import requests

url = "https://api.memic.ai/api/v1/search"

payload = {
    "query": "<string>",
    "project_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
    "file_ids": ["3c90c3cc-0d44-4b50-8888-8dd25736052a"],
    "environment_slug": "<string>",
    "top_k": 10,
    "min_score": 0.7,
    "include_metadata": True,
    "connector_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
    "metadata_filters": {
        "reference_id": "<string>",
        "reference_ids": ["<string>"],
        "page_number": 2,
        "page_numbers": [123],
        "page_range": {
            "gte": 2,
            "lte": 2
        },
        "category": "<string>",
        "document_type": "<string>"
    }
}
headers = {
    "X-API-Key": "<api-key>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

curl --request POST \
  --url https://api.memic.ai/api/v1/search \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "query": "<string>",
  "project_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "file_ids": [
    "3c90c3cc-0d44-4b50-8888-8dd25736052a"
  ],
  "environment_slug": "<string>",
  "top_k": 10,
  "min_score": 0.7,
  "include_metadata": true,
  "connector_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "metadata_filters": {
    "reference_id": "<string>",
    "reference_ids": [
      "<string>"
    ],
    "page_number": 2,
    "page_numbers": [
      123
    ],
    "page_range": {
      "gte": 2,
      "lte": 2
    },
    "category": "<string>",
    "document_type": "<string>"
  }
}
'

const options = {
  method: 'POST',
  headers: {'X-API-Key': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    query: '<string>',
    project_id: '3c90c3cc-0d44-4b50-8888-8dd25736052a',
    file_ids: ['3c90c3cc-0d44-4b50-8888-8dd25736052a'],
    environment_slug: '<string>',
    top_k: 10,
    min_score: 0.7,
    include_metadata: true,
    connector_id: '3c90c3cc-0d44-4b50-8888-8dd25736052a',
    metadata_filters: {
      reference_id: '<string>',
      reference_ids: ['<string>'],
      page_number: 2,
      page_numbers: [123],
      page_range: {gte: 2, lte: 2},
      category: '<string>',
      document_type: '<string>'
    }
  })
};

fetch('https://api.memic.ai/api/v1/search', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

{
  "filters_applied": {
    "project_id": "d1710969-5973-4cf1-8c4e-3264158e765f"
  },
  "namespace": "8e9fd1e0-e355-4394-9878-b59ec0fffcfd",
  "query": "quarterly revenue",
  "results": {
    "semantic": [
      {
        "category": "Security",
        "chunk_id": "123e4567-e89b-12d3-a456-426614174000",
        "chunk_index": 5,
        "content": "The cybersecurity landscape shows increasing threats...",
        "file_id": "123e4567-e89b-12d3-a456-426614174001",
        "file_name": "Cyber_Report_2024.pdf",
        "mime_type": "application/pdf",
        "page_number": 3,
        "score": 0.92,
        "tags": [
          "cybersecurity",
          "report"
        ],
        "token_count": 256
      }
    ],
    "structured": {
      "columns": [
        {
          "description": "Customer name",
          "name": "customer",
          "type": "varchar"
        },
        {
          "name": "revenue",
          "type": "integer"
        }
      ],
      "rows": [
        {
          "customer": "Acme",
          "revenue": 50000
        },
        {
          "customer": "Beta",
          "revenue": 75000
        }
      ]
    }
  },
  "routing": {
    "reasoning": "Query combines document search with structured data",
    "route": "hybrid"
  },
  "search_time_ms": 125.5,
  "total_results": 3
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}