{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Integrates AWS Bedrock LLMs into LlamaIndex, enabling you to use Bedrock-hosted language models for text completion, chat, and streaming operations within LlamaIndex workflows.","skillfed_tags":["bedrock-integration","rag-framework","aws-llm"],"use_cases":["Build LlamaIndex RAG pipelines using Bedrock models as the generation backend instead of OpenAI or other providers.","Stream long-form completions from Bedrock models in real-time within LlamaIndex chat or agentic workflows.","Use Bedrock's managed LLM infrastructure with LlamaIndex's retrieval and indexing tools for cost-optimized deployments.","Integrate multi-turn conversations with Bedrock models while leveraging LlamaIndex's message history and context management.","Deploy LlamaIndex applications on AWS without external LLM API dependencies by using Bedrock as the sole LLM provider."],"what_it_does":"This package bridges AWS Bedrock LLMs into the LlamaIndex ecosystem, letting you use Bedrock-hosted models (like Amazon Titan) as the language model backend for LlamaIndex applications. It wraps boto3 calls and exposes Bedrock models through LlamaIndex's standard LLM interface, supporting text completion, chat with message history, and streaming variants of both.\n\nYou instantiate a Bedrock object with a model name and AWS credentials (profile, access keys, or session token), then call complete(), chat(), stream_complete(), or stream_chat() just as you would with any other LlamaIndex LLM. The package handles authentication and request marshaling to Bedrock, making it straightforward to swap Bedrock into existing LlamaIndex pipelines or build new ones around Bedrock models.","worth_installing":"Yes, if you are already using LlamaIndex and need to route requests through AWS Bedrock. The package has low install friction, active maintenance, MIT licensing, no known vulnerabilities, and clear integration points. Install it only if you have AWS Bedrock access and want to use it as your LlamaIndex LLM backend; otherwise it adds no value."},"id":"llama-index-llms-bedrock","links":{"html":"https://skillfed.io/packages/llama-index-llms-bedrock","md":"https://skillfed.io/packages/llama-index-llms-bedrock.md","pypi":"https://pypi.org/project/llama-index-llms-bedrock/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-12","license_spdx":"MIT","license_treatment":"permissive","name":"llama-index-llms-bedrock","python_support":"supports_current","summary":"llama-index llms bedrock integration"},"popularity":{"monthly_downloads":105529,"position":12693,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.0"}