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      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">G</span><h2 id=\"sheet-title\">Google: Gemini Embedding 001</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Close\">×</button></div><div class=\"idline\"><span>Google /</span><code>google/gemini-embedding-001</code><button type=\"button\" id=\"copy-id\">Copy ID</button></div><nav class=\"card-jump-nav\" aria-label=\"Model card sections\"><button type=\"button\" data-card-jump=\"overview\">Overview</button><button type=\"button\" data-card-jump=\"methods\">Inputs &amp; methods</button><button type=\"button\" data-card-jump=\"pricing\">Pricing</button><button type=\"button\" data-card-jump=\"examples\">Examples</button><button type=\"button\" data-card-jump=\"results\">Results</button><button type=\"button\" data-card-jump=\"aliases\">Aliases</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"API methods\"><button type=\"button\" role=\"tab\" tabindex=\"0\" aria-controls=\"card-method-content\" aria-selected=\"true\" id=\"card-method-tab-0\" data-card-method=\"Ultra|embeddings|/v1/embeddings|google%2Fgemini-embedding-001\">Embeddings</button></div><div id=\"card-method-content\" role=\"tabpanel\" aria-labelledby=\"card-method-tab-0\"><section class=\"card-section\" data-card-section=\"overview\"><h3 tabindex=\"-1\">Overview</h3><p class=\"mdesc-full\">Text or token-ID input to vectors, with float encoding and a listed 20,000-token hosted context.</p><dl class=\"card-io\" data-card-io><div><dt>Accepted inputs</dt><dd>Text · token_ids</dd></div><div><dt>Results</dt><dd>embedding</dd></div></dl><details class=\"card-about\"><summary>About the model</summary><p class=\"note-s\" data-model-method-boundary>This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.</p><div class=\"mabout\" id=\"m-about\"><p>google/gemini-embedding-001 represents text as vectors for retrieval. A practical use is finding the relevant technical passage when a question uses different wording from the document. The Ailoot route lists a 20,000-token hosted context and accepts text strings, string arrays or token-ID arrays, using one consistent input kind per request.</p><p>The result contains indexed finite-float vectors. The accepted contract does not publish a default vector width and explicitly excludes a dimensions override; establish your index width from the actual returned vector length and keep it fixed for that index. encoding_format supports float. No task-type or query/document selector is exposed on this route. Use a consistent preparation convention for questions and documents, and keep the exact model and vector width together when building or updating the index.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Available in Ailoot · inputs &amp; methods</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · Embeddings</b> <code>POST /v1/embeddings</code></summary><dl class=\"method-capabilities\"><div><dt>Input types</dt><dd><code>text</code>, <code>token_ids</code></dd></div><div><dt>Output types</dt><dd><code>embedding</code></dd></div><div><dt>Context</dt><dd>20,000 tokens</dd></div><div><dt>Output format</dt><dd>json</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Field</th><th>Type</th><th>Requirement</th><th>Values &amp; limits</th><th>Default</th></tr></thead><tbody><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Required</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>google/gemini-embedding-001</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">input</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string|string[]|integer[]|integer[][]</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Required</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>Minimum items: 1</div><div>Conditions: Nonempty homogeneous batch. Token IDs are nonnegative integers.</div><div>Input budget: Maximum: 20,000 · Text unit: UTF8 bytes · Token unit: token-array elements</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">encoding_format</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>string</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Optional</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>float</div></td><td data-cell-label=\"Default\" data-default=\"known\"><span class=\"card-cell-label\">Default</span>float</td></tr><tr data-parameter-availability=\"unsupported\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">dimensions</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>integer</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Not supported</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>Minimum: 1</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">user</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>json_value</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Optional</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific.</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">session_id</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>json_value</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Optional</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific.</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr><tr data-parameter-availability=\"pass_through_metadata\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">trace</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>json_value</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Optional</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific.