mirror of
https://github.com/infiniflow/ragflow.git
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273 lines
8.2 KiB
Go
273 lines
8.2 KiB
Go
//
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// Copyright 2026 The InfiniFlow Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//
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// DOCX vision figure dispatch: enriches the parse result with
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// LLM-generated descriptions of embedded images, mirroring
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// Python's enhance_media_sections_with_vision in
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// rag/flow/parser/utils.py (invoked from parser.py:_doc's JSON branch).
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//
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// Unlike the PDF vision path (which replaces dispatchParse entirely),
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// DOCX vision is a post-processing step. It mirrors Python exactly:
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// vision enrichment happens ONLY on the JSON output path, where each
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// item carries a doc_type_kwd and an optional image. The markdown path
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// performs no vision enrichment in Python, so it must not here either.
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package component
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import (
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"context"
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"fmt"
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"os"
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"path/filepath"
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"strings"
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"sync"
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"ragflow/internal/entity"
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modelModule "ragflow/internal/entity/models"
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"ragflow/internal/ingestion/component/schema"
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"ragflow/internal/utility"
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)
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var (
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docxVisionPromptBuilder = buildDOCXVisionPrompt
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visionChatInvoker = defaultVisionChatInvoker
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docxVisionConcurrency uint = 10
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)
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const (
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docxVisionPromptFile = "vision_llm_figure_describe_prompt.md"
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docxVisionPromptWithContextFile = "vision_llm_figure_describe_prompt_with_context.md"
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)
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var (
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docxVisionPromptsBase string
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docxVisionPromptsOnce sync.Once
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docxVisionPromptCache = make(map[string]string)
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docxVisionPromptMu sync.RWMutex
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)
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// maybeDispatchDOCXVision enriches a DOCX parse result with vision-model
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// descriptions of embedded images. It mirrors Python's
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// enhance_media_sections_with_vision (rag/flow/parser/utils.py:162), which
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// runs only in the JSON output branch of parser.py:_doc.
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//
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// For each JSON item whose doc_type_kwd is "image" or "table" AND that
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// carries a non-empty "image" field, the vision model describes the image
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// and the description is appended to the item's text (Python:
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// item["text"] = f"{text}\n{parsed_text}" if text else parsed_text). Items
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// without an image (e.g. DOCX tables) are left untouched, exactly as Python
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// skips them via `if item.get("image") is None: continue`.
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//
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// The markdown output path receives no vision enrichment — Python's DOCX
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// markdown branch only concatenates text and never calls the vision model.
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func maybeDispatchDOCXVision(
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ctx context.Context,
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fileType utility.FileType,
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dispatched parserDispatchResult,
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inputs map[string]any,
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setups map[string]schema.ParserSetup,
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) (parserDispatchResult, bool, error) {
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if fileType != utility.FileTypeDOCX {
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return dispatched, false, nil
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}
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// Python triggers vision enrichment only on the JSON path
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// (parser.py:_doc → enhance_media_sections_with_vision).
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if dispatched.Err != nil || dispatched.OutputFormat != "json" || len(dispatched.JSON) == 0 {
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return dispatched, false, nil
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}
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tenantID := getStringOr(inputs, "tenant_id", "")
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if tenantID == "" {
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return dispatched, false, nil
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}
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// Resolve the tenant's IMAGE2TEXT model.
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driver, modelName, apiConfig, _, err := resolveTenantModelByType(tenantID, entity.ModelTypeImage2Text)
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if err != nil {
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// Model not available — skip vision enhancement silently,
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// matching Python's try/except pass behaviour.
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return dispatched, false, nil
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}
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// Collect the indices of JSON items that carry an embeddable image.
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type target struct {
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idx int
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}
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var targets []target
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for i, item := range dispatched.JSON {
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kd, _ := item["doc_type_kwd"].(string)
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if kd != "image" && kd != "table" {
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continue
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}
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img, _ := item["image"].(string)
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if img == "" {
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continue
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}
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targets = append(targets, target{idx: i})
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}
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if len(targets) == 0 {
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return dispatched, false, nil
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}
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descriptions := make([]string, len(targets))
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var wg sync.WaitGroup
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sem := make(chan struct{}, docxVisionConcurrency)
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for slot, tg := range targets {
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wg.Add(1)
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go func(slot int, itemIdx int) {
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defer wg.Done()
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sem <- struct{}{}
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defer func() { <-sem }()
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img, _ := dispatched.JSON[itemIdx]["image"].(string)
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if img == "" {
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return
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}
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// DOCX JSON items have no surrounding context (unlike the
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// former markdown path), so use the bare figure prompt —
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// matching Python's VisionFigureParser(context_size=0).
