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#
# 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)
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