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--- |
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title: Template Final Assignment |
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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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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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## 🚀 Quick Start |
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1. Clone the repository: |
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```bash |
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git clone https://huggingface.co/spaces/Lucas-C-R/Agents |
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cd Agents |
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``` |
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2. Create and activate a virtual environment: |
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- Linux: |
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```bash |
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python3 -m venv .venv |
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source .venv/bin/activate |
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``` |
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- Windows: |
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```bash |
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python -m venv .venv |
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.venv\Scripts\activate |
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``` |
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1. Install dependencies: |
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```bash |
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pip install -r requirements.txt |
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``` |
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1. Configure environment variables: |
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```bash |
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# API Keys |
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export OPENAI_API_KEY="sk-proj-..." # Your OpenAI API key |
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export TAVILY_API_KEY="tvly-dev-..." # Your Tavily API key |
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export GROQ_API_KEY="gsk_..." # Your Groq API key |
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export HF_TOKEN="hf_..." # Your Hugging Face token |
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# Space Configuration |
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export SPACE_ID="<your user>/<space name>" # Your Hugging Face space ID |
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# Supabase Configuration |
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export SUPABASE_URL="your_supabase_url" # Your Supabase project URL |
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export SUPABASE_KEY="your_supabase_key" # Your Supabase API key |
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``` |
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5. Run the application: |
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```bash |
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python app.py |
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``` |
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## 🔧 Detailed Configuration |
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### 1. Supabase Setup |
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1. Create an organization on [Supabase](https://supabase.com/) |
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2. Create a new project |
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3. Set up a password and select the nearest region |
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4. In the "Project Overview" tab, locate the "Project API" section |
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5. Copy the Project URL to the `SUPABASE_URL` environment variable |
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6. Copy the API Key to the `SUPABASE_KEY` environment variable |
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### 2. Database Configuration |
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Execute the following SQL in the Supabase SQL editor: |
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```sql |
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create extension if not exists vector; |
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create table if not exists documents ( |
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id uuid primary key default gen_random_uuid(), |
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content text, |
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metadata jsonb, |
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embedding vector(768) |
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); |
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DROP FUNCTION IF EXISTS match_documents_langchain(vector, integer); |
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create or replace function match_documents_langchain( |
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query_embedding vector(768), |
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match_count int default 5 |
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) |
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returns table ( |
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id uuid, |
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content text, |
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metadata jsonb, |
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similarity float |
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) |
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as $$ |
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begin |
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return query |
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select |
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documents.id, |
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documents.content, |
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documents.metadata, |
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1 - (documents.embedding <=> query_embedding) as similarity |
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from documents |
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order by documents.embedding <=> query_embedding |
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limit match_count; |
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end; |
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$$ language plpgsql; |
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``` |
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### 3. Data Import |
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1. Access the "Table Editor" tab in Supabase |
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2. Select the "documents" table |
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3. Click on "Import data from CSV" |
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4. Select the `supabase_docs.csv` file from the project directory |
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5. Click "Import data" and wait for completion |
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## 📝 License |
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This project is under the MIT License. See the LICENSE file for more details. |
