wehub-resource-sync
104 lines
2.4 KiB
Python
104 lines
2.4 KiB
Python
"""
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Example of custom graph using existing nodes
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"""
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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from scrapegraphai.graphs import BaseGraph
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from scrapegraphai.nodes import (
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FetchNode,
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GenerateAnswerNode,
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ParseNode,
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RobotsNode,
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)
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# ************************************************
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# Define the configuration for the graph
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# ************************************************
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graph_config = {
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"llm": {
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"model": "ollama/mistral",
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"temperature": 0,
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"format": "json", # Ollama needs the format to be specified explicitly
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# "model_tokens": 2000, # set context length arbitrarily
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"base_url": "http://localhost:11434",
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},
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"verbose": True,
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}
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# ************************************************
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# Define the graph nodes
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# ************************************************
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llm_model = ChatOpenAI(graph_config["llm"])
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embedder = OpenAIEmbeddings(api_key=llm_model.openai_api_key)
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# define the nodes for the graph
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robot_node = RobotsNode(
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input="url",
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output=["is_scrapable"],
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node_config={
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"llm_model": llm_model,
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"force_scraping": True,
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"verbose": True,
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},
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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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node_config={
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"verbose": True,
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"headless": True,
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},
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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={
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"chunk_size": 4096,
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"verbose": True,
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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={
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"llm_model": llm_model,
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"verbose": True,
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},
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)
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# ************************************************
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# Create the graph by defining the connections
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# ************************************************
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graph = BaseGraph(
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nodes=[
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robot_node,
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fetch_node,
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parse_node,
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generate_answer_node,
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],
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edges=[
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(robot_node, fetch_node),
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(fetch_node, parse_node),
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(parse_node, generate_answer_node),
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],
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entry_point=robot_node,
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)
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# ************************************************
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# Execute the graph
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# ************************************************
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result, execution_info = graph.execute(
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{"user_prompt": "Describe the content", "url": "https://example.com/"}
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)
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# get the answer from the result
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result = result.get("answer", "No answer found.")
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print(result)
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