import asyncio
import json
import logging
import os
import warnings
from abc import ABC
from typing import (
Any,
AsyncGenerator,
AsyncIterator,
Dict,
Iterator,
List,
Mapping,
Optional,
Tuple,
TypedDict,
Union,
)
import boto3
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import LLM, BaseLanguageModel, LangSmithParams
from langchain_core.messages import AIMessageChunk, ToolCall
from langchain_core.messages.tool import tool_call, tool_call_chunk
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
from langchain_core.utils import secret_from_env
from pydantic import ConfigDict, Field, SecretStr, model_validator
from typing_extensions import Self
from langchain_aws.function_calling import _tools_in_params
from langchain_aws.utils import (
enforce_stop_tokens,
get_num_tokens_anthropic,
get_token_ids_anthropic,
)
AMAZON_BEDROCK_TRACE_KEY = "amazon-bedrock-trace"
GUARDRAILS_BODY_KEY = "amazon-bedrock-guardrailAction"
HUMAN_PROMPT = "\n\nHuman:"
ASSISTANT_PROMPT = "\n\nAssistant:"
ALTERNATION_ERROR = (
"Error: Prompt must alternate between '\n\nHuman:' and '\n\nAssistant:'."
)
def _add_newlines_before_ha(input_text: str) -> str:
new_text = input_text
for word in ["Human:", "Assistant:"]:
new_text = new_text.replace(word, "\n\n" + word)
for i in range(2):
new_text = new_text.replace("\n\n\n" + word, "\n\n" + word)
return new_text
def _human_assistant_format(input_text: str) -> str:
if input_text.count("Human:") == 0 or (
input_text.find("Human:") > input_text.find("Assistant:")
and "Assistant:" in input_text
):
input_text = HUMAN_PROMPT + " " + input_text # SILENT CORRECTION
if input_text.count("Assistant:") == 0:
input_text = input_text + ASSISTANT_PROMPT # SILENT CORRECTION
if input_text[: len("Human:")] == "Human:":
input_text = "\n\n" + input_text
input_text = _add_newlines_before_ha(input_text)
count = 0
# track alternation
for i in range(len(input_text)):
if input_text[i : i + len(HUMAN_PROMPT)] == HUMAN_PROMPT:
if count % 2 == 0:
count += 1
else:
warnings.warn(ALTERNATION_ERROR + f" Received {input_text}")
if input_text[i : i + len(ASSISTANT_PROMPT)] == ASSISTANT_PROMPT:
if count % 2 == 1:
count += 1
else:
warnings.warn(ALTERNATION_ERROR + f" Received {input_text}")
if count % 2 == 1: # Only saw Human, no Assistant
input_text = input_text + ASSISTANT_PROMPT # SILENT CORRECTION
return input_text
def _stream_response_to_generation_chunk(
stream_response: Dict[str, Any],
provider: str,
output_key: str,
messages_api: bool,
coerce_content_to_string: bool,
) -> Union[GenerationChunk, AIMessageChunk, None]: # type ignore[return]
"""Convert a stream response to a generation chunk."""
