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https://github.com/VinciGit00/Scrapegraph-ai.git
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266 lines
11 KiB
Python
266 lines
11 KiB
Python
"""
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RAGNode Module
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"""
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import os
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import sys
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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 langchain_community.chat_models import ChatOllama
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from langchain_aws import BedrockEmbeddings, ChatBedrock
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from langchain_community.embeddings import OllamaEmbeddings
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from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
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from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbeddings, ChatOpenAI, AzureChatOpenAI
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from ..utils.logging import get_logger
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from .base_node import BaseNode
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from ..helpers import models_tokens
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from ..models import DeepSeek
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optional_modules = {"langchain_anthropic", "langchain_fireworks", "langchain_groq", "langchain_google_vertexai"}
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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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self.cache_path = node_config.get("cache_path", False)
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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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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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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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if self.embedder_model is not None:
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embeddings = self.embedder_model
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elif 'embeddings' in self.node_config:
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try:
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embeddings = self._create_embedder(self.node_config['embedder_config'])
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except Exception:
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try:
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embeddings = self._create_default_embedder()
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self.embedder_model = embeddings
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except ValueError:
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embeddings = self.llm_model
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self.embedder_model = self.llm_model
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else:
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embeddings = self.llm_model
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self.embedder_model = self.llm_model
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folder_name = self.node_config.get("cache_path", "cache")
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if self.node_config.get("cache_path", False) and not os.path.exists(folder_name):
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index = FAISS.from_documents(chunked_docs, embeddings)
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os.makedirs(folder_name)
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index.save_local(folder_name)
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self.logger.info("--- (indexes saved to cache) ---")
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elif self.node_config.get("cache_path", False) and os.path.exists(folder_name):
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index = FAISS.load_local(folder_path=folder_name,
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embeddings=embeddings,
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allow_dangerous_deserialization=True)
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self.logger.info("--- (indexes loaded from cache) ---")
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else:
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index = FAISS.from_documents(chunked_docs, embeddings)
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retriever = index.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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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.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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def _create_default_embedder(self, llm_config=None) -> object:
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"""
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Create an embedding model instance based on the chosen llm model.
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Returns:
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object: An instance of the embedding model client.
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Raises:
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ValueError: If the model is not supported.
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"""
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if isinstance(self.llm_model, ChatGoogleGenerativeAI):
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return GoogleGenerativeAIEmbeddings(
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google_api_key=llm_config["api_key"], model="models/embedding-001"
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)
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if isinstance(self.llm_model, ChatOpenAI):
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return OpenAIEmbeddings(api_key=self.llm_model.openai_api_key,
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base_url=self.llm_model.openai_api_base)
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elif isinstance(self.llm_model, DeepSeek):
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return OpenAIEmbeddings(api_key=self.llm_model.openai_api_key)
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elif isinstance(self.llm_model, AzureOpenAIEmbeddings):
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return self.llm_model
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elif isinstance(self.llm_model, AzureChatOpenAI):
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return AzureOpenAIEmbeddings()
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elif isinstance(self.llm_model, ChatOllama):
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# unwrap the kwargs from the model whihc is a dict
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params = self.llm_model._lc_kwargs
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# remove streaming and temperature
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params.pop("streaming", None)
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params.pop("temperature", None)
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return OllamaEmbeddings(**params)
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elif isinstance(self.llm_model, ChatBedrock):
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return BedrockEmbeddings(client=None, model_id=self.llm_model.model_id)
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elif all(key in sys.modules for key in optional_modules):
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if isinstance(self.llm_model, ChatFireworks):
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return FireworksEmbeddings(model=self.llm_model.model_name)
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if isinstance(self.llm_model, ChatNVIDIA):
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return NVIDIAEmbeddings(model=self.llm_model.model_name)
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if isinstance(self.llm_model, ChatHuggingFace):
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return HuggingFaceEmbeddings(model=self.llm_model.model)
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if isinstance(self.llm_model, ChatVertexAI):
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return VertexAIEmbeddings()
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else:
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raise ValueError("Embedding Model missing or not supported")
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def _create_embedder(self, embedder_config: dict) -> object:
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"""
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Create an embedding model instance based on the configuration provided.
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Args:
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embedder_config (dict): Configuration parameters for the embedding model.
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Returns:
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object: An instance of the embedding model client.
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Raises:
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KeyError: If the model is not supported.
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"""
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embedder_params = {**embedder_config}
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if "model_instance" in embedder_config:
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return embedder_params["model_instance"]
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if "openai" in embedder_params["model"]:
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return OpenAIEmbeddings(api_key=embedder_params["api_key"])
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if "azure" in embedder_params["model"]:
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return AzureOpenAIEmbeddings()
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if "ollama" in embedder_params["model"]:
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embedder_params["model"] = "/".join(embedder_params["model"].split("/")[1:])
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try:
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models_tokens["ollama"][embedder_params["model"]]
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except KeyError as exc:
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raise KeyError("Model not supported") from exc
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return OllamaEmbeddings(**embedder_params)
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if "gemini" in embedder_params["model"]:
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try:
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models_tokens["gemini"][embedder_params["model"]]
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except KeyError as exc:
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raise KeyError("Model not supported") from exc
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return GoogleGenerativeAIEmbeddings(model=embedder_params["model"])
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if "bedrock" in embedder_params["model"]:
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embedder_params["model"] = embedder_params["model"].split("/")[-1]
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client = embedder_params.get("client", None)
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try:
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models_tokens["bedrock"][embedder_params["model"]]
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except KeyError as exc:
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raise KeyError("Model not supported") from exc
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return BedrockEmbeddings(client=client, model_id=embedder_params["model"])
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if all(key in sys.modules for key in optional_modules):
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if "hugging_face" in embedder_params["model"]:
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embedder_params["model"] = "/".join(embedder_params["model"].split("/")[1:])
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try:
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models_tokens["hugging_face"][embedder_params["model"]]
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except KeyError as exc:
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raise KeyError("Model not supported") from exc
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return HuggingFaceEmbeddings(model=embedder_params["model"])
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if "fireworks" in embedder_params["model"]:
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embedder_params["model"] = "/".join(embedder_params["model"].split("/")[1:])
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try:
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models_tokens["fireworks"][embedder_params["model"]]
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except KeyError as exc:
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raise KeyError("Model not supported") from exc
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return FireworksEmbeddings(model=embedder_params["model"])
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if "nvidia" in embedder_params["model"]:
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embedder_params["model"] = "/".join(embedder_params["model"].split("/")[1:])
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try:
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models_tokens["nvidia"][embedder_params["model"]]
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except KeyError as exc:
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raise KeyError("Model not supported") from exc
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return NVIDIAEmbeddings(model=embedder_params["model"],
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nvidia_api_key=embedder_params["api_key"])
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raise ValueError("Model provided by the configuration not supported")
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