Langchain pandas agent example 9 Photo by Hitesh Choudhary on Unsplash Building the Agent. 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. ) I am trying to use local model Vicuna 13b v1. Review full docs for full user-facing oauth developer support. Provide details and share your research! But avoid . py: Chatbot to ask questions about a pandas DF (Note: uses PythonAstREPLTool which is vulnerable to arbitrary code execution, see langchain #7700) Apps feature LangChain 🤝 Streamlit integrations such as the Callback integration and StreamlitChatMessageHistory . Details. You signed in with another tab or window. I am attempting to write a simple script to provide CSV data analysis to a user. In this article, You signed in with another tab or window. csv" ) chat_pandas_df. For those who might not be familiar, an agent is is a software program that can access and use a large language model (LLM). Example Code. . Reload to refresh your session. manager import The Agent class in the PandasAI library is a powerful tool for interacting with your data using natural language. This notebook shows how to use agents to interact with a pandas dataframe. agent_toolkits. agents import Tool from langchain_experimental. 5 kwargs (Any) – Additional kwargs to pass to langchain_experimental. csv' with the actual path to your CSV file and adjust the pandas_kwargs dictionary according to your needs. This function enables the agent to perform complex data manipulation and analysis tasks by leveraging the powerful pandas library. E2B's Data Analysis sandbox allows for safe code execution in a sandboxed environment, which is ideal for building tools such as code interpreters or advanced data analysis. We can interact with this agent using natural language and ask it to perform To start using LangChain, import the necessary libraries. 0 Please replace 'path_to_your_file. llms import OpenAI agent = create_csv_agent(OpenAI(temperature=0), I am trying to add memory to create_pandas_dataframe_agent to perform post processing on a model that I trained using Langchain. agent_toolkits import create_pandas_dataframe_agent from Context. Check for Known Issues: There might be known issues or bugs related to the ChatOllama model when used with the create_pandas_dataframe_agent function. agent_toolkits import create_python_agent from langchain. run(user_message) . However, in my pandas_dataframe_agent, I am using 'df' not a vector store. savefig() is called after Yes, LangChain has built-in support for querying Pandas DataFrames using natural language. For anything else, say you do not know. You can adjust it to keep the context of the conversation. xlsx", sheet_name = "Jun24", Hi - I am using the langchain pandas dataframe agent. agents import load_tools, AgentExecutor, Issue you'd like to raise. I'm working with a DataFrame that contains enterprise data from our employees, and the main objective is to retrieve information from our employees using the agent Yes, LangChain has built-in support for querying Pandas DataFrames using natural language. The Pandas DataFrame Agent is the agent responsible for interacting with DataFrames. manager import CallbackManager from langchain. create_pandas_dataframe_agent (llm: Runnable [PromptValue | str Construct a Pandas agent from an LLM and dataframe(s). Rather, they have their own independent scratchpads, and then their final responses are appended to a LangChain makes it easier to build agents thanks to lightweight libraries which provide our LLM with the ReAct-based prompt template that makes the agent capable of both reasoning and acting. 5-turbo", temperature = 0) # Define your prompt template TEMPLATE = """You Please note that this is a simplified example and the actual implementation may vary based on the specific requirements of your application. For example: df has columns department, salary, manager_name, emp_name. agents import create_csv_agent from langchain. Unfortunately, I couldn't find specific information on this, but you might want to check the LangChain repository for any updates or discussions. import pandas as pd from langchain. prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Respond to the human as helpfully and accurately as possible. The last thing we need to do is to initialize the agent. get_input_schema. 5. I searched the LangChain documentation with the integrated search. I wanted to let you know that we are marking this issue as stale. Then, you can use the format method of the PromptTemplate object to generate the 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). Memory in Agent. This is generally the most reliable way to create agents. Agent is a class that uses an LLM to choose a sequence of actions to take. agents import create_react_agent from langchain. read_csv("titanic. This tool uses the pandaslibrary to manipulate the data and the ChatOpenAIagent to run the code. In this example, we will use OpenAI Tool Calling to create this agent. Next, define the tools that your agent will use. The python LangChain framework allows you to develop applications integrating large language models (LLMs). 