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docs: drop docs changes from this PR (defer to follow-up) 

Restores docs/v3/documentation/features/chat.mdx to main's version. This
also puts back the peer-chat Structured Outputs section (#896) that the
workspace-chat commit removed as a rebase artifact.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
adavyas 2026-07-24 16:02:27 -04:00
parent 1af690bcdb
commit e15123a997
1 changed files with 37 additions and 42 deletions
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docs/v3/documentation/features/chat.mdx
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@ -94,6 +94,43 @@ for await (const chunk of responseStream.iter_text()) {
Streaming is useful for displaying real-time responses in chat interfaces or when asking complex questions that require longer answers.
## Structured Outputs
When your application needs a machine-readable answer instead of prose, pass a schema as `response_format` and the answer is guaranteed to conform to it:
<CodeGroup>
```python Python
from pydantic import BaseModel
class OnboardingStatus(BaseModel):
completed: bool
remaining_steps: list[str]
status = peer.chat(
"Has the user completed the onboarding flow?",
response_format=OnboardingStatus,
)
# status is a parsed OnboardingStatus instance
```
```typescript TypeScript
import { z } from 'zod';
const OnboardingStatus = z.object({
completed: z.boolean(),
remainingSteps: z.array(z.string()),
});
const status = await peer.chat(
"Has the user completed the onboarding flow?",
{ responseFormat: OnboardingStatus },
);
// status is typed as z.infer<typeof OnboardingStatus>
```
</CodeGroup>
The agent runs its full reasoning loop either way — only the final answer is formatted to your schema. See [Structured Outputs](/v3/documentation/features/advanced/structured-outputs) for the supported schema subset, streaming behavior, and best practices.
## Integration Patterns
### Dynamic Prompt Enhancement
@ -189,48 +226,6 @@ const goals = await peer.chat("What are the user's main goals or objectives?");
```
</CodeGroup>
## Workspace-Level Chat
While `peer.chat()` queries knowledge about a single peer, `honcho.chat()` searches across **all peers and observations** in the workspace. This is useful for cross-peer analysis, discovering common themes, or asking workspace-wide questions.
<CodeGroup>
```python Python
from honcho import Honcho
honcho = Honcho()
# Ask about the entire workspace
answer = honcho.chat("What are common themes across all users?")
print(answer)
# With streaming
stream = honcho.chat_stream("Summarize all peer activity this week.")
for chunk in stream:
print(chunk, end="", flush=True)
# Async
answer = await honcho.aio.chat("Which users have discussed topic X?")
```
```typescript TypeScript
import { Honcho } from '@honcho-ai/sdk';
const honcho = new Honcho({});
// Ask about the entire workspace
const answer = await honcho.chat("What are common themes across all users?");
console.log(answer);
// With streaming
const stream = await honcho.chatStream("Summarize all peer activity this week.");
for await (const chunk of stream) {
process.stdout.write(chunk);
}
```
</CodeGroup>
Workspace chat accepts `reasoning_level` and optional `session` scoping. For streaming, use the separate `chat_stream()` / `chatStream()` method rather than a `stream` parameter.
## How Honcho Answers
When you call `peer.chat(query)`: