mirror of
https://github.com/VinciGit00/Scrapegraph-ai.git
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126 lines
5.3 KiB
Python
126 lines
5.3 KiB
Python
from typing import List, Optional
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from langchain.prompts import PromptTemplate
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from langchain_core.output_parsers import JsonOutputParser
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from langchain_core.runnables import RunnableParallel
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from langchain_openai import ChatOpenAI, AzureChatOpenAI
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from langchain_aws import ChatBedrock
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from langchain_mistralai import ChatMistralAI
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from langchain_community.chat_models import ChatOllama
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from tqdm import tqdm
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from .base_node import BaseNode
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from ..utils.output_parser import get_structured_output_parser, get_pydantic_output_parser
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from ..prompts import (
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TEMPLATE_CHUNKS, TEMPLATE_NO_CHUNKS, TEMPLATE_MERGE,
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TEMPLATE_CHUNKS_MD, TEMPLATE_NO_CHUNKS_MD, TEMPLATE_MERGE_MD
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)
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class GenerateAnswerNode(BaseNode):
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def __init__(
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self,
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input: str,
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output: List[str],
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node_config: Optional[dict] = None,
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node_name: str = "GenerateAnswer",
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):
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super().__init__(node_name, "node", input, output, 2, node_config)
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self.llm_model = node_config["llm_model"]
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if isinstance(node_config["llm_model"], ChatOllama):
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self.llm_model.format = "json"
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self.verbose = node_config.get("verbose", False)
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self.force = node_config.get("force", False)
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self.script_creator = node_config.get("script_creator", False)
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self.is_md_scraper = node_config.get("is_md_scraper", False)
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self.additional_info = node_config.get("additional_info")
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def execute(self, state: dict) -> dict:
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self.logger.info(f"--- Executing {self.node_name} Node ---")
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input_keys = self.get_input_keys(state)
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input_data = [state[key] for key in input_keys]
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user_prompt = input_data[0]
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doc = input_data[1]
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if self.node_config.get("schema", None) is not None:
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if isinstance(self.llm_model, (ChatOpenAI, ChatMistralAI)):
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self.llm_model = self.llm_model.with_structured_output(
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schema=self.node_config["schema"]
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)
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output_parser = get_structured_output_parser(self.node_config["schema"])
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format_instructions = "NA"
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else:
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if not isinstance(self.llm_model, ChatBedrock):
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output_parser = get_pydantic_output_parser(self.node_config["schema"])
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format_instructions = output_parser.get_format_instructions()
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else:
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output_parser = None
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format_instructions = ""
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else:
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if not isinstance(self.llm_model, ChatBedrock):
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output_parser = JsonOutputParser()
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format_instructions = output_parser.get_format_instructions()
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else:
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output_parser = None
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format_instructions = ""
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if isinstance(self.llm_model, (ChatOpenAI, AzureChatOpenAI)) \
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and not self.script_creator \
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or self.force \
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and not self.script_creator or self.is_md_scraper:
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template_no_chunks_prompt = TEMPLATE_NO_CHUNKS_MD
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template_chunks_prompt = TEMPLATE_CHUNKS_MD
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template_merge_prompt = TEMPLATE_MERGE_MD
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else:
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template_no_chunks_prompt = TEMPLATE_NO_CHUNKS
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template_chunks_prompt = TEMPLATE_CHUNKS
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template_merge_prompt = TEMPLATE_MERGE
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if self.additional_info is not None:
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template_no_chunks_prompt = self.additional_info + template_no_chunks_prompt
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template_chunks_prompt = self.additional_info + template_chunks_prompt
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template_merge_prompt = self.additional_info + template_merge_prompt
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if len(doc) == 1:
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prompt = PromptTemplate(
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template=template_no_chunks_prompt,
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input_variables=["question"],
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partial_variables={"context": doc, "format_instructions": format_instructions}
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)
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chain = prompt | self.llm_model
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if output_parser:
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chain = chain | output_parser
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answer = chain.invoke({"question": user_prompt})
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state.update({self.output[0]: answer})
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return state
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chains_dict = {}
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for i, chunk in enumerate(tqdm(doc, desc="Processing chunks", disable=not self.verbose)):
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prompt = PromptTemplate(
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template=template_chunks_prompt,
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input_variables=["question"],
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partial_variables={"context": chunk, "chunk_id": i + 1, "format_instructions": format_instructions}
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)
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chain_name = f"chunk{i+1}"
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chains_dict[chain_name] = prompt | self.llm_model
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if output_parser:
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chains_dict[chain_name] = chains_dict[chain_name] | output_parser
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async_runner = RunnableParallel(**chains_dict)
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batch_results = async_runner.invoke({"question": user_prompt})
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merge_prompt = PromptTemplate(
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template=template_merge_prompt,
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input_variables=["context", "question"],
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partial_variables={"format_instructions": format_instructions}
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)
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merge_chain = merge_prompt | self.llm_model
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if output_parser:
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merge_chain = merge_chain | output_parser
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answer = merge_chain.invoke({"context": batch_results, "question": user_prompt})
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state.update({self.output[0]: answer})
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return state
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