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The tool allows you to load PDF documents into a vector store, where they can be queried using OpenAI's language models. The retrieval process aims to simulate a customer support assistant by fetching relevant information from the document database in response to user queries.\n\n## Features\n\n- **PDF Document Processing**: Extracts text from PDFs and splits it into manageable chunks.\n- **Vector Storage with Pinecone**: Stores text chunks in Pinecone for efficient retrieval.\n- **Intelligent Query Handling**: Utilizes OpenAI's language model to generate human-like responses to user queries.\n- **Customizable Settings**: The tool can be tailored through various environment variables for API keys and service configurations.\n\n## Installation\n\n### Prerequisites\n\n- Python 3.7+\n- A Pinecone account and API key\n- An OpenAI account and API key\n- Required Python packages as listed in `requirements.txt`\n\n### Environment Setup\n\n1. **Clone Repository**\n   ```bash\n   git clone https://github.com/hi-tech-AI/help-scout-assistant-using-Pinecone-vector-database.git\n   cd help-scout-assistant-using-Pinecone-vector-database\n   ```\n\n2. **Install Requirements**\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. **Environment Variables**\n\n   Set up your environment variables using a `.env` file:\n\n   ```plaintext\n   PINECONE_API_KEY=your_pinecone_api_key\n   PINECONE_CLOUD=aws\n   PINECONE_REGION=us-east-1\n   OPENAI_API_KEY=your_openai_api_key\n   ```\n\n## Usage\n\nThe primary interaction with this system happens through the `main` function, which coordinates the entire workflow of loading documents, creating/updating the index, and handling user queries.\n\n```python\nfrom your_script import main\n\nuser_query = \"Your question here\"\nresponse = main(user_query)\nprint(response)\n```\n\n## Structure\n\n- `extract_text_from_pdf`: Loads and splits the text from a given PDF path.\n- `get_index_from_pinecone`: Ensures the specified index exists in Pinecone.\n- `upsert_doc_to_pinecone`: Inserts or updates the document in the Pinecone vector store.\n- `search_query`: Executes a search query on the document store and returns the response.\n- `main`: Orchestrates the flow from PDF extraction to query response.\n\n## Customization\n\nYou can modify the tool’s behavior by adjusting parameters in functions like `CharacterTextSplitter`, `ChatOpenAI`, and changing environment variable values in `.env`.\n\n## Contributing\n\nFeel free to open issues or submit pull requests to discuss and improve the functionality.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhi-tech-ai%2Fhelp-scout-assistant-using-pinecone-vector-database","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhi-tech-ai%2Fhelp-scout-assistant-using-pinecone-vector-database","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhi-tech-ai%2Fhelp-scout-assistant-using-pinecone-vector-database/lists"}