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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
@ -14,13 +14,18 @@
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# limitations under the License.
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#
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import logging
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import os
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import re
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from abc import ABC
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from api.db import LLMType
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from api.db.services.llm_service import LLMBundle
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from agent.component import GenerateParam, Generate
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from agent.component import LLMParam, LLM
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from api.utils.api_utils import timeout
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from rag.llm.chat_model import ERROR_PREFIX
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class CategorizeParam(GenerateParam):
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class CategorizeParam(LLMParam):
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"""
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Define the Categorize component parameters.
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@ -28,10 +33,12 @@ class CategorizeParam(GenerateParam):
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def __init__(self):
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super().__init__()
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self.category_description = {}
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self.prompt = ""
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self.query = "sys.query"
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self.message_history_window_size = 1
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self.update_prompt()
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def check(self):
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super().check()
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self.check_positive_integer(self.message_history_window_size, "[Categorize] Message window size > 0")
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self.check_empty(self.category_description, "[Categorize] Category examples")
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for k, v in self.category_description.items():
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if not k:
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@ -39,76 +46,90 @@ class CategorizeParam(GenerateParam):
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if not v.get("to"):
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raise ValueError(f"[Categorize] 'To' of category {k} can not be empty!")
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def get_prompt(self, chat_hist):
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def get_input_form(self) -> dict[str, dict]:
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return {
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"query": {
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"type": "line",
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"name": "Query"
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}
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}
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def update_prompt(self):
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cate_lines = []
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for c, desc in self.category_description.items():
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for line in desc.get("examples", "").split("\n"):
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for line in desc.get("examples", []):
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if not line:
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continue
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cate_lines.append("USER: {}\nCategory: {}".format(line, c))
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cate_lines.append("USER: \"" + re.sub(r"\n", " ", line, flags=re.DOTALL) + "\" → "+c)
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descriptions = []
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for c, desc in self.category_description.items():
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if desc.get("description"):
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descriptions.append(
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"\nCategory: {}\nDescription: {}".format(c, desc["description"]))
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"\n------\nCategory: {}\nDescription: {}".format(c, desc["description"]))
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self.prompt = """
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Role: You're a text classifier.
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Task: You need to categorize the user’s questions into {} categories, namely: {}
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self.sys_prompt = """
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You are an advanced classification system that categorizes user questions into specific types. Analyze the input question and classify it into ONE of the following categories:
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{}
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Here's description of each category:
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{}
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- {}
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You could learn from the following examples:
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{}
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You could learn from the above examples.
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Requirements:
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- Just mention the category names, no need for any additional words.
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---- Real Data ----
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USER: {}\n
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""".format(
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len(self.category_description.keys()),
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"/".join(list(self.category_description.keys())),
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"\n".join(descriptions),
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"\n\n- ".join(cate_lines),
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chat_hist
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---- Instructions ----
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- Consider both explicit mentions and implied context
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- Prioritize the most specific applicable category
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- Return only the category name without explanations
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- Use "Other" only when no other category fits
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""".format(
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"\n - ".join(list(self.category_description.keys())),
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"\n".join(descriptions)
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)
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return self.prompt
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if cate_lines:
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self.sys_prompt += """
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---- Examples ----
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{}
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""".format("\n".join(cate_lines))
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class Categorize(Generate, ABC):
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class Categorize(LLM, ABC):
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component_name = "Categorize"
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def _run(self, history, **kwargs):
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input = self.get_input()
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input = " - ".join(input["content"]) if "content" in input else ""
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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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msg = self._canvas.get_history(self._param.message_history_window_size)
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if not msg:
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msg = [{"role": "user", "content": ""}]
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if kwargs.get("sys.query"):
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msg[-1]["content"] = kwargs["sys.query"]
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self.set_input_value("sys.query", kwargs["sys.query"])
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else:
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msg[-1]["content"] = self._canvas.get_variable_value(self._param.query)
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self.set_input_value(self._param.query, msg[-1]["content"])
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self._param.update_prompt()
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chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
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self._canvas.set_component_infor(self._id, {"prompt":self._param.get_prompt(input),"messages": [{"role": "user", "content": "\nCategory: "}],"conf": self._param.gen_conf()})
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ans = chat_mdl.chat(self._param.get_prompt(input), [{"role": "user", "content": "\nCategory: "}],
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self._param.gen_conf())
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logging.debug(f"input: {input}, answer: {str(ans)}")
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user_prompt = """
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---- Real Data ----
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{} →
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""".format(" | ".join(["{}: \"{}\"".format(c["role"].upper(), re.sub(r"\n", "", c["content"], flags=re.DOTALL)) for c in msg]))
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ans = chat_mdl.chat(self._param.sys_prompt, [{"role": "user", "content": user_prompt}], self._param.gen_conf())
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logging.info(f"input: {user_prompt}, answer: {str(ans)}")
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if ERROR_PREFIX in ans:
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raise Exception(ans)
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# Count the number of times each category appears in the answer.
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category_counts = {}
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for c in self._param.category_description.keys():
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count = ans.lower().count(c.lower())
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category_counts[c] = count
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# If a category is found, return the category with the highest count.
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cpn_ids = list(self._param.category_description.items())[-1][1]["to"]
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max_category = list(self._param.category_description.keys())[0]
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if any(category_counts.values()):
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max_category = max(category_counts.items(), key=lambda x: x[1])
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res = Categorize.be_output(self._param.category_description[max_category[0]]["to"])
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self.set_output(res)
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return res
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max_category = max(category_counts.items(), key=lambda x: x[1])[0]
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cpn_ids = self._param.category_description[max_category]["to"]
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res = Categorize.be_output(list(self._param.category_description.items())[-1][1]["to"])
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self.set_output(res)
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return res
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def debug(self, **kwargs):
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df = self._run([], **kwargs)
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cpn_id = df.iloc[0, 0]
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return Categorize.be_output(self._canvas.get_component_name(cpn_id))
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self.set_output("category_name", max_category)
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self.set_output("_next", cpn_ids)
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