Support for AWS Bedrock Embeddings (#2660)

mem0/embeddings/aws_bedrock.py

@@ -0,0 +1,78 @@
+import json
+from typing import Literal, Optional
+
+try:
+    import boto3
+except ImportError:
+    raise ImportError("The 'boto3' library is required. Please install it using 'pip install boto3'.")
+
+import numpy as np
+
+from mem0.configs.embeddings.base import BaseEmbedderConfig
+from mem0.embeddings.base import EmbeddingBase
+
+
+class AWSBedrockEmbedding(EmbeddingBase):
+    """AWS Bedrock embedding implementation.
+
+    This class uses AWS Bedrock's embedding models.
+    """
+
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        super().__init__(config)
+
+        self.config.model = self.config.model or "amazon.titan-embed-text-v1"
+        self.client = boto3.client("bedrock-runtime")
+
+    def _normalize_vector(self, embeddings):
+        """Normalize the embedding to a unit vector."""
+        emb = np.array(embeddings)
+        norm_emb = emb / np.linalg.norm(emb)
+        return norm_emb.tolist()
+
+    def _get_embedding(self, text):
+        """Call out to Bedrock embedding endpoint."""
+
+        # Format input body based on the provider
+        provider = self.config.model.split(".")[0]
+        input_body = {}
+
+        if provider == "cohere":
+            input_body["input_type"] = "search_document"
+            input_body["texts"] = [text]
+        else:
+            # Amazon and other providers
+            input_body["inputText"] = text
+
+        body = json.dumps(input_body)
+
+        try:
+            response = self.client.invoke_model(
+                body=body,
+                modelId=self.config.model,
+                accept="application/json",
+                contentType="application/json",
+            )
+
+            response_body = json.loads(response.get("body").read())
+
+            if provider == "cohere":
+                embeddings = response_body.get("embeddings")[0]
+            else:
+                embeddings = response_body.get("embedding")
+
+            return embeddings
+        except Exception as e:
+            raise ValueError(f"Error getting embedding from AWS Bedrock: {e}")
+
+    def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
+        """
+        Get the embedding for the given text using AWS Bedrock.
+
+        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.
+        """
+        return self._get_embedding(text)

mem0/embeddings/configs.py

@@ -23,6 +23,7 @@ class EmbedderConfig(BaseModel):
             "together",
             "lmstudio",
             "langchain",
+            "aws_bedrock",
         ]:
             return v
         else:

mem0/utils/factory.py

@@ -53,6 +53,7 @@ class EmbedderFactory:
         "together": "mem0.embeddings.together.TogetherEmbedding",
         "lmstudio": "mem0.embeddings.lmstudio.LMStudioEmbedding",
         "langchain": "mem0.embeddings.langchain.LangchainEmbedding",
+        "aws_bedrock": "mem0.embeddings.aws_bedrock.AWSBedrockEmbedding",
     }
 
     @classmethod