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 In this article, we will explore the collaboration of LangChain, GPT-4, and Pandas to create an interactive DataFrame in the form of an agent. llms import OpenAI import pandas as pd Getting down with the code. agent_toolkits module of LangChain version '0. Tools can be anything from APIs to local functions. 350' is designed to create a CSV agent by loading the data into a pandas Example Code. The create_csv_agent() function will return an AgentExecutor instance that you can use in your chain. The outputs of the CSV agent and Pandas Dataframe agents are similar, which makes sense because both agents call the Pandas DataFrame agent under the hood, which in turn calls the Python agent. 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; In order to add a memory to an agent we are going to perform the following steps: We are going to create an LLMChain This agent relies on access to a python repl tool which can execute arbitrary code. By leveraging large language models (LLMs), it allows users to ask questions and receive answers in a conversational manner. For example, if they ask for Alice West, split it I have a Datarame and want to query it using langchain create_pandas_dataframe_agent. Jump to Example Using OAuth Access Token to see a short example how to set up Zapier for user-facing situations. from langchain_experimental. Iterate through the smaller DataFrames, running the CSV Agent on each chunk. However I want to pass one dynamic variable with the prompt. from langchain_community. savefig() should be called before plt. create() got an unexpected keyword argument 'tools') Checked other resources I added a very descriptive title to this question. also, show more code on how are you But as you can see, not even on the official Langchain website is there memory for a pandas agent or a CSV agent (which uses the create_pandas_agent function). This example goes over how to use the Zapier integration with a SimpleSequentialChain, then an Sep 11, 2024 · Example #1: LangChain’s create_pandas_dataframe_agent In this example, we’re going to try a more advanced example that uses toolkits(a suit of tools) from LangChain to create a tool that can manipulate and analyze tabular data by actually running Python code. agents import create_pandas_dataframe_agent # Example usage of the function agent = create_pandas_dataframe_agent(dataframe) agent. Example #1: LangChain’s create_pandas_dataframe_agent In this example, we’re going to try a more advanced example that uses toolkits(a suit of tools) from LangChain to create a tool that can manipulate and analyze tabular data by actually running Python code. This section delves into how to effectively utilize the Agent class with Pandas DataFrames, enhancing your data analysis In this example, the dataframe df is created by another agent (e. chat_models import ChatOpenAI from langchain. linear_model import LogisticRegression import pandas as pd import numpy as np from sklearn , model_name='gpt-3. agents. base. streaming_stdout import StreamingStdOutCallbackHandler # CSV file path csv_file = "bike_sales_100k. We will use the LangChain wrap Wondering about Pandas Query Engine in Langchain; How can we use create_pandas_dataframe_agent in Multi-agent Collaboration? Create_pandas_dataframe_agent as a tool; To incorporate your Python_Pandas agent into a LangGraph workflow, you can follow the example provided below: How can I prevent the agent from creating a sample and make it use the real data instead? from langchain_experimental. However, it currently runs for approximately 10 seconds before stopping. The ask and process methods used in this example are placeholders and may not exist in the actual LangChain framework. This method is responsible for clearing the memory contents. output_parsers import StrOutputParser from langchain_core. agent_toolkits import create_csv_agent data_filename Invoking: `python_repl_ast` with `{'query': "import pandas as pd\n\n# Assuming df is already defined as per the provided dataframe\n# Let's create a This modification uses the create_pandas_dataframe_agent to create an agent that can handle the DataFrame and then invokes it with the question. llms import LlamaCpp from langchain. Based on my understanding, the issue is about a pandas dataframe agent in the Langchain library returning incorrect results even though the action input is correct. The available agent types are action agents or plan-and-execute agents. Alternatively (e. Yes, it is possible to use a pandas DataFrame with the E2B Code Interpreter and LangGraph to execute queries. I am using the following code at the moment. Parameters: llm (BaseLanguageModel) – Language model to use for the agent. Here is an example: import os import pandas as pd from langchain. agents import AgentExecutor import matplotlib. Here’s the setup that is used for this example. , by reading a CSV file), and then it is passed to the create_pandas_dataframe_agent function to create a new agent that can work with this dataframe . run() Conclusion By staying updated with the official documentation and adjusting your code accordingly, you can effectively manage deprecations and ensure your code remains functional. Great to see you again and thanks for reaching out with your question! To incorporate a prompt template into the create_csv_agent function in the LangChain framework, you would need to modify the function to accept the prompt template as an argument. 