import re
from collections import defaultdict
from operator import itemgetter
from typing import (
Any,
Callable,
Dict,
Iterator,
List,
Literal,
Optional,
Sequence,
Tuple,
Union,
cast,
)
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models import (
BaseChatModel,
LangSmithParams,
LanguageModelInput,
)
from langchain_core.language_models.chat_models import generate_from_stream
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
ChatMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.messages.ai import UsageMetadata
from langchain_core.messages.tool import ToolCall, ToolMessage
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass
from pydantic import BaseModel, ConfigDict
from langchain_aws.chat_models.bedrock_converse import ChatBedrockConverse
from langchain_aws.function_calling import (
ToolsOutputParser,
_lc_tool_calls_to_anthropic_tool_use_blocks,
convert_to_anthropic_tool,
get_system_message,
)
from langchain_aws.llms.bedrock import (
BedrockBase,
_combine_generation_info_for_llm_result,
)
from langchain_aws.utils import (
get_num_tokens_anthropic,
get_token_ids_anthropic,
)
def _convert_one_message_to_text_llama(message: BaseMessage) -> str:
if isinstance(message, ChatMessage):
message_text = f"\n\n{message.role.capitalize()}: {message.content}"
elif isinstance(message, HumanMessage):
message_text = f"[INST] {message.content} [/INST]"
elif isinstance(message, AIMessage):
message_text = f"{message.content}"
elif isinstance(message, SystemMessage):
message_text = f"<<SYS>> {message.content} <</SYS>>"
else:
raise ValueError(f"Got unknown type {message}")
return message_text
[docs]
def convert_messages_to_prompt_llama(messages: List[BaseMessage]) -> str:
"""Convert a list of messages to a prompt for llama."""
return "\n".join(
[_convert_one_message_to_text_llama(message) for message in messages]
)
def _convert_one_message_to_text_llama3(message: BaseMessage) -> str:
if isinstance(message, ChatMessage):
message_text = (
f"<|start_header_id|>{message.role}"
f"<|end_header_id|>{message.content}<|eot_id|>"
)
elif isinstance(message, HumanMessage):
message_text = (
f"<|start_header_id|>user" f"<|end_header_id|>{message.content}<|eot_id|>"
)
elif isinstance(message, AIMessage):
message_text = (
f"<|start_header_id|>assistant"
f"<|end_header_id|>{message.content}<|eot_id|>"
)
elif isinstance(message, SystemMessage):
message_text = (
f"<|start_header_id|>system" f"<|end_header_id|>{message.content}<|eot_id|>"
)
else:
raise ValueError(f"Got unknown type {message}")
return message_text
[docs]
def convert_messages_to_prompt_llama3(messages: List[BaseMessage]) -> str:
"""Convert a list of messages to a prompt for llama."""
return "\n".join(
["<|begin_of_text|>"]
+ [_convert_one_message_to_text_llama3(message) for message in messages]
+ ["<|start_header_id|>assistant<|end_header_id|>\n\n"]
)
def _convert_one_message_to_text_anthropic(
message: BaseMessage,
human_prompt: str,
ai_prompt: str,
) -> str:
content = cast(str, message.content)
if isinstance(message, ChatMessage):
message_text = f"\n\n{message.role.capitalize()}: {content}"
elif isinstance(message, HumanMessage):
message_text = f"{human_prompt} {content}"
elif isinstance(message, AIMessage):
message_text = f"{ai_prompt} {content}"
elif isinstance(message, SystemMessage):
message_text = content
else:
raise ValueError(f"Got unknown type {message}")
return message_text
[docs]
def convert_messages_to_prompt_anthropic(
messages: List[BaseMessage],
*,
human_prompt: str = "\n\nHuman:",
ai_prompt: str = "\n\nAssistant:",
) -> str:
"""Format a list of messages into a full prompt for the Anthropic model
Args:
messages (List[BaseMessage]): List of BaseMessage to combine.
human_prompt (str, optional): Human prompt tag. Defaults to "\n\nHuman:".
ai_prompt (str, optional): AI prompt tag. Defaults to "\n\nAssistant:".
