Fixed HTTPClient::make() missing event dispatcher refs

evals/LLMModes/run.php

@@ -44,11 +44,7 @@
 );
 
 $experiment = new Experiment(
-    cases: InferenceCases::except(
-        connections: ['ollama'],
-        modes: [],
-        stream: [],
-    ),
+    cases: InferenceCases::all(),
     executor: new RunInference($data),
     processors: [
         new CompanyEval(

examples/A05_Extras/Embeddings/run.php

@@ -1,57 +1,65 @@
----
-title: 'Embeddings'
-docname: 'embeddings'
----
-
-## Overview
-
-`Embeddings` class offers access to embeddings APIs and convenient methods
-to find top K vectors or documents most similar to provided query.
-
-`Embeddings` class supports following embeddings providers:
- - Azure
- - Cohere
- - Gemini
- - Jina
- - Mistral
- - OpenAI
-
-Embeddings providers access details can be found and modified via
-`/config/embed.php`.
-
-
-## Example
-
-```php
-<?php
-$loader = require 'vendor/autoload.php';
-$loader->add('Cognesy\\Instructor\\', __DIR__ . '../../src/');
-
-use Cognesy\Instructor\Extras\Embeddings\Embeddings;
-
-$documents = [
-    'Computer vision models are used to analyze images and videos.',
-    'The bakers at the Nashville Bakery baked 200 loaves of bread on Monday morning.',
-    'The new movie starring Tom Hanks is now playing in theaters.',
-    'Famous soccer player Lionel Messi has arrived in town.',
-    'News about the latest iPhone model has been leaked.',
-    'New car model by Tesla is now available for pre-order.',
-    'Philip K. Dick is an author of many sci-fi novels.',
-];
-
-$query = "technology news";
-
-$connections = ['azure', 'cohere1', 'gemini', 'jina', 'mistral', 'ollama', 'openai'];
-
-foreach($connections as $connection) {
-    $bestMatches = (new Embeddings)->withConnection($connection)->findSimilar(
-        query: $query,
-        documents: $documents,
-        topK: 3
-    );
-
-    echo "\n[$connection]\n";
-    dump($bestMatches);
-}
-?>
-```
+---
+title: 'Embeddings'
+docname: 'embeddings'
+---
+
+## Overview
+
+`Embeddings` class offers access to embeddings APIs and convenient methods
+to find top K vectors or documents most similar to provided query.
+
+`Embeddings` class supports following embeddings providers:
+ - Azure
+ - Cohere
+ - Gemini
+ - Jina
+ - Mistral
+ - OpenAI
+
+Embeddings providers access details can be found and modified via
+`/config/embed.php`.
+
+
+## Example
+
+```php
+<?php
+$loader = require 'vendor/autoload.php';
+$loader->add('Cognesy\\Instructor\\', __DIR__ . '../../src/');
+
+use Cognesy\Instructor\Extras\Embeddings\Embeddings;
+
+$documents = [
+    'Computer vision models are used to analyze images and videos.',
+    'The bakers at the Nashville Bakery baked 200 loaves of bread on Monday morning.',
+    'The new movie starring Tom Hanks is now playing in theaters.',
+    'Famous soccer player Lionel Messi has arrived in town.',
+    'News about the latest iPhone model has been leaked.',
+    'New car model by Tesla is now available for pre-order.',
+    'Philip K. Dick is an author of many sci-fi novels.',
+];
+
+$query = "technology news";
+
+$connections = [
+    'azure',
+    'cohere1',
+    'gemini',
+    'jina',
+    'mistral',
+    //'ollama',
+    'openai'
+];
+
+foreach($connections as $connection) {
+    $bestMatches = (new Embeddings)->withConnection($connection)->findSimilar(
+        query: $query,
+        documents: $documents,
+        topK: 3
+    );
+
+    echo "\n[$connection]\n";
+    dump($bestMatches);
+}
+?>
+```

