import re
from collections import defaultdict
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import (
CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
ChatMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_community.chat_models.anthropic import (
convert_messages_to_prompt_anthropic,
)
from langchain_community.chat_models.meta import convert_messages_to_prompt_llama
from langchain_community.llms.bedrock import BedrockBase
from langchain_community.utilities.anthropic import (
get_num_tokens_anthropic,
get_token_ids_anthropic,
)
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 _format_anthropic_messages(
messages: List[BaseMessage],
) -> Tuple[Optional[str], List[Dict]]:
"""Format messages for anthropic."""
"""
[
{
"role": _message_type_lookups[m.type],
"content": [_AnthropicMessageContent(text=m.content).dict()],
}
for m in messages
]
"""
system: Optional[str] = None
formatted_messages: List[Dict] = []
for i, message in enumerate(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[Dict]]
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")
if item["type"] == "image_url":
# convert format
source = _format_image(item["image_url"]["url"])
content.append(
{
"type": "image",
"source": source,
}
)
else:
content.append(item)
else:
raise ValueError(
f"Content items must be str or dict, instead was: {type(item)}"
)
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]
) -> str:
if provider == "anthropic":
prompt = convert_messages_to_prompt_anthropic(messages=messages)
elif provider == "meta":
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",
"function": "user",
}
[docs]@deprecated(
since="0.0.34", removal="1.0", alternative_import="langchain_aws.ChatBedrock"
)
class BedrockChat(BaseChatModel, BedrockBase):
"""Chat model that uses the Bedrock API."""
@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
class Config:
extra = "forbid"
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
provider = self._get_provider()
prompt, system, formatted_messages = None, None, None
if provider == "anthropic":
system, formatted_messages = ChatPromptAdapter.format_messages(
provider, messages
)
else:
prompt = ChatPromptAdapter.convert_messages_to_prompt(
provider=provider, messages=messages
)
for chunk in self._prepare_input_and_invoke_stream(
prompt=prompt,
system=system,
messages=formatted_messages,
stop=stop,
run_manager=run_manager,
**kwargs,
):
delta = chunk.text
yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
completion = ""
llm_output: Dict[str, Any] = {"model_id": self.model_id}
if self.streaming:
for chunk in self._stream(messages, stop, run_manager, **kwargs):
completion += chunk.text
else:
provider = self._get_provider()
prompt, system, formatted_messages = None, None, None
params: Dict[str, Any] = {**kwargs}
if provider == "anthropic":
system, formatted_messages = ChatPromptAdapter.format_messages(
provider, messages
)
else:
prompt = ChatPromptAdapter.convert_messages_to_prompt(
provider=provider, messages=messages
)
if stop:
params["stop_sequences"] = stop
completion, usage_info = self._prepare_input_and_invoke(
prompt=prompt,
stop=stop,
run_manager=run_manager,
system=system,
messages=formatted_messages,
**params,
)
llm_output["usage"] = usage_info
return ChatResult(
generations=[ChatGeneration(message=AIMessage(content=completion))],
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)
