csv_scraper_graph_multi_ollama

"""
Basic example of scraping pipeline using CSVScraperMultiGraph from CSV documents
"""

import os
import pandas as pd
from scrapegraphai.graphs import CSVScraperMultiGraph
from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info

# ************************************************
# Read the CSV file
# ************************************************

FILE_NAME = "inputs/username.csv"
curr_dir = os.path.dirname(os.path.realpath(__file__))
file_path = os.path.join(curr_dir, FILE_NAME)

text = pd.read_csv(file_path)

# ************************************************
# Define the configuration for the graph
# ************************************************

graph_config = {
    "llm": {
        "model": "ollama/llama3",
        "temperature": 0,
        "format": "json",  # Ollama needs the format to be specified explicitly
        # "model_tokens": 2000, # set context length arbitrarily
        "base_url": "http://localhost:11434",
    },
    "embeddings": {
        "model": "ollama/nomic-embed-text",
        "temperature": 0,
        "base_url": "http://localhost:11434",
    },
    "verbose": True,
}

# ************************************************
# Create the CSVScraperMultiGraph instance and run it
# ************************************************

csv_scraper_graph = CSVScraperMultiGraph(
    prompt="List me all the last names",
    source=[str(text), str(text)],
    config=graph_config
)

result = csv_scraper_graph.run()
print(result)

# ************************************************
# Get graph execution info
# ************************************************

graph_exec_info = csv_scraper_graph.get_execution_info()
print(prettify_exec_info(graph_exec_info))

# Save to json or csv
convert_to_csv(result, "result")
convert_to_json(result, "result")