From 908480f35e3b8c1a9fff7fc2b9552daf7a899351 Mon Sep 17 00:00:00 2001 From: Hermes Date: Thu, 30 Jul 2026 14:24:49 +0800 Subject: [PATCH] fix: file_url uses file_path directly with /idfile prefix --- build/lib/rag/__init__.py | 9 + build/lib/rag/init.py | 873 +++++ build/lib/rag/pipeline.py | 208 + rag.egg-info/PKG-INFO | 3 + rag.egg-info/SOURCES.txt | 9 + rag.egg-info/dependency_links.txt | 1 + rag.egg-info/top_level.txt | 1 + rag/__pycache__/pipeline.cpython-310.pyc | Bin 0 -> 4346 bytes rag/files/3a71de7efcb84ba8.txt | 3457 +++++++++++++++++ rag/files/4XPZJxScRnhF8hC3.xlsx | Bin 0 -> 5829 bytes rag/files/5_ukt_PrM8mQNany.xlsx | Bin 0 -> 2151880 bytes rag/files/5fcrX01E4PfyX9oa.xlsx | Bin 0 -> 5317 bytes rag/files/Aq84F3Fv7ctEkrwn.pptx | Bin 0 -> 84348 bytes rag/files/G9ZUHQ23bwrwuFJN.txt | 1 + rag/files/QvVq8vaVajS8G_3N.txt | 1 + rag/files/TQRrReBGtEGscp6B.txt | 1 + rag/files/TSczgYAoejFIztzY.txt | 1 + rag/files/UXFEvwHtCEDgb5QW.docx | Bin 0 -> 13848 bytes rag/files/W4DHTtrgeqmIAY6j.txt | 1 + rag/files/_YahIghaUsLj2AKS.xlsx | Bin 0 -> 2151880 bytes rag/files/aOxjCdUwNKE6Xcyw.txt | 1 + rag/files/be5e85e8a6734aae.txt | 3457 +++++++++++++++++ rag/files/d4arz15O8KqNrgU8.mp4 | Bin 0 -> 2326 bytes rag/files/hJ9WG7qG6DoxYIx2.png | Bin 0 -> 589 bytes rag/files/j0G36PxRI3Bkgak5.txt | 1 + rag/files/ltarC48J9aAFtm5W.txt | 1 + wwwroot/knowledge_bases_list/file_list.dspy | 2 +- wwwroot/knowledge_bases_list/index.ui.bak | 43 + .../knowledge_bases_list/storage_card.dspy | 23 + 29 files changed, 8093 insertions(+), 1 deletion(-) create mode 100644 build/lib/rag/__init__.py create mode 100644 build/lib/rag/init.py create mode 100644 build/lib/rag/pipeline.py create mode 100644 rag.egg-info/PKG-INFO create mode 100644 rag.egg-info/SOURCES.txt create mode 100644 rag.egg-info/dependency_links.txt create mode 100644 rag.egg-info/top_level.txt create mode 100644 rag/__pycache__/pipeline.cpython-310.pyc create mode 100644 rag/files/3a71de7efcb84ba8.txt create mode 100644 rag/files/4XPZJxScRnhF8hC3.xlsx create mode 100644 rag/files/5_ukt_PrM8mQNany.xlsx create mode 100644 rag/files/5fcrX01E4PfyX9oa.xlsx create mode 100644 rag/files/Aq84F3Fv7ctEkrwn.pptx create mode 100644 rag/files/G9ZUHQ23bwrwuFJN.txt create mode 100644 rag/files/QvVq8vaVajS8G_3N.txt create mode 100644 rag/files/TQRrReBGtEGscp6B.txt create mode 100644 rag/files/TSczgYAoejFIztzY.txt create mode 100644 rag/files/UXFEvwHtCEDgb5QW.docx create mode 100644 rag/files/W4DHTtrgeqmIAY6j.txt create mode 100644 rag/files/_YahIghaUsLj2AKS.xlsx create mode 100644 rag/files/aOxjCdUwNKE6Xcyw.txt create mode 100644 rag/files/be5e85e8a6734aae.txt create mode 100644 rag/files/d4arz15O8KqNrgU8.mp4 create mode 100644 rag/files/hJ9WG7qG6DoxYIx2.png create mode 100644 rag/files/j0G36PxRI3Bkgak5.txt create mode 100644 rag/files/ltarC48J9aAFtm5W.txt create mode 100644 wwwroot/knowledge_bases_list/index.ui.bak create mode 100644 wwwroot/knowledge_bases_list/storage_card.dspy diff --git a/build/lib/rag/__init__.py b/build/lib/rag/__init__.py new file mode 100644 index 0000000..8e7eac3 --- /dev/null +++ b/build/lib/rag/__init__.py @@ -0,0 +1,9 @@ +# -*- coding:utf-8 -*- +""" +RAG业务模块 - 知识库、文档、引擎配置、订阅管理 +作为独立模块被ragserver或其他应用引用 +""" + +from .init import init_rag_module + +__all__ = ['init_rag_module'] diff --git a/build/lib/rag/init.py b/build/lib/rag/init.py new file mode 100644 index 0000000..24624f4 --- /dev/null +++ b/build/lib/rag/init.py @@ -0,0 +1,873 @@ +# -*- coding:utf-8 -*- +""" +RagServer DSPY Handlers - RAG 核心业务逻辑 + 文件上传/删除 +""" +from traceback import format_exc +from ahserver.serverenv import ServerEnv +from appPublic.registerfunction import RegisterFunction +from appPublic.log import debug, exception +from sqlor.dbpools import get_sor_context +import json, os, uuid, time + + +async def status_handler(request, params_kw, *args, **kwargs): + return json.dumps({ + "service": "ragserver", "version": "0.1.0", + "endpoints": ["/api/status", "/api/kb/list", "/api/doc/upload", + "/api/doc/delete", "/api/search", "/api/engines", + "/api/dir/create", "/api/dir/delete", "/api/dir/list", + "/api/tag/create", "/api/tag/list", "/api/tag/delete", + "/api/tag/assign", "/api/tag/unassign", "/api/tag/media_tags", + "/api/tag/search"] + }, indent=2, ensure_ascii=False) + + +async def kb_list_handler(request, params_kw, *args, **kwargs): + env = request._run_ns + try: + userorgid = await env.get_userorgid() + async with get_sor_context(env, 'rag') as sor: + recs = await sor.R("knowledge_bases", {}) + cards = [] + for r in recs: + doc_recs = await sor.R("documents", {"kb_id": r.id}) + doc_count = len(doc_recs) + total_size = r.total_size or 0 + if total_size >= 1073741824: + size_str = f"{total_size/1073741824:.1f}GB" + elif total_size >= 1048576: + size_str = f"{total_size/1048576:.0f}MB" + elif total_size >= 1024: + size_str = f"{total_size/1024:.0f}KB" + else: + size_str = f"{total_size}B" + engine = r.embedding_engine or "CLIP" + card = {"widgettype":"VBox","options":{"cwidth":16,"cheight":12,"bgcolor":"#f0f7ff","padding":"16px","css":"card clickable","border":"1px solid #d0e4f7"},"subwidgets":[ + {"widgettype":"Text","options":{"text":"📚 " + str(r.name),"cfontsize":16,"fontWeight":"bold"}}, + {"widgettype":"Text","options":{"text":str(engine)+" · 1024维","cfontsize":12,"color":"#888"}}, + {"widgettype":"HBox","options":{"spacing":"12px"},"subwidgets":[ + {"widgettype":"Text","options":{"text":"📄 "+str(doc_count)+"文档","cfontsize":12,"color":"#666"}}, + {"widgettype":"Text","options":{"text":"💾 "+size_str,"cfontsize":12,"color":"#666"}}]}], + "binds":[{"wid":"self","event":"click","actiontype":"urlwidget","target":"app.rag_main_content","mode":"replace","options":{"url":"/rag/knowledge_bases_list/detail.ui","params":{"kb_id":str(r.id),"kb_name":str(r.name)}}}]} + cards.append(card) + if not cards: + cards.append({"widgettype":"Text","options":{"text":"暂无知识库","color":"#aaa","cfontsize":14,"halign":"center"}}) + result = {"widgettype":"HBox","options":{"width":"100%","spacing":"12px","wrap":True},"subwidgets":cards} + return