</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr><tr data-parameter-availability=\"source_interface\"><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">provider</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span><code>object</code></td><td data-cell-label=\"Requirement\"><span class=\"card-cell-label\">Requirement</span>Optional</td><td data-cell-label=\"Values &amp; limits\"><span class=\"card-cell-label\">Values &amp; limits</span><div>Conditions: provider.options rejected</div></td><td data-cell-label=\"Default\" data-default=\"unspecified\"><span class=\"card-cell-label\">Default</span>—</td></tr></tbody></table></div><details class=\"card-about\" data-material-combinations><summary>Input material combinations</summary><p class=\"note-s\"><b>text_to_embedding</b></p></details><details class=\"note-s\" data-method-contract><summary>Method parameters and lifecycle</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parameter</th><th>Contract</th></tr></thead><tbody><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>content_type</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>application/json</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>required</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>model, input</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>input_shapes</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>string, string[], nonnegative_integer_token_array, nonnegative_integer_token_array[]</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>encoding_format</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>float</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>dimensions_override</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>false</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>max_input_budget_units</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>20000</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>multimodal_objects</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>false</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>material_types</code></th><td data-cell-label=\"Contract\"><span class=\"card-cell-label\">Contract</span>text, token_ids</td></tr></tbody></table></div><p>Lifecycle: <code>synchronous_json</code></p><p class=\"note-s\">Keep every vector index and dimension.</p><p class=\"note-s\">Multimodal model name does not admit image objects on this exact path.</p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Pricing</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>Tariff references do not add request options. Supported parameters are listed for the selected method.</p><dl data-price-conditions><dt>Fast mode</dt><dd>Standard rate <span class=\"note-s\">When fast mode is selected</span></dd><dt>Flex mode</dt><dd>Standard rate <span class=\"note-s\">When flex mode is selected</span></dd><dt>Maximum reasoning effort</dt><dd>Standard rate <span class=\"note-s\">When maximum reasoning effort uses the default adjustment</span></dd></dl></div><div data-declared-tariff-pricing><h4>Ailoot rates</h4><h4>Prices by configuration</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Configuration</th><th>Rate schedule</th><th>Charge</th></tr></thead><tbody><tr><th data-cell-label=\"Configuration\" scope=\"row\"><span class=\"card-cell-label\">Configuration</span>Base token rates</th><td data-cell-label=\"Rate schedule\"><span class=\"card-cell-label\">Rate schedule</span>Base rate</td><td data-cell-label=\"Charge\"><span class=\"card-cell-label\">Charge</span><div><span>Input · Input tokens</span> <b>$0.0003</b> <span class=\"price-unit\">/ 1K tokens</span></div><div><span>Output · Output tokens</span> <b>$0</b> <span class=\"price-unit\">/ 1K tokens</span></div><div><span>Input · Cache read</span> <b>—</b> <span class=\"price-unit\">/ 1K tokens</span></div><div><span>Input · Cache write</span> <b>—</b> <span class=\"price-unit\">/ 1K tokens</span></div><div><span>Input · Cache write, 1 hour</span> <b>—</b> <span class=\"price-unit\">/ 1K tokens</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. <a href=\"/docs/en/billing/reserve/\">How reservation and settlement work →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">Example requests</h3><h4>Ailoot Ultra · Embeddings</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Code options\"><button class=\"cg-tab\" id=\"model-code-1486-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1486-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl</button><button class=\"cg-tab\" id=\"model-code-1486-tab-1\" role=\"tab\" type=\"button\" aria-controls=\"model-code-1486-panel-1\" aria-selected=\"false\" tabindex=\"-1\">Python SDK</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Copy code\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-1486-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-1486-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;google/gemini-embedding-001&quot;, &quot;input&quot;: [&quot;First document&quot;, &quot;Second document&quot;]}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - &lt;&lt;&#39;PY_RESULT&#39;\nimport json\nfrom pathlib import