fix: HuggingFace tokenizer (#752)

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## Description
Resolve issue noticed by [RyabykinIlya](https://github.com/RyabykinIlya)
where too many HuggingFace requests have been sent due to the embedding
engine not working as a singleton per config

## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin.

---------

Signed-off-by: Ryabykin Ilya <ryabykinia@sibur.ru>
Co-authored-by: greshish <ryabykinia@yandex.ru>
Co-authored-by: Ryabykin Ilya <ryabykinia@sibur.ru>

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

@@ -14,6 +14,21 @@ class EmbeddingConfig(BaseSettings):
     huggingface_tokenizer: Optional[str] = None
     model_config = SettingsConfigDict(env_file=".env", extra="allow")
 
+    def to_dict(self) -> dict:
+        """
+        Serialize all embedding configuration settings to a dictionary.
+        """
+        return {
+            "embedding_provider": self.embedding_provider,
+            "embedding_model": self.embedding_model,
+            "embedding_dimensions": self.embedding_dimensions,
+            "embedding_endpoint": self.embedding_endpoint,
+            "embedding_api_key": self.embedding_api_key,
+            "embedding_api_version": self.embedding_api_version,
+            "embedding_max_tokens": self.embedding_max_tokens,
+            "huggingface_tokenizer": self.huggingface_tokenizer,
+        }
+
 
 @lru_cache
 def get_embedding_config():

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

@@ -1,40 +1,66 @@
 from cognee.infrastructure.databases.vector.embeddings.config import get_embedding_config
 from cognee.infrastructure.llm.config import get_llm_config
 from .EmbeddingEngine import EmbeddingEngine
+from functools import lru_cache
 
 
 def get_embedding_engine() -> EmbeddingEngine:
     config = get_embedding_config()
     llm_config = get_llm_config()
+    # Embedding engine has to be a singleton based on configuration to ensure too many requests won't be sent to HuggingFace
+    return create_embedding_engine(
+        config.embedding_provider,
+        config.embedding_model,
+        config.embedding_dimensions,
+        config.embedding_max_tokens,
+        config.embedding_endpoint,
+        config.embedding_api_key,
+        config.embedding_api_version,
+        config.huggingface_tokenizer,
+        llm_config.llm_api_key,
+    )
 
-    if config.embedding_provider == "fastembed":
+
+@lru_cache
+def create_embedding_engine(
+    embedding_provider,
+    embedding_model,
+    embedding_dimensions,
+    embedding_max_tokens,
+    embedding_endpoint,
+    embedding_api_key,
+    embedding_api_version,
+    huggingface_tokenizer,
+    llm_api_key,
+):
+    if embedding_provider == "fastembed":
         from .FastembedEmbeddingEngine import FastembedEmbeddingEngine
 
         return FastembedEmbeddingEngine(
-            model=config.embedding_model,
-            dimensions=config.embedding_dimensions,
-            max_tokens=config.embedding_max_tokens,
+            model=embedding_model,
+            dimensions=embedding_dimensions,
+            max_tokens=embedding_max_tokens,
         )
 
-    if config.embedding_provider == "ollama":
+    if embedding_provider == "ollama":
         from .OllamaEmbeddingEngine import OllamaEmbeddingEngine
 
         return OllamaEmbeddingEngine(
-            model=config.embedding_model,
-            dimensions=config.embedding_dimensions,
-            max_tokens=config.embedding_max_tokens,
-            endpoint=config.embedding_endpoint,
-            huggingface_tokenizer=config.huggingface_tokenizer,
+            model=embedding_model,
+            dimensions=embedding_dimensions,
+            max_tokens=embedding_max_tokens,
+            endpoint=embedding_endpoint,
+            huggingface_tokenizer=huggingface_tokenizer,
         )
 
     from .LiteLLMEmbeddingEngine import LiteLLMEmbeddingEngine
 
     return LiteLLMEmbeddingEngine(
-        provider=config.embedding_provider,
-        api_key=config.embedding_api_key or llm_config.llm_api_key,
-        endpoint=config.embedding_endpoint,
-        api_version=config.embedding_api_version,
-        model=config.embedding_model,
-        dimensions=config.embedding_dimensions,
-        max_tokens=config.embedding_max_tokens,
+        provider=embedding_provider,
+        api_key=embedding_api_key or llm_api_key,
+        endpoint=embedding_endpoint,
+        api_version=embedding_api_version,
+        model=embedding_model,
+        dimensions=embedding_dimensions,
+        max_tokens=embedding_max_tokens,
     )