diff --git a/lightrag/llm.py b/lightrag/llm.py
index 06d75d01..5c923acb 100644
--- a/lightrag/llm.py
+++ b/lightrag/llm.py
@@ -5,9 +5,12 @@ import aioboto3
 import aiohttp
 import numpy as np
 import ollama
+
+from openai import AsyncOpenAI, APIConnectionError, RateLimitError, Timeout, AsyncAzureOpenAI
+
 import base64
 import struct
-from openai import AsyncOpenAI, APIConnectionError, RateLimitError, Timeout
+
 from tenacity import (
     retry,
     stop_after_attempt,
@@ -64,6 +67,49 @@ async def openai_complete_if_cache(
         )
     return response.choices[0].message.content
 
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=10),
+    retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
+)
+async def azure_openai_complete_if_cache(model,
+    prompt,
+    system_prompt=None,
+    history_messages=[],
+    base_url=None,
+    api_key=None,
+    **kwargs):
+    if api_key:
+        os.environ["AZURE_OPENAI_API_KEY"] = api_key
+    if base_url:
+        os.environ["AZURE_OPENAI_ENDPOINT"] = base_url
+
+    openai_async_client = AsyncAzureOpenAI(azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
+                                           api_key=os.getenv("AZURE_OPENAI_API_KEY"),
+                                           api_version=os.getenv("AZURE_OPENAI_API_VERSION"))
+
+    hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
+    messages = []
+    if system_prompt:
+        messages.append({"role": "system", "content": system_prompt})
+    messages.extend(history_messages)
+    if prompt is not None:
+        messages.append({"role": "user", "content": prompt})
+    if hashing_kv is not None:
+        args_hash = compute_args_hash(model, messages)
+        if_cache_return = await hashing_kv.get_by_id(args_hash)
+        if if_cache_return is not None:
+            return if_cache_return["return"]
+
+    response = await openai_async_client.chat.completions.create(
+        model=model, messages=messages, **kwargs
+    )
+
+    if hashing_kv is not None:
+        await hashing_kv.upsert(
+            {args_hash: {"return": response.choices[0].message.content, "model": model}}
+        )
+    return response.choices[0].message.content
 
 class BedrockError(Exception):
     """Generic error for issues related to Amazon Bedrock"""
@@ -273,6 +319,16 @@ async def gpt_4o_mini_complete(
         **kwargs,
     )
 
+async def azure_openai_complete(
+        prompt, system_prompt=None, history_messages=[], **kwargs
+) -> str:
+    return await azure_openai_complete_if_cache(
+        "conversation-4o-mini",
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        **kwargs,
+    )
 
 async def bedrock_complete(
     prompt, system_prompt=None, history_messages=[], **kwargs
@@ -335,6 +391,34 @@ async def openai_embedding(
     )
     return np.array([dp.embedding for dp in response.data])
 
+
+@wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192)
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=10),
+    retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
+)
+async def azure_openai_embedding(
+    texts: list[str],
+    model: str = "text-embedding-3-small",
+    base_url: str = None,
+    api_key: str = None,
+) -> np.ndarray:
+    if api_key:
+        os.environ["AZURE_OPENAI_API_KEY"] = api_key
+    if base_url:
+        os.environ["AZURE_OPENAI_ENDPOINT"] = base_url
+
+    openai_async_client = AsyncAzureOpenAI(azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
+                                           api_key=os.getenv("AZURE_OPENAI_API_KEY"),
+                                           api_version=os.getenv("AZURE_OPENAI_API_VERSION"))
+
+    response = await openai_async_client.embeddings.create(
+        model=model, input=texts, encoding_format="float"
+    )
+    return np.array([dp.embedding for dp in response.data])
+
+
 @retry(
     stop=stop_after_attempt(3),
     wait=wait_exponential(multiplier=1, min=4, max=60),
@@ -379,6 +463,7 @@ async def siliconcloud_embedding(
         embeddings.append(float_array)
     return np.array(embeddings)
 
+
 # @wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192)
 # @retry(
 #     stop=stop_after_attempt(3),