Path\nr=json.loads(Path(&quot;result.json&quot;).read_text())\nfor x in r.get(&quot;data&quot;,[]): print(json.dumps({&quot;index&quot;:x.get(&quot;index&quot;),&quot;embedding&quot;:x.get(&quot;embedding&quot;)}))\nPY_RESULT\n</code></pre></div><div class=\"cg-pane\" id=\"model-code-1486-panel-1\" role=\"tabpanel\" aria-labelledby=\"model-code-1486-tab-1\" hidden><pre data-lang=\"python\"><code>from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=&quot;https://api.ailoot.ai/v1&quot;,api_key=os.environ[&quot;AILOOT_API_KEY&quot;],max_retries=0)\nresult=client.embeddings.create(model=&#39;google/gemini-embedding-001&#39;,input=[&quot;First document&quot;,&quot;Second document&quot;])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Receive and save results</h3><h4>Ailoot Ultra · Embeddings</h4><p class=\"note-s\">json</p><div class=\"card-result-process\" data-all-results><h5>Keep all completed results</h5><p class=\"note-s\">Keep every complete returned response, file and content part.</p><p class=\"note-s\">Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>If the original response needs checking</h5><p class=\"note-s\">No embedding job endpoint is introduced.</p></div><details class=\"card-about\" data-output-fields><summary>Response fields</summary><div class=\"tw\" data-card-table=\"output\"><table><thead><tr><th>Field</th><th>Type</th></tr></thead><tbody><tr><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">object</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>string</td></tr><tr><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>string</td></tr><tr><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">data[].index</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>integer</td></tr><tr><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">data[].embedding</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>finite float[]</td></tr><tr><th data-cell-label=\"Field\" scope=\"row\"><span class=\"card-cell-label\">Field</span><code class=\"card-field-name\">usage</code></th><td data-cell-label=\"Type\" data-field-type><span class=\"card-cell-label\">Type</span>object</td></tr></tbody></table></div></details><details class=\"card-about\" data-method-errors><summary>Errors and next steps</summary><div class=\"tw\" data-card-table=\"errors\"><table><thead><tr><th>Response</th><th>Condition</th><th>Next step</th></tr></thead><tbody><tr><th data-cell-label=\"Response\" scope=\"row\"><span class=\"card-cell-label\">Response</span>400 · <code>invalid_request_error</code></th><td data-cell-label=\"Condition\"><span class=\"card-cell-label\">Condition</span>Invalid required fields, material representation or parameter value</td><td data-cell-label=\"Next step\"><span class=\"card-cell-label\">Next step</span>Correct the indicated fields before a new creation request.</td></tr></tbody></table></div></details></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Aliases</h3><h4>Model aliases</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=google%2Fgemini-embedding-001&amp;route=Ultra&amp;caller=google%2Fgemini-embedding-001&amp;fmt=embeddings\"><code>google/gemini-embedding-001</code></a></span></p><h4>Method path aliases</h4><p class=\"note-s\">Use the method paths shown above.</p><h4>Related caller IDs</h4><p class=\"note-s\"><a href=\"#m=google%2Fgemini-embedding-001&amp;route=Ultra&amp;caller=google%2Fgemini-embedding-001&amp;fmt=embeddings\"><code>google/gemini-embedding-001</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "google/gemini-embedding-001",
        "fmt": "embeddings",
        "http": "POST",
        "path": "/v1/embeddings",
        "request_model_id": "google/gemini-embedding-001",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|embeddings|/v1/embeddings|google%2Fgemini-embedding-001",
      "ordinal": 0,
      "rawMethod": {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Invalid required fields, material representation or parameter value",
                "http_status": 400,
                "recovery": "Correct the indicated fields before a new creation request.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "No embedding job endpoint is introduced."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "google/gemini-embedding-001"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 20000,
                  "text_unit": "UTF8 bytes",
                  "token_unit": "token-array elements"
                },
                "conditions": "Nonempty homogeneous batch. Token IDs are nonnegative integers.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Source allows this JSON metadata field; exact upstream semantics are model-specific.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Source allows this JSON metadata field; exact upstream semantics are model-specific.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Source allows this JSON metadata field; exact upstream semantics are model-specific.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options rejected",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Keep every vector index and dimension.",
