# Chat-with-Dataframe **Repository Path**: deepbluethinker/Chat-with-Dataframe ## Basic Information - **Project Name**: Chat-with-Dataframe - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2024-11-12 - **Last Updated**: 2024-11-12 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Chat-with-Dataframe Chat with Dataframe - Streamlit Chatbot using Langchian Agent and Ollama | Generative AI | LLM. The chatbot processes user-uploaded data by displaying a preview of the data frame, which allows users to ask questions. Building a Streamlit Chatbot with DataFrame Functionality This approach can also utilize other similar models such as Lama 2 or Lama 3, ensuring that the process remains consistent regardless of the model used. Once the file is uploaded, the application previews the data frame and enables user inquiries. The chatbot utilizes an agent-based method through a DataFrame agent that communicates with a language model (LLM) to generate responses or Python code tailored to the user's questions. "The entire process allows users to interactively explore the data by asking questions and receiving structured responses." # Required Libraries for Implementation: To start developing the application, the necessary libraries must be installed. The presenter outlines several libraries needed, such as streamlit for the user interface, pandas for data manipulation, and specific LangChain utilities for handling LLMs and agents. The instructions include running a pip install command to set up these libraries listed in a requirements.txt file. A virtual environment named "DF chat" is created specifically for this project to maintain dependency management. "You need to install all the libraries mentioned in the requirements.txt file to ensure smooth execution." # Setting Up the Chatbot Interface: The video demonstrates the configuration of the Streamlit application's layout, where users can customize the page title and icon. The structure is key to making the chatbot interface appealing and user-friendly. The chatbot's title could be customized with Emojis for visual appeal. The layout can either be centered or wide, and adjusting these settings affects how the content is presented on the screen. "A well-configured interface enhances user engagement and makes the chatbot more delightful to interact with." Reading Data from User Files: A function is defined to read user-uploaded CSV or Excel files. The function checks the file extension to determine the appropriate reading method using pandas. If a file ends with .csv, it employs the read_csv method; otherwise, it uses read_excel for Excel files. This function ensures that the uploaded data can be easily converted into a Pandas DataFrame for subsequent interactions. "This function will seamlessly load user data into a DataFrame for further querying and analysis." # Maintaining Chat History: The design of the chatbot includes the ability to retain chat history, allowing users to refer back to previous interactions. Utilizing Streamlit's session state, the chatbot can keep track of previous questions and answers, creating a more coherent conversational experience. This initialization sets up a session history in the application, making it possible for the LLM to maintain context throughout the conversation. "Maintaining chat history allows for a richer and more contextual conversation, enhancing user experience." Initialization of Chat History and Data Frame: The first step involves checking if there is a key called "chat history" in the session state. If it does not exist, a new one is created and initialized as an empty list. This ensures that when the Streamlit app starts, it has an established place to store chat messages. Additionally, the video explains the need for initializing a data frame in session state. If the data frame does not exist, it is set to None. Both chat history and data frame are essential for storing user interactions and the data that will be used in the chatbot's responses. "We are storing the data frame in the session state instead of just a variable." # Handling Uploaded Files: After a user uploads a file, the application checks if the uploaded file variable is not None. If a file is uploaded, it is read into a data frame, replacing the previous None value. The application then processes the uploaded data to ensure it is the correct type, using either read_csv or read_excel functions based on the input. Following this, a preview of the data frame is displayed to the user, showing the first five rows. This helps users understand what data they have uploaded and what questions they can ask regarding it. "Once the user has uploaded a file, we are loading it to a data frame and then showing a preview of the database." Displaying Chat History and User Interaction: The video explains how to display chat history, which is crucial for providing context for user interactions. A loop iterates over previous messages stored in the session state, displaying both user queries and the chatbot's responses. An input field is also established where users can type in their questions. Once the user submits a question, it is appended to the chat history, and a message will be sent based on this input, ensuring a seamless conversation flow within the app. "We are just trying to display all the previous messages from the chat history." # Model Size and Temperature Settings: The ability to select a language model (LM) often depends on the size of the GPU available. Larger GPUs can handle models that exceed 4.7 