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"""

Example of custom graph using existing nodes

"""

from langchain_openai import OpenAIEmbeddings

from langchain_openai import ChatOpenAI

from scrapegraphai.graphs import BaseGraph

from scrapegraphai.nodes import FetchNode, ParseNode, RAGNode, GenerateAnswerNode, RobotsNode

# ************************************************

# Define the configuration for the graph

# ************************************************

graph_config = {

"llm": {

"model": "ernie/ernie-bot-turbo",

"ernie_client_id": "<ernie_client_id>",

"ernie_client_secret": "<ernie_client_secret>",

"temperature": 0.1

}

}

# ************************************************

# Define the graph nodes

# ************************************************

llm_model = ChatOpenAI(graph_config["llm"])

embedder = OpenAIEmbeddings(api_key=llm_model.openai_api_key)

# define the nodes for the graph

robot_node = RobotsNode(

input="url",

output=["is_scrapable"],

node_config={

"llm_model": llm_model,

"force_scraping": True,

"verbose": True,

}

)

fetch_node = FetchNode(

input="url | local_dir",

output=["doc"],

node_config={

"verbose": True,

"headless": True,

}

)

parse_node = ParseNode(

input="doc",

output=["parsed_doc"],

node_config={

"chunk_size": 4096,

"verbose": True,

}

)

rag_node = RAGNode(

input="user_prompt & (parsed_doc | doc)",

output=["relevant_chunks"],

node_config={

"llm_model": llm_model,

"embedder_model": embedder,

"verbose": True,

}

)

generate_answer_node = GenerateAnswerNode(

input="user_prompt & (relevant_chunks | parsed_doc | doc)",

output=["answer"],

node_config={

"llm_model": llm_model,

"verbose": True,

}

)

# ************************************************

# Create the graph by defining the connections

# ************************************************

graph = BaseGraph(

nodes=[

robot_node,

fetch_node,

parse_node,

rag_node,

generate_answer_node,

],

edges=[

(robot_node, fetch_node),

(fetch_node, parse_node),

(parse_node, rag_node),

(rag_node, generate_answer_node)

],

entry_point=robot_node

)

# ************************************************

# Execute the graph

# ************************************************

result, execution_info = graph.execute({

"user_prompt": "Describe the content",

"url": "https://example.com/"

})

# get the answer from the result

result = result.get("answer", "No answer found.")

print(result)