Fix raptor issue (#3737)

### What problem does this PR solve?

#3732

### Type of change

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

rag/raptor.py

@@ -33,7 +33,7 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
         self._prompt = prompt
         self._max_token = max_token
 
-    def _get_optimal_clusters(self, embeddings: np.ndarray, random_state:int):
+    def _get_optimal_clusters(self, embeddings: np.ndarray, random_state: int):
         max_clusters = min(self._max_cluster, len(embeddings))
         n_clusters = np.arange(1, max_clusters)
         bics = []
@@ -44,7 +44,7 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
         optimal_clusters = n_clusters[np.argmin(bics)]
         return optimal_clusters
 
-    def __call__(self, chunks: tuple[str, np.ndarray], random_state, callback=None):
+    def __call__(self, chunks, random_state, callback=None):
         layers = [(0, len(chunks))]
         start, end = 0, len(chunks)
         if len(chunks) <= 1: return
@@ -54,13 +54,15 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
             nonlocal chunks
             try:
                 texts = [chunks[i][0] for i in ck_idx]
-                len_per_chunk = int((self._llm_model.max_length - self._max_token)/len(texts))
+                len_per_chunk = int((self._llm_model.max_length - self._max_token) / len(texts))
                 cluster_content = "\n".join([truncate(t, max(1, len_per_chunk)) for t in texts])
                 cnt = self._llm_model.chat("You're a helpful assistant.",
-                                             [{"role": "user", "content": self._prompt.format(cluster_content=cluster_content)}],
+                                           [{"role": "user",
-                                             {"temperature": 0.3, "max_tokens": self._max_token}
+                                             "content": self._prompt.format(cluster_content=cluster_content)}],
-                                             )
+                                           {"temperature": 0.3, "max_tokens": self._max_token}
-                cnt = re.sub("(······\n由于长度的原因，回答被截断了，要继续吗？|For the content length reason, it stopped, continue?)", "", cnt)
+                                           )
+                cnt = re.sub("(······\n由于长度的原因，回答被截断了，要继续吗？|For the content length reason, it stopped, continue?)", "",
+                             cnt)
                 logging.debug(f"SUM: {cnt}")
                 embds, _ = self._embd_model.encode([cnt])
                 with lock:
@@ -74,10 +76,10 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
         while end - start > 1:
             embeddings = [embd for _, embd in chunks[start: end]]
             if len(embeddings) == 2:
-                summarize([start, start+1], Lock())
+                summarize([start, start + 1], Lock())
                 if callback:
-                    callback(msg="Cluster one layer: {} -> {}".format(end-start, len(chunks)-end))
+                    callback(msg="Cluster one layer: {} -> {}".format(end - start, len(chunks) - end))
-                labels.extend([0,0])
+                labels.extend([0, 0])
                 layers.append((end, len(chunks)))
                 start = end
                 end = len(chunks)
@@ -85,7 +87,7 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
 
             n_neighbors = int((len(embeddings) - 1) ** 0.8)
             reduced_embeddings = umap.UMAP(
-                n_neighbors=max(2, n_neighbors), n_components=min(12, len(embeddings)-2), metric="cosine"
+                n_neighbors=max(2, n_neighbors), n_components=min(12, len(embeddings) - 2), metric="cosine"
             ).fit_transform(embeddings)
             n_clusters = self._get_optimal_clusters(reduced_embeddings, random_state)
             if n_clusters == 1:
@@ -100,7 +102,7 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
             with ThreadPoolExecutor(max_workers=12) as executor:
                 threads = []
                 for c in range(n_clusters):
-                    ck_idx = [i+start for i in range(len(lbls)) if lbls[i] == c]
+                    ck_idx = [i + start for i in range(len(lbls)) if lbls[i] == c]
                     threads.append(executor.submit(summarize, ck_idx, lock))
                 wait(threads, return_when=ALL_COMPLETED)
                 logging.debug(str([t.result() for t in threads]))
@@ -109,7 +111,9 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
             labels.extend(lbls)
             layers.append((end, len(chunks)))
             if callback:
-                callback(msg="Cluster one layer: {} -> {}".format(end-start, len(chunks)-end))
+                callback(msg="Cluster one layer: {} -> {}".format(end - start, len(chunks) - end))
             start = end
             end = len(chunks)
 
+        return chunks
+

rag/svr/task_executor.py

@@ -344,7 +344,7 @@ def run_raptor(row, chat_mdl, embd_mdl, callback=None):
         row["parser_config"]["raptor"]["threshold"]
     )
     original_length = len(chunks)
-    raptor(chunks, row["parser_config"]["raptor"]["random_seed"], callback)
+    chunks = raptor(chunks, row["parser_config"]["raptor"]["random_seed"], callback)
     doc = {
         "doc_id": row["doc_id"],
         "kb_id": [str(row["kb_id"])],