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<?php |
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/** |
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* OpenStation — AI Copilot: WordPress AI Client adapter. |
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* |
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* Thin wrappers around `wp_ai_client_prompt()` that the agentic search loop |
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* and the comment-scoring job use to generate. Credentials are injected by |
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* Core from the configured Connector — nothing here ever handles an API key. |
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* |
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* The search loop advertises its tools — built-in WordPress Abilities (see |
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* abilities.php) plus client command tools — as function declarations, and |
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* dispatches ability calls through `wp_get_ability()->execute()`. |
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* |
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* All SDK classes referenced here ship with WordPress 7.0+. The `use` |
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* statements are compile-time aliases only; every call site is reached solely |
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* through {@see openstation_ai_is_available()}, so this file is inert (and |
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* never resolves the classes) on older WordPress. |
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* |
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* @package OpenStation |
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*/ |
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|
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use WordPress\AiClient\Messages\DTO\Message; |
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use WordPress\AiClient\Messages\DTO\MessagePart; |
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use WordPress\AiClient\Messages\DTO\UserMessage; |
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use WordPress\AiClient\Providers\Models\Contracts\ModelInterface; |
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use WordPress\AiClient\Providers\Models\DTO\ModelConfig; |
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use WordPress\AiClient\Tools\DTO\FunctionCall; |
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use WordPress\AiClient\Tools\DTO\FunctionDeclaration; |
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use WordPress\AiClient\Tools\DTO\FunctionResponse; |
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|
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defined( 'ABSPATH' ) || exit; |
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|
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/** |
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* Builds SDK function declarations from the loop's tool definitions. |
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* |
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* Each definition is the neutral tool shape the registry already produces: |
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* `{ type: 'function', name, description, parameters (JSON Schema) }`. |
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* |
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* @param array $tool_defs List of tool definitions. |
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* @return FunctionDeclaration[] |
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*/ |
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function openstation_ai_build_function_declarations( array $tool_defs ) { |
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$declarations = array(); |
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foreach ( $tool_defs as $def ) { |
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if ( ! is_array( $def ) ) { |
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continue; |
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} |
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$name = isset( $def['name'] ) ? (string) $def['name'] : ''; |
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if ( '' === $name ) { |
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continue; |
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} |
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$description = isset( $def['description'] ) ? (string) $def['description'] : ''; |
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$parameters = isset( $def['parameters'] ) && is_array( $def['parameters'] ) ? $def['parameters'] : null; |
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|
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$declarations[] = new FunctionDeclaration( $name, $description, $parameters ); |
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} |
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return $declarations; |
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} |
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|
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/** |
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* Wraps a user query as a text message for the conversation history. |
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* |
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* @param string $text |
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* @return UserMessage |
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*/ |
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function openstation_ai_user_text_message( $text ) { |
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return new UserMessage( array( new MessagePart( (string) $text ) ) ); |
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} |
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|
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/** |
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* Wraps tool results as a user message of function-response parts. |
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* |
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* @param array $tool_outputs List of `{ call_id, name, response }` entries. |
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* @return UserMessage |
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*/ |
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function openstation_ai_tool_result_message( array $tool_outputs ) { |
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$parts = array(); |
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foreach ( $tool_outputs as $output ) { |
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$parts[] = new MessagePart( |
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new FunctionResponse( |
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isset( $output['call_id'] ) && '' !== $output['call_id'] ? (string) $output['call_id'] : null, |
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isset( $output['name'] ) && '' !== $output['name'] ? (string) $output['name'] : null, |
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isset( $output['response'] ) ? $output['response'] : null |
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) |
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); |
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} |
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return new UserMessage( $parts ); |
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} |
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|
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/** |
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* Strips thought-channel parts from a message before it re-enters history. |
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* |
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* Providers cannot reliably round-trip reasoning blocks: the Anthropic |
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* provider drops the cryptographic `signature` when parsing a `thinking` |
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* block, and the API rejects any replayed thinking block without one |
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* (`thinking.signature: Field required`). Thought parts carry no information |
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* the next turn needs — the model re-reasons from the visible conversation — |
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* so the agentic loop replays assistant turns without them. |
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* |
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* If every part is a thought (no text, no function call), the message is |
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* returned unchanged rather than emptied; the loop never replays such a |
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* turn anyway. |
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* |
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* @param Message $message Assistant message as returned by the AI Client. |
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* @return Message Message safe to append to the conversation history. |
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*/ |
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function openstation_ai_strip_thought_parts( Message $message ) { |
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$kept = array(); |
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$stripped = false; |
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foreach ( $message->getParts() as $part ) { |
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if ( $part->getChannel()->isThought() ) { |
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$stripped = true; |
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continue; |
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} |
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$kept[] = $part; |
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} |
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|
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if ( ! $stripped || empty( $kept ) ) { |
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return $message; |
