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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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hf_oauth_expiration_minutes: 480 |
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--- |
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# LLM-Enhanced Internet Search Agent |
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This agent uses a two-step approach to answer questions: |
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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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## Features |
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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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## Setup Requirements |
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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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## 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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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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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
