> ## Documentation Index > Fetch the complete documentation index at: https://docs.tryfltr.com/llms.txt > Use this file to discover all available pages before exploring further. # Getting Started > Start using FLTR in 5 minutes with your first dataset and query # Getting Started with FLTR This guide will walk you through creating your first dataset, uploading a document, and running semantic search queries using the FLTR API. ## Prerequisites Before you begin, make sure you have: * A FLTR account (sign up at [www.tryfltr.com](https://www.tryfltr.com)) * An API key (we'll generate one in the next step) * Basic familiarity with REST APIs ## Step 1: Generate an API Key 1. Log in to your [FLTR Dashboard](https://www.tryfltr.com) 2. Navigate to **Settings** → **API Keys** 3. Click **Create API Key** 4. Give your key a descriptive name (e.g., "Development Key") 5. Copy and save your API key securely API keys grant full access to your account. Never share them publicly or commit them to version control. Use environment variables to store them securely. ## Step 2: Create Your First Dataset Datasets are containers for related documents. Let's create one using curl: ```bash theme={null} curl -X POST https://api.fltr.com/v1/datasets \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "name": "My First Dataset", "description": "Testing FLTR semantic search", "is_public": false }' ``` The response will include a `dataset_id` - save this for the next steps: ```json theme={null} { "id": "ds_abc123", "name": "My First Dataset", "description": "Testing FLTR semantic search", "is_public": false, "created_at": "2024-01-10T12:00:00Z" } ``` ## Step 3: Upload a Document Now let's add a document to your dataset. You can upload text, PDFs, or images: ### Option A: Upload Text Content ```bash theme={null} curl -X POST https://api.fltr.com/v1/datasets/ds_abc123/documents \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "content": "FLTR is a powerful semantic search platform that makes your documents AI-ready. It supports hybrid search combining vector similarity and keyword matching.", "metadata": { "title": "About FLTR", "category": "documentation" } }' ``` ### Option B: Upload a File ```bash theme={null} curl -X POST https://api.fltr.com/v1/datasets/ds_abc123/documents \ -H "Authorization: Bearer YOUR_API_KEY" \ -F "file=@/path/to/document.pdf" \ -F 'metadata={"title":"My Document","category":"research"}' ``` The API will automatically: * Extract text from PDFs and images * Chunk the content into searchable segments * Generate vector embeddings * Index for hybrid search Document processing happens asynchronously. Large files may take a few seconds to become searchable. ## Step 4: Run Your First Query Now you can search your dataset using semantic search: ```bash theme={null} curl -X POST https://api.fltr.com/v1/mcp/query \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "query": "What is FLTR?", "dataset_id": "ds_abc123", "limit": 5 }' ``` Response: ```json theme={null} { "results": [ { "chunk_id": "ch_xyz789", "content": "FLTR is a powerful semantic search platform that makes your documents AI-ready...", "score": 0.89, "metadata": { "title": "About FLTR", "category": "documentation" }, "document_id": "doc_def456" } ], "total": 1, "query_time_ms": 45 } ``` ## Code Examples ```python Python theme={null} import requests # Configuration API_KEY = "your_api_key_here" BASE_URL = "https://api.fltr.com/v1" headers = { "Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json" } # Create dataset dataset_response = requests.post( f"{BASE_URL}/datasets", headers=headers, json={ "name": "My First Dataset", "description": "Testing FLTR", "is_public": False } ) dataset_id = dataset_response.json()["id"] # Upload document doc_response = requests.post( f"{BASE_URL}/datasets/{dataset_id}/documents", headers=headers, json={ "content": "FLTR is a