diff --git a/memgraph-toolbox/pyproject.toml b/memgraph-toolbox/pyproject.toml index a519818..3732c78 100644 --- a/memgraph-toolbox/pyproject.toml +++ b/memgraph-toolbox/pyproject.toml @@ -22,12 +22,18 @@ classifiers = [ dependencies = [ "deepeval>=3.5.2", + "fastmcp>=2.12.4", + "httpx>=0.27.0", "litellm>=1.77.1", "mcp>=1.14.1", "neo4j>=5.28.1", "numpy>=2.2.6", + "orjson>=3.11.3", + "pydantic>=2.11.10", + "rich>=13.0.0", "sentence-transformers>=5.1.0", "torch>=2.8.0", + "typer>=0.9.0", ] [project.optional-dependencies] @@ -36,6 +42,9 @@ test = [ "pytest-asyncio>=0.26.0", ] +[project.scripts] +mcp-chat = "memgraph_toolbox.client.mcp_full:app" + [project.urls] "Homepage" = "https://github.com/memgraph/ai-toolkit" "Source" = "https://github.com/memgraph/ai-toolkit" diff --git a/memgraph-toolbox/src/memgraph_toolbox/client/dummy/__init__.py b/memgraph-toolbox/src/memgraph_toolbox/client/dummy/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/memgraph-toolbox/src/memgraph_toolbox/client/dummy/fastmcp_client.py b/memgraph-toolbox/src/memgraph_toolbox/client/dummy/fastmcp_client.py new file mode 100644 index 0000000..e182663 --- /dev/null +++ b/memgraph-toolbox/src/memgraph_toolbox/client/dummy/fastmcp_client.py @@ -0,0 +1,21 @@ +import asyncio +from fastmcp.client import Client +from fastmcp.client.elicitation import ElicitResult + + +async def handle_elicitation(message, response_type, *_): + print(f"Server asks: {message}") + comment = input("INPUT:") + return ElicitResult(action="accept", content=response_type(**{"comment": comment})) + + +async def main(): + async with Client( + "http://localhost:8000/mcp", elicitation_handler=handle_elicitation + ) as c: + result = await c.call_tool("ask_feedback", {}) + print("Tool result:", result.data) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/memgraph-toolbox/src/memgraph_toolbox/client/dummy/fastmcp_server.py b/memgraph-toolbox/src/memgraph_toolbox/client/dummy/fastmcp_server.py new file mode 100644 index 0000000..4b47ba4 --- /dev/null +++ b/memgraph-toolbox/src/memgraph_toolbox/client/dummy/fastmcp_server.py @@ -0,0 +1,22 @@ +from fastmcp import FastMCP, Context +from pydantic import BaseModel + +mcp = FastMCP("demo-server") + + +class Feedback(BaseModel): + comment: str + + +@mcp.tool +async def ask_feedback(ctx: Context) -> str: + result = await ctx.elicit("Please write a comment:", response_type=Feedback) + print(f"Result: {result}") + if result.action == "accept": + return f"You said: {result.data.comment}" + else: + return "You declined to provide feedback." + + +if __name__ == "__main__": + mcp.run(transport="streamable-http") diff --git a/memgraph-toolbox/src/memgraph_toolbox/client/mcp_full.py b/memgraph-toolbox/src/memgraph_toolbox/client/mcp_full.py new file mode 100644 index 0000000..deca0d7 --- /dev/null +++ b/memgraph-toolbox/src/memgraph_toolbox/client/mcp_full.py @@ -0,0 +1,186 @@ +import asyncio +from fastmcp import Client +from fastmcp.exceptions import ToolError +from mcp.types import ElicitResult +from rich.console import Console + +# https://github.com/prompt-toolkit/python-prompt-toolkit is also very interesting. +import typer +from litellm import acompletion +from tools import atool_calls + +LLM_MODEL = "openai/gpt-4o" +app = typer.Typer(help="MCP Terminal Client (Streaming HTTP)") +console = Console() + + +async def describe_mcp_server(client: Client): + tools = await client.list_tools() + prompts = await client.list_prompts() + tool_names = [tool.name for tool in tools] + console.print(f"[bold green]Available Tools:[/bold green] {tool_names}") + prompt_names = [prompt.name for prompt in prompts] + console.print(f"[bold green]Available Prompts:[/bold green] {prompt_names}") + console.print(f"Getting all the prompts...") + for prompt_name in prompt_names: + result = await client.get_prompt(prompt_name) + console.print(f" Prompt: [yellow]{prompt_name}[/yellow]") + for message in result.messages: + console.print(f" Role: [blue]{message.role}[/blue]") + console.print(f" Content Text:") + console.print(f" [blue]{message.content.text}[/blue]") + return tools, prompts + + +async def my_elicitation_handler(message: str, response_type: type, params, context): + """ + User adds additonal feedback to the failed query. + Actions are: accept, decline, cancel. + """ + console.print(f"[bold yellow]Server asks:[/bold yellow] {message}") + console.print(f"[dim]Response type: {response_type}[/dim]") + # Use an editor to get user input + content = typer.edit(message) + # Decline + if not content or content.strip() == "": + console.print("[red]Declining elicitation request[/red]") + return ElicitResult(action="decline") + # Accept + console.print(f"[green]Sending response:[/green] {content}") + # NOTE: None of the below pass the pydantic validation for some reason. + # return response_type(**{"data": content}) + # return ElicitResult(action="accept", content=response_type(**{"data": content})) + # return ElicitResult(action="accept", content={"data": content}) + return {"data": content} + + +async def my_sampling_handler(message, params, context) -> str: + """ + Iterate in a loop and try to generate valid responses. + """ + return "Generated sample response" + + +def convert_mcp_tools_to_litellm(mcp_tools): + """Convert MCP tools to litellm format""" + litellm_tools = [] + for tool in mcp_tools: + litellm_tool = { + "type": "function", + "function": { + "name": tool.name, + "description": tool.description or f"Tool: {tool.name}", + "parameters": tool.inputSchema or {}, + }, + } + litellm_tools.append(litellm_tool) + return litellm_tools + + +@app.command() +def chat( + base_url: str = typer.Option( + "http://localhost:8000/mcp", help="MCP server base URL" + ), + interactivity_type: str = typer.Option( + "llm-use", # TODO: There should be one-shot, iterate, etc... + help="Interactivity type (llm-use, no-llm-use)", + case_sensitive=False, + metavar="INTERACTIVITY_TYPE", + ), +): + config = { + "mcpServers": { + "memgraph": {"url": base_url}, + } + } + client = Client( + config, + elicitation_handler=my_elicitation_handler, + sampling_handler=my_sampling_handler, + ) + print(f"Interactivity type: {interactivity_type}") + + async def _run(): + async with client: + tools, prompts = await describe_mcp_server(client) + litellm_tools = convert_mcp_tools_to_litellm(tools) + # TODO(gitbuda): Fill this in from the templates. + chat_messages = [ + { + "role": "user", + "content": """ + You are an expert in Cypher and Memgraph graph databases. + You have access to the following tools: + - `run_query`: Run a Cypher query on the Memgraph database. + Input is a Cypher query string. Output is a list of records or an error message. + - `get_schema`: Get the schema information of the Memgraph database. Input is empty. + You should first call `get_schema` to understand the database structure, + then use `run_query` to execute Cypher queries. + IMPORTANT: Do not call run_query if you don't have the schema information. + """, + } + ] + + while True: + user_input = input("\n> ").strip() + if user_input.lower() in ("exit", "quit"): + break + # TODO(gitbuda): Expose prompts user can pick from. + + if interactivity_type == "llm-use": + console.print(f"\n[bold yellow]Prompt:[/bold yellow] {user_input}") + # Convert MCP tools to litellm format + chat_messages.append({"role": "user", "content": user_input}) + pick_tool_resp = await acompletion( + LLM_MODEL, chat_messages, tools=litellm_tools + ) + # print(pick_tool_resp) + # TODO(gitbuda): Here should be a prompt to allow tool call. + tool_call_msg = await atool_calls( + pick_tool_resp, chat_messages, client + ) + print(tool_call_msg) + chat_messages.append( + { + "role": "assistant", + "content": """Based on the given data, generate the run_query call.""", + } + ) + the_query_resp = await acompletion( + LLM_MODEL, tool_call_msg, tools=litellm_tools + ) + print(the_query_resp) + tool_call_msg = await atool_calls( + the_query_resp, chat_messages, client + ) + mg_data = tool_call_msg[-1]["content"] + console.print( + f"\n[bold yellow]Query Result:[/bold yellow] {mg_data}" + ) + + if interactivity_type == "no-llm-use": + query_result = await client.call_tool( + "run_query", {"query": user_input} + ) + console.print( + f"\n[bold yellow]Query Result:[/bold yellow] {query_result.data}" + ) + # Print any additional content if available + for content in query_result.content: + if hasattr(content, "text"): + console.print( + f"[bold blue]Content:[/bold blue] {content.text}" + ) + elif hasattr(content, "data"): + console.print( + f"[bold blue]Data:[/bold blue] {content.data}" + ) + + # TODO(gitbuda): If something fails or empty, don't take user input -> rerun the whole thing again (make sure the train history is there). + + asyncio.run(_run()) + + +if __name__ == "__main__": + app() diff --git a/memgraph-toolbox/src/memgraph_toolbox/client/mcp_prompt.py b/memgraph-toolbox/src/memgraph_toolbox/client/mcp_prompt.py index b2ad98b..d54d81b 100644 --- a/memgraph-toolbox/src/memgraph_toolbox/client/mcp_prompt.py +++ b/memgraph-toolbox/src/memgraph_toolbox/client/mcp_prompt.py @@ -68,6 +68,7 @@ async def prompt_with_tools( messages=messages, tools=tools, ) + # TODO(gitbuda): Replace the code below with tools.atool_calls msg = resp["choices"][0]["message"] # Tool calls by the MCP server. tool_calls = msg.get("tool_calls", []) diff --git a/memgraph-toolbox/src/memgraph_toolbox/client/tools.py b/memgraph-toolbox/src/memgraph_toolbox/client/tools.py new file mode 100644 index 0000000..ae3ec4d --- /dev/null +++ b/memgraph-toolbox/src/memgraph_toolbox/client/tools.py @@ -0,0 +1,80 @@ +import json, logging + +logger = logging.getLogger(__name__) + + +async def atool_calls(resp, messages, session): + """ + Execute tool calls by the MCP server. + """ + msg = resp["choices"][0]["message"] + # Tool calls by the MCP server. + tool_calls = msg.get("tool_calls", []) + if not tool_calls: + logger.info("LLM Response: %s", msg.get("content", "")) + else: + logger.info("LLM wants to use %d tools:", len(tool_calls)) + for tc in tool_calls: + logger.info( + "- %s: %s", + tc["function"]["name"], + tc["function"].get("description", "No description"), + ) + # Ensure all tool call IDs are within OpenAI's 40-character limit + for tc in tool_calls: + if len(tc["id"]) > 40: + logger.warning( + "Tool call ID is too long, truncating to 40 characters: %s", + tc["id"], + ) + tc["id"] = tc["id"][:40] + messages.append( + { + "role": "assistant", + "content": msg.get("content", ""), + "tool_calls": tool_calls, + } + ) + + for tc in tool_calls: + try: + arguments = tc["function"].get("arguments", {}) + logger.info("Arguments: %s", arguments) + if isinstance(arguments, str): + arguments = json.loads(arguments) + result = await session.call_tool( + name=tc["function"]["name"], arguments=arguments + ) + if hasattr(result, "content") and result.content: + if isinstance(result.content, list): + content_text = [] + for content_item in result.content: + if hasattr(content_item, "text"): + content_text.append(content_item.text) + elif isinstance(content_item, str): + content_text.append(content_item) + else: + content_text.append(str(content_item)) + content_str = "\n".join(content_text) + elif hasattr(result.content, "text"): + content_str = result.content.text + else: + content_str = str(result.content) + else: + content_str = "No content returned" + logger.debug( + "Tool %s result: %s", + tc["function"]["name"], + content_str, + ) + messages.append( + { + "role": "tool", + "tool_call_id": tc["id"], + "name": tc["function"]["name"], + "content": content_str, + } + ) + except Exception as e: + logger.error(str(e)) + return messages diff --git a/memgraph-toolbox/uv.lock b/memgraph-toolbox/uv.lock index 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