1+ import { AzureOpenAI } from 'openai' ;
2+ import { Embedding , EmbeddingVector } from './base-embedding' ;
3+
4+ export interface AzureOpenAIEmbeddingConfig {
5+ deploymentName : string ; // Azure deployment name (not model name)
6+ apiKey : string ; // Azure OpenAI API key
7+ azureEndpoint : string ; // Required: Azure endpoint URL
8+ apiVersion ?: string ; // Optional: defaults to stable version
9+ }
10+
11+ export class AzureOpenAIEmbedding extends Embedding {
12+ private client : AzureOpenAI ;
13+ private config : AzureOpenAIEmbeddingConfig ;
14+ private dimension : number = 1536 ; // Default dimension for text-embedding-3-small
15+ protected maxTokens : number = 8192 ; // Maximum tokens for OpenAI embedding models
16+
17+ constructor ( config : AzureOpenAIEmbeddingConfig ) {
18+ super ( ) ;
19+ this . config = config ;
20+
21+ // Validate endpoint format
22+ if ( ! config . azureEndpoint . startsWith ( 'https://' ) ) {
23+ throw new Error ( 'Azure OpenAI endpoint must start with https://' ) ;
24+ }
25+
26+ // Initialize Azure OpenAI client with API key authentication
27+ this . client = new AzureOpenAI ( {
28+ apiKey : config . apiKey ,
29+ apiVersion : config . apiVersion || '2024-02-01' , // Use stable version
30+ endpoint : config . azureEndpoint ,
31+ } ) ;
32+ }
33+
34+ async detectDimension ( testText : string = "test" ) : Promise < number > {
35+ const knownModels = AzureOpenAIEmbedding . getSupportedModels ( ) ;
36+
37+ // Try to infer from deployment name if it matches known patterns
38+ // Azure deployment names often include the model name with dashes
39+ for ( const [ modelName , info ] of Object . entries ( knownModels ) ) {
40+ // Check if deployment name contains model pattern (with dashes instead of dots)
41+ const modelPattern = modelName . replace ( / \. / g, '-' ) ;
42+ if ( this . config . deploymentName . toLowerCase ( ) . includes ( modelPattern ) ) {
43+ return info . dimension ;
44+ }
45+ }
46+
47+ // Dynamic detection via API call for custom deployments
48+ try {
49+ const processedText = this . preprocessText ( testText ) ;
50+ const response = await this . client . embeddings . create ( {
51+ model : this . config . deploymentName , // Use deployment name
52+ input : processedText ,
53+ encoding_format : 'float' ,
54+ } ) ;
55+ return response . data [ 0 ] . embedding . length ;
56+ } catch ( error ) {
57+ const errorMessage = error instanceof Error ? error . message : 'Unknown error' ;
58+
59+ // Re-throw authentication errors
60+ if ( errorMessage . includes ( 'API key' ) || errorMessage . includes ( 'unauthorized' ) || errorMessage . includes ( 'authentication' ) ) {
61+ throw new Error ( `Azure OpenAI authentication failed: ${ errorMessage } ) ;
62+ }
63+
64+ // Handle deployment not found errors
65+ if ( errorMessage . includes ( 'deployment' ) || errorMessage . includes ( 'not found' ) ) {
66+ throw new Error ( `Azure OpenAI deployment '${ this . config . deploymentName } ${ errorMessage } ) ;
67+ }
68+
69+ throw new Error ( `Failed to detect dimension for Azure deployment ${ this . config . deploymentName } ${ errorMessage } ) ;
70+ }
71+ }
72+
73+ async embed ( text : string ) : Promise < EmbeddingVector > {
74+ const processedText = this . preprocessText ( text ) ;
75+
76+ // Check if we need to detect dimension
77+ const knownModels = AzureOpenAIEmbedding . getSupportedModels ( ) ;
78+ let needsDimensionDetection = true ;
79+
80+ for ( const [ modelName , info ] of Object . entries ( knownModels ) ) {
81+ const modelPattern = modelName . replace ( / \. / g, '-' ) ;
82+ if ( this . config . deploymentName . toLowerCase ( ) . includes ( modelPattern ) ) {
83+ this . dimension = info . dimension ;
84+ needsDimensionDetection = false ;
85+ break ;
86+ }
87+ }
88+
89+ if ( needsDimensionDetection && this . dimension === 1536 ) {
90+ // Only detect if we haven't already and are using default
91+ this . dimension = await this . detectDimension ( ) ;
92+ }
93+
94+ try {
95+ const response = await this . client . embeddings . create ( {
96+ model : this . config . deploymentName , // Use deployment name
97+ input : processedText ,
98+ encoding_format : 'float' ,
99+ } ) ;
100+
101+ // Update dimension from actual response
102+ this . dimension = response . data [ 0 ] . embedding . length ;
103+
104+ return {
105+ vector : response . data [ 0 ] . embedding ,
106+ dimension : this . dimension
107+ } ;
108+ } catch ( error ) {
109+ const errorMessage = error instanceof Error ? error . message : 'Unknown error' ;
110+
111+ // Provide specific error messages for common Azure issues
112+ if ( errorMessage . includes ( 'API key' ) || errorMessage . includes ( 'unauthorized' ) ) {
113+ throw new Error ( `Azure OpenAI authentication failed: ${ errorMessage } ) ;
114+ }
115+
116+ if ( errorMessage . includes ( 'deployment' ) || errorMessage . includes ( 'not found' ) ) {
117+ throw new Error ( `Azure OpenAI deployment '${ this . config . deploymentName } ${ errorMessage } ) ;
118+ }
119+
120+ if ( errorMessage . includes ( 'rate limit' ) || errorMessage . includes ( 'quota' ) ) {
