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"path": "/t/appraising-the-value-of-an-item-based-on-photos/175732#post_2",
"publishedAt": "2026-05-03T23:22:01.000Z",
"site": "https://discuss.huggingface.co",
"tags": [
"even slight differences in usage can lead to performance differences",
"Gemini Apps file upload docs",
"Gemini image understanding",
"Image understanding",
"Structured outputs",
"Function calling",
"File Search / RAG",
"Embeddings / Gemini Embedding 2",
"Grounding with Google Search",
"URL Context",
"Google Lens: how it works",
"eBay Browse API: search by image",
"Florence-2",
"PaddleOCR-VL",
"PaddleOCR GitHub",
"Gemini structured outputs",
"Gemini embeddings",
"Qdrant",
"VaseVQA",
"Ceramic artifact classification/valuation paper",
"Gemini File Search",
"WorthPoint",
"LiveAuctioneers auction results",
"BGE-reranker-v2-m3",
"ValueMyStuff: how it works",
"Appraisal Foundation / USPAP / AO-41",
"Google ADK",
"Gemini function calling",
"Gemini File Search / RAG",
"Gemini Apps file upload behavior",
"VaseVQA: Multimodal Agent and Benchmark for Ancient Greek Pottery",
"Ceramic artifact classification and valuation paper"
],
"textContent": "> I also wonder how come Gemini Chat managed to recognize it, but Gemini API didn’t, despite using the same model.\n\nWhile Gemini x.x and GPT x.x are the models themselves—such as LLMs, VLMs, or multimodal LLMs—Gemini Chat and ChatGPT are Agentic RAGs (or more advanced services), so their behavior differs significantly.\n\nIn some cases, Gemini Chat and ChatGPT automatically even create and execute Python scripts within the cloud runtime, process the results, and—if necessary—call other Gemini or GPT models… essentially building and using a fairly complex pipeline like this internally on their own to provide the final answer based on those results. Seriously.\n\nIt’s quite difficult to replicate all of that as-is (though it is possible since there are OSS frameworks for that), and because it’s fully automated, the results are prone to variation (to the point where even slight differences in usage can lead to performance differences…), so it’s better to leave that for later.\n\nIt’s probably more realistic to start by building a pipeline that mimics only the parts that are important for your own project. Also, that approach is likely to be lighter:\n\n* * *\n\n# Evidence-based photo appraisal for antiques\n\n## Gemini API + Lens + FAISS/RAG + OCR + expert-style QC\n\nThe key shift:\n\n\n Generic resale items: photo appraisal is mostly object recognition.\n\n Antiques: photo appraisal is hypothesis testing.\n\n\nA simple item can work like this:\n\n\n photo → “generic brass candle holder” → similar records → ~$10\n\n\nA difficult antique should not work like this:\n\n\n photo → “Meiji vase” → similar records → ~$500\n\n\nFor antiques, the valuable label is usually what must be proven. A vase can look like a Meiji-period Japanese vase while actually being a later decorative reproduction, Satsuma-style imitation, tube-lined revival piece, tourist/export ware, or seller-mislabeled object.\n\nA better target pipeline:\n\n\n photos\n → visible evidence\n → competing hypotheses\n → positive + negative retrieval\n → expert rubric checks\n → comp filtering\n → QC / skeptic review\n → supported value + upside-if-authenticated\n\n\nNot:\n\n\n photo\n → one best caption\n → FAISS text search\n → average price\n\n\n* * *\n\n# 1. Why Gemini Chat likely beat the Gemini API call\n\nThe likely reason is not simply “Gemini Chat used a better model.”\n\nMore likely:\n\n\n Gemini model = the engine.\n Gemini Chat / Gemini Apps = finished assistant product around the engine.\n Gemini API = programmable access to the engine/tools, but you build the assistant behavior.\n\n\nGemini Apps support uploaded files/photos/videos in a chat workflow: Gemini Apps file upload docs. The Gemini API also supports image understanding, but you must explicitly send the right images, label them, prompt correctly, and attach tools/retrieval: Gemini image understanding.\n\nIn the vase case, Gemini Chat saw multiple views including the bottom. For ceramics, the base, foot rim, underside wear, mark, glaze/body transition, decoration detail, and restoration clues can matter more than the front view.\n\nSo the API probably failed because it behaved like a one-shot captioner. Gemini Chat succeeded because it behaved more like a multi-photo inspection assistant.\n\nTo match or beat Chat, replicate:\n\n * multi-photo context\n * labeled views\n * detail crops\n * OCR on marks/labels\n * visual search as candidate discovery\n * RAG over rubrics and prior cases\n * structured JSON outputs\n * tool calling\n * comp filtering\n * QC gates\n\n\n\nUseful Gemini building blocks:\n\n * Image understanding\n * Structured outputs\n * Function calling\n * File Search / RAG\n * Embeddings / Gemini Embedding 2\n * Grounding with Google Search\n * URL Context\n\n\n\n* * *\n\n# 2. Three use cases, three routes\n\n## A. Generic brass candle holder\n\nMostly object recognition plus broad comp retrieval.\n\n\n photo → “brass candle holder” → FAISS/text/image comps → ~$10\n\n\nRoute:\n\n\n cheap Gemini call or OSS VLM\n → object type\n → visible material\n → broad database search\n → simple value range\n\n\nExample:\n\n\n Likely identity:\n Generic brass candle holder.\n\n Supported resale estimate:\n ~$8–15, depending on size, condition, and local marketplace.\n\n Confidence:\n Medium-high if no maker, designer, age, or unusual quality indicators are visible.\n\n\n* * *\n\n## B. Branded brass candle holder\n\nThis is exact-identity / maker recognition.\n\nGemini may see:\n\n\n brass candle holder\n\n\nGoogle Lens may find:\n\n\n specific branded brass candle holder\n\n\nThat is where visual search helps. Google Lens discovers visually similar images and related content from an image: Google Lens: how it works.\n\nRoute:\n\n\n full photo + mark/logo crop\n → OCR if needed\n → Google Lens / visual search\n → exact or near-exact comp retrieval\n → database price lookup\n → Gemini reconciles evidence\n\n\nFor branded objects, the best Lens image is often:\n\n\n logo\n maker mark\n label\n pattern number\n base stamp\n distinctive design detail\n\n\nExample:\n\n\n Likely identity:\n [Brand/model] brass candle holder.\n\n Evidence:\n Visible maker/brand clue + visual-search match + matching database records.\n\n Supported estimate:\n ~$100, assuming same model, material, size, and condition.\n\n\n* * *\n\n## C. Japanese vase\n\nThis is authentication / attribution / comp-validity.\n\nDangerous near-neighbors:\n\n\n Meiji-period Satsuma vase\n Meiji-style vase\n Satsuma-style decorative vase\n moriage tourist ware\n tube-lined imitation\n modern decorative reproduction\n Chinese/Japanese-style decorative ceramic\n\n\nGoogle Lens may surface the expensive visual hypothesis:\n\n\n Meiji-period Japanese vase → maybe ~$500\n\n\nBut multi-photo evidence may support:\n\n\n tube-lined or revival technique used to mimic older Meiji-period appearance\n → likely later/revival/reproduction\n → maybe ~$50\n\n\nThe QC question is not:\n\n\n Which answer is cheaper?\n\n\nIt is:\n\n\n Which hypothesis is best supported by visible evidence?\n What contradicts the expensive hypothesis?\n What evidence is missing?\n Which sold comps actually match the supported hypothesis?\n What upside remains if the item is later authenticated?\n\n\n* * *\n\n# 3. Recommended architecture\n\n\n 1. Photo intake / sufficiency gate\n 2. Risk routing\n 3. Crop and detail extraction\n 4. OCR and mark interpretation\n 5. Visual evidence extraction\n 6. Lens / visual-search candidate discovery\n 7. Multimodal retrieval\n 8. Negative-example retrieval\n 9. Category-specific rubric checks\n 10. Comparable-sales filtering\n 11. QC / skeptic review\n 12. Final appraisal report\n\n\nRole separation:\n\nComponent | Correct role | Incorrect role\n---|---|---\nGemini API | Evidence extraction, hypotheses, rubric reasoning, comp filtering, QC, report | One-shot appraiser\nGoogle Lens | Candidate labels, visually similar web/listing discovery | Authenticator or price authority\nOCR | Read