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bf0d516e49
### What problem does this PR solve? **generate.py 更新：** 问题：部分模型提供商对输入对话内容的格式有严格校验，要求第一条内容的 role 不能为 assistant，否则会报错。 解决：删除了系统设置的 agent 开场白，确保传递给模型的对话内容中，第一条内容的 role 不为 assistant。 **retrieval.py 更新：** 问题：当前知识库检索使用全部对话内容作为输入，可能导致检索结果不准确。 解决：改为仅使用用户最后提出的一个问题进行知识库检索，提高检索的准确性。 **Update generate.py:** Issue: Some model providers have strict validation rules for the format of input conversation content, requiring that the role of the first content must not be assistant. Otherwise, an error will occur. Solution: Removed the system-set agent opening statement to ensure that the role of the first content in the conversation passed to the model is not assistant. **Update retrieval.py:** Issue: The current knowledge base retrieval uses the entire conversation content as input, which may lead to inaccurate retrieval results. Solution: Changed the retrieval logic to use only the last question asked by the user for knowledge base retrieval, improving retrieval accuracy. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) - [x] Performance Improvement
251 lines
11 KiB
Python
251 lines
11 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import re
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from functools import partial
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import pandas as pd
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from api.db import LLMType
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from api.db.services.conversation_service import structure_answer
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from api.db.services.llm_service import LLMBundle
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from api import settings
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from agent.component.base import ComponentBase, ComponentParamBase
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from rag.prompts import message_fit_in
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class GenerateParam(ComponentParamBase):
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"""
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Define the Generate component parameters.
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"""
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def __init__(self):
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super().__init__()
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self.llm_id = ""
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self.prompt = ""
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self.max_tokens = 0
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self.temperature = 0
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self.top_p = 0
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self.presence_penalty = 0
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self.frequency_penalty = 0
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self.cite = True
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self.parameters = []
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def check(self):
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self.check_decimal_float(self.temperature, "[Generate] Temperature")
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self.check_decimal_float(self.presence_penalty, "[Generate] Presence penalty")
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self.check_decimal_float(self.frequency_penalty, "[Generate] Frequency penalty")
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self.check_nonnegative_number(self.max_tokens, "[Generate] Max tokens")
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self.check_decimal_float(self.top_p, "[Generate] Top P")
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self.check_empty(self.llm_id, "[Generate] LLM")
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# self.check_defined_type(self.parameters, "Parameters", ["list"])
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def gen_conf(self):
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conf = {}
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if self.max_tokens > 0:
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conf["max_tokens"] = self.max_tokens
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if self.temperature > 0:
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conf["temperature"] = self.temperature
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if self.top_p > 0:
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conf["top_p"] = self.top_p
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if self.presence_penalty > 0:
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conf["presence_penalty"] = self.presence_penalty
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if self.frequency_penalty > 0:
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conf["frequency_penalty"] = self.frequency_penalty
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return conf
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class Generate(ComponentBase):
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component_name = "Generate"
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def get_dependent_components(self):
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inputs = self.get_input_elements()
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cpnts = set([i["key"] for i in inputs[1:] if i["key"].lower().find("answer") < 0 and i["key"].lower().find("begin") < 0])
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return list(cpnts)
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def set_cite(self, retrieval_res, answer):
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retrieval_res = retrieval_res.dropna(subset=["vector", "content_ltks"]).reset_index(drop=True)
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if "empty_response" in retrieval_res.columns:
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retrieval_res["empty_response"].fillna("", inplace=True)
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answer, idx = settings.retrievaler.insert_citations(answer,
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[ck["content_ltks"] for _, ck in retrieval_res.iterrows()],
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[ck["vector"] for _, ck in retrieval_res.iterrows()],
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LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING,
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self._canvas.get_embedding_model()), tkweight=0.7,
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vtweight=0.3)
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doc_ids = set([])
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recall_docs = []
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for i in idx:
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did = retrieval_res.loc[int(i), "doc_id"]
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if did in doc_ids:
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continue
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doc_ids.add(did)
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recall_docs.append({"doc_id": did, "doc_name": retrieval_res.loc[int(i), "docnm_kwd"]})
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del retrieval_res["vector"]
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del retrieval_res["content_ltks"]
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reference = {
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"chunks": [ck.to_dict() for _, ck in retrieval_res.iterrows()],
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"doc_aggs": recall_docs
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}
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if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
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answer += " Please set LLM API-Key in 'User Setting -> Model providers -> API-Key'"
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res = {"content": answer, "reference": reference}
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res = structure_answer(None, res, "", "")
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return res
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def get_input_elements(self):
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key_set = set([])
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res = [{"key": "user", "name": "Input your question here:"}]
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for r in re.finditer(r"\{([a-z]+[:@][a-z0-9_-]+)\}", self._param.prompt, flags=re.IGNORECASE):
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cpn_id = r.group(1)
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if cpn_id in key_set:
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continue
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if cpn_id.lower().find("begin@") == 0:
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cpn_id, key = cpn_id.split("@")
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for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
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if p["key"] != key:
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continue
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res.append({"key": r.group(1), "name": p["name"]})
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key_set.add(r.group(1))
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continue
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cpn_nm = self._canvas.get_component_name(cpn_id)
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if not cpn_nm:
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continue
