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61 lines
1.5 KiB
Python
61 lines
1.5 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 FetchTextNode, ParseNode, RAGNode, GenerateAnswerNode
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load_dotenv()
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# Define the configuration for the language model
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openai_key = os.getenv("OPENAI_APIKEY")
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llm_config = {
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"api_key": openai_key,
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"model_name": "gpt-3.5-turbo",
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"temperature": 0,
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"streaming": True
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}
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model = OpenAI(llm_config)
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curr_dir = os.path.dirname(__file__)
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file_path = os.path.join(curr_dir, "text_example.txt")
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with open(file_path, "r", encoding="utf-8") as file:
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text = file.read()
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# define the nodes for the graph
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fetch_text_node = FetchTextNode("load_html_from_text")
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parse_document_node = ParseNode(
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doc_type="text", chunks_size=4000, node_name="parse_document")
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rag_node = RAGNode(model, "rag")
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generate_answer_node = GenerateAnswerNode(model, "generate_answer")
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# create the graph
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graph = BaseGraph(
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nodes={
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fetch_text_node,
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parse_document_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_text_node, parse_document_node),
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(parse_document_node, rag_node),
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(rag_node, generate_answer_node)
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},
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entry_point=fetch_text_node
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)
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# execute the graph
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inputs = {"user_input": "Give me the name of all the news",
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"text": text}
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result = graph.execute(inputs)
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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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