json.dumps(result, ensure_ascii=False) + except Exception as e: + exception(f"kb_list: {e}, {format_exc()}") + return json.dumps({"error": str(e)}) + + +async def engines_handler(request, params_kw, *args, **kwargs): + env = request._run_ns + try: + userorgid = await env.get_userorgid() + async with get_sor_context(env, 'rag') as sor: + sql = "SELECT * FROM engine_configs WHERE status='active' AND (org_id IS NULL OR org_id=${org_id}$) ORDER BY engine_type, priority DESC" + recs = await sor.sqlExe(sql, {"org_id": userorgid}) + rows = [dict(r) for r in recs] + return json.dumps({"status": "SUCCEEDED", "data": {"rows": rows, "total": len(rows)}}, ensure_ascii=False, default=str) + except Exception as e: + exception(f"engines: {e}, {format_exc()}") + return json.dumps({"error": str(e)}) + + +async def search_handler(request, params_kw, *args, **kwargs): + """统一检索：文本 + 多媒体(图/音/视/文) → embedding → VDB → 重排 → 返回""" + env = request._run_ns + try: + userorgid = await env.get_userorgid() + query = (params_kw.get("query") or "").strip() + kb_id = (params_kw.get("kb_id") or "").strip() + top_k = int(params_kw.get("top_k", 10)) + recall_k = int(params_kw.get("recall_k", top_k * 3)) + + # Resolve KBs + kb_ids = await _resolve_search_kbs(env, userorgid, kb_id) + if not kb_ids: + return json.dumps({"status": "SUCCEEDED", "data": {"results": [], "total": 0, "message": "no knowledge bases"}}, ensure_ascii=False) + + # Read uploaded file if present + file_data = None + file_name = None + content_type = request.headers.get("Content-Type", "") + if "multipart" in content_type: + reader = await request.multipart() + async for part in reader: + if part.name == "file" and part.filename: + file_data = await part.read() + file_name = part.filename + break + if not file_data: + # Try raw body as file + body = await request.read() + if body and len(body) > 10: + # Check if it's a form-encoded request + if not query and b'=' in body[:100]: + pass # form data, not a file + elif not body.startswith(b'{') and not body.startswith(b'['): + file_data = body + file_name = params_kw.get("file_name", "search_upload") + + # Process query + media → embedding vector + query_vec = None + if query or file_data: + query_vec = await _build_search_vector(query, file_data, file_name) + + if not query_vec: + return json.dumps({"status": "SUCCEEDED", "data": {"results": [], "total": 0, "message": "no query or file provided"}}, ensure_ascii=False) + + # Multi-KB VDB search + all_hits = [] + for kid in kb_ids: + try: + vdb_resp = await _call_uapi("rag-vdb", "search", { + "collection": kid, + "vector": query_vec, + "topK": recall_k + }) + hits = _parse_vdb_hits(vdb_resp, kid) + all_hits.extend(hits) + except Exception as e: + exception(f"vdb search kb={kid}: {e}") + + # Deduplicate + sort by score + seen = set() + unique_hits = [] + for h in sorted(all_hits, key=lambda x: x.get("score", 0), reverse=True): + hid = h.get("id", h.get("text", "")) + if hid not in seen: + seen.add(hid) + unique_hits.append(h) + + # Rerank if query text provided + if query and unique_hits: + try: + documents = [h.get("text", h.get("content", "")) for h in unique_hits[:recall_k]] + rerank_resp = await _call_uapi("rag-reranker", "rerank", { + "query": query, + "documents": documents + }) + reranked = _apply_rerank(unique_hits, rerank_resp) + unique_hits = reranked + except Exception as e: + exception(f"rerank failed: {e}") + + # Limit + enrich with DB metadata + final = unique_hits[:top_k] + enriched = await _enrich_search_results(env, final) + + return json.dumps({ + "status": "SUCCEEDED", + "data": {"results": enriched, "total": len(enriched), + "recall": len(all_hits), "kbs_searched": len(kb_ids)} + }, ensure_ascii=False, default=str) + + except Exception as e: + exception(f"search: {e}, {format_exc()}") + return json.dumps({"error": str(e)}) + + +async def _resolve_search_kbs(env, userorgid, kb_id): + """Resolve KB IDs: specific or all org KBs""" + async with get_sor_context(env, 'rag') as sor: + if kb_id: + recs = await sor.R("knowledge_bases", {"id": kb_id}) + return [r.id for r in recs] + # All org KBs (global + org-specific) + sql = "SELECT id FROM knowledge_bases WHERE org_id IS NULL OR org_id=${org_id}$" + recs = await sor.sqlExe(sql, {"org_id": userorgid}) + return [r.id for r in recs] + + +async def _build_search_vector(query, file_data, file_name): + """Build search embedding from text query + media file""" + texts = [] + if query: + texts.append(query) + + if file_data and file_name: + ext = os.path.splitext(file_name)[1].lower() + if ext in ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp'): + # Image → embed as-is (CLIP multimodal) + texts.append(f"[IMAGE:{file_name}]") + elif ext in ('.txt', '.md', '.json', '.csv', '.html', '.py'): + # Text file → extract content + try: + content = file_data.decode("utf-8", errors="replace")[:4000] + texts.append(content) + except Exception: + pass + elif ext in ('.mp3', '.wav', '.flac', '.ogg'): + # Audio → placeholder (would need ASR service) + texts.append(f"[AUDIO:{file_name}]") + elif ext in ('.mp4', '.avi', '.mov', '.mkv'): + texts.append(f"[VIDEO:{file_name}]") + else: + # Try as text + try: + texts.append(file_data.decode("utf-8", errors="replace")[:2000]) + except Exception: + pass + + if not texts: + return None + + combined = " ".join(texts) + try: + resp = await _call_uapi("rag-embedding", "embed", { + "texts": [combined], + "model": "CLIP-ViT-H-14" + }) + vecs = resp.get("embeddings", []) if isinstance(resp, dict) else [] + return vecs[0] if vecs else None + except Exception as e: + exception(f"query embedding failed: {e}") + return None + + +def _parse_vdb_hits(vdb_resp, kb_id): + """Parse VDB search response into uniform hit format""" + hits = [] + data = vdb_resp + if isinstance(data, dict): + for key in ("results", "data", "hits", "rows"): + candidates = data.get(key) + if isinstance(candidates, list): + data = candidates + break + if not isinstance(data, list): + return hits + for item in data: + if isinstance(item, dict): + hits.append({ + "id": item.get("id", item.get("doc_id", "")), + "text": item.get("text", item.get("content", "")), + "score": item.get("score", item.get("distance", 0)), + "kb_id": kb_id, + "metadata": item.get("metadata", item.get("meta", {})) + }) + return hits + + +def _apply_rerank(hits, rerank_resp): + """Apply reranker scores to reorder hits""" + scores = [] + if isinstance(rerank_resp, dict): + scores = rerank_resp.get("scores", rerank_resp.get("results", [])) + if isinstance(scores, list) and len(scores) == len(hits): + for i, s in enumerate(scores): + if isinstance(s, dict): + hits[i]["rerank_score"] = s.get("score", s.get("relevance_score", 0)) + else: + hits[i]["rerank_score"] = float(s) if s else 0 + hits.sort(key=lambda x: x.get("rerank_score", 0), reverse=True) + return hits + + +async def _enrich_search_results(env, hits): + """Enrich hits with document metadata from DB""" + doc_ids = list(set(h.get("id", "") for h in hits if h.get("id"))) + if not doc_ids: + return hits + + async with get_sor_context(env, 'rag') as sor: + recs = await sor.sqlExe( + "SELECT id, file_name, file_type, file_size, status, kb_id, created_at " + "FROM documents WHERE id IN (" + ",".join(repr(d) for d in doc_ids) + ")", {}) + doc_map = {r.id: dict(r) for r in recs} + + for h in hits: + did = h.get("id", "") + if did in doc_map: + h["document"] = doc_map[did] + return hits + + +async def doc_upload_handler(request, params_kw, *args, **kwargs): + """文件上传 → 保存 → DB记录 → 触发RAG入库""" + env = request._run_ns + try: + userorgid = await env.get_userorgid() + kb_id = params_kw.get("kb_id", "") + file_name = params_kw.get("file_name", "upload.bin") + if not kb_id: + return json.dumps({"error": "kb_id required"}) + + # Read raw file data from request body + file_data = await request.read() + + if not file_data: + return json.dumps({"error": "no file data"}) + + # Save file + doc_id = uuid.uuid4().hex[:16] + ext = os.path.splitext(file_name)[1] or ".bin" + saved_name = f"{doc_id}{ext}" + files_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files") + os.makedirs(files_dir, exist_ok=True) + file_path = os.path.join(files_dir, saved_name) + with open(file_path, "wb") as f: + f.write(file_data) + + file_size = len(file_data) + file_type = _detect_file_type(file_name, "application/octet-stream") + + # Create document record + async with get_sor_context(env, 'rag') as sor: + await sor.sqlExe( + "INSERT INTO documents (id, kb_id, file_name, file_type, file_size, file_path, mime_type, status, org_id, created_at, updated_at) " + "VALUES (${id}$, ${kb_id}$, ${file_name}$, ${file_type}$, ${file_size}$, ${file_path}$, ${mime_type}$, 'pending', ${org_id}$, NOW(), NOW())", + {"id": doc_id, "kb_id": kb_id, "file_name": file_name, "file_type": file_type, + "file_size": file_size, "file_path": "/idfile/files/" + saved_name, + "mime_type": "application/octet-stream", "org_id": userorgid}) + + # Update KB stats + await sor.sqlExe( + "UPDATE knowledge_bases SET doc_count=doc_count+1, total_size=total_size+${size}$ WHERE id=${kb_id}$", + {"size": file_size, "kb_id": kb_id}) + + # Trigger async ingest for text-based files via uapi + ingest_result = None + if file_type == "text": + try: + text = file_data.decode("utf-8", errors="replace") + ingest_result = await _rag_ingest_async(env, text, kb_id, doc_id) + # Update status to done + async with get_sor_context(env, 'rag') as sor: + chunks_n = ingest_result.get("chunks", 0) if ingest_result else 0 + await sor.sqlExe( + "UPDATE documents SET status='done', chunk_count=${chunks}$ WHERE id=${id}$", + {"chunks": chunks_n, "id": doc_id}) + if chunks_n: + await sor.sqlExe( + "UPDATE knowledge_bases SET chunk_count=chunk_count+${n}$ WHERE id=${kb_id}$", + {"n": chunks_n, "kb_id": kb_id}) + except Exception as e: + exception(f"uapi ingest failed: {e}") + async with get_sor_context(env, 'rag') as sor: + await sor.sqlExe( + "UPDATE documents SET status='error' WHERE id=${id}$", {"id": doc_id}) + + return json.dumps({ + "status": "SUCCEEDED", + "doc_id": doc_id, + "file_name": file_name, + "file_size": file_size, + "file_type": file_type, + "ingest": ingest_result + }, ensure_ascii=False, default=str) + + except Exception as e: + exception(f"doc_upload: {e}, {format_exc()}") + return json.dumps({"error": str(e)}) + + +async def doc_delete_handler(request, params_kw, *args, **kwargs): + """文件删除 → 清理VDB → 清理图 → 清理DB → 删文件""" + env = request._run_ns + try: + doc_id = params_kw.get("doc_id", "") + if not doc_id: + return json.dumps({"error": "doc_id required"}) + + async with get_sor_context(env, 'rag') as sor: + # Get document info + recs = await sor.R("documents", {"id": doc_id}) + if not recs: + return json.dumps({"error": "document not found"}) + doc = recs[0] + + # Get chunks to clean VDB + chunks = await sor.R("document_chunks", {"doc_id": doc_id}) + + # Delete from VDB + if chunks: + vector_ids = [c.vector_id for c in chunks if c.vector_id] + if vector_ids: + try: + await _call_uapi("rag-vdb", "delete", + {"colname": doc.kb_id, "ids": vector_ids}) + except Exception as e: + exception(f"vdb delete failed: {e}") + + # Delete entities from graph + try: + await _call_uapi("rag-graph", "delete", {"graph": doc.kb_id}) + except Exception as e: + exception(f"graph delete failed: {e}") + + # Delete DB records + await sor.sqlExe("DELETE FROM document_chunks WHERE doc_id=${id}$", {"id": doc_id}) + await sor.sqlExe("DELETE FROM entities WHERE kb_id=${kb_id}$", {"kb_id": doc.kb_id}) + await sor.sqlExe("DELETE FROM entity_relations WHERE kb_id=${kb_id}$", {"kb_id": doc.kb_id}) + await sor.sqlExe("DELETE FROM documents WHERE id=${id}$", {"id": doc_id}) + + # Update KB stats + await sor.sqlExe( + "UPDATE knowledge_bases SET doc_count=GREATEST(doc_count-1,0), total_size=GREATEST(total_size-${size}$,0), chunk_count=GREATEST(chunk_count-${n}$,0) WHERE id=${kb_id}$", + {"size": doc.file_size, "n": len(chunks), "kb_id": doc.kb_id}) + + # Delete file from disk + file_path = doc.file_path + if file_path and file_path.startswith("/idfile/"): + real_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "files", + os.path.basename(file_path)) + if os.path.exists(real_path): + os.remove(real_path) + + return json.dumps({"status": "SUCCEEDED", "doc_id": doc_id, "chunks_deleted": len(chunks)}) + + except Exception as e: + exception(f"doc_delete: {e}, {format_exc()}") + return json.dumps({"error": str(e)}) + + +def _detect_file_type(name, mime): + """Detect file type from name and MIME""" + ext = os.path.splitext(name)[1].lower() + if ext in ('.txt', '.md', '.json', '.csv', '.xml', '.html', '.py', '.js', '.css', '.yaml', '.yml'): + return "text" + if ext in ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp', '.svg'): + return "image" + if ext in ('.mp3', '.wav', '.flac', '.ogg', '.m4a', '.aac'): + return "audio" + if ext in ('.mp4', '.avi', '.mov', '.mkv', '.webm'): + return "video" + if ext == '.pdf': + return "text" + return "other" + + +async def _call_uapi(upappid, apiname, data, timeout=10): + """Call GPU service via uapi config in rag database""" + import aiohttp + env = ServerEnv() + async with get_sor_context(env, 'rag') as sor: + recs = await sor.sqlExe( + "SELECT a.path, a.httpmethod, a.data as tmpl, b.baseurl " + "FROM uapi a JOIN upapp b ON a.upappid=b.id " + "WHERE a.upappid=${upappid}$ AND a.name=${apiname}$", + {"upappid": upappid, "apiname": apiname}) + if not recs: + raise Exception(f"uapi not found: {upappid}/{apiname}") + cfg = recs[0] + body = await _render_tmpl(cfg.tmpl, data) + url = f"{cfg.baseurl}{cfg.path}" + async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=timeout)) as session: + async with session.post(url, data=body, headers={"Content-Type": "application/json"}) as resp: + return await resp.json() + + +async def _rag_ingest_async(env, text, kb_id, doc_id): + """RAG ingestion pipeline via uapi: chunk → embed → VDB → NER → graph""" + chunks = _split_text(text, chunk_size=512, overlap=64) + if not chunks: + return {"chunks": 0} + chunk_count = len(chunks) + + # 1. Embedding + try: + emb_resp = await _call_uapi("rag-embedding", "embed", + {"texts": chunks, "model": "CLIP-ViT-H-14"}) + embeddings = emb_resp.get("embeddings", []) if isinstance(emb_resp, dict) else [] + except Exception as e: + exception(f"embedding failed: {e}") + embeddings = [] + + # 2. VDB upsert + vector_ids = [] + if embeddings: + try: + vdb_data = { + "collection": kb_id, + "data": [{"id": f"{doc_id}_{i}", "vector": emb, "text": chunks[i]} + for i, emb in enumerate(embeddings)] + } + vdb_resp = await _call_uapi("rag-vdb", "upsert", vdb_data) + vector_ids = [f"{doc_id}_{i}" for i in range(len(embeddings))] + except Exception as e: + exception(f"vdb upsert failed: {e}") + + # 3. NER entity extraction + entities_found = [] + try: + full_text = " ".join(chunks[:20]) # first 20 chunks for NER + ner_resp = await _call_uapi("rag-ner", "entities", {"text": full_text}) + entities_found = ner_resp.get("entities", []) if isinstance(ner_resp, dict) else [] + except Exception as e: + exception(f"ner failed: {e}") + + # 4. Save to graph + if entities_found: + try: + graph_data = { + "graph": kb_id, + "data": {"entities": entities_found, "source_doc": doc_id} + } + await _call_uapi("rag-graph", "save", graph_data) + except Exception as e: + exception(f"graph save failed: {e}") + + # 5. Record chunks in DB + async with get_sor_context(env, 'rag') as sor: + for i, (chunk_text, vid) in enumerate(zip(chunks, vector_ids)): + await sor.sqlExe( + "INSERT INTO document_chunks (id, doc_id, kb_id, chunk_index, content, vector_id, created_at) " + "VALUES (${id}$, ${doc_id}$, ${kb_id}$, ${idx}$, ${content}$, ${vid}$, NOW())", + {"id": f"{doc_id}_c{i}", "doc_id": doc_id, "kb_id": kb_id, + "idx": i, "content": chunk_text[:2000], "vid": vid}) + + return {"chunks": chunk_count, "vectors": len(vector_ids), + "entities": len(entities_found)} + + +def _split_text(text, chunk_size=512, overlap=64): + """Simple text chunker: paragraph-based with size limits""" + paragraphs = text.split('\n') + chunks = [] + current = "" + for p in paragraphs: + p = p.strip() + if not p: + continue + if len(current) + len(p) < chunk_size: + current = (current + " " + p).strip() + else: + if current: + chunks.append(current) + current = p + if current: + chunks.append(current) + # If still no chunks (single giant paragraph), force-split by size + if not chunks and text.strip(): + for i in range(0, len(text), chunk_size - overlap): + chunks.append(text[i:i + chunk_size]) + return chunks + + +async def _render_tmpl(tmpl, data): + """Simple Jinja2-style template rendering for uapi data templates""" + import re + result = tmpl + for key, val in data.items(): + result = result.replace("{{" + key + "}}", str(val)) + result = result.replace("{{json.dumps(" + key + ")}}", json.dumps(val, ensure_ascii=False)) + return result + + +async def _call_vdb_async(path, data, timeout=10): + """Call VDB service via uapi (mapping path to apiname)""" + apiname_map = {"/v1/upsert": "upsert", "/v1/search": "search", "/v1/delete": "delete"} + apiname = apiname_map.get(path, "search") + return await _call_uapi("rag-vdb", apiname, data, timeout) + + +async def _call_graph_async(path, data, timeout=10): + """Call Graph service via uapi""" + apiname_map = {"/api/graph/save": "save", "/api/graph/query": "query", "/api/graph/delete": "delete"} + apiname = apiname_map.get(path, "save") + return await _call_uapi("rag-graph", apiname, data, timeout) + + +# Keep sync wrappers for backward compat (note: these block in async context, prefer async versions) +def _call_vdb(path, data, timeout=10): + """Synchronous VDB call — deprecated, use _call_vdb_async in async context""" + import urllib.request + url = f"http://localhost:8886{path}" + req = urllib.request.Request(url, data=json.dumps(data).encode(), + headers={"Content-Type": "application/json"}) + with urllib.request.urlopen(req, timeout=timeout) as resp: + return json.loads(resp.read()) + + +def _call_graph(path, data, timeout=10): + """Synchronous Graph call — deprecated, use _call_graph_async in async context""" + import urllib.request + url = f"http://localhost:9092{path}" + req = urllib.request.Request(url, data=json.dumps(data).encode(), + headers={"Content-Type": "application/json"}) + with urllib.request.urlopen(req, timeout=timeout) as resp: + return json.loads(resp.read()) + + +async def dir_create_handler(request, params_kw, *args, **kwargs): + """创建目录""" + env = request._run_ns + try: + kb_id = params_kw.get("kb_id", "") + parent_id = params_kw.get("parent_id", "") + dir_name = params_kw.get("dir_name", "") + if not kb_id or not dir_name: + return json.dumps({"error": "kb_id and dir_name required"}) + async with get_sor_context(env, 'rag') as sor: + dir_id = uuid.uuid4().hex[:16] + await sor.sqlExe( + "INSERT INTO document_chunks (id, doc_id, kb_id, chunk_index, chunk_type, content, description, created_at) " + "VALUES (${id}$, '', ${kb_id}$, 0, 'directory', ${name}$, ${parent}$, NOW())", + {"id": dir_id, "kb_id": kb_id, "name": dir_name, "parent": parent_id}) + return json.dumps({"status": "SUCCEEDED", "dir_id": dir_id}) + except Exception as e: + exception(f"dir_create: {e}") + return json.dumps({"error": str(e)}) + + +async def dir_delete_handler(request, params_kw, *args, **kwargs): + """删除目录/文件""" + env = request._run_ns + try: + item_id = params_kw.get("item_id", "") + if not item_id: + return json.dumps({"error": "item_id required"}) + async with get_sor_context(env, 'rag') as sor: + await sor.sqlExe("DELETE FROM document_chunks WHERE id=${id}$ OR doc_id=${id}$", {"id": item_id}) + return json.dumps({"status": "SUCCEEDED"}) + except Exception as e: + exception(f"dir_delete: {e}") + return json.dumps({"error": str(e)}) + + +async def dir_list_handler(request, params_kw, *args, **kwargs): + """列出知识库的目录树""" + env = request._run_ns + try: + kb_id = params_kw.get("kb_id", "") + if not kb_id: + return json.dumps({"error": "kb_id required"}) + async with get_sor_context(env, 'rag') as sor: + # Get directories (chunk_type='directory') + dirs = await sor.sqlExe( + "SELECT id, content as label, description as parent_id FROM document_chunks WHERE kb_id=${kb_id}$ AND chunk_type='directory'", + {"kb_id": kb_id}) + # Get files (documents table) + docs = await sor.sqlExe( + "SELECT id, file_name as label, '' as parent_id FROM documents WHERE kb_id=${kb_id}$", + {"kb_id": kb_id}) + items = [] + for d in dirs: + items.append({"id": d.id, "label": d.label, "parent_id": d.parent_id or "", "type": "dir"}) + for d in docs: + items.append({"id": d.id, "label": d.label, "parent_id": "", "type": "file"}) + return json.dumps({"status": "SUCCEEDED", "items": items}) + except Exception as e: + exception(f"dir_list: {e}") + return json.dumps({"error": str(e)}) + + +async def tag_create_handler(request, params_kw, *args, **kwargs): + """创建标签""" + env = request._run_ns + try: + kb_id = params_kw.get("kb_id", "") + name = params_kw.get("name", "").strip() + color = params_kw.get("color", "#3b82f6") + if not kb_id or not name: + return json.dumps({"error": "kb_id and name required"}) + userorgid = await env.get_userorgid() + async with get_sor_context(env, 'rag') as sor: + tag_id = uuid.uuid4().hex[:16] + await sor.sqlExe( + "INSERT INTO tags (id, kb_id, name, color, org_id, created_at) " + "VALUES (${id}$, ${kb_id}$, ${name}$, ${color}$, ${org_id}$, NOW())", + {"id": tag_id, "kb_id": kb_id, "name": name, "color": color, "org_id": userorgid}) + return json.dumps({"status": "SUCCEEDED", "tag_id": tag_id, "name": name, "color": color}) + except Exception as e: + exception(f"tag_create: {e}, {format_exc()}") + return json.dumps({"error": str(e)}) + + +async def tag_list_handler(request, params_kw, *args, **kwargs): + """列出知识库的所有标签""" + env = request._run_ns + try: + kb_id = params_kw.get("kb_id", "") + if not kb_id: + return json.dumps({"error": "kb_id required"}) + userorgid = await env.get_userorgid() + async with get_sor_context(env, 'rag') as sor: + recs = await sor.R("tags", {"kb_id": kb_id, "org_id": userorgid}) + tags = [{"id": r.id, "name": r.name, "color": r.color, "created_at": str(r.created_at)} for r in recs] + return json.dumps({"status": "SUCCEEDED", "tags": tags}, ensure_ascii=False, default=str) + except Exception as e: + exception(f"tag_list: {e}, {format_exc()}") + return json.dumps({"error": str(e)}) + + +async def tag_delete_handler(request, params_kw, *args, **kwargs): + """删除标签（级联删除关联）""" + env = request._run_ns + try: + tag_id = params_kw.get("tag_id", "") + if not tag_id: + return json.dumps({"error": "tag_id required"}) + async with get_sor_context(env, 'rag') as sor: + await sor.sqlExe("DELETE FROM media_tags WHERE tag_id=${id}$", {"id": tag_id}) + await sor.sqlExe("DELETE FROM tags WHERE id=${id}$", {"id": tag_id}) + return json.dumps({"status": "SUCCEEDED", "tag_id": tag_id}) + except Exception as e: + exception(f"tag_delete: {e}, {format_exc()}") + return json.dumps({"error": str(e)}) + + +async def tag_assign_handler(request, params_kw, *args, **kwargs): + """给媒体/人脸/声纹打标签""" + env = request._run_ns + try: + kb_id = params_kw.get("kb_id", "") + media_type = params_kw.get("media_type", "") # document / face / voice + media_id = params_kw.get("media_id", "") + tag_id = params_kw.get("tag_id", "") + if not all([kb_id, media_type, media_id, tag_id]): + return json.dumps({"error": "kb_id, media_type, media_id, tag_id required"}) + if media_type not in ("document", "face", "voice"): + return json.dumps({"error": "media_type must be document/face/voice"}) + async with get_sor_context(env, 'rag') as sor: + mt_id = uuid.uuid4().hex[:16] + await sor.sqlExe( + "INSERT INTO media_tags (id, kb_id, media_type, media_id, tag_id, created_at) " + "VALUES (${id}$, ${kb_id}$, ${type}$, ${mid}$, ${tid}$, NOW())", + {"id": mt_id, "kb_id": kb_id, "type": media_type, "mid": media_id, "tid": tag_id}) + return json.dumps({"status": "SUCCEEDED", "media_tag_id": mt_id}) + except Exception as e: + exception(f"tag_assign: {e}, {format_exc()}") + return json.dumps({"error": str(e)}) + + +async def tag_unassign_handler(request, params_kw, *args, **kwargs): + """取消标签关联""" + env = request._run_ns + try: + media_type = params_kw.get("media_type", "") + media_id = params_kw.get("media_id", "") + tag_id = params_kw.get("tag_id", "") + if not all([media_type, media_id, tag_id]): + return json.dumps({"error": "media_type, media_id, tag_id required"}) + async with get_sor_context(env, 'rag') as sor: + await sor.sqlExe( + "DELETE FROM media_tags WHERE media_type=${type}$ AND media_id=${mid}$ AND tag_id=${tid}$", + {"type": media_type, "mid": media_id, "tid": tag_id}) + return json.dumps({"status": "SUCCEEDED"}) + except Exception as e: + exception(f"tag_unassign: {e}, {format_exc()}") + return json.dumps({"error": str(e)}) + + +async def tag_media_tags_handler(request, params_kw, *args, **kwargs): + """查询某媒体的所有标签""" + env = request._run_ns + try: + media_type = params_kw.get("media_type", "") + media_id = params_kw.get("media_id", "") + if not all([media_type, media_id]): + return json.dumps({"error": "media_type and media_id required"}) + async with get_sor_context(env, 'rag') as sor: + recs = await sor.sqlExe( + "SELECT t.id, t.name, t.color FROM media_tags mt " + "JOIN tags t ON mt.tag_id=t.id " + "WHERE mt.media_type=${type}$ AND mt.media_id=${mid}$", + {"type": media_type, "mid": media_id}) + tags = [{"id": r.id, "name": r.name, "color": r.color} for r in recs] + return json.dumps({"status": "SUCCEEDED", "tags": tags}) + except Exception as e: + exception(f"tag_media_tags: {e}, {format_exc()}") + return json.dumps({"error": str(e)}) + + +async def tag_search_handler(request, params_kw, *args, **kwargs): + """组合标签检索：按 tag_ids 过滤，再语义检索""" + env = request._run_ns + try: + query = params_kw.get("query", "") + kb_id = params_kw.get("kb_id", "") + tag_ids_str = params_kw.get("tag_ids", "") # comma-separated tag IDs + top_k = int(params_kw.get("top_k", 5)) + match_mode = params_kw.get("match_mode", "any") # any / all + if not kb_id: + return json.dumps({"error": "kb_id required"}) + async with get_sor_context(env, 'rag') as sor: + if tag_ids_str: + tag_ids = [t.strip() for t in tag_ids_str.split(",") if t.strip()] + media_ids_by_tag = [] + for tid in tag_ids: + recs = await sor.sqlExe( + "SELECT media_type, media_id FROM media_tags WHERE kb_id=${kb_id}$ AND tag_id=${tid}$", + {"kb_id": kb_id, "tid": tid}) + mids = {(r.media_type, r.media_id) for r in recs} + media_ids_by_tag.append(mids) + if match_mode == "all": + matched = media_ids_by_tag[0] + for s in media_ids_by_tag[1:]: + matched = matched & s + else: + matched = set() + for s in media_ids_by_tag: + matched |= s + if not matched: + return json.dumps({"status": "SUCCEEDED", "results": [], "message": "no media match tags"}) + doc_ids = [mid for mt, mid in matched if mt == "document"] + face_ids = [mid for mt, mid in matched if mt == "face"] + voice_ids = [mid for mt, mid in matched if mt == "voice"] + results = [] + if doc_ids: + docs = await sor.sqlExe( + "SELECT id, file_name, file_type, file_size, status, created_at FROM documents WHERE id IN (${ids}$)", + {"ids": doc_ids}) + for d in docs: + results.append({"type": "document", "id": d.id, "name": d.file_name, "file_type": d.file_type, "size": d.file_size, "status": d.status, "created_at": str(d.created_at)}) + if face_ids: + faces = await sor.sqlExe( + "SELECT id, name, description, face_embedding_id, created_at FROM entities WHERE id IN (${ids}$) AND entity_type='person'", + {"ids": face_ids}) + for f in faces: + results.append({"type": "face", "id": f.id, "name": f.name, "description": f.description, "created_at": str(f.created_at)}) + if voice_ids: + voices = await sor.sqlExe( + "SELECT id, name, description, voice_embedding_id, created_at FROM entities WHERE id IN (${ids}$) AND entity_type='voice'", + {"ids": voice_ids}) + for v in voices: + results.append({"type": "voice", "id": v.id, "name": v.name, "description": v.description, "created_at": str(v.created_at)}) + if query: + import urllib.request + tag_filtered_docs = [r for r in results if r["type"] == "document"] + vdb_docs = [] + for doc in tag_filtered_docs: + chunks = await sor.sqlExe( + "SELECT content FROM document_chunks WHERE doc_id=${id}$ LIMIT 3", + {"id": doc["id"]}) + for c in chunks: + vdb_docs.append({"doc_id": doc["id"], "content": c.content}) + results = {"documents": [{"id": r["id"], "name": r["name"], "file_type": r["file_type"]} for r in tag_filtered_docs], + "faces": [r for r in results if r["type"] == "face"], + "voices": [r for r in results if r["type"] == "voice"], + "chunks": vdb_docs, + "query": query, + "tag_ids": tag_ids, + "match_mode": match_mode} + return json.dumps({"status": "SUCCEEDED", "results": results, "tag_ids": tag_ids, "match_mode": match_mode}, ensure_ascii=False, default=str) + else: + return json.dumps({"status": "SUCCEEDED", "results": [], "message": "no tag_ids provided"}) + except Exception as e: + exception(f"tag_search: {e}, {format_exc()}") + return json.dumps({"error": str(e)}) + + +def init_rag_module(): + env = ServerEnv() + rf = RegisterFunction() + rf.register("status", status_handler) + rf.register("kb_list", kb_list_handler) + rf.register("engines", engines_handler) + rf.register("search", search_handler) + rf.register("doc_upload", doc_upload_handler) + rf.register("doc_delete", doc_delete_handler) + rf.register("dir_create", dir_create_handler) + rf.register("dir_delete", dir_delete_handler) + rf.register("dir_list", dir_list_handler) + rf.register("tag_create", tag_create_handler) + rf.register("tag_list", tag_list_handler) + rf.register("tag_delete", tag_delete_handler) + rf.register("tag_assign", tag_assign_handler) + rf.register("tag_unassign", tag_unassign_handler) + rf.register("tag_media_tags", tag_media_tags_handler) + rf.register("tag_search", tag_search_handler) diff --git a/build/lib/rag/pipeline.py b/build/lib/rag/pipeline.py new file mode 100644 index 0000000..48cb8e8 --- /dev/null +++ b/build/lib/rag/pipeline.py @@ -0,0 +1,208 @@ +""" +RAG 文件处理管线 — 文档解析 / 人脸检测 / 声纹处理 +GPU 服务映射: + 人脸: https://media.opencomputing.net:10443/face/api/detect + 声纹: https://media.opencomputing.net:10443/voiceprint/extract/submit +""" +import json, os, uuid, base64 + +# File type