llama-index-embeddings-bedrock
llama-index embeddings bedrock integration
Decision gist · record as of 2026-08-14
Yes. Low install friction, active maintenance, no known vulnerabilities, and permissive MIT license. Install if you need Bedrock embeddings and have AWS access. Skip if you prefer self-hosted embeddings or use a different embedding provider.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires AWS credentials (via environment variables, profile, or direct parameters) and access to Amazon Bedrock in your AWS account.
- Low install friction with a pure-Python wheel.
- Actively maintained as of 2026-08-14.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making this suitable for most production deployments.
last release 2026-08-14 (0 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,304,795 downloads/mo, #3,154 on PyPI
Alternatives
Verify before relying
pip install llama-index-embeddings-bedrock
from llama_index.embeddings.bedrock import BedrockEmbedding
embed_model = BedrockEmbedding(
model_name="cohere.embed-english-v3",
region_name="us-east-1",
)
embedding = embed_model.get_text_embedding("Hello world")- Performance characteristics and latency for batch embedding operations at scale.
- Cost implications of different Bedrock models and request volumes.
- Whether Application Inference Profile ARN mismatches produce silent failures or explicit errors.
What it is and what it does
This package integrates Amazon Bedrock embedding models with the embedding interface, letting you generate text embeddings using AWS-hosted models without managing your own embedding infrastructure. It supports multiple Bedrock foundation models—Amazon Titan variants and Cohere v3/v4—and handles the API differences between them transparently, including Cohere v4's multimodal capabilities and response format changes.
You configure it with a model name and AWS region, then call `get_text_embedding()` or `get_text_embedding_batch()` to convert text into vectors. AWS credentials flow through boto3's standard chain (environment variables, IAM roles, profiles, or explicit parameters). The integration also supports Application Inference Profiles for cost tracking and model usage governance in enterprise Bedrock deployments.
Use it for
- Embed documents in a RAG pipeline using Bedrock models without running your own embedding server.
- Build semantic search over text corpora by generating embeddings via Cohere or Titan through the integration.
- Use Cohere v4 multimodal embeddings to embed both text and images in a single unified vector space.
- Integrate Bedrock embeddings with automatic response format handling across model versions.
- Track embedding costs and usage through AWS Application Inference Profiles in governed environments.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Low install friction, active maintenance, no known vulnerabilities, and permissive MIT license. Install if you need Bedrock embeddings and have AWS access. Skip if you prefer self-hosted embeddings or use a different embedding provider.
Install
llama-index-embeddings-bedrock on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained as of 2026-08-14. Requires boto3 and llama-index-core as runtime dependencies.
Requires AWS credentials (via environment variables, profile, or direct parameters) and access to Amazon Bedrock in your AWS account.
License in practice
MIT license permits commercial and private use with minimal restrictions, making this suitable for most production deployments.
Quickstart
pip install llama-index-embeddings-bedrock
from llama_index.embeddings.bedrock import BedrockEmbedding
embed_model = BedrockEmbedding(
model_name="cohere.embed-english-v3",
region_name="us-east-1",
)
embedding = embed_model.get_text_embedding("Hello world")Verify before relying
- Performance characteristics and latency for batch embedding operations at scale.
- Cost implications of different Bedrock models and request volumes.
- Whether Application Inference Profile ARN mismatches produce silent failures or explicit errors.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesboto3llama-index-core |
| Maintenance | Actively maintained 0 days since the last release |
| First released | |
| Downloads | 2,304,795 / month, #3,154 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_embeddings_bedrock-0.8.3-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “bedrock embeddings integration”
- llama-index-embeddings-bedrockProvides Amazon Bedrock embedding models integration for LlamaIndex,…
- uipath-langchain-clientProvides LangChain-compatible chat models and embeddings that route…
- opentelemetry-instrumentation-bedrockAdds distributed tracing to AWS Bedrock API calls made through Boto3,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also llama-index-llms-bedrock-converse · llama-index-llms-bedrock · llama-index-embeddings-ollama · llama-index-embeddings-vertex · llama-index-embeddings-google-genai · aws_sdk_bedrock_runtime · aws-bedrock-token-generator · llama-index-embeddings-openai · llama-index-embeddings-langchain · llama-index-embeddings-huggingface