powerful semantic search platform...", "metadata": {"title": "About FLTR"} } ) # Query query_response = requests.post( f"{BASE_URL}/mcp/query", headers=headers, json={ "query": "What is FLTR?", "dataset_id": dataset_id, "limit": 5 } ) print(query_response.json()) ``` ```javascript JavaScript theme={null} const API_KEY = "your_api_key_here"; const BASE_URL = "https://api.fltr.com/v1"; const headers = { "Authorization": `Bearer ${API_KEY}`, "Content-Type": "application/json" }; // Create dataset const datasetResponse = await fetch(`${BASE_URL}/datasets`, { method: "POST", headers, body: JSON.stringify({ name: "My First Dataset", description: "Testing FLTR", is_public: false }) }); const { id: datasetId } = await datasetResponse.json(); // Upload document await fetch(`${BASE_URL}/datasets/${datasetId}/documents`, { method: "POST", headers, body: JSON.stringify({ content: "FLTR is a powerful semantic search platform...", metadata: { title: "About FLTR" } }) }); // Query const queryResponse = await fetch(`${BASE_URL}/mcp/query`, { method: "POST", headers, body: JSON.stringify({ query: "What is FLTR?", dataset_id: datasetId, limit: 5 }) }); const results = await queryResponse.json(); console.log(results); ``` ```bash cURL theme={null} # Set your API key export FLTR_API_KEY="your_api_key_here" # Create dataset DATASET_ID=$(curl -s -X POST https://api.fltr.com/v1/datasets \ -H "Authorization: Bearer $FLTR_API_KEY" \ -H "Content-Type: application/json" \ -d '{"name":"My First Dataset","is_public":false}' \ | jq -r '.id') # Upload document curl -X POST https://api.fltr.com/v1/datasets/$DATASET_ID/documents \ -H "Authorization: Bearer $FLTR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "content": "FLTR is a powerful semantic search platform...", "metadata": {"title": "About FLTR"} }' # Query curl -X POST https://api.fltr.com/v1/mcp/query \ -H "Authorization: Bearer $FLTR_API_KEY" \ -H "Content-Type: application/json" \ -d "{\"query\":\"What is FLTR?\",\"dataset_id\":\"$DATASET_ID\",\"limit\":5}" ``` ## Rate Limits FLTR has three authentication tiers with different rate limits: | Method | Rate Limit | Best For | | --------- | -------------------- | ------------------- | | Anonymous | 50 requests/hour | Quick testing | | API Key | 1,000 requests/hour | Production services | | OAuth/MCP | 15,000 requests/hour | MCP clients | Rate limits are per account, not per API key. If you need higher limits, [contact support](mailto:support@fltr.com). ## Common Errors ### 401 Unauthorized ```json theme={null} {"error": "Invalid API key"} ``` **Solution**: Double-check your API key and ensure it's properly formatted in the Authorization header: ``` Authorization: Bearer YOUR_API_KEY ``` ### 404 Dataset Not Found ```json theme={null} {"error": "Dataset not found"} ``` **Solution**: Verify the dataset ID exists and belongs to your account. List your datasets: ```bash theme={null} curl https://api.fltr.com/v1/datasets \ -H "Authorization: Bearer YOUR_API_KEY" ``` ### 429 Rate Limit Exceeded ```json theme={null} {"error": "Rate limit exceeded", "retry_after": 3600} ``` **Solution**: Wait for the retry period (in seconds) or upgrade to OAuth authentication for higher limits. ### 413 Payload Too Large ```json theme={null} {"error": "Document exceeds maximum size of 10MB"} ``` **Solution**: Split large documents into smaller chunks before uploading, or use our chunking API. ## Next Steps Build a complete RAG application with FLTR Explore all available endpoints Connect FLTR to Claude Desktop or VS Code Use FLTR with Zapier, Make, or n8n ## Need Help? * **Documentation**: Browse our comprehensive [API reference](/api-reference/introduction) * **Examples**: Check out [integration examples](/integrations/zapier) * **Support**: Email us at [support@fltr.com](mailto:support@fltr.com) * **Status**: Monitor uptime at [status.fltr.com](https://status.fltr.com)