From 801552f99ece512bfb497977c6879edd6ca7ff9a Mon Sep 17 00:00:00 2001 From: furyhawk Date: Thu, 19 Jun 2025 10:36:34 +0800 Subject: [PATCH] Implement chat application with SQLite backend and TypeScript frontend - Added SQLite database to store chat messages. - Created HTML interface for the chat application using Bootstrap. - Developed backend logic in FastAPI to handle chat requests and responses. - Implemented TypeScript for dynamic message rendering and interaction. - Integrated logging with Logfire for monitoring. - Configured Ollama model for OpenAI-compatible API usage. - Established a question graph for evaluating user responses. --- .../chat/.chat_app_messages.sqlite | Bin 0 -> 12288 bytes notebooks/pydantic_ai/chat/chat_app.html | 81 ++ notebooks/pydantic_ai/chat/chat_app.py | 230 ++++ notebooks/pydantic_ai/chat/chat_app.ts | 90 ++ notebooks/pydantic_ai/pyproject.toml | 2 +- notebooks/pydantic_ai/question_graph.py | 182 +++ notebooks/uv.lock | 1107 +++++++++-------- 7 files changed, 1168 insertions(+), 524 deletions(-) create mode 100644 notebooks/pydantic_ai/chat/.chat_app_messages.sqlite create mode 100644 notebooks/pydantic_ai/chat/chat_app.html create mode 100644 notebooks/pydantic_ai/chat/chat_app.py create mode 100644 notebooks/pydantic_ai/chat/chat_app.ts create mode 100644 notebooks/pydantic_ai/question_graph.py diff --git a/notebooks/pydantic_ai/chat/.chat_app_messages.sqlite b/notebooks/pydantic_ai/chat/.chat_app_messages.sqlite new file mode 100644 index 0000000000000000000000000000000000000000..9eb5e549ecb2f6671668a9ea38d40feb87e48844 GIT binary patch literal 12288 zcmeI1%}x|S5P)YEB5EY-%@av67c?0b7UXY3R3w`~0Do`~f=f2dPO&qYneJh_$7LnN zc<^F;0B=5yujA2!F;*`OiN=Jpq&m~-?&|8Q?yqVOm3{ScJ2D7IDo=%hdFQULqbry5tkU3Gx>nE(@D0!)AjFaajO1egF5 zU;<2l3H;{-_T9026BF)-F(VEWOi^pmLtT!Q_1)%Ls|l^Or`t^^rEotAVDniEUhHn} ztnKc>R&&oUcRNX>4YZoCTMuR`H}0)ZxK1PkJkiGq)$|=v7&TmXN^6~ZIkR>BP0Mqf zx_jrBLmxLLzyz286JP>NfC(@GCcp%kz%?fD@x43he6GAX^)itg?KNoDRnnj|Uc(D9 zNtEY%CZe**h%_VF-0a-K^z7nveYsViZ`79>bBnd*rIp3mxqZ^KRyr{iU8GrP%%`(l zrJ3>04ty_?+T=yoL`sq)StNVaAR!tbm_)%LvkHx?T6CzmapaoTj z?elxKy&7zS4por^kfY9&3?Ngs5=;g)*b#$61bRhopdXnK1oU!56UiP3(iA#|Q1z|t z!BmbAQ0dsK!4_f$k%6N;LK!53p{~kMPSX+t&}w)Bjt~>*s+9gm9#*$tXhS4%4Ytux zy$Y#_$$deV?d^?{2$6=q$kTK88Icm*&{oE^wp^VlU)+`(HVTulL6;MBe4q+C9*DtX z`2O|FC)k`yAw+9e(U@cxuag67p*K{Qh=FgfI=tl(J=-el(3NfC(@GCcp%k025#WOn?b60VZ$_35<+5w|)caDnugy literal 0 HcmV?d00001 diff --git a/notebooks/pydantic_ai/chat/chat_app.html b/notebooks/pydantic_ai/chat/chat_app.html new file mode 100644 index 0000000..b08f956 --- /dev/null +++ b/notebooks/pydantic_ai/chat/chat_app.html @@ -0,0 +1,81 @@ + + + + + + Chat App + + + + +
+

Chat App

+

Ask me anything...

+
+
+
+
+
+ +
+ +
+
+
+ Error occurred, check the browser developer console for more information. +
+
+ + + + \ No newline at end of file diff --git a/notebooks/pydantic_ai/chat/chat_app.py b/notebooks/pydantic_ai/chat/chat_app.py new file mode 100644 index 0000000..a9569ee --- /dev/null +++ b/notebooks/pydantic_ai/chat/chat_app.py @@ -0,0 +1,230 @@ +from __future__ import annotations as _annotations + +import asyncio +import json +import sqlite3 +from collections.abc import AsyncIterator +from concurrent.futures.thread import ThreadPoolExecutor +from contextlib import asynccontextmanager +from dataclasses import dataclass +from datetime import datetime, timezone +from functools import partial +from pathlib import Path +from typing import Annotated, Any, Callable, Literal, TypeVar + +import fastapi +import logfire +from fastapi import Depends, Request +from fastapi.responses import FileResponse, Response, StreamingResponse +from typing_extensions import LiteralString, ParamSpec, TypedDict + +from pydantic_ai.models.openai import OpenAIModel +from pydantic_ai.providers.openai import OpenAIProvider +from pydantic_ai import Agent +from pydantic_ai.exceptions import UnexpectedModelBehavior +from pydantic_ai.messages import ( + ModelMessage, + ModelMessagesTypeAdapter, + ModelRequest, + ModelResponse, + TextPart, + UserPromptPart, +) + +# 'if-token-present' means nothing will be sent (and the example will work) if you don't have logfire configured +logfire.configure(send_to_logfire="if-token-present") +logfire.instrument_pydantic_ai() + +# Configuration for Ollama using OpenAI-compatible endpoint +OLLAMA_BASE_URL = "http://localhost:11434" +MODEL_NAME = "qwen3:8b" # Updated to use available model + +ollama_model = OpenAIModel( + model_name=MODEL_NAME, provider=OpenAIProvider(base_url=f"{OLLAMA_BASE_URL}/v1") +) + +agent = Agent(ollama_model) +THIS_DIR = Path(__file__).parent + + +@asynccontextmanager +async def lifespan(_app: fastapi.FastAPI): + async with Database.connect() as db: + yield {"db": db} + + +app = fastapi.FastAPI(lifespan=lifespan) +logfire.instrument_fastapi(app) + + +@app.get("/") +async def index() -> FileResponse: + return FileResponse((THIS_DIR / "chat_app.html"), media_type="text/html") + + +@app.get("/chat_app.ts") +async def main_ts() -> FileResponse: + """Get the raw typescript code, it's compiled in the browser, forgive me.""" + return FileResponse((THIS_DIR / "chat_app.ts"), media_type="text/plain") + + +async def get_db(request: Request) -> Database: + return request.state.db + + +@app.get("/chat/") +async def get_chat(database: Database = Depends(get_db)) -> Response: + msgs = await database.get_messages() + return Response( + b"\n".join(json.dumps(to_chat_message(m)).encode("utf-8") for m in msgs), + media_type="text/plain", + ) + + +class ChatMessage(TypedDict): + """Format of messages sent to the browser.""" + + role: Literal["user", "model"] + timestamp: str + content: str + + +def to_chat_message(m: ModelMessage) -> ChatMessage: + first_part = m.parts[0] + if isinstance(m, ModelRequest): + if isinstance(first_part, UserPromptPart): + assert isinstance(first_part.content, str) + return { + "role": "user", + "timestamp": first_part.timestamp.isoformat(), + "content": first_part.content, + } + elif isinstance(m, ModelResponse): + if isinstance(first_part, TextPart): + return { + "role": "model", + "timestamp": m.timestamp.isoformat(), + "content": first_part.content, + } + raise UnexpectedModelBehavior(f"Unexpected message type for chat app: {m}") + + +@app.post("/chat/") +async def post_chat( + prompt: Annotated[str, fastapi.Form()], database: Database = Depends(get_db) +) -> StreamingResponse: + async def stream_messages(): + """Streams new line delimited JSON `Message`s to the client.""" + # stream the user prompt so that can be displayed straight away + yield ( + json.dumps( + { + "role": "user", + "timestamp": datetime.now(tz=timezone.utc).isoformat(), + "content": prompt, + } + ).encode("utf-8") + + b"\n" + ) + # get the chat history so far to pass as context to the agent + messages = await database.get_messages() + # run the agent with the user prompt and the chat history + async with