            "Multimodal model name does not admit image objects on this exact path."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 20000,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": 20000,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"google/gemini-embedding-001\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='google/gemini-embedding-001',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0.3,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Standard rate",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Standard rate",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Standard rate",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "google/gemini-embedding-001",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.0003000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000003",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    }
  ],
  "model_id": "google/gemini-embedding-001",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "0759870f1ac196cc7298f0c192c75aebffb77b99df2d137b11224ea830692536",
    "input_html_sha256": "185652756d2ee1ade9fac76e3d08c94b7e54c5277255beae747dba006eabe090",
    "private_audit_sha256": "6719681a98f19241db0b9a499e4eb3b1abf2bef1de1ca8111f7bf04eb45251a4",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/en/google-gemini-embedding-001/",
    "description": "Text or token-ID input to vectors, with float encoding and a listed 20,000-token hosted context.",
    "locale": "en",
    "md_url": "https://ailoot.ai/models/en/google-gemini-embedding-001/index.md",
    "title": "Google: Gemini Embedding 001 — Ailoot",
    "url": "https://ailoot.ai/models/en/google-gemini-embedding-001/"
  },
  "rawModel": {
    "about": "Gemini Embedding 001 is Google’s text embedding model for representing written material as vectors. Its published role covers several domains, including science, legal text, finance and coding. Text is the input and embedding vectors are the output; a retrieval application uses those vectors to find related source items.\n\n### Prepare source items that remain identifiable\n\nChoose a passage or document item that contains the information a user needs to find. Preserve its document name, section label and source identifier. A code item can retain its file and surrounding purpose; a technical passage can retain the terminology and units needed to interpret it. These labels make a later match useful as evidence rather than only as a similarity score.\n\nKeep item boundaries meaningful. A definition and its explanation may belong together, while unrelated subjects should retain separate identities. Preserve the original text alongside the vector so a retrieved item can be inspected or passed to a later answer-generation stage.\n\n### Use vectors in a retrieval workflow\n\nKeep the query and indexed material in a compatible embedding setup. The application stores the vectors, compares them and returns the corresponding source items. Review representative queries against the passages that should be found, including difficult vocabulary and different ways of expressing the same need.\n\nA useful retrieval result includes the source identifier and relevant text. For a later answer, retain the passage that supports each finding. The embedding model supplies the representation; a separate application step performs search or generates a prose answer from the retrieved evidence.\n\n### Input allowance and index continuity\n\nThe documented hosted profile accepts up to 20,000 tokens per input. Split longer collections into separate, labeled items and preserve their relationships. Record the model and preparation used for an index, so changes to the embedding setup can be distinguished from changes in the source collection.\n\nA completed search artifact joins the query with the selected source passages and their identifiers. Inspect wrong or missing matches against the actual text and item boundaries, then carry the relevant evidence into the task that follows retrieval.",
    "alias_of": null,
    "aliases": [],
    "author": "Google",
    "available": null,
    "canonical_model": "google/gemini-embedding-001",
    "capabilities": {
      "context_length": 20000,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "embeddings"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0.0003000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000003",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          }
        ],
        "summary": "Full configuration pricing in details."
      }
    ],
    "ctx": 20000,
    "entity_type": "model",
    "formats": [
      "embeddings"
    ],
    "id": "google/gemini-embedding-001",
    "input": [
      "text",
      "token_ids"
    ],
    "kind": "embedding",
    "max_out": null,
    "methods": [
      {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
            "errors": [
              {
                "code": null,
                "condition": "Invalid required fields, material representation or parameter value",
                "http_status": 400,
                "recovery": "Correct the indicated fields before a new creation request.",
                "type": "invalid_request_error"
              }
            ],
            "hints": [],
            "ignored_fields": [],
            "lifecycle": {
              "create": {
                "http": "POST",
                "path": "/v1/embeddings"
              },
              "job_read": null,
              "mode": "synchronous_json",
              "unknown": "No embedding job endpoint is introduced."
