support graph (#1152)

### What problem does this PR solve?

#918 
### Type of change

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

graph/component/generate.py

@@ -0,0 +1,156 @@
+#
+#  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 functools import partial
+
+import pandas as pd
+
+from api.db import LLMType
+from api.db.services.llm_service import LLMBundle
+from api.settings import retrievaler
+from graph.component.base import ComponentBase, ComponentParamBase
+
+
+class GenerateParam(ComponentParamBase):
+    """
+    Define the Generate component parameters.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.llm_id = ""
+        self.prompt = ""
+        self.max_tokens = 256
+        self.temperature = 0.1
+        self.top_p = 0.3
+        self.presence_penalty = 0.4
+        self.frequency_penalty = 0.7
+        self.cite = True
+        #self.parameters = []
+
+    def check(self):
+        self.check_decimal_float(self.temperature, "Temperature")
+        self.check_decimal_float(self.presence_penalty, "Presence penalty")
+        self.check_decimal_float(self.frequency_penalty, "Frequency penalty")
+        self.check_positive_number(self.max_tokens, "Max tokens")
+        self.check_decimal_float(self.top_p, "Top P")
+        self.check_empty(self.llm_id, "LLM")
+        #self.check_defined_type(self.parameters, "Parameters", ["list"])
+
+    def gen_conf(self):
+        return {
+            "max_tokens": self.max_tokens,
+            "temperature": self.temperature,
+            "top_p": self.top_p,
+            "presence_penalty": self.presence_penalty,
+            "frequency_penalty": self.frequency_penalty,
+        }
+
+
+class Generate(ComponentBase):
+    component_name = "Generate"
+
+    def _run(self, history, **kwargs):
+        chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
+        prompt = self._param.prompt
+
+        retrieval_res = self.get_input()
+        input = "\n- ".join(retrieval_res["content"])
+
+
+        kwargs["input"] = input
+        for n, v in kwargs.items():
+            #prompt = re.sub(r"\{%s\}"%n, re.escape(str(v)), prompt)
+            prompt = re.sub(r"\{%s\}"%n, str(v), prompt)
+
+        if kwargs.get("stream"):
+            return partial(self.stream_output, chat_mdl, prompt, retrieval_res)
+
+        if "empty_response" in retrieval_res.columns:
+            return Generate.be_output(input)
+
+        ans = chat_mdl.chat(prompt, self._canvas.get_history(self._param.message_history_window_size), self._param.gen_conf())
+
+        if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" in retrieval_res.columns:
+            ans, idx = retrievaler.insert_citations(ans,
+                                                   [ck["content_ltks"]
+                                                    for _, ck in retrieval_res.iterrows()],
+                                                   [ck["vector"]
+                                                    for _,ck in retrieval_res.iterrows()],
+                                                   LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING, self._canvas.get_embedding_model()),
+                                                   tkweight=0.7,
+                                                   vtweight=0.3)
+            del retrieval_res["vector"]
+            retrieval_res = retrieval_res.to_dict("records")
+            df = []
+            for i in idx:
+                df.append(retrieval_res[int(i)])
+                r = re.search(r"^((.|[\r\n])*? ##%s\$\$)"%str(i), ans)
+                assert r, f"{i} => {ans}"
+                df[-1]["content"] = r.group(1)
+                ans = re.sub(r"^((.|[\r\n])*? ##%s\$\$)" % str(i), "", ans)
+            if ans: df.append({"content": ans})
+            return pd.DataFrame(df)
+
+        return Generate.be_output(ans)
+
+    def stream_output(self, chat_mdl, prompt, retrieval_res):
+        res = None
+        if "empty_response" in retrieval_res.columns and "\n- ".join(retrieval_res["content"]):
+            res = {"content": "\n- ".join(retrieval_res["content"]), "reference": []}
+            yield res
+            self.set_output(res)
+            return
+
+        answer = ""
+        for ans in chat_mdl.chat_streamly(prompt, self._canvas.get_history(self._param.message_history_window_size), self._param.gen_conf()):
+            res = {"content": ans, "reference": []}
+            answer = ans
+            yield res
+
+        if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" in retrieval_res.columns:
+            answer, idx = retrievaler.insert_citations(answer,
+                                                   [ck["content_ltks"]
+                                                    for _, ck in retrieval_res.iterrows()],
+                                                   [ck["vector"]
+                                                    for _, ck in retrieval_res.iterrows()],
+                                                   LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING, self._canvas.get_embedding_model()),
+                                                   tkweight=0.7,
+                                                   vtweight=0.3)
+            doc_ids = set([])
+            recall_docs = []
+            for i in idx:
+                did = retrieval_res.loc[int(i), "doc_id"]
+                if did in doc_ids: continue
+                doc_ids.add(did)
+                recall_docs.append({"doc_id": did, "doc_name": retrieval_res.loc[int(i), "docnm_kwd"]})
+
+            del retrieval_res["vector"]
+            del retrieval_res["content_ltks"]
+
+            reference = {
+                "chunks": [ck.to_dict() for _, ck in retrieval_res.iterrows()],
+                "doc_aggs": recall_docs
+            }
+
+            if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
+                answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
+            res = {"content": answer, "reference": reference}
+            yield res
+
+        self.set_output(res)
+
+