Spaces:
Runtime error
Runtime error
File size: 7,274 Bytes
ed4d993 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 |
import asyncio
import json
import os
from typing import Any, Dict, List, Optional
import numpy as np
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
from langchain_core.runnables.config import run_in_executor
class BedrockEmbeddings(BaseModel, Embeddings):
"""Bedrock embedding models.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Bedrock service.
"""
"""
Example:
.. code-block:: python
from langchain_community.bedrock_embeddings import BedrockEmbeddings
region_name ="us-east-1"
credentials_profile_name = "default"
model_id = "amazon.titan-embed-text-v1"
be = BedrockEmbeddings(
credentials_profile_name=credentials_profile_name,
region_name=region_name,
model_id=model_id
)
"""
client: Any #: :meta private:
"""Bedrock client."""
region_name: Optional[str] = None
"""The aws region e.g., `us-west-2`. Fallsback to AWS_DEFAULT_REGION env variable
or region specified in ~/.aws/config in case it is not provided here.
"""
credentials_profile_name: Optional[str] = None
"""The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
"""
model_id: str = "amazon.titan-embed-text-v1"
"""Id of the model to call, e.g., amazon.titan-embed-text-v1, this is
equivalent to the modelId property in the list-foundation-models api"""
model_kwargs: Optional[Dict] = None
"""Keyword arguments to pass to the model."""
endpoint_url: Optional[str] = None
"""Needed if you don't want to default to us-east-1 endpoint"""
normalize: bool = False
"""Whether the embeddings should be normalized to unit vectors"""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that AWS credentials to and python package exists in environment."""
if values["client"] is not None:
return values
try:
import boto3
if values["credentials_profile_name"] is not None:
session = boto3.Session(profile_name=values["credentials_profile_name"])
else:
# use default credentials
session = boto3.Session()
client_params = {}
if values["region_name"]:
client_params["region_name"] = values["region_name"]
if values["endpoint_url"]:
client_params["endpoint_url"] = values["endpoint_url"]
values["client"] = session.client("bedrock-runtime", **client_params)
except ImportError:
raise ImportError(
"Could not import boto3 python package. "
"Please install it with `pip install boto3`."
)
except Exception as e:
raise ValueError(
"Could not load credentials to authenticate with AWS client. "
"Please check that credentials in the specified "
f"profile name are valid. Bedrock error: {e}"
) from e
return values
def _embedding_func(self, text: str) -> List[float]:
"""Call out to Bedrock embedding endpoint."""
# replace newlines, which can negatively affect performance.
text = text.replace(os.linesep, " ")
# format input body for provider
provider = self.model_id.split(".")[0]
_model_kwargs = self.model_kwargs or {}
input_body = {**_model_kwargs}
if provider == "cohere":
if "input_type" not in input_body.keys():
input_body["input_type"] = "search_document"
input_body["texts"] = [text]
else:
# includes common provider == "amazon"
input_body["inputText"] = text
body = json.dumps(input_body)
try:
# invoke bedrock API
response = self.client.invoke_model(
body=body,
modelId=self.model_id,
accept="application/json",
contentType="application/json",
)
# format output based on provider
response_body = json.loads(response.get("body").read())
if provider == "cohere":
return response_body.get("embeddings")[0]
else:
# includes common provider == "amazon"
return response_body.get("embedding")
except Exception as e:
raise ValueError(f"Error raised by inference endpoint: {e}")
def _normalize_vector(self, embeddings: List[float]) -> List[float]:
"""Normalize the embedding to a unit vector."""
emb = np.array(embeddings)
norm_emb = emb / np.linalg.norm(emb)
return norm_emb.tolist()
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a Bedrock model.
Args:
texts: The list of texts to embed
Returns:
List of embeddings, one for each text.
"""
results = []
for text in texts:
response = self._embedding_func(text)
if self.normalize:
response = self._normalize_vector(response)
results.append(response)
return results
def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a Bedrock model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
embedding = self._embedding_func(text)
if self.normalize:
return self._normalize_vector(embedding)
return embedding
async def aembed_query(self, text: str) -> List[float]:
"""Asynchronous compute query embeddings using a Bedrock model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return await run_in_executor(None, self.embed_query, text)
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
"""Asynchronous compute doc embeddings using a Bedrock model.
Args:
texts: The list of texts to embed
Returns:
List of embeddings, one for each text.
"""
result = await asyncio.gather(*[self.aembed_query(text) for text in texts])
return list(result)
|