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Merge pull request #481 from ScrapeGraphAI/479-using-nemotron-from-nvidia
feat: add nvidia connection
2 parents 72f18d1 + c7bac98 commit 46d72a3

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‎examples/nemotron/csv_scraper_graph_multi_nemotron.py

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"""
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Basic example of scraping pipeline using CSVScraperMultiGraph from CSV documents
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"""
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import os
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import pandas as pd
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from dotenv import load_dotenv
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from scrapegraphai.graphs import CSVScraperMultiGraph
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from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
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load_dotenv()
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# ************************************************
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# Read the CSV file
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# ************************************************
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FILE_NAME = "inputs/username.csv"
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curr_dir = os.path.dirname(os.path.realpath(__file__))
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file_path = os.path.join(curr_dir, FILE_NAME)
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text = pd.read_csv(file_path)
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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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"api_key": os.getenv("NEMOTRON_APIKEY"),
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"model": "nvidia/meta/llama3-70b-instruct",
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}
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}
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# ************************************************
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# Create the CSVScraperMultiGraph instance and run it
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# ************************************************
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csv_scraper_graph = CSVScraperMultiGraph(
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prompt="List me all the last names",
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source=[str(text), str(text)],
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config=graph_config
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)
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result = csv_scraper_graph.run()
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print(result)
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# ************************************************
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# Get graph execution info
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# ************************************************
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graph_exec_info = csv_scraper_graph.get_execution_info()
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print(prettify_exec_info(graph_exec_info))
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# Save to json or csv
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convert_to_csv(result, "result")
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convert_to_json(result, "result")

‎examples/nemotron/csv_scraper_nemotron.py

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"""
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Basic example of scraping pipeline using CSVScraperGraph from CSV documents
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"""
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import os
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from dotenv import load_dotenv
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import pandas as pd
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from scrapegraphai.graphs import CSVScraperGraph
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from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
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load_dotenv()
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# ************************************************
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# Read the CSV file
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# ************************************************
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FILE_NAME = "inputs/username.csv"
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curr_dir = os.path.dirname(os.path.realpath(__file__))
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file_path = os.path.join(curr_dir, FILE_NAME)
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text = pd.read_csv(file_path)
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# ************************************************
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# Define the configuration for the graph
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# ************************************************
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nemotron_key = os.getenv("NEMOTRON_APIKEY")
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graph_config = {
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"llm": {
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"api_key": nemotron_key,
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"model": "nvidia/meta/llama3-70b-instruct",
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},
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}
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# ************************************************
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# Create the CSVScraperGraph instance and run it
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# ************************************************
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csv_scraper_graph = CSVScraperGraph(
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prompt="List me all the last names",
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source=str(text), # Pass the content of the file, not the file object
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config=graph_config
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)
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result = csv_scraper_graph.run()
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print(result)
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# ************************************************
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# Get graph execution info
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# ************************************************
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graph_exec_info = csv_scraper_graph.get_execution_info()
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print(prettify_exec_info(graph_exec_info))
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# Save to json or csv
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convert_to_csv(result, "result")
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convert_to_json(result, "result")

‎examples/nemotron/custom_graph_nemotron.py

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"""
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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 langchain_openai import OpenAIEmbeddings
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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, RobotsNode
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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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"api_key": os.getenv("NEMOTRON_KEY"),
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"model": "claude-3-haiku-20240307",
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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 = OpenAI(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", "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": "Describe the content",
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"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)

‎examples/nemotron/deep_scraper_nemotron.py

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"""
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Basic example of scraping pipeline using SmartScraper
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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.graphs import DeepScraperGraph
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from scrapegraphai.utils import prettify_exec_info
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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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nemotron_key = os.getenv("NEMOTRON_APIKEY")
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graph_config = {
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"llm": {
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"api_key": nemotron_key,
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"model": "nvidia/meta/llama3-70b-instruct",
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},
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"verbose": True,
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"max_depth": 1
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}
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# ************************************************
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# Create the SmartScraperGraph instance and run it
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# ************************************************
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deep_scraper_graph = DeepScraperGraph(
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prompt="List me all the job titles and detailed job description.",
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# also accepts a string with the already downloaded HTML code
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source="https://www.google.com/about/careers/applications/jobs/results/?location=Bangalore%20India",
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config=graph_config
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)
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result = deep_scraper_graph.run()
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print(result)
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# ************************************************
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# Get graph execution info
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# ************************************************
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graph_exec_info = deep_scraper_graph.get_execution_info()
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print(deep_scraper_graph.get_state("relevant_links"))
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print(prettify_exec_info(graph_exec_info))