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} |
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|
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return new Message( $message->getRole(), $kept ); |
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} |
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|
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/** |
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* Builds the error for a final turn that produced no answer text. |
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* |
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* Observed live with the Anthropic provider under agent runs: a hard task |
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* spends the entire `max_tokens` budget inside a thinking block |
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* (`stop_reason: "max_tokens"`, a single text-less thought part), so the |
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* turn carries neither function calls nor extractable text. Callers that |
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* can meaningfully degrade instead (the command follow-up turn) match on |
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* this code and keep their own fallback. |
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* |
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* @param string $detail Underlying extraction failure, preserved for logs. |
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* @return WP_Error |
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*/ |
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function openstation_ai_empty_answer_error( $detail ) { |
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return new WP_Error( |
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'openstation_ai_empty_answer', |
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__( 'The AI provider returned no answer text.', 'desktop-mode' ), |
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array( |
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'status' => 502, |
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'detail' => (string) $detail, |
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) |
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); |
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} |
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|
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/** |
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* Applies the site's model config to a prompt builder. |
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* |
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* @param mixed $builder WP_AI_Client_Prompt_Builder. |
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* @param array $context Partial filter context; missing keys are defaulted. |
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* @return mixed |
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*/ |
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function openstation_ai_apply_model_config( $builder, array $context ) { |
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$context = array_merge( |
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array( |
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'user_id' => 0, |
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'request_id' => '', |
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'source' => '', |
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'has_tools' => false, |
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'has_schema' => false, |
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), |
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$context |
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); |
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|
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/** |
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* Filters the model config for one AI turn. |
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* |
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* Defaults to empty. Recipe: `docs/examples/ai-model-config.md`. |
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* |
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* @param array $config { model?: string|ModelInterface, max_tokens?: int, temperature?: float, custom_options?: array<string, mixed> }. |
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* @param array $context { user_id, request_id, source, has_tools, has_schema }. |
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*/ |
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$config = apply_filters( 'openstation_ai_model_config', array(), $context ); |
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if ( ! is_array( $config ) ) { |
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return $builder; |
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} |
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|
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$model_config = new ModelConfig(); |
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|
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if ( isset( $config['max_tokens'] ) && is_numeric( $config['max_tokens'] ) && (int) $config['max_tokens'] > 0 ) { |
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$model_config->setMaxTokens( (int) $config['max_tokens'] ); |
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} |
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|
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// Unlike max_tokens, 0.0 is a legitimate temperature (deterministic). The |
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// 2.0 ceiling is the range the SDK's own schema declares. |
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if ( isset( $config['temperature'] ) && is_numeric( $config['temperature'] ) |
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&& (float) $config['temperature'] >= 0.0 && (float) $config['temperature'] <= 2.0 ) { |
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$model_config->setTemperature( (float) $config['temperature'] ); |
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} |
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|
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$custom_options = array(); |
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if ( isset( $config['custom_options'] ) && is_array( $config['custom_options'] ) ) { |
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foreach ( $config['custom_options'] as $key => $value ) { |
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// A list would reach the provider as parameters named `0`, `1`, …. |
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if ( is_string( $key ) && '' !== $key ) { |
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$custom_options[ $key ] = $value; |
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} |
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} |
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} |
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|
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if ( ! empty( $custom_options ) ) { |
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$model_config->setCustomOptions( $custom_options ); |
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} |
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|
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$builder = $builder->using_model_config( $model_config ); |
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|
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// After the config: `using_model()` merges the model's own defaults under |
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// whatever the builder already carries, so ours has to land first. |
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$model = isset( $config['model'] ) ? $config['model'] : null; |
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if ( $model instanceof ModelInterface ) { |
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$builder = $builder->using_model( $model ); |
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} elseif ( is_string( $model ) && '' !== trim( $model ) ) { |
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// `using_model()` needs a ModelInterface, so a bare model id goes |
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// through `using_model_preference()`, which throws on anything that |
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// isn't a non-empty string. |
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$builder = $builder->using_model_preference( trim( $model ) ); |
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} |
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|
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return $builder; |
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} |
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|
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/** |
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* Runs one generation turn through the AI Client. |
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* |
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* Rebuilds the prompt from the full ordered message list each turn (the |
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* builder's `with_history()` prepends, so it can't append turns in a loop), |
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* advertises the tools as function declarations, and constrains the final |
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* answer to `$answer_schema` when given. Returns the assistant turn normalized |
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* to the shape the loop consumes; `message` has thought-channel parts stripped |
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* ({@see openstation_ai_strip_thought_parts()}) so it is safe to replay. |
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* |
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* @param int $user_id Requesting user id. |
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* @param array $messages Ordered conversation as SDK Message objects. |
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* @param array $tool_defs Tool definitions to advertise. |
