add text extraction for PDF/DOCX/PPTX/XLSX and MP4 keyframe CLIP embedding in upload_file.dspy

wwwroot/knowledge_bases_list/upload_file.dspy

@@ -1,3 +1,4 @@
+import base64, os, subprocess
 ns = params_kw.copy()
 kb_id = ns.get('kb_id', '')
 folder_id = ns.get('folder', '')
@@ -32,6 +33,33 @@ face_count = 0
 voice_speakers = 0
 meta_parts = {}
 
+# --- OFFICE DOCS: text extraction (PDF/DOCX/PPTX/XLSX) ---
+if ext_l == '.pdf' and not text:
+    import io; from PyPDF2 import PdfReader
+    reader = PdfReader(io.BytesIO(file_data))
+    text = '\n'.join(p.extract_text() or '' for p in reader.pages)
+elif ext_l == '.docx' and not text:
+    import io; from docx import Document
+    doc = Document(io.BytesIO(file_data))
+    text = '\n'.join(p.text for p in doc.paragraphs)
+elif ext_l == '.pptx' and not text:
+    import io; from pptx import Presentation
+    prs = Presentation(io.BytesIO(file_data))
+    parts = []
+    for slide in prs.slides:
+        for shape in slide.shapes:
+            if hasattr(shape, 'text') and shape.text:
+                parts.append(shape.text)
+    text = '\n'.join(parts)
+elif ext_l == '.xlsx' and not text:
+    import io; from openpyxl import load_workbook
+    wb = load_workbook(io.BytesIO(file_data), data_only=True)
+    parts = []
+    for sheet in wb.worksheets:
+        for row in sheet.iter_rows(values_only=True):
+            parts.append('\t'.join(str(c or '') for c in row))
+    text = '\n'.join(parts)
+
 # --- TEXT EXTRACTION ---
 if ext_l in text_exts:
     text = file_data.decode('utf-8', errors='replace')
@@ -83,6 +111,30 @@ if ext_l in video_exts:
                         if results and isinstance(results[0], dict):
                             face_count = len(results[0].get("faces", results[0].get("detections", [])))
                 except: pass
+                # --- CLIP image embedding for video frame ---
+                if os.path.exists(tmp_img):
+                    img_b64_frame = base64.b64encode(frame_data).decode()
+                    try:
+                        r2 = await s.post('https://embedding.opencomputing.net/api/embed',
+                            json={"images": [img_b64_frame], "model": "CLIP-ViT-H-14"})
+                        emb_data = await r2.json() if r2.status == 200 else {}
+                        img_embeddings = emb_data.get("image_embeddings", emb_data.get("embeddings", []))
+                    except:
+                        img_embeddings = []
+                    if img_embeddings:
+                        try:
+                            vdb_data = {"colname": kb_id, "data": [
+                                {"id": doc_id + "_frame0", "vector": img_embeddings[0], "text": file_name}
+                            ]}
+                            await s.post('https://vectordb.opencomputing.net/v1/upsert', json=vdb_data)
+                            async with get_sor_context(env, 'rag') as sor:
+                                await sor.sqlExe(
+                                    "INSERT INTO document_chunks (id, doc_id, kb_id, chunk_index, content, vector_id, created_at) "
+                                    "VALUES (${id}$, ${doc_id}$, ${kb_id}$, 0, ${content}$, ${vid}$, NOW())",
+                                    {"id": doc_id + "_c0", "doc_id": doc_id, "kb_id": kb_id,
+                                     "content": file_name, "vid": doc_id + "_frame0"})
+                        except:
+                            pass
             os.remove(tmp_img)
     except: pass