            },
            "material_combinations": [
              {
                "alternatives": [
                  "string",
                  "string[]",
                  "nonnegative_integer_token_array",
                  "nonnegative_integer_token_array[]"
                ],
                "mode": "text_to_embedding"
              }
            ],
            "operation_modes": [
              "single_embedding",
              "batched_embeddings"
            ],
            "outputs": {
              "all_results": "Keep every vector with its index. Vector count must equal input count; dimensions must be consistent.",
              "default_dimensions": null,
              "family": "embedding",
              "fields": [
                {
                  "description": null,
                  "name": "object",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "model",
                  "type": "string"
                },
                {
                  "description": null,
                  "name": "data[].index",
                  "type": "integer"
                },
                {
                  "description": null,
                  "name": "data[].embedding",
                  "type": "finite float[]"
                },
                {
                  "description": null,
                  "name": "usage",
                  "type": "object"
                }
              ],
              "format": "json"
            },
            "parameters": [
              {
                "availability": "source_interface",
                "default": null,
                "default_known": false,
                "enum": [
                  "google/gemini-embedding-001"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "availability": "source_interface",
                "budget": {
                  "maximum": 20000,
                  "text_unit": "UTF8 bytes",
                  "token_unit": "token-array elements"
                },
                "conditions": "Nonempty homogeneous batch. Token IDs are nonnegative integers.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "min_items": 1,
                "name": "input",
                "required": true,
                "type": "string|string[]|integer[]|integer[][]"
              },
              {
                "availability": "source_interface",
                "default": "float",
                "default_known": true,
                "enum": [
                  "float"
                ],
                "enum_known": true,
                "name": "encoding_format",
                "required": false,
                "type": "string"
              },
              {
                "availability": "unsupported",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "minimum": 1,
                "name": "dimensions",
                "required": false,
                "type": "integer"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Source allows this JSON metadata field; exact upstream semantics are model-specific.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Source allows this JSON metadata field; exact upstream semantics are model-specific.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "session_id",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "pass_through_metadata",
                "conditions": "Source allows this JSON metadata field; exact upstream semantics are model-specific.",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "trace",
                "required": false,
                "type": "json_value"
              },
              {
                "availability": "source_interface",
                "conditions": "provider.options rejected",
                "default": null,
                "default_known": false,
                "enum": null,
                "enum_known": false,
                "name": "provider",
                "required": false,
                "type": "object"
              }
            ],
            "profile_binding": "Exact existing Ailoot model and method; interface constraints below do not assert provider qualification.",
            "unsupported": [
              "mixed input kinds",
              "empty input",
              "image/audio/file objects",
              "encoding_format=base64",
              "dimensions override"
            ]
          },
          "hints": [
            "Keep every vector index and dimension.",
            "Multimodal model name does not admit image objects on this exact path."