marks, labels, stamps, signatures | Maker/authenticity proof by itself\nFAISS/Qdrant/vector DB | Retrieve positive comps, negatives, marks, details | Final price calculator\nRubric/RAG | Tell model what to check per category | Generic background only\nQC agent | Block unsupported claims, preserve upside, decide escalation | Pick cheaper answer\n\n* * *\n\n# 4. Photo sufficiency gate\n\nFor Japanese ceramics, require:\n\n\n front view\n back view\n left and right side views\n top / mouth / interior\n bottom / base\n foot rim close-up\n mark / backstamp close-up\n decoration macro\n damage / restoration close-ups\n scale photo\n\n\nInternal schema:\n\n\n {\n \"object_category_guess\": \"Japanese ceramic vase\",\n \"sufficient_for_generic_identification\": true,\n \"sufficient_for_authentication\": false,\n \"missing_required_views\": [\n \"clear bottom/base photo\",\n \"foot rim close-up\",\n \"legible mark/backstamp close-up\",\n \"decoration macro\"\n ],\n \"valuation_allowed\": \"low_confidence_only\",\n \"blocked_claims\": [\n \"Meiji-period attribution\",\n \"verified maker\",\n \"high-confidence high-value appraisal\"\n ]\n }\n\n\nHard rule:\n\n\n If category = Japanese ceramic vase\n and no clear base/foot/mark views are present,\n then block high-confidence period and maker claims.\n\n\n* * *\n\n# 5. Route by risk\n\n\n Route A: generic low-risk item\n Route B: branded / marked / exact-product item\n Route C: antique / authenticity-sensitive item\n\n\n## Route A\n\n\n Gemini object ID\n → broad text/image database search\n → simple comp estimate\n\n\n## Route B\n\n\n crop mark/logo\n → OCR\n → Lens / eBay / visual search\n → exact or near-exact comps\n → value estimate\n\n\neBay’s Browse API includes search by image for product-like discovery: eBay Browse API: search by image.\n\n## Route C\n\n\n multi-photo intake\n → detail crops\n → OCR\n → positive retrieval\n → negative retrieval\n → category rubric\n → comp filtering\n → QC\n → supported value + upside scenario\n\n\n* * *\n\n# 6. Crop decisive details\n\nFull-object views identify the broad object. Detail crops contain appraisal evidence.\n\nFor ceramics, crop:\n\n\n base\n foot rim\n mark / backstamp\n decoration detail\n glaze/body transition\n top/interior\n damage/restoration\n scale reference\n\n\nUseful tools:\n\n * Florence-2 for captioning/detection/segmentation-style tasks.\n * Grounding DINO for text-prompted region detection.\n * SAM / SAM 2 for segmentation after detection.\n * YOLO variants if you train fixed appraisal-detail categories.\n\n\n\nThe cropper is not the appraiser. It ensures the model sees the same details a human specialist would inspect.\n\n* * *\n\n# 7. OCR and mark interpretation\n\nA mark can be:\n\n\n maker mark\n workshop mark\n retailer mark\n import mark\n country-of-origin mark\n pattern number\n decorative mark\n apocryphal mark\n fake mark\n later-added label\n\n\nSplit mark handling:\n\n\n 1. detect mark region\n 2. transcribe mark\n 3. classify mark type\n 4. interpret appraisal implication\n\n\nOCR candidates:\n\n * PaddleOCR-VL\n * PaddleOCR GitHub\n * EasyOCR\n * Tesseract\n * DeepSeek-OCR\n * Chandra OCR\n\n\n\nExample:\n\n\n {\n \"ocr_text\": \"MADE IN JAPAN\",\n \"mark_type\": \"country_of_origin_mark\",\n \"appraisal_implication\": \"If original to the object, this conflicts with a 19th-century Meiji-period attribution.\",\n \"confidence\": \"medium\",\n \"needs_human_check\": false\n }\n\n\nNever jump from:\n\n\n mark visible\n\n\nto:\n\n\n maker verified\n\n\nwithout corroboration.\n\n* * *\n\n# 8. Extract visible evidence, not final value\n\nBad prompt:\n\n\n What is this item and what is it worth?\n\n\nBetter prompt:\n\n\n You are not appraising yet.\n\n Extract visible evidence only.\n Separate visible facts, uncertain observations, interpretations, missing views, and risk flags.\n Do not state period, maker, authenticity, or value as fact unless directly supported.\n Do not generate a final price.\n\n\nExample structured output:\n\n\n {\n \"visible_facts\": [\n {\n \"fact\": \"The object is a ceramic vase form.