src/Extras/Embeddings/Drivers/AzureOpenAIDriver.php

@@ -1,81 +1,84 @@
-<?php
-namespace Cognesy\Instructor\Extras\Embeddings\Drivers;
-
-use Cognesy\Instructor\Extras\Embeddings\Contracts\CanVectorize;
-use Cognesy\Instructor\Extras\Embeddings\Data\EmbeddingsConfig;
-use Cognesy\Instructor\Extras\Embeddings\Data\Vector;
-use Cognesy\Instructor\Extras\Embeddings\EmbeddingsResponse;
-use Cognesy\Instructor\Features\Http\Contracts\CanHandleHttp;
-use Cognesy\Instructor\Features\Http\HttpClient;
-use Cognesy\Instructor\Features\LLM\Data\Usage;
-
-class AzureOpenAIDriver implements CanVectorize
-{
-    public function __construct(
-        protected EmbeddingsConfig $config,
-        protected ?CanHandleHttp $httpClient = null,
-    ) {
-        $this->httpClient = $httpClient ?? HttpClient::make();
-    }
-
-    public function vectorize(array $input, array $options = []): EmbeddingsResponse {
-        $response = $this->httpClient->handle(
-            $this->getEndpointUrl(),
-            $this->getRequestHeaders(),
-            $this->getRequestBody($input, $options),
-        );
-        return $this->toResponse(json_decode($response->getContents(), true));
-    }
-
-    // INTERNAL /////////////////////////////////////////////////
-
-    protected function getEndpointUrl(): string {
-        return str_replace(
-                search: array_map(fn($key) => "{".$key."}", array_keys($this->config->metadata)),
-                replace: array_values($this->config->metadata),
-                subject: "{$this->config->apiUrl}{$this->config->endpoint}"
-            ) . $this->getUrlParams();
-    }
-
-    protected function getUrlParams(): string {
-        $params = array_filter([
-            'api-version' => $this->config->metadata['apiVersion'] ?? '',
-        ]);
-        if (!empty($params)) {
-            return '?' . http_build_query($params);
-        }
-        return '';
-    }
-
-    protected function getRequestHeaders(): array {
-        return [
-            'Api-Key' => $this->config->apiKey,
-            'Content-Type' => 'application/json',
-        ];
-    }
-
-    protected function getRequestBody(array $input, array $options) : array {
-        return array_filter(array_merge([
-            'input' => $input,
-            'model' => $this->config->model,
-            'encoding_format' => 'float',
-        ], $options));
-    }
-
-    protected function toResponse(array $response) : EmbeddingsResponse {
-        return new EmbeddingsResponse(
-            vectors: array_map(
-                callback: fn($item) => new Vector(values: $item['embedding'], id: $item['index']),
-                array: $response['data']
-            ),
-            usage: $this->makeUsage($response),
-        );
-    }
-
-    protected function makeUsage(array $response): Usage {
-        return new Usage(
-            inputTokens: $response['usage']['prompt_tokens'] ?? 0,
-            outputTokens: ($response['usage']['total_tokens'] ?? 0) - ($response['usage']['prompt_tokens'] ?? 0),
-        );
-    }
+<?php
+namespace Cognesy\Instructor\Extras\Embeddings\Drivers;
+
+use Cognesy\Instructor\Events\EventDispatcher;
+use Cognesy\Instructor\Extras\Embeddings\Contracts\CanVectorize;
+use Cognesy\Instructor\Extras\Embeddings\Data\EmbeddingsConfig;
+use Cognesy\Instructor\Extras\Embeddings\Data\Vector;
+use Cognesy\Instructor\Extras\Embeddings\EmbeddingsResponse;
+use Cognesy\Instructor\Features\Http\Contracts\CanHandleHttp;
+use Cognesy\Instructor\Features\Http\HttpClient;
+use Cognesy\Instructor\Features\LLM\Data\Usage;
+
+class AzureOpenAIDriver implements CanVectorize
+{
+    public function __construct(
+        protected EmbeddingsConfig $config,
+        protected ?CanHandleHttp $httpClient = null,
+        protected ?EventDispatcher $events = null,
+    ) {
+        $this->events = $events ?? new EventDispatcher();
+        $this->httpClient = $httpClient ?? HttpClient::make(events: $this->events);
+    }
+
+    public function vectorize(array $input, array $options = []): EmbeddingsResponse {
+        $response = $this->httpClient->handle(
+            $this->getEndpointUrl(),
+            $this->getRequestHeaders(),
+            $this->getRequestBody($input, $options),
+        );
+        return $this->toResponse(json_decode($response->getContents(), true));
+    }
+
+    // INTERNAL /////////////////////////////////////////////////
+
+    protected function getEndpointUrl(): string {
+        return str_replace(
+                search: array_map(fn($key) => "{".$key."}", array_keys($this->config->metadata)),
+                replace: array_values($this->config->metadata),
+                subject: "{$this->config->apiUrl}{$this->config->endpoint}"
+            ) . $this->getUrlParams();
+    }
+
+    protected function getUrlParams(): string {
+        $params = array_filter([
+            'api-version' => $this->config->metadata['apiVersion'] ?? '',
+        ]);
+        if (!empty($params)) {
+            return '?' . http_build_query($params);
+        }
+        return '';
+    }
+
+    protected function getRequestHeaders(): array {
+        return [
+            'Api-Key' => $this->config->apiKey,
+            'Content-Type' => 'application/json',
+        ];
+    }
+
+    protected function getRequestBody(array $input, array $options) : array {
+        return array_filter(array_merge([
+            'input' => $input,
+            'model' => $this->config->model,
+            'encoding_format' => 'float',
+        ], $options));
+    }
+
+    protected function toResponse(array $response) : EmbeddingsResponse {
+        return new EmbeddingsResponse(
+            vectors: array_map(
+                callback: fn($item) => new Vector(values: $item['embedding'], id: $item['index']),
+                array: $response['data']
+            ),
+            usage: $this->makeUsage($response),
+        );
+    }
+
+    protected function makeUsage(array $response): Usage {
+        return new Usage(
+            inputTokens: $response['usage']['prompt_tokens'] ?? 0,
+            outputTokens: ($response['usage']['total_tokens'] ?? 0) - ($response['usage']['prompt_tokens'] ?? 0),
+        );
+    }
 }