          ],
          "input": {
            "content_type": "application/json",
            "dimensions_override": false,
            "encoding_format": [
              "float"
            ],
            "input_shapes": [
              "string",
              "string[]",
              "nonnegative_integer_token_array",
              "nonnegative_integer_token_array[]"
            ],
            "material_types": [
              "text",
              "token_ids"
            ],
            "max_input_budget_units": 20000,
            "multimodal_objects": false,
            "required": [
              "model",
              "input"
            ]
          },
          "lifecycle": {
            "mode": "synchronous_json",
            "unknown": "no embedding job endpoint inferred"
          },
          "manufacturer_capability": null,
          "method": {
            "http": "POST",
            "path": "/v1/embeddings"
          },
          "method_aliases": [],
          "operation_modes": [
            "text_to_embedding"
          ],
          "output": {
            "default_dimensions": null,
            "family": "embedding",
            "format": "json",
            "http_success": 200,
            "vectors": "one dense float vector per input; indices/model/usage validated"
          }
        },
        "ctx": 20000,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"google/gemini-embedding-001\", \"input\": [\"First document\", \"Second document\"]}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"data\",[]): print(json.dumps({\"index\":x.get(\"index\"),\"embedding\":x.get(\"embedding\")}))\nPY_RESULT\n",
            "label": "curl · retained exact creation",
            "language": "bash"
          },
          {
            "code": "from openai import OpenAI\nimport os\n\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.embeddings.create(model='google/gemini-embedding-001',input=[\"First document\",\"Second document\"])\nprint(result)\nfor item in result.data:print(item.index,item.embedding)\n",
            "label": "Python SDK · all vectors",
            "language": "python"
          }
        ],
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "embeddings",
        "hints": [
          "Keep every vector index and dimension.",
          "Multimodal model name does not admit image objects on this exact path."
        ],
        "http": "POST",
        "in": 0.3,
        "input": [
          "text",
          "token_ids"
        ],
        "label": "Эмбеддинги",
        "max_out": null,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "embedding"
        ],
        "path": "/v1/embeddings",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Standard rate",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Standard rate",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Standard rate",
            "when": "When maximum reasoning effort uses the default adjustment"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "image_input_price": null,
          "image_output_price": null,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0.0003000",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0.0000003",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "google/gemini-embedding-001",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "units": [
          {
            "amount": "0.0003000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000003",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    ],
    "model_aliases": [
      {
        "canonical_model_id": "google/gemini-embedding-001",
        "request_model_id": "google/gemini-embedding-001",
        "route_label": "Ultra"
      }
    ],
    "name": "Google: Gemini Embedding 001",
    "price": {
      "in": 0.3,
      "in_label": null,
      "out": 0,
      "out_label": null,
      "scope_label": "Token rates",
      "sort_in": 0.3,
      "sort_out": 0,
      "varies": false
    },
    "pricing_profiles": [
      {
        "dimensions": {
          "regime": "base"
        },
        "label": "Эмбеддинги · Base token rates",
        "units": [
          {
            "amount": "0.0003000",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0.0000003",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    ],
    "public_id": "google/gemini-embedding-001",
    "short": "Gemini Embedding 001 is Google’s text embedding model for retrieval across domains such as science, legal material, finance and code. It converts text into vectors, with a documented hosted allowance of up to 20,000 tokens per input."
  },
  "renderer_schema": {
    "method_order": "Original DTO methods order; every exact route/format/path/caller survives.",
    "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers."
  },
  "representation": "exact-public-dto-and-native-renderer-html",
  "representationNotes": {
    "current_reference": {
      "about_json_pointer": "/aboutHtml",
      "methods": [
        {
          "contract_json_pointer": "/methods/0/rawMethod/contract",
          "html_json_pointer": "/methods/0/html",
          "method_ordinal": 0
        }
      ],
      "role": "current-semantic-reference"
    },
    "raw_input": {
      "examples_role": "historical-input-not-current-ready-examples",
      "json_pointers": [
        "/rawModel",
        "/methods/0/rawMethod"
      ],
      "role": "historical-owner-input"
    },
    "schema": "ailoot-native-model-representations-v1"
  },
  "schema": "ailoot-static-model-facts-v1",
  "schema_version": 1,
  "shortText": "Text or token-ID input to vectors, with float encoding and a listed 20,000-token hosted context.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "517e37219d7cfeca68e36bb8f81d2ba885b0c47e060e63463cf75fbfa0ad844d",
    "renderer_sha256": "fed50305bd3bcc4980ac14df23640efb8c1bc1e819f9d53643d6f43e8974d3fe"
  },
  "sourceRevision": "5f973a386ac9ed680421aae03325af17a96f15ca938f9c8db5975d08d7745185"
}