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[BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type #65

Description

@braintrust-bot

Summary

When client.models.embedContent() is called through the instrumented Google GenAI client, the call is captured at the HTTP level but the span contains almost no useful embedding-specific detail. The tagSpan() method in BraintrustApiClient only extracts fields relevant to generateContent (like contents, generationConfig, usageMetadata), which are absent from embedding requests and responses.

The span is created with the correct operation name (embed_content) but has:

  • Empty input_json: only {"model": "..."} — the actual content being embedded, taskType, title, and outputDimensionality are not extracted
  • No metrics: embedding responses use metadata.billableCharacterCount instead of usageMetadata, so no token/character counts are captured
  • Incorrect span type: marked as type: "llm" rather than type: "embedding" (or equivalent)

The full response is stored in output_json as a raw dump, so the embedding vector data is technically present but not meaningfully structured.

What is missing

In BraintrustApiClient.tagSpan() (lines 50–161):

Request parsing (lines 97–112):

  • Checks for contents (generateContent field) but embedContent uses content (singular)
  • Checks for generationConfig but embedContent uses taskType, title, outputDimensionality
  • Result: input_json only contains {"model": "..."} for embedding calls

Response parsing (lines 116–149):

  • Checks for usageMetadata with promptTokenCount/candidatesTokenCount but embedContent responses have metadata with billableCharacterCount
  • Result: no metrics are captured

Span attributes (line 156):

  • Hardcodes type: "llm" for all calls including embeddings

Braintrust docs status

  • The Braintrust Gemini integration docs at braintrust.dev/docs/integrations/ai-providers/gemini do not mention embeddings: not_found
  • No embeddings instrumentation is documented for any provider in Java

Upstream sources

  • Google GenAI embeddings docs: https://ai.google.dev/gemini-api/docs/embeddings — documents embedContent as a stable, first-class API with models like gemini-embedding-001
  • Google GenAI Java SDK: client.models.embedContent() is available with EmbedContentConfig (taskType, title, outputDimensionality) and returns EmbedContentResponse with embeddings and metadata
  • embedContent request format: uses content (singular), taskType, title, outputDimensionality — none of which match the generateContent fields currently extracted
  • embedContent response format: returns embedding.values array and metadata.billableCharacterCount — not usageMetadata

Local files inspected

  • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustApiClient.java — lines 50–161 (tagSpan only extracts generateContent-relevant fields), lines 325–333 (getOperation correctly parses embedContent to embed_content)
  • braintrust-sdk/instrumentation/genai_1_18_0/src/test/java/dev/braintrust/instrumentation/genai/v1_18_0/BraintrustGenAITest.java — no embedContent test exists
  • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustInstrumentation.java — wraps ApiClient generically, no embedding-specific logic

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      [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type · Issue #65 · braintrustdata/braintrust-sdk-java · GitHub
      Skip to content

      [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type #65

      Description

      @braintrust-bot

      Summary

      When client.models.embedContent() is called through the instrumented Google GenAI client, the call is captured at the HTTP level but the span contains almost no useful embedding-specific detail. The tagSpan() method in BraintrustApiClient only extracts fields relevant to generateContent (like contents, generationConfig, usageMetadata), which are absent from embedding requests and responses.

      The span is created with the correct operation name (embed_content) but has:

      • Empty input_json: only {"model": "..."} — the actual content being embedded, taskType, title, and outputDimensionality are not extracted
      • No metrics: embedding responses use metadata.billableCharacterCount instead of usageMetadata, so no token/character counts are captured
      • Incorrect span type: marked as type: "llm" rather than type: "embedding" (or equivalent)

      The full response is stored in output_json as a raw dump, so the embedding vector data is technically present but not meaningfully structured.

