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127 lines
4.8 KiB
Python
127 lines
4.8 KiB
Python
"""
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Module for parsing the HTML node
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"""
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from typing import List
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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 EmbeddingsFilter, DocumentCompressorPipeline
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from langchain_community.document_transformers import EmbeddingsRedundantFilter
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from langchain_community.embeddings import HuggingFaceHubEmbeddings
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from langchain_community.vectorstores import FAISS
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from langchain_openai import OpenAIEmbeddings, AzureOpenAIEmbeddings
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from ..models import OpenAI, Ollama, AzureOpenAI, HuggingFace
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from langchain_community.embeddings import OllamaEmbeddings
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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.
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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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node_name (str): The unique identifier name for the node, defaulting to "ParseHTMLNode".
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node_type (str): The type of the node, set to "node" indicating a standard operational node.
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Args:
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node_name (str, optional): The unique identifier name for the node.
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Defaults to "ParseHTMLNode".
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Methods:
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execute(state): Parses the HTML document contained within the state using
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the specified tags, if provided, and updates the state with the parsed content.
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"""
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def __init__(self, input: str, output: List[str], node_config: dict, node_name: str = "RAG"):
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"""
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Initializes the ParseHTMLNode with a node name.
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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"]
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self.embedder_model = node_config.get("embedder_model", None)
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def execute(self, state):
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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 state containing the 'document' key with the HTML content
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Returns:
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dict: The updated state containing the 'relevant_chunks' key with the relevant chunks.
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Raises:
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KeyError: If 'document' is not found in the state, indicating that the necessary
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information for parsing is missing.
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"""
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print(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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print("--- (updated chunks metadata) ---")
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# check if embedder_model is provided, if not use llm_model
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embedding_model = self.embedder_model if self.embedder_model else self.llm_model
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if isinstance(embedding_model, OpenAI):
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embeddings = OpenAIEmbeddings(
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api_key=embedding_model.openai_api_key)
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elif isinstance(embedding_model, AzureOpenAI):
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embeddings = AzureOpenAIEmbeddings()
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elif isinstance(embedding_model, Ollama):
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embeddings = OllamaEmbeddings(model=embedding_model.model)
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elif isinstance(embedding_model, HuggingFace):
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embeddings = HuggingFaceHubEmbeddings(model=embedding_model.model)
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else:
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raise ValueError("Embedding Model missing or not supported")
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retriever = FAISS.from_documents(
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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.get_relevant_documents(
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user_prompt)
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print("--- (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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