🦃 Search graph with schema

"""
Example of Search Graph
"""

import os
from dotenv import load_dotenv
from langchain_openai import AzureChatOpenAI
from langchain_openai import AzureOpenAIEmbeddings
from scrapegraphai.graphs import SearchGraph
from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
load_dotenv()

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

with open(file_path, 'r', encoding="utf-8") as file:
    text = file.read()

# ************************************************
# Initialize the model instances
# ************************************************

llm_model_instance = AzureChatOpenAI(
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
    azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"]
)

embedder_model_instance = AzureOpenAIEmbeddings(
    azure_deployment=os.environ["AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

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

graph_config = {
    "llm": {"model_instance": llm_model_instance},
    "embeddings": {"model_instance": embedder_model_instance}
}

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

search_graph = SearchGraph(
    prompt="List me the best escursions near Trento",
    config=graph_config
)

result = search_graph.run()
print(result)

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

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

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