#
# 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.dialog_service import message_fit_in
from api.db.services.llm_service import LLMBundle
from api.settings import retrievaler
from agent.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 = 0
self.temperature = 0
self.top_p = 0
self.presence_penalty = 0
self.frequency_penalty = 0
self.cite = True
self.parameters = []
def check(self):
self.check_decimal_float(self.temperature, "[Generate] Temperature")
self.check_decimal_float(self.presence_penalty, "[Generate] Presence penalty")
self.check_decimal_float(self.frequency_penalty, "[Generate] Frequency penalty")
self.check_nonnegative_number(self.max_tokens, "[Generate] Max tokens")
self.check_decimal_float(self.top_p, "[Generate] Top P")
self.check_empty(self.llm_id, "[Generate] LLM")
# self.check_defined_type(self.parameters, "Parameters", ["list"])
def gen_conf(self):
conf = {}
if self.max_tokens > 0: conf["max_tokens"] = self.max_tokens
if self.temperature > 0: conf["temperature"] = self.temperature
if self.top_p > 0: conf["top_p"] = self.top_p
if self.presence_penalty > 0: conf["presence_penalty"] = self.presence_penalty
if self.frequency_penalty > 0: conf["frequency_penalty"] = self.frequency_penalty
return conf
class Generate(ComponentBase):
component_name = "Generate"
def get_dependent_components(self):
cpnts = [para["component_id"] for para in self._param.parameters if para["component_id"].lower().find("answer") < 0]
return cpnts
def set_cite(self, retrieval_res, answer):
retrieval_res = retrieval_res.dropna(subset=["vector", "content_ltks"]).reset_index(drop=True)
if "empty_response" in retrieval_res.columns:
retrieval_res["empty_response"].fillna("", inplace=True)
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}
return res
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._param.inputs = []
for para in self._param.parameters:
if para["component_id"].split("@")[0].lower().find("begin") > 0:
cpn_id, key = para["component_id"].split("@")
for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
if p["key"] == key:
kwargs[para["key"]] = p["value"]
self._param.inputs.append(
{"component_id": para["component_id"], "content": kwargs[para["key"]]})
break
else:
assert False, f"Can't find parameter '{key}' for {cpn_id}"
continue
cpn = self._canvas.get_component(para["component_id"])["obj"]
if cpn.component_name.lower() == "answer":
kwargs[para["key"]] = self._canvas.get_history(1)[0]["content"]
continue
_, out = cpn.output(allow_partial=False)
if "content" not in out.columns:
kwargs[para["key"]] = "Nothing"
else:
if cpn.component_name.lower() == "retrieval":
retrieval_res.append(out)
kwargs[para["key"]] = " - "+"\n - ".join([o if isinstance(o, str) else str(o) for o in out["content"]])
self._param.inputs.append({"component_id": para["component_id"], "content": kwargs[para["key"]]})
if retrieval_res:
retrieval_res = pd.concat(retrieval_res, ignore_index=True)
else: retrieval_res = pd.DataFrame([])
for n, v in kwargs.items():
prompt = re.sub(r"\{%s\}" % re.escape(n), re.escape(str(v)), prompt)
if not self._param.inputs and prompt.find("{input}") >= 0:
retrieval_res = self.get_input()
input = (" - " + "\n - ".join(
[c for c in retrieval_res["content"] if isinstance(c, str)])) if "content" in retrieval_res else ""
prompt = re.sub(r"\{input\}", re.escape(input), prompt)
downstreams = self._canvas.get_component(self._id)["downstream"]
if kwargs.get("stream") and len(downstreams) == 1 and self._canvas.get_component(downstreams[0])[
"obj"].component_name.lower() == "answer":
return partial(self.stream_output, chat_mdl, prompt, retrieval_res)
if "empty_response" in retrieval_res.columns and not "".join(retrieval_res["content"]):
res = {"content": "\n- ".join(retrieval_res["empty_response"]) if "\n- ".join(
retrieval_res["empty_response"]) else "Nothing found in knowledgebase!", "reference": []}
return pd.DataFrame([res])
msg = self._canvas.get_history(self._param.message_history_window_size)
_, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
if len(msg) < 2: msg.append({"role": "user", "content": ""})
ans = chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf())
if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" in retrieval_res.columns:
res = self.set_cite(retrieval_res, ans)
return pd.DataFrame([res])
return Generate.be_output(ans)
def stream_output(self, chat_mdl, prompt, retrieval_res):
res = None
if "empty_response" in retrieval_res.columns and not "".join(retrieval_res["content"]):
res = {"content": "\n- ".join(retrieval_res["empty_response"]) if "\n- ".join(
retrieval_res["empty_response"]) else "Nothing found in knowledgebase!", "reference": []}
yield res
self.set_output(res)
return
msg = self._canvas.get_history(self._param.message_history_window_size)
_, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
if len(msg) < 2: msg.append({"role": "user", "content": ""})
answer = ""
for ans in chat_mdl.chat_streamly(msg[0]["content"], msg[1:], 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:
res = self.set_cite(retrieval_res, answer)
yield res
self.set_output(res)