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* @param array|null $answer_schema JSON Schema for the final answer, or null. |
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* @param string $instructions System instruction. |
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* @param array $context Optional. `{ source?: string, request_id?: string }` |
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* for the model-config filter. |
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* @return array{ text: ?string, function_calls: array, message: mixed, usage: ?array, model: ?array }|WP_Error |
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*/ |
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function openstation_ai_client_generate( $user_id, array $messages, array $tool_defs, $answer_schema, $instructions, array $context = array() ) { |
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$builder = wp_ai_client_prompt( $messages ); |
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|
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if ( is_string( $instructions ) && '' !== $instructions ) { |
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$builder = $builder->using_system_instruction( $instructions ); |
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} |
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|
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// Provider + model selection is delegated to the Core AI Client |
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// (Connector-backed) unless the model-config filter says otherwise. |
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|
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$declarations = openstation_ai_build_function_declarations( $tool_defs ); |
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if ( ! empty( $declarations ) ) { |
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$builder = $builder->using_function_declarations( ...$declarations ); |
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} |
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|
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if ( is_array( $answer_schema ) ) { |
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// Strict structured output: providers reject an object subschema that |
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// doesn't set `additionalProperties: false`, and one such node 400s the |
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// whole turn. Normalize here so no schema author has to know that. |
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$builder = $builder->as_json_response( openstation_ai_normalize_response_schema( $answer_schema ) ); |
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} |
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|
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$builder = openstation_ai_apply_model_config( |
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$builder, |
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array_merge( |
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$context, |
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array( |
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'user_id' => (int) $user_id, |
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'has_tools' => ! empty( $declarations ), |
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'has_schema' => is_array( $answer_schema ), |
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) |
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) |
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); |
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|
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$result = $builder->generate_result(); |
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if ( is_wp_error( $result ) ) { |
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return $result; |
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} |
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|
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$message = $result->toMessage(); |
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$function_calls = array(); |
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foreach ( $message->getParts() as $part ) { |
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if ( ! $part->getType()->isFunctionCall() ) { |
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continue; |
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} |
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$call = $part->getFunctionCall(); |
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if ( ! $call instanceof FunctionCall ) { |
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continue; |
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} |
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$args = $call->getArgs(); |
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$function_calls[] = array( |
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'name' => (string) $call->getName(), |
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'call_id' => (string) $call->getId(), |
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'arguments' => wp_json_encode( is_array( $args ) ? $args : array() ), |
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); |
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} |
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|
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$text = null; |
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if ( empty( $function_calls ) ) { |
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// A turn with no function calls IS the final answer, so failing to |
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// extract its text is a failed generation, not a valid empty one. |
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// Swallowing it here used to surface as a "successful" run with an |
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// empty answer, invisible to the retry and error paths alike. |
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try { |
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$text = $result->toText(); |
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} catch ( \Throwable $e ) { |
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return openstation_ai_empty_answer_error( $e->getMessage() ); |
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} |
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if ( ! is_string( $text ) || '' === trim( $text ) ) { |
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return openstation_ai_empty_answer_error( 'The provider response contains no text part.' ); |
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} |
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} |
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|
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return array( |
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'text' => $text, |
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'function_calls' => $function_calls, |
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'message' => openstation_ai_strip_thought_parts( $message ), |
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'usage' => openstation_ai_result_token_usage( $result ), |
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'model' => openstation_ai_result_model_metadata( $result ), |
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); |
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} |
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|
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/** |
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* Extracts normalized token usage from a generation result. |
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* |
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* @param mixed $result GenerativeAiResult. |
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* @return array{ prompt: int, completion: int, total: int }|null |
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*/ |
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function openstation_ai_result_token_usage( $result ) { |
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try { |
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$usage = $result->getTokenUsage(); |
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return array( |
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'prompt' => (int) $usage->getPromptTokens(), |
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'completion' => (int) $usage->getCompletionTokens(), |
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'total' => (int) $usage->getTotalTokens(), |
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); |
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} catch ( \Throwable $e ) { |
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return null; |
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} |
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} |
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|
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/** |
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* Extracts the resolved model's id + name from a generation result. |
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* |
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* @param mixed $result GenerativeAiResult. |
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* @return array{ id: string, name: string }|null |
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*/ |
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function openstation_ai_result_model_metadata( $result ) { |
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try { |
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$model = $result->getModelMetadata(); |
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return array( |
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'id' => (string) $model->getId(), |
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'name' => (string) $model->getName(), |
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); |
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} catch ( \Throwable $e ) { |
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return null; |
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} |
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} |
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|
