Result docs

docs/agents.md

@@ -66,7 +66,7 @@ There are three ways to run an agent:
 
 Here's a simple example demonstrating all three:
 
-```python title="run_agent.py"
+```py title="run_agent.py"
 from pydantic_ai import Agent
 
 agent = Agent('openai:gpt-4o')
@@ -95,7 +95,7 @@ An agent **run** might represent an entire conversation — there's no limit to
 
 Here's an example of a conversation comprised of multiple runs:
 
-```python title="conversation_example.py" hl_lines="13"
+```py title="conversation_example.py" hl_lines="13"
 from pydantic_ai import Agent
 
 agent = Agent('openai:gpt-4o')
@@ -131,7 +131,7 @@ You can add both to a single agent; they're concatenated in the order they're de
 
 Here's an example using both types of system prompts:
 
-```python title="system_prompts.py"
+```py title="system_prompts.py"
 from datetime import date
 
 from pydantic_ai import Agent, CallContext
@@ -228,7 +228,7 @@ _(This example is complete, it can be run "as is")_
 
 Let's print the messages from that game to see what happened:
 
-```python title="dice_game_messages.py"
+```py title="dice_game_messages.py"
 from dice_game import dice_result
 
 print(dice_result.all_messages())
@@ -417,7 +417,7 @@ If models behave unexpectedly (e.g., the retry limit is exceeded, or their API r
 
 In these cases, [`agent.last_run_messages`][pydantic_ai.Agent.last_run_messages] can be used to access the messages exchanged during the run to help diagnose the issue.
 
-```python
+```py
 from pydantic_ai import Agent, ModelRetry, UnexpectedModelBehaviour
 
 agent = Agent('openai:gpt-4o')

docs/dependencies.md

@@ -55,7 +55,7 @@ _(This example is complete, it can be run "as is")_
 Dependencies are accessed through the [`CallContext`][pydantic_ai.dependencies.CallContext] type, this should be the first parameter of system prompt functions etc.
 
 
-```python title="system_prompt_dependencies.py" hl_lines="20-27"
+```py title="system_prompt_dependencies.py" hl_lines="20-27"
 from dataclasses import dataclass
 
 import httpx
@@ -113,7 +113,7 @@ to use `async` methods where dependencies perform IO, although synchronous depen
 
 Here's the same example as above, but with a synchronous dependency:
 
-```python title="sync_dependencies.py"
+```py title="sync_dependencies.py"
 from dataclasses import dataclass
 
 import httpx
@@ -161,7 +161,7 @@ _(This example is complete, it can be run "as is")_
 
 As well as system prompts, dependencies can be used in [retrievers](agents.md#retrievers) and [result validators](results.md#result-validators-functions).
 
-```python title="full_example.py" hl_lines="27-35 38-48"
+```py title="full_example.py" hl_lines="27-35 38-48"
 from dataclasses import dataclass
 
 import httpx

docs/index.md

@@ -136,7 +136,7 @@ async def main():
 10. The result will be validated with Pydantic to guarantee it is a `SupportResult`, since the agent is generic, it'll also be typed as a `SupportResult` to aid with static type checking.
 11. In a real use case, you'd add many more retrievers and a longer system prompt to the agent to extend the context it's equipped with and support it can provide.
 12. This is a simple sketch of a database connection, used to keep the example short and readable. In reality, you'd be connecting to an external database (e.g. PostgreSQL) to get information about customers.
-13. This [Pydantic](https://docs.pydantic.dev) model is used to constrain the structured data returned by the agent. From this simple definition, Pydantic builds teh JSON Schema that tells the LLM how to return the data, and performs validation to guarantee the data is correct at the end of the conversation.
+13. This [Pydantic](https://docs.pydantic.dev) model is used to constrain the structured data returned by the agent. From this simple definition, Pydantic builds the JSON Schema that tells the LLM how to return the data, and performs validation to guarantee the data is correct at the end of the conversation.
 
 !!! tip "Complete `bank_support.py` example"
     This example is incomplete for the sake of brevity (the definition of `DatabaseConn` is missing); you can find a complete `bank_support.py` example [here](examples/bank-support.md).

docs/message-history.md

@@ -25,7 +25,7 @@ and [`StreamedRunResult`][pydantic_ai.result.StreamedRunResult] (returned by [`A
 
 Example of accessing methods on a [`RunResult`][pydantic_ai.result.RunResult] :
 
-```python title="run_result_messages.py" hl_lines="10 28"
+```py title="run_result_messages.py" hl_lines="10 28"
 from pydantic_ai import Agent
 
 agent = Agent('openai:gpt-4o', system_prompt='Be a helpful assistant.')
@@ -73,7 +73,7 @@ _(This example is complete, it can be run "as is")_
 
 Example of accessing methods on a [`StreamedRunResult`][pydantic_ai.result.StreamedRunResult] :
 
-```python title="streamed_run_result_messages.py" hl_lines="9 31"
+```py title="streamed_run_result_messages.py" hl_lines="9 31"
 from pydantic_ai import Agent
 
 agent = Agent('openai:gpt-4o', system_prompt='Be a helpful assistant.')
@@ -96,11 +96,9 @@ async def main():
 
         async for text in result.stream():
             print(text)
-            #> Did you
-            #> Did you hear about
+            #> Did you hear
             #> Did you hear about the toothpaste
-            #> Did you hear about the toothpaste scandal? They
-            #> Did you hear about the toothpaste scandal? They called it
+            #> Did you hear about the toothpaste scandal? They called
             #> Did you hear about the toothpaste scandal? They called it Colgate.
 
         # complete messages once the stream finishes
@@ -190,7 +188,7 @@ Since messages are defined by simple dataclasses, you can manually create and ma
 
 The message format is independent of the model used, so you can use messages in different agents, or the same agent with different models.
 
-```python
+```py
 from pydantic_ai import Agent
 
 agent = Agent('openai:gpt-4o', system_prompt='Be a helpful assistant.')