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79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
"""
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DescriptionNode Module
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"""
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from typing import List, Optional
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from tqdm import tqdm
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from langchain.prompts import PromptTemplate
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from langchain_core.runnables import RunnableParallel
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from .base_node import BaseNode
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from ..prompts.description_node_prompts import DESCRIPTION_NODE_PROMPT
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class DescriptionNode(BaseNode):
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"""
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A node responsible for compressing the input tokens and storing the document
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in a vector database for retrieval. Relevant chunks are stored in the state.
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It allows scraping of big documents without exceeding the token limit of the language model.
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Attributes:
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llm_model: An instance of a language model client, configured for generating answers.
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verbose (bool): A flag indicating whether to show print statements during execution.
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Args:
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input (str): Boolean expression defining the input keys needed from the state.
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output (List[str]): List of output keys to be updated in the state.
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node_config (dict): Additional configuration for the node.
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node_name (str): The unique identifier name for the node, defaulting to "Parse".
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"""
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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 = "RAG",
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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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self.embedder_model = node_config.get("embedder_model", None)
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self.verbose = (
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False if node_config is None else node_config.get("verbose", False)
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)
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self.cache_path = node_config.get("cache_path", False)
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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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docs = input_data[1]
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chains_dict = {}
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for i, chunk in enumerate(tqdm(docs, desc="Processing chunks", disable=not self.verbose)):
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prompt = PromptTemplate(
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template=DESCRIPTION_NODE_PROMPT,
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partial_variables={"context": chunk,
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"chunk_id": i + 1
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}
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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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async_runner = RunnableParallel(**chains_dict)
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batch_results = async_runner.invoke()
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temp_res = {}
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for i, (summary, document) in enumerate(zip(batch_results, docs)):
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temp_res[summary] = {
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"id": i,
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"summary": summary,
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"document": document
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}
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state["descriptions"] = temp_res
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return state
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