chore: add feature spec for responses/AI matching/assignment rules
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James
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# Feature Spec: Responses, AI Matching, Assignment Rules
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## Context
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Dealspace needs to separate *what buyers ask* (requests) from *what sellers provide* (responses), with AI automatically discovering which responses satisfy which requests via embeddings. Confirmed by a human before being counted as answered.
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## Locked Decisions
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- Assignment rules: per deal
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- Statements (typed text answers): IN SCOPE
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- Extraction: async background worker
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- Confirmation: internal users only (RBAC refinement later)
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---
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## 1. Schema Changes
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### New tables (add to migrate.go as CREATE TABLE IF NOT EXISTS in the migrations slice)
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```sql
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-- Responses: seller-provided answers (document OR typed statement)
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CREATE TABLE IF NOT EXISTS responses (
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id TEXT PRIMARY KEY,
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deal_id TEXT NOT NULL,
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type TEXT NOT NULL CHECK (type IN ('document','statement')),
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title TEXT NOT NULL,
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body TEXT DEFAULT '', -- markdown: extracted doc content OR typed text
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file_id TEXT DEFAULT '', -- populated for type='document'
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extraction_status TEXT DEFAULT 'pending'
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CHECK (extraction_status IN ('pending','processing','done','failed')),
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created_by TEXT DEFAULT '',
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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updated_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (deal_id) REFERENCES deals(id)
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);
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-- Chunks: segments of a response body for fine-grained matching
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CREATE TABLE IF NOT EXISTS response_chunks (
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id TEXT PRIMARY KEY,
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response_id TEXT NOT NULL,
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chunk_index INTEGER NOT NULL,
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text TEXT NOT NULL,
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vector BLOB NOT NULL, -- []float32 serialised as little-endian bytes
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FOREIGN KEY (response_id) REFERENCES responses(id)
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);
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-- N:M: AI-discovered links between requests and response chunks
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CREATE TABLE IF NOT EXISTS request_links (
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request_id TEXT NOT NULL,
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response_id TEXT NOT NULL,
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chunk_id TEXT NOT NULL,
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confidence REAL NOT NULL, -- cosine similarity 0-1
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auto_linked BOOLEAN DEFAULT 1,
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confirmed BOOLEAN DEFAULT 0,
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confirmed_by TEXT DEFAULT '',
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confirmed_at DATETIME,
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PRIMARY KEY (request_id, response_id, chunk_id)
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);
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-- Assignment rules: keyword → assignee, per deal
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CREATE TABLE IF NOT EXISTS assignment_rules (
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id TEXT PRIMARY KEY,
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deal_id TEXT NOT NULL,
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keyword TEXT NOT NULL, -- e.g. "Legal", "Tax", "HR"
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assignee_id TEXT NOT NULL, -- profile ID
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (deal_id) REFERENCES deals(id)
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);
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```
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### Additive migrations (append to additiveMigrationStmts in migrate.go)
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```go
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`ALTER TABLE diligence_requests ADD COLUMN assignee_id TEXT DEFAULT ''`,
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`ALTER TABLE diligence_requests ADD COLUMN status TEXT DEFAULT 'open'`,
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`ALTER TABLE files ADD COLUMN response_id TEXT DEFAULT ''`,
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```
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Note: status CHECK constraint can't be added via ALTER TABLE in SQLite — enforce in handler.
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---
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## 2. Fireworks Client
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Create `internal/fireworks/client.go`:
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```
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Package: fireworks
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Fireworks API key: fw_RVcDe4c6mN4utKLsgA7hTm
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Base URL: https://api.fireworks.ai/inference/v1
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Functions needed:
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1. ExtractToMarkdown(ctx, imageBase64 []string, filename string) (string, error)
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- Model: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
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- System prompt: "You are a document extraction expert. Extract ALL content from this document into clean markdown. Preserve headings, tables, lists, and structure. Do not summarise — extract everything."
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- Send up to 10 images per call (multi-page docs: batch into 10-page chunks, concatenate results)
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- For XLSX files (no images): use a different path — just send the structured data as text
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- Return full markdown string
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2. EmbedText(ctx, texts []string) ([][]float32, error)
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- Model: nomic-ai/nomic-embed-text-v1.5
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- POST /embeddings (OpenAI-compatible)
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- Batch up to 50 texts per call
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- Return [][]float32
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3. CosineSimilarity(a, b []float32) float32
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- Pure Go dot product (normalised vectors)
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```
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---
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## 3. PDF-to-Images Conversion
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Create `internal/extract/pdf.go`:
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```
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Use exec("pdftoppm") subprocess:
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pdftoppm -jpeg -r 150 input.pdf /tmp/prefix
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→ produces /tmp/prefix-1.jpg, /tmp/prefix-2.jpg, ...