Returns:
str: Combined string with necessary human_prompt and ai_prompt tags.
"""
messages = messages.copy() # don't mutate the original list
if not isinstance(messages[-1], AIMessage):
messages.append(AIMessage(content=""))
text = "".join(
_convert_one_message_to_text_anthropic(message, human_prompt, ai_prompt)
for message in messages
)
# trim off the trailing ' ' that might come from the "Assistant: "
return text.rstrip()
def _convert_one_message_to_text_mistral(message: BaseMessage) -> str:
if isinstance(message, ChatMessage):
message_text = f"\n\n{message.role.capitalize()}: {message.content}"
elif isinstance(message, HumanMessage):
message_text = f"[INST] {message.content} [/INST]"
elif isinstance(message, AIMessage):
message_text = f"{message.content}"
elif isinstance(message, SystemMessage):
message_text = f"<<SYS>> {message.content} <</SYS>>"
else:
raise ValueError(f"Got unknown type {message}")
return message_text
[docs]
def convert_messages_to_prompt_mistral(messages: List[BaseMessage]) -> str:
"""Convert a list of messages to a prompt for mistral."""
return "\n".join(
[_convert_one_message_to_text_mistral(message) for message in messages]
)
def _format_image(image_url: str) -> Dict:
"""
Formats an image of format data:image/jpeg;base64,{b64_string}
to a dict for anthropic api
{
"type": "base64",
"media_type": "image/jpeg",
"data": "/9j/4AAQSkZJRg...",
}
And throws an error if it's not a b64 image
"""
regex = r"^data:(?P<media_type>image/.+);base64,(?P<data>.+)$"
match = re.match(regex, image_url)
if match is None:
raise ValueError(
"Anthropic only supports base64-encoded images currently."
" Example: data:image/png;base64,'/9j/4AAQSk'..."
)
return {
"type": "base64",
"media_type": match.group("media_type"),
"data": match.group("data"),
}
def _merge_messages(
messages: Sequence[BaseMessage],
) -> List[Union[SystemMessage, AIMessage, HumanMessage]]:
"""Merge runs of human/tool messages into single human messages with content blocks.""" # noqa: E501
merged: list = []
for curr in messages:
curr = curr.model_copy(deep=True)
if isinstance(curr, ToolMessage):
if isinstance(curr.content, list) and all(
isinstance(block, dict) and block.get("type") == "tool_result"
for block in curr.content
):
curr = HumanMessage(curr.content) # type: ignore[misc]
else:
curr = HumanMessage( # type: ignore[misc]
[
{
"type": "tool_result",
"content": curr.content,
"tool_use_id": curr.tool_call_id,
}
]
)
last = merged[-1] if merged else None
if isinstance(last, HumanMessage) and isinstance(curr, HumanMessage):
if isinstance(last.content, str):
new_content: List = [{"type": "text", "text": last.content}]
else:
new_content = last.content
if isinstance(curr.content, str):
new_content.append({"type": "text", "text": curr.content})
else:
new_content.extend(curr.content)
last.content = new_content
else:
merged.append(curr)
return merged
def _format_anthropic_messages(
messages: List[BaseMessage],
) -> Tuple[Optional[str], List[Dict]]:
"""Format messages for anthropic."""
system: Optional[str] = None
formatted_messages: List[Dict] = []
merged_messages = _merge_messages(messages)
for i, message in enumerate(merged_messages):
if message.type == "system":
if i != 0:
raise ValueError("System message must be at beginning of message list.")
if not isinstance(message.content, str):
raise ValueError(
"System message must be a string, "
f"instead was: {type(message.content)}"
)
system = message.content
continue
role = _message_type_lookups[message.type]
content: Union[str, List]
if not isinstance(message.content, str):
# parse as dict
assert isinstance(
message.content, list
), "Anthropic message content must be str or list of dicts"
# populate content
content = []
for item in message.content:
if isinstance(item, str):
content.append({"type": "text", "text": item})
elif isinstance(item, dict):
if "type" not in item:
raise ValueError("Dict content item must have a type key")
elif item["type"] == "image_url":
# convert format
source = _format_image(item["image_url"]["url"])
content.append({"type": "image", "source": source})
elif item["type"] == "tool_use":
# If a tool_call with the same id as a tool_use content block
# exists, the tool_call is preferred.
if isinstance(message, AIMessage) and item["id"] in [
tc["id"] for tc in message.tool_calls
]:
overlapping = [
tc
for tc in message.tool_calls
if tc["id"] == item["id"]
]
content.extend(
_lc_tool_calls_to_anthropic_tool_use_blocks(overlapping)
)
else:
item.pop("text", None)
content.append(item)
elif item["type"] == "text":
text = item.get("text", "")
# Only add non-empty strings for now as empty ones are not
# accepted.
# https://github.com/anthropics/anthropic-sdk-python/issues/461
if text.strip():
content.append({"type": "text", "text": text})
else:
content.append(item)
else:
raise ValueError(
f"Content items must be str or dict, instead was: {type(item)}"
)
elif isinstance(message, AIMessage) and message.tool_calls:
content = (
[]
if not message.content
else [{"type": "text", "text": message.content}]
)
# Note: Anthropic can't have invalid tool calls as presently defined,
# since the model already returns dicts args not JSON strings, and invalid
# tool calls are those with invalid JSON for args.
content += _lc_tool_calls_to_anthropic_tool_use_blocks(message.tool_calls)
else:
content = message.content
formatted_messages.append({"role": role, "content": content})
return system, formatted_messages
[docs]
class ChatPromptAdapter:
"""Adapter class to prepare the inputs from Langchain to prompt format
that Chat model expects.
"""
[docs]
@classmethod
def convert_messages_to_prompt(
cls, provider: str, messages: List[BaseMessage], model: str
) -> str:
if provider == "anthropic":
prompt = convert_messages_to_prompt_anthropic(messages=messages)
elif provider == "meta":
if "llama3" in model:
prompt = convert_messages_to_prompt_llama3(messages=messages)
else:
prompt = convert_messages_to_prompt_llama(messages=messages)
elif provider == "mistral":
prompt = convert_messages_to_prompt_mistral(messages=messages)
elif provider == "amazon":
prompt = convert_messages_to_prompt_anthropic(
messages=messages,
human_prompt="\n\nUser:",
ai_prompt="\n\nBot:",
)
else:
raise NotImplementedError(
f"Provider {provider} model does not support chat."
)
return prompt
_message_type_lookups = {
"human": "user",
"ai": "assistant",
"AIMessageChunk": "assistant",
"HumanMessageChunk": "user",
}
[docs]
class ChatBedrock(BaseChatModel, BedrockBase):
"""A chat model that uses the Bedrock API."""
system_prompt_with_tools: str = ""
beta_use_converse_api: bool = False
"""Use the new Bedrock ``converse`` API which provides a standardized interface to
all Bedrock models. Support still in beta. See ChatBedrockConverse docs for more."""