      What is missing

      In BraintrustApiClient.tagSpan() (lines 50–161):

      Request parsing (lines 97–112):

      • Checks for contents (generateContent field) but embedContent uses content (singular)
      • Checks for generationConfig but embedContent uses taskType, title, outputDimensionality
      • Result: input_json only contains {"model": "..."} for embedding calls

      Response parsing (lines 116–149):

      • Checks for usageMetadata with promptTokenCount/candidatesTokenCount but embedContent responses have metadata with billableCharacterCount
      • Result: no metrics are captured

      Span attributes (line 156):

      • Hardcodes type: "llm" for all calls including embeddings

      Braintrust docs status

      • The Braintrust Gemini integration docs at braintrust.dev/docs/integrations/ai-providers/gemini do not mention embeddings: not_found
      • No embeddings instrumentation is documented for any provider in Java

      Upstream sources

      • Google GenAI embeddings docs: https://ai.google.dev/gemini-api/docs/embeddings — documents embedContent as a stable, first-class API with models like gemini-embedding-001
      • Google GenAI Java SDK: client.models.embedContent() is available with EmbedContentConfig (taskType, title, outputDimensionality) and returns EmbedContentResponse with embeddings and metadata
      • embedContent request format: uses content (singular), taskType, title, outputDimensionality — none of which match the generateContent fields currently extracted
      • embedContent response format: returns embedding.values array and metadata.billableCharacterCount — not usageMetadata

      Local files inspected

      • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustApiClient.java — lines 50–161 (tagSpan only extracts generateContent-relevant fields), lines 325–333 (getOperation correctly parses embedContent to embed_content)
      • braintrust-sdk/instrumentation/genai_1_18_0/src/test/java/dev/braintrust/instrumentation/genai/v1_18_0/BraintrustGenAITest.java — no embedContent test exists
      • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustInstrumentation.java — wraps ApiClient generically, no embedding-specific logic

      Metadata

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          , 'i'); if (__m === '*' || __re.test(location.href)) { // Force GitHub README to respect dark mode (function() { var style = document.createElement('style'); style.textContent = ' .markdown-body { color-scheme: dark light; } .markdown-body pre { background: #161b22 !important; } .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; } .markdown-body table th, .markdown-body table td { border-color: #30363d !important; } .markdown-body img { background: #0d1117; } .markdown-body blockquote { border-left-color: #8b949e; } .markdown-body hr { border-color: #30363d; } '; document.head.appendChild(style); })(); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type · Issue #65 · braintrustdata/braintrust-sdk-java · GitHub
          Skip to content

          [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type #65

          Description

          @braintrust-bot

          Summary

          When client.models.embedContent() is called through the instrumented Google GenAI client, the call is captured at the HTTP level but the span contains almost no useful embedding-specific detail. The tagSpan() method in BraintrustApiClient only extracts fields relevant to generateContent (like contents, generationConfig, usageMetadata), which are absent from embedding requests and responses.

          The span is created with the correct operation name (embed_content) but has:

          • Empty input_json: only {"model": "..."} — the actual content being embedded, taskType, title, and outputDimensionality are not extracted
          • No metrics: embedding responses use metadata.billableCharacterCount instead of usageMetadata, so no token/character counts are captured
          • Incorrect span type: marked as type: "llm" rather than type: "embedding" (or equivalent)

          The full response is stored in output_json as a raw dump, so the embedding vector data is technically present but not meaningfully structured.

          What is missing

          In BraintrustApiClient.tagSpan() (lines 50–161):

          Request parsing (lines 97–112):

          • Checks for contents (generateContent field) but embedContent uses content (singular)
          • Checks for generationConfig but embedContent uses taskType, title, outputDimensionality
          • Result: input_json only contains {"model": "..."} for embedding calls

          Response parsing (lines 116–149):

          • Checks for usageMetadata with promptTokenCount/candidatesTokenCount but embedContent responses have metadata with billableCharacterCount
          • Result: no metrics are captured

          Span attributes (line 156):

          • Hardcodes type: "llm" for all calls including embeddings

          Braintrust docs status

          • The Braintrust Gemini integration docs at braintrust.dev/docs/integrations/ai-providers/gemini do not mention embeddings: not_found
          • No embeddings instrumentation is documented for any provider in Java

          Upstream sources

          • Google GenAI embeddings docs: https://ai.google.dev/gemini-api/docs/embeddings — documents embedContent as a stable, first-class API with models like gemini-embedding-001
          • Google GenAI Java SDK: client.models.embedContent() is available with EmbedContentConfig (taskType, title, outputDimensionality) and returns EmbedContentResponse with embeddings and metadata
          • embedContent request format: uses content (singular), taskType, title, outputDimensionality — none of which match the generateContent fields currently extracted
          • embedContent response format: returns embedding.values array and metadata.billableCharacterCount — not usageMetadata

