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Add Amazon Bedrock Text vectorizer (#143)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,201 @@ | ||
| import json | ||
| import os | ||
| from typing import Any, Callable, Dict, List, Optional | ||
|
|
||
| from pydantic.v1 import PrivateAttr | ||
| from tenacity import retry, stop_after_attempt, wait_random_exponential | ||
| from tenacity.retry import retry_if_not_exception_type | ||
|
|
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| from redisvl.utils.vectorize.base import BaseVectorizer | ||
|
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|
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| class BedrockTextVectorizer(BaseVectorizer): | ||
| """The AmazonBedrockTextVectorizer class utilizes Amazon Bedrock's API to generate | ||
| embeddings for text data. | ||
| This vectorizer is designed to interact with Amazon Bedrock API, | ||
| requiring AWS credentials for authentication. The credentials can be provided | ||
| directly in the `api_config` dictionary or through environment variables: | ||
| - AWS_ACCESS_KEY_ID | ||
| - AWS_SECRET_ACCESS_KEY | ||
| - AWS_REGION (defaults to us-east-1) | ||
| The vectorizer supports synchronous operations with batch processing and | ||
| preprocessing capabilities. | ||
| .. code-block:: python | ||
| # Initialize with explicit credentials | ||
| vectorizer = AmazonBedrockTextVectorizer( | ||
| model_id="amazon.titan-embed-text-v2:0", | ||
| api_config={ | ||
| "aws_access_key_id": "your_access_key", | ||
| "aws_secret_access_key": "your_secret_key", | ||
| "region_name": "us-east-1" | ||
| } | ||
| ) | ||
| # Initialize using environment variables | ||
| vectorizer = AmazonBedrockTextVectorizer() | ||
| # Generate embeddings | ||
| embedding = vectorizer.embed("Hello, world!") | ||
| embeddings = vectorizer.embed_many(["Hello", "World"], batch_size=2) | ||
| """ | ||
|
|
||
| _client: Any = PrivateAttr() | ||
|
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||
| def __init__( | ||
| self, | ||
| model_id: str = "amazon.titan-embed-text-v2:0", | ||
| api_config: Optional[Dict[str, str]] = None, | ||
| ) -> None: | ||
| """Initialize the AWS Bedrock Vectorizer. | ||
| Args: | ||
| model_id (str): The Bedrock model ID to use. Defaults to amazon.titan-embed-text-v2:0 | ||
| api_config (Optional[Dict[str, str]]): AWS credentials and config. | ||
| Can include: aws_access_key_id, aws_secret_access_key, region_name | ||
| If not provided, will use environment variables. | ||
| Raises: | ||
| ValueError: If credentials are not provided in config or environment. | ||
| ImportError: If boto3 is not installed. | ||
| """ | ||
| try: | ||
| import boto3 # type: ignore | ||
| except ImportError: | ||
| raise ImportError( | ||
| "Amazon Bedrock vectorizer requires boto3. " | ||
| "Please install with `pip install boto3`" | ||
| ) | ||
|
|
||
| if api_config is None: | ||
| api_config = {} | ||
|
|
||
| aws_access_key_id = api_config.get( | ||
| "aws_access_key_id", os.getenv("AWS_ACCESS_KEY_ID") | ||
| ) | ||
| aws_secret_access_key = api_config.get( | ||
| "aws_secret_access_key", os.getenv("AWS_SECRET_ACCESS_KEY") | ||
| ) | ||
| region_name = api_config.get( | ||
| "region_name", os.getenv("AWS_REGION", "us-east-1") | ||
| ) | ||
|
|
||
| if not aws_access_key_id or not aws_secret_access_key: | ||
| raise ValueError( | ||
| "AWS credentials required. Provide via api_config or environment variables " | ||
| "AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY" | ||
| ) | ||
|
|
||
| self._client = boto3.client( | ||
| "bedrock-runtime", | ||
| aws_access_key_id=aws_access_key_id, | ||
| aws_secret_access_key=aws_secret_access_key, | ||
| region_name=region_name, | ||
| ) | ||
|
|
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| super().__init__(model=model_id, dims=self._set_model_dims(model_id)) | ||
|
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||
| def _set_model_dims(self, model_id: str) -> int: | ||
| """Initialize model and determine embedding dimensions.""" | ||
| try: | ||
| response = self._client.invoke_model( | ||
| modelId=model_id, body=json.dumps({"inputText": "dimension test"}) | ||
| ) | ||
| response_body = json.loads(response["body"].read()) | ||
| embedding = response_body["embedding"] | ||
| return len(embedding) | ||
| except Exception as e: | ||
| raise ValueError(f"Error initializing Bedrock model: {str(e)}") | ||
|
|
||
| @retry( | ||
| wait=wait_random_exponential(min=1, max=60), | ||
| stop=stop_after_attempt(6), | ||
| retry=retry_if_not_exception_type(TypeError), | ||
| ) | ||
| def embed( | ||
| self, | ||
| text: str, | ||
| preprocess: Optional[Callable] = None, | ||
| as_buffer: bool = False, | ||
| **kwargs, | ||
| ) -> List[float]: | ||
| """Embed a chunk of text using Amazon Bedrock. | ||
| Args: | ||
| text (str): Text to embed. | ||
| preprocess (Optional[Callable]): Optional preprocessing function. | ||
| as_buffer (bool): Whether to return as byte buffer. | ||
| Returns: | ||
| List[float]: The embedding vector. | ||
| Raises: | ||
| TypeError: If text is not a string. | ||
| """ | ||
| if not isinstance(text, str): | ||
| raise TypeError("Text must be a string") | ||
|
|
||
| if preprocess: | ||
| text = preprocess(text) | ||
|
|
||
| response = self._client.invoke_model( | ||
| modelId=self.model, body=json.dumps({"inputText": text}) | ||
| ) | ||
| response_body = json.loads(response["body"].read()) | ||
| embedding = response_body["embedding"] | ||
|
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||
| dtype = kwargs.pop("dtype", None) | ||
| return self._process_embedding(embedding, as_buffer, dtype) | ||
|
|
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| @retry( | ||
| wait=wait_random_exponential(min=1, max=60), | ||
| stop=stop_after_attempt(6), | ||
| retry=retry_if_not_exception_type(TypeError), | ||
| ) | ||
| def embed_many( | ||
| self, | ||
| texts: List[str], | ||
| preprocess: Optional[Callable] = None, | ||
| batch_size: int = 10, | ||
| as_buffer: bool = False, | ||
| **kwargs, | ||
| ) -> List[List[float]]: | ||
| """Embed multiple texts using Amazon Bedrock. | ||
| Args: | ||
| texts (List[str]): List of texts to embed. | ||
| preprocess (Optional[Callable]): Optional preprocessing function. | ||
| batch_size (int): Size of batches for processing. | ||
| as_buffer (bool): Whether to return as byte buffers. | ||
| Returns: | ||
| List[List[float]]: List of embedding vectors. | ||
| Raises: | ||
| TypeError: If texts is not a list of strings. | ||
| """ | ||
| if not isinstance(texts, list): | ||
| raise TypeError("Texts must be a list of strings") | ||
| if texts and not isinstance(texts[0], str): | ||
| raise TypeError("Texts must be a list of strings") | ||
|
|
||
| embeddings: List[List[float]] = [] | ||
| dtype = kwargs.pop("dtype", None) | ||
|
|
||
| for batch in self.batchify(texts, batch_size, preprocess): | ||
| for text in batch: | ||
| response = self._client.invoke_model( | ||
| modelId=self.model, body=json.dumps({"inputText": text}) | ||
| ) | ||
| response_body = json.loads(response["body"].read()) | ||
| embedding = response_body["embedding"] | ||
| embeddings.append(self._process_embedding(embedding, as_buffer, dtype)) | ||
|
|
||
| return embeddings | ||
|
|
||
| @property | ||
| def type(self) -> str: | ||
| return "bedrock" |
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