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Feat: Redesign and refactor agent module (#9113)
### What problem does this PR solve? #9082 #6365 <u> **WARNING: it's not compatible with the older version of `Agent` module, which means that `Agent` from older versions can not work anymore.**</u> ### Type of change - [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Kevin Hu
242
agent/component/llm.py
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242
agent/component/llm.py
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#
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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 json
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import logging
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import os
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import re
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from typing import Any
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import json_repair
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from copy import deepcopy
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from functools import partial
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from api.db.services.llm_service import LLMBundle, TenantLLMService
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from agent.component.base import ComponentBase, ComponentParamBase
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from api.utils.api_utils import timeout
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from rag.prompts import message_fit_in, citation_prompt
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from rag.prompts.prompts import tool_call_summary
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class LLMParam(ComponentParamBase):
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"""
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Define the LLM 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.sys_prompt = ""
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self.prompts = [{"role": "user", "content": "{sys.query}"}]
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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.output_structure = None
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self.cite = True
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self.visual_files_var = None
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def check(self):
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self.check_decimal_float(self.temperature, "[Agent] Temperature")
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self.check_decimal_float(self.presence_penalty, "[Agent] Presence penalty")
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self.check_decimal_float(self.frequency_penalty, "[Agent] Frequency penalty")
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self.check_nonnegative_number(self.max_tokens, "[Agent] Max tokens")
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self.check_decimal_float(self.top_p, "[Agent] Top P")
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self.check_empty(self.llm_id, "[Agent] LLM")
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self.check_empty(self.sys_prompt, "[Agent] System prompt")
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self.check_empty(self.prompts, "[Agent] User prompt")
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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 LLM(ComponentBase):
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component_name = "LLM"
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def __init__(self, canvas, id, param: ComponentParamBase):
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super().__init__(canvas, id, param)
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self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), TenantLLMService.llm_id2llm_type(self._param.llm_id),
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self._param.llm_id, max_retries=self._param.max_retries,
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retry_interval=self._param.delay_after_error
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)
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self.imgs = []
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def get_input_form(self) -> dict[str, dict]:
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res = {}
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for k, v in self.get_input_elements().items():
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res[k] = {
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"type": "line",
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"name": v["name"]
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}
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return res
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def get_input_elements(self) -> dict[str, Any]:
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res = self.get_input_elements_from_text(self._param.sys_prompt)
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for prompt in self._param.prompts:
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d = self.get_input_elements_from_text(prompt["content"])
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res.update(d)
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return res
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def set_debug_inputs(self, inputs: dict[str, dict]):
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self._param.debug_inputs = inputs
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def add2system_prompt(self, txt):
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self._param.sys_prompt += txt
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def _prepare_prompt_variables(self):
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if self._param.visual_files_var:
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self.imgs = self._canvas.get_variable_value(self._param.visual_files_var)
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if not self.imgs:
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self.imgs = []
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self.imgs = [img for img in self.imgs if img[:len("data:image/")] == "data:image/"]
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args = {}
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vars = self.get_input_elements() if not self._param.debug_inputs else self._param.debug_inputs
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prompt = self._param.sys_prompt
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for k, o in vars.items():
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args[k] = o["value"]
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if not isinstance(args[k], str):
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try:
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args[k] = json.dumps(args[k], ensure_ascii=False)
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except Exception:
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args[k] = str(args[k])
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self.set_input_value(k, args[k])
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msg = self._canvas.get_history(self._param.message_history_window_size)[:-1]
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msg.extend(deepcopy(self._param.prompts))
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prompt = self.string_format(prompt, args)
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for m in msg:
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m["content"] = self.string_format(m["content"], args)
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if self._canvas.get_reference()["chunks"]:
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prompt += citation_prompt()
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return prompt, msg
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def _generate(self, msg:list[dict], **kwargs) -> str:
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if not self.imgs:
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return self.chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf(), **kwargs)
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return self.chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf(), images=self.imgs, **kwargs)
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def _generate_streamly(self, msg:list[dict], **kwargs) -> str:
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ans = ""
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last_idx = 0
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endswith_think = False
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def delta(txt):
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nonlocal ans, last_idx, endswith_think
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delta_ans = txt[last_idx:]
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ans = txt
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if delta_ans.find("<think>") == 0:
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last_idx += len("<think>")
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return "<think>"
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elif delta_ans.find("<think>") > 0:
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delta_ans = txt[last_idx:last_idx+delta_ans.find("<think>")]
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last_idx += delta_ans.find("<think>")
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return delta_ans
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elif delta_ans.endswith("</think>"):
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endswith_think = True
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elif endswith_think:
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endswith_think = False
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return "</think>"
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last_idx = len(ans)
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if ans.endswith("</think>"):
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last_idx -= len("</think>")
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return re.sub(r"(<think>|</think>)", "", delta_ans)
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if not self.imgs:
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for txt in self.chat_mdl.chat_streamly(msg[0]["content"], msg[1:], self._param.gen_conf(), **kwargs):
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yield delta(txt)
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else:
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for txt in self.chat_mdl.chat_streamly(msg[0]["content"], msg[1:], self._param.gen_conf(), images=self.imgs, **kwargs):
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yield delta(txt)
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@timeout(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60))
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def _invoke(self, **kwargs):
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def clean_formated_answer(ans: str) -> str:
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ans = re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
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ans = re.sub(r"^.*```json", "", ans, flags=re.DOTALL)
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return re.sub(r"```\n*$", "", ans, flags=re.DOTALL)
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prompt, msg = self._prepare_prompt_variables()
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error = ""
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if self._param.output_structure:
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prompt += "\nThe output MUST follow this JSON format:\n"+json.dumps(self._param.output_structure, ensure_ascii=False, indent=2)
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prompt += "\nRedundant information is FORBIDDEN."
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for _ in range(self._param.max_retries+1):
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_, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
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error = ""
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ans = self._generate(msg)
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msg.pop(0)
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if ans.find("**ERROR**") >= 0:
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logging.error(f"LLM response error: {ans}")
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error = ans
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continue
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try:
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self.set_output("structured_content", json_repair.loads(clean_formated_answer(ans)))
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return
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except Exception:
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msg.append({"role": "user", "content": "The answer can't not be parsed as JSON"})
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error = "The answer can't not be parsed as JSON"
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if error:
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self.set_output("_ERROR", error)
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return
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downstreams = self._canvas.get_component(self._id)["downstream"] if self._canvas.get_component(self._id) else []
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if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not self._param.output_structure:
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self.set_output("content", partial(self._stream_output, prompt, msg))
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return
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for _ in range(self._param.max_retries+1):
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_, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
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error = ""
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ans = self._generate(msg)
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msg.pop(0)
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if ans.find("**ERROR**") >= 0:
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logging.error(f"LLM response error: {ans}")
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error = ans
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continue
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self.set_output("content", ans)
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break
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if error:
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self.set_output("_ERROR", error)
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if self.get_exception_default_value():
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self.set_output("content", self.get_exception_default_value())
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def _stream_output(self, prompt, msg):
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_, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
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answer = ""
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for ans in self._generate_streamly(msg):
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yield ans
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answer += ans
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self.set_output("content", answer)
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def add_memory(self, user:str, assist:str, func_name: str, params: dict, results: str):
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summ = tool_call_summary(self.chat_mdl, func_name, params, results)
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logging.info(f"[MEMORY]: {summ}")
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self._canvas.add_memory(user, assist, summ)
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