          Local files inspected

          • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustApiClient.java — lines 50–161 (tagSpan only extracts generateContent-relevant fields), lines 325–333 (getOperation correctly parses embedContent to embed_content)
          • braintrust-sdk/instrumentation/genai_1_18_0/src/test/java/dev/braintrust/instrumentation/genai/v1_18_0/BraintrustGenAITest.java — no embedContent test exists
          • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustInstrumentation.java — wraps ApiClient generically, no embedding-specific logic

          Metadata

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              , 'i'); if (__m === '*' || __re.test(location.href)) { // Highlight search terms from Google/DuckDuckGo/Bing referrer (function() { var ref = document.referrer; var terms = []; if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) { var url = new URL(ref); var q = url.searchParams.get('q') || url.searchParams.get('p'); if (q) { terms = q.split(/\s+/).filter(function(t) { return t.length > 2; }); } } if (terms.length === 0) return; var style = document.createElement('style'); style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }'; document.head.appendChild(style); function highlight(node) { if (node.nodeType === 3) { // text node var text = node.textContent; var found = false; terms.forEach(function(term) { var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi'); if (regex.test(text)) { found = true; var frag = document.createDocumentFragment(); var parts = text.split(regex); parts.forEach(function(part, i) { if (i % 2 === 0) { frag.appendChild(document.createTextNode(part)); } else { var span = document.createElement('span'); span.className = 'userscript-highlight'; span.textContent = part; frag.appendChild(span); } }); node.parentNode.replaceChild(frag, node); } }); } else if (node.nodeType === 1 && node.childNodes) { // element var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT']; if (!skipTags.includes(node.tagName)) { Array.from(node.childNodes).forEach(highlight); } } } highlight(document.body); // Re-highlight on dynamic content var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1 || node.nodeType === 3) highlight(node); }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type · Issue #65 · braintrustdata/braintrust-sdk-java · GitHub
              Skip to content

              [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type #65

              Description

              @braintrust-bot

              Summary

              When client.models.embedContent() is called through the instrumented Google GenAI client, the call is captured at the HTTP level but the span contains almost no useful embedding-specific detail. The tagSpan() method in BraintrustApiClient only extracts fields relevant to generateContent (like contents, generationConfig, usageMetadata), which are absent from embedding requests and responses.

              The span is created with the correct operation name (embed_content) but has:

              • Empty input_json: only {"model": "..."} — the actual content being embedded, taskType, title, and outputDimensionality are not extracted
              • No metrics: embedding responses use metadata.billableCharacterCount instead of usageMetadata, so no token/character counts are captured
              • Incorrect span type: marked as type: "llm" rather than type: "embedding" (or equivalent)

              The full response is stored in output_json as a raw dump, so the embedding vector data is technically present but not meaningfully structured.

              What is missing

              In BraintrustApiClient.tagSpan() (lines 50–161):

              Request parsing (lines 97–112):

              • Checks for contents (generateContent field) but embedContent uses content (singular)
              • Checks for generationConfig but embedContent uses taskType, title, outputDimensionality
              • Result: input_json only contains {"model": "..."} for embedding calls

              Response parsing (lines 116–149):

              • Checks for usageMetadata with promptTokenCount/candidatesTokenCount but embedContent responses have metadata with billableCharacterCount
              • Result: no metrics are captured

              Span attributes (line 156):

              • Hardcodes type: "llm" for all calls including embeddings

              Braintrust docs status

              • The Braintrust Gemini integration docs at braintrust.dev/docs/integrations/ai-providers/gemini do not mention embeddings: not_found
              • No embeddings instrumentation is documented for any provider in Java

              Upstream sources

              • Google GenAI embeddings docs: https://ai.google.dev/gemini-api/docs/embeddings — documents embedContent as a stable, first-class API with models like gemini-embedding-001
              • Google GenAI Java SDK: client.models.embedContent() is available with EmbedContentConfig (taskType, title, outputDimensionality) and returns EmbedContentResponse with embeddings and metadata
              • embedContent request format: uses content (singular), taskType, title, outputDimensionality — none of which match the generateContent fields currently extracted
              • embedContent response format: returns embedding.values array and metadata.billableCharacterCount — not usageMetadata

