TradingAgents/tests/fixtures/api_responses/openai_embeddings.json

{
  "description": "Mock OpenAI API embedding responses for testing",
  "model": "text-embedding-3-small",
  "examples": {
    "single_text_embedding": {
      "object": "list",
      "data": [
        {
          "object": "embedding",
          "index": 0,
          "embedding": [
            -0.006929283495992422,
            -0.005336422007530928,
            0.00047350498218461871,
            -0.024047505110502243,
            0.013851520791649818,
            -0.02005230076611042,
            0.0052589345723390579,
            -0.011878303624689579,
            -0.00025520036462694407,
            0.015827439725399017,
            -0.010150175541639328,
            0.023847095295786858,
            -0.0088148806244134903,
            0.019137535244226456,
            -0.003254246478900313,
            0.007801613025367260,
            -0.012429786287248135,
            0.009863543696701527,
            -0.002845674939453602,
            0.004567321203649044
          ]
        }
      ],
      "model": "text-embedding-3-small",
      "usage": {
        "prompt_tokens": 8,
        "total_tokens": 8
      }
    },
    "batch_text_embeddings": {
      "object": "list",
      "data": [
        {
          "object": "embedding",
          "index": 0,
          "embedding": [
            -0.006929283495992422,
            -0.005336422007530928,
            0.00047350498218461871,
            -0.024047505110502243,
            0.013851520791649818,
            -0.02005230076611042,
            0.0052589345723390579,
            -0.011878303624689579,
            -0.00025520036462694407,
            0.015827439725399017
          ]
        },
        {
          "object": "embedding",
          "index": 1,
          "embedding": [
            0.012345678901234567,
            -0.009876543210987654,
            0.005432109876543211,
            -0.018765432109876543,
            0.007654321098765432,
            -0.015432109876543211,
            0.009123456789012345,
            -0.013456789012345678,
            0.002345678901234567,
            0.011234567890123456
          ]
        },
        {
          "object": "embedding",
          "index": 2,
          "embedding": [
            -0.008765432109876543,
            0.013456789012345678,
            -0.003456789012345678,
            0.009876543210987654,
            -0.007654321098765432,
            0.015432109876543211,
            -0.011234567890123456,
            0.006789012345678901,
            -0.002345678901234567,
            0.010123456789012345
          ]
        }
      ],
      "model": "text-embedding-3-small",
      "usage": {
        "prompt_tokens": 24,
        "total_tokens": 24
      }
    },
    "financial_situation_embedding": {
      "description": "Embedding for a financial situation used in memory system",
      "input": "Market showing strong bullish momentum with RSI at 68.5 and price breaking above resistance at $240",
      "object": "list",
      "data": [
        {
          "object": "embedding",
          "index": 0,
          "embedding": [
            0.023847095295786858,
            -0.0088148806244134903,
            0.019137535244226456,
            -0.003254246478900313,
            0.007801613025367260,
            -0.012429786287248135,
            0.009863543696701527,
            -0.002845674939453602,
            0.004567321203649044,
            -0.006929283495992422,
            -0.005336422007530928,
            0.00047350498218461871,
            -0.024047505110502243,
            0.013851520791649818,
            -0.02005230076611042,
            0.0052589345723390579,
            -0.011878303624689579,
            -0.00025520036462694407,
            0.015827439725399017,
            -0.010150175541639328
          ]
        }
      ],
      "model": "text-embedding-3-small",
      "usage": {
        "prompt_tokens": 17,
        "total_tokens": 17
      }
    },
    "large_embedding_1536": {
      "description": "Full-size embedding (1536 dimensions) - truncated for brevity in fixture",
      "note": "In real usage, this would have 1536 float values. For testing, use smaller dimension or generate random values.",
      "object": "list",
      "data": [
        {
          "object": "embedding",
          "index": 0,
          "embedding": [
            -0.006929283495992422,
            -0.005336422007530928,
            0.00047350498218461871
          ],
          "_note": "... (1533 more values truncated for readability)"
        }
      ],
      "model": "text-embedding-3-small",
      "usage": {
        "prompt_tokens": 8,
        "total_tokens": 8
      }
    }
  },
  "error_responses": {
    "rate_limit_error": {
      "error": {
        "message": "Rate limit exceeded. Please try again later.",
        "type": "rate_limit_error",
        "param": null,
        "code": "rate_limit_exceeded"
      }
    },
    "invalid_api_key": {
      "error": {
        "message": "Incorrect API key provided. You can find your API key at https://platform.openai.com/account/api-keys.",
        "type": "invalid_request_error",
        "param": null,
        "code": "invalid_api_key"
      }
    },
    "model_not_found": {
      "error": {
        "message": "The model `invalid-model` does not exist",
        "type": "invalid_request_error",
        "param": null,
        "code": "model_not_found"
      }
    }
  },
  "usage_notes": {
    "embedding_dimensions": {
      "text-embedding-3-small": 1536,
      "text-embedding-3-large": 3072,
      "text-embedding-ada-002": 1536
    },
    "testing_recommendations": [
      "For unit tests, use small embeddings (10-20 dimensions) to save memory",
      "For integration tests, use realistic dimension counts but mock the API",
      "Use consistent random seeds for reproducible test embeddings",
      "Test edge cases: empty text, very long text, special characters"
    ]
  }
}