Langchain create csv agent example python. Mar 1, 2023 · Today, we're announcing agent toolkits, a new abstraction that allows developers to create agents designed for a particular use-case (for example, interacting with a relational database or interacting with an OpenAPI spec). base. Agents are responsible for taking user input, processing it, and generating a response. These applications use a technique known as Retrieval Augmented Generation, or RAG. Here's a quick example of how we Aug 28, 2023 · long story short, and thanks to google,python and streamlit, i can upload . ChatOpenAI (View the app) basic_memory. agents import create_pandas_dataframe_agent from langchain. Oct 29, 2023 · There is a lot of human ingenuity involved in getting this agent to work as intended. In order to easily do that, we provide a simple Python REPL to execute commands in. Memory is needed to enable conversation. Ready to support ollama. OPENAI_FUNCTIONS) response = agent. Sep 27, 2023 · 🤖 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 Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. llms import Nov 1, 2023 · agent. Dec 9, 2024 · langchain_experimental. tool_names: contains all tool names. Jun 29, 2024 · LangChain provides tools to create agents that can interact with CSV files. Functions ¶ Building a CSV Assistant with LangChain: MLQ Academy In this video tutorial, we’ll walk through how to use LangChain and OpenAI to create a CSV assistant that allows you to chat with and visualize data with natural language. Once you've done this you can use all of the chain and agent-creating techniques outlined in the SQL tutorial. An artificial intelligence (AI) agent is a system that performs tasks on behalf of a user or another system by designing its own workflow and utilizing available tools. Additionally, we import Bedrock from LangChain for accessing models and boto3 for AWS SDK to Dec 20, 2023 · I am using langchain version '0. Oct 13, 2023 · To create an agent that accesses tools, import the load_tools, initialize_agent methods, and AgentType object from the langchain. 2 days ago · LangChain is a powerful framework that simplifies the development of applications powered by large language models (LLMs). language_models import LanguageModelLike This walkthrough showcases using an agent to implement the ReAct logic. These agents can streamline operations, enhance user experiences, and handle complex processes with minimal human intervention. The function first checks if the pandas package is installed. Use cautiously. Example:- Consider CSV file having data:- question, answer 2+3, 22/7, 9+2, I want to fetch data from CSV file (database in your case), then use PythonREPLTool to process data and save it back to the CSV file. Each row of the CSV file is translated to one document. Stay ahead with this up-to-the-minute resource and start your LLM development journey now. agents import AgentExecutor, create_tool_calling_agent from langchain_core. Jan 20, 2025 · Why LangChain? LangChain is a powerful framework for building applications that leverage language models. Jul 1, 2024 · CSV Agent of LangChain uses CSV (Comma-Separated Values) format, which is a simple file format for storing tabular data. New to LangChain or LLM app development in general? Read this material to quickly get up and running building your first applications. We will first create it WITHOUT memory, but we will then show how to add memory in. from __future__ import annotations from io import IOBase from typing import TYPE_CHECKING, Any, List, Optional, Union from langchain_experimental. A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. 1. 构建代理 LangChain 支持创建 智能体，即使用 大型语言模型 作为推理引擎来决定采取哪些行动以及执行行动所需的输入。执行行动后，可以将结果反馈给大型语言模型，以判断是否需要更多行动，或者是否可以结束。这通常通过 工具调用 实现。 在本教程中，我们将构建一个可以与搜索引擎交互的 In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. LangChain Python API Reference langchain-cohere: 0. Agent Deep dive To understand primarily the first two aspects of agent design, I took a deep dive into Langchain’s CSV Agent that lets you ask natural language query on the data stored in your csv file. Each project is presented in a Jupyter notebook and showcases various functionalities such as creating simple chains, using tools, querying CSV files, and interacting with SQL databases. g whats the best performing month, can you predict future sales based on data. Pass the tool you want an agent to access in a list to the load_tools () method. One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. Sep 27, 2023 · The create_csv_agent() function in the LangChain codebase is used to create a CSV agent by loading data into a pandas DataFrame and using a pandas agent. Aug 6, 2023 · After this configuration, you can run queries that refer to 'df', and the tool will recognize it as your dataframe. In this tutorial, we will use the LangChain Python package to build an AI agent