Keysat
e824ff2206
Completes business-card contact capture. The transcription prompt now labels Phone/Mobile/Fax on separate lines, and the extractor maps an office/main number -> phone and a cell -> mobile, never a fax. Both carry the same digit-in-source integrity rule as email/LinkedIn: a number is kept only if its digits literally appear in the source (or, on revise, the instruction) -- never minted. The proposal card shows Phone + Mobile and they're editable (aliases phone/tel/office, mobile/cell). Server: _upsert_contact_from_fundraising now accepts contact.phone + contact.mobile and writes them to the canonical contact record (contact-level, not grid pills), shipped in s9pk v0.1.0:98. No schema change -- the contacts columns already exist. 41/41 backend suite green + the matrix_intake units; card flow end-to-end is live-smoke.
57 lines
3.2 KiB
Python
57 lines
3.2 KiB
Python
"""Thin reuse of the in-repo local-Qwen client (backend/ingest/llm.py) via Spark Control.
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We import the ingest client rather than re-implementing the HTTP call so the intake bot
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speaks the exact same Spark contract (model, /v1/chat/completions, TLS verify, .env load).
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The intake message is real LP substance, but it goes ONLY to the local Qwen on Ten31 infra
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— never Claude — so no scrub boundary applies (same basis as the daily digest). Never call a
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Spark directly; everything goes through SPARK_CONTROL_URL.
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"""
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import os
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import sys
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_INGEST = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "ingest")
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if _INGEST not in sys.path:
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sys.path.insert(0, _INGEST)
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import llm # noqa: E402 (backend/ingest/llm.py — chat / chat_json over Spark Control)
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def parse_json(prompt, system=None, max_tokens=400):
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"""Send to local Qwen (temp 0, thinking off) and parse the first JSON object, or None."""
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return llm.chat_json(prompt, system=system, max_tokens=max_tokens)
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# The vision model only TRANSCRIBES the card; the existing text-parse flow then extracts the
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# structured proposal from that transcription. Keeping the two steps separate (vs. asking the
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# vision model for JSON directly) is deliberate: the transcription becomes the source text the
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# email-integrity check runs against, so the "only keep an address that literally appears in the
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# source, never let the model mint one" rule (parse.normalize) protects card intake too.
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CARD_SYSTEM = (
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"You are transcribing a photo of a business card. Copy the text EXACTLY as printed — never "
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"paraphrase, translate, complete, normalize, or correct anything.\n"
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"Read each of these character-by-character and reproduce every glyph precisely. Do NOT 'fix' "
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"them toward a more common spelling or a well-known company's domain, and never add or drop a "
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"character:\n"
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" - Email: check the local part, the @, and the domain separately (transcribe 'mara.com' as "
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"'mara.com', never 'marac.com').\n"
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" - Phone, cell/mobile, and fax numbers — keep each on its own labeled line so they aren't "
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"confused (put an office/main/direct number on Phone:, a cell/mobile number on Mobile:, and a "
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"fax on Fax:).\n"
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" - Website / LinkedIn URL.\n"
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"Then list, each on its own labeled line and ONLY if present on the card:\n"
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" Name: Title: Company: Email: Phone: Mobile: Fax: LinkedIn: City:\n"
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"If a character is genuinely ambiguous, give your single best reading — never invent extra "
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"characters to fill a gap. If the image is not a readable business card, reply with the single "
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"word NONE. Output only the labeled lines, nothing else."
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)
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def transcribe_card(image_b64, mime="image/jpeg", chat_fn=None):
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"""Vision-transcribe a business card to faithful text via the local VL model (same model and
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Spark Control endpoint as the text parse). Returns the transcription string, or '' if the model
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saw no readable card. `chat_fn` is injectable for offline tests (defaults to Spark/VL)."""
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chat_fn = chat_fn or llm.chat_vision
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out = (chat_fn("Transcribe this business card.", image_b64, mime=mime,
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system=CARD_SYSTEM, max_tokens=600) or "").strip()
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return "" if out.upper() == "NONE" else out
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