other code

docs/examples/chat-app.md

@@ -8,10 +8,12 @@ Demonstrates:
 
 * reusing chat history
 * serializing messages
+* streaming responses
 
 This demonstrates storing chat history between requests and using it to give the model context for new responses.
 
-Most of the complex logic here is in `chat_app.html` which includes the page layout and JavaScript to handle the chat.
+Most of the complex logic here is between `chat_app.py` which streams the response to the browser,
+and `chat_app.ts` which renders messages in the browser.
 
 ## Running the Example
 
@@ -27,10 +29,20 @@ TODO screenshot.
 
 ## Example Code
 
+Python code that runs the chat app:
+
 ```py title="chat_app.py"
 #! pydantic_ai_examples/chat_app.py
 ```
 
+Simple HTML page to render the app:
+
 ```html title="chat_app.html"
 #! pydantic_ai_examples/chat_app.html
 ```
+
+TypeScript to handle rendering the messages, to keep this simple (and at the risk of offending frontend developers) the typescript code is passed to the browser as plain text and transpiled in the browser.
+
+```ts title="chat_app.ts"
+#! pydantic_ai_examples/chat_app.ts
+```

pydantic_ai/result.py

@@ -3,6 +3,7 @@
 from abc import ABC, abstractmethod
 from collections.abc import AsyncIterator
 from dataclasses import dataclass
+from datetime import datetime
 from typing import Generic, TypeVar, cast
 
 import logfire_api
@@ -273,6 +274,10 @@ def cost(self) -> Cost:
         """
         return self.cost_so_far + self._stream_response.cost()
 
+    def timestamp(self) -> datetime:
+        """Get the timestamp of the response."""
+        return self._stream_response.timestamp()
+
     async def validate_structured_result(
         self, message: messages.ModelStructuredResponse, *, allow_partial: bool = False
     ) -> ResultData:

pydantic_ai_examples/chat_app.html

@@ -58,71 +58,24 @@ <h1>Chat App</h1>
   </main>
 </body>
 </html>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/typescript/5.6.3/typescript.min.js" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
 <script type="module">
-  import { marked } from 'https://cdn.jsdelivr.net/npm/marked/lib/marked.esm.js';
-
-  function addMessages(lines) {
-    const messages = lines.filter(line => line.length > 1).map((line) => JSON.parse(line))
-    const parent = document.getElementById('conversation');
-    for (const message of messages) {
-      let msgDiv = document.createElement('div');
-      msgDiv.classList.add('border-top', 'pt-2', message.role);
-      msgDiv.innerHTML = marked.parse(message.content);
-      parent.appendChild(msgDiv);
-    }
+  // to let me write TypeScript, without adding the burden of npm we do a dirty, non-production-ready hack
+  // and transpile the TypeScript code in the browser
+  // this is (arguably) A neat demo trick, but not suitable for production!
+  async function loadTs() {
+    const response = await fetch('/chat_app.ts');
+    const tsCode = await response.text();
+    const jsCode = window.ts.transpile(tsCode, { target: "es2015" });
+    let script = document.createElement('script');
+    script.type = 'module';
+    script.text = jsCode;
+    document.body.appendChild(script);
   }
 
-  function onError(error) {
-    console.error(error);
+  loadTs().catch((e) => {
+    console.error(e);
     document.getElementById('error').classList.remove('d-none');
     document.getElementById('spinner').classList.remove('active');
-  }
-
-  async function fetchResponse(response) {
-    let text = '';
-    if (response.ok) {
-      const reader = response.body.getReader();
-      while (true) {
-        const {done, value} = await reader.read();
-        if (done) {
-          break;
-        }
-        text += new TextDecoder().decode(value);
-        const lines = text.split('\n');
-        if (lines.length > 1) {
-          addMessages(lines.slice(0, -1));
-          text = lines[lines.length - 1];
-        }
-      }
-      addMessages(text.split('\n'));
-      let input = document.getElementById('prompt-input')
-      input.disabled = false;
-      input.focus();
-    } else {
-      const text = await response.text();
-      console.error(`Unexpected response: ${response.status}`, {response, text});
-      throw new Error(`Unexpected response: ${response.status}`);
-    }
-  }
-
-  async function onSubmit(e) {
-    e.preventDefault();
-    const spinner = document.getElementById('spinner');
-    spinner.classList.add('active');
-    const body = new FormData(e.target);
-
-    let input = document.getElementById('prompt-input')
-    input.value = '';
-    input.disabled = true;
-
-    const response = await fetch('/chat/', {method: 'POST', body});
-    await fetchResponse(response);
-    spinner.classList.remove('active');
-  }
-
-  // call onSubmit when 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(fetchResponse).catch(onError);
+  });
 </script>

pydantic_ai_examples/chat_app.py

@@ -16,7 +16,12 @@
 from pydantic import Field, TypeAdapter
 
 from pydantic_ai import Agent
-from pydantic_ai.messages import Message, MessagesTypeAdapter, UserPrompt
+from pydantic_ai.messages import (
+    Message,
+    MessagesTypeAdapter,
+    ModelTextResponse,
+    UserPrompt,
+)
 
 # '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')
@@ -32,6 +37,12 @@ async def index() -> HTMLResponse:
     return HTMLResponse((THIS_DIR / 'chat_app.html').read_bytes())
 
 
+@app.get('/chat_app.ts')
+async def main_ts() -> Response:
+    """Get the raw typescript code, it's compiled in the browser, forgive me."""
+    return Response((THIS_DIR / 'chat_app.ts').read_bytes(), media_type='text/plain')
+
+
 @app.get('/chat/')
 async def get_chat() -> Response:
     msgs = database.get_messages()
@@ -49,12 +60,16 @@ async def stream_messages():
         yield MessageTypeAdapter.dump_json(UserPrompt(content=prompt)) + b'\n'
         # get the chat history so far to pass as context to the agent
         messages = list(database.get_messages())
-        response = await agent.run(prompt, message_history=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 ModelTextResponse, so we create one
+                m = ModelTextResponse(content=text, timestamp=result.timestamp())
+                yield MessageTypeAdapter.dump_json(m) + b'\n'
+
         # add new messages (e.g. the user prompt and the agent response in this case) to the database
-        database.add_messages(response.new_messages_json())
-        # stream the last message which will be the agent response, we can't just yield `new_messages_json()`
-        # since we already stream the user prompt
-        yield MessageTypeAdapter.dump_json(response.all_messages()[-1]) + b'\n'
+        database.add_messages(result.new_messages_json())
 
     return StreamingResponse(stream_messages(), media_type='text/plain')
 

tests/test_streaming.py

@@ -21,6 +21,7 @@
 )
 from pydantic_ai.models.function import AgentInfo, DeltaToolCall, DeltaToolCalls, FunctionModel
 from pydantic_ai.models.test import TestModel
+from pydantic_ai.result import Cost
 from tests.conftest import IsNow
 
 pytestmark = pytest.mark.anyio
@@ -51,6 +52,8 @@ async def ret_a(x: str) -> str:
         response = await result.get_data()
         assert response == snapshot('{"ret_a":"a-apple"}')
         assert result.is_complete
+        assert result.cost() == snapshot(Cost())
+        assert result.timestamp() == IsNow(tz=timezone.utc)
         assert result.all_messages() == snapshot(
             [
                 UserPrompt(content='Hello', timestamp=IsNow(tz=timezone.utc)),