that uses its custom tools to return a URL directing to NASA's Astronomy Picture of the Day. We hope to continue developing different toolkits that can enable agents to do amazing feats. It can read and write data from CSV files and perform primary operations on the data. Agents select and use Tools and Toolkits for actions. We will use create_csv_agent to build our agent. They allow a LLM to access Google search, perform complex calculations with Python, and even make SQL queries. This tutorial, published following the release of LangChain 0. Pandas Dataframe This notebook shows how to use agents to interact with a Pandas DataFrame. Returns: An AgentExecutor with the specified agent_type agent and access to a PythonAstREPLTool with the loaded DataFrame (s) and any user-provided extra_tools. 65 ¶ langchain_experimental. csv files and use chatGPT to talk with them: Oct 17, 2024 · This project demonstrates the integration of Google's Gemini AI model with LangChain framework, specifically focusing on CSV data analysis using agents. May 21, 2025 · In this tutorial, you’ll learn how to build a local Retrieval-Augmented Generation (RAG) AI agent using Python, leveraging Ollama, LangChain and SingleStore. create_python_agent(llm: BaseLanguageModel, tool: PythonREPLTool, agent_type: AgentType = AgentType. The CSV agent then uses tools to find solutions to your questions and generates an appropriate response with the help of a LLM. For those who might not be familiar, an agent is is a software program that can access and use a large language model (LLM). May 7, 2025 · Learn how to build agentic systems using Python and LangChain. Here's a quick example of how we May 17, 2023 · Setting up the agent is fairly straightforward as we're going to be using the create_pandas_dataframe_agent that comes with langchain. langchain_experimental. These are applications that can answer questions about specific source information. py: Simple app using StreamlitChatMessageHistory for LLM conversation memory (View the app) mrkl_demo. agents module. However, if you want to create an agent that can interact with a pandas dataframe, you can use the create_pandas_dataframe_agent function from the langchain. Aug 28, 2023 · long story short, and thanks to google,python and streamlit, i can upload . Analyze and interact with data files. from langchain. May 14, 2023 · Disclaimer: After conducting further research upon completing this article, I found no evidence that using Langchain’s create_csv_agent tool can prevent data leakage. Toolkits are supported Jan 11, 2024 · Discover the ultimate guide to LangChain agents. Load the LLM First, let's load the language model we're going to Welcome to the LangChain Sample Projects repository! This repository contains four example projects demonstrating different capabilities of the LangChain library. py: An agent that replicates the MRKL demo (View the app) minimal_agent. note Aug 5, 2024 · Whether you are developing a conversational agent, an automated research assistant, or a complex data analysis tool, LangChain agents offer a robust solution to enhance your project’s capabilities. agent_toolkits module of LangChain version '0. You can order the results by a relevant column to return the most May 17, 2023 · Setting up the agent is fairly straightforward as we're going to be using the create_pandas_dataframe_agent that comes with langchain. In this notebook we'll explore agents and how to use them in LangChain. It’s particularly useful for creating modular and reusable components, such as agents, that can: Execute Python code. For this example, we will create custom tools from functions. The available agent types are action agents or plan-and-execute agents. For example, the CSV Agent can be used to load data from CSV files and perform queries, while the Pandas Agent can be used to load data from Pandas data frames and process user queries. ") However, I want to make the chatbot more advanced by enabling it to remember previous conversations. How to: pass in callbacks at runtime How to: attach callbacks to a module How to: pass callbacks into a module constructor How to: create custom callback handlers How to: use callbacks in Dec 9, 2024 · langchain_experimental 0. The memory allows the model to handle sequential conversations, keeping track of prior exchanges to ensure the system responds appropriately. Dec 9, 2024 · from datetime import datetime from io import IOBase from typing import List, Optional, Union from langchain. This agent is more focused on working with CSV files specifically. Agents determine which actions to take and in what order. py: Simple streaming app with langchain. Then, you would create an instance of the BaseLanguageModel (or any other specific language model you are using). The prompt must have input keys: tools: contains descriptions and arguments for each tool. prompt (BasePromptTemplate) – The prompt to use. 