Cache the result from llm for graphrag and raptor (#4051)

### What problem does this PR solve?

#4045

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

graphrag/extractor.py

@@ -0,0 +1,34 @@
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+from graphrag.utils import get_llm_cache, set_llm_cache
+from rag.llm.chat_model import Base as CompletionLLM
+
+
+class Extractor:
+    _llm: CompletionLLM
+
+    def __init__(self, llm_invoker: CompletionLLM):
+        self._llm = llm_invoker
+
+    def _chat(self, system, history, gen_conf):
+        response = get_llm_cache(self._llm.llm_name, system, history, gen_conf)
+        if response:
+            return response
+        response = self._llm.chat(system, history, gen_conf)
+        if response.find("**ERROR**") >= 0:
+            raise Exception(response)
+        set_llm_cache(self._llm.llm_name, system, response, history, gen_conf)
+        return response