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Read each JPEG → base64 encode → pass to fireworks.ExtractToMarkdown
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For non-PDF files that are images (jpg/png): base64 encode directly, skip pdftoppm.
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For XLSX: use excelize GetRows on all sheets → format as markdown table → skip vision model entirely.
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For other binary types: attempt pdftoppm, fall back to filename+extension as minimal context.
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Function signature:
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FileToImages(path string) ([]string, error) // returns base64-encoded JPEG strings
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```
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---
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## 4. Chunker
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Create `internal/extract/chunker.go`:
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```
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ChunkMarkdown(text string) []string
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- Split on markdown headings (## or ###) first
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- If a section > 600 tokens (approx 2400 chars): split further at paragraph breaks (\n\n)
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- If a paragraph > 600 tokens: split at sentence boundary (". ")
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- Overlap: prepend last 80 chars of previous chunk to each chunk (context continuity)
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- Minimum chunk length: 50 chars (discard shorter)
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- Return []string of chunks
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```
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---
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## 5. Extraction Worker
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Create `internal/worker/extractor.go`:
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```
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type ExtractionJob struct {
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ResponseID string
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FilePath string // absolute path to uploaded file (or "" for statements)
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DealID string
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}
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type Extractor struct {
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db *sql.DB
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fw *fireworks.Client
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jobs chan ExtractionJob
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}
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func NewExtractor(db *sql.DB, fw *fireworks.Client) *Extractor
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func (e *Extractor) Start() // launch 2 worker goroutines
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func (e *Extractor) Enqueue(job ExtractionJob)
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Worker loop:
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1. Set responses.extraction_status = 'processing'
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2. If file:
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a. Convert to images (extract.FileToImages)
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b. Call fw.ExtractToMarkdown → markdown body
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c. UPDATE responses SET body=?, extraction_status='done'
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3. If statement (body already set, skip extraction):
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a. extraction_status → 'done' immediately
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4. Chunk: extract.ChunkMarkdown(body)
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5. Embed: fw.EmbedText(chunks) → [][]float32
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6. Store each chunk: INSERT INTO response_chunks (id, response_id, chunk_index, text, vector)
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- Serialise []float32 as little-endian bytes: each float32 = 4 bytes
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7. Match against all open requests in this deal:
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a. Load all diligence_requests for deal_id (embed their descriptions if not already embedded)
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b. Embed request descriptions that have no embedding yet (store in a simple in-memory cache or re-embed each run — re-embed is fine for now)
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c. For each (chunk, request) pair: compute cosine similarity
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d. If similarity >= 0.72: INSERT OR IGNORE INTO request_links (request_id, response_id, chunk_id, confidence, auto_linked=1, confirmed=0)
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8. Log summary: "Response {id}: {N} chunks, {M} request links auto-created"
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On error: SET extraction_status = 'failed', log error
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```
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---
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## 6. Handler: Responses & Assignment Rules
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Create `internal/handler/responses.go`:
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```
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Handlers:
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POST /deals/responses/statement
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- Fields: deal_id, title, body (markdown text)
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- Create responses row (type='statement', extraction_status='pending')
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- Enqueue extraction job (body already set, worker will chunk+embed+match)
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- Redirect to /deals/{deal_id}?tab=requests
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POST /deals/responses/confirm
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- Fields: request_id, response_id, chunk_id
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- UPDATE request_links SET confirmed=1, confirmed_by=profile.ID, confirmed_at=now
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- Return 200 OK (HTMX partial or redirect)
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POST /deals/responses/reject
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- Fields: request_id, response_id, chunk_id
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- DELETE FROM request_links WHERE ...