              Local files inspected

              • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustApiClient.java — lines 50–161 (tagSpan only extracts generateContent-relevant fields), lines 325–333 (getOperation correctly parses embedContent to embed_content)
              • braintrust-sdk/instrumentation/genai_1_18_0/src/test/java/dev/braintrust/instrumentation/genai/v1_18_0/BraintrustGenAITest.java — no embedContent test exists
              • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustInstrumentation.java — wraps ApiClient generically, no embedding-specific logic

              Metadata

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                  , 'i'); if (__m === '*' || __re.test(location.href)) { // Strip utm_, fbclid, gclid, etc. from all links on page (function() { var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content', 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid', 'ref', 'ref_src', 'source', 'medium', 'campaign']; function cleanUrl(url) { try { var u = new URL(url, window.location.origin); var changed = false; trackingParams.forEach(function(p) { if (u.searchParams.has(p)) { u.searchParams.delete(p); changed = true; } }); return changed ? u.toString() : url; } catch (e) { return url; } } function cleanLinks() { document.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } cleanLinks(); var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1) { if (node.tagName === 'A') cleanLinks(); node.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + ' [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type · Issue #65 · braintrustdata/braintrust-sdk-java · GitHub
                  Skip to content

                  [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type #65

                  Description

                  @braintrust-bot

                  Summary

                  When client.models.embedContent() is called through the instrumented Google GenAI client, the call is captured at the HTTP level but the span contains almost no useful embedding-specific detail. The tagSpan() method in BraintrustApiClient only extracts fields relevant to generateContent (like contents, generationConfig, usageMetadata), which are absent from embedding requests and responses.

                  The span is created with the correct operation name (embed_content) but has:

                  • Empty input_json: only {"model": "..."} — the actual content being embedded, taskType, title, and outputDimensionality are not extracted
                  • No metrics: embedding responses use metadata.billableCharacterCount instead of usageMetadata, so no token/character counts are captured
                  • Incorrect span type: marked as type: "llm" rather than type: "embedding" (or equivalent)

                  The full response is stored in output_json as a raw dump, so the embedding vector data is technically present but not meaningfully structured.

                  What is missing

                  In BraintrustApiClient.tagSpan() (lines 50–161):

                  Request parsing (lines 97–112):

                  • Checks for contents (generateContent field) but embedContent uses content (singular)
                  • Checks for generationConfig but embedContent uses taskType, title, outputDimensionality
                  • Result: input_json only contains {"model": "..."} for embedding calls

                  Response parsing (lines 116–149):

                  • Checks for usageMetadata with promptTokenCount/candidatesTokenCount but embedContent responses have metadata with billableCharacterCount
                  • Result: no metrics are captured

                  Span attributes (line 156):

                  • Hardcodes type: "llm" for all calls including embeddings

                  Braintrust docs status

                  • The Braintrust Gemini integration docs at braintrust.dev/docs/integrations/ai-providers/gemini do not mention embeddings: not_found
                  • No embeddings instrumentation is documented for any provider in Java

                  Upstream sources

                  • Google GenAI embeddings docs: https://ai.google.dev/gemini-api/docs/embeddings — documents embedContent as a stable, first-class API with models like gemini-embedding-001
                  • Google GenAI Java SDK: client.models.embedContent() is available with EmbedContentConfig (taskType, title, outputDimensionality) and returns EmbedContentResponse with embeddings and metadata
                  • embedContent request format: uses content (singular), taskType, title, outputDimensionality — none of which match the generateContent fields currently extracted
                  • embedContent response format: returns embedding.values array and metadata.billableCharacterCount — not usageMetadata

                  Local files inspected

                  • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustApiClient.java — lines 50–161 (tagSpan only extracts generateContent-relevant fields), lines 325–333 (getOperation correctly parses embedContent to embed_content)
                  • braintrust-sdk/instrumentation/genai_1_18_0/src/test/java/dev/braintrust/instrumentation/genai/v1_18_0/BraintrustGenAITest.java — no embedContent test exists
                  • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustInstrumentation.java — wraps ApiClient generically, no embedding-specific logic