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. run(user_message). Learn more with Twilio. openai Parameters: llm (BaseLanguageModel) – LLM to use as the agent. Dec 27, 2023 · That‘s where LangChain comes in handy. agent_toolkits. pandas. 350' is designed to create a CSV agent by loading the data into a pandas DataFrame and using a pandas agent. It provides essential building blocks like chains, agents, and memory components that enable developers to create sophisticated AI workflows beyond simple prompt-response interactions. The function primarily focuses on creating a CSV agent by loading data into a pandas DataFrame and Create tools First, we need to create some tool to call. I am using a sample small csv file with 101 rows to test create_csv_agent. Jun 17, 2025 · Build an Agent LangChain supports the creation of agents, or systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. We'll start by installing the prerequisite libraries that we'll be using in this example. The implementation allows for interactive chat-based analysis of CSV data using Gemini's advanced language capabilities. This repository contains reference implementations of various LangChain agents as Streamlit apps including: basic_streaming. py: A This guide provides explanations of the key concepts behind the LangChain framework and AI applications more broadly. In this notebook we will show how those parameters map to the LangGraph react agent executor using the create_react_agent prebuilt helper method. This is often achieved via tool-calling. language_models import LanguageModelLike from langchain_core. Dec 22, 2023 · I am attempting to write a simple script to provide CSV data analysis to a user. python. LangChain agents (the AgentExecutor in particular) have multiple configuration parameters. Here’s an example: Jun 23, 2024 · LangChain is a tool for more easily creating AI agents that can autonomously perform tasks. Explore agents, tools, memory, and real-world AI applications in this practical guide. For more information on creating custom tools, please see this guide. Commit to Help I commit to help with one of those options 👆 Example Code agent = create_csv_agent (llm, file_paths, verbose=True, agent_type=AgentType. kwargs (Any) – Additional kwargs to pass to langchain_experimental. output_parser (AgentOutputParser | None) – AgentOutputParser for parse the LLM output. For example, you can use LangChain agents to access information on the web, to interact with CSV files, Pandas DataFrames, SQL databases, and so on. 0. I am using the CSV agent which is essentially a wrapper for the Pandas Dataframe agent, both of which are included in langchain-experimental. Unless the user specifies in his question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. Aug 10, 2023 · You can create two or more agents and use them as tools with initialize_agent (). Jun 17, 2025 · In this tutorial we will build an agent that can interact with a search engine. ). 0 in January 2024, is your key to creating your first agent with Python. agent_toolkits import create_csv_agent Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. 4csv_agent # Functions Jul 11, 2023 · In this tutorial, you will learn how to query LangChain Agents in Python with an OpenAPI Agent, CSV Agent, and Pandas Dataframe Agent. You will be able to ask this agent questions, watch it call the search tool, and have conversations with it. Here's a quick example of how Dec 22, 2023 · I am using the CSV agent which is essentially a wrapper for the Pandas Dataframe agent, both of which are included in langchain-experimental. In Chains, a sequence of actions is hardcoded. The file has the column Customer with 101 unique names from Cust1 to Cust101. The goal of this python app is to incorporate Azure OpenAI GPT4 with Langchain CSV and Pandas agents to allow a user to query the CSV and get answers in in text, linge graphs or bar charts. Here we focus on how to move from legacy LangChain agents to more flexible LangGraph agents. By combining LangChain’s capabilities with OpenAI’s GPT-4, we can Feb 16, 2025 · Agents in LangChain are advanced components that enable AI models to decide when and how to use tools dynamically. SQL Using SQL to interact with CSV data is the recommended approach because it is easier to limit permissions and sanitize queries than with arbitrary Python. The application employs Streamlit to create the graphical user interface (GUI) and utilizes Langchain to interact with the LLM. CSV 代理 这个笔记本展示了如何使用代理与 csv 进行交互。主要优化了问答功能。 注意: 这个代理在内部调用了 Pandas DataFrame 代理，而 Pandas DataFrame 代理又调用了 Python 代理，后者执行 LLM 生成的 Python 代码 - 如果 LLM 生成的 Python 代码有害的话，这可能会造成问题。请谨慎使用。 这个模板使用一个csv代理，通过工具（Python REPL）和内存（vectorstore）与文本数据进行交互（问答）。 kwargs (Any) – Additional kwargs to pass to langchain_experimental. Apr 26, 2024 · The python LangChain framework allows you to develop applications integrating large language models (LLMs). 