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- Return 200 OK
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GET /deals/responses/pending/{dealID}
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- Returns all request_links WHERE confirmed=0 AND auto_linked=1
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- Joined with requests (description) and responses (title, type)
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- Returns JSON for HTMX partial
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POST /deals/assignment-rules/save
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- Fields: deal_id, rules[] (keyword + assignee_id pairs, JSON array)
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- DELETE existing rules for deal, INSERT new set
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- On save: re-run auto-assignment for all unassigned requests in deal
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- Redirect back to deal settings
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GET /deals/assignment-rules/{dealID}
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- Returns JSON array of {id, keyword, assignee_id, assignee_name}
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Auto-assignment function (call on: rule save, request import):
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func autoAssignRequests(db, dealID):
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- Load all assignment_rules for deal_id
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- For each diligence_request WHERE assignee_id = '':
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- Check if section contains any rule keyword (case-insensitive)
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- If match: UPDATE diligence_requests SET assignee_id = rule.assignee_id
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```
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---
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## 7. Wire Up in handler.go
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Add to RegisterRoutes:
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```go
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// Responses & AI matching
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mux.HandleFunc("/deals/responses/statement", h.requireAuth(h.handleCreateStatement))
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mux.HandleFunc("/deals/responses/confirm", h.requireAuth(h.handleConfirmLink))
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mux.HandleFunc("/deals/responses/reject", h.requireAuth(h.handleRejectLink))
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mux.HandleFunc("/deals/responses/pending/", h.requireAuth(h.handlePendingLinks))
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mux.HandleFunc("/deals/assignment-rules/save", h.requireAuth(h.handleSaveAssignmentRules))
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mux.HandleFunc("/deals/assignment-rules/", h.requireAuth(h.handleGetAssignmentRules))
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```
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In Handler struct, add:
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```go
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extractor *worker.Extractor
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fw *fireworks.Client
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```
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In New(): initialise both, call extractor.Start().
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In handleFileUpload (files.go): after saving file, create a responses row (type='document') and enqueue extraction job.
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---
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## 8. Template Changes
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### dealroom.templ — Requests tab
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Current requests tab shows a list of requests. Add:
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**A) Per-request: assignee + status badge**
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- Show assignee name (or "Unassigned" in gray) next to each request
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- Status pill: open (gray), in_progress (blue), answered (green), not_applicable (muted)
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- If confirmed link exists: show "✓ Answered" with link to the response
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- If pending auto-links exist: show "🤖 N AI matches — review" button (teal outline)
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**B) Pending AI matches panel** (shown above request list if any pending)
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- Collapsible section: "🤖 X AI-suggested matches waiting for review"
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- Each row: Request description | → | Response title | Confidence % | [Confirm] [Reject]
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- Confirm/Reject use fetch() POST to /deals/responses/confirm or /reject, then reload
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**C) "Add Statement" button** (in requests toolbar)
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- Opens a modal: Title + markdown textarea
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- Submits to POST /deals/responses/statement
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- After submit: shows in pending matches if AI matched any requests
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**D) Assignment rules** (accessible via a gear icon or "Settings" in requests tab header)
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- Inline expandable panel or small modal
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- Table: Keyword | Assignee (dropdown of internal team members) | [Remove]
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- [Add Rule] row at bottom
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- Save button → POST /deals/assignment-rules/save
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### Keep it clean
