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+ ---
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+ title: LLM-Enhanced Internet Search Agent
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+ emoji: 🕵🏻‍♂️
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+ colorFrom: indigo
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+ colorTo: indigo
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+ sdk: gradio
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+ sdk_version: 5.25.2
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+ app_file: app.py
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+ pinned: false
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+ hf_oauth: true
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+ # optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.
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+ hf_oauth_expiration_minutes: 480
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+ ---
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+
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+ # LLM-Enhanced Internet Search Agent
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+
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+ This agent uses a two-step approach to answer questions:
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+
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+ 1. **Question Breakdown**: The agent first uses an LLM (GPT-3.5) to break down complex questions into 2-3 key search queries
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+ 2. **Targeted Search**: Each search query is sent to Wikipedia's API to retrieve relevant information
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+ 3. **Answer Synthesis**: The agent then uses the LLM to synthesize a comprehensive answer based on all search results
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+
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+ ## Features
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+
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+ - **Smart Query Generation**: Transforms natural language questions into optimized search queries
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+ - **Parallel Search Processing**: Searches for multiple key aspects of the question simultaneously
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+ - **Knowledge Synthesis**: Combines information from multiple sources into a cohesive answer
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+ - **Fallback Mechanisms**: Graceful handling of errors at each step of the process
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+
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+ ## Setup Requirements
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+
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+ 1. Clone this repository
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+ 2. Install required packages: `pip install -r requirements.txt`
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+ 3. Set your OpenAI API key as an environment variable: `OPENAI_API_KEY=your-api-key`
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+
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+ ## How It Works
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+ 1. User submits a question
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+ 2. LLM breaks down the question into key search terms
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+ 3. Search terms are used to query Wikipedia API
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+ 4. Results from multiple searches are collected
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+ 5. LLM synthesizes the information into a comprehensive answer
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+ 6. Answer is returned to the user
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+
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+ This approach is more effective than direct internet searches because:
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+ - It identifies the most relevant aspects of complex questions
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+ - It can break multi-part questions into their components
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+ - It leverages the LLM's understanding of natural language
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+ - It provides more targeted and accurate search results
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference