diff --git a/README.md b/README.md
index d793006..0a61b3e 100644
--- a/README.md
+++ b/README.md
@@ -51,7 +51,7 @@ A Model Context Protocol (MCP) server that enables semantic search and retrieval
 
 ## Version
 
-Current version: 0.1.6
+Current version: 0.1.7
 
 ## Features
 
@@ -140,6 +140,24 @@ For OpenAI instead of Ollama:
 }
 ```
 
+For OpenAI with a proxy service:
+```json
+{
+		"mcpServers": {
+				"ragdocs": {
+						"command": "node",
+      "args": ["C:/Users/YOUR_USERNAME/AppData/Roaming/npm/node_modules/@qpd-v/mcp-server-ragdocs/build/index.js"],
+      "env": {
+        "QDRANT_URL": "http://127.0.0.1:6333",
+        "EMBEDDING_PROVIDER": "openai",
+        "OPENAI_API_KEY": "your-openai-api-key",
+        "OPENAI_BASE_URL": "https://your-proxy-service.example.com/v1"
+      }
+    }
+  }
+}
+```
+
 2. Using local development setup:
 ```json
 {
@@ -201,6 +219,26 @@ Windows Setup with OpenAI:
 }
 ```
 
+Windows Setup with OpenAI proxy:
+```json
+{
+		"mcpServers": {
+				"ragdocs": {
+						"command": "C:\\Program Files\\nodejs\\node.exe",
+						"args": [
+								"C:\\Users\\YOUR_USERNAME\\AppData\\Roaming\\npm\\node_modules\\@qpd-v/mcp-server-ragdocs\\build\\index.js"
+						],
+						"env": {
+								"QDRANT_URL": "http://127.0.0.1:6333",
+								"EMBEDDING_PROVIDER": "openai",
+								"OPENAI_API_KEY": "your-openai-api-key",
+								"OPENAI_BASE_URL": "https://your-proxy-service.example.com/v1"
+						}
+				}
+		}
+}
+```
+
 2. macOS Setup with Ollama:
 ```json
 {
@@ -248,6 +286,19 @@ With OpenAI:
 }
 ```
 
+With OpenAI proxy:
+```json
+{
+		"env": {
+				"QDRANT_URL": "https://your-cluster-url.qdrant.tech",
+				"QDRANT_API_KEY": "your-qdrant-api-key",
+				"EMBEDDING_PROVIDER": "openai",
+				"OPENAI_API_KEY": "your-openai-api-key",
+				"OPENAI_BASE_URL": "https://your-proxy-service.example.com/v1"
+		}
+}
+```
+
 ### Environment Variables
 
 #### Qdrant Configuration
@@ -263,6 +314,7 @@ With OpenAI:
   - For OpenAI: defaults to 'text-embedding-3-small'
 - `OLLAMA_URL` (optional): URL of your Ollama instance (defaults to http://localhost:11434)
 - `OPENAI_API_KEY` (required if using OpenAI): Your OpenAI API key
+- `OPENAI_BASE_URL` (optional): Base URL for OpenAI API when using proxy services
 
 ## Available Tools
 
@@ -356,6 +408,11 @@ MIT
    - Check npm is in PATH: `npm -v`
    - Verify global installation: `npm list -g @qpd-v/mcp-server-ragdocs`
 
+5. **OpenAI API Proxy Issues**
+   - Ensure the base URL is properly formatted (e.g., https://your-proxy-service.example.com/v1)
+   - Verify the proxy service is compatible with OpenAI's API format
+   - Check if your API key is valid for the proxy service
+
 For other issues, please check:
 - Docker logs: `docker logs $(docker ps -q --filter ancestor=qdrant/qdrant)`
 - Ollama status: `ollama list`
diff --git a/package.json b/package.json
index feac11f..137f81e 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "@qpd-v/mcp-server-ragdocs",
-  "version": "0.1.6",
+  "version": "0.1.7",
   "description": "A Model Context Protocol server for fetching and storing documentation in a vector database, enabling semantic search and retrieval to augment LLM capabilities with relevant documentation context.",
   "private": false,
   "type": "module",
diff --git a/src/embeddings.ts b/src/embeddings.ts
index fe70ceb..4f9b542 100644
--- a/src/embeddings.ts
+++ b/src/embeddings.ts
@@ -42,8 +42,11 @@ export class OpenAIProvider implements EmbeddingProvider {
   private client: OpenAI;
   private model: string;
 
-  constructor(apiKey: string, model: string = 'text-embedding-3-small') {
-    this.client = new OpenAI({ apiKey });
+  constructor(apiKey: string, model: string = 'text-embedding-3-small', baseURL?: string) {
+    this.client = new OpenAI({ 
+      apiKey,
+      baseURL: baseURL || undefined
+    });
     this.model = model;
   }
 
@@ -91,6 +94,7 @@ export class EmbeddingService {
     provider: 'ollama' | 'openai';
     apiKey?: string;
     model?: string;
+    baseURL?: string;
   }): EmbeddingService {
     switch (config.provider) {
       case 'ollama':
@@ -102,7 +106,7 @@ export class EmbeddingService {
             'OpenAI API key is required'
           );
         }
-        return new EmbeddingService(new OpenAIProvider(config.apiKey, config.model));
+        return new EmbeddingService(new OpenAIProvider(config.apiKey, config.model, config.baseURL));
       default:
         throw new McpError(
           ErrorCode.InvalidParams,
diff --git a/src/index.ts b/src/index.ts
index 045820e..4765ce0 100644
--- a/src/index.ts
+++ b/src/index.ts
@@ -22,6 +22,7 @@ const COLLECTION_NAME = 'documentation';
 const EMBEDDING_PROVIDER = process.env.EMBEDDING_PROVIDER || 'ollama';
 const EMBEDDING_MODEL = process.env.EMBEDDING_MODEL;
 const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
+const OPENAI_BASE_URL = process.env.OPENAI_BASE_URL;
 
 interface QdrantCollectionConfig {
 		params: {
@@ -116,7 +117,8 @@ class RagDocsServer {
     this.embeddingService = EmbeddingService.createFromConfig({
       provider: EMBEDDING_PROVIDER as 'ollama' | 'openai',
       model: EMBEDDING_MODEL,
-      apiKey: OPENAI_API_KEY
+      apiKey: OPENAI_API_KEY,
+      baseURL: OPENAI_BASE_URL
     });
 
     this.setupToolHandlers();
@@ -365,6 +367,10 @@ class RagDocsServer {
                 type: 'string',
                 description: 'Model to use for embeddings',
               },
+              baseURL: {
+                type: 'string',
+                description: 'Base URL for OpenAI API (optional, for proxy services)',
+              },
             },
             required: ['text'],
           },
@@ -409,7 +415,8 @@ class RagDocsServer {
       const tempEmbeddingService = EmbeddingService.createFromConfig({
         provider: args.provider || 'ollama',
         apiKey: args.apiKey,
-        model: args.model
+        model: args.model,
+        baseURL: args.baseURL
       });
 
       const embedding = await tempEmbeddingService.generateEmbeddings(args.text);