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. ). 0. This notebook goes over adding memory to an Agent. agents import initialize_agent from langchain Nov 17, 2023 · This quick start focus mostly on the server-side use case for brevity. Here's an example: About LangChain. I'm working with a langchain pandas agent using GPT-4 from Azure OpenAI as the then include a str. prompts import ChatPromptTemplate, MessagesPlaceholder import pandas as pd df = pd. import pandas as pd from langchain_experimental. create_csv_agent: Based on the information you've provided and the context from the LangChain repository, it seems like you're trying to conditionally create or update a Pandas DataFrame agent using create_pandas_dataframe_agent in the LangChain framework, especially when dealing with dynamically created DataFrames from functions that agents may or may not call. This notebook shows how to use agents to interact with a Pandas DataFrame. run("Who are Oct 2, 2023 · If you want to apply the Tree-Of-Thought (ToT) change the format prompt to:. 3 days ago · In this guide, we'll learn how to create a simple prompt template that provides the model with example inputs and outputs when generating. Output parsers accept a string or BaseMessage as input and can return an arbitrary type. Let's create a sequence of steps that, given a Applications of LangChain include documentation, creating agents, and Chatbots. langchain_pandas. It provides a comprehensive set of tools for working with structured data, making it a versatile option for tasks such LLM Agent: Build an agent that leverages a modified version of the ReAct framework to do chain-of-thought reasoning. kwargs (Any) – Additional kwargs to pass to langchain_experimental. llms import OpenAI from langchain. The Retriever class in LangChain is designed to return documents given a text query and does not need to store documents, making it more general than a vector store. For example, I ask it about the longest name in a dataframe containing a column named "name" and it returns the following: from langchain. 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. This is a simple example, but in the case of the actual dataframe that I have (which contains a lot of textual data), the agent runs for around one minute before I 1 day ago · Azure Container Apps dynamic sessions provides a secure and scalable way to run a Python code interpreter in Hyper-V isolated sandboxes. This notebook goes over how to load data from a xorbits. This agent is more And also I like to build 'Define the Query Generator' for my pandas_dataframe agent as well. After this configuration, import pandas as pd from langchain. You signed out in another tab or window. Hello @nithinreddyyyyyy,. Providing the LLM with a few such examples is called few-shotting, and is a simple yet powerful way to guide generation and in some cases drastically improve model performance. agent_toolkits module. Nov 17, 2023 · from langchain. This allows the react agent to use the CSVAgent when it needs to perform CSV-related tasks. agents import create_pandas_dataframe_agent from langchain. An example of this could be: p_agent. The file extension determines the format in which the file will be saved. My company is preparing to integrate this file into a live web application. Python agent - an agent capable of producing and executing Python code. In this example, CSVAgent is assumed to be a BaseTool that you have implemented. Agents select and from langchain_openai import ChatOpenAI from langchain_experimental. For the Pandas Dataframe Agent# This notebook shows how to use agents to interact with a pandas dataframe. __call__ expects a single input dictionary with all the inputs. jpg, . I am trying to in corporate 'create_question_rewriter' with pandas_dataframe_agent. You can use the create_pandas_dataframe_agent function to construct an agent that interacts with Pandas DataFrames. You would need to replace them with the appropriate methods for querying the conversational agent and 🤖. I have tried the code in these Stack Overflow posts: How to add conversational memory to pandas toolkit agent? add memory to create_pandas_dataframe_agent in Langchain Issues with CSV agent and Pandas agent ( Completions. Agents select and Memory is a crucial aspect of LangChain that allows for the persistence of state between calls of a chain or agent. Now we will use the LangChain library to create a DataFrame agent which can answer natural language questions based off of the CSV data. Here is an example: However, upon reviewing the source code, I believe this could also be applied to the CSV agent. This means they support invoke, ainvoke, stream, astream, batch, abatch, astream_log calls. Example:- Consider CSV file from langchain. 