ragflow/graph/component/generate.py

#
#  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 _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"])
        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)

        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)