mirror of
https://github.com/VinciGit00/Scrapegraph-ai.git
synced 2026-06-23 21:00:30 +08:00
71 lines
1.6 KiB
Python
71 lines
1.6 KiB
Python
"""
|
|
Example of custom graph using existing nodes
|
|
"""
|
|
|
|
import os
|
|
from dotenv import load_dotenv
|
|
from scrapegraphai.models import OpenAI
|
|
from scrapegraphai.graphs import BaseGraph
|
|
from scrapegraphai.nodes import FetchNode, ParseNode, RAGNode, GenerateAnswerNode
|
|
|
|
load_dotenv()
|
|
openai_key = os.getenv("OPENAI_APIKEY")
|
|
|
|
# Define the configuration for the graph
|
|
graph_config = {
|
|
"llm": {
|
|
"api_key": openai_key,
|
|
"model": "gpt-3.5-turbo",
|
|
"temperature": 0,
|
|
"streaming": True
|
|
},
|
|
}
|
|
|
|
llm_model = OpenAI(graph_config["llm"])
|
|
|
|
# define the nodes for the graph
|
|
fetch_node = FetchNode(
|
|
input="url | local_dir",
|
|
output=["doc"],
|
|
)
|
|
parse_node = ParseNode(
|
|
input="doc",
|
|
output=["parsed_doc"],
|
|
)
|
|
rag_node = RAGNode(
|
|
input="user_prompt & (parsed_doc | doc)",
|
|
output=["relevant_chunks"],
|
|
model_config={"llm_model": llm_model},
|
|
)
|
|
generate_answer_node = GenerateAnswerNode(
|
|
input="user_prompt & (relevant_chunks | parsed_doc | doc)",
|
|
output=["answer"],
|
|
model_config={"llm_model": llm_model},
|
|
)
|
|
|
|
# create the graph by defining the nodes and their connections
|
|
graph = BaseGraph(
|
|
nodes={
|
|
fetch_node,
|
|
parse_node,
|
|
rag_node,
|
|
generate_answer_node,
|
|
},
|
|
edges={
|
|
(fetch_node, parse_node),
|
|
(parse_node, rag_node),
|
|
(rag_node, generate_answer_node)
|
|
},
|
|
entry_point=fetch_node
|
|
)
|
|
|
|
# execute the graph
|
|
result = graph.execute({
|
|
"user_prompt": "List me the projects with their description",
|
|
"url": "https://perinim.github.io/projects/"
|
|
})
|
|
|
|
# get the answer from the result
|
|
result = result.get("answer", "No answer found.")
|
|
print(result)
|