2, ' "Wins"': 97} Hi, @ayush-1506!I'm Dosu, and I'm here to help the LangChain team manage their backlog. It provides a unified interface to create agents based on different language models such as OpenAI. base import (create_pandas_dataframe_agent,) from langchain_openai import Jul 20, 2023 · System Info I am using the below code to make an agent that decides, upon the question, whether to use semantic search or pandas agent to answer the question about a dataset of employees Who can help? No response Information The official 3 days ago · Create csv agent with the specified language model. LangChain has from langchain_openai import ChatOpenAI from langchain_experimental. read_csv('titanic. structured_chat. pdf, etc. LangChain is an advanced framework that utilizes language models to handle complex natural language processing tasks. csv") llm = ChatOpenAI (model = "gpt-3. prompts import ChatPromptTemplate, MessagesPlaceholder from langchain. You will need to import initialize_agent from langchain. So I need to pass the department name as variable Yes, it is possible to apply the concept of MultiQueryRetriever to a pandas DataFrame instead of a vector database. llms import OpenAI import pandas as pd df = pd. number_of_head_rows (int) – Number of rows to display in the prompt for sample data Jul 12, 2023 · 1- To maintain contextual memory during the conversation, you can modify the clear method in the create_pandas_dataframe_agent class. Jan 1, 2009 · SQL数据库代理 本笔记本展示了一个与sql数据库交互的代理。该代理基于SQLDatabaseChain构建，并旨在回答有关数据库的更一般的问题，并从错误中恢复。 请注意，由于该代理正在积极开发中，所有答案可能不正确。此 Sep 12, 2023 · Expected behavior. Agents in LangChain are components that allow you to interact with third-party tools via natural language. See the Azure Container App 3 days ago · LCEL . tools import PythonAstREPLTool from langchain. From your description, it seems like you're expecting the test_tool to be included in the prompt when you run the agent. Now let's try hooking it up to an LLM. In this example, multiple agents are connected, but compared to above they do NOT share a shared scratchpad. df1 = pd. agent = initialize_agent(tools, llm, agent="zero-shot For example: Thought: Hmm, this looks like a pandas dataframe. llms import OpenAI import pandas as pd We need to import the libraries we installed, note that we are also importing OpenAI. The prompt is generated based on You can create two or more agents and use them as tools with initialize_agent(). This flexibility allows it to be adapted to different data sources, including pandas DataFrames . 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 In this example, custom_tool_func is the function that implements your custom tool. 🤖. png' with the actual path where you want to save the file. Security Notice: This agent relies on access to a python repl tool which I am trying to use Langchain for structured data using these steps from the official document. read_excel ("niftywtsjun24. langchain python agent react differently, for one prompt, _sha3 dis packaging tracemalloc _sha512 distro pandas tty _signal distutils pathlib turtle _sitebuiltins doctest patsy turtledemo _socket dotenv pdb types _sqlite3 email pickle typing _sre encodings pickletools typing_extensions _ssl ensurepip pip typing how to read Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. For a list of all Groq models, visit this link. MULTI_DF_PREFIX = """ You are working with {num_dfs} pandas dataframes in Python named df1, df2, etc. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. Example:. LangChain’s Pandas Agent enables users to harness the power of LLMs to perform data processing and analysis with Pandas. prompts import To handle multiple CSVs (or dataframes) we just need to pass multiple dataframes to our Python tool. import pandas as pd from langchain_openai import Here's a quick example of how we might do this with SQLite: import pandas as pd from langchain_core. It just displays the answer and output variable says "as shown in the observation above". Agent. The create_pandas_dataframe_agent function constructs a Pandas agent from a language model and DataFrame(s), allowing for the execution of arbitrary code in a sandboxed environment . Output parsers implement the Runnable interface, the basic building block of the LangChain Expression Language (LCEL). Whether you’re a seasoned Take advantage of the LangChain create_pandas_dataframe_agent API to use Vertex AI Generative AI in Google Cloud to answer English-language questions about Pandas dataframes. The max_execution_time is set to 1, indicating that the query should run for one second before stopping. We will first create it kwargs (Any) – Additional kwargs to pass to langchain_experimental. It enables developers to create sophisticated conversational agents that can understand and respond to user queries by dynamically generating SQL queries and other data manipulations. In the given Field from langchain_experimental. agent_toolkits import create_pandas_dataframe_agent from langchain. png, . I am using the CSV agent which is essentially a wrapper for the Pandas Dataframe agent, both of which are included in langchain-experimental. You have access to the following tools: No response Information The official example notebooks/scripts My own modified scripts Related Components LLMs/Chat Models Embedding Models Prompts from langchain_experimental. Link for the query For example, a create_pandas_dataframe_agent might need access to data retrieval and processing tools. About Dosu This response is meant to be useful and save you time. However, the extra_tools argument in the create_pandas_dataframe_agent() function is used to extend the base tools used by the agent, not to modify the prompt. Like to have a sample code. Please note that plt. The CSV agent from langchain_openai import ChatOpenAI from langchain_experimental. __call__ is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas Chain. I should check its shape import boto3 import json import os import sys from langchain. from_function(), and then you add this tool to the list of tools that you provide to the agent when you initialize it. Our Pandas Dataframe Agent# This notebook shows how to use agents to interact with a pandas dataframe. Check out the LangChain documentation on pandas DataFrame Agent. from langchain. This allows your agents to run potentially untrusted code in a secure environment. You can access them via AgentType() from langchain. The prefix and suffix are used to construct the prompt that is sent to the Here is I am using the CSV agent which is essentially a wrapper for the Pandas Dataframe agent, both of which are included in langchain-experimental. It takes a natural language query and interprets it into data manipulation commands that can be executed on a from langchain_experimental. (the same scripts work well with gpt3. In this example, the prefix includes the previous question and answer, followed by a newline character (\n). Collect the results and aggregate them as per your requirements. The create_csv_agent() function in the LangChain codebase is used to create a CSV agent by loading data into a pandas DataFrame Convenience method for executing chain. Finally, you In this tutorial, we'll be using the pandas DataFrame Agent, which can be created using create_pandas_dataframe_agent() from langchain. This notebook goes through how to create your own custom agent. You should use the tools below to answer the question posed of you: 1) Only answer questions related to the dataframes. Once plt. When you create the react agent, you include the CSVAgent in the tools sequence. create_pandas_dataframe_agent function in LangChain is designed to enable large language models (LLMs) Example Applications: - Research Projects: import os import json import pandas as pd from typing import Dict from langchain_core. df = pd . agents import AgentExecutor from langchain_community. What is Gradio ? Gradio is an open-source Python library that allows developers and researchers to quickly create customizable UIs for their machine-learning models, without requiring any web development experience. The CSVAgent should be able to handle CSV-related tasks. show(). create_pandas_dataframe_agent(). pandas. The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package). agents import create_csv_agent from langchain_ollama import OllamaLLM from langchain. llms import OpenAI Next, we will be reading in data from a CSV file to create a DataFrame. agents along with any tools you plan to use: from langchain. agents import create_pandas_dataframe_agent from langchain. read_csv ( "/content/package-manifest. LangChain: Pros: import pandas as pd from langchain. 5-turbo' ) # In[74]: from langchain. agents # Agent is a class that uses an LLM to choose a sequence of actions to take. However, when the model can't find the answers from the data frame, I want the model to google the question and try to get the answers from the To add a custom template to the create_pandas_dataframe_agent in LangChain, you can provide your custom template as the prefix and suffix parameters when calling the function. agents ¶. agents import YourAgentClass Function Definition: Define the create_pandas_dataframe_agent function. Visual Studio Code; An OpenAI API Key; Python version 3. Chains . chat_models import AzureChatOpenAI from langchain. TEMPLATE = """ You are working with a pandas dataframe in Python. In the given example, it is using vector store. pyplot as plt Custom agent. Oct 3, 2024 · import pandas as pd from langchain_experimental. csv') agent = create_pandas_dataframe_agent(OpenAI(temperature=0), [df], verbose=True) Regarding the pandas_dataframe_agent in the LangChain library, it is designed to facilitate operations on pandas DataFrame objects within the LangChain framework. 5-turbo", temperature = 0) agent_executor = (llm, df, I'm working with a langchain pandas agent using GPT-4 from Azure OpenAI as the LLM. csv") llm = ChatOpenAI(model="gpt-3. callbacks. # ### Basic Agent # - example of basic chat scores pre-trained classifier on previously unseen data from sklearn. base import (create_pandas_dataframe_agent,) from langchain_openai import You signed in with another tab or window. Toolkits are supported To create a Pandas DataFrame agent using LangChain, you can utilize the create_pandas_dataframe_agent function from the langchain_experimental. agents import create_pandas_dataframe_agent agent = create_pandas_dataframe_agent( OpenAI(temperature=0), df, verbose=True ) Share. You can load them via load_tools() from langchain. , if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema. agent_toolkits import create_pandas_dataframe_agent from langchain_openai import ChatOpenAI. This will ensure that the language model has the context of the previous question and answer when generating a In this video, we are going to explore the Pandas data frame agent to try to understand what the future of data analysis holds. Please note that the actual implementation of This is a Jupyter Notebook which explains how to use LangChain and the Open AI API to create a PandasDataFrame Agent. py: loads required libraries This notebook shows how to use agents to interact with a pandas dataframe. document_loaders import DataFrameLoader API Reference: DataFrameLoader loader = DataFrameLoader ( df , page_content_column = "Team" ) Setting up the agent I have included all the code for this project on my github. import pandas as pd. csv" import pandas as pd df = pd. 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 LangChain is a library that utilizes natural language processing and machine learning algorithms to create agents to answer questions from CSV data. Below is the snippet of my code - from langchain. Feb 21, 2024 · I have a Python file that utilizes AzureChatOpenAI, langchain agents (specifically the create_pandas_dataframe_agent) and Pandas to create an application that allows the user to ask questions based on different SQL tables (loaded as dataframes). 65¶ langchain_experimental. For example, you can use . It is not meant to be a precise solution, but rather a starting point for your own research. In this example, tool_names is a list of the names of the tools that the agent can use. agents import initialize_agent, Tool Defining Tools. code-block:: python from langchain_openai import ChatOpenAI from langchain_experimental. Vectorstore agent - an agent capable of interacting with vector stores. ipynb notebook in the LangChain repository. show() is called, a new figure is created, and if plt. We will create an agent using LangChain’s capabilities, integrating the LLAMA 3 model from Ollama and utilizing the Tavily search tool For example, the support tool should be used to optimize or debug a Cypher statement and the input to the tool should be a fully formed question. below is a snippet of code for the agent that I have created : tools = [ python_repl Create csv agent with the specified language model. text_splitter import TextSplitter. create_structured_chat_agent (llm: Here’s an example: from langchain_core. agent_df_base_company = create_pandas_dataframe_agent(ChatOpenAI(temperature=0, model="gpt-3. The code interpreter environment includes many popular Python packages, such as NumPy, pandas, and scikit-learn. read_csv(). read_csv(csv_file) # Begin by importing the required modules. In LangGraph, we can represent a chain via simple sequence of nodes. g. In this tutorial, we'll be using the pandas DataFrame Agent . I changed it a bit as I am using Azure OpenAI account referring this. We’ve already seen an example of the Read-Retrieve-Read technique in my latest article. Pandas Dataframe Agent# This notebook shows how to use agents to interact with a pandas dataframe. You switched accounts on another tab or window. I want to find top 3 manager names for the department "CSE". Parameters: *args (Any) – If the chain expects a single input, it can be passed in as the Create a BaseTool from a Runnable. agent_types import AgentType from langsmith import Client from add some sample custom prompts and the results you are getting. agents import create_pandas_dataframe_agent import pandas as pd df = pd. A few-shot prompt template can be constructed I am sure that this is a bug in LangChain rather than my code. as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. Use cautiously. 5-turbo", temperature=0) agent_executor = create_pandas_dataframe_agent(llm, df, agent_type="tool-calling", In this example, 'df' is the pandas DataFrame that you want the tool to recognize. Setting up the agent is fairly straightforward as we're going to be using the create_pandas_dataframe_agent that comes with langchain. Asking for help, clarification, or responding to other answers. read_csv ("titanic. langchain. We hope to continue developing different toolkits that can enable agents to do amazing feats. Langchain pandas agents (create_pandas_dataframe_agent ) is hard to work with llama models. Hello, Thank you for bringing this to our attention. Example Pre-Requisites. Somehow it is not returning the output in the output variable. Enter the LangChain Pandas Agent, a groundbreaking tool that democratizes data insights by enabling users to interact with complex datasets using plain language. LLM Agent with History: Provide the LLM with access to previous steps in the conversation. This notebook is accompanied a more detailed Medium walk-thru page_content='Nationals' metadata={' "Payroll (millions)"': 81. 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. You can find more information about this in the custom_tools. path (str | List[str]) – A string path, or a list of string paths that can be read in as pandas DataFrames with pd. agent_types import AgentType from langchain. 34, ' "Wins"': 98} page_content='Reds' metadata={' "Payroll (millions)"': 82. contains search ignoring NaNs and case insensitive in this fashion. Where possible, schemas are inferred from runnable. agent How to pass a CSV file or a dataframe to Pandas REPL tool in Langchain? Related. number_of_head_rows (int) – Number of rows to display in the prompt for sample data langchain_experimental 0. 