classification +TEXT_EXTS = {'.txt', '.md', '.csv', '.json', '.xml', '.html', '.htm', '.py', '.js', '.css', '.yaml', '.yml', '.log', '.rst'} +DOC_EXTS = {'.pdf', '.docx', '.pptx', '.xlsx', '.doc', '.ppt', '.xls'} +IMAGE_EXTS = {'.jpg', '.jpeg', '.png', '.bmp', '.gif', '.webp'} +AUDIO_EXTS = {'.mp3', '.wav', '.flac', '.ogg', '.m4a', '.aac'} +VIDEO_EXTS = {'.mp4', '.avi', '.mov', '.mkv', '.webm'} + +# GPU service endpoints +FACE_DETECT_URL = "https://media.opencomputing.net:10443/face/api/detect" +FACE_RECOGNIZE_URL = "https://media.opencomputing.net:10443/face/api/recognize" +VOICEPRINT_EXTRACT_URL = "https://media.opencomputing.net:10443/voiceprint/extract/submit" +VOICEPRINT_STATUS_URL = "https://media.opencomputing.net:10443/voiceprint/extract/status" + + +def classify_file(file_name): + ext = os.path.splitext(file_name)[1].lower() + if ext in TEXT_EXTS: + return 'text' + if ext in DOC_EXTS: + return 'document' + if ext in IMAGE_EXTS: + return 'image' + if ext in AUDIO_EXTS: + return 'audio' + if ext in VIDEO_EXTS: + return 'video' + return 'other' + + +async def extract_text(file_data, file_name): + ext = os.path.splitext(file_name)[1].lower() + text = "" + + if ext in TEXT_EXTS: + text = file_data.decode('utf-8', errors='replace') + + elif ext == '.pdf': + try: + import io + from PyPDF2 import PdfReader + reader = PdfReader(io.BytesIO(file_data)) + text = '\n'.join(p.extract_text() or '' for p in reader.pages) + except Exception as e: + text = "[PDF error: " + str(e) + "]" + + elif ext == '.docx': + try: + import io + from docx import Document + doc = Document(io.BytesIO(file_data)) + text = '\n'.join(p.text for p in doc.paragraphs if p.text.strip()) + except Exception as e: + text = "[DOCX error: " + str(e) + "]" + + elif ext == '.pptx': + try: + import io + from pptx import Presentation + prs = Presentation(io.BytesIO(file_data)) + slides = [] + for slide in prs.slides: + parts = [s.text for s in slide.shapes if hasattr(s, 'text') and s.text.strip()] + slides.append(' '.join(parts)) + text = '\n'.join(slides) + except Exception as e: + text = "[PPTX error: " + str(e) + "]" + + elif ext in {'.xlsx', '.xls'}: + try: + import io + from openpyxl import load_workbook + wb = load_workbook(io.BytesIO(file_data), read_only=True, data_only=True) + rows = [] + for ws in wb.worksheets: + for row in ws.iter_rows(values_only=True): + r = ' | '.join(str(c) if c is not None else '' for c in row) + if r.strip(): + rows.append(r) + text = '\n'.join(rows) + except Exception as e: + text = "[XLSX error: " + str(e) + "]" + + return text.strip() + + +async def ingest_to_rag(env, text, kb_id, doc_id): + try: + from pipeline import ingest as pipeline_ingest + result = pipeline_ingest( + text, pipeline_name="kg-rag-standard", + collection=kb_id, graph_name=kb_id, llm_func=None) + return result + except Exception as e: + return {"error": str(e), "chunks": 0} + + +async def detect_faces(file_data, file_name): + """Detect faces via GPU face-service (InsightFace buffalo_l)""" + import aiohttp + try: + b64 = base64.b64encode(file_data).decode() + payload = {"image": b64} + async with aiohttp.ClientSession() as session: + async with session.post( + FACE_DETECT_URL, + json=payload, + timeout=aiohttp.ClientTimeout(total=30) + ) as resp: + if resp.status == 200: + result = await resp.json() + return { + "faces": result.get("faces", result.get("count", 0)), + "details": result + } + return {"error": "face service returned " + str(resp.status), "faces": 0} + except Exception as e: + return {"error": str(e), "faces": 0} + + +async def extract_voiceprint(file_data, file_name): + import aiohttp + try: + async with aiohttp.ClientSession() as session: + form = aiohttp.FormData() + form.add_field('file', file_data, filename=file_name, content_type='audio/wav') + async with session.post(VOICEPRINT_EXTRACT_URL, data=form, timeout=aiohttp.ClientTimeout(total=60)) as resp: + if resp.status == 200: + r = await resp.json() + if r.get('status') == 'SUCCEEDED': + return {'voiceprint': r, 'speakers': 1} + return {'error': r.get('error', ''), 'speakers': 0} + return {'error': 'service returned ' + str(resp.status), 'speakers': 0} + except Exception as e: + return {'error': str(e), 'speakers': 0} + +async def process_upload(env, file_data, kb_id, folder_id, file_name): + """Full upload pipeline — save + classify + process + DB record""" + import uuid as _uuid + doc_id = str(_uuid.uuid4()).hex[:16] + ext = '.' + file_name.rsplit('.', 1)[1] if '.' in file_name else '.bin' + saved_name = doc_id + ext + file_path = '/d/rag/ragserver/pkgs/rag/rag/files/' + saved_name + with open(file_path, 'wb') as f: + f.write(file_data) + file_size = len(file_data) + + ft = classify_file(file_name) + ingest_result = None + face_result = None + voice_result = None + status = 'pending' + + if ft in ('text', 'document'): + text = await extract_text(file_data, file_name) + if text and len(text) > 10: + ingest_result = await ingest_to_rag(env, text, kb_id, doc_id) + status = 'done' if ingest_result and not ingest_result.get('error') else 'error' + else: + status = 'done' + elif ft == 'image': + face_result = await detect_faces(file_data, file_name) + status = 'done' + elif ft == 'audio': + voice_result = await extract_voiceprint(file_data, file_name) + status = 'done' + elif ft == 'video': + face_result = await detect_faces(file_data, file_name) + voice_result = await extract_voiceprint(file_data, file_name) + status = 'done' + else: + status = 'done' + + import json as _json + meta = _json.dumps({'file_type': ft, 'face': face_result, 'voice': voice_result, 'ingest': str(ingest_result)[:500]}, ensure_ascii=False, default=str) + + userorgid = await env.get_userorgid() + from sqlor.dbpools import get_sor_context + async with get_sor_context(env, 'rag') as sor: + await sor.sqlExe( + "INSERT INTO documents (id, kb_id, folder_id, file_name, file_type, file_size, file_path, mime_type, status, metadata, org_id, created_at, updated_at) " + "VALUES (" + "${id}$, ${kb_id}$, ${folder_id}$, ${file_name}$, 'other', ${file_size}$, ${file_path}$, 'application/octet-stream', ${status}$, ${meta}$, ${org_id}$, NOW(), NOW())", + {"id": doc_id, "kb_id": kb_id, "folder_id": folder_id, "file_name": file_name, + "file_size": file_size, "file_path": "/idfile/files/" + saved_name, + "status": status, "meta": meta, "org_id": userorgid}) + await sor.sqlExe( + "UPDATE knowledge_bases SET doc_count=doc_count+1, total_size=total_size+" + "${size}$ WHERE id=${kb_id}$", + {"size": file_size, "kb_id": kb_id}) + if ingest_result and status == 'done': + chunks_n = ingest_result.get('chunks', 0) if isinstance(ingest_result, dict) else 0 + if chunks_n: + await sor.sqlExe("UPDATE documents SET chunk_count=${chunks}$ WHERE id=${id}$", {"chunks": chunks_n, "id": doc_id}) + await sor.sqlExe("UPDATE knowledge_bases SET chunk_count=chunk_count+${n}$ WHERE id=${kb_id}$", {"n": chunks_n, "kb_id": kb_id}) + + faces_n = face_result.get('faces', 0) if face_result and isinstance(face_result, dict) else 0 + speakers_n = voice_result.get('speakers', 0) if voice_result and isinstance(voice_result, dict) else 0 + return _json.dumps({ + "status": "SUCCEEDED", "doc_id": doc_id, "file_name": file_name, + "file_size": file_size, "folder_id": folder_id, "file_type": ft, + "rag_status": status, "faces": faces_n, "speakers": speakers_n + }, ensure_ascii=False, default=str) diff --git a/rag.egg-info/PKG-INFO b/rag.egg-info/PKG-INFO new file mode 100644 index 0000000..2894f97 --- /dev/null +++ b/rag.egg-info/PKG-INFO @@ -0,0 +1,3 @@ +Metadata-Version: 2.4 +Name: rag +Version: 0.1.0 diff --git a/rag.egg-info/SOURCES.txt b/rag.egg-info/SOURCES.txt new file mode 100644 index 0000000..bec9d13 --- /dev/null +++ b/rag.egg-info/SOURCES.txt @@ -0,0 +1,9 @@ +pyproject.toml +setup.py +rag/__init__.py +rag/init.py +rag/pipeline.py +rag.egg-info/PKG-INFO +rag.egg-info/SOURCES.txt +rag.egg-info/dependency_links.txt 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a/rag/files/j0G36PxRI3Bkgak5.txt b/rag/files/j0G36PxRI3Bkgak5.txt new file mode 100644 index 0000000..733e4cf --- /dev/null +++ b/rag/files/j0G36PxRI3Bkgak5.txt @@ -0,0 +1 @@ +hello world test doc \ No newline at end of file diff --git a/rag/files/ltarC48J9aAFtm5W.txt b/rag/files/ltarC48J9aAFtm5W.txt new file mode 100644 index 0000000..b6fc4c6 --- /dev/null +++ b/rag/files/ltarC48J9aAFtm5W.txt @@ -0,0 +1 @@ +hello \ No newline at end of file diff --git a/wwwroot/knowledge_bases_list/file_list.dspy b/wwwroot/knowledge_bases_list/file_list.dspy index fdc1d3b..3f2a749 100644 --- a/wwwroot/knowledge_bases_list/file_list.dspy +++ b/wwwroot/knowledge_bases_list/file_list.dspy @@ -63,7 +63,7 @@ else: if f["status"] == "pending": sc = "#e6a23c"; st = "处理中" elif f["status"] == "error": sc = "#f56c6c"; st = "失败" - file_url = "/idfile/" + f["id"] + "." + f["file_name"].rsplit(".")[1] if "." in f["file_name"] else "/idfile/" + f["id"] + file_url = f["file_path"] if f["file_path"].startswith("/idfile/") else "/idfile" + f["file_path"] display_name = trunc_name(f["file_name"]) row = { diff --git a/wwwroot/knowledge_bases_list/index.ui.bak b/wwwroot/knowledge_bases_list/index.ui.bak new file mode 100644 index 0000000..0d92287 --- /dev/null +++ b/wwwroot/knowledge_bases_list/index.ui.bak @@ -0,0 +1,43 @@ +{ + "widgettype": "VBox", + "options": { + "cheight": 40, + "width": "100%", + "padding": "16px", + "spacing": "16px" + }, + "subwidgets": [ + { + "widgettype": "HBox", + "options": {"width": "100%", "justifyContent": "space-between", "alignItems": "center"}, + "subwidgets": [ + {"widgettype": "Text", "options": {"text": "知识库", "cfontsize": 22, "fontWeight": "bold", "color": "#333"}}, + {"widgettype": "Button", "options": {"label": "+ 新建知识库", "bgcolor": "#4a90d9", "color": "#fff"}, "binds": [ + {"wid": "self", "event": "click", "actiontype": "urlwidget", + "target": "Popup", + "popup_options": {"auto_dismiss": true}, + "options": {"url": "{{entire_url('/knowledge_bases_list/new_kb_form.ui')}}"}} + ]} + ] + }, + { + "widgettype": "VBox", + "options": {"width": "100%", "bgcolor": "#f8f9fa", "padding": "16px", "css": "card", "spacing": "4px"}, + "subwidgets": [ + {"widgettype": "HBox", "options": {"width": "100%", "justifyContent": "space-between"}, "subwidgets": [ + {"widgettype": "Text", "options": {"text": "💾 存储容量", "cfontsize": 14, "fontWeight": "bold", "color": "#333"}}, + {"widgettype": "Text", "options": {"text": "已用 0MB / 总计 100MB", "cfontsize": 12, "color": "#888"}} + ]}, + {"widgettype": "VBox", "options": {"width": "100%", "cheight": 0.5, "bgcolor": "#e0e0e0", "css": ""}, "subwidgets": [ + {"widgettype": "VBox", "options": {"cwidth": 0, "cheight": 0.5, "bgcolor": "#4a90d9", "css": ""}} + ]} + ] + }, + { + "widgettype": "urlwidget", + "options": { + "url": "{{entire_url('/api/kb/list')}}" + } + } + ] +} diff --git a/wwwroot/knowledge_bases_list/storage_card.dspy b/wwwroot/knowledge_bases_list/storage_card.dspy new file mode 100644 index 0000000..f409fe9 --- /dev/null +++ b/wwwroot/knowledge_bases_list/storage_card.dspy @@ -0,0 +1,23 @@ +ns = params_kw.copy() +env = request._run_ns + +async with get_sor_context(env, 'rag') as sor: + rec = await sor.sqlExe("SELECT COALESCE(SUM(total_size),0) used FROM knowledge_bases", {}) + used_bytes = int(rec[0].used) if rec else 0 + +used_mb = round(used_bytes / 1048576, 1) +pct = min(round(used_bytes / 104857600 * 100), 100) if used_bytes > 0 else 0 + +return { + "widgettype": "VBox", + "options": {"width": "100%", "bgcolor": "#f8f9fa", "padding": "16px", "css": "card", "spacing": "4px"}, + "subwidgets": [ + {"widgettype": "HBox", "options": {"width": "100%", "justifyContent": "space-between"}, "subwidgets": [ + {"widgettype": "Text", "options": {"text": "💾 存储容量", "cfontsize": 14, "fontWeight": "bold", "color": "#333"}}, + {"widgettype": "Text", "options": {"text": "已用 " + str(used_mb) + "MB / 总计 100MB", "cfontsize": 12, "color": "#888"}} + ]}, + {"widgettype": "VBox", "options": {"width": "100%", "cheight": 0.5, "bgcolor": "#e0e0e0"}, "subwidgets": [ + {"widgettype": "VBox", "options": {"cwidth": pct, "cheight": 0.5, "bgcolor": "#4a90d9"}} + ]} + ] +}