the latest LLMs Jan 22, 2024 路 Today’s tutorial is done using Windows. AI’s Mistral/Mixtral, and Cohere’s Command R models. I have a laptop and a rig and was thinking of trying different model choices for each. CSV AI Agent Using Ollama This project is an AI-powered CSV analysis tool using Ollama. How could I give a local llm (windows 11 pro, ollama, RTX 3090, 128 gigs RAM) a chain (where each each row is a prompt with the name) from the csv list (or json?) and capture the response from the LLM on each prompt? Ollama Ollama is a local inference engine that enables you to run open-weight LLMs in your environment. Would appreciate any thoughts! Here's what's new in ollama-webui: 馃攳 Completely Local RAG Suppor t - Dive into rich, contextualized responses with our newly integrated Retriever-Augmented Generation (RAG) feature, all processed locally for enhanced privacy and speed. Updates 3. Each cell contains a question I want the LLM (local, using Ollama) to answer. openai compatibility has been a major pain point for using ollama previously, with people like myself needing to implement an open ai proxy inbetween like litellm. Sep 6, 2024 路 This project uses LangChain to load CSV documents, split them into chunks, store them in a Chroma database, and query this database using a language model. To ensure we have it enabled on our local machine, just go to the start menu, type in turn Windows features on or off, and make sure We would like to show you a description here but the site won’t allow us. Nov 6, 2024 路 Sample summary of a Reddit Post Conclusion In this blog, we explored how to build a Reddit Summarizer and Q&A Bot using a Retrieval-Augmented Generation (RAG) approach. Try to run it first with Ollama or gpt4all. basically i am new to local llms. For example, if I run it with the -y option, it'll quite quickly figure out what packages are needed, what I have installed etc. md at main · Tlecomte13 Jun 5, 2025 路 In this article, I'll demonstrate how to build a sophisticated Reddit intelligence engine that goes beyond basic web scraping to deliver actionable analytical insights using Ollama for local LLM Was curious as to what other users are using for selecting a model for the "embedding" and "agent" portions of the workspace. Loading into Ollama via the cli and testing, it is clearly using the GPU. i really apologize if i missed it but i looked for a little bit on internet and reddit but couldnt find anything. Unlike traditional AI chatbots, this agent thinks in Python code to solve Resources Hey everyone, I have been working on AnythingLLM for a few months now, I wanted to just build a simple to install, dead simple to use, LLM chat with built-in RAG, tooling, data connectors, and privacy-focus all in a single open-source repo and app. 5 Pro. Their performance is not great. I have tested it, and it seems to work but the only thing is that my We would like to show you a description here but the site won’t allow us. Replies are instant, but if try to access using the api (either curl or via another api I am building) with an identical query, the response time is far longer. Each record consists of one or more fields, separated by commas. It depends of course on your hardware as well. CrewAI works with local models downloaded via Ollama or remote models like OpenAI. this means we can simplify our stacks if we're using ollama and the only reason litellm or similar projects were included was for openai proxy. In this tutorial, we will not spend a lot of time explaining the power of AI agents. I would recommend checking it out, it's been fun tinkering with so far. There are a lot of features in the webui to make the user experience more pleasant than using the cli. Feb 20, 2025 路 I will show you how I use RAG (Retrieval-Augmented Generation) and Ollama Deepseek-R1 to build a powerful chatbot backend that can answer customer queries efficiently and accurately tailor to your business policy. It does have some flaws. Some have endorsed us publicly. . These are well proven frameworks Jan 28, 2024 路 *RAG with ChromaDB + Llama Index + Ollama + CSV * curl https://ollama. I have a CSV with values in the first column, going down 10 rows. Parameters: llm (LanguageModelLike) – Language model to use for the agent. Jan 2, 2025 路 Install docker, and learn basic docker commands Follow ollama documentation online to run in a docker container Follow open-webui documentation to run in a docker container and point it at the ollama IP address Install ollama models and chat on open-webui! Any debugging questions you have should be in ollama or open-webui docs or Google! :) I'm working on a project where I'll be using an open-source llm - probably quantized Mistral 7B. I still don't get what it does. 28. Hii, I am trying to develop a data analysis agent, and using langchain CSV agent with local llm