import os import pipmaster as pm # Pipmaster for dynamic library install # install specific modules if not pm.is_installed("aiohttp"): pm.install("aiohttp") if not pm.is_installed("tenacity"): pm.install("tenacity") import numpy as np import base64 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() # Check if the error response is HTML (common for 502, 503, etc.) content_type = response.headers.get("content-type", "").lower() is_html_error = ( error_text.strip().startswith("") or "text/html" in content_type ) if is_html_error: # Provide clean, user-friendly error messages for HTML error pages if response.status == 502: clean_error = "Bad Gateway (502) - Jina AI service temporarily unavailable. Please try again in a few minutes." elif response.status == 503: clean_error = "Service Unavailable (503) - Jina AI service is temporarily overloaded. Please try again later." elif response.status == 504: clean_error = "Gateway Timeout (504) - Jina AI service request timed out. Please try again." else: clean_error = f"HTTP {response.status} - Jina AI service error. Please try again later." else: # Use original error text if it's not HTML clean_error = error_text logger.error(f"Jina API error {response.status}: {clean_error}") raise aiohttp.ClientResponseError( request_info=response.request_info, history=response.history, status=response.status, message=f"Jina API error: {clean_error}", ) 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, model_name="jina-embeddings-v4", supports_asymmetric=True, ) @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], model: str = "jina-embeddings-v4", embedding_dim: int = 2048, late_chunking: bool = False, base_url: str = None, api_key: str = None, context: str | None = None, task: str | None = None, ) -> np.ndarray: """Generate embeddings for a list of texts using Jina AI's API. Args: texts: List of texts to embed. model: The Jina embedding model to use (default: jina-embeddings-v4). Supported models: jina-embeddings-v3, jina-embeddings-v4, etc. embedding_dim: The embedding dimensions (default: 2048 for jina-embeddings-v4). **IMPORTANT**: This parameter is automatically injected by the EmbeddingFunc wrapper. Do NOT manually pass this parameter when calling the function directly. The dimension is controlled by the @wrap_embedding_func_with_attrs decorator. Manually passing a different value will trigger a warning and be ignored. When provided (by EmbeddingFunc), it will be passed to the Jina API for dimension reduction. 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. context: The embedding context - "query" for search queries, "document" for indexed content. **IMPORTANT**: This parameter is automatically injected by the EmbeddingFunc wrapper when supports_asymmetric=True. When ``task`` is left at its default of None, ``context`` drives the task selection. task: Embedding task mode. Default is None so that ``context`` (when present) picks the right Jina task: - "retrieval.query" for context="query" - "retrieval.passage" for context="document" - "text-matching" otherwise (true backward-compatible default) Any explicit non-None task value overrides context-based selection. 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 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']}", } # Determine task based on context if not explicitly provided if task is None: if context == "query": task = "retrieval.query" elif context == "document": task = "retrieval.passage" else: task = "text-matching" # Default for backward compatibility data = { "model": model, "task": task, "dimensions": embedding_dim, "embedding_type": "base64", "input": texts, } # 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: {embedding_dim}" ) 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( [ np.frombuffer(base64.b64decode(dp["embedding"]), dtype=np.float32) 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