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+from lynxkite_core import ops +from lynxkite_graph_analytics import core +import fastapi +from lynxscribe.components.chat.api import ChatAPI +from lynxscribe.components.rag.rag_chatbot import Mode, RAGChatbot, Scenario, ScenarioSelector +from lynxscribe.core.llm.base import get_llm_engine +from lynxscribe.core.models.prompts import ChatCompletionPrompt + +lsop = ops.op_registration( + "LynxKite Graph Analytics", "LynxScribe", dir="bottom-to-top", color="blue" +) + +dsop = ops.op_registration("LynxKite Graph Analytics", "Data Science") + +Placeholder = str + + +@lsop("Chat frontend", color="gray", outputs=[], view="service") +def chat_frontend(agent: Placeholder): + return ChatAPIService(agent) + + +@lsop("Agent") +def agent( + tools: list[Placeholder], + *, + purpose: str = "", + description: ops.LongStr = "This agent helps with various tasks.", + system_prompt: ops.LongStr = "You are a helpful assistant.", +): + ts = [] + for t in tools: + t = "\n ".join(t.split("\n")) + ts.append(f"- {t}\n") + return f"Agent for {purpose} with {len(tools)} tools:\n{''.join(ts)}" + + +@lsop("MCP: Query database with SQL", color="green") +def sql_tool(db: Placeholder): + return f"SQL over {db}" + + +@ops.output_position(output="top") +@dsop("Database") +def db(data_pipeline: list[core.Bundle]): + return f"DB with {len(data_pipeline)} bundles" + + +class MCPSearchEngine(str, enum.Enum): + Google = "Google" + Bing = "Bing" + DuckDuckGo = "DuckDuckGo" + + +@lsop("MCP: Search web", color="green") +def web_search(*, engine: MCPSearchEngine = MCPSearchEngine.Google): + return f"Web search ({engine.name})" + + +@lsop("MCP: Run ComfyUI workflow", color="green") +def run_comfyui_workflow(*, workflow_name: str): + return f"Run comfyui workflow ({workflow_name})" + + +@lsop("MCP: Calculator", color="green") +def calculator(): + return "Calculator" + + +class ChatAPIService: + def __init__(self, agent: str): + self.agent = agent + self.chat_api = ChatAPI( + chatbot=RAGChatbot( + None, + ScenarioSelector([Scenario(name="single", mode=Mode.LLM_ONLY)]), + llm=get_llm_engine(name="openai"), + ), + model="gpt-4o-mini", + ) + + async def get(self, request: fastapi.Request) -> dict: + if request.state.remaining_path == "models": + return { + "object": "list", + "data": [ + { + "id": "LynxScribe", + "object": "model", + "created": 0, + "owned_by": "lynxkite", + "meta": {"profile_image_url": "https://lynxkite.com/favicon.png"}, + } + ], + } + return {"error": "Not found"} + + async def post(self, request: fastapi.Request) -> dict: + if request.state.remaining_path == "chat/completions": + request = await request.json() + if request["stream"]: + from sse_starlette.sse import EventSourceResponse + + return EventSourceResponse(self.stream_chat_api_response(request)) + else: + return await self.get_chat_api_response(request) + return {"error": "Not found"} + + async def stream_chat_api_response(self, request): + request = ChatCompletionPrompt(**request) + async for chunk in await self.chat_api.answer(request, stream=True): + chunk.choices[0].delta.content += f"({self.agent})" + yield chunk.model_dump_json() + + async def get_chat_api_response(self, request): + request = ChatCompletionPrompt(**request) + response = await self.chat_api.answer(request, stream=False) + return response.model_dump() diff --git a/examples/LynxScribe/ChatAPI/demo.sh b/examples/LynxScribe/ChatAPI/demo.sh new file mode 100644 index 0000000000000000000000000000000000000000..51de9ad75874df12580e3332f99045411b107ff5 --- /dev/null +++ b/examples/LynxScribe/ChatAPI/demo.sh @@ -0,0 +1,13 @@ +#!/bin/bash -xue +# xh POST 'https://api.openai.com/v1/chat/completions' \ +# Authorization:"Bearer $OPENAI_API_KEY" \ +# model=gpt-4o-mini \ +# stream:=false \ +# messages:='[{"role": "user", "content": "what does the fox say"}]' + +xh 'http://localhost:8000/api/service/lynxkite_graph_analytics/LynxScribe%20MCP/Demo.lynxkite.json/Chat%20frontend%201/models' + +xh POST 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Always set a limit of 10 when using `read_records`. Unfortunately, `read_records` +is out of order currently. Please use `query` instead." + """.strip(), + } + + +@ops.output_position(output="top") +@dsop("Database") +def db(data_pipeline: list[core.Bundle]): + # The source of this file: https://github.com/biggraph/lynxscribe/pull/416 + return "movie_data.sqlite.db" diff --git a/examples/LynxScribe/MCP/demo.sh b/examples/LynxScribe/MCP/demo.sh new file mode 100644 index 0000000000000000000000000000000000000000..201d15e03c5f7c9193dbc7f734aa341f5f46e342 --- /dev/null +++ b/examples/LynxScribe/MCP/demo.sh @@ -0,0 +1,5 @@ +#!