Registry / llm-agents / llama-index-llms-bedrock

llama-index-llms-bedrock

library0.5.0pypypi✓ verified 85d ago

This library provides an integration for connecting LlamaIndex with AWS Bedrock Large Language Models (LLMs). It allows users to leverage various Bedrock models for text completion and chat functionalities within their LlamaIndex applications, including streaming responses. The library is part of the broader LlamaIndex ecosystem, which maintains an active development pace with frequent updates and a move towards modular integrations.

llm-agents aws ai-ml

pip install llama-index-llms-bedrock

package ↗homepage ↗

INSTALL

IMPORT

SIG · LLAMA-INDEX-LLMS-B

llama-index-llms-bedrock

llm-agentspythonv0.5.0

Install

24.8s avg

Import

7876ms

Disk

348MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.5.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

installs and imports cleanly · install 0.0s · import 6.536s · 318.8MB

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 24.8s · import 6.066s · 315MB

348MB installed

● package 348MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

Bedrock

✓ from llama_index.llms.bedrock import Bedrock

This quickstart demonstrates how to initialize the `Bedrock` LLM, perform a simple text completion, engage in a chat conversation using `ChatMessage` objects, and stream a completion response. Authentication is handled via environment variables for AWS credentials (or a configured AWS CLI profile).

import os from llama_index.llms.bedrock import Bedrock from llama_index.core.llms import ChatMessage # Configure AWS credentials via environment variables for security # Ensure AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_REGION are set # Or configure AWS CLI profile and use profile_name parameter # Initialize the Bedrock LLM llm = Bedrock( model=os.environ.get('BEDROCK_MODEL_ID', 'amazon.titan-text-express-v1'), region_name=os.environ.get('AWS_REGION', 'us-east-1'), temperature=0.7, max_tokens=512 ) # Example 1: Simple completion print("\n--- Completion Example ---") response_complete = llm.complete("What is the capital of France?") print(response_complete) # Example 2: Chat with messages print("\n--- Chat Example ---") messages = [ ChatMessage(role="system", content="You are a helpful AI assistant."), ChatMessage(role="user", content="Tell me a short story about a brave knight."), ] response_chat = llm.chat(messages) print(response_chat.message.content) # Example 3: Streaming completion print("\n--- Streaming Completion Example ---") print("Streaming response: ", end="") stream_resp = llm.stream_complete("Explain the concept of AI in simple terms.") for r in stream_resp: print(r.delta, end="") print("\n")

Debug

Known issues

deprecatedThe `llama-index-llms-bedrock` package is officially deprecated in favor of `llama-index-llms-bedrock-converse`. The `bedrock-converse` package utilizes the newer Bedrock Converse API, which is the recommended way to interact with Bedrock LLMs and provides enhanced capabilities like native function calling.

fix

Migrate your code to use `llama-index-llms-bedrock-converse`. Install it with `pip install llama-index-llms-bedrock-converse` and import `BedrockConverse` from `llama_index.llms.bedrock_converse`.

affects: 0.5.0 and earlier

gotchaAWS region configuration is critical. Ensure the `region_name` parameter passed to `Bedrock` matches the AWS region where your Bedrock model access is granted and where you want to invoke the model. Mismatched regions or an unset region will lead to `NoRegionError` or invocation failures.

fix

Explicitly set `region_name` when initializing `Bedrock` (e.g., `region_name='us-east-1'`) or ensure the `AWS_REGION` environment variable is correctly set.

affects: All versions

gotchaIncorrect IAM permissions on your AWS role or user can lead to `BedrockException` indicating 'User is not authorized to perform: bedrock:InvokeModel'.

fix

Verify that the IAM role or user credentials used have the necessary `bedrock:InvokeModel` permissions for the specific foundation model and region you are trying to access. Check for any explicit deny policies.

affects: All versions

gotchaFor Anthropic Claude models (and potentially others) within `llama-index-llms-bedrock`, there can be prompt formatting differences or type mismatches when using `llm.complete()` versus `llm.chat()`. Specifically, `complete` expects a string prompt, while `chat` expects a list of `ChatMessage` objects. Attempting to pass a list of messages to `complete` (or a string to `chat` without proper wrapping) can result in a `TypeError` or unexpected behavior.

fix

Ensure you use `llm.complete(prompt_string)` for single-turn text completions and `llm.chat([ChatMessage(role='user', content='...')])` for multi-turn conversations or when expecting a chat-like interface. Check model-specific documentation for any unique prompt requirements.

affects: 0.5.0 and earlier

gotchaPassing `guardrail_config` parameters to `Bedrock.complete` might not be correctly processed due to an underlying issue where `kwargs` can be lost. This means Bedrock Guardrails configured this way might not apply to your model invocations.

fix

This is a known issue. Consider using `llama-index-llms-bedrock-converse` as it has seen fixes for similar issues regarding parameter passing. Alternatively, implement guardrails at the AWS service level or ensure your `guardrail_config` is being correctly translated and applied in your specific `llama-index` version (check the relevant GitHub issues for updates).

affects: 0.5.0 and earlier

Errors

Common errors & fixes

botocore.exceptions.NoRegionError: You must specify a region.

The AWS region was not specified during Bedrock LLM initialization or via environment variables (AWS_REGION).

fix

When initializing `Bedrock`, provide the `region_name` argument (e.g., `Bedrock(..., region_name='us-west-2')`) or set the `AWS_REGION` environment variable.

ValueError: Provider bedrock/mistral for model bedrock/mistral.mixtral-8x7b-instruct-v0:1 is not supported

The model ID string format or the model itself is not recognized by the underlying Bedrock integration layer (e.g., LiteLLM if used indirectly), or the model is not accessible in the specified region.

fix

Verify the exact model ID string from AWS Bedrock documentation (e.g., 'mistral.mixtral-8x7b-instruct-v0:1' or 'amazon.titan-text-express-v1'). Also, ensure the model is enabled and available in your AWS account and the specified `region_name`.

litellm.APIConnectionError: BedrockException - {"Message":"User: arn:aws:sts::xxxxxxxxxxxxxxxx is not authorized to perform: bedrock:InvokeModel on resource: arn:aws:bedrock:us-west-2::foundation-model/mistral.mixtral-8x7b-instruct-v0:1 with an explicit deny in a service control policy"}

The AWS IAM user or role attempting to invoke the Bedrock model lacks the necessary `bedrock:InvokeModel` permission for the specific model and region.

fix

Review your AWS IAM policies and ensure the user/role has permission to invoke Bedrock models, specifically `bedrock:InvokeModel` on the target resource (e.g., `arn:aws:bedrock:us-west-2::foundation-model/mistral.mixtral-8x7b-instruct-v0:1`). Also, check for any Service Control Policies (SCPs) that might be explicitly denying access.

TypeError: 'prompt' parameter must be a string for completion, not a list of messages (or vice-versa for chat)

Attempting to use `llm.complete()` with a list of `ChatMessage` objects, or `llm.chat()` with a raw string prompt, when the underlying model or `llama-index` wrapper expects a different type.

fix

For `llm.complete(prompt_string)`, `prompt_string` must be a `str`. For `llm.chat(messages)`, `messages` must be a `list` of `ChatMessage` objects. Convert your input to the expected type for the method being called.

Upgrade

Version history

0.5.0latest on PyPI · released Mar 12, 2026

Audit

Dependencies

llama-index-corerequiredProvides core LlamaIndex abstractions and functionalities required by the LLM integration.

boto3requiredAWS SDK for Python, essential for interacting with AWS Bedrock services.

aioboto3optionalAsynchronous AWS SDK client, likely used internally for async operations. (Often a dependency of llama-index-llms-bedrock-converse)

Agent activity

15 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/llama-index-llms-bedrock/ ↗

homepagehttps://llamaindex.ai ↗