Add text extraction (OCR / document parsing) capability - #269
Add text extraction (OCR / document parsing) capability#269saarnilauri wants to merge 2 commits into
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Adds a provider-agnostic text extraction capability, following the structural pattern established by embedding generation in 1.4.0 (dedicated builder, result type, and requirements factory): - CapabilityEnum::TEXT_EXTRACTION with magic accessors. - TextExtractionModelInterface::extractTextResult(File): TextExtractionResult. - Result DTOs: TextExtractionResult (ResultInterface, non-candidate-based), ExtractedPage (1-based page numbers, markdown content), ExtractedImage, BoundingBox (normalized 0-1 coordinates), PageDimensions. - ModelRequirements::fromExtractionData() mapping the document's MIME type to a document or image input-modality requirement. - TextExtractionBuilder with withDocument($document, $mimeType) plus AiClient::document() / extractTextResult() / extractText() entry points. Validated end to end by two downstream provider PoCs with intentionally different API shapes: ai-provider-for-mistral (synchronous dedicated OCR endpoint) and ai-provider-for-llamaparse (async job-based parsing with internal polling). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## trunk #269 +/- ##
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+ Coverage 86.49% 87.67% +1.17%
- Complexity 1327 1418 +91
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Files 68 74 +6
Lines 4295 4696 +401
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+ Hits 3715 4117 +402
+ Misses 580 579 -1
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Adds a provider-agnostic text extraction capability, following the structural pattern established by embedding generation in 1.4.0 (dedicated builder, result type, and requirements factory):
Validated end to end by two downstream provider PoCs with intentionally different API shapes: ai-provider-for-mistral (synchronous dedicated OCR endpoint) and ai-provider-for-llamaparse (async job-based parsing with internal polling).
For more details see the issue that this PR closes #268
Use of AI Tools
This implementations was drafted with the assistance of Claude Code (Anthropic), used for researching the provider APIs, analyzing the SDK architecture, and writing code. All work was done with a human in the loop: the design direction, scope decisions, and API trade-offs were made or reviewed by the author, and the proof of concept was verified by the author against the live Mistral and LlamaParse APIs (including real integration test runs and inspection of the extracted output).