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feat: add extraction schema, sidebar nav, few-shot prompting, and prompt settings

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Overhaul extraction pipeline with new TradeItem model, conversation flow,
and dedicated extraction endpoint. Add sidebar navigation with NavMenu
component and landing page. Introduce few-shot prompting service and
tests. Add prompt settings and email upload specs. Update OpenSpec
tooling with improved export-spec and extract-feature commands. Archive
completed changes and export full specs.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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2026-04-06 23:39:23 +01:00
parent 7a5c22593a
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openspec/specs/chat-streaming/spec.md
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@@ -6,12 +6,27 @@ Define the streaming AI response pipeline — backend chat endpoint using Semant
### Requirement: Chat endpoint proxies to Responses API
The API backend SHALL expose `POST /api/chat` that accepts a list of messages and processes them using a Semantic Kernel chat completion service. The kernel is configured with an OpenAI connector pointed at the existing CLIProxyAPI proxy.
The API backend SHALL expose `POST /api/chat` that accepts a `ChatRequest` containing messages, an optional system prompt, and optional model settings. The request is processed using a Semantic Kernel chat completion service. When a system prompt is provided, it SHALL be added as the first system message in the ChatHistory. When model settings are provided, non-null values SHALL be applied to the execution settings. A separate `POST /api/chat/extract` endpoint SHALL handle extraction-specific requests with few-shot prompting.
#### Scenario: Successful chat request
#### Scenario: Successful chat request with system prompt
- **WHEN** the client sends a POST to `/api/chat` with a message list
- **THEN** the API processes the messages through the Semantic Kernel and returns the response
- **WHEN** the client sends a POST to `/api/chat` with messages and a system prompt
- **THEN** the API creates a ChatHistory with the system prompt as the first message, followed by the conversation messages, and processes them through Semantic Kernel
#### Scenario: Successful chat request with model settings
- **WHEN** the client sends a POST to `/api/chat` with messages and model settings (e.g., Temperature=0.3)
- **THEN** the API applies the settings to OpenAIPromptExecutionSettings before calling the Semantic Kernel
#### Scenario: Successful chat request without optional fields
- **WHEN** the client sends a POST to `/api/chat` with only messages (no system prompt, no settings)
- **THEN** the API processes the request with default behavior (no system message, default execution settings)
#### Scenario: Extraction request routed to dedicated endpoint
- **WHEN** the client sends a POST to `/api/chat/extract` with email HTML
- **THEN** the API uses the few-shot ChatHistory prefix and extraction tools instead of the general chat configuration
### Requirement: Streaming response delivery