\",\n \"source_image\": \"front_view\",\n \"confidence\": 0.92\n },\n {\n \"fact\": \"Raised decorative outlines are visible around motifs.\",\n \"source_image\": \"decoration_macro\",\n \"confidence\": 0.86\n }\n ],\n \"uncertain_observations\": [\n {\n \"observation\": \"Raised decoration may be tube-lined, molded, or applied.\",\n \"needed_evidence\": \"macro photo under angled light\"\n }\n ],\n \"missing_evidence\": [\n \"clear base photo\",\n \"foot rim macro\",\n \"legible mark close-up\",\n \"measurements\",\n \"condition close-ups\",\n \"provenance\"\n ],\n \"risk_flags\": [\n \"period_style_mismatch_possible\",\n \"reproduction_possible\",\n \"visual_similarity_not_authentication\"\n ]\n }\n\n\nUse structured outputs for schema validation: Gemini structured outputs.\n\n* * *\n\n# 9. Lens as candidate discovery\n\nGood representation:\n\n\n {\n \"lens_candidates\": [\n \"Meiji Satsuma vase\",\n \"Japanese moriage vase\",\n \"Satsuma-style decorative vase\",\n \"Japanese export pottery\"\n ],\n \"status\": \"candidate_discovery_only\",\n \"warning\": \"Visual similarity does not establish period, maker, authenticity, condition, or value.\"\n }\n\n\nRule:\n\n\n Lens creates hypotheses.\n Rubrics and comps test hypotheses.\n QC decides whether claims are allowed, blocked, upside-only, or need expert review.\n\n\n* * *\n\n# 10. Add multimodal retrieval\n\nAvoid the bottleneck:\n\n\n image → Gemini description → FAISS text search\n\n\nIf the description misses “tube-lined imitation,” “moriage tourist ware,” “Satsuma-style reproduction,” or a mark clue, the right records may never be searched.\n\nAdd:\n\n\n full image → similar object images\n base crop → similar bases / foot rims\n mark crop → similar marks\n decoration crop → similar techniques\n text query → similar descriptions\n negative query → similar reproductions\n\n\nGemini Embedding 2 supports cross-modal retrieval: Gemini embeddings.\n\nUse multiple indexes:\n\n\n full_object_image_index\n text_description_index\n mark_crop_index\n base_crop_index\n foot_rim_index\n decoration_detail_index\n damage_detail_index\n negative_example_index\n auction_catalogue_page_index\n\n\nIf FAISS is enough, keep it. If metadata filtering becomes painful, consider Qdrant.\n\nExample metadata filters:\n\n\n {\n \"object_type\": \"vase\",\n \"material\": \"ceramic\",\n \"sale_status\": \"sold\",\n \"source_type\": \"auction_result\",\n \"has_base_photo\": true,\n \"condition_known\": true,\n \"period_claim\": \"Meiji-style\",\n \"attribution_strength\": \"seller_claim | auction_house | specialist | authenticated\"\n }\n\n\n* * *\n\n# 11. Add negative retrieval\n\nMost systems retrieve only positives:\n\n\n Meiji Satsuma vase\n Japanese vase\n antique ceramic vase\n\n\nAlso retrieve lower-value confusables:\n\n\n modern Satsuma-style reproduction\n tube-lined imitation vase\n moriage tourist ware\n Meiji-style decorative vase\n fake/apocryphal mark\n Japanese-style ceramic reproduction\n Chinese/Japanese-style decorative ceramic\n\n\nAsk:\n\n\n What lower-value confusable class could explain the same visual evidence?\n\n\nThen compare:\n\n\n Does the base match period examples or reproduction examples?\n Does the decoration look hand-applied or mechanically uniform?\n Does the mark support maker/period or merely style/import?\n Do sold comps match the same material, technique, size, condition, and attribution strength?\n\n\nQC should choose the best-supported hypothesis, not the cheaper one.\n\n* * *\n\n# 12. Use category-specific rubrics\n\nThere is no single perfect “all antiques” dataset. But cultural-heritage work shows the right pattern: expert-defined visual questions.\n\nUseful references:\n\n * VaseVQA — ancient Greek pottery benchmark with expert-defined categories.\n * Ceramic artifact classification/valuation paper — attribute extraction first, valuation second.