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- Don't clutter the existing request rows — use progressive disclosure
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- The "N AI matches" prompt should be prominent but not alarming
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- Confidence shown as percentage (e.g. "87%"), not raw float
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---
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## 9. Files tab: extraction status
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In the files table, add a small status indicator per file:
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- ⏳ Extracting... (extraction_status = 'pending' or 'processing')
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- ✓ (extraction_status = 'done') — subtle, no noise
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- ⚠ (extraction_status = 'failed') — show tooltip with reason
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Poll via a simple setInterval (every 5s) that reloads the file list if any files are pending — only while any files are pending, stop polling once all done.
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---
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## 10. Build & Deploy
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After all code changes:
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1. Run: cd ~/dev/dealroom && PATH=$PATH:/home/johan/go/bin:/usr/local/go/bin make build
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2. Run: systemctl --user stop dealroom && cp bin/dealroom dealroom && systemctl --user start dealroom
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3. Verify: curl -s -o /dev/null -w "%{http_code}" http://localhost:9300/ (expect 303)
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4. Check logs: journalctl --user -u dealroom -n 30 --no-pager
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5. Run: cd ~/dev/dealroom && git add -A && git commit -m "feat: responses, AI matching, assignment rules" && git push origin main
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---
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## Key Constants
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Fireworks API key: fw_RVcDe4c6mN4utKLsgA7hTm
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Extraction model: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
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Embedding model: nomic-ai/nomic-embed-text-v1.5
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Match threshold: 0.72 cosine similarity
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Chunk size: ~400 tokens / ~1600 chars max
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Chunk overlap: ~80 chars
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Max images per vision call: 10
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Worker concurrency: 2 goroutines
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Files are stored at: data/uploads/ (relative to WorkingDirectory in the service)
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DB path: data/db/dealroom.db
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@ -6,8 +6,12 @@ import (
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"encoding/csv"
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"encoding/csv"
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"fmt"
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"fmt"
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"io"
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"io"
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"log"
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"net/http"
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"net/http"
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"os"
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"path/filepath"
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"strings"
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"strings"
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"time"
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"dealroom/internal/rbac"
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"dealroom/internal/rbac"
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"dealroom/templates"
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"dealroom/templates"
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@ -89,6 +93,17 @@ func (h *Handler) handleRequestListUpload(w http.ResponseWriter, r *http.Request
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return
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return
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}
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}
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// Save uploaded file to data/uploads/ for inspection and reprocessing
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uploadsDir := "data/uploads"
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os.MkdirAll(uploadsDir, 0755)
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stamp := time.Now().Format("20060102-150405")
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saveName := filepath.Join(uploadsDir, stamp+"-"+dealID+"-"+header.Filename)
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if err := os.WriteFile(saveName, raw, 0644); err != nil {
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log.Printf("Warning: could not save uploaded file: %v", err)
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} else {
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log.Printf("Saved upload: %s (%d bytes)", saveName, len(raw))
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}
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// Detect XLSX by filename extension or magic bytes (PK = zip/xlsx)
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// Detect XLSX by filename extension or magic bytes (PK = zip/xlsx)
|
||||||
fname := strings.ToLower(header.Filename)
|
fname := strings.ToLower(header.Filename)
|
||||||
isXLSX := strings.HasSuffix(fname, ".xlsx") || strings.HasSuffix(fname, ".xls") ||
