100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
import json
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import os
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from typing import Literal, Optional
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try:
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import boto3
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except ImportError:
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raise ImportError("The 'boto3' library is required. Please install it using 'pip install boto3'.")
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import numpy as np
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from mem0.configs.embeddings.base import BaseEmbedderConfig
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from mem0.embeddings.base import EmbeddingBase
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from mem0.memory.utils import extract_json
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class AWSBedrockEmbedding(EmbeddingBase):
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"""AWS Bedrock embedding implementation.
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This class uses AWS Bedrock's embedding models.
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"""
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def __init__(self, config: Optional[BaseEmbedderConfig] = None):
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super().__init__(config)
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self.config.model = self.config.model or "amazon.titan-embed-text-v1"
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# Get AWS config from environment variables or use defaults
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aws_access_key = os.environ.get("AWS_ACCESS_KEY_ID", "")
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aws_secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY", "")
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aws_region = os.environ.get("AWS_REGION", "us-west-2")
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# Check if AWS config is provided in the config
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if hasattr(self.config, "aws_access_key_id"):
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aws_access_key = self.config.aws_access_key_id
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if hasattr(self.config, "aws_secret_access_key"):
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aws_secret_key = self.config.aws_secret_access_key
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if hasattr(self.config, "aws_region"):
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aws_region = self.config.aws_region
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self.client = boto3.client(
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"bedrock-runtime",
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region_name=aws_region,
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aws_access_key_id=aws_access_key if aws_access_key else None,
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aws_secret_access_key=aws_secret_key if aws_secret_key else None,
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)
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def _normalize_vector(self, embeddings):
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"""Normalize the embedding to a unit vector."""
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emb = np.array(embeddings)
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norm_emb = emb / np.linalg.norm(emb)
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return norm_emb.tolist()
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def _get_embedding(self, text):
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"""Call out to Bedrock embedding endpoint."""
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# Format input body based on the provider
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provider = self.config.model.split(".")[0]
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input_body = {}
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if provider == "cohere":
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input_body["input_type"] = "search_document"
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input_body["texts"] = [text]
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else:
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# Amazon and other providers
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input_body["inputText"] = text
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body = json.dumps(input_body)
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try:
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response = self.client.invoke_model(
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body=body,
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modelId=self.config.model,
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accept="application/json",
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contentType="application/json",
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)
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response_body = json.loads(extract_json(response.get("body").read()))
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if provider == "cohere":
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embeddings = response_body.get("embeddings")[0]
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else:
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embeddings = response_body.get("embedding")
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return embeddings
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except Exception as e:
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raise ValueError(f"Error getting embedding from AWS Bedrock: {e}")
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def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
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"""
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Get the embedding for the given text using AWS Bedrock.
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Args:
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text (str): The text to embed.
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memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
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Returns:
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list: The embedding vector.
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"""
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return self._get_embedding(text)
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