diff --git a/lightrag/llm.py b/lightrag/llm.py
index b2bb99b7..99179128 100644
--- a/lightrag/llm.py
+++ b/lightrag/llm.py
@@ -583,6 +583,40 @@ async def openai_embedding(
     return np.array([dp.embedding for dp in response.data])
 
 
+async def fetch_data(url, headers, data):
+    async with aiohttp.ClientSession() as session:
+        async with session.post(url, headers=headers, json=data) as response:
+            response_json = await response.json()
+            data_list = response_json.get("data", [])
+            return data_list
+
+
+async def jina_embedding(
+    texts: list[str],
+    dimensions: int = 1024,
+    late_chunking: bool = False,
+    base_url: str = None,
+    api_key: str = None,
+) -> np.ndarray:
+    if api_key:
+        os.environ["JINA_API_KEY"] = api_key
+    url = "https://api.jina.ai/v1/embeddings" if not base_url else base_url
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {os.environ["JINA_API_KEY"]}",
+    }
+    data = {
+        "model": "jina-embeddings-v3",
+        "normalized": True,
+        "embedding_type": "float",
+        "dimensions": f"{dimensions}",
+        "late_chunking": late_chunking,
+        "input": texts,
+    }
+    data_list = await fetch_data(url, headers, data)
+    return np.array([dp["embedding"] for dp in data_list])
+
+
 @wrap_embedding_func_with_attrs(embedding_dim=2048, max_token_size=512)
 @retry(
     stop=stop_after_attempt(3),
diff --git a/lightrag_jinaai_demo.py b/lightrag_jinaai_demo.py
new file mode 100644
index 00000000..4daead75
--- /dev/null
+++ b/lightrag_jinaai_demo.py
@@ -0,0 +1,114 @@
+import numpy as np
+from lightrag import LightRAG, QueryParam
+from lightrag.utils import EmbeddingFunc
+from lightrag.llm import jina_embedding, openai_complete_if_cache
+import os
+import asyncio
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    return await jina_embedding(texts, api_key="YourJinaAPIKey")
+
+
+WORKING_DIR = "./dickens"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+
+async def llm_model_func(
+    prompt, system_prompt=None, history_messages=[], **kwargs
+) -> str:
+    return await openai_complete_if_cache(
+        "solar-mini",
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        api_key=os.getenv("UPSTAGE_API_KEY"),
+        base_url="https://api.upstage.ai/v1/solar",
+        **kwargs,
+    )
+
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=llm_model_func,
+    embedding_func=EmbeddingFunc(
+        embedding_dim=1024, max_token_size=8192, func=embedding_func
+    ),
+)
+
+
+async def lightraginsert(file_path, semaphore):
+    async with semaphore:
+        try:
+            with open(file_path, "r", encoding="utf-8") as f:
+                content = f.read()
+        except UnicodeDecodeError:
+            # If UTF-8 decoding fails, try other encodings
+            with open(file_path, "r", encoding="gbk") as f:
+                content = f.read()
+        await rag.ainsert(content)
+
+
+async def process_files(directory, concurrency_limit):
+    semaphore = asyncio.Semaphore(concurrency_limit)
+    tasks = []
+    for root, dirs, files in os.walk(directory):
+        for f in files:
+            file_path = os.path.join(root, f)
+            if f.startswith("."):
+                continue
+            tasks.append(lightraginsert(file_path, semaphore))
+    await asyncio.gather(*tasks)
+
+
+async def main():
+    try:
+        rag = LightRAG(
+            working_dir=WORKING_DIR,
+            llm_model_func=llm_model_func,
+            embedding_func=EmbeddingFunc(
+                embedding_dim=1024,
+                max_token_size=8192,
+                func=embedding_func,
+            ),
+        )
+
+        asyncio.run(process_files(WORKING_DIR, concurrency_limit=4))
+
+        # Perform naive search
+        print(
+            await rag.aquery(
+                "What are the top themes in this story?", param=QueryParam(mode="naive")
+            )
+        )
+
+        # Perform local search
+        print(
+            await rag.aquery(
+                "What are the top themes in this story?", param=QueryParam(mode="local")
+            )
+        )
+
+        # Perform global search
+        print(
+            await rag.aquery(
+                "What are the top themes in this story?",
+                param=QueryParam(mode="global"),
+            )
+        )
+
+        # Perform hybrid search
+        print(
+            await rag.aquery(
+                "What are the top themes in this story?",
+                param=QueryParam(mode="hybrid"),
+            )
+        )
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())