> ## Documentation Index
> Fetch the complete documentation index at: https://docs.raglight.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AWS Bedrock

> Use Claude, Titan, Llama and other Bedrock models for LLM inference and embeddings.

# AWS Bedrock

## Overview

RAGLight supports **AWS Bedrock** for both LLM inference and embeddings. You can use it as a drop-in replacement for any other provider — just set `provider=Settings.AWS_BEDROCK`.

<Info>
  `langchain-aws` is included in RAGLight's core dependencies. No extra install is needed.
</Info>

***

## Authentication

Authentication relies on the standard **boto3 credential chain** — the first available source is used:

1. **Environment variables**: `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_DEFAULT_REGION`
2. **Credentials file**: `~/.aws/credentials`
3. **IAM role**: automatic when running on EC2, ECS, or Lambda

```bash theme={null}
export AWS_ACCESS_KEY_ID=AKIA...
export AWS_SECRET_ACCESS_KEY=...
export AWS_DEFAULT_REGION=us-east-1
```

***

## Supported models

### LLM

| Model ID                                    | Description                 |
| :------------------------------------------ | :-------------------------- |
| `anthropic.claude-3-5-sonnet-20241022-v2:0` | Default — Claude 3.5 Sonnet |
| `anthropic.claude-3-haiku-20240307-v1:0`    | Fast and lightweight        |
| `amazon.titan-text-express-v1`              | Amazon Titan text           |
| `meta.llama3-8b-instruct-v1:0`              | Meta Llama 3 8B             |

<Warning>
  **Newer Claude models require cross-region inference profile IDs.** On-demand invocation of recent Claude versions (e.g. Claude 3.7, Claude 4) is not supported with a bare `anthropic.*` model ID. You must use a cross-region inference profile prefixed with `us.`, `eu.`, or `ap.` — for example `us.anthropic.claude-sonnet-4-5-20250929-v1:0`.

  To list available profiles in your account:

  ```bash theme={null}
  aws bedrock list-inference-profiles
  ```
</Warning>

### Embeddings

| Model ID                       | Description                       |
| :----------------------------- | :-------------------------------- |
| `amazon.titan-embed-text-v2:0` | Default — Amazon Titan embeddings |
| `cohere.embed-english-v3`      | Cohere English embeddings         |

Constants are available in `Settings`:

```python theme={null}
Settings.AWS_BEDROCK_LLM_MODEL       # anthropic.claude-3-5-sonnet-20241022-v2:0
Settings.AWS_BEDROCK_EMBEDDING_MODEL  # amazon.titan-embed-text-v2:0
```

***

## Full example

```python theme={null}
from raglight.rag.simple_rag_api import RAGPipeline
from raglight.config.settings import Settings
from raglight.config.rag_config import RAGConfig
from raglight.config.vector_store_config import VectorStoreConfig
from raglight.models.data_source_model import GitHubSource

Settings.setup_logging()

# Embeddings via Bedrock Titan
vector_store_config = VectorStoreConfig(
    provider=Settings.AWS_BEDROCK,
    embedding_model=Settings.AWS_BEDROCK_EMBEDDING_MODEL,
    database=Settings.CHROMA,
    persist_directory="./bedrockDb",
    collection_name="bedrock_collection",
)

# LLM via Bedrock Claude
config = RAGConfig(
    provider=Settings.AWS_BEDROCK,
    llm=Settings.AWS_BEDROCK_LLM_MODEL,
    knowledge_base=[GitHubSource(url="https://github.com/Bessouat40/RAGLight")],
    k=5,
)

pipeline = RAGPipeline(config, vector_store_config)
pipeline.build()

response = pipeline.generate(
    "How can I create a RAGPipeline using raglight? Give me a Python example."
)
print(response)
```

***

## Custom region

By default, `AWS_DEFAULT_REGION` (env var) or `us-east-1` is used. You can override it per-model via the Builder API:

```python theme={null}
from raglight.rag.builder import Builder
from raglight.config.settings import Settings

rag = (
    Builder()
    .with_embeddings(
        Settings.AWS_BEDROCK,
        model_name=Settings.AWS_BEDROCK_EMBEDDING_MODEL,
        region_name="eu-west-1",
    )
    .with_vector_store(Settings.CHROMA, persist_directory="./db", collection_name="col")
    .with_llm(
        Settings.AWS_BEDROCK,
        model_name=Settings.AWS_BEDROCK_LLM_MODEL,
        region_name="eu-west-1",
    )
    .build_rag(k=5)
)
```

***

## Mix and match

You can combine Bedrock with any other provider. For example, local HuggingFace embeddings with a Bedrock LLM:

```python theme={null}
vector_store_config = VectorStoreConfig(
    provider=Settings.HUGGINGFACE,
    embedding_model=Settings.DEFAULT_EMBEDDINGS_MODEL,
    database=Settings.CHROMA,
    persist_directory="./db",
    collection_name="my_collection",
)

config = RAGConfig(
    provider=Settings.AWS_BEDROCK,
    llm=Settings.AWS_BEDROCK_LLM_MODEL,
)
```

***

## Use with `raglight serve --ui`

You can run the RAGLight REST API + chat UI with Bedrock by creating a `.env` file:

```bash theme={null}
# .env
RAGLIGHT_DB=Chroma
RAGLIGHT_PERSIST_DIR=./bedrockDb
RAGLIGHT_COLLECTION=bedrock_collection
RAGLIGHT_LLM_PROVIDER=AWSBedrock
RAGLIGHT_LLM_MODEL=us.anthropic.claude-sonnet-4-5-20250929-v1:0

RAGLIGHT_EMBEDDINGS_PROVIDER=AWSBedrock
RAGLIGHT_EMBEDDINGS_MODEL=amazon.titan-embed-text-v2:0

AWS_ACCESS_KEY_ID=AKIA...
AWS_SECRET_ACCESS_KEY=...
AWS_DEFAULT_REGION=us-east-1
```

Then launch:

```bash theme={null}
raglight serve --ui
```

<Tip>
  If you use named profiles in `~/.aws/credentials`, prefer setting explicit `AWS_ACCESS_KEY_ID` / `AWS_SECRET_ACCESS_KEY` in `.env` — profile names with special characters (parentheses, spaces) can cause parsing issues.
</Tip>

***

## Summary

* Set `provider=Settings.AWS_BEDROCK` in both `RAGConfig` and `VectorStoreConfig`
* No extra install required — `langchain-aws` is bundled
* Credentials are resolved automatically via the boto3 chain
* Region defaults to `AWS_DEFAULT_REGION` env var or `us-east-1`
* Newer Claude models require **cross-region inference profile IDs** (e.g. `us.anthropic.claude-...`)
* Mix Bedrock with any other provider freely