350'. invoke ("show graph for each year sales") answer = response ['output'] print (answer) Sep 27, 2024 · Create an app that communicates with LLM APIs. In this tutorial, we'll be using the pandas DataFrame Agent, which can be created using create_pandas_dataframe_agent() from langchain. Aug 25, 2024 · AgentExecutor and create_react_agent : Classes and functions used to create and manage agents in LangChain. I‘ll explain what LangChain is, the CSV format, and provide step-by-step examples of loading CSV data into a project. For this, I can create two agents as follows:- Step 0:- Import dependencies from langchain. 5rc1 Jan 7, 2024 · In this post, I’ll guide you through building an AI Agent from scratch using OpenAI models and Python, with a particular focus on the Langchain library. You can access them via AgentType() from langchain. embeddings. Jul 1, 2024 · Learn how to query structured data with CSV Agents of LangChain and Pandas to get data insights with complete implementation. (Update when i a This template creates an agent that uses Google Gemini function calling to communicate its decisions on what actions to take. Agents in LangChain are components that allow you to interact with third-party tools via natural language. In this example, we will use OpenAI Tool Calling to create this agent. If not, it raises an ImportError. Oct 10, 2023 · Learn about the essential components of LangChain — agents, models, chunks and chains — and how to harness the power of LangChain in Python. LangChain implements a CSV Loader that will load CSV files into a sequence of Document objects. It is equipped with a generic search tool. The code snippet below represents a fully functional agent that uses an LLM to decide which tools to use. Agent. Return type: AgentExecutor Example Jul 23, 2025 · 6. In this step-by-step tutorial, you'll leverage LLMs to build your own retrieval-augmented generation (RAG) chatbot using synthetic data with LangChain and Neo4j. tools_renderer (Callable[[list[BaseTool]], str]) – This controls how the tools are Large language models (LLMs) have taken the world by storm, demonstrating unprecedented capabilities in natural language tasks. This is generally the most reliable way to create agents. Therefore, it is May 12, 2023 · Agents: Agents in LangChain interact with user inputs and process them using different models. Each line of the file is a data record. prompts import ChatPromptTemplate system_message = """ Given an input question, create a syntactically correct {dialect} query to run to help find the answer. An AgentExecutor with the specified agent_type agent and access to a PythonAstREPLTool with the loaded DataFrame (s) and any user-provided extra_tools. 3. In this Langchain video, we take a look at how you can use CSV agents and the OpenAI API to talk directly to a CSV file. 2. NOTE: Since langchain migrated to v0. LangChain Python API Reference langchain-experimental: 0. How to: use legacy LangChain Agents (AgentExecutor) How to: migrate from legacy LangChain agents to LangGraph Callbacks Callbacks allow you to hook into the various stages of your LLM application's execution. This is especially useful for creating conversational agents that need context across multiple inputs. agent_scratchpad: contains previous agent actions and tool outputs as a string. ZERO_SHOT_REACT_DESCRIPTION, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = False, prefix: str = 'You are an agent designed to write and execute python code to An examples code to make langchain agents without openai API key (Google Gemini), Completely free unlimited and open source, run it yourself on website. Once you've done this you can use all of the chain and agent-creating techniques outlined in the SQL use case guide. llms import OpenAI import pandas as pd Getting down with the code SQL Using SQL to interact with CSV data is the recommended approach because it is easier to limit permissions and sanitize queries than with arbitrary Python. After that, you would call the create_csv_agent() function with the language model instance, the path to your CSV Nov 17, 2023 · Import all the necessary packages into your application. See Prompt section below for more. Overview of AI agents One of the most common Sometimes, for complex calculations, rather than have an LLM generate the answer directly, it can be better to have the LLM generate code to calculate the answer, and then run that code to get the answer. from langchain_experimental. . By combining LangChain’s capabilities with OpenAI’s GPT-4, we can Jan 20, 2025 · Why LangChain? LangChain is a powerful framework for building applications that leverage language models. This notebook shows how to use agents to interact with a csv. 3 you should upgrade langchain_openai and Agents 🤖 Agents are like "tools" for