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res.append({"key": cpn_id, "name": cpn_nm})
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key_set.add(cpn_id)
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return res
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def _run(self, history, **kwargs):
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chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
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prompt = self._param.prompt
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retrieval_res = []
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self._param.inputs = []
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for para in self.get_input_elements()[1:]:
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if para["key"].lower().find("begin@") == 0:
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cpn_id, key = para["key"].split("@")
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for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
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if p["key"] == key:
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kwargs[para["key"]] = p.get("value", "")
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self._param.inputs.append(
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{"component_id": para["key"], "content": kwargs[para["key"]]})
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break
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else:
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assert False, f"Can't find parameter '{key}' for {cpn_id}"
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continue
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component_id = para["key"]
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cpn = self._canvas.get_component(component_id)["obj"]
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if cpn.component_name.lower() == "answer":
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hist = self._canvas.get_history(1)
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if hist:
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hist = hist[0]["content"]
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else:
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hist = ""
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kwargs[para["key"]] = hist
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continue
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_, out = cpn.output(allow_partial=False)
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if "content" not in out.columns:
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kwargs[para["key"]] = ""
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else:
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if cpn.component_name.lower() == "retrieval":
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retrieval_res.append(out)
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kwargs[para["key"]] = " - " + "\n - ".join([o if isinstance(o, str) else str(o) for o in out["content"]])
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self._param.inputs.append({"component_id": para["key"], "content": kwargs[para["key"]]})
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if retrieval_res:
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retrieval_res = pd.concat(retrieval_res, ignore_index=True)
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else:
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retrieval_res = pd.DataFrame([])
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for n, v in kwargs.items():
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prompt = re.sub(r"\{%s\}" % re.escape(n), str(v).replace("\\", " "), prompt)
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if not self._param.inputs and prompt.find("{input}") >= 0:
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retrieval_res = self.get_input()
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input = (" - " + "\n - ".join(
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[c for c in retrieval_res["content"] if isinstance(c, str)])) if "content" in retrieval_res else ""
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prompt = re.sub(r"\{input\}", re.escape(input), prompt)
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downstreams = self._canvas.get_component(self._id)["downstream"]
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if kwargs.get("stream") and len(downstreams) == 1 and self._canvas.get_component(downstreams[0])[
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"obj"].component_name.lower() == "answer":
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return partial(self.stream_output, chat_mdl, prompt, retrieval_res)
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if "empty_response" in retrieval_res.columns and not "".join(retrieval_res["content"]):
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empty_res = "\n- ".join([str(t) for t in retrieval_res["empty_response"] if str(t)])
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res = {"content": empty_res if empty_res else "Nothing found in knowledgebase!", "reference": []}
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return pd.DataFrame([res])
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msg = self._canvas.get_history(self._param.message_history_window_size)
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if len(msg) < 1:
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msg.append({"role": "user", "content": "Output: "})
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_, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
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if len(msg) < 2:
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msg.append({"role": "user", "content": "Output: "})
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ans = chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf())
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ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
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if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" in retrieval_res.columns:
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res = self.set_cite(retrieval_res, ans)
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return pd.DataFrame([res])
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return Generate.be_output(ans)
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def stream_output(self, chat_mdl, prompt, retrieval_res):
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res = None
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if "empty_response" in retrieval_res.columns and not "".join(retrieval_res["content"]):
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empty_res = "\n- ".join([str(t) for t in retrieval_res["empty_response"] if str(t)])
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res = {"content": empty_res if empty_res else "Nothing found in knowledgebase!", "reference": []}
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yield res
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self.set_output(res)
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return
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msg = self._canvas.get_history(self._param.message_history_window_size)
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if msg and msg[0]['role'] == 'assistant':
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msg.pop(0)
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if len(msg) < 1:
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msg.append({"role": "user", "content": "Output: "})
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_, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
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if len(msg) < 2:
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msg.append({"role": "user", "content": "Output: "})
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answer = ""
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for ans in chat_mdl.chat_streamly(msg[0]["content"], msg[1:], self._param.gen_conf()):
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res = {"content": ans, "reference": []}
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answer = ans
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yield res
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if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" in retrieval_res.columns:
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res = self.set_cite(retrieval_res, answer)
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yield res
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self.set_output(Generate.be_output(res))
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def debug(self, **kwargs):
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chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
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prompt = self._param.prompt
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for para in self._param.debug_inputs:
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kwargs[para["key"]] = para.get("value", "")
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for n, v in kwargs.items():
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prompt = re.sub(r"\{%s\}" % re.escape(n), str(v).replace("\\", " "), prompt)
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u = kwargs.get("user")
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ans = chat_mdl.chat(prompt, [{"role": "user", "content": u if u else "Output: "}], self._param.gen_conf())
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return pd.DataFrame([ans])
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