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115 lines
4.4 KiB
Python
115 lines
4.4 KiB
Python
"""
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Module for parsing the HTML node
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"""
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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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 Html2TextTransformer, EmbeddingsRedundantFilter
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from langchain_community.vectorstores import FAISS
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from langchain_openai import OpenAIEmbeddings
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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 parsing HTML content from a document using specified tags.
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It uses BeautifulSoupTransformer for parsing, providing flexibility in extracting
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specific parts of an HTML document based on the tags provided in the state.
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This node enhances the scraping workflow by allowing for targeted extraction of
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content, thereby optimizing the processing of large HTML documents.
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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, llm, node_name="TestRagNode"):
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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")
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self.llm = llm
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def execute(self, state):
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"""
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Executes the node's logic to parse the HTML document based on specified tags.
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If tags are provided in the state, the document is parsed accordingly; otherwise,
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the document remains unchanged. The method updates the state with either the original
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or parsed document under the 'parsed_document' key.
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Args:
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state (dict): The current state of the graph, expected to contain
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'document' within 'keys', and optionally 'tags' for targeted parsing.
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Returns:
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dict: The updated state with the 'parsed_document' key containing the parsed content,
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if tags were provided, or the original document otherwise.
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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("---PARSING HTML DOCUMENT---")
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try:
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user_input = state["user_input"]
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document = state["document"]
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except KeyError as e:
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print(f"Error: {e} not found in state.")
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raise
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text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
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chunk_size=4000,
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chunk_overlap=0,
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)
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docs_transformed = Html2TextTransformer(
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).transform_documents(document)[0]
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chunks = text_splitter.split_text(docs_transformed.page_content)
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chunked_docs = []
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for i, chunk in enumerate(chunks):
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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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openai_key = self.llm.openai_api_key
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retriever = FAISS.from_documents(chunked_docs,
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OpenAIEmbeddings(api_key=openai_key)).as_retriever()
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# could be any embedding of your choice
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embeddings = OpenAIEmbeddings(api_key=openai_key)
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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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compression_retriever = ContextualCompressionRetriever(
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base_compressor=pipeline_compressor, base_retriever=retriever
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)
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compressed_docs = compression_retriever.get_relevant_documents(
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user_input)
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print("Documents compressed and stored in a vector database.")
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state.update({"document_chunks": compressed_docs})
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return state
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