add rerank module + bug fixes from e2e test

- New reranker.py: SiliconFlow bge-reranker-v2-m3 integration
- config.py: add RERANK_MODEL
- api_client.py: fix get_next_job/claim_job/get_job_detail unwrapping
- candidate_generator.py: fix .format() conflict with JSON braces
- import_pipeline.py: fix file existence check + UUID point IDs
- Add .gitignore for __pycache__

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

rag-worker/.gitignore

@@ -0,0 +1 @@
+__pycache__/

rag-worker/api_client.py

@@ -16,7 +16,9 @@ async def get_next_job() -> dict | None:
         )
         if resp.status_code == 200:
             data = resp.json()
-            return data.get("data") or data.get("job")
+            result = data.get("data") or data
+            if isinstance(result, dict):
+                return result.get("job")
         return None
 
 
@@ -26,8 +28,9 @@ async def claim_job(job_id: str) -> bool:
         resp = await client.post(
             f"{API_BASE_URL}/internal/rag/jobs/{job_id}/claim",
             headers=_auth_headers,
+            json={"workerId": WORKER_ID},
         )
-        return resp.status_code == 200
+        return resp.status_code in (200, 201)
 
 
 async def heartbeat(job_id: str) -> bool:
@@ -90,5 +93,6 @@ async def get_job_detail(job_id: str) -> dict | None:
             headers=_auth_headers,
         )
         if resp.status_code == 200:
-            return resp.json()
+            data = resp.json()
+            return data.get("data") or data
         return None

rag-worker/candidate_generator.py

@@ -43,7 +43,7 @@ async def generate_candidates(text: str) -> list[dict]:
     text_len = len(text)
     expected_count = max(MIN_CANDIDATES, min(MAX_CANDIDATES, text_len // CHARS_PER_CANDIDATE))
 
-    prompt = _PROMPT.format(text=text[:16000])  # 限制上下文长度
+    prompt = _PROMPT.replace("{text}", text[:16000])  # 限制上下文长度
 
     async with httpx.AsyncClient(timeout=120) as client:
         resp = await client.post(
@@ -71,7 +71,31 @@ async def generate_candidates(text: str) -> list[dict]:
 
 def _parse_json_response(raw: str, expected_count: int) -> list[dict]:
     """从 AI 回复中提取 JSON 数组"""
-    # 尝试直接解析
+    import re
+
+    # 1. 提取 ```json ... ``` 块
+    m = re.search(r"```(?:json)?\s*(.*?)\s*```", raw, re.DOTALL)
+    if m:
+        inner = m.group(1).strip()
+        try:
+            candidates = json.loads(inner)
+            if isinstance(candidates, list):
+                return candidates[:MAX_CANDIDATES]
+        except json.JSONDecodeError:
+            pass
+
+    # 2. 提取 [ ... ] 块（从第一个 [ 到最后一个 ]）
+    start = raw.find("[")
+    end = raw.rfind("]")
+    if start != -1 and end != -1 and end > start:
+        try:
+            candidates = json.loads(raw[start:end + 1])
+            if isinstance(candidates, list):
+                return candidates[:MAX_CANDIDATES]
+        except json.JSONDecodeError:
+            pass
+
+    # 3. 直接解析整个回复
     try:
         candidates = json.loads(raw)
         if isinstance(candidates, list):
@@ -79,25 +103,4 @@ def _parse_json_response(raw: str, expected_count: int) -> list[dict]:
     except json.JSONDecodeError:
         pass
 
-    # 提取 ```json ... ``` 块
-    import re
-    m = re.search(r"```(?:json)?\s*\n?(.*?)\n?```", raw, re.DOTALL)
-    if m:
-        try:
-            candidates = json.loads(m.group(1))
-            if isinstance(candidates, list):
-                return candidates[:MAX_CANDIDATES]
-        except json.JSONDecodeError:
-            pass
-
-    # 提取 [ ... ] 块
-    m = re.search(r"\[.*\]", raw, re.DOTALL)
-    if m:
-        try:
-            candidates = json.loads(m.group(0))
-            if isinstance(candidates, list):
-                return candidates[:MAX_CANDIDATES]
-        except json.JSONDecodeError:
-            pass
-
     raise ValueError(f"无法解析 AI 候选知识点回复: {raw[:500]}")

rag-worker/config.py

@@ -1,4 +1,14 @@
 import os
+from pathlib import Path
+
+# 加载 .env 文件（systemd 可通过 EnvironmentFile 设置，此处作为手动运行的兜底）
+_dotenv_path = Path(__file__).resolve().parent / ".env"
+if _dotenv_path.exists():
+    try:
+        from dotenv import load_dotenv
+        load_dotenv(_dotenv_path)
+    except ImportError:
+        pass
 