|
isXLSX := strings.HasSuffix(fname, ".xlsx") || strings.HasSuffix(fname, ".xls") ||
|
||||||
|
|
@ -126,33 +141,105 @@ func (h *Handler) handleRequestListUpload(w http.ResponseWriter, r *http.Request
|
||||||
rows = csvRows
|
rows = csvRows
|
||||||
}
|
}
|
||||||
|
|
||||||
// Detect column indices from header row using common DD checklist naming conventions
|
// Log first 12 rows of the file for debugging
|
||||||
// Falls back to positional (col 0=section, 1=item#, 2=description, 3=priority)
|
for ri, row := range rows {
|
||||||
idxSection := 0
|
if ri >= 12 {
|
||||||
idxItem := 1
|
break
|
||||||
idxDesc := 2
|
}
|
||||||
idxPriority := -1
|
log.Printf("[upload-debug] row %d: %v\n", ri, row)
|
||||||
|
}
|
||||||
|
|
||||||
if len(rows) > 0 {
|
// Scan up to first 12 rows to find the actual header row (highest keyword score).
|
||||||
for ci, cell := range rows[0] {
|
// Many DD checklists have title/metadata rows before the real column headers.
|
||||||
|
idxSection := -1
|
||||||
|
idxItem := -1
|
||||||
|
idxDesc := -1
|
||||||
|
idxPriority := -1
|
||||||
|
headerRowIdx := 0
|
||||||
|
bestScore := 0
|
||||||
|
|
||||||
|
for ri, record := range rows {
|
||||||
|
if ri >= 12 {
|
||||||
|
break
|
||||||
|
}
|
||||||
|
score := 0
|
||||||
|
tmpSection, tmpItem, tmpDesc, tmpPri := -1, -1, -1, -1
|
||||||
|
for ci, cell := range record {
|
||||||
h := strings.ToLower(strings.TrimSpace(cell))
|
h := strings.ToLower(strings.TrimSpace(cell))
|
||||||
switch {
|
if h == "" {
|
||||||
case contains(h, "section", "category", "topic", "area", "phase", "workstream"):
|
continue
|
||||||
idxSection = ci
|
}
|
||||||
case contains(h, "item #", "item#", "item no", "no.", "ref", "number", "#"):
|
if contains(h, "section", "category", "topic", "area", "phase", "workstream") {
|
||||||
idxItem = ci
|
tmpSection = ci
|
||||||
case contains(h, "description", "request", "document", "information", "detail", "item") && ci != idxSection:
|
score += 3
|
||||||
|
} else if contains(h, "description", "request", "document", "information requested", "detail") {
|
||||||
|
tmpDesc = ci
|
||||||
|
score += 3
|
||||||
|
} else if contains(h, "priority", "urgency", "importance", "criticality") {
|
||||||
|
tmpPri = ci
|
||||||
|
score += 2
|
||||||
|
} else if h == "#" || h == "no." || h == "no" || h == "item #" || h == "item#" ||
|
||||||
|
contains(h, "item no", "ref no", "ref #") {
|
||||||
|
tmpItem = ci
|
||||||
|
score += 2
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if score > bestScore {
|
||||||
|
bestScore = score
|
||||||
|
headerRowIdx = ri
|
||||||
|
if tmpSection >= 0 {
|
||||||
|
idxSection = tmpSection
|
||||||
|
}
|
||||||
|
if tmpItem >= 0 {
|
||||||
|
idxItem = tmpItem
|
||||||
|
}
|
||||||
|
if tmpDesc >= 0 {
|
||||||
|
idxDesc = tmpDesc
|
||||||
|
}
|
||||||
|
if tmpPri >= 0 {
|
||||||
|
idxPriority = tmpPri
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// If no header found, fall back to positional
|
||||||
|
if bestScore < 2 {
|
||||||
|
headerRowIdx = 0
|
||||||
|
idxSection = 0
|
||||||
|
idxItem = 1
|
||||||
|
idxDesc = 2
|
||||||
|
}
|
||||||
|
|
||||||
|
// If desc still not found, guess: pick the column with the longest average text
|
||||||
|
if idxDesc < 0 && len(rows) > headerRowIdx+1 {
|
||||||
|
maxLen := 0
|
||||||
|
for ci := range rows[headerRowIdx] {
|
||||||
|
total := 0
|
||||||
|
count := 0
|
||||||
|
for ri := headerRowIdx + 1; ri < len(rows) && ri < headerRowIdx+20; ri++ {
|
||||||
|
if ci < len(rows[ri]) {
|
||||||
|
total += len(strings.TrimSpace(rows[ri][ci]))
|
||||||
|
count++
|
||||||
|
}
|
||||||
|
}
|
||||||
|
avg := 0
|
||||||
|
if count > 0 {
|
||||||
|
avg = total / count
|
||||||
|
}
|
||||||
|
if avg > maxLen && ci != idxSection && ci != idxItem {
|
||||||
|
maxLen = avg
|
||||||
idxDesc = ci
|
idxDesc = ci
|
||||||
case contains(h, "priority", "urgency", "importance", "criticality"):
|
|
||||||
idxPriority = ci
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
log.Printf("[upload-debug] header at row %d (score=%d) | section=%d item=%d desc=%d priority=%d\n",
|
||||||
|
headerRowIdx, bestScore, idxSection, idxItem, idxDesc, idxPriority)
|
||||||
|
|
||||||
var items []reqRow
|
var items []reqRow
|
||||||
for ri, record := range rows {
|
for ri, record := range rows {
|
||||||
if ri == 0 {
|
if ri <= headerRowIdx {
|
||||||
continue // skip header
|
continue // skip title rows + header row itself
|
||||||
}
|
}
|
||||||
if len(record) == 0 {
|
if len(record) == 0 {
|
||||||
continue
|
continue
|
||||||
|
|
@ -253,7 +340,7 @@ func (h *Handler) handleRequestListUpload(w http.ResponseWriter, r *http.Request
|
||||||
|
|
||||||
h.logActivity(dealID, profile.ID, profile.OrganizationID, "upload", "request_list", fmt.Sprintf("%d items", len(items)), "")
|
h.logActivity(dealID, profile.ID, profile.OrganizationID, "upload", "request_list", fmt.Sprintf("%d items", len(items)), "")
|
||||||
|
|
||||||
http.Redirect(w, r, "/deals/"+dealID, http.StatusSeeOther)
|
http.Redirect(w, r, "/deals/"+dealID+"?tab=requests", http.StatusSeeOther)
|
||||||
}
|
}
|
||||||
|
|
||||||
func (h *Handler) autoAssignFilesToRequests(dealID string) {
|
func (h *Handler) autoAssignFilesToRequests(dealID string) {
|
||||||
|
|
|
||||||
|
|
@ -516,6 +516,12 @@ templ DealRoomDetail(profile *model.Profile, deal *model.Deal, folders []*model.
|
||||||
: 'px-4 py-2 text-sm font-medium border-b-2 border-transparent text-gray-500 hover:text-gray-300';
|
: 'px-4 py-2 text-sm font-medium border-b-2 border-transparent text-gray-500 hover:text-gray-300';
|
||||||
}
|
}
|
||||||
|
|
||||||
|
// Auto-switch tab based on ?tab= query param (e.g. after request list upload)
|
||||||
|
(function() {
|
||||||
|
var tab = new URLSearchParams(window.location.search).get('tab');
|
||||||
|
if (tab) { showTab(tab); }
|
||||||
|
})();
|
||||||
|
|
||||||
function filterFiles() {
|
function filterFiles() {
|
||||||
var q = document.getElementById('fileSearch').value.toLowerCase();
|
var q = document.getElementById('fileSearch').value.toLowerCase();
|
||||||
document.querySelectorAll('.file-row').forEach(function(row) {
|
document.querySelectorAll('.file-row').forEach(function(row) {
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue