bambooai 0.3.18

A lightweight library for working with pandas dataframes using natural language queries

BambooAI

A lightweight library that leverages Large Language Models (LLMs) to enable natural language interactions, allowing you to converse with your pandas DataFrames.

Objective

The BambooAI library is an experimental user-friendly tool designed to make data analysis more accessible to non-programmers. Utilizing the power of Large Language Models (LLM), BambooAI can comprehend your questions about a dataset and automatically generate and execute the appropriate Python code for both analysis and plotting. Users can effortlessly gain valuable insights from their data without writing complex code or mastering advanced programming techniques. With BambooAI, simply input your dataset, ask questions in plain English, and receive answers along with relevant out of the box visualizations if asked for to help you better understand your data.

My main goal was to create a tool to help analysts at all levels, not to replace them. This library makes data analysis and visualization easier, helping to improve workflows. It's designed to be easy to use, efficient, and adaptable to different users' needs. As a supportive tool, not the main operator, it helps users apply their own analytical skills more effectively and increase their productivity.

Preview

Try it out in Google Colab:

How it works

The BambooAI agent operates through several key steps to interact with users and generate responses:

1. Initiation

• The user starts the BambooAI agent.
• If there's no input question, the agent prompts the user to either input a question or type 'exit' to terminate the program.

2. Task Evaluation

• The agent stores the received question and utilizes the Large Language Model (LLM) to evaluate and categorize it.
• The LLM determines whether the question necessitates a textual response, additional information (Google search), or can be resolved using code.
• Depending on the task evaluation and classification the agent calls the appropriate agent.

3. Dynamic Prompt Build

• If code can resolve the question, the agent formulates an algorithm expressed as a task list.
• The original question is modified to align with this algorithm. The agent conducts a semantic search against a vector db for similar questions.
• Any matching questions found are appended to the prompt as examples, and GPT-3 is used to generate code based on the algorithm.

4. Debugging, Execution, and Error Correction

• If the generated code needs debugging, GPT-4 is engaged.
• The code is executed, and if errors occur, the agent logs the error message and refers it to the LLM (GPT-3 or GPT-4, depending on the settings) for correction.
• This process continues until successful code execution.

5. Results, Ranking, and Knowledge Base Build

• Post successful execution, GPT-4 is used to rank the answer.
• If the rank surpasses a set threshold, the question, answer, code, and rank are stored in the Pinecone vector database.
• Regardless of the rank, the final answer or visualization is formatted and presented to the user.

6. Human Feedback and Loop Continuation

• The agent seeks feedback from the user.
• If the user validates the auto-generated ranking, the question/answer pair is stored in the vector database.
• If not, a new execution loop begins.

Throughout this process, the agent continuously solicits user input, stores messages for context, and generates and executes code to ensure optimal results. Various AI models and a vector database are employed in this process to provide accurate and helpful responses to user's questions.

How to use

Installation

pip install bambooai

Usage

Parameters

df: pd.DataFrame - Dataframe(Required)

max_conversations: int - Number of "user:assistant" conversation pairs to keep in memory for a context. Default=2

llm: str - Base LLM model. Default = gpt-3.5-turbo-0613

llm_switch: bool - If True, the agent will switch to gpt-4 after error,or if a self reflection is required (code debug, solution ranking).

debug: bool - If True, the received code is sent back to the Language Learning Model (LLM) for an evaluation of its relevance to the user's question, along with code error checking and debugging.

search_tool: bool - If True, the agent will switch to a google search if the answer is not available or satisfactory.

vector_db: bool - If True, each answer will first be ranked from 1 to 10. If the rank surpasses a certain threshold (8), the corresponding question (vectorised), answer, code, and rank (metadata) are all stored in the Pinecone database. Each time a new question is asked, these records will be searched. If the similarity score is above 0.9, they will be offered as examples and included in the prompt (in a one-shot learning scenario)

exploratory: bool - If set to True, the LLM will evaluate the user's question and select an "Expert" that is best suited to address the question (experts: Internet Search Specialist, Data Analisys Theoretician, Data Analyst). For instance, if the task involves code generation/execution, it will generate a task list detailing the steps, which will subsequently be sent to the LLM as a prompt for the next action. This method is particularly effective for vague user prompts, but it might not perform as efficiently with more specific prompts. The default setting is True.


e.g. bamboo = BambooAI(df, debug=True, vector_db=True, llm_switch=True, search_tool=True, exploratory=True)

Run in a loop

# Run in a loop remembering the conversation history
import pandas as pd
from bambooai import BambooAI

df = pd.read_csv('test_activity_data.csv')
bamboo = BambooAI(df)
bamboo.pd_agent_converse()

Single execution

# Run programaticaly (Single execution).
import pandas as pd
from bambooai import BambooAI

df = pd.read_csv('test_activity_data.csv')
bamboo = BambooAI(df)
bamboo.pd_agent_converse("Calculate 30, 50, 75 and 90 percentiles of the heart rate column")

Visualize the data (Uses Matplotlib). Works with both Loop and Single execution

Environment Variables

The library requires an OpenAI API account and the API key to connect to OpenAI LLMs. The OpenAI API key needs to be stored in a OPENAI_API_KEY environment variable. The key can be obtained from here: https://platform.openai.com/account/api-keys.

The Pincone vector db is optional. If you don want to use it, you dont need to do anything. If you have an account with Pinecone and would like to use the knowledge base and ranking features, you will be required to setup PINECONE_API_KEY and PINECONE_ENV envirooment variables, and set the 'vector_db' parameter to True. The vector db index is created upon first execution.

The Google Search is also optional. If you don want to use it, you dont need to do anything. If you have an account with Serper and would like to use the Google Search feature, you will be required to setup SERPER_API_KEY environment variable, and set the 'search_tool' parameter to True.

Notes

• The library currently supports only OpenAI Chat models. It has been tested with both gpt-3.5-turbo and gpt-4. The gpt-3.5-turbo seems to perform well and is the preferred option due to its 10x lower cost.
• The library executes LLM generated Python code, this can be bad if the LLM generated Python code is harmful. Use cautiously.
• Be sure to monitor your token usage. At the time of writing, the cost per 1K tokens is $0.03 USD for GPT-4 and $0.002 USD for GPT-3.5-turbo. It's important to keep these costs in mind when using the library, particularly when using the more expensive models.
• Supported models: gpt-3.5-turbo, gpt-3.5-turbo-613, gpt-3.5-turbo-16k, gpt-4, gpt-4-0613.

Contributing

Contributions are welcome; please feel free to open a pull request. Keep in mind that our goal is to maintain a concise codebase with high readability.

ToDo

• Implement ReAct https://arxiv.org/abs/2201.11903, where a question can be decomposed into individual sub-steps and then sequentially routed to the appropriate Experts for processing. This approach should enhance the reasoning capabilities when dealing with multifaceted tasks.
• Add support for aditional LLMs.