agent.run_stream(prompt, message_history=messages) as result: + async for text in result.stream(debounce_by=0.01): + # text here is a `str` and the frontend wants + # JSON encoded ModelResponse, so we create one + m = ModelResponse(parts=[TextPart(text)], timestamp=result.timestamp()) + yield json.dumps(to_chat_message(m)).encode("utf-8") + b"\n" + + # add new messages (e.g. the user prompt and the agent response in this case) to the database + await database.add_messages(result.new_messages_json()) + + return StreamingResponse(stream_messages(), media_type="text/plain") + + +P = ParamSpec("P") +R = TypeVar("R") + + +@dataclass +class Database: + """Rudimentary database to store chat messages in SQLite. + + The SQLite standard library package is synchronous, so we + use a thread pool executor to run queries asynchronously. + """ + + con: sqlite3.Connection + _loop: asyncio.AbstractEventLoop + _executor: ThreadPoolExecutor + + @classmethod + @asynccontextmanager + async def connect( + cls, file: Path = THIS_DIR / ".chat_app_messages.sqlite" + ) -> AsyncIterator[Database]: + with logfire.span("connect to DB"): + loop = asyncio.get_event_loop() + executor = ThreadPoolExecutor(max_workers=1) + con = await loop.run_in_executor(executor, cls._connect, file) + slf = cls(con, loop, executor) + try: + yield slf + finally: + await slf._asyncify(con.close) + + @staticmethod + def _connect(file: Path) -> sqlite3.Connection: + con = sqlite3.connect(str(file)) + con = logfire.instrument_sqlite3(con) + cur = con.cursor() + cur.execute( + "CREATE TABLE IF NOT EXISTS messages (id INT PRIMARY KEY, message_list TEXT);" + ) + con.commit() + return con + + async def add_messages(self, messages: bytes): + await self._asyncify( + self._execute, + "INSERT INTO messages (message_list) VALUES (?);", + messages, + commit=True, + ) + await self._asyncify(self.con.commit) + + async def get_messages(self) -> list[ModelMessage]: + c = await self._asyncify( + self._execute, "SELECT message_list FROM messages order by id" + ) + rows = await self._asyncify(c.fetchall) + messages: list[ModelMessage] = [] + for row in rows: + messages.extend(ModelMessagesTypeAdapter.validate_json(row[0])) + return messages + + def _execute( + self, sql: LiteralString, *args: Any, commit: bool = False + ) -> sqlite3.Cursor: + cur = self.con.cursor() + cur.execute(sql, args) + if commit: + self.con.commit() + return cur + + async def _asyncify( + self, func: Callable[P, R], *args: P.args, **kwargs: P.kwargs + ) -> R: + return await self._loop.run_in_executor( # type: ignore + self._executor, + partial(func, **kwargs), + *args, # type: ignore + ) + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run( + "chat_app:app", reload=True, reload_dirs=[str(THIS_DIR)] + ) diff --git a/notebooks/pydantic_ai/chat/chat_app.ts b/notebooks/pydantic_ai/chat/chat_app.ts new file mode 100644 index 0000000..c8878b8 --- /dev/null +++ b/notebooks/pydantic_ai/chat/chat_app.ts @@ -0,0 +1,90 @@ +// BIG FAT WARNING: to avoid the complexity of npm, this typescript is compiled in the browser +// there's currently no static type checking + +import { marked } from 'https://cdnjs.cloudflare.com/ajax/libs/marked/15.0.0/lib/marked.esm.js' +const convElement = document.getElementById('conversation') + +const promptInput = document.getElementById('prompt-input') as HTMLInputElement +const spinner = document.getElementById('spinner') + +// stream the response and render messages as each chunk is received +// data is sent as newline-delimited JSON +async function onFetchResponse(response: Response): Promise { + let text = '' + let decoder = new TextDecoder() + if (response.ok) { + const reader = response.body.getReader() + while (true) { + const { done, value } = await reader.read() + if (done) { + break + } + text += decoder.decode(value) + addMessages(text) + spinner.classList.remove('active') + } + addMessages(text) + promptInput.disabled = false + promptInput.focus() + } else { + const text = await response.text() + console.error(`Unexpected response: ${response.status}`, { response, text }) + throw new Error(`Unexpected response: ${response.status}`) + } +} + +// The format of messages, this matches pydantic-ai