\n\n\n\nStart rubrics for:\n\n\n Japanese / Chinese ceramics\n silver vs silverplate\n brass / bronze / resin\n paintings vs prints\n signed glass\n furniture / joinery\n designer decor\n jewelry\n\n\nExample rubric:\n\n\n {\n \"category\": \"Japanese ceramic vase\",\n \"required_views\": [\n \"front\",\n \"back\",\n \"sides\",\n \"top/interior\",\n \"bottom/base\",\n \"foot rim close-up\",\n \"mark close-up\",\n \"decoration macro\",\n \"damage close-ups\",\n \"scale photo\"\n ],\n \"attributes_to_check\": [\n \"object type\",\n \"material/body\",\n \"glaze\",\n \"decoration technique\",\n \"raised decoration method\",\n \"mark type\",\n \"foot rim\",\n \"wear pattern\",\n \"condition\",\n \"restoration\",\n \"period vs style\",\n \"maker attribution strength\"\n ],\n \"high_value_claims\": [\n {\n \"claim\": \"Meiji-period Satsuma vase\",\n \"required_evidence\": [\n \"period-consistent base and foot rim\",\n \"period-consistent decoration technique\",\n \"credible mark or provenance\",\n \"no modern country-of-origin/import mark\",\n \"matching sold comps from reliable sources\",\n \"condition sufficiently documented\"\n ]\n }\n ],\n \"common_false_positives\": [\n \"modern Satsuma-style decorative ware\",\n \"tube-lined imitation\",\n \"moriage tourist ware\",\n \"Chinese/Japanese style confusion\",\n \"seller-labeled Meiji without evidence\",\n \"apocryphal or decorative marks\"\n ],\n \"blocked_without_evidence\": [\n \"authentic Meiji-period\",\n \"verified maker\",\n \"museum-quality\",\n \"rare signed workshop piece\"\n ],\n \"safe_language\": [\n \"Satsuma-style\",\n \"Japanese-style\",\n \"unverified age\",\n \"decorative ceramic vase\",\n \"possibly later/revival\"\n ]\n }\n\n\nStore rubrics, known reproductions, expert notes, and prior corrections in RAG/File Search: Gemini File Search.\n\n* * *\n\n# 13. Filter comps aggressively\n\nA visually similar listing is not necessarily a valid comp.\n\nA valid comp should match:\n\n\n object type\n material\n technique\n size\n period/style\n maker/attribution strength\n condition\n sale status\n sale venue\n source quality\n photo completeness\n sale date\n\n\nExamples/inspiration:\n\n * WorthPoint\n * LiveAuctioneers auction results\n\n\n\nComp decision schema:\n\n\n {\n \"comp_id\": \"lot_123\",\n \"include_in_valuation\": false,\n \"reason\": \"Rejected: visually similar but active listing only; no sold price; no base photo; period attribution unsupported.\",\n \"matched_fields\": [\n \"object_type\",\n \"broad_style\"\n ],\n \"missing_or_mismatched_fields\": [\n \"sale_status\",\n \"period\",\n \"technique\",\n \"condition\",\n \"attribution_strength\",\n \"base_photo\"\n ]\n }\n\n\nAccepted comp schema:\n\n\n {\n \"comp_id\": \"lot_456\",\n \"include_in_valuation\": true,\n \"reason\": \"Accepted: sold result, similar object type, similar size, similar later Satsuma-style decorative category, unsigned, comparable condition.\",\n \"adjustments\": [\n \"condition report incomplete\",\n \"size within acceptable range\",\n \"no verified maker, matching current item\"\n ]\n }\n\n\nDo not average all retrieved prices.\n\nGood report line:\n\n\n Retrieved 12 visually similar records.\n Rejected 8 as invalid comps.\n Used 4 closer comps for supported valuation.\n\n\nFor reranking after retrieval, consider BGE-reranker-v2-m3.\n\n* * *\n\n# 14. QC with claim permissions\n\nClassify claims:\n\n\n allowed\n blocked\n upside-only\n needs-human-review\n\n\nExamples:\n\n\n {\n \"claim\": \"Meiji-period Satsuma vase\",\n \"status\": \"upside_only\",\n \"reason\": \"Visual style is suggestive, but base/foot/mark/technique evidence is insufficient.\",\n \"required_next_evidence\": [\n \"clear base photo\",\n \"foot rim macro\",\n \"legible mark close-up\",\n \"decoration macro under angled light\",\n \"matching specialist sold comps\"\n ]\n }\n\n\n\n {\n \"claim\": \"Japanese Satsuma-style decorative vase\",\n \"status\": \"allowed\",\n \"reason\": \"Supported by broad visual vocabulary and decoration style, while avoiding unsupported period authentication.\"\n }\n\n\n\n {\n \"claim\": \"authentic Meiji-period vase worth $500\",\n \"status\": \"blocked\",\n \"reason\": \"Current evidence does not establish period, maker, or technique strongly enough.