if messages_api:
msg_type = stream_response.get("type")
if msg_type == "message_start":
return AIMessageChunk(
content="" if coerce_content_to_string else [],
)
elif (
msg_type == "content_block_start"
and stream_response["content_block"] is not None
and stream_response["content_block"]["type"] == "tool_use"
):
content_block = stream_response["content_block"]
content_block["index"] = stream_response["index"]
tc_chunk = tool_call_chunk(
index=stream_response["index"],
id=stream_response["content_block"]["id"],
name=stream_response["content_block"]["name"],
args="",
)
return AIMessageChunk(
content=[content_block],
tool_call_chunks=[tc_chunk], # type: ignore
)
elif msg_type == "content_block_delta":
if not stream_response["delta"]:
return AIMessageChunk(content="")
if stream_response["delta"]["type"] == "text_delta":
if coerce_content_to_string:
return AIMessageChunk(content=stream_response["delta"]["text"])
else:
content_block = stream_response["delta"]
content_block["index"] = stream_response["index"]
content_block["type"] = "text"
return AIMessageChunk(content=[content_block])
elif stream_response["delta"]["type"] == "input_json_delta":
content_block = stream_response["delta"]
content_block["index"] = stream_response["index"]
content_block["type"] = "tool_use"
tc_chunk = {
"index": stream_response["index"],
"id": None,
"name": None,
"args": stream_response["delta"]["partial_json"],
}
return AIMessageChunk(
content=[content_block],
tool_call_chunks=[tc_chunk], # type: ignore
)
elif msg_type == "message_delta":
return AIMessageChunk(
content="",
response_metadata={
"stop_reason": stream_response["delta"].get("stop_reason"),
"stop_sequence": stream_response["delta"].get("stop_sequence"),
},
)
else:
return None
# chunk obj format varies with provider
generation_info = {
k: v
for k, v in stream_response.items()
if k not in [output_key, "prompt_token_count", "generation_token_count"]
}
return GenerationChunk(
text=(
stream_response[output_key]
if provider != "mistral"
else stream_response[output_key][0]["text"]
),
generation_info=generation_info,
)
def _combine_generation_info_for_llm_result(
chunks_generation_info: List[Dict[str, Any]], provider_stop_code: str
) -> Dict[str, Any]:
"""
Returns usage and stop reason information with the intent to pack into an LLMResult
Takes a list of generation_info from GenerationChunks
If the messages api is being used,
the generation_info from some of these chunks should contain "usage" keys
if not, the token counts should be found within "amazon-bedrock-invocationMetrics"
"""
total_usage_info = {"prompt_tokens": 0, "completion_tokens": 0}
stop_reason = ""
for generation_info in chunks_generation_info:
if "usage" in generation_info:
usage_info = generation_info["usage"]
if "input_tokens" in usage_info:
total_usage_info["prompt_tokens"] += sum(usage_info["input_tokens"])
if "output_tokens" in usage_info:
total_usage_info["completion_tokens"] += sum(
usage_info["output_tokens"]
)
if "amazon-bedrock-invocationMetrics" in generation_info:
usage_info = generation_info["amazon-bedrock-invocationMetrics"]
if "inputTokenCount" in usage_info:
total_usage_info["prompt_tokens"] += usage_info["inputTokenCount"]
if "outputTokenCount" in usage_info:
total_usage_info["completion_tokens"] += usage_info["outputTokenCount"]
if provider_stop_code is not None and provider_stop_code in generation_info:
# uses the last stop reason
stop_reason = generation_info[provider_stop_code]
total_usage_info["total_tokens"] = (
total_usage_info["prompt_tokens"] + total_usage_info["completion_tokens"]
)
return {"usage": total_usage_info, "stop_reason": stop_reason}
def _get_invocation_metrics_chunk(chunk: Dict[str, Any]) -> GenerationChunk:
generation_info = {}
if metrics := chunk.get("amazon-bedrock-invocationMetrics"):
input_tokens = metrics.get("inputTokenCount", 0)
output_tokens = metrics.get("outputTokenCount", 0)
generation_info["usage_metadata"] = {
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"total_tokens": input_tokens + output_tokens,
}
return GenerationChunk(text="", generation_info=generation_info)
[docs]
class BedrockBase(BaseLanguageModel, ABC):
"""Base class for Bedrock models."""
client: Any = Field(default=None, exclude=True) #: :meta private:
region_name: Optional[str] = Field(default=None, alias="region")
"""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] = Field(default=None, exclude=True)
"""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
"""
aws_access_key_id: Optional[SecretStr] = Field(
default_factory=secret_from_env("AWS_ACCESS_KEY_ID", default=None)
)
"""AWS access key id.
If provided, aws_secret_access_key must also be provided.
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
If not provided, will be read from 'AWS_ACCESS_KEY_ID' environment variable.
"""
aws_secret_access_key: Optional[SecretStr] = Field(
default_factory=secret_from_env("AWS_SECRET_ACCESS_KEY", default=None)
)
"""AWS secret_access_key.
If provided, aws_access_key_id must also be provided.
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
If not provided, will be read from 'AWS_SECRET_ACCESS_KEY' environment variable.
"""
aws_session_token: Optional[SecretStr] = Field(
default_factory=secret_from_env("AWS_SESSION_TOKEN", default=None)
)
"""AWS session token.
If provided, aws_access_key_id and aws_secret_access_key must also be provided.
Not required unless using temporary credentials.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If not provided, will be read from 'AWS_SESSION_TOKEN' environment variable.
"""
config: Any = None
"""An optional botocore.config.Config instance to pass to the client."""
provider: Optional[str] = None
"""The model provider, e.g., amazon, cohere, ai21, etc. When not supplied, provider
is extracted from the first part of the model_id e.g. 'amazon' in
'amazon.titan-text-express-v1'. This value should be provided for model ids that do
not have the provider in them, e.g., custom and provisioned models that have an ARN
associated with them."""
model_id: str = Field(alias="model")
"""Id of the model to call, e.g., amazon.titan-text-express-v1, this is
equivalent to the modelId property in the list-foundation-models api. For custom and
provisioned models, an ARN value is expected."""
model_kwargs: Optional[Dict[str, Any]] = 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"""
streaming: bool = False
"""Whether to stream the results."""
provider_stop_sequence_key_name_map: Mapping[str, str] = {
"anthropic": "stop_sequences",
"amazon": "stopSequences",
"ai21": "stop_sequences",
"cohere": "stop_sequences",
"mistral": "stop_sequences",
}
provider_stop_reason_key_map: Mapping[str, str] = {
"anthropic": "stop_reason",
"amazon": "completionReason",
"ai21": "finishReason",
"cohere": "finish_reason",
"mistral": "stop_reason",
}
guardrails: Optional[Mapping[str, Any]] = {
"trace": None,
"guardrailIdentifier": None,
"guardrailVersion": None,
}
"""
An optional dictionary to configure guardrails for Bedrock.
This field 'guardrails' consists of two keys: 'guardrailId' and
'guardrailVersion', which should be strings, but are initialized to None.
It's used to determine if specific guardrails are enabled and properly set.
Type:
Optional[Mapping[str, str]]: A mapping with 'guardrailId' and
'guardrailVersion' keys.
Example:
llm = BedrockLLM(model_id="<model_id>", client=<bedrock_client>,
model_kwargs={},
guardrails={
"guardrailId": "<guardrail_id>",
"guardrailVersion": "<guardrail_version>"})
To enable tracing for guardrails, set the 'trace' key to True and pass a callback handler to the
'run_manager' parameter of the 'generate', '_call' methods.
Example:
llm = BedrockLLM(model_id="<model_id>", client=<bedrock_client>,
model_kwargs={},
guardrails={
"guardrailId": "<guardrail_id>",
"guardrailVersion": "<guardrail_version>",
"trace": True},
callbacks=[BedrockAsyncCallbackHandler()])
[https://python.langchain.com/docs/modules/callbacks/] for more information on callback handlers.
class BedrockAsyncCallbackHandler(AsyncCallbackHandler):
async def on_llm_error(
self,
error: BaseException,
**kwargs: Any,
) -> Any:
reason = kwargs.get("reason")
if reason == "GUARDRAIL_INTERVENED":
...Logic to handle guardrail intervention...
""" # noqa: E501
temperature: Optional[float] = None
max_tokens: Optional[int] = None
@property
def lc_secrets(self) -> Dict[str, str]:
return {
"aws_access_key_id": "AWS_ACCESS_KEY_ID",
"aws_secret_access_key": "AWS_SECRET_ACCESS_KEY",
"aws_session_token": "AWS_SESSION_TOKEN",
}
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that AWS credentials to and python package exists in environment."""
if self.model_kwargs:
if "temperature" in self.model_kwargs:
if self.temperature is None:
self.temperature = self.model_kwargs["temperature"]
self.model_kwargs.pop("temperature")
if "max_tokens" in self.model_kwargs:
if not self.max_tokens:
self.max_tokens = self.model_kwargs["max_tokens"]
self.model_kwargs.pop("max_tokens")
# Skip creating new client if passed in constructor
if self.client is not None:
return self
creds = {
"aws_access_key_id": self.aws_access_key_id,
"aws_secret_access_key": self.aws_secret_access_key,
"aws_session_token": self.aws_session_token,
}
if creds["aws_access_key_id"] and creds["aws_secret_access_key"]:
session_params = {k: v.get_secret_value() for k, v in creds.items() if v}
elif any(creds.values()):
raise ValueError(
f"If any of aws_access_key_id, aws_secret_access_key, or "
f"aws_session_token are specified then both aws_access_key_id and "
f"aws_secret_access_key must be specified. Only received "
f"{(k for k, v in creds.items() if v)}."
)
elif self.credentials_profile_name is not None:
session_params = {"profile_name": self.credentials_profile_name}
else:
# use default credentials
session_params = {}
try:
session = boto3.Session(**session_params)
self.region_name = (
self.region_name
or os.getenv("AWS_DEFAULT_REGION")
or session.region_name
)
client_params = {
"endpoint_url": self.endpoint_url,
"config": self.config,
"region_name": self.region_name,
}
client_params = {k: v for k, v in client_params.items() if v}
self.client = session.client("bedrock-runtime", **client_params)
except ValueError as e:
raise ValueError(f"Error raised by bedrock service:\n\n{e}") from e
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:\n\n{e}"
) from e
return self
@property
def _identifying_params(self) -> Dict[str, Any]:
_model_kwargs = self.model_kwargs or {}
return {
"model_id": self.model_id,
"provider": self._get_provider(),
"stream": self.streaming,
"trace": self.guardrails.get("trace"), # type: ignore[union-attr]
"guardrailIdentifier": self.guardrails.get("guardrailIdentifier", None), # type: ignore[union-attr]
"guardrailVersion": self.guardrails.get("guardrailVersion", None), # type: ignore[union-attr]
**_model_kwargs,
}
def _get_provider(self) -> str:
# If provider supplied by user, return as-is
if self.provider:
return self.provider
# If model_id is an arn, can't extract provider from model_id,
# so this requires passing in the provider by user
if self.model_id.startswith("arn"):
raise ValueError(
"Model provider should be supplied when passing a model ARN as "
"model_id"
)
# If model_id has region prefixed to them,
# for example eu.anthropic.claude-3-haiku-20240307-v1:0,
# provider is the second part, otherwise, the first part
parts = self.model_id.split(".", maxsplit=2)
return (
parts[1]
if (len(parts) > 1 and parts[0].lower() in {"eu", "us", "ap", "sa"})
else parts[0]
)
def _get_model(self) -> str:
return self.model_id.split(".", maxsplit=1)[-1]
@property
def _model_is_anthropic(self) -> bool:
return self._get_provider() == "anthropic"
@property
def _guardrails_enabled(self) -> bool:
"""
Determines if guardrails are enabled and correctly configured.
Checks if 'guardrails' is a dictionary with non-empty 'id' and 'version' keys.
Checks if 'guardrails.trace' is true.
Returns:
bool: True if guardrails are correctly configured, False otherwise.
Raises:
TypeError: If 'guardrails' lacks 'id' or 'version' keys.
"""
try:
return (
isinstance(self.guardrails, dict)
and bool(self.guardrails["guardrailIdentifier"])
and bool(self.guardrails["guardrailVersion"])
)
except KeyError as e:
raise TypeError(
"Guardrails must be a dictionary with 'guardrailIdentifier' \
and 'guardrailVersion' keys."
) from e
def _prepare_input_and_invoke(
self,
prompt: Optional[str] = None,
system: Optional[str] = None,
messages: Optional[List[Dict]] = None,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Tuple[
str,
List[ToolCall],
Dict[str, Any],
]:
_model_kwargs = self.model_kwargs or {}
provider = self._get_provider()
params = {**_model_kwargs, **kwargs}
if "claude-3" in self._get_model() and _tools_in_params(params):
input_body = LLMInputOutputAdapter.prepare_input(
provider=provider,
model_kwargs=params,
prompt=prompt,
system=system,
messages=messages,
tools=params["tools"],
max_tokens=self.max_tokens,
temperature=self.temperature,
)
else:
input_body = LLMInputOutputAdapter.prepare_input(
provider=provider,
model_kwargs=params,
prompt=prompt,
system=system,
messages=messages,
max_tokens=self.max_tokens,
temperature=self.temperature,
)
body = json.dumps(input_body)
accept = "application/json"
contentType = "application/json"
request_options = {
"body": body,
"modelId": self.model_id,
"accept": accept,
"contentType": contentType,
}
if self._guardrails_enabled:
request_options["guardrailIdentifier"] = self.guardrails.get( # type: ignore[union-attr]
"guardrailIdentifier", ""
)
request_options["guardrailVersion"] = self.guardrails.get( # type: ignore[union-attr]
"guardrailVersion", ""
)
if self.guardrails.get("trace"): # type: ignore[union-attr]
request_options["trace"] = "ENABLED"
try:
response = self.client.invoke_model(**request_options)
(
text,
tool_calls,
body,
usage_info,
stop_reason,
) = LLMInputOutputAdapter.prepare_output(provider, response).values()
except Exception as e:
logging.error(f"Error raised by bedrock service: {e}")
if run_manager is not None:
run_manager.on_llm_error(e)
raise e
if stop is not None:
text = enforce_stop_tokens(text, stop)
llm_output = {"usage": usage_info, "stop_reason": stop_reason}
# Verify and raise a callback error if any intervention occurs or a signal is
# sent from a Bedrock service,
# such as when guardrails are triggered.
services_trace = self._get_bedrock_services_signal(body) # type: ignore[arg-type]
if run_manager is not None and services_trace.get("signal"):
run_manager.on_llm_error(
Exception(
f"Error raised by bedrock service: {services_trace.get('reason')}"
),
**services_trace,
)
return text, tool_calls, llm_output
def _get_bedrock_services_signal(self, body: dict) -> dict:
"""
This function checks the response body for an interrupt flag or message that indicates
whether any of the Bedrock services have intervened in the processing flow. It is
primarily used to identify modifications or interruptions imposed by these services
during the request-response cycle with a Large Language Model (LLM).
""" # noqa: E501
if (
self._guardrails_enabled
and self.guardrails.get("trace") # type: ignore[union-attr]
and self._is_guardrails_intervention(body)
):
return {
"signal": True,
"reason": "GUARDRAIL_INTERVENED",
"trace": body.get(AMAZON_BEDROCK_TRACE_KEY),
}
return {
"signal": False,
"reason": None,
"trace": None,
}
def _is_guardrails_intervention(self, body: dict) -> bool:
return body.get(GUARDRAILS_BODY_KEY) == "INTERVENED"
def _prepare_input_and_invoke_stream(
self,
prompt: Optional[str] = None,
system: Optional[str] = None,
messages: Optional[List[Dict]] = None,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[Union[GenerationChunk, AIMessageChunk]]:
_model_kwargs = self.model_kwargs or {}
provider = self._get_provider()
if stop:
if provider not in self.provider_stop_sequence_key_name_map:
raise ValueError(
f"Stop sequence key name for {provider} is not supported."