mistral through Ollama. I will warn you though, if you decide to use local lm for this, it can be a real hassle to get working (if at all). May 3, 2024 路 Simple wonders of RAG using Ollama, Langchain and ChromaDB Harness the powers of RAG to turbocharge your LLM experience Sep 8, 2023 路 As there is already support for llama. where user will ask question in natural language and llms will wrtie sql query, run it on my database and then give me result in natural language. Are the models downloaded from ollama scanned for malware? We would like to show you a description here but the site won’t allow us. It has native support for a large number of models such as Google’s Gemma, Meta’s Llama 2/3/3. create_csv_agent(llm: LanguageModelLike, path: Union[str, IOBase, List[Union[str, IOBase]]], pandas_kwargs: Optional[dict] = None, **kwargs: Any) → AgentExecutor [source] ¶ Create pandas dataframe agent by loading csv to a dataframe. Subreddit to discuss about locally run large language models and related topics. CrewAI is a framework for orchestrating role-playing, autonomous AI agents. Other specialized agents include SQLChatAgent, Neo4jChatAgent, TableChatAgent (csv, etc). Looking to see if there are other tools that make local LLM runs smoother than what I Shameless plug: I have made my own agent toolkit for the frontend and Nodejs that matches your description: it's called Agent Smith. Contribute to AIAnytime/AI-Agents-from-Scratch-using-Ollama development by creating an account on GitHub. It's the only "agent" that I've tried that seems to work. It works with Ollama and other local servers. Parameters llm Jul 1, 2024 路 Learn how to query structured data with CSV Agents of LangChain and Pandas to get data insights with complete implementation. Get up and running with large language models. I'd personally write a python script to inventory all the Metadata associated with the Plex files, and output an array or dictionary using CSV. As per the requirements for a language model to be compatible with LangChain's CSV and pandas dataframe agents, the language model should be an instance of BaseLanguageModel or a subclass of it. Instead, we will explain how to install and use smolagents library “locally” by using Ollama and Llama 3. Mainly: 1. tl;dr: A new open-source Ollama macOS client that looks like ChatGPT. The downside is that it can be a bit slow to generate responses with the bigger models (but worth it if you want to wait). Could you do one for excel and csv files? Are there and good models that do analytics on files and run locally? Great Has anybody tried to combine this with a rag that reads csv to perform data analysis? 152 votes, 78 comments. Sep 26, 2023 路 I understand you're trying to use the LangChain CSV and pandas dataframe agents with open-source language models, specifically the LLama 2 models. It allows adding documents to the database, resetting the database, and generating context-based responses from the stored documents. 3, DeepSeek-R1, Phi-4, Gemma 2, and other large language models. The problem is schema of database is huge and tables names,column names are not self explanatory. agent_toolkits. Observability, lineage: All multi-agent chats are logged, and lineage of messages is tracked. What is Ollama? Jan 29, 2025 路 Awhile back I wrote about how you can run your own local ChatGPT experience for free using Ollama and OpenWebUI with support for LLMs like DeepSeek R1, Llama3, Microsoft Phi, Mistral and more! With the recent, open source release of DeepSeek R1, it’s also supported to run locally with Ollama too! This article will take you through the steps to do this. Can someone suggest me how can I plot charts using agents. Nov 15, 2024 路 In this blog, we’ll walk through creating an interactive Gradio application that allows users to upload a CSV file and query its data using a conversational AI model powered by LangChain’s create_pandas_dataframe_agent and Ollama's Llama 3. Hi, So I learning to build RAG system with LLaMa 2 and local embeddings. Most of the times two tables need to joined on more than one column and in where We would like to show you a description here but the site won’t allow us. - example-rag-csv-ollama/README. Would you guys have some advice on the best current tools for the following two use cases for local LLMs that I have at home ? 