15571014953

Committed 10 Jun 2025 09:48PM UTC coverage: 75.613% (-10.8%) from 86.441%

Build # 15571014953

Build Type

push

github

Committed by

web-flow

Commit Message

Merge pull request #49 from joagonzalez/rc-v0.1.0

Rc v0.1.0

Run Details

110 of 230 new or added lines in 11 files covered. (47.83%)

617 of 816 relevant lines covered (75.61%)

0.76 hits per line

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76.19

/src/cyclops_graph.py

from langgraph.graph import StateGraph, END, START
from src.services.prometheus import PrometheusClient
from src.services.vectordb import MetricVectorStore
from src.services.chatgpt import ChatGPTService
from src.config.settings import config

from src.tools.promql_executor_tool import PromQLExecutorTool
from src.tools.vector_search_tool import VectorSearchTool
from src.tools.llm_decompose_tool import LLMDecomposeTool
from src.tools.llm_generate_promql_tool import LLMGeneratePromQLTool
from src.tools.llm_summarize_tool import LLMSummarizeTool


# Setup shared clients
prom_client = PrometheusClient(base_url=config["PROMETHEUS_URL"])
vector_store = MetricVectorStore(persist_directory="./chroma_db")
llm_client = ChatGPTService(api_key=config["OPENAI"]["API_KEY"], model=config["OPENAI"]["MODEL"])

# Instantiate tools
promql_tool = PromQLExecutorTool(prometheus_client=prom_client)
vector_tool = VectorSearchTool(vector_store=vector_store, default_top_k=5)
llm_decompose_tool = LLMDecomposeTool(llm_client)
llm_generate_promql_tool = LLMGeneratePromQLTool(llm_client)
llm_summarize_tool = LLMSummarizeTool(llm_client)

# Define LangGraph
graph = StateGraph(dict)  # we use simple dict state for now

# Add nodes
graph.add_node("Decompose", llm_decompose_tool)
graph.add_node("VectorSearch", vector_tool)
graph.add_node("GeneratePromQL", llm_generate_promql_tool)
graph.add_node("ExecutePromQL", promql_tool)
graph.add_node("Summarize", llm_summarize_tool)

# Add edges (pipeline)
graph.add_edge(START, "Decompose")
graph.add_edge("Decompose", "VectorSearch")
graph.add_edge("VectorSearch", "GeneratePromQL")
graph.add_edge("GeneratePromQL", "ExecutePromQL")
graph.add_edge("ExecutePromQL", "Summarize")
graph.add_edge("Summarize", END)

# Compile graph
cyclops = graph.compile()

# Run example (main)
if __name__ == "__main__":
    import asyncio

    async def run_agent() -> None:
        # Example user prompt
        user_prompt = "Show average CPU usage of Kubernetes nodes"

        # Initial state
        input_state = {"user_prompt": user_prompt}

        print("\nRunning Cyclops agent...")
        result = await cyclops.invoke(input_state) # type: ignore

        print("\nFinal state:")
        for key, value in result.items():
            print(f"{key}: {value}")

    asyncio.run(run_agent())

1	from langgraph.graph import StateGraph, END, START	1✔
2	from src.services.prometheus import PrometheusClient	1✔
3	from src.services.vectordb import MetricVectorStore	1✔
4	from src.services.chatgpt import ChatGPTService	1✔
5	from src.config.settings import config	1✔
6
7	from src.tools.promql_executor_tool import PromQLExecutorTool	1✔
8	from src.tools.vector_search_tool import VectorSearchTool	1✔
9	from src.tools.llm_decompose_tool import LLMDecomposeTool	1✔
10	from src.tools.llm_generate_promql_tool import LLMGeneratePromQLTool	1✔
11	from src.tools.llm_summarize_tool import LLMSummarizeTool	1✔
12
13
14	# Setup shared clients
15	prom_client = PrometheusClient(base_url=config["PROMETHEUS_URL"])	1✔
16	vector_store = MetricVectorStore(persist_directory="./chroma_db")	1✔
17	llm_client = ChatGPTService(api_key=config["OPENAI"]["API_KEY"], model=config["OPENAI"]["MODEL"])	1✔
18
19	# Instantiate tools
20	promql_tool = PromQLExecutorTool(prometheus_client=prom_client)	1✔
21	vector_tool = VectorSearchTool(vector_store=vector_store, default_top_k=5)	1✔
22	llm_decompose_tool = LLMDecomposeTool(llm_client)	1✔
23	llm_generate_promql_tool = LLMGeneratePromQLTool(llm_client)	1✔
24	llm_summarize_tool = LLMSummarizeTool(llm_client)	1✔
25
26	# Define LangGraph
27	graph = StateGraph(dict) # we use simple dict state for now	1✔
28
29	# Add nodes
30	graph.add_node("Decompose", llm_decompose_tool)	1✔
31	graph.add_node("VectorSearch", vector_tool)	1✔
32	graph.add_node("GeneratePromQL", llm_generate_promql_tool)	1✔
33	graph.add_node("ExecutePromQL", promql_tool)	1✔
34	graph.add_node("Summarize", llm_summarize_tool)	1✔
35
36	# Add edges (pipeline)
37	graph.add_edge(START, "Decompose")	1✔
38	graph.add_edge("Decompose", "VectorSearch")	1✔
39	graph.add_edge("VectorSearch", "GeneratePromQL")	1✔
40	graph.add_edge("GeneratePromQL", "ExecutePromQL")	1✔
41	graph.add_edge("ExecutePromQL", "Summarize")	1✔
42	graph.add_edge("Summarize", END)	1✔
43
44	# Compile graph
45	cyclops = graph.compile()	1✔
46
47	# Run example (main)
48	if __name__ == "__main__":	1✔
NEW 49	import asyncio	×
50
NEW 51	async def run_agent() -> None:	×
52	# Example user prompt
NEW 53	user_prompt = "Show average CPU usage of Kubernetes nodes"	×
54
55	# Initial state
NEW 56	input_state = {"user_prompt": user_prompt}	×
57
NEW 58	print("\nRunning Cyclops agent...")	×
NEW 59	result = await cyclops.invoke(input_state) # type: ignore	×
60
NEW 61	print("\nFinal state:")	×
NEW 62	for key, value in result.items():	×
NEW 63	print(f"{key}: {value}")	×
64
NEW 65	asyncio.run(run_agent())	×

joagonzalez / cyclops-devops-agent / 15571014953

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