both for brevity and understanding +// in production, you might not want to keep this format all the way to the frontend +interface Message { + role: string + content: string + timestamp: string +} + +// take raw response text and render messages into the `#conversation` element +// Message timestamp is assumed to be a unique identifier of a message, and is used to deduplicate +// hence you can send data about the same message multiple times, and it will be updated +// instead of creating a new message elements +function addMessages(responseText: string) { + const lines = responseText.split('\n') + const messages: Message[] = lines.filter(line => line.length > 1).map(j => JSON.parse(j)) + for (const message of messages) { + // we use the timestamp as a crude element id + const { timestamp, role, content } = message + const id = `msg-${timestamp}` + let msgDiv = document.getElementById(id) + if (!msgDiv) { + msgDiv = document.createElement('div') + msgDiv.id = id + msgDiv.title = `${role} at ${timestamp}` + msgDiv.classList.add('border-top', 'pt-2', role) + convElement.appendChild(msgDiv) + } + msgDiv.innerHTML = marked.parse(content) + } + window.scrollTo({ top: document.body.scrollHeight, behavior: 'smooth' }) +} + +function onError(error: any) { + console.error(error) + document.getElementById('error').classList.remove('d-none') + document.getElementById('spinner').classList.remove('active') +} + +async function onSubmit(e: SubmitEvent): Promise { + e.preventDefault() + spinner.classList.add('active') + const body = new FormData(e.target as HTMLFormElement) + + promptInput.value = '' + promptInput.disabled = true + + const response = await fetch('/chat/', { method: 'POST', body }) + await onFetchResponse(response) +} + +// call onSubmit when the form is submitted (e.g. user clicks the send button or hits Enter) +document.querySelector('form').addEventListener('submit', (e) => onSubmit(e).catch(onError)) + +// load messages on page load +fetch('/chat/').then(onFetchResponse).catch(onError) \ No newline at end of file diff --git a/notebooks/pydantic_ai/pyproject.toml b/notebooks/pydantic_ai/pyproject.toml index 927825a..61fe058 100644 --- a/notebooks/pydantic_ai/pyproject.toml +++ b/notebooks/pydantic_ai/pyproject.toml @@ -17,5 +17,5 @@ dependencies = [ "uvicorn>=0.32.0", "devtools>=0.12.2", "gradio>=5.9.0; python_version>'3.9'", - "mcp[cli]>=1.4.1; python_version >= '3.10'" + "mcp[cli]>=1.4.1; python_version >= '3.10'", ] diff --git a/notebooks/pydantic_ai/question_graph.py b/notebooks/pydantic_ai/question_graph.py new file mode 100644 index 0000000..37f6824 --- /dev/null +++ b/notebooks/pydantic_ai/question_graph.py @@ -0,0 +1,182 @@ +from __future__ import annotations as _annotations + +from dataclasses import dataclass, field +from pathlib import Path + +import logfire + +from groq import BaseModel +from pydantic_graph import ( + BaseNode, + End, + Graph, + GraphRunContext, +) +from pydantic_graph.persistence.file import FileStatePersistence + +from pydantic_ai.models.openai import OpenAIModel +from pydantic_ai.providers.openai import OpenAIProvider +from pydantic_ai import Agent, format_as_xml +from pydantic_ai.messages import ModelMessage + +# 'if-token-present' means nothing will be sent (and the example will work) if you don't have logfire configured +logfire.configure(send_to_logfire="if-token-present") +logfire.instrument_pydantic_ai() + +# Configuration for Ollama using OpenAI-compatible endpoint +OLLAMA_BASE_URL = "http://localhost:11434" +MODEL_NAME = "qwen3:8b" # Updated to use available model +# Ollama provides OpenAI-compatible API at /v1/ endpoint +ollama_model = OpenAIModel( + model_name=MODEL_NAME, provider=OpenAIProvider(base_url=f"{OLLAMA_BASE_URL}/v1") +) + +# MODEL_NAME = "hf.