)
# stop sequence from _generate() overrides
# stop sequences in the class attribute
if k := self.provider_stop_sequence_key_name_map.get(provider):
_model_kwargs[k] = stop
if provider == "cohere":
_model_kwargs["stream"] = True
params = {**_model_kwargs, **kwargs}
input_body = LLMInputOutputAdapter.prepare_input(
provider=provider,
prompt=prompt,
system=system,
messages=messages,
model_kwargs=params,
max_tokens=self.max_tokens,
temperature=self.temperature,
)
coerce_content_to_string = True
if "claude-3" in self._get_model():
if _tools_in_params(params):
coerce_content_to_string = False
input_body = LLMInputOutputAdapter.prepare_input(
provider=provider,
model_kwargs=params,
prompt=prompt,
system=system,
messages=messages,
tools=params["tools"],
max_tokens=self.max_tokens,
temperature=self.temperature,
)
body = json.dumps(input_body)
request_options = {
"body": body,
"modelId": self.model_id,
"accept": "application/json",
"contentType": "application/json",
}
if self._guardrails_enabled:
request_options["guardrailIdentifier"] = self.guardrails.get( # type: ignore[union-attr]
"guardrailIdentifier", ""
)
request_options["guardrailVersion"] = self.guardrails.get( # type: ignore[union-attr]
"guardrailVersion", ""
)
if self.guardrails.get("trace"): # type: ignore[union-attr]
request_options["trace"] = "ENABLED"
try:
response = self.client.invoke_model_with_response_stream(**request_options)
except Exception as e:
logging.error(f"Error raised by bedrock service: {e}")
if run_manager is not None:
run_manager.on_llm_error(e)
raise e
for chunk in LLMInputOutputAdapter.prepare_output_stream(
provider,
response,
stop,
True if messages else False,
coerce_content_to_string=coerce_content_to_string,
):
yield chunk
# verify and raise callback error if any middleware intervened
if not isinstance(chunk, AIMessageChunk):
self._get_bedrock_services_signal(chunk.generation_info) # type: ignore[arg-type]
async def _aprepare_input_and_invoke_stream(
self,
prompt: str,
system: Optional[str] = None,
messages: Optional[List[Dict]] = None,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[Union[GenerationChunk, AIMessageChunk]]:
_model_kwargs = self.model_kwargs or {}
provider = self._get_provider()
if stop:
if provider not in self.provider_stop_sequence_key_name_map:
raise ValueError(
f"Stop sequence key name for {provider} is not supported."
)
if k := self.provider_stop_sequence_key_name_map.get(provider):
_model_kwargs[k] = stop
if provider == "cohere":
_model_kwargs["stream"] = True
params = {**_model_kwargs, **kwargs}
if "claude-3" in self._get_model() and _tools_in_params(params):
input_body = LLMInputOutputAdapter.prepare_input(
provider=provider,
model_kwargs=params,
prompt=prompt,
system=system,
messages=messages,
tools=params["tools"],
max_tokens=self.max_tokens,
temperature=self.temperature,
)
else:
input_body = LLMInputOutputAdapter.prepare_input(
provider=provider,
prompt=prompt,
system=system,
messages=messages,
model_kwargs=params,
max_tokens=self.max_tokens,
temperature=self.temperature,
)
body = json.dumps(input_body)
response = await asyncio.get_running_loop().run_in_executor(
None,
lambda: self.client.invoke_model_with_response_stream(
body=body,
modelId=self.model_id,
accept="application/json",
contentType="application/json",
),
)
async for chunk in LLMInputOutputAdapter.aprepare_output_stream(
provider,
response,
stop,
True if messages else False,
):
yield chunk
[docs]
class BedrockLLM(LLM, BedrockBase):
"""Bedrock 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 bedrock_langchain.bedrock_llm import BedrockLLM
llm = BedrockLLM(
credentials_profile_name="default",
model_id="amazon.titan-text-express-v1",
streaming=True
)
"""
@model_validator(mode="after")
def validate_environment_llm(self) -> Self:
model_id = self.model_id
if model_id.startswith("anthropic.claude-3"):
raise ValueError(
"Claude v3 models are not supported by this LLM."
"Please use `from langchain_aws import ChatBedrock` "
"instead."
)
return self
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "amazon_bedrock"
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this model can be serialized by Langchain."""
return True
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "llms", "bedrock"]
@property
def lc_attributes(self) -> Dict[str, Any]:
attributes: Dict[str, Any] = {}
if self.region_name:
attributes["region_name"] = self.region_name
return attributes
def _get_ls_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
ls_params = super()._get_ls_params(stop=stop, **kwargs)
ls_params["ls_provider"] = "amazon_bedrock"
ls_params["ls_model_name"] = self.model_id
return ls_params
model_config = ConfigDict(
extra="forbid",
populate_by_name=True,
)
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
"""Call out to Bedrock service with streaming.
Args:
prompt (str): The prompt to pass into the model
stop (Optional[List[str]], optional): Stop sequences. These will
override any stop sequences in the `model_kwargs` attribute.
Defaults to None.
run_manager (Optional[CallbackManagerForLLMRun], optional): Callback
run managers used to process the output. Defaults to None.
Returns:
Iterator[GenerationChunk]: Generator that yields the streamed responses.
Yields:
Iterator[GenerationChunk]: Responses from the model.
"""
return self._prepare_input_and_invoke_stream( # type: ignore
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
)
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Bedrock service model.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = llm("Tell me a joke.")
"""
provider = self._get_provider()
provider_stop_reason_code = self.provider_stop_reason_key_map.get(
provider, "stop_reason"
)
if self.streaming:
all_chunks: List[GenerationChunk] = []
completion = ""
for chunk in self._stream(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
):
completion += chunk.text
all_chunks.append(chunk)
if run_manager is not None:
chunks_generation_info = [
chunk.generation_info
for chunk in all_chunks
if chunk.generation_info is not None
]
llm_output = _combine_generation_info_for_llm_result(
chunks_generation_info, provider_stop_code=provider_stop_reason_code
)
all_generations = [
Generation(text=chunk.text, generation_info=chunk.generation_info)
for chunk in all_chunks
]
run_manager.on_llm_end(
LLMResult(generations=[all_generations], llm_output=llm_output)
)
return completion
text, tool_calls, llm_output = self._prepare_input_and_invoke(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
)
if run_manager is not None:
run_manager.on_llm_end(
LLMResult(generations=[[Generation(text=text)]], llm_output=llm_output)
)
return text
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncGenerator[GenerationChunk, None]:
"""Call out to Bedrock service with streaming.
Args:
prompt (str): The prompt to pass into the model
stop (Optional[List[str]], optional): Stop sequences. These will
override any stop sequences in the `model_kwargs` attribute.
Defaults to None.
run_manager (Optional[CallbackManagerForLLMRun], optional): Callback
run managers used to process the output. Defaults to None.
Yields:
AsyncGenerator[GenerationChunk, None]: Generator that asynchronously yields
the streamed responses.
"""
async for chunk in self._aprepare_input_and_invoke_stream(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
):
yield chunk # type: ignore
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Bedrock service model.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = await llm._acall("Tell me a joke.")
"""
if not self.streaming:
raise ValueError("Streaming must be set to True for async operations. ")
provider = self._get_provider()
provider_stop_reason_code = self.provider_stop_reason_key_map.get(
provider, "stop_reason"
)
chunks = [
chunk
async for chunk in self._astream(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
)
]
if run_manager is not None:
chunks_generation_info = [
chunk.generation_info
for chunk in chunks
if chunk.generation_info is not None
]
llm_output = _combine_generation_info_for_llm_result(
chunks_generation_info, provider_stop_code=provider_stop_reason_code
)
generations = [
Generation(text=chunk.text, generation_info=chunk.generation_info)
for chunk in chunks
]
await run_manager.on_llm_end(
LLMResult(generations=[generations], llm_output=llm_output)
)
return "".join([chunk.text for chunk in chunks])
[docs]
def get_num_tokens(self, text: str) -> int:
if self._model_is_anthropic:
return get_num_tokens_anthropic(text)
else:
return super().get_num_tokens(text)
[docs]
def get_token_ids(self, text: str) -> List[int]:
if self._model_is_anthropic:
return get_token_ids_anthropic(text)
else:
return super().get_token_ids(text)
