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76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
"""
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Module for making the search on the intenet
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"""
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from .base_graph import BaseGraph
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from ..nodes import (
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SearchInternetNode,
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FetchNode,
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ParseNode,
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RAGNode,
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GenerateAnswerNode
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)
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from .abstract_graph import AbstractGraph
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class SearchGraph(AbstractGraph):
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"""
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Module for searching info on the internet
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"""
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def _create_graph(self):
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"""
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Creates the graph of nodes representing the workflow for web scraping and searching.
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"""
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search_internet_node = SearchInternetNode(
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input="user_prompt",
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output=["url"],
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node_config={"llm": self.llm_model}
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)
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fetch_node = FetchNode(
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input="url | local_dir",
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output=["doc"],
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)
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parse_node = ParseNode(
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input="doc",
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output=["parsed_doc"],
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node_config={"chunk_size": self.model_token}
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)
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rag_node = RAGNode(
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input="user_prompt & (parsed_doc | doc)",
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output=["relevant_chunks"],
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node_config={
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"llm": self.llm_model,
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"embedder_model": self.embedder_model
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}
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)
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generate_answer_node = GenerateAnswerNode(
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input="user_prompt & (relevant_chunks | parsed_doc | doc)",
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output=["answer"],
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node_config={"llm": self.llm_model},
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)
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return BaseGraph(
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nodes={
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search_internet_node,
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fetch_node,
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parse_node,
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rag_node,
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generate_answer_node,
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},
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edges={
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(search_internet_node, fetch_node),
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(fetch_node, parse_node),
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(parse_node, rag_node),
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(rag_node, generate_answer_node)
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},
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entry_point=search_internet_node
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)
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def run(self) -> str:
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"""
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Executes the web scraping and searching process.
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"""
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inputs = {"user_prompt": self.prompt}
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self.final_state, self.execution_info = self.graph.execute(inputs)
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return self.final_state.get("answer", "No answer found.")
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