\"\n }\n\n\n* * *\n\n# 15. Supported value vs upside scenario\n\nDo not output one number. Output:\n\n\n supported value\n conservative resale value\n upside if authenticated\n\n\nExample:\n\n\n Supported value:\n $40–80 as a later Satsuma-style decorative ceramic vase.\n\n Conservative resale value:\n $40–60 if listed honestly as unverified age/style only.\n\n Upside scenario:\n Potentially much higher if authenticated as Meiji-period or a signed workshop piece, but that requires stronger evidence: base, foot rim, mark, technique, condition, and specialist comps.\n\n Current claim permission:\n Do not list as authentic Meiji-period.\n\n\nThis avoids:\n\n\n always choose cheap → miss rare finds\n accept expensive visual match → overvalue fakes\n\n\n* * *\n\n# Recommended final report format\n\n\n Likely supported identity:\n Japanese Satsuma-style decorative ceramic vase, likely later/revival rather than verified Meiji-period.\n\n Supported value:\n $40–80, assuming no major hidden damage.\n\n Conservative resale value:\n $40–60 if listed honestly as unverified age/style only.\n\n Upside scenario:\n Could be materially higher if authenticated as Meiji-period or a signed workshop piece, but current evidence does not support that claim.\n\n Evidence supporting the supported identity:\n - Japanese/Satsuma-style decorative vocabulary.\n - Raised decorative outlines visible.\n - Current photos do not verify period or maker.\n - Technique may indicate later/revival/tube-lined imitation.\n\n Evidence against the high-value Meiji claim:\n - Visual similarity alone is not authentication.\n - Mark/base/foot evidence is insufficient.\n - Decoration technique needs closer verification.\n - Valid comps must match size, material, technique, condition, and attribution.\n\n Missing evidence:\n - Sharp base photo.\n - Foot rim macro.\n - Legible mark close-up.\n - Decoration macro under angled light.\n - Measurements.\n - Provenance or prior auction record.\n\n Accepted comps:\n - [comp IDs + reason]\n\n Rejected comps:\n - [comp IDs + reason]\n\n Safe listing title:\n Japanese Satsuma-style decorative ceramic vase, raised decoration, unverified age.\n\n Do not list as:\n Authentic Meiji-period Satsuma vase.\n\n Confidence:\n Medium-low from photos only.\n\n Escalation:\n Human expert review recommended if the user wants to sell, insure, purchase, donate, or consign based on the high-value scenario.\n\n\n* * *\n\n# Human expert fallback\n\nAI can triage and produce evidence reports, but human review is needed for high-stakes decisions. Human appraisal services use photo/info intake plus expert review, not one-shot photo guessing: ValueMyStuff: how it works. Appraisal guidance also increasingly treats generative AI as a tool that still requires professional judgment: Appraisal Foundation / USPAP / AO-41.\n\nUse expert review when:\n\n\n price spread across hypotheses is large\n upside exceeds threshold\n mark is visible but unclear\n period/maker claim drives value\n condition/restoration is uncertain\n provenance is claimed\n user wants insurance/tax/estate/sale support\n\n\nStore expert corrections as future training/evaluation data.\n\n* * *\n\n# Suggested stack\n\n## Gemini-centered core\n\n\n Gemini image understanding\n Gemini structured outputs\n Gemini function calling\n Gemini File Search / RAG\n Gemini Embedding 2\n Google Search grounding\n URL Context\n\n\n## OSS / preprocessing\n\n\n PaddleOCR / PaddleOCR-VL for marks and labels\n Florence-2 / Grounding DINO / SAM / YOLO for crops\n FAISS or Qdrant for vector retrieval\n BM25 + dense embeddings for hybrid text search\n BGE rerankers for comp filtering\n Qwen3-VL / InternVL as optional OSS VLM baselines\n Qwen3-VL-Embedding / Jina CLIP / CLIP / SigLIP as retrieval alternatives\n\n\n## External candidate discovery\n\n\n Google Lens wrapper\n eBay image search\n Google Vision Web Detection\n auction/sold-price databases\n\n\n## Workflow frameworks\n\n\n Google ADK if you want Gemini-native agent orchestration\n LangGraph if you want deterministic gates and human review\n LlamaIndex if your RAG layer grows large\n\n\nSee also: Google ADK.