added fixes to litellm

cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py

@@ -14,6 +14,8 @@ from tenacity import (
 )
 import litellm
 import os
+from urllib.parse import urlparse
+import httpx
 from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine
 from cognee.infrastructure.databases.exceptions import EmbeddingException
 from cognee.infrastructure.llm.tokenizer.HuggingFace import (
@@ -79,10 +81,26 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
             enable_mocking = str(enable_mocking).lower()
         self.mock = enable_mocking in ("true", "1", "yes")
 
+        # Validate provided custom embedding endpoint early to avoid long hangs later
+        if self.endpoint:
+            try:
+                parsed = urlparse(self.endpoint)
+            except Exception:
+                parsed = None
+            if not parsed or parsed.scheme not in ("http", "https") or not parsed.netloc:
+                logger.error(
+                    "Invalid EMBEDDING_ENDPOINT configured: '%s'. Expected a URL starting with http:// or https://",
+                    str(self.endpoint),
+                )
+                raise EmbeddingException(
+                    "Invalid EMBEDDING_ENDPOINT. Please set a valid URL (e.g., https://host:port) "
+                    "via environment variable EMBEDDING_ENDPOINT."
+                )
+
     @retry(
-        stop=stop_after_delay(128),
+        stop=stop_after_delay(30),
         wait=wait_exponential_jitter(2, 128),
-        retry=retry_if_not_exception_type(litellm.exceptions.NotFoundError),
+        retry=retry_if_not_exception_type((litellm.exceptions.NotFoundError, EmbeddingException)),
         before_sleep=before_sleep_log(logger, logging.DEBUG),
         reraise=True,
     )
@@ -111,12 +129,16 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
                 return [data["embedding"] for data in response["data"]]
             else:
                 async with embedding_rate_limiter_context_manager():
-                    response = await litellm.aembedding(
-                        model=self.model,
-                        input=text,
-                        api_key=self.api_key,
-                        api_base=self.endpoint,
-                        api_version=self.api_version,
+                    # Ensure each attempt does not hang indefinitely
+                    response = await asyncio.wait_for(
+                        litellm.aembedding(
+                            model=self.model,
+                            input=text,
+                            api_key=self.api_key,
+                            api_base=self.endpoint,
+                            api_version=self.api_version,
+                        ),
+                        timeout=30.0,
                     )
 
                 return [data["embedding"] for data in response.data]
@@ -154,6 +176,27 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
             logger.error("Context window exceeded for embedding text: %s", str(error))
             raise error
 
+        except asyncio.TimeoutError as e:
+            # Per-attempt timeout – likely an unreachable endpoint
+            logger.error(
+                "Embedding endpoint timed out. EMBEDDING_ENDPOINT='%s'. "
+                "Verify that the endpoint is reachable and correct.",
+                str(self.endpoint),
+            )
+            raise EmbeddingException(
+                "Embedding request timed out. Check EMBEDDING_ENDPOINT connectivity."
+            ) from e
+
+        except (httpx.ConnectError, httpx.ReadTimeout) as e:
+            logger.error(
+                "Failed to connect to embedding endpoint. EMBEDDING_ENDPOINT='%s'. "
+                "Ensure the URL is correct and the server is running.",
+                str(self.endpoint),
+            )
+            raise EmbeddingException(
+                "Cannot connect to embedding endpoint. Check EMBEDDING_ENDPOINT."
+            ) from e
+
         except (
             litellm.exceptions.BadRequestError,
             litellm.exceptions.NotFoundError,
@@ -162,8 +205,15 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
             raise EmbeddingException(f"Failed to index data points using model {self.model}") from e
 
         except Exception as error:
-            logger.error("Error embedding text: %s", str(error))
-            raise error
+            # Fall back to a clear, actionable message for connectivity/misconfiguration issues
+            logger.error(
+                "Error embedding text: %s. EMBEDDING_ENDPOINT='%s'.",
+                str(error),
+                str(self.endpoint),
+            )
+            raise EmbeddingException(
+                "Embedding failed due to an unexpected error. Verify EMBEDDING_ENDPOINT and provider settings."
+            ) from error
 
     def get_vector_size(self) -> int:
         """