                  Metadata

                  Metadata

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                      No milestone

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                      No branches or pull requests

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                      , 'i'); if (__m === '*' || __re.test(location.href)) { // Auto-enable theater mode on YouTube (function() { function tryTheater() { var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]'); if (btn && !btn.classList.contains('activated')) { btn.click(); } } // Try immediately tryTheater(); // Try after navigation (SPA) var lastUrl = location.href; setInterval(function() { if (location.href !== lastUrl) { lastUrl = location.href; setTimeout(tryTheater, 500); } }, 1000); // Also try on player load var observer = new MutationObserver(tryTheater); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type · Issue #65 · braintrustdata/braintrust-sdk-java · GitHub
                      Skip to content

                      [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type #65

                      Description

                      @braintrust-bot

                      Summary

                      When client.models.embedContent() is called through the instrumented Google GenAI client, the call is captured at the HTTP level but the span contains almost no useful embedding-specific detail. The tagSpan() method in BraintrustApiClient only extracts fields relevant to generateContent (like contents, generationConfig, usageMetadata), which are absent from embedding requests and responses.

                      The span is created with the correct operation name (embed_content) but has:

                      • Empty input_json: only {"model": "..."} — the actual content being embedded, taskType, title, and outputDimensionality are not extracted
                      • No metrics: embedding responses use metadata.billableCharacterCount instead of usageMetadata, so no token/character counts are captured
                      • Incorrect span type: marked as type: "llm" rather than type: "embedding" (or equivalent)

                      The full response is stored in output_json as a raw dump, so the embedding vector data is technically present but not meaningfully structured.

                      What is missing

                      In BraintrustApiClient.tagSpan() (lines 50–161):

                      Request parsing (lines 97–112):

                      • Checks for contents (generateContent field) but embedContent uses content (singular)
                      • Checks for generationConfig but embedContent uses taskType, title, outputDimensionality
                      • Result: input_json only contains {"model": "..."} for embedding calls

                      Response parsing (lines 116–149):

                      • Checks for usageMetadata with promptTokenCount/candidatesTokenCount but embedContent responses have metadata with billableCharacterCount
                      • Result: no metrics are captured

                      Span attributes (line 156):

                      • Hardcodes type: "llm" for all calls including embeddings

                      Braintrust docs status

                      • The Braintrust Gemini integration docs at braintrust.dev/docs/integrations/ai-providers/gemini do not mention embeddings: not_found
                      • No embeddings instrumentation is documented for any provider in Java

                      Upstream sources

                      • Google GenAI embeddings docs: https://ai.google.dev/gemini-api/docs/embeddings — documents embedContent as a stable, first-class API with models like gemini-embedding-001
                      • Google GenAI Java SDK: client.models.embedContent() is available with EmbedContentConfig (taskType, title, outputDimensionality) and returns EmbedContentResponse with embeddings and metadata
                      • embedContent request format: uses content (singular), taskType, title, outputDimensionality — none of which match the generateContent fields currently extracted
                      • embedContent response format: returns embedding.values array and metadata.billableCharacterCount — not usageMetadata

                      Local files inspected

                      • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustApiClient.java — lines 50–161 (tagSpan only extracts generateContent-relevant fields), lines 325–333 (getOperation correctly parses embedContent to embed_content)
                      • braintrust-sdk/instrumentation/genai_1_18_0/src/test/java/dev/braintrust/instrumentation/genai/v1_18_0/BraintrustGenAITest.java — no embedContent test exists
                      • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustInstrumentation.java — wraps ApiClient generically, no embedding-specific logic

                      Metadata

                      Metadata

                      Assignees

                      No one assigned

                        Labels

                        No labels
                        No labels

                        Type

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                          No milestone

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                          , 'i'); if (__m === '*' || __re.test(location.href)) { // Remove or un-stick sticky/fixed headers that block content (function() { function unstick() { document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) { if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') { el.style.position = 'static'; el.style.top = 'auto'; el.style.zIndex = 'auto'; } }); } unstick(); var observer = new MutationObserver(unstick); observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] }); })(); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type · Issue #65 · braintrustdata/braintrust-sdk-java · GitHub
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                          [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type #65

                          Description

                          @braintrust-bot

                          Summary

                          When client.models.embedContent() is called through the instrumented Google GenAI client, the call is captured at the HTTP level but the span contains almost no useful embedding-specific detail. The tagSpan() method in BraintrustApiClient only extracts fields relevant to generateContent (like contents, generationConfig, usageMetadata), which are absent from embedding requests and responses.

