π’ JSONScraperGraph Module
The JSONScraperGraph
module defines a class for creating and executing a graph that automates the process of extracting information from JSON files using a natural language model.
Classesβ
JSONScraperGraph
β
JSONScraperGraph
defines a scraping pipeline for JSON files.
Attributesβ
- prompt (str): The prompt for the graph.
- source (str): The source of the graph.
- config (dict): Configuration parameters for the graph.
- schema (str): The schema for the graph output.
- llm_model: An instance of a language model client, configured for generating answers.
- embedder_model: An instance of an embedding model client, configured for generating embeddings.
- verbose (bool): A flag indicating whether to show print statements during execution.
- headless (bool): A flag indicating whether to run the graph in headless mode.
- input_key (str): The key for the input source (either
json
orjson_dir
). - graph: The graph of nodes representing the workflow for web scraping.
- final_state: The final state of the graph after execution.
- execution_info: Information about the execution of the graph.
Methodsβ
-
__init__(self, prompt: str, source: str, config: dict, schema: Optional[str] = None)
- Initializes the
JSONScraperGraph
with a prompt, source, and configuration. - Args:
prompt (str)
: The prompt for the graph.source (str)
: The source of the graph.config (dict)
: Configuration parameters for the graph.schema (Optional[str])
: The schema for the graph output.
- Initializes the
-
_create_graph(self) -> BaseGraph
- Creates the graph of nodes representing the workflow for web scraping.
- Returns: An instance of
BaseGraph
.
-
run(self) -> str
- Executes the web scraping process and returns the answer to the prompt.
- Returns: The answer to the prompt.
Example Usageβ
Here is an example of how to use the JSONScraperGraph
class:
from json_scraper_graph import JSONScraperGraph
# Define the prompt, source, and configuration
prompt = "List me all the attractions in Chioggia."
source = "data/chioggia.json"
config = {
"llm": {"model": "gpt-3.5-turbo"},
"verbose": True,
"headless": False,
}
# Create the scraper graph
json_scraper = JSONScraperGraph(prompt, source, config)
# Run the scraper graph
result = json_scraper.run()
print(result)