#
# 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 = 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]
return cpnts
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"])) if "content" in retrieval_res else ""
for para in self._param.parameters:
cpn = self._canvas.get_component(para["component_id"])["obj"]
_, out = cpn.output(allow_partial=False)
if "content" not in out.columns:
kwargs[para["key"]] = "Nothing"
else:
kwargs[para["key"]] = " - " + "\n - ".join(out["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)
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:
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)