> ## Documentation Index
> Fetch the complete documentation index at: https://cortex-ad5578da.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Start building amazing LLM experiences in under 5 minutes

> Base URL: `https://api.usecortex.ai`
>
> Contact us to get your API key at [founders@usecortex.ai](mailto:founders@usecortex.ai)

All endpoints require an API key sent as a Bearer token in the `Authorization` header.

```mdx theme={null}
Authorization: Bearer <your_api_key>
```

### Prerequisites

* Node.js / Python (or any backend language)
* Basic knowledge of HTTP requests
* An API key from [Cortex](https://usecortex.ai/) (`Authorization: Bearer <your_api_key>`)
* A document or webpage you want your AI to read

### **Step 1: Upload Your Knowledge Source**

You can upload:

* A public webpage
* A document (PDF, DOCX, etc.)
* Multiple documents in bulk

#### Option A: Upload a document

```mdx theme={null}
POST /upload/upload_document
```

**Form Data:**

* `file`: your document
* `tenant_id`: your unique tenant ID

#### Option B: Upload a webpage

```mdx theme={null}
POST /upload/scrape_webpage
```

```mdx theme={null}
curl -X POST 'https://api.usecortex.ai/upload/scrape_webpage?web_url=https://example.com&tenant_id=your_tenant_id' \
  -H "Authorization: Bearer <your_api_key>"
```

### **Step 2 (Optional): Verify Processing**

Check if the document is fully indexed:

```mdx theme={null}
POST /upload/verify_processing?file_id=YOUR_FILE_ID
```

Returns success once the document is ready for querying.

### **Step 3: Ask Questions Using the AI Retrieval API**

```mdx theme={null}
POST /search/qna
```

**Request Body:**

```json theme={null}
{
  "question": "What did the document say about pricing?",
  "session_id": "user-session-123",
  "tenant_id": "your_tenant_id",
  "context_list": ["optional_specific_file_id"],
  "ai_generation": true,
  "highlight_chunks": true,
  "tenant_metadata": {"source_title": "contract.pdf"}
  "document_metadata": {"source_title": "contract.pdf"}
}
```

**Example (cURL):**

```mdx theme={null}
curl -X POST 'https://api.usecortex.ai/search/qna' \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "question": "Summarize the contract terms",
    "session_id": "user-123-session",
    "tenant_id": "your_tenant_id",
    "tenant_metadata": {"source_title": "contract.pdf"}
    "document_metadata": {"source_title": "contract.pdf"}
  }'
```

### **Step 5: Display the Answer in Your App**

You'll get a JSON response like:

```json theme={null}
{
  "answer": "The contract specifies a 12-month term with a 30-day opt-out period...",
  "sources": [...],
  "chunks": [...]
}
```

Render the answer in your UI and include clickable citations if desired. Citations can reference:

* **Source filename** (e.g., `contract.pdf`)
* **Page number**
* **Snippet preview**

If available, use the `bounding_box` to enable clickable highlights or coordinate-based jumping in PDFs.

You can use the `tenant_metadata` and `document_metadata` parameters to restrict the context to only sources matching specific criteria:

* **`tenant_metadata`**: Filter by organizational attributes (department, compliance framework) that apply to all documents in your tenant
* **`document_metadata`**: Filter by document-specific attributes (title, author, document type) that vary per document

For example, `{ "source_title": "contract.pdf" }` will only use sources with that title for answering the question. You can combine both metadata types for precise filtering: `{ "department": "Legal", "document_type": "contract" }` to find all legal contracts.

### **Step 6: Iterate (Optional)**

Fine-tune the user experience with:

* `AI response (true or false)`: Lets you decide if you only want the retrieved context or the AI generated response to user queries using retrieved context
* `search_alpha (0-1)`: Prioritize keyword vs. semantic match. An Alpha of "1" means semantic match. An Alpha of "0" means exact keyword match
* `recency_bias (0-1)`: Control how much you want to favour newly added knowledge. 1 you strongly favour recently added knowledge. 0 means freshness of knowledge doesn't matter.
* `highlight_chunks`: Get the actual context/chunks can be used for generating answers for RAG or Agentic purposes
* `stream`: for real-time, streaming answers

### 🧩 Optional Extensions

* **Batch Upload**: `/upload/batch_upload` for multiple files
* **Delete Memory**: `/delete_source` to remove documents
* **List Sources**: `/list/sources` to display all uploaded documents