mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-23 11:30:37 +00:00
community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
This commit is contained in:
Bagatur
GitHub
226
libs/community/langchain_community/chat_models/anthropic.py
Normal file
226
libs/community/langchain_community/chat_models/anthropic.py
Normal file
@@ -0,0 +1,226 @@
|
||||
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, cast
|
||||
|
||||
from langchain_core.callbacks import (
|
||||
AsyncCallbackManagerForLLMRun,
|
||||
CallbackManagerForLLMRun,
|
||||
)
|
||||
from langchain_core.language_models.chat_models import (
|
||||
BaseChatModel,
|
||||
agenerate_from_stream,
|
||||
generate_from_stream,
|
||||
)
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
AIMessageChunk,
|
||||
BaseMessage,
|
||||
ChatMessage,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
)
|
||||
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
|
||||
from langchain_core.prompt_values import PromptValue
|
||||
|
||||
from langchain_community.llms.anthropic import _AnthropicCommon
|
||||
|
||||
|
||||
def _convert_one_message_to_text(
|
||||
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
|
||||
|
||||
|
||||
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(message, human_prompt, ai_prompt)
|
||||
for message in messages
|
||||
)
|
||||
|
||||
# trim off the trailing ' ' that might come from the "Assistant: "
|
||||
return text.rstrip()
|
||||
|
||||
|
||||
class ChatAnthropic(BaseChatModel, _AnthropicCommon):
|
||||
"""`Anthropic` chat large language models.
|
||||
|
||||
To use, you should have the ``anthropic`` python package installed, and the
|
||||
environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass
|
||||
it as a named parameter to the constructor.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
import anthropic
|
||||
from langchain_community.chat_models import ChatAnthropic
|
||||
model = ChatAnthropic(model="<model_name>", anthropic_api_key="my-api-key")
|
||||
"""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
allow_population_by_field_name = True
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@property
|
||||
def lc_secrets(self) -> Dict[str, str]:
|
||||
return {"anthropic_api_key": "ANTHROPIC_API_KEY"}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of chat model."""
|
||||
return "anthropic-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", "anthropic"]
|
||||
|
||||
def _convert_messages_to_prompt(self, messages: List[BaseMessage]) -> str:
|
||||
"""Format a list of messages into a full prompt for the Anthropic model
|
||||
Args:
|
||||
messages (List[BaseMessage]): List of BaseMessage to combine.
|
||||
Returns:
|
||||
str: Combined string with necessary HUMAN_PROMPT and AI_PROMPT tags.
|
||||
"""
|
||||
prompt_params = {}
|
||||
if self.HUMAN_PROMPT:
|
||||
prompt_params["human_prompt"] = self.HUMAN_PROMPT
|
||||
if self.AI_PROMPT:
|
||||
prompt_params["ai_prompt"] = self.AI_PROMPT
|
||||
return convert_messages_to_prompt_anthropic(messages=messages, **prompt_params)
|
||||
|
||||
def convert_prompt(self, prompt: PromptValue) -> str:
|
||||
return self._convert_messages_to_prompt(prompt.to_messages())
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[ChatGenerationChunk]:
|
||||
prompt = self._convert_messages_to_prompt(messages)
|
||||
params: Dict[str, Any] = {"prompt": prompt, **self._default_params, **kwargs}
|
||||
if stop:
|
||||
params["stop_sequences"] = stop
|
||||
|
||||
stream_resp = self.client.completions.create(**params, stream=True)
|
||||
for data in stream_resp:
|
||||
delta = data.completion
|
||||
yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(delta)
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[ChatGenerationChunk]:
|
||||
prompt = self._convert_messages_to_prompt(messages)
|
||||
params: Dict[str, Any] = {"prompt": prompt, **self._default_params, **kwargs}
|
||||
if stop:
|
||||
params["stop_sequences"] = stop
|
||||
|
||||
stream_resp = await self.async_client.completions.create(**params, stream=True)
|
||||
async for data in stream_resp:
|
||||
delta = data.completion
|
||||
yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(delta)
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
if self.streaming:
|
||||
stream_iter = self._stream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return generate_from_stream(stream_iter)
|
||||
prompt = self._convert_messages_to_prompt(
|
||||
messages,
|
||||
)
|
||||
params: Dict[str, Any] = {
|
||||
"prompt": prompt,
|
||||
**self._default_params,
|
||||
**kwargs,
|
||||
}
|
||||
if stop:
|
||||
params["stop_sequences"] = stop
|
||||
response = self.client.completions.create(**params)
|
||||
completion = response.completion
|
||||
message = AIMessage(content=completion)
|
||||
return ChatResult(generations=[ChatGeneration(message=message)])
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
if self.streaming:
|
||||
stream_iter = self._astream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return await agenerate_from_stream(stream_iter)
|
||||
prompt = self._convert_messages_to_prompt(
|
||||
messages,
|
||||
)
|
||||
params: Dict[str, Any] = {
|
||||
"prompt": prompt,
|
||||
**self._default_params,
|
||||
**kwargs,
|
||||
}
|
||||
if stop:
|
||||
params["stop_sequences"] = stop
|
||||
response = await self.async_client.completions.create(**params)
|
||||
completion = response.completion
|
||||
message = AIMessage(content=completion)
|
||||
return ChatResult(generations=[ChatGeneration(message=message)])
|
||||
|
||||
def get_num_tokens(self, text: str) -> int:
|
||||
"""Calculate number of tokens."""
|
||||
if not self.count_tokens:
|
||||
raise NameError("Please ensure the anthropic package is loaded")
|
||||
return self.count_tokens(text)
|
||||
Reference in New Issue
Block a user