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+ ## Functionality Breakdown
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+ ### Internal Knowledge Exploration
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+ - **memory_search**: Searches a Chroma vector store containing past findings, utilizing sentence embeddings to identify relevant information.
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+ - **knowledgeBase_search**: Similar to `memory_search`, but specifically explores a collection within Chroma potentially holding research papers or relevant information chunks.
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+ ### External Information Retrieval
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+ - **arxiv_search**: Retrieves scientific research papers from the Arxiv database based on user queries, stores metadata, and formats papers for display.
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+ - **get_arxiv_paper**: Downloads full PDF content of a specific Arxiv paper based on its ID and saves it to a designated directory.
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+ - **wikipedia_search**: Queries Wikipedia for relevant summaries based on user input, formats and stores retrieved summaries.
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+ - **google_search**: Performs a Google Search using user queries, retrieves relevant results, and stores them for later use.
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+ ### Data Storage and Management
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+ - **Chroma Vector Store**: Stores text content embeddings for similarity search (`memory_search`) and Arxiv paper embeddings (`embed_arvix_paper`).
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+ - **Global `all_sources` Variable**: Accumulates retrieved information from various sources (Arxiv, Wikipedia, Google Search).
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+ ## Additional Notes
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+ - These tools are integrated with a larger LangChain framework, as indicated by the `@tool` decorator.
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+ - Configuration files (`config.ini`) define details like vector store location and collection names.
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+ ## Getting Started
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+ 1. Install the LangChain framework and required libraries.
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+ 2. Configure access to the Chroma vector store (likely involves environment variables or configuration files).
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+ 3. Refer to the InnovationPathfinderAI project documentation for detailed integration instructions.
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+ ## Disclaimer
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+ This documentation provides an overview of the tools' functionalities.