AWS Bedrock Embeddings

Overview

AWS Bedrock provides access to multiple embedding models including Amazon Titan, Cohere, and Marengo through a unified API. Offers enterprise-grade security, guardrails, and CloudWatch integration. Provider Class: BedrockEmbedding Config Class: BedrockEmbeddingConfig

Dependencies

pip install boto3

Examples

from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import BedrockEmbedding, BedrockEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Create embedding provider
embedding = BedrockEmbedding(BedrockEmbeddingConfig(
    model_name="amazon.titan-embed-text-v1",
    region_name="us-east-1"
))

# Setup KnowledgeBase
vectordb = ChromaProvider(ChromaConfig(
    collection_name="bedrock_docs",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.IN_MEMORY)
))

kb = KnowledgeBase(
    sources=["document.txt"],
    embedding_provider=embedding,
    vectordb=vectordb
)

# Query with Agent
agent = Agent("openai/gpt-4o")
task = Task("What is this document about?", context=[kb])
result = agent.do(task)
print(result)

Parameters

Parameter	Type	Description	Default	Source
`model_name`	`str`	Bedrock embedding model name	`"amazon.titan-embed-text-v1"`	Specific
`model_id`	`str \| None`	Full Bedrock model ID (overrides model_name)	`None`	Specific
`aws_access_key_id`	`str \| None`	AWS access key ID	`None`	Specific
`aws_secret_access_key`	`str \| None`	AWS secret access key	`None`	Specific
`aws_session_token`	`str \| None`	AWS session token	`None`	Specific
`region_name`	`str`	AWS region	`"us-east-1"`	Specific
`profile_name`	`str \| None`	AWS profile name	`None`	Specific
`inference_profile`	`str \| None`	Bedrock inference profile	`None`	Specific
`enable_guardrails`	`bool`	Enable Bedrock guardrails	`True`	Specific
`guardrail_id`	`str \| None`	Custom guardrail ID	`None`	Specific
`enable_model_caching`	`bool`	Enable model response caching	`True`	Specific
`prefer_provisioned_throughput`	`bool`	Prefer provisioned throughput models	`False`	Specific
`enable_cloudwatch_logging`	`bool`	Enable CloudWatch logging	`True`	Specific
`log_group_name`	`str \| None`	CloudWatch log group name	`None`	Specific
`batch_size`	`int`	Batch size for document embedding	`100`	Base
`max_retries`	`int`	Maximum number of retries on failure	`3`	Base
`normalize_embeddings`	`bool`	Whether to normalize embeddings to unit length	`True`	Base

GET STARTED

CONCEPTS

DEPLOYMENT

FURTHER READINGS

AWS Bedrock Embeddings

Overview

Dependencies

Examples

Parameters

GET STARTED

CONCEPTS

DEPLOYMENT

FURTHER READINGS

​Overview

​Dependencies

​Examples

​Parameters

Overview

Dependencies

Examples

Parameters