‎examples/nemotron/inputs/books.xml

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<?xml version="1.0"?>
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<catalog>
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<book id="bk101">
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<author>Gambardella, Matthew</author>
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<title>XML Developer's Guide</title>
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<genre>Computer</genre>
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<price>44.95</price>
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<publish_date>2000-10-01</publish_date>
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<description>An in-depth look at creating applications
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with XML.</description>
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</book>
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<book id="bk102">
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<author>Ralls, Kim</author>
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<title>Midnight Rain</title>
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<genre>Fantasy</genre>
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<price>5.95</price>
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<publish_date>2000-12-16</publish_date>
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<description>A former architect battles corporate zombies,
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an evil sorceress, and her own childhood to become queen
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of the world.</description>
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</book>
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<book id="bk103">
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<author>Corets, Eva</author>
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<title>Maeve Ascendant</title>
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<genre>Fantasy</genre>
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<price>5.95</price>
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<publish_date>2000-11-17</publish_date>
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<description>After the collapse of a nanotechnology
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society in England, the young survivors lay the
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foundation for a new society.</description>
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</book>
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<book id="bk104">
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<author>Corets, Eva</author>
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<title>Oberon's Legacy</title>
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<genre>Fantasy</genre>
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<price>5.95</price>
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<publish_date>2001-03-10</publish_date>
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<description>In post-apocalypse England, the mysterious
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agent known only as Oberon helps to create a new life
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for the inhabitants of London. Sequel to Maeve
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Ascendant.</description>
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</book>
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<book id="bk105">
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<author>Corets, Eva</author>
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<title>The Sundered Grail</title>
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<genre>Fantasy</genre>
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<price>5.95</price>
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<publish_date>2001-09-10</publish_date>
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<description>The two daughters of Maeve, half-sisters,
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battle one another for control of England. Sequel to
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Oberon's Legacy.</description>
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</book>
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<book id="bk106">
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<author>Randall, Cynthia</author>
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<title>Lover Birds</title>
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<genre>Romance</genre>
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<price>4.95</price>
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<publish_date>2000-09-02</publish_date>
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<description>When Carla meets Paul at an ornithology
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conference, tempers fly as feathers get ruffled.</description>
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</book>
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<book id="bk107">
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<author>Thurman, Paula</author>
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<title>Splish Splash</title>
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<genre>Romance</genre>
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<price>4.95</price>
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<publish_date>2000-11-02</publish_date>
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<description>A deep sea diver finds true love twenty
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thousand leagues beneath the sea.</description>
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</book>
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<book id="bk108">
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<author>Knorr, Stefan</author>
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<title>Creepy Crawlies</title>
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<genre>Horror</genre>
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<price>4.95</price>
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<publish_date>2000-12-06</publish_date>
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<description>An anthology of horror stories about roaches,
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centipedes, scorpions and other insects.</description>
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</book>
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<book id="bk109">
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<author>Kress, Peter</author>
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<title>Paradox Lost</title>
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<genre>Science Fiction</genre>
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<price>6.95</price>
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<publish_date>2000-11-02</publish_date>
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<description>After an inadvertant trip through a Heisenberg
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Uncertainty Device, James Salway discovers the problems
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of being quantum.</description>
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</book>
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<book id="bk110">
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<author>O'Brien, Tim</author>
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<title>Microsoft .NET: The Programming Bible</title>
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<genre>Computer</genre>
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<price>36.95</price>
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<publish_date>2000-12-09</publish_date>
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<description>Microsoft's .NET initiative is explored in
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detail in this deep programmer's reference.</description>
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</book>
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<book id="bk111">
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<author>O'Brien, Tim</author>
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<title>MSXML3: A Comprehensive Guide</title>
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<genre>Computer</genre>
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<price>36.95</price>
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<publish_date>2000-12-01</publish_date>
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<description>The Microsoft MSXML3 parser is covered in
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detail, with attention to XML DOM interfaces, XSLT processing,
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SAX and more.</description>
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</book>
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<book id="bk112">
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<author>Galos, Mike</author>
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<title>Visual Studio 7: A Comprehensive Guide</title>
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<genre>Computer</genre>
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<price>49.95</price>
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<publish_date>2001-04-16</publish_date>
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<description>Microsoft Visual Studio 7 is explored in depth,
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looking at how Visual Basic, Visual C++, C#, and ASP+ are
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integrated into a comprehensive development
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environment.</description>
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</book>
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</catalog>

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