1. I run Ollama on Windows and have an AMD RX 6800. It can define multiple agents and their system prompt, the model they use, the prompt template. Read about source, fine tune, embedding and multimodal models. Its strong performance in coding and reasoning makes it particularly useful for developers and technical users. I am developing a text-to-sql project with llms and sql server. sh | sh ollama OpenAI-compliant Python client API for client-server control Web-Search integration with Chat and Document Q/A Agents for Search, Document Q/A, Python Code, CSV frames (Experimental, best with OpenAI currently) Evaluate performance using reward models Quality maintained with over 1000 unit and integration tests taking over 4 GPU-hours We would like to show you a description here but the site won’t allow us. After install and loading up, the logs say that it is using the GPU. I am a beginner in this field. Just download and use: Download… Jul 7, 2024 路 In preparation for building intelligent AI agents with Crew AI and Ollama, we’ve taken the following steps: installed essential libraries, imported crucial modules for agent creation, data Full-stack framework for building Multi-Agent Systems with memory, knowledge and reasoning. I'd then include that file as part of a RAG integration so the LLM can retrieve it. I want to test a local LLM on my proprietary data. Mar 29, 2024 路 I noticed some similar questions from Nov 2023 about reading a CSV in, but those pertained to analyzing the entire file at once. Its not even close. Dec 23, 2024 路 Discover the different types of Ollama models and how each one can be used for your case. - Tlecomte13/example-rag-csv-ollama Learn how to build a powerful AI agent that runs entirely on your computer using Ollama and Hugging Face's smolagents. 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. Thanks! Ollama Python library. base. Each row is a book and the columns are author (s), genres, publisher (s), release dates, ratings, and then one column is the brief summaries of the books. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. Contribute to ollama/ollama-python development by creating an account on GitHub. How safe are models from ollama? Id like to get started using local LLMs with ollama, however Id like to know about the safety of using ollama models given some reports I have seen about LLMs containing malware. Visual Understanding: Need a way to analyze screenshots, identify buttons, and understand website layouts for interaction. CrewAI What is better than an agent? Multiple agents. agents. May 21, 2025 路 In this tutorial, you’ll learn how to build a local Retrieval-Augmented Generation (RAG) AI agent using Python, leveraging Ollama, LangChain and SingleStore. truewhoa this is HUGE news. Here's a breakdown of the core components: Multi-Agent Architecture: I'm using AutoGen to create a team of specialized AI agents (built on models like Ollama) that collaborate to handle different parts of the task. 42 votes, 36 comments. We would like to show you a description here but the site won’t allow us. AI Agents from Scratch using Ollama Local LLMs. Hi, I'm an engineer who uses ChatGPT Plus and played with early Ollama / LMStudio some months ago, but I stopped short of using agents, and things have moved so fast that I now feel completely out of the loop. 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 want to use the mistral model, but create a lora to act as an assistant that primarily references data I've supplied during training. i tried using a lot of apps etc on windows but failed msierably (at best my models somehow start talking in gibberish) so i just installed openwebui docker container with ollama, downloading codellama as i write this but i would like to know which I've been a bit obsessed with the recent MoA paper and its implementation. Now it can be used directly and Feb 24, 2025 路 DeepSeek-R1 with Ollama provides a powerful, locally-run AI solution for various technical tasks. Companies using it in production, after evaluating CrewAI, Autogen, Langgraph, LangChain, etc. ? Does Ollama quantize the models to make it easier to run them locally? If that's what it Feb 26, 2025 路 Download and running with Llama 3. 37 votes, 11 comments. Nov 7, 2024 路 The create_csv_agent function in LangChain works by chaining several layers of agents under the hood to interpret and execute natural language queries on a CSV file. I plan to add an ollama component as a built-in, which looks like the best use of cycles on this front, but have not had a chance to get to it yet. In this guide, we’ll walk you through the process of setting up and running DeepSeek-R1 locally using Ollama. Dec 9, 2024 路 langchain_experimental. regardless, I was not able to get this to work. 