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68 lines
2.3 KiB
Python
68 lines
2.3 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 .base_node import BaseNode
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from qdrant_client import QdrantClient
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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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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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if self.node_config.get("client_type") == "memory":
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client = QdrantClient(":memory:")
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elif self.node_config.get("client_type") == "local_db":
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client = QdrantClient(path="path/to/db")
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elif self.node_config.get("client_type") == "image":
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client = QdrantClient(url="http://localhost:6333")
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else:
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raise ValueError("client_type provided not correct")
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docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"]
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metadata = [
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{"source": "Langchain-docs"},
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{"source": "Linkedin-docs"},
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]
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ids = [42, 2]
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client.add(
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collection_name="demo_collection",
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documents=docs,
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metadata=metadata,
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ids=ids
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)
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state["vectorial_db"] = client
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return state
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