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128 lines
2.9 KiB
Python
128 lines
2.9 KiB
Python
"""
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Example of custom graph using existing nodes
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"""
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import json
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from dotenv import load_dotenv
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from langchain_aws import BedrockEmbeddings
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from scrapegraphai.models import Bedrock
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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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ParseNode,
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RAGNode,
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GenerateAnswerNode,
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RobotsNode
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)
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load_dotenv()
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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": "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
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"temperature": 0.0
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},
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"embeddings": {
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"model": "bedrock/cohere.embed-multilingual-v3"
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}
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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 = Bedrock({
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'model_id': graph_config["llm"]["model"].split("/")[-1],
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'model_kwargs': {
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'temperature': 0.0
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}})
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embedder = BedrockEmbeddings(model_id=graph_config["embeddings"]["model"].split("/")[-1])
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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", "link_urls", "img_urls"],
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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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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_model": llm_model,
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"embedder_model": embedder,
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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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rag_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, rag_node),
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(rag_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": "List me all the articles",
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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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result = result.get("answer", "No answer found.")
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print(json.dumps(result, indent=4))
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