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_XL" # Updated to use available model +# BaseModel = OpenAIModel( +# model_name=MODEL_NAME, +# provider=OpenAIProvider(base_url=f"{OLLAMA_BASE_URL}/v1"), +# ) + +ask_agent = Agent(ollama_model, output_type=str) + + +@dataclass +class QuestionState: + question: str | None = None + ask_agent_messages: list[ModelMessage] = field(default_factory=list) + evaluate_agent_messages: list[ModelMessage] = field(default_factory=list) + + +@dataclass +class Ask(BaseNode[QuestionState]): + async def run(self, ctx: GraphRunContext[QuestionState]) -> Answer: + result = await ask_agent.run( + "Ask a simple question with a single correct answer.", + message_history=ctx.state.ask_agent_messages, + ) + ctx.state.ask_agent_messages += result.all_messages() + ctx.state.question = result.output + return Answer(result.output) + + +@dataclass +class Answer(BaseNode[QuestionState]): + question: str + + async def run(self, ctx: GraphRunContext[QuestionState]) -> Evaluate: + answer = input(f"{self.question}: ") + return Evaluate(answer) + + +class EvaluationOutput(BaseModel, use_attribute_docstrings=True): + correct: bool + """Whether the answer is correct.""" + comment: str + """Comment on the answer, reprimand the user if the answer is wrong.""" + + +evaluate_agent = Agent( + ollama_model, + output_type=EvaluationOutput, + system_prompt="Given a question and answer, evaluate if the answer is correct.", +) + + +@dataclass +class Evaluate(BaseNode[QuestionState, None, str]): + answer: str + + async def run( + self, + ctx: GraphRunContext[QuestionState], + ) -> End[str] | Reprimand: + assert ctx.state.question is not None + result = await evaluate_agent.run( + format_as_xml({"question": ctx.state.question, "answer": self.answer}), + message_history=ctx.state.evaluate_agent_messages, + ) + ctx.state.evaluate_agent_messages += result.all_messages() + if result.output.correct: + return End(result.output.comment) + else: + return Reprimand(result.output.comment) + + +@dataclass +class Reprimand(BaseNode[QuestionState]): + comment: str + + async def run(self, ctx: GraphRunContext[QuestionState]) -> Ask: + print(f"Comment: {self.comment}") + ctx.state.question = None + return Ask() + + +question_graph = Graph( + nodes=(Ask, Answer, Evaluate, Reprimand), state_type=QuestionState +) + + +async def run_as_continuous(): + state = QuestionState() + node = Ask() + end = await question_graph.run(node, state=state) + print("END:", end.output) + + +async def run_as_cli(answer: str | None): + persistence = FileStatePersistence(Path("question_graph.json")) + persistence.set_graph_types(question_graph) + + if snapshot := await persistence.load_next(): + state = snapshot.state + assert ( + answer is not None + ), 'answer required, usage "uv run -m pydantic_ai_examples.question_graph cli "' + node = Evaluate(answer) + else: + state = QuestionState() + node = Ask() + # debug(state, node) + + async with question_graph.iter(node, state=state, persistence=persistence) as run: + while True: + node = await run.next() + if isinstance(node, End): + print("END:", node.data) + history = await persistence.load_all() + print("history:", "\n".join(str(e.node) for e in history), sep="\n") + print("Finished!") + break + elif isinstance(node, Answer): + print(node.question) + break + # otherwise just continue + + +if __name__ == "__main__": + import asyncio + import sys + + try: + sub_command = sys.argv[1] + assert sub_command in ("continuous", "cli", "mermaid") + except (IndexError, AssertionError): + print( + "Usage:\n" + " uv run -m pydantic_ai_examples.question_graph mermaid\n" + "or:\n" + " uv run -m pydantic_ai_examples.question_graph continuous\n" + "or:\n" + " uv run -m pydantic_ai_examples.question_graph cli [answer]", + file=sys.stderr, + ) + sys.exit(1) + + if sub_command == "mermaid": + print(question_graph.mermaid_code(start_node=Ask)) + elif sub_command == "continuous": + asyncio.run(run_as_continuous()) + else: + a = sys.argv[2] if len(sys.argv) > 2 else None + asyncio.run(run_as_cli(a)) diff --git a/notebooks/uv.lock b/notebooks/uv.lock index 5ed7ec3..3e32c60 100644 --- a/notebooks/uv.lock +++ b/notebooks/uv.lock @@ -3,12 +3,12 @@ revision = 2 requires-python = ">=3.12" resolution-markers = [ "python_full_version >= '3.13' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')", "python_full_version >= '3.12.4' and python_full_version < '3.13' and sys_platform == 'darwin'", "python_full_version < '3.12.4' and sys_platform == 'darwin'", - "python_full_version >= '3.13' and platform_machine == 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