Let ThreadPool exit gracefully. (#3653)

### What problem does this PR solve?

#3646

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

agent/component/crawler.py

@@ -65,7 +65,3 @@ class Crawler(ComponentBase, ABC):
             elif self._param.extract_type == 'content':
                 result.extracted_content
             return result.markdown
-            
-
-
-    

graphrag/index.py

@@ -64,27 +64,27 @@ def build_knowledge_graph_chunks(tenant_id: str, chunks: list[str], callback, en
     BATCH_SIZE=4
     texts, graphs = [], []
     cnt = 0
-    threads = []
     max_workers = int(os.environ.get('GRAPH_EXTRACTOR_MAX_WORKERS', 50))
-    exe = ThreadPoolExecutor(max_workers=max_workers)
-    for i in range(len(chunks)):
-        tkn_cnt = num_tokens_from_string(chunks[i])
-        if cnt+tkn_cnt >= left_token_count and texts:
+    with ThreadPoolExecutor(max_workers=max_workers) as exe:
+        threads = []
+        for i in range(len(chunks)):
+            tkn_cnt = num_tokens_from_string(chunks[i])
+            if cnt+tkn_cnt >= left_token_count and texts:
+                for b in range(0, len(texts), BATCH_SIZE):
+                    threads.append(exe.submit(ext, ["\n".join(texts[b:b+BATCH_SIZE])], {"entity_types": entity_types}, callback))
+                texts = []
+                cnt = 0
+            texts.append(chunks[i])
+            cnt += tkn_cnt
+        if texts:
             for b in range(0, len(texts), BATCH_SIZE):
                 threads.append(exe.submit(ext, ["\n".join(texts[b:b+BATCH_SIZE])], {"entity_types": entity_types}, callback))
-            texts = []
-            cnt = 0
-        texts.append(chunks[i])
-        cnt += tkn_cnt
-    if texts:
-        for b in range(0, len(texts), BATCH_SIZE):
-            threads.append(exe.submit(ext, ["\n".join(texts[b:b+BATCH_SIZE])], {"entity_types": entity_types}, callback))
-
-    callback(0.5, "Extracting entities.")
-    graphs = []
-    for i, _ in enumerate(threads):
-        graphs.append(_.result().output)
-        callback(0.5 + 0.1*i/len(threads), f"Entities extraction progress ... {i+1}/{len(threads)}")
+
+        callback(0.5, "Extracting entities.")
+        graphs = []
+        for i, _ in enumerate(threads):
+            graphs.append(_.result().output)
+            callback(0.5 + 0.1*i/len(threads), f"Entities extraction progress ... {i+1}/{len(threads)}")
 
     graph = reduce(graph_merge, graphs) if graphs else nx.Graph()
     er = EntityResolution(llm_bdl)

graphrag/mind_map_extractor.py

@@ -88,26 +88,26 @@ class MindMapExtractor:
             prompt_variables = {}
 
         try:
-            max_workers = int(os.environ.get('MINDMAP_EXTRACTOR_MAX_WORKERS', 12))
-            exe = ThreadPoolExecutor(max_workers=max_workers)
-            threads = []
-            token_count = max(self._llm.max_length * 0.8, self._llm.max_length - 512)
-            texts = []
             res = []
-            cnt = 0
-            for i in range(len(sections)):
-                section_cnt = num_tokens_from_string(sections[i])
-                if cnt + section_cnt >= token_count and texts:
+            max_workers = int(os.environ.get('MINDMAP_EXTRACTOR_MAX_WORKERS', 12))
+            with ThreadPoolExecutor(max_workers=max_workers) as exe:
+                threads = []
+                token_count = max(self._llm.max_length * 0.8, self._llm.max_length - 512)
+                texts = []
+                cnt = 0
+                for i in range(len(sections)):
+                    section_cnt = num_tokens_from_string(sections[i])
+                    if cnt + section_cnt >= token_count and texts:
+                        threads.append(exe.submit(self._process_document, "".join(texts), prompt_variables))
+                        texts = []
+                        cnt = 0
+                    texts.append(sections[i])
+                    cnt += section_cnt
+                if texts:
                     threads.append(exe.submit(self._process_document, "".join(texts), prompt_variables))
-                    texts = []
-                    cnt = 0
-                texts.append(sections[i])
-                cnt += section_cnt
-            if texts:
-                threads.append(exe.submit(self._process_document, "".join(texts), prompt_variables))
-
-            for i, _ in enumerate(threads):
-                res.append(_.result())
+
+                for i, _ in enumerate(threads):
+                    res.append(_.result())
 
             if not res:
                 return MindMapResult(output={"id": "root", "children": []})

rag/llm/chat_model.py

@@ -366,7 +366,7 @@ class OllamaChat(Base):
                 keep_alive=-1
             )
             ans = response["message"]["content"].strip()
-            return ans, response["eval_count"] + response.get("prompt_eval_count", 0)
+            return ans, response.get("eval_count", 0) + response.get("prompt_eval_count", 0)
         except Exception as e:
             return "**ERROR**: " + str(e), 0
 

rag/svr/task_executor.py

@@ -492,6 +492,7 @@ def report_status():
             logging.exception("report_status got exception")
         time.sleep(30)
 
+
 def analyze_heap(snapshot1: tracemalloc.Snapshot, snapshot2: tracemalloc.Snapshot, snapshot_id: int, dump_full: bool):
     msg = ""
     if dump_full:
@@ -508,6 +509,7 @@ def analyze_heap(snapshot1: tracemalloc.Snapshot, snapshot2: tracemalloc.Snapsho
         msg += '\n'.join(stat.traceback.format())
     logging.info(msg)
 
+
 def main():
     settings.init_settings()
     background_thread = threading.Thread(target=report_status)