 # NestJS 内部 API
 API_BASE_URL = os.getenv("API_BASE_URL", "http://127.0.0.1:3000")
@@ -9,6 +19,7 @@ SILICONFLOW_API_KEY = os.getenv("SILICONFLOW_API_KEY", "")
 SILICONFLOW_BASE_URL = os.getenv("SILICONFLOW_BASE_URL", "https://api.siliconflow.cn/v1")
 EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "BAAI/bge-m3")
 EMBEDDING_DIM = int(os.getenv("EMBEDDING_DIM", "1024"))
+RERANK_MODEL = os.getenv("RERANK_MODEL", "BAAI/bge-reranker-v2-m3")
 
 # DeepSeek
 DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY", "")

rag-worker/pipelines/import_pipeline.py

@@ -45,11 +45,14 @@ async def run_import(job: dict):
 
         # 2. 解析
         await update_job_status(job_id, "PARSING", {"progress": 20})
+        text = ""
+        if os.path.exists(file_path):
             text = await parse_document(file_path, mime_type)
 
         # 如果文件不在本地（纯文本导入），直接从 source/import 中取文本
-        if not text and (job.get("rawText") or source.get("rawText")):
+        detail_job = (detail or {}).get("job", {})
-            text = job.get("rawText", "") or source.get("rawText", "")
+        if not text:
+            text = job.get("rawText") or source.get("rawText") or detail_job.get("rawText") or ""
 
         if not text or len(text.strip()) < 10:
             raise ValueError("文档解析后内容过少，可能为空白或损坏文件")
@@ -72,7 +75,7 @@ async def run_import(job: dict):
         points = []
         chunk_records = []
         for i, (chunk, vec) in enumerate(zip(chunks, vectors)):
-            chunk_id = f"chunk_{source_id}_{i}"
+            chunk_id = str(uuid.uuid4())
             points.append({
                 "id": chunk_id,
                 "vector": vec,
@@ -104,11 +107,14 @@ async def run_import(job: dict):
         await upsert_points(points)
         await save_chunks(chunk_records)
 
-        # 7. 生成候选知识点
+        # 7. 生成候选知识点（非致命：失败不影响导入完成）
         await update_job_status(job_id, "GENERATING_CANDIDATES", {"progress": 90})
+        try:
             candidates = await generate_candidates(text)
             if candidates:
                 await save_candidates(user_id, kb_id, source_id, job_id, candidates)
+        except Exception as e:
+            print(f"[worker] 候选知识点生成失败（非致命）: {e}")
 
         # 8. 完成
         await update_job_status(job_id, "COMPLETED", {"progress": 100})

rag-worker/requirements.txt

@@ -6,3 +6,5 @@ markdown>=3.5
 pandas>=2.0
 openpyxl>=3.1
 Pillow>=10.0
+qdrant-client>=1.9
+python-dotenv>=1.0

rag-worker/reranker.py

@@ -0,0 +1,46 @@
+"""Rerank 服务：调用硅基流动 bge-reranker-v2-m3 对检索结果精排"""
+
+import httpx
+from config import SILICONFLOW_API_KEY, SILICONFLOW_BASE_URL, RERANK_MODEL
+
+
+async def rerank(
+    query: str,
+    documents: list[str],
+    top_n: int = 5,
+) -> list[dict]:
+    """对候选文档重新打分排序，返回 top_n 结果。
+
+    每个结果包含:
+        - index: 原始 documents 数组中的位置
+        - score: 相关性分数 (0.0-1.0)
+        - text: 文档原文
+    """
+    if not documents:
+        return []
+
+    async with httpx.AsyncClient(timeout=30) as client:
+        resp = await client.post(
+            f"{SILICONFLOW_BASE_URL}/rerank",
+            headers={"Authorization": f"Bearer {SILICONFLOW_API_KEY}"},
+            json={
+                "model": RERANK_MODEL,
+                "query": query,
+                "documents": documents,
+                "top_n": min(top_n, len(documents)),
+                "return_documents": True,
+                "max_chunks_per_doc": 1024,
+            },
+        )
+        if resp.status_code != 200:
+            raise RuntimeError(f"Rerank API error: {resp.status_code} {resp.text}")
+
+        data = resp.json()
+        return [
+            {
+                "index": r["index"],
+                "score": r["relevance_score"],
+                "text": r.get("document", {}).get("text", documents[r["index"]]),
+            }
+            for r in data["results"]
+        ]