BambooAI

It is a Python library powered by Language Models (LLMs) designed for conversational data discovery and analysis.

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BambooAI AI Agent Competitors

It is a Python library powered by Language Models (LLMs) designed for conversational data discovery and analysis. The BambooAI library enables users to interact with their data through natural language, making data analysis accessible even to those without programming expertise. It allows users to analyze and explore datasets by asking questions in plain English, generating and executing Python code for data analysis, and providing visualizations to help interpret the results. The library supports local datasets and can fetch data from external sources or APIs, integrating internet searches and external API access to enhance its functionality.

BambooAI operates as an assistant for research and data analysis, streamlining workflows by simplifying data analysis and visualization. It processes natural language queries, generates Python code, and executes it to derive insights from data. Users can input their dataset, ask questions, and receive answers along with visualizations. The library is vendor-agnostic, supporting various open-source or proprietary models via API or locally. Users can configure the models and providers by modifying the `LLM_CONFIG` file, which specifies details like model name, provider, temperature, and token limits for each agent.

The library requires an OpenAI API key for default configurations but also supports models from providers like Groq, Gemini, Mistral, and Anthropic. Optional features include integration with Pinecone for vector database functionality and Serper for Google Search. Local open-source models, such as Phind-CodeLlama-34B-v2, are supported for specific tasks, requiring CUDA-enabled GPUs and compatible libraries. Additionally, Ollama models can be used by configuring the `LLM_CONFIG` file accordingly.

BambooAI logs all LLM interactions in a consolidated log file, capturing details of tasks, datasets, and models used. Contributions to the library are welcome, with a focus on maintaining a concise and readable codebase. The library is open-source and aims to augment the capabilities of data analysts across all levels by providing a user-friendly, efficient, and adaptable tool for data analysis.

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