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125 lines
4.5 KiB
Python
125 lines
4.5 KiB
Python
"""
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RAGNode Module
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"""
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from typing import List, Optional
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from langchain.docstore.document import Document
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from langchain.retrievers import ContextualCompressionRetriever
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from langchain.retrievers.document_compressors import (
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DocumentCompressorPipeline,
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EmbeddingsFilter,
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)
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from langchain_community.document_transformers import EmbeddingsRedundantFilter
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from langchain_community.vectorstores import FAISS
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from ..utils.logging import get_logger
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from .base_node import BaseNode
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class RAGNode(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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embedder_model: An instance of an embedding model client, configured for generating embeddings.
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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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def execute(self, state: dict) -> dict:
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"""
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Executes the node's logic to implement RAG (Retrieval-Augmented Generation).
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The method updates the state with relevant chunks of the document.
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Args:
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state (dict): The current state of the graph. The input keys will be used to fetch the
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correct data from the state.
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Returns:
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dict: The updated state with the output key containing the relevant chunks of the document.
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Raises:
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KeyError: If the input keys are not found in the state, indicating that the
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necessary information for compressing the content is missing.
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"""
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self.logger.info(f"--- Executing {self.node_name} Node ---")
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# Interpret input keys based on the provided input expression
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input_keys = self.get_input_keys(state)
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# Fetching data from the state based on the input keys
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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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chunked_docs = []
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for i, chunk in enumerate(doc):
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doc = Document(
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page_content=chunk,
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metadata={
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"chunk": i + 1,
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},
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)
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chunked_docs.append(doc)
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self.logger.info("--- (updated chunks metadata) ---")
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# check if embedder_model is provided, if not use llm_model
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self.embedder_model = (
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self.embedder_model if self.embedder_model else self.llm_model
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)
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embeddings = self.embedder_model
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retriever = FAISS.from_documents(chunked_docs, embeddings).as_retriever()
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redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
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# similarity_threshold could be set, now k=20
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relevant_filter = EmbeddingsFilter(embeddings=embeddings)
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pipeline_compressor = DocumentCompressorPipeline(
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transformers=[redundant_filter, relevant_filter]
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)
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# redundant + relevant filter compressor
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compression_retriever = ContextualCompressionRetriever(
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base_compressor=pipeline_compressor, base_retriever=retriever
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)
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# relevant filter compressor only
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# compression_retriever = ContextualCompressionRetriever(
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# base_compressor=relevant_filter, base_retriever=retriever
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# )
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compressed_docs = compression_retriever.invoke(user_prompt)
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self.logger.info("--- (tokens compressed and vector stored) ---")
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state.update({self.output[0]: compressed_docs})
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return state
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