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71 lines
1.6 KiB
Python
71 lines
1.6 KiB
Python
"""
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Example of custom graph using existing nodes
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"""
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import os
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from dotenv import load_dotenv
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from scrapegraphai.models import OpenAI
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from scrapegraphai.graphs import BaseGraph
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from scrapegraphai.nodes import FetchNode, ParseNode, RAGNode, GenerateAnswerNode
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load_dotenv()
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openai_key = os.getenv("OPENAI_APIKEY")
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# Define the configuration for the graph
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graph_config = {
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"llm": {
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"api_key": openai_key,
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"model": "gpt-3.5-turbo",
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"temperature": 0,
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"streaming": True
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},
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}
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llm_model = OpenAI(graph_config["llm"])
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# define the nodes for the graph
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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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)
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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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model_config={"llm_model": llm_model},
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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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model_config={"llm_model": llm_model},
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)
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# create the graph by defining the nodes and their connections
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graph = BaseGraph(
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nodes={
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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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(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=fetch_node
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)
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# execute the graph
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result = graph.execute({
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"user_prompt": "List me the projects with their description",
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"url": "https://perinim.github.io/projects/"
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})
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# get the answer from the result
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answer = result.get("answer", "No answer found.")
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print(answer)
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