/bin/bash -xue +xh POST 'http://localhost:8000/api/service/lynxkite_graph_analytics/LynxScribe%20Task%20Solver/Demo.lynxkite.json/Chat%20frontend%201/chat/completions' \ + model=LynxScribe \ + stream:=false \ + messages:='[{"role": "user", "content": "Investigate the database schema, then use a series of SQL queries to find movies featuring Bette Davis and Ann Sheridan together."}]' diff --git a/examples/LynxScribe/MCP/ui/main.py b/examples/LynxScribe/MCP/ui/main.py new file mode 100644 index 0000000000000000000000000000000000000000..fa29bc10736c0aa4556156f8bed19e027b78b8b6 --- /dev/null +++ b/examples/LynxScribe/MCP/ui/main.py @@ -0,0 +1,29 @@ +import streamlit as st +import requests + +node_url = st.query_params["service"] +api_url = f"http://localhost:8000{node_url}" + +st.title("LynxScribe Task Solver") + +examples = [ + "Find the value of x given that 4*x^4 = 44.", + "Which movies feature Bette Davis and Ann Sheridan together? Include the year of the movie's release.", + "What is the current stock price of Alphabet?", + "What is the scientific name of Atlantic cod?", +] +selected_example = st.radio("Choose an example:", examples, index=None) +question = st.text_area( + "Your question:", value="" if selected_example is None else selected_example +) +if question: + payload = { + "model": "LynxScribe", + "stream": False, + "messages": [{"role": "user", "content": question}], + } + response = requests.post(api_url, json=payload) + if response.ok: + st.write("\n".join(c["message"]["content"] for c in response.json()["choices"])) + else: + st.error(f"Error: {response.status_code}") diff --git a/examples/LynxScribe/MCP/ui/multi_agent.py b/examples/LynxScribe/MCP/ui/multi_agent.py new file mode 100644 index 0000000000000000000000000000000000000000..e303c25a7900b00ac57627f9fa6396b6973e8315 --- /dev/null +++ b/examples/LynxScribe/MCP/ui/multi_agent.py @@ -0,0 +1,28 @@ +import streamlit as st +import requests + +API_URL = "http://localhost:8000/api/service/lynxkite_graph_analytics/LynxScribe/MCP/Multi-agent.lynxkite.json/Chat%20frontend%201/chat/completions" + +st.title("LynxScribe Multi-Agent Example") + +examples = [ + "Find the value of x given that 4*x^4 = 44.", + "Which movies feature Bette Davis and Ann Sheridan together? Include the year of the movie's release.", + "What is the current stock price of Alphabet?", + "What is the scientific name of Atlantic cod?", +] +selected_example = st.radio("Choose an example:", examples, index=None) +question = st.text_area( + "Your question:", value="" if selected_example is None else selected_example +) +if question: + payload = { + "model": "LynxScribe", + "stream": False, + "messages": [{"role": "user", "content": question}], + } + response = requests.post(API_URL, json=payload) + if response.ok: + st.write("\n".join(c["message"]["content"] for c in response.json()["choices"])) + else: + st.error(f"Error: {response.status_code}") diff --git a/lynxkite-lynxscribe/src/lynxkite_lynxscribe/__init__.py b/lynxkite-lynxscribe/src/lynxkite_lynxscribe/__init__.py index 33f6dea73bc7828c66a2245c6ff2b0a465e31417..1a7947c4f8e831133c2f87c7ad3db73df8dee723 100644 --- a/lynxkite-lynxscribe/src/lynxkite_lynxscribe/__init__.py +++ b/lynxkite-lynxscribe/src/lynxkite_lynxscribe/__init__.py @@ -1,3 +1,4 @@ +from . import agentic # noqa (imported to trigger registration) from . import lynxscribe_ops # noqa (imported to trigger registration) from . import llm_ops # noqa (imported to trigger registration) from .lynxscribe_ops import api_service_post, api_service_get diff --git a/lynxkite-lynxscribe/src/lynxkite_lynxscribe/agentic.py b/lynxkite-lynxscribe/src/lynxkite_lynxscribe/agentic.py new file mode 100644 index 0000000000000000000000000000000000000000..4f6e48ac95794d1461fd945c3976da15ec9929dd --- /dev/null +++ b/lynxkite-lynxscribe/src/lynxkite_lynxscribe/agentic.py @@ -0,0 +1,190 @@ +"""Operations that allow LynxScribe agents and MCP servers to be combined with LynxKite pipelines.""" + +import enum +import os +import typing + +from lynxkite_core import ops +from lynxscribe.components.task_solver import TaskSolver +from lynxscribe.components.mcp import MCPClient +from lynxscribe.core.llm.base import get_llm_engine +from lynxscribe.core.models.prompts import Function, Tool + +if typing.TYPE_CHECKING: + import fastapi + +CHAT_FRONTEND_URL = os.environ.get("CHAT_FRONTEND_URL", "http://localhost:8501/") + +op = ops.op_registration( + "LynxKite Graph Analytics", "LynxScribe", dir="bottom-to-top", color="blue" +) + + +@op("Chat frontend", color="gray", outputs=[], view="service") +def chat_frontend(agent: dict): + agent = agent["agent"] + return Agent( + agent["name"], + agent["description"], + agent["system_prompt"], + agent["mcp_servers"], + [chat_frontend(a) for a in agent["sub_agents"]], + ) + + +@op("Agent") +def agent( + tools: list[dict], + *, + name: str = "", + description: ops.LongStr = "This agent helps with various tasks.", + system_prompt: ops.LongStr = "You are a helpful assistant.", +): + prompt = [system_prompt] + for tool in tools: + if tool.get("extra_prompt"): + prompt.append(tool["extra_prompt"]) + return { + "agent": { + "name": name, + "description": description, + "system_prompt": "\n".join(prompt), + "mcp_servers": [t["command"] for t in tools if "command" in t], + "sub_agents": [t for t in tools if "agent" in t], + } + } + + +@op("MCP: Custom", color="green") +def custom_mcp(*, command: str, extra_prompt: ops.LongStr): + if command.strip(): + return {"command": command.strip().split(), "extra_prompt": extra_prompt} + + +# A few basic MCP integrations to serve as examples. + + +class MCPSearchEngine(str, enum.Enum): + Google = "Google" + Bing = "Bing" + DuckDuckGo = "DuckDuckGo" + + +@op("MCP: Search web", color="green") +def web_search(*, engine: MCPSearchEngine = MCPSearchEngine.Google): + match engine: + case MCPSearchEngine.Google: + return {"command": ["npx", "-y", "https://github.com/pskill9/web-search"]} + case MCPSearchEngine.Bing: + return {"command": ["uvx", "bing-search-mcp"]} + case MCPSearchEngine.DuckDuckGo: + return {"command": ["uvx", "duckduckgo-mcp-server"]} + + +@op("MCP: Financial data", color="green") +def financial_data(): + return {"command": ["uvx", "investor-agent"]} + + +@op("MCP: Calculator", color="green") +def calculator(): + return {"command": ["uvx", "mcp-server-calculator"]} + + +@op("MCP: Time", color="green") +def time(): + return {"command": ["uvx", "mcp-server-time"]} + + +class Agent: + def __init__( + self, + name: str, + description: str, + prompt: str, + mcp_servers: list[list[str]], + agents: list["Agent"], + ): + self.name = name + self.description = description + self.prompt = prompt + self.mcp_servers = mcp_servers + self.agents = agents + self.mcp_client = None + self.task_solver = None + + async def init(self): + if self.task_solver is not None: + return + self.mcp_client = MCPClient() + await self.mcp_client.connect(self.mcp_servers) + agents_as_functions = [agent.as_function() for agent in self.agents] + self.task_solver = TaskSolver( + llm=get_llm_engine(name="openai"), + model="gpt-4.1-nano", + initial_messages=[self.prompt], + functions=[*self.mcp_client.tools, *agents_as_functions], + tool_choice="required", + temperature=0.0, + max_tool_call_steps=999, + ) + + def get_description(self, url: str) -> str: + return f"[Go to frontend]({CHAT_FRONTEND_URL}?service={url}/chat/completions)" + + async def get(self, request: "fastapi.Request") -> dict: + if request.state.remaining_path == "models": + return { + "object": "list", + "data": [ + { + "id": "LynxScribe", + "object": "model", + "created": 0, + "owned_by": "lynxkite", + "meta": {"profile_image_url": "https://lynxkite.com/favicon.png"}, + } + ], + } + return {"error": "Not found"} + + async def post(self, request: "fastapi.Request") -> dict: + if request.state.remaining_path == "chat/completions": + request = await request.json() + assert not request["stream"] + await self.init() + res = await self.task_solver.solve(request["messages"][-1]["content"]) + return {"choices": [{"message": {"role": "assistant", "content": res}}]} + + return {"error": "Not found"} + + def as_function(self): + """A callable that can be used as a tool by another Agent.""" + + # Find the value of x given that 4*x^4 = 44. Compute the numerical value. + async def ask(message): + print(f"Calling agent {self.name} with message: {message}") + await self.init() + res = await self.task_solver.solve(message) + print(f"Agent {self.name} response: {res}") + return res + + ask.__name__ = "ask_" + self.name.lower().replace(" ", "_") + ask._tool = Tool( + type="function", + function=Function( + name=ask.__name__, + description=self.description, + parameters={ + "type": "object", + "required": ["message"], + "properties": { + "message": { + "type": "string", + "description": "The question to ask the agent.", + } + }, + }, + ), + ) + return ask diff --git a/uv.lock b/uv.lock index 517e5a989734ada64d698f97cbbcf27308b5e3ab..6796ff6c193241a5eb5e66366809615f93dbef7f 100644 --- a/uv.lock +++ b/uv.lock @@ -19,7 +19,7 @@ members = [ [[package]] name = "aiobotocore" -version = "2.23.1" +version = "2.24.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohttp" }, @@ -30,9 +30,9 @@ dependencies = 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