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "amazon_bedrock_chat"
@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", "chat_models", "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
model_config = ConfigDict(
extra="forbid",
populate_by_name=True,
)
def _get_ls_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
ls_params = LangSmithParams(
ls_provider="amazon_bedrock",
ls_model_name=self.model_id,
ls_model_type="chat",
)
if ls_temperature := params.get("temperature", self.temperature):
ls_params["ls_temperature"] = ls_temperature
if ls_max_tokens := params.get("max_tokens", self.max_tokens):
ls_params["ls_max_tokens"] = ls_max_tokens
if ls_stop := stop or params.get("stop", None):
ls_params["ls_stop"] = ls_stop
return ls_params
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
if self.beta_use_converse_api:
yield from self._as_converse._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return
provider = self._get_provider()
prompt, system, formatted_messages = None, None, None
if provider == "anthropic":
system, formatted_messages = ChatPromptAdapter.format_messages(
provider, messages
)
if self.system_prompt_with_tools:
if system:
system = self.system_prompt_with_tools + f"\n{system}"
else:
system = self.system_prompt_with_tools
else:
prompt = ChatPromptAdapter.convert_messages_to_prompt(
provider=provider, messages=messages, model=self._get_model()
)
for chunk in self._prepare_input_and_invoke_stream(
prompt=prompt,
system=system,
messages=formatted_messages,
stop=stop,
run_manager=run_manager,
**kwargs,
):
if isinstance(chunk, AIMessageChunk):
generation_chunk = ChatGenerationChunk(message=chunk)
if run_manager:
run_manager.on_llm_new_token(
generation_chunk.text, chunk=generation_chunk
)
yield generation_chunk
else:
delta = chunk.text
if generation_info := chunk.generation_info:
usage_metadata = generation_info.pop("usage_metadata", None)
else:
usage_metadata = None
generation_chunk = ChatGenerationChunk(
message=AIMessageChunk(
content=delta,
response_metadata=chunk.generation_info,
usage_metadata=usage_metadata,
)
if chunk.generation_info is not None
else AIMessageChunk(content=delta)
)
if run_manager:
run_manager.on_llm_new_token(
generation_chunk.text, chunk=generation_chunk
)
yield generation_chunk
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.beta_use_converse_api:
return self._as_converse._generate(
messages, stop=stop, run_manager=run_manager, **kwargs
)
completion = ""
llm_output: Dict[str, Any] = {}
tool_calls: List[ToolCall] = []
provider_stop_reason_code = self.provider_stop_reason_key_map.get(
self._get_provider(), "stop_reason"
)
provider = self._get_provider()
if self.streaming:
if provider == "anthropic":
stream_iter = self._stream(messages, stop, run_manager, **kwargs)
return generate_from_stream(stream_iter)
response_metadata: List[Dict[str, Any]] = []
for chunk in self._stream(messages, stop, run_manager, **kwargs):
completion += chunk.text
response_metadata.append(chunk.message.response_metadata)
if "tool_calls" in chunk.message.additional_kwargs.keys():
tool_calls = chunk.message.additional_kwargs["tool_calls"]
llm_output = _combine_generation_info_for_llm_result(
response_metadata, provider_stop_reason_code
)
else:
prompt, system, formatted_messages = None, None, None
params: Dict[str, Any] = {**kwargs}
if provider == "anthropic":
system, formatted_messages = ChatPromptAdapter.format_messages(
provider, messages
)
# use tools the new way with claude 3
if self.system_prompt_with_tools:
if system:
system = self.system_prompt_with_tools + f"\n{system}"
else:
system = self.system_prompt_with_tools
else:
prompt = ChatPromptAdapter.convert_messages_to_prompt(
provider=provider, messages=messages, model=self._get_model()
)
if stop:
params["stop_sequences"] = stop
completion, tool_calls, llm_output = self._prepare_input_and_invoke(
prompt=prompt,
stop=stop,
run_manager=run_manager,
system=system,
messages=formatted_messages,
**params,
)
# usage metadata
if usage := llm_output.get("usage"):
input_tokens = usage.get("prompt_tokens", 0)
output_tokens = usage.get("completion_tokens", 0)
usage_metadata = UsageMetadata(
input_tokens=input_tokens,
output_tokens=output_tokens,
total_tokens=usage.get("total_tokens", input_tokens + output_tokens),
)
else:
usage_metadata = None
llm_output["model_id"] = self.model_id
msg = AIMessage(
content=completion,
additional_kwargs=llm_output,
tool_calls=cast(List[ToolCall], tool_calls),
usage_metadata=usage_metadata,
)
return ChatResult(
generations=[
ChatGeneration(
message=msg,
)
],
llm_output=llm_output,
)
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
final_usage: Dict[str, int] = defaultdict(int)
final_output = {}
for output in llm_outputs:
output = output or {}
usage = output.get("usage", {})
for token_type, token_count in usage.items():
final_usage[token_type] += token_count
final_output.update(output)
final_output["usage"] = final_usage
return final_output
[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)
[docs]
def with_structured_output(
self,
schema: Union[Dict, TypeBaseModel],
*,
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
"""Model wrapper that returns outputs formatted to match the given schema.