                          The span is created with the correct operation name (embed_content) but has:

                          • Empty input_json: only {"model": "..."} — the actual content being embedded, taskType, title, and outputDimensionality are not extracted
                          • No metrics: embedding responses use metadata.billableCharacterCount instead of usageMetadata, so no token/character counts are captured
                          • Incorrect span type: marked as type: "llm" rather than type: "embedding" (or equivalent)

                          The full response is stored in output_json as a raw dump, so the embedding vector data is technically present but not meaningfully structured.

                          What is missing

                          In BraintrustApiClient.tagSpan() (lines 50–161):

                          Request parsing (lines 97–112):

                          • Checks for contents (generateContent field) but embedContent uses content (singular)
                          • Checks for generationConfig but embedContent uses taskType, title, outputDimensionality
                          • Result: input_json only contains {"model": "..."} for embedding calls

                          Response parsing (lines 116–149):

                          • Checks for usageMetadata with promptTokenCount/candidatesTokenCount but embedContent responses have metadata with billableCharacterCount
                          • Result: no metrics are captured

                          Span attributes (line 156):

                          • Hardcodes type: "llm" for all calls including embeddings

                          Braintrust docs status

                          • The Braintrust Gemini integration docs at braintrust.dev/docs/integrations/ai-providers/gemini do not mention embeddings: not_found
                          • No embeddings instrumentation is documented for any provider in Java

                          Upstream sources

                          • Google GenAI embeddings docs: https://ai.google.dev/gemini-api/docs/embeddings — documents embedContent as a stable, first-class API with models like gemini-embedding-001
                          • Google GenAI Java SDK: client.models.embedContent() is available with EmbedContentConfig (taskType, title, outputDimensionality) and returns EmbedContentResponse with embeddings and metadata
                          • embedContent request format: uses content (singular), taskType, title, outputDimensionality — none of which match the generateContent fields currently extracted
                          • embedContent response format: returns embedding.values array and metadata.billableCharacterCount — not usageMetadata

                          Local files inspected

                          • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustApiClient.java — lines 50–161 (tagSpan only extracts generateContent-relevant fields), lines 325–333 (getOperation correctly parses embedContent to embed_content)
                          • braintrust-sdk/instrumentation/genai_1_18_0/src/test/java/dev/braintrust/instrumentation/genai/v1_18_0/BraintrustGenAITest.java — no embedContent test exists
                          • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustInstrumentation.java — wraps ApiClient generically, no embedding-specific logic

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                              , 'i'); if (__m === '*' || __re.test(location.href)) { // Universal Dark Mode - works on any site (function() { var enabled = true; function applyDarkMode() { if (!enabled) return; // Create style element if it doesn't exist var style = document.getElementById('universal-dark-mode-style'); if (!style) { style = document.createElement('style'); style.id = 'universal-dark-mode-style'; document.head.appendChild(style); } // Dark mode CSS - inverts colors but preserves images/video style.textContent = ' /* Invert everything except media */ html { filter: invert(1) hue-rotate(180deg) !important; background: #1a1a2e !important; } /* Restore images, videos, iframes, canvas */ img, video, iframe, canvas, svg, picture, [style*="background-image"] { filter: invert(1) hue-rotate(180deg) !important; } /* Preserve specific elements that should not be inverted */ .no-dark-mode, .no-dark-mode *, [data-theme="light"], [data-theme="light"], .ace_editor, .ace_editor *, .CodeMirror, .CodeMirror *, .monaco-editor, .monaco-editor *, .markdown-body pre, .markdown-body pre *, .highlight, .highlight *, pre code, pre code * { filter: none !important; } /* Fix common UI elements */ .modal, .popup, .dropdown-menu, .tooltip, .popover { filter: invert(1) hue-rotate(180deg) !important; background: #2d2d44 !important; border-color: #444 !important; } /* Scrollbars */ ::-webkit-scrollbar { background: #1a1a2e !important; } ::-webkit-scrollbar-thumb { background: #444 !important; } ::-webkit-scrollbar-thumb:hover { background: #555 !important; } /* Selection */ ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; } ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; } '; } function removeDarkMode() { var style = document.getElementById('universal-dark-mode-style'); if (style) style.remove(); } // Toggle with Alt+Shift+D document.addEventListener('keydown', function(e) { if (e.altKey && e.shiftKey && e.key === 'D') { e.preventDefault(); enabled = !enabled; if (enabled) { applyDarkMode(); console.log('[Universal Dark Mode] Enabled'); } else { removeDarkMode(); console.log('[Universal Dark Mode] Disabled'); } } }); // Apply on load applyDarkMode(); // Re-apply on dynamic content var observer = new MutationObserver(function(mutations) { if (enabled && !document.getElementById('universal-dark-mode-style')) { applyDarkMode(); } }); observer.observe(document.head, { childList: true }); console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle'); })(); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })(); [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type · Issue #65 · braintrustdata/braintrust-sdk-java · GitHub
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                              [BOT ISSUE] Google GenAI embedContent spans lack embedding-specific input, metrics, and span type #65

                              Description

                              @braintrust-bot

                              Summary

                              When client.models.embedContent() is called through the instrumented Google GenAI client, the call is captured at the HTTP level but the span contains almost no useful embedding-specific detail. The tagSpan() method in BraintrustApiClient only extracts fields relevant to generateContent (like contents, generationConfig, usageMetadata), which are absent from embedding requests and responses.

                              The span is created with the correct operation name (embed_content) but has:

                              • Empty input_json: only {"model": "..."} — the actual content being embedded, taskType, title, and outputDimensionality are not extracted
                              • No metrics: embedding responses use metadata.billableCharacterCount instead of usageMetadata, so no token/character counts are captured
                              • Incorrect span type: marked as type: "llm" rather than type: "embedding" (or equivalent)

                              The full response is stored in output_json as a raw dump, so the embedding vector data is technically present but not meaningfully structured.

                              What is missing

                              In BraintrustApiClient.tagSpan() (lines 50–161):

                              Request parsing (lines 97–112):

                              • Checks for contents (generateContent field) but embedContent uses content (singular)
                              • Checks for generationConfig but embedContent uses taskType, title, outputDimensionality
                              • Result: input_json only contains {"model": "..."} for embedding calls

                              Response parsing (lines 116–149):

                              • Checks for usageMetadata with promptTokenCount/candidatesTokenCount but embedContent responses have metadata with billableCharacterCount
                              • Result: no metrics are captured

                              Span attributes (line 156):

                              • Hardcodes type: "llm" for all calls including embeddings

                              Braintrust docs status

                              • The Braintrust Gemini integration docs at braintrust.dev/docs/integrations/ai-providers/gemini do not mention embeddings: not_found
                              • No embeddings instrumentation is documented for any provider in Java

                              Upstream sources

                              • Google GenAI embeddings docs: https://ai.google.dev/gemini-api/docs/embeddings — documents embedContent as a stable, first-class API with models like gemini-embedding-001
                              • Google GenAI Java SDK: client.models.embedContent() is available with EmbedContentConfig (taskType, title, outputDimensionality) and returns EmbedContentResponse with embeddings and metadata
                              • embedContent request format: uses content (singular), taskType, title, outputDimensionality — none of which match the generateContent fields currently extracted
                              • embedContent response format: returns embedding.values array and metadata.billableCharacterCount — not usageMetadata

                              Local files inspected

                              • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustApiClient.java — lines 50–161 (tagSpan only extracts generateContent-relevant fields), lines 325–333 (getOperation correctly parses embedContent to embed_content)
                              • braintrust-sdk/instrumentation/genai_1_18_0/src/test/java/dev/braintrust/instrumentation/genai/v1_18_0/BraintrustGenAITest.java — no embedContent test exists
                              • braintrust-sdk/instrumentation/genai_1_18_0/src/main/java/com/google/genai/BraintrustInstrumentation.java — wraps ApiClient generically, no embedding-specific logic

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