LLMs. After initializing the the LLM and the agent (the csv agent is initialized with a csv file containing data from an online retailer), I run the agent with agent. Perform reasoning and decision-making tasks using tools. Most SQL databases make it easy to load a CSV file in as a table (DuckDB, SQLite, etc. While still a bit buggy, this is a pretty cool feature to implement in a Jan 26, 2024 · 🤖 Based on the context provided, it seems like the create_csv_agent function in LangChain does not directly handle the external_tools parameter. After executing actions, the results can be fed back into the LLM to determine whether more actions are needed, or whether it is okay to finish. Here's an example of how you can do it: Jul 21, 2023 · You can load them via load_tools() from langchain. We will also demonstrate how to use few-shot prompting in this context to improve performance. How LangChain Works Apr 13, 2023 · I've a folder with multiple csv files, I'm trying to figure out a way to load them all into langchain and ask questions over all of them. A key distinction between chains May 5, 2024 · In this section, we import the necessary modules to create and interact with the LangChain CSV Agent. create_pandas_dataframe_agent (). agents ¶ Agent is a class that uses an LLM to choose a sequence of actions to take. messages import BaseMessage, HumanMessage, SystemMessage from langchain_core. Nov 7, 2024 · 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. Here's what I have so far. agent import AgentExecutor from langchain_core. How to load CSVs A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. The function signature does not include an external_tools parameter, and the function's body does not reference or use external_tools in any way. The Oct 17, 2023 · In this article, we’ll walk through an example of how you can use Python and the Langchain library to create a simple, yet powerful, tool for processing data from a CSV file based on user queries. language_models import BaseLanguageModel from langchain_core. 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). It is mostly optimized for question answering. Build an Extraction Chain In this tutorial, we will use tool-calling features of chat models to extract structured information from unstructured text. Instead of relying on predefined scripts, agents analyze user queries and choose Custom agent This notebook goes through how to create your own custom agent. 3 days ago · Learn how to use the LangChain ecosystem to build, test, deploy, monitor, and visualize complex agentic workflows. DataStax walks you through a build with the LangChain Python framework step by step. In this tutorial we This template uses a csv agent with tools (Python REPL) and memory (vectorstore) for interaction (question-answering) with text data. Oct 1, 2023 · Does Langchain's create_csv_agent and create_pandas_dataframe_agent functions work with non-OpenAl LLM models too like Llama 2 and Vicuna? The only example I have seen in the documentation (in the links below) are only using OpenAI API. Each record consists of one or more fields, separated by commas. ZERO_SHOT_REACT_DESCRIPTION, callback_manager: BaseCallbackManager | None = None, verbose: bool = False, prefix: str = 'You are an agent designed to write and execute python code to answer from __future__ import annotations from io import IOBase from typing import TYPE_CHECKING, Any, List, Optional, Union from langchain_experimental. 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. run("chat sentence about csv, e. Then, it checks the type of the path parameter. Building these AI agents can be difficult and this is the reason for why LangChain exists. base import ( create_pandas_dataframe_agent, ) if TYPE_CHECKING: from langchain. It can recover from errors by running a generated query, catching the traceback and regenerating it This notebook showcases an agent designed to write and execute Python code to answer a question. In this comprehensive guide, you‘ll learn how LangChain provides a straightforward way to import CSV files using its built-in CSV loader. chat_models. tools (Sequence[BaseTool]) – Tools this agent has access to. Memory Management LangChain supports memory management, allowing the LLM to "remember" context from previous interactions. prompts import Dec 9, 2024 · from __future__ import annotations from io import IOBase from typing import TYPE_CHECKING, Any, List, Optional, Union from langchain_experimental. Tool : A class from LangChain that represents a tool the agent can use. CSV Agent # This notebook shows how to use agents to interact with a csv. agents. language_models import LanguageModelLike Mar 9, 2024 · I used the GitHub search to find a similar question and didn't find it. Dec 20, 2023 · The create_csv_agent function in the langchain_experimental. gqbj dqha mkamoo nim qwvgy osxrd ndpb foglqgi qean lmix