GB, while smaller GPUs may only support models less than 2 GB, such as GMA 2on. The temperature setting in the LM influences the randomness of responses generated. A temperature value closer to zero results in consistent outputs for the same inputs, while a value closer to one leads to a diverse range of responses for the identical question. Users need to pull the desired model beforehand, as illustrated through terminal commands, before they can utilize features like temperature settings. "The temperature setting influences the randomness of responses generated by the LM." # Creating the DataFrame Agent: The process includes creating a DataFrame agent using the create Pandas DataFrame agent function. This requires passing the LM and the DataFrame to the function, specifying additional parameters like verbosity and agent type. Incorporating OpenAI functions is essential when utilizing LLMs from sources like OpenAI's GPT series, and the agent can also work with open-source models like Llama 3. A critical parameter, "allow dangerous code," should be set to true only if you are confident that no harmful code will be executed. "Incorporating OpenAI functions is essential when utilizing LLMs." # User Interaction and Response Generation: The interaction begins by setting up an initial list of messages for communication. The system role can be defined to indicate that the LM is expected to be helpful. When a user poses a question—for example, inquiring about missing values in a DataFrame—the DataFrame agent invokes a function that generates and executes corresponding Python code based on the user’s prompt. This allows the model to return precise results from the executed code. Users can then save this response as the assistant's reply and log the entire interaction, including user prompts and assistant messages, into a chat history for record-keeping and debugging. "The DataFrame agent generates and executes corresponding Python code based on the user's prompt." # Chat History Management: The workflow allows for ongoing chat history management. Initially, the chat history is empty until the user engages in conversation by entering their question. When a user submits a question, it gets displayed and is stored in session state. This storage is implemented as a list of dictionaries, where each dictionary consists of a role identifier (user or assistant) and the content of the message. The chat history thus maintains a structured record of interactions, showcasing which messages originated from the user and which came from the assistant while also enabling the visualization of this chat in a user-friendly format. "The chat history maintains a structured record of interactions, showcasing which messages originated from the user and which came from the assistant." # User Prompt and Chat History Management: The system processes user prompts by first displaying the questions entered through a widget. This input is stored in the chat history for later reference. Chat history is utilized to provide context to the language model (LLM), allowing the model to generate responses based on previous exchanges. When initiating the LLM with a chat prompt, the system ensures that it includes both the current user input and the stored chat history. "Chat history is crucial for maintaining context and guiding the LLM's responses." # LangChain Agent and DataFrame Interaction The application utilizes a LangChain agent that interacts with a Pandas DataFrame. This agent receives the user prompt and uses it to query the LLM. The process involves three key components: the LLM, the DataFrame, and the user prompt, which together enable the agent to generate relevant outputs. The LLM returns Python code relevant to the user’s question, which the agent can then execute in a sandbox environment to get actual data results. "The agent executes the Python code generated by the LLM to provide real-time data insights." Storing Assistant Responses and Iterative Learning Once the agent executes the code and receives a response, this output is stored to enhance future interactions. The application employs a mechanism for maintaining messages, where the entire chat history is sent along with user queries to enrich the LLM's contextual understanding. Notably, this system emphasizes continuous learning and refinement, as it allows the LLM to respond dynamically to the evolving conversation. "Continuously feeding chat history to the LLM enriches its ability to respond accurately." # Processing Multiple User Queries: The framework allows users to make various inquiries about the DataFrame, such as identifying column names or checking for missing values. The response quality may vary depending on the size and capability of the LLM model being used, with larger models generally offering better performance. An agent can summarize the DataFrame or answer complex queries, demonstrating the system’s flexibility in handling user requests. "Response efficacy improves with more capable models, enhancing user interaction quality." # Enhancing Contextual Awareness: A critical part of the system is sending entire chat histories in response to user queries, rather than just the latest question. This strategy enhances the LLM's understanding of previous interactions. The inclusion of chat context allows the LLM to formulate more detailed and relevant answers, especially when users ask for explanations without context. The implementation focuses on fostering a conversation-flow dynamic rather than a simple Q&A framework.