Add graphrag (#1793)

### What problem does this PR solve?

#1594

### Type of change

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

agent/component/keyword.py

@@ -0,0 +1,65 @@
+#
+#  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.
+#
+import re
+from abc import ABC
+from api.db import LLMType
+from api.db.services.llm_service import LLMBundle
+from agent.component import GenerateParam, Generate
+from agent.settings import DEBUG
+
+
+class KeywordExtractParam(GenerateParam):
+    """
+    Define the KeywordExtract component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.top_n = 1
+
+    def check(self):
+        super().check()
+        self.check_positive_integer(self.top_n, "Top N")
+
+    def get_prompt(self):
+        self.prompt = """
+- Role: You're a question analyzer. 
+- Requirements: 
+  - Summarize user's question, and give top %s important keyword/phrase.
+  - Use comma as a delimiter to separate keywords/phrases.
+- Answer format: (in language of user's question)
+  - keyword: 
+""" % self.top_n
+        return self.prompt
+
+
+class KeywordExtract(Generate, ABC):
+    component_name = "KeywordExtract"
+
+    def _run(self, history, **kwargs):
+        q = ""
+        for r, c in self._canvas.history[::-1]:
+            if r == "user":
+                q += c
+                break
+
+        chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
+        ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": q}],
+                            self._param.gen_conf())
+
+        ans = re.sub(r".*keyword:", "", ans).strip()
+        if DEBUG: print(ans, ":::::::::::::::::::::::::::::::::")
+        return KeywordExtract.be_output(ans)