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 :/ The other example I am desperate to examine is a simple agent/task that actually works with ANY local LLM, via Ollama, LMS, or whatever because while all my working tests work perfectly file with any of the GPT LLMs, they all fail miserably with any local LLM, and I have tried dozens of LLMs x Oct 3, 2024 路 What if you could quickly read in any CSV file and have summary statistics provided to you without any further user intervention? Have you tried different agents, or for starters, without? Your model runs on my MacBook M2 with about 30-50s response time. create_csv_agent # langchain_experimental. I am trying to build an agent to answer questions on this csv. From basic lookups like 'what books were published in the last two A community to learn how to implement AI in Flowise. Like can't you already run models locally if you have enough RAM, a good GPU etc. In tools like CrewAI this is implemented directly with the Ollama client, so i was hoping there was a contributed ollama client for AutoGen that implements the new ModelClient pattern. It works really well for the most part though can be glitchy at times. create_csv_agent ¶ langchain_experimental. I will give it few shot examples in the prompt. Anyone here has experience using a Local LLM (thru Ollama or any other service) where you bring an open source LLM, and ask it to explore a CSV file in your local dir? Have you fine tuned the model for your own data analysis needs? Basically, I want to do what GPT Data Analyst does without uploading files there. By leveraging PRAW for data Chat with RTX is VERY fast (it's the only local LLM that uses Nvidia's Tensor cores) We would like to show you a description here but the site won’t allow us. Model: Mixtral via Ollama Method: create a SQLite database that I have created a simple CSV file that contains a simple database, the header of my database is the date, amount, and description Why can I not get a correct result when I ask a very very simple query information about my database? This project uses LangChain to load CSV documents, split them into chunks, store them in a Chroma database, and query this database using a language model. Reddit gives you the best of the internet in one place. Aug 20, 2024 路 KNIME and CrewAI - use an AI-Agent system to scan your CSV files and let Ollama / Llama3 write the SQL code The agents will 'discuss' among themselvesm use the documents provided and come back with a (hopefully) perfect soltion to your task based on the instructions you gave --- Adapted from: A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Jun 29, 2024 路 Step 2: Create the CSV Agent LangChain provides tools to create agents that can interact with CSV files. Jan 27, 2025 路 DeepSeek-R1 is a powerful AI model designed for advanced data exploration and analysis. I have Nvidia 3090 (24gb vRAM) on my PC and I want to implement function calling with ollama as building applications with ollama is easier when using Langchain. If you’re looking for an intro to I'm using Langchain for RAG, and i've been switching between using Ollama and OpenAi embedders. It is really good at dependency issues on linux, even if I already know how to solve the issue. 2B. 2. It allows users to process CSV files, extract insights, and interact with data intelligently. If you're looking to run it locally for better control, security, and efficiency, Ollama offers an excellent platform to manage it. I developed a simple agent which is able to answer simple queries like , how many rows in dataframe, list all transaction realated to Open-WebUI (former ollama-webui) is alright, and provides a lot of things out of the box, like using PDF or Word documents as a context, however I like it less and less because since ollama-webui it accumulated some bloat and the container size is ~2Gb, with quite rapid release cycle hence watchtower has to download ~2Gb every second night to The closest thing you can get to this is by using autogen or something similar like micro-agent. Langchain moved CSV agent to experimental package , so you should import it as from langchain_experimental. path (Union[str, IOBase After iterating on this over the holiday break, I think I've finally got it working! Goal: Present a natural language question that the model translates to a SQL query and / or maps to a set of tools (functions). Llama3-8b is good but often mixes up with multiple tool calls. We will use create_csv_agent to build our agent. 8b for using function calling. ai/install. This is definitely something I want though, so if you end up making a custom component, I would love if you contributed it back to the project. 1, Microsoft’s Phi 3, Mistral. How to build things with Flowise 2. Share Projects This template uses a csv agent with tools (Python REPL) and memory (vectorstore) for interaction (question-answering) with text data. Jan 9, 2024 路 A short tutorial on how to get an LLM to answer questins from your own data by hosting a local open source LLM through Ollama, LangChain and a Vector DB in just a few lines of code. - OllamaRelease/Ollama I'm using ollama to run my models. I have tried llama3-8b and phi3-3. I've noticed a HUGE upgrade in the final output and it seems to really be a great way to harness the power of a team of different LLMs. Each line of the file is a data record. ywxhw kiytam hzqikg usmuue dor cfjmm odvqr phbp nyc ijpqqn