Add LM Studio support (#2425)

mem0/embeddings/configs.py

@@ -13,7 +13,7 @@ class EmbedderConfig(BaseModel):
     @field_validator("config")
     def validate_config(cls, v, values):
         provider = values.data.get("provider")
-        if provider in ["openai", "ollama", "huggingface", "azure_openai", "gemini", "vertexai", "together"]:
+        if provider in ["openai", "ollama", "huggingface", "azure_openai", "gemini", "vertexai", "together", "lmstudio"]:
             return v
         else:
             raise ValueError(f"Unsupported embedding provider: {provider}")

mem0/embeddings/lmstudio.py

@@ -0,0 +1,33 @@
+from typing import Literal, Optional
+
+from openai import OpenAI
+
+from mem0.configs.embeddings.base import BaseEmbedderConfig
+from mem0.embeddings.base import EmbeddingBase
+
+
+class LMStudioEmbedding(EmbeddingBase):
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        super().__init__(config)
+
+        self.config.model = self.config.model or "nomic-ai/nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
+        self.config.embedding_dims = self.config.embedding_dims or 1536
+        self.config.api_key = self.config.api_key or "lm-studio"
+
+        self.client = OpenAI(base_url=self.config.lmstudio_base_url, api_key=self.config.api_key)
+
+    def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
+        """
+        Get the embedding for the given text using LM Studio.
+        Args:
+            text (str): The text to embed.
+            memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
+        Returns:
+            list: The embedding vector.
+        """
+        text = text.replace("\n", " ")
+        return (
+            self.client.embeddings.create(input=[text], model=self.config.model)
+            .data[0]
+            .embedding
+        )