feat: Integrate Jina embeddings API support

- Implemented Jina embedding function
- Add new EMBEDDING_BINDING type of jina for LightRAG Server
- Add env var sample

env.example

@@ -123,7 +123,7 @@ LLM_BINDING_API_KEY=your_api_key
 ####################################################################################
 ### Embedding Configuration (Should not be changed after the first file processed)
 ####################################################################################
-### Embedding Binding type: openai, ollama, lollms, azure_openai
+### Embedding Binding type: openai, ollama, lollms, azure_openai, jina
 EMBEDDING_BINDING=ollama
 EMBEDDING_MODEL=bge-m3:latest
 EMBEDDING_DIM=1024
@@ -139,6 +139,13 @@ EMBEDDING_BINDING_HOST=http://localhost:11434
 # AZURE_EMBEDDING_ENDPOINT=your_endpoint
 # AZURE_EMBEDDING_API_KEY=your_api_key
 
+### Jina AI Embedding 
+EMBEDDING_BINDING=jina
+EMBEDDING_BINDING_HOST=https://api.jina.ai/v1/embeddings
+EMBEDDING_MODEL=jina-embeddings-v4
+EMBEDDING_DIM=2048
+EMBEDDING_BINDING_API_KEY=your_api_key
+
 ############################
 ### Data storage selection
 ############################

lightrag/api/lightrag_server.py

@@ -89,7 +89,7 @@ def create_app(args):
     ]:
         raise Exception("llm binding not supported")
 
-    if args.embedding_binding not in ["lollms", "ollama", "openai", "azure_openai"]:
+    if args.embedding_binding not in ["lollms", "ollama", "openai", "azure_openai", "jina"]:
         raise Exception("embedding binding not supported")
 
     # Set default hosts if not provided
@@ -213,6 +213,8 @@ def create_app(args):
     if args.llm_binding_host == "openai-ollama" or args.embedding_binding == "ollama":
         from lightrag.llm.openai import openai_complete_if_cache
         from lightrag.llm.ollama import ollama_embed
+    if args.embedding_binding == "jina":
+        from lightrag.llm.jina import jina_embed
 
     async def openai_alike_model_complete(
         prompt,
@@ -284,6 +286,13 @@ def create_app(args):
             api_key=args.embedding_binding_api_key,
         )
         if args.embedding_binding == "azure_openai"
+        else jina_embed(
+            texts,
+            dimensions=args.embedding_dim,
+            base_url=args.embedding_binding_host,
+            api_key=args.embedding_binding_api_key,
+        )
+        if args.embedding_binding == "jina"
         else openai_embed(
             texts,
             model=args.embedding_model,

lightrag/llm/jina.py

@@ -2,45 +2,111 @@ import os
 import pipmaster as pm  # Pipmaster for dynamic library install
 
 # install specific modules
-if not pm.is_installed("lmdeploy"):
-    pm.install("lmdeploy")
+if not pm.is_installed("aiohttp"):
+    pm.install("aiohttp")
 if not pm.is_installed("tenacity"):
     pm.install("tenacity")
 
-
 import numpy as np
 import aiohttp
+from tenacity import (
+    retry,
+    stop_after_attempt,
+    wait_exponential,
+    retry_if_exception_type,
+)
+from lightrag.utils import wrap_embedding_func_with_attrs, logger
 
 
 async def fetch_data(url, headers, data):
     async with aiohttp.ClientSession() as session:
         async with session.post(url, headers=headers, json=data) as response:
+            if response.status != 200:
+                error_text = await response.text()
+                logger.error(f"Jina API error {response.status}: {error_text}")
+                raise aiohttp.ClientResponseError(
+                    request_info=response.request_info,
+                    history=response.history,
+                    status=response.status,
+                    message=f"Jina API error: {error_text}"
+                )
             response_json = await response.json()
             data_list = response_json.get("data", [])
             return data_list
 
 
+@wrap_embedding_func_with_attrs(embedding_dim=2048, max_token_size=8192)
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=60),
+    retry=(
+        retry_if_exception_type(aiohttp.ClientError)
+        | retry_if_exception_type(aiohttp.ClientResponseError)
+    ),
+)
 async def jina_embed(
     texts: list[str],
-    dimensions: int = 1024,
+    dimensions: int = 2048,
     late_chunking: bool = False,
     base_url: str = None,
     api_key: str = None,
 ) -> np.ndarray:
+    """Generate embeddings for a list of texts using Jina AI's API.
+
+    Args:
+        texts: List of texts to embed.
+        dimensions: The embedding dimensions (default: 2048 for jina-embeddings-v4).
+        late_chunking: Whether to use late chunking.
+        base_url: Optional base URL for the Jina API.
+        api_key: Optional Jina API key. If None, uses the JINA_API_KEY environment variable.
+
+    Returns:
+        A numpy array of embeddings, one per input text.
+
+    Raises:
+        aiohttp.ClientError: If there is a connection error with the Jina API.
+        aiohttp.ClientResponseError: If the Jina API returns an error response.
+    """
     if api_key:
         os.environ["JINA_API_KEY"] = api_key
-    url = "https://api.jina.ai/v1/embeddings" if not base_url else base_url
+    
+    if "JINA_API_KEY" not in os.environ:
+        raise ValueError("JINA_API_KEY environment variable is required")
+    
+    url = base_url or "https://api.jina.ai/v1/embeddings"
     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,
+        "model": "jina-embeddings-v4",
+        "task": "text-matching",
+        "dimensions": dimensions,
         "input": texts,
     }
-    data_list = await fetch_data(url, headers, data)
-    return np.array([dp["embedding"] for dp in data_list])
+    
+    # Only add optional parameters if they have non-default values
+    if late_chunking:
+        data["late_chunking"] = late_chunking
+    
+    logger.debug(f"Jina embedding request: {len(texts)} texts, dimensions: {dimensions}")
+    
+    try:
+        data_list = await fetch_data(url, headers, data)
+        
+        if not data_list:
+            logger.error("Jina API returned empty data list")
+            raise ValueError("Jina API returned empty data list")
+        
+        if len(data_list) != len(texts):
+            logger.error(f"Jina API returned {len(data_list)} embeddings for {len(texts)} texts")
+            raise ValueError(f"Jina API returned {len(data_list)} embeddings for {len(texts)} texts")
+        
+        embeddings = np.array([dp["embedding"] for dp in data_list])
+        logger.debug(f"Jina embeddings generated: shape {embeddings.shape}")
+        
+        return embeddings
+        
+    except Exception as e:
+        logger.error(f"Jina embedding error: {e}")
+        raise