less text, better extraction (#1869)

### What problem does this PR solve?

#1861

### Type of change

- [x] Refactoring

graphrag/index.py

@@ -75,10 +75,11 @@ def build_knowlege_graph_chunks(tenant_id: str, chunks: List[str], callback, ent
     llm_bdl = LLMBundle(tenant_id, LLMType.CHAT, tenant.llm_id)
     ext = GraphExtractor(llm_bdl)
     left_token_count = llm_bdl.max_length - ext.prompt_token_count - 1024
-    left_token_count = max(llm_bdl.max_length * 0.8, left_token_count)
+    left_token_count = max(llm_bdl.max_length * 0.6, left_token_count)
 
     assert left_token_count > 0, f"The LLM context length({llm_bdl.max_length}) is smaller than prompt({ext.prompt_token_count})"
 
+    BATCH_SIZE=1
     texts, graphs = [], []
     cnt = 0
     threads = []
@@ -86,15 +87,15 @@ def build_knowlege_graph_chunks(tenant_id: str, chunks: List[str], callback, ent
     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), 16):
-                threads.append(exe.submit(ext, ["\n".join(texts[b:b+16])], {"entity_types": entity_types}, callback))
+            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), 16):
-            threads.append(exe.submit(ext, ["\n".join(texts[b:b+16])], {"entity_types": entity_types}, callback))
+        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 = []