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prompt, perr := docxVisionPromptBuilder("", "")
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if perr != nil {
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return
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}
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messages := buildVisionMessages(prompt, img)
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resp, ierr := visionChatInvoker(ctx, driver, modelName, messages, apiConfig)
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if ierr != nil {
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return
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}
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descriptions[slot] = extractDOCXVisionAnswer(resp)
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}(slot, tg.idx)
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}
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wg.Wait()
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modified := false
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for slot, tg := range targets {
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desc := strings.TrimSpace(descriptions[slot])
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if desc == "" {
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continue
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}
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existing, _ := dispatched.JSON[tg.idx]["text"].(string)
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if existing != "" {
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dispatched.JSON[tg.idx]["text"] = existing + "\n" + desc
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} else {
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dispatched.JSON[tg.idx]["text"] = desc
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}
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modified = true
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}
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return dispatched, modified, nil
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}
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// buildDOCXVisionPrompt loads the figure-describe prompt template
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// and, when context text is available, renders it with the
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// with-context variant. Mirrors Python:
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//
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// if context_above or context_below:
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// prompt = vision_llm_figure_describe_prompt_with_context(context_above, context_below)
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// else:
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// prompt = vision_llm_figure_describe_prompt()
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func buildDOCXVisionPrompt(contextAbove, contextBelow string) (string, error) {
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hasContext := strings.TrimSpace(contextAbove) != "" || strings.TrimSpace(contextBelow) != ""
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var templateName string
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if hasContext {
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templateName = docxVisionPromptWithContextFile
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} else {
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templateName = docxVisionPromptFile
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}
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template, err := loadDOCXVisionPromptFile(templateName)
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if err != nil {
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return "", err
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}
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if hasContext {
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template = strings.ReplaceAll(template, "{{ context_above }}", contextAbove)
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template = strings.ReplaceAll(template, "{{ context_below }}", contextBelow)
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}
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return template, nil
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}
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func loadDOCXVisionPromptFile(filename string) (string, error) {
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docxVisionPromptMu.RLock()
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if cached, ok := docxVisionPromptCache[filename]; ok {
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docxVisionPromptMu.RUnlock()
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return cached, nil
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}
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docxVisionPromptMu.RUnlock()
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baseDir, err := docxVisionPromptsBaseDir()
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if err != nil {
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return "", err
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}
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promptPath := filepath.Join(baseDir, "rag", "prompts", filename)
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content, err := os.ReadFile(promptPath)
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if err != nil {
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return "", fmt.Errorf("docx vision prompt %q: %w", filename, err)
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}
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cached := strings.TrimSpace(string(content))
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docxVisionPromptMu.Lock()
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docxVisionPromptCache[filename] = cached
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docxVisionPromptMu.Unlock()
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return cached, nil
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}
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func docxVisionPromptsBaseDir() (string, error) {
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var initErr error
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docxVisionPromptsOnce.Do(func() {
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root := utility.GetProjectRoot()
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if _, statErr := os.Stat(filepath.Join(root, "rag", "prompts")); statErr == nil {
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docxVisionPromptsBase = root
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return
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}
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initErr = fmt.Errorf("rag/prompts not found under project root %q", root)
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})
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if initErr != nil {
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return "", initErr
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}
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return docxVisionPromptsBase, nil
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}
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func buildVisionMessages(prompt, imageBase64 string) []modelModule.Message {
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dataURI := "data:image/png;base64," + imageBase64
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return []modelModule.Message{{
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Role: "user",
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Content: []interface{}{
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map[string]any{"type": "text", "text": prompt},
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map[string]any{"type": "image_url", "image_url": map[string]any{"url": dataURI}},
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},
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}}
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}
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func extractDOCXVisionAnswer(resp *modelModule.ChatResponse) string {
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if resp == nil || resp.Answer == nil {
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return ""
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}
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return strings.TrimSpace(*resp.Answer)
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}
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func defaultVisionChatInvoker(
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ctx context.Context,
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driver modelModule.ModelDriver,
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modelName string,
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messages []modelModule.Message,
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apiConfig *modelModule.APIConfig,
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) (*modelModule.ChatResponse, error) {
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vision := true
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return driver.ChatWithMessages(ctx, modelName, messages, apiConfig, &modelModule.ChatConfig{Vision: &vision}, nil)
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}
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