Args:
schema: The output schema as a dict or a Pydantic class. If a Pydantic class
then the model output will be an object of that class. If a dict then
the model output will be a dict. With a Pydantic class the returned
attributes will be validated, whereas with a dict they will not be.
include_raw: If False then only the parsed structured output is returned. If
an error occurs during model output parsing it will be raised. If True
then both the raw model response (a BaseMessage) and the parsed model
response will be returned. If an error occurs during output parsing it
will be caught and returned as well. The final output is always a dict
with keys "raw", "parsed", and "parsing_error".
Returns:
A Runnable that takes any ChatModel input. The output type depends on
include_raw and schema.
If include_raw is True then output is a dict with keys:
raw: BaseMessage,
parsed: Optional[_DictOrPydantic],
parsing_error: Optional[BaseException],
If include_raw is False and schema is a Dict then the runnable outputs a Dict.
If include_raw is False and schema is a Type[BaseModel] then the runnable
outputs a BaseModel.
Example: Pydantic schema (include_raw=False):
.. code-block:: python
from langchain_aws.chat_models.bedrock import ChatBedrock
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm =ChatBedrock(
model_id="anthropic.claude-3-sonnet-20240229-v1:0",
model_kwargs={"temperature": 0.001},
) # type: ignore[call-arg]
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> AnswerWithJustification(
# answer='They weigh the same',
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )
Example: Pydantic schema (include_raw=True):
.. code-block:: python
from langchain_aws.chat_models.bedrock import ChatBedrock
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm =ChatBedrock(
model_id="anthropic.claude-3-sonnet-20240229-v1:0",
model_kwargs={"temperature": 0.001},
) # type: ignore[call-arg]
structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
# 'parsing_error': None
# }
Example: Dict schema (include_raw=False):
.. code-block:: python
from langchain_aws.chat_models.bedrock import ChatBedrock
schema = {
"name": "AnswerWithJustification",
"description": "An answer to the user question along with justification for the answer.",
"input_schema": {
"type": "object",
"properties": {
"answer": {"type": "string"},
"justification": {"type": "string"},
},
"required": ["answer", "justification"]
}
}
llm =ChatBedrock(
model_id="anthropic.claude-3-sonnet-20240229-v1:0",
model_kwargs={"temperature": 0.001},
) # type: ignore[call-arg]
structured_llm = llm.with_structured_output(schema)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
""" # noqa: E501
if self.beta_use_converse_api:
return self._as_converse.with_structured_output(
schema, include_raw=include_raw, **kwargs
)
if "claude-3" not in self._get_model():
ValueError(
f"Structured output is not supported for model {self._get_model()}"
)
tool_name = convert_to_anthropic_tool(schema)["name"]
llm = self.bind_tools([schema], tool_choice=tool_name)
if isinstance(schema, type) and is_basemodel_subclass(schema):
output_parser = ToolsOutputParser(
first_tool_only=True, pydantic_schemas=[schema]
)
else:
output_parser = ToolsOutputParser(first_tool_only=True, args_only=True)
if include_raw:
parser_assign = RunnablePassthrough.assign(
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
)
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
parser_with_fallback = parser_assign.with_fallbacks(
[parser_none], exception_key="parsing_error"
)
return RunnableMap(raw=llm) | parser_with_fallback
else:
return llm | output_parser
@property
def _as_converse(self) -> ChatBedrockConverse:
kwargs = {
k: v
for k, v in (self.model_kwargs or {}).items()
if k in ("stop", "stop_sequences", "max_tokens", "temperature", "top_p")
}
if self.max_tokens:
kwargs["max_tokens"] = self.max_tokens
if self.temperature is not None:
kwargs["temperature"] = self.temperature
return ChatBedrockConverse(
model=self.model_id,
region_name=self.region_name,
credentials_profile_name=self.credentials_profile_name,
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,
config=self.config,
provider=self.provider or "",
base_url=self.endpoint_url,
guardrail_config=(self.guardrails if self._guardrails_enabled else None), # type: ignore[call-arg]
**kwargs,
)