5-turbo-0125"), df, verbose=True, Dec 21, 2024 · from pandasai import Agent agent = Agent(df, config={"llm": llm}, memory_size=10) result = agent. For detailed documentation of all ChatGroq features and configurations head to the API reference. Return Example:. And also I like to build 'Define the Query Generator' for my pandas_dataframe agent as well. The main difference between this method and Chain. This project demonstrates the integration of Google's Gemini AI model with LangChain framework, specifically focusing on CSV data analysis using agents. Great! We've got a SQL database that we can query. 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. Let's langchain_experimental. 5-turbo", temperature=0) agent_executor = create_pandas_dataframe_agent(llm, df, agent_type="tool-calling", The script begins by importing the necessary libraries, including os for operating system interactions, streamlit for building web applications, pandas for data manipulation, dotenv for loading environment variables, and langchain to for interacting with the openAI API and creating a Pandas DF agent. This function allows you to build an agent that can interact with a Pandas DataFrame, enabling it to perform data manipulation and analysis tasks effectively. def chunk_data_frame(df, chunk_size): Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company I'm using the create_pandas_dataframe_agent to create an agent that does the analysis with OpenAI's GPT-3. You create a Tool instance from this function using Tool. 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 In this article, we dive into the simplicity and effectiveness of using LangChain’s Pandas Agent to uncover hidden patterns and valuable insights within your data. This section delves into the ConversationBufferMemory, a straightforward implementation that maintains a list of chat messages in a I am trying to make an LLM model that answers questions from the panda's data frame by using Langchain agent. Define these tools based on your agent's objectives. Chains are compositions of predictable steps. import openai import pandas as pd from dotenv import load_dotenv from langchain. You must The create_pandas_dataframe_agent function is a pivotal component for integrating pandas DataFrame operations within a LangChain agent. For example, you can use LangChain agents to access information on the web, to interact with CSV files, Pandas DataFrames, SQL databases, and I worked around with a different agent and this did the trick for me: from langchain_openai import ChatOpenAI from langchain_core. 2) Each row of the df1 corresponds to demographics of study participants in clinical study called CDISCPILOT01. This can be dangerous and requires a specially sandboxed environment to be safely used. chat("Which are top 5 items with highest MRP") result Contrary to a SmartDataframe or a SmartDatalake, an agent will keep track of the state of the conversation and will be able to answer multi-turn conversations. 5-turbo-0613 model. tools is a dictionary where the keys are the names of the tools and the values are dictionaries that define the state of the tools. agents import Issue you'd like to raise. Pandas DataFrame agent - an agent capable of question-answering over Pandas dataframes, builds on top Aug 17, 2023 · Pandas Dataframe Agent 这个笔记本展示了如何使用代理与pandas dataframe进行交互。它主要用于问答。注意：这个代理在底层调用Python代理，执行LLM生成的Python代码 - 如果LLM生成的Python代码有害，这可能是不好的。 4 days ago · This will help you getting started with Groq chat models. In this article, we will explore how to use Langchain Pandas Agent to guide a dataset. This agent is not explicitly named pandas_dataframe_agent in the given code, but the functionality described aligns with what one would expect from an agent handling pandas DataFrames. Here is some example code to help you implement this approach with Pandas: python. A Pandas DataFrame is a popular data structure in the Python programming language, commonly used for data manipulation and analysis. The name of the dataframe is `df` You are an Mar 1, 2023 · Other agent toolkit examples: JSON agent - an agent capable of interacting with a large JSON blob. However, I haven't found a way to actually filter a dataframe and save (or access) the result. Next, we will be reading in data from a CSV file to create a DataFrame. In Chains, a sequence of actions is hardcoded. Implementation of CSV Agents CSV Agent of LangChain uses CSV (Comma-Separated Values) format, which is a simple file format for storing tabular data. llms import AzureOpenAI llm = AzureOpenAI ( model = "gpt-35-turbo-16k" create_pandas_dataframe_agent( llm, df, verbose=True, allow_dangerous_code=True) Beta Was this translation helpful? In this code, replace 'path/to/your/file. It can read and write data from CSV files and perform primary operations on the data. So, I am working on a project that involves data extraction from csv files and involves creating charts and graphs from them. It is mostly optimized for question answering. 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. pandas DataFrame. It's easy to get the agent going, I followed the examples in the Langchain Docs. prompts import PromptTemplate from langchain. 5-turbo", temperature = 0) # Define your prompt template TEMPLATE = """You agents # Agent is a class that uses an LLM to choose a sequence of actions to take. tools import tool from langchain_groq import ChatGroq from langchain_core. The create_csv_agent function in the langchain_experimental. xuvv pqqxq obv ekaor nsrxh njqlg owe xwwzh tghbs zsdku