support custom openAi models + breaking change: on query method

README.md

@@ -632,13 +632,17 @@ Once these models are deployed, using Azure OpenAI instead of the regular OpenAI
 
 ```bash
 # Set this to `azure`
-export OPENAI_API_TYPE=azure
+OPENAI_API_TYPE=azure
 # The API version you want to use
-export OPENAI_API_VERSION=2023-03-15-preview
+AZURE_OPENAI_API_VERSION=2023-05-15
 # The base URL for your Azure OpenAI resource.  You can find this in the Azure portal under your Azure OpenAI resource.
-export OPENAI_API_BASE=https://your-resource-name.openai.azure.com
-# The API key for your Azure OpenAI resource.  You can find this in the Azure portal under your Azure OpenAI resource.
-export OPENAI_API_KEY=<Your Azure OpenAI API key>
+export AZURE_OPENAI_BASE_PATH=https://your-resource-name.openai.azure.com/openai/deployments
+# The API key1 or key2 for your Azure OpenAI resource
+export AZURE_OPENAI_API_KEY=<Your Azure OpenAI API key>
+# The deployment name you used for your embedding model
+AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=text-embedding-ada-002
+# The deployment name you used for your llm
+AZURE_OPENAI_API_DEPLOYMENT_NAME=gpt-35-turbo
 ```
 
 # Examples

package.json

@@ -1,6 +1,6 @@
 {
     "name": "@llm-tools/embedjs",
-    "version": "0.0.48",
+    "version": "0.0.49",
     "description": "A NodeJS RAG framework to easily work with LLMs and custom datasets",
     "main": "dist/index.js",
     "types": "dist/index.d.ts",

src/core/llm-application-builder.ts

@@ -88,11 +88,12 @@ export class LLMApplicationBuilder {
         return this;
     }
 
-    setModel(model: SIMPLE_MODELS | BaseModel) {
+    setModel(model: string | SIMPLE_MODELS | BaseModel) {
         if (typeof model === 'object') this.model = model;
         else {
             if (model === SIMPLE_MODELS.OPENAI_GPT3_TURBO) this.model = new OpenAi(this.temperature, 'gpt-3.5-turbo');
             else if (model === SIMPLE_MODELS.OPENAI_GPT4) this.model = new OpenAi(this.temperature, 'gpt-4');
+            else this.model = new OpenAi(this.temperature, model);
         }
 
         return this;

src/core/llm-application.ts

@@ -154,8 +154,19 @@ export class LLMApplication {
         };
     }
 
-    public async query(userQuery: string, conversationId?: string): Promise<string> {
+    public async query(
+        userQuery: string,
+        conversationId?: string,
+    ): Promise<{
+        result: string;
+        sources: string[];
+    }> {
         const context = await this.getContext(userQuery);
-        return this.model.query(context.prompt, userQuery, context.supportingContext, conversationId);
+        const sources = context.supportingContext.map((chunk) => chunk.metadata.source);
+
+        return {
+            sources,
+            result: await this.model.query(context.prompt, userQuery, context.supportingContext, conversationId),
+        };
     }
 }