\n\n* * *\n\n# Build order\n\n## Phase 1 — Make Gemini API match Gemini Chat\n\n\n 5–10 labeled photos\n base/foot/mark/detail views\n appraisal-specific prompt\n structured output\n hypotheses + evidence + missing evidence\n\n\nGoal:\n\n\n Gemini API should reproduce the “tube-lined imitation / likely later decorative” insight when given the same evidence.\n\n\n## Phase 2 — Add crop + OCR\n\n\n base crop\n foot rim crop\n mark crop\n decoration crop\n damage crop\n OCR result\n mark interpretation\n\n\n## Phase 3 — Add multimodal retrieval\n\n\n full object\n text description\n mark crop\n base crop\n foot rim\n decoration detail\n negative examples\n auction catalogue pages\n\n\n## Phase 4 — Add category rubrics\n\n\n Japanese ceramics\n silver vs silverplate\n brass/bronze/resin\n paintings vs prints\n branded/decorator objects\n\n\n## Phase 5 — Add comp filtering\n\nReject:\n\n\n active listings as primary comps\n wrong material\n wrong size\n wrong period\n wrong technique\n wrong condition\n signed vs unsigned mismatch\n seller-label-only attribution\n no base photo\n no condition report\n\n\n## Phase 6 — Add QC and claim permissions\n\n\n allowed\n blocked\n upside-only\n needs-human-review\n\n\n## Phase 7 — Build a private benchmark\n\nCreate at least 100 labeled cases:\n\n\n generic low-value items\n branded/maker-mark items\n Japanese/Asian ceramic confusables\n OCR/mark-heavy items\n damaged/restored items\n\n\nTrack:\n\n\n unsupported high-value claim rate\n reproduction detection\n mark-reading accuracy\n missing-photo detection\n negative retrieval success\n comp-filter precision\n value-range overlap\n confidence calibration\n escalation accuracy\n safe-title accuracy\n\n\nMain metric:\n\n\n Does the system avoid unsupported expensive claims while preserving rare-find upside?\n\n\n* * *\n\n# Curated links\n\n## Gemini / Google API\n\n * Gemini image understanding\n * Gemini structured outputs\n * Gemini function calling\n * Gemini File Search / RAG\n * Gemini embeddings\n * Grounding with Google Search\n * URL Context\n * Gemini Apps file upload behavior\n\n\n\n## Visual search\n\n * Google Lens: how it works\n * eBay Browse API: search by image\n\n\n\n## Cultural heritage / appraisal-style research\n\n * VaseVQA: Multimodal Agent and Benchmark for Ancient Greek Pottery\n * Ceramic artifact classification and valuation paper\n\n\n\n## Market data / appraisal practice\n\n * WorthPoint\n * LiveAuctioneers auction results\n * ValueMyStuff: how it works\n * Appraisal Foundation / USPAP / AO-41\n\n\n\n## OSS / infrastructure\n\n * PaddleOCR-VL\n * PaddleOCR GitHub\n * Florence-2\n * Qdrant\n * BGE-reranker-v2-m3\n * Google ADK\n\n\n\n* * *\n\n# Final answer\n\nYour simple case is object recognition. Your branded case is exact-identity search. Your Japanese vase case is authenticity reasoning.\n\nSo the system should be three-tiered:\n\n\n Generic item:\n Gemini API + simple FAISS retrieval.\n\n Branded item:\n Gemini + OCR + Lens / image search + exact comps.\n\n Antique item:\n Gemini + multi-photo evidence + crops/OCR + Lens candidates + multimodal retrieval + negative examples + expert rubrics + comp filtering + QC gates.\n\n\nThe winning architecture is not:\n\n\n Gemini vs Google Lens\n\n\nIt is:\n\n\n Gemini for reasoning\n + Lens for candidate discovery\n + OCR/crops for evidence\n + multimodal retrieval for comps\n + negative examples for fake/reproduction detection\n + expert rubrics for domain checks\n + sold-comp filtering for valuation\n + QC gates for safe claims\n + human escalation for high-risk cases\n\n\nThat architecture can match or surpass Gemini Chat because it replicates what made the chat product useful—multi-photo context, conversational inspection, and tool-like behavior—while adding what Gemini Chat does not have by default: your private price database, negative-example corpus, category rubrics, comp filters, structured outputs, and deterministic appraisal safeguards.",
"title": "Appraising the value of an item based on photos"
}