121+ throw new Error ( `Azure OpenAI rate limit exceeded: ${ errorMessage } ) ;
122+ }
123+
124+ throw new Error ( `Failed to generate Azure OpenAI embedding: ${ errorMessage } ) ;
125+ }
126+ }
127+
128+ async embedBatch ( texts : string [ ] ) : Promise < EmbeddingVector [ ] > {
129+ const processedTexts = this . preprocessTexts ( texts ) ;
130+
131+ // Check if we need to detect dimension
132+ const knownModels = AzureOpenAIEmbedding . getSupportedModels ( ) ;
133+ let needsDimensionDetection = true ;
134+
135+ for ( const [ modelName , info ] of Object . entries ( knownModels ) ) {
136+ const modelPattern = modelName . replace ( / \. / g, '-' ) ;
137+ if ( this . config . deploymentName . toLowerCase ( ) . includes ( modelPattern ) ) {
138+ this . dimension = info . dimension ;
139+ needsDimensionDetection = false ;
140+ break ;
141+ }
142+ }
143+
144+ if ( needsDimensionDetection && this . dimension === 1536 ) {
145+ this . dimension = await this . detectDimension ( ) ;
146+ }
147+
148+ try {
149+ const response = await this . client . embeddings . create ( {
150+ model : this . config . deploymentName , // Use deployment name
151+ input : processedTexts ,
152+ encoding_format : 'float' ,
153+ } ) ;
154+
155+ // Update dimension from actual response
156+ this . dimension = response . data [ 0 ] . embedding . length ;
157+
158+ return response . data . map ( ( item ) => ( {
159+ vector : item . embedding ,
160+ dimension : this . dimension
161+ } ) ) ;
162+ } catch ( error ) {
163+ const errorMessage = error instanceof Error ? error . message : 'Unknown error' ;
164+
165+ // Provide specific error messages for common Azure issues
166+ if ( errorMessage . includes ( 'API key' ) || errorMessage . includes ( 'unauthorized' ) ) {
167+ throw new Error ( `Azure OpenAI authentication failed: ${ errorMessage } ) ;
168+ }
169+
170+ if ( errorMessage . includes ( 'deployment' ) || errorMessage . includes ( 'not found' ) ) {
171+ throw new Error ( `Azure OpenAI deployment '${ this . config . deploymentName } ${ errorMessage } ) ;
172+ }
173+
174+ if ( errorMessage . includes ( 'rate limit' ) || errorMessage . includes ( 'quota' ) ) {
175+ throw new Error ( `Azure OpenAI rate limit exceeded: ${ errorMessage } ) ;
176+ }
177+
178+ throw new Error ( `Failed to generate Azure OpenAI batch embeddings: ${ errorMessage } ) ;
179+ }
180+ }
181+
182+ getDimension ( ) : number {
183+ // Check if deployment name matches known models
184+ const knownModels = AzureOpenAIEmbedding . getSupportedModels ( ) ;
185+
186+ for ( const [ modelName , info ] of Object . entries ( knownModels ) ) {
187+ const modelPattern = modelName . replace ( / \. / g, '-' ) ;
188+ if ( this . config . deploymentName . toLowerCase ( ) . includes ( modelPattern ) ) {
189+ return info . dimension ;
190+ }
191+ }
192+
193+ // For custom deployments, return the current dimension
194+ // Note: This may be incorrect until detectDimension() is called
195+ console . warn ( `[AzureOpenAIEmbedding] ⚠️ getDimension() called for deployment '${ this . config . deploymentName } ${ this . dimension } ) ;
196+ return this . dimension ;
197+ }
198+
199+ getProvider ( ) : string {
200+ return 'Azure OpenAI' ;
201+ }
202+
203+ /**
204+ * Set deployment name
205+ * @param deploymentName Azure deployment name
206+ */
207+ async setDeployment ( deploymentName : string ) : Promise < void > {
208+ this . config . deploymentName = deploymentName ;
209+
210+ // Check if this matches a known model
211+ const knownModels = AzureOpenAIEmbedding . getSupportedModels ( ) ;
212+ let foundKnownModel = false ;
213+
214+ for ( const [ modelName , info ] of Object . entries ( knownModels ) ) {
215+ const modelPattern = modelName . replace ( / \. / g, '-' ) ;
216+ if ( deploymentName . toLowerCase ( ) . includes ( modelPattern ) ) {
217+ this . dimension = info . dimension ;
218+ foundKnownModel = true ;
219+ break ;
220+ }
221+ }
222+
223+ if ( ! foundKnownModel ) {
224+ // Detect dimension for custom deployment
225+ this . dimension = await this . detectDimension ( ) ;
226+ }
227+ }
228+
229+ /**
230+ * Get client instance (for advanced usage)
231+ */
232+ getClient ( ) : AzureOpenAI {
233+ return this . client ;
234+ }
235+
236+ /**
237+ * Get list of supported models (these are OpenAI model names, not Azure deployment names)
238+ * Azure deployments can be named anything, but often include the model name
239+ */
240+ static getSupportedModels ( ) : Record < string , { dimension : number ; description : string } > {
241+ return {
242+ 'text-embedding-3-small' : {
243+ dimension : 1536 ,
244+ description : 'High performance and cost-effective embedding model (recommended)'
245+ } ,
246+ 'text-embedding-3-large' : {
247+ dimension : 3072 ,
248+ description : 'Highest performance embedding model with larger dimensions'
249+ } ,
250+ 'text-embedding-ada-002' : {
251+ dimension : 1536 ,
252+ description : 'Legacy model (use text-embedding-3-small instead)'
253+ }
254+ } ;
255+ }
256+ }
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