mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
anthropic[minor]: package move (#17974)
This commit is contained in:
Erick Friis
GitHub
parent
a2d5fa7649
commit
3b5bdbfee8
@ -1,3 +1,4 @@
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from langchain_anthropic.chat_models import ChatAnthropicMessages
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from langchain_anthropic.chat_models import ChatAnthropic, ChatAnthropicMessages
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from langchain_anthropic.llms import Anthropic, AnthropicLLM
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__all__ = ["ChatAnthropicMessages"]
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__all__ = ["ChatAnthropicMessages", "ChatAnthropic", "Anthropic", "AnthropicLLM"]
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@ -2,6 +2,7 @@ import os
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Tuple
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import anthropic
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from langchain_core._api.deprecation import deprecated
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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@ -14,7 +15,11 @@ from langchain_core.messages import (
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
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from langchain_core.utils import convert_to_secret_str
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from langchain_core.utils import (
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build_extra_kwargs,
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convert_to_secret_str,
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get_pydantic_field_names,
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)
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_message_type_lookups = {"human": "user", "ai": "assistant"}
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@ -50,7 +55,7 @@ def _format_messages(messages: List[BaseMessage]) -> Tuple[Optional[str], List[D
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return system, formatted_messages
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class ChatAnthropicMessages(BaseChatModel):
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class ChatAnthropic(BaseChatModel):
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"""ChatAnthropicMessages chat model.
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Example:
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@ -61,13 +66,18 @@ class ChatAnthropicMessages(BaseChatModel):
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model = ChatAnthropicMessages()
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"""
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_client: anthropic.Client = Field(default_factory=anthropic.Client)
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_async_client: anthropic.AsyncClient = Field(default_factory=anthropic.AsyncClient)
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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_client: anthropic.Client = Field(default=None)
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_async_client: anthropic.AsyncClient = Field(default=None)
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model: str = Field(alias="model_name")
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"""Model name to use."""
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max_tokens: int = Field(default=256)
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max_tokens: int = Field(default=256, alias="max_tokens_to_sample")
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"""Denotes the number of tokens to predict per generation."""
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temperature: Optional[float] = None
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@ -88,16 +98,20 @@ class ChatAnthropicMessages(BaseChatModel):
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "chat-anthropic-messages"
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@root_validator(pre=True)
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def build_extra(cls, values: Dict) -> Dict:
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extra = values.get("model_kwargs", {})
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all_required_field_names = get_pydantic_field_names(cls)
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values["model_kwargs"] = build_extra_kwargs(
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extra, values, all_required_field_names
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)
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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anthropic_api_key = convert_to_secret_str(
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@ -130,6 +144,7 @@ class ChatAnthropicMessages(BaseChatModel):
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"top_p": self.top_p,
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"stop_sequences": stop,
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"system": system,
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**self.model_kwargs,
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}
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rtn = {k: v for k, v in rtn.items() if v is not None}
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@ -145,7 +160,10 @@ class ChatAnthropicMessages(BaseChatModel):
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params = self._format_params(messages=messages, stop=stop, **kwargs)
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with self._client.messages.stream(**params) as stream:
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for text in stream.text_stream:
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yield ChatGenerationChunk(message=AIMessageChunk(content=text))
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chunk = ChatGenerationChunk(message=AIMessageChunk(content=text))
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if run_manager:
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run_manager.on_llm_new_token(text, chunk=chunk)
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yield chunk
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async def _astream(
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self,
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@ -157,7 +175,10 @@ class ChatAnthropicMessages(BaseChatModel):
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params = self._format_params(messages=messages, stop=stop, **kwargs)
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async with self._async_client.messages.stream(**params) as stream:
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async for text in stream.text_stream:
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yield ChatGenerationChunk(message=AIMessageChunk(content=text))
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chunk = ChatGenerationChunk(message=AIMessageChunk(content=text))
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if run_manager:
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await run_manager.on_llm_new_token(text, chunk=chunk)
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yield chunk
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def _generate(
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self,
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@ -190,3 +211,8 @@ class ChatAnthropicMessages(BaseChatModel):
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],
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llm_output=data,
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)
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@deprecated(since="0.1.0", removal="0.2.0", alternative="ChatAnthropic")
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class ChatAnthropicMessages(ChatAnthropic):
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pass
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352
libs/partners/anthropic/langchain_anthropic/llms.py
Normal file
352
libs/partners/anthropic/langchain_anthropic/llms.py
Normal file
@ -0,0 +1,352 @@
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import re
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import warnings
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from typing import (
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Any,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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Mapping,
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Optional,
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)
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import anthropic
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from langchain_core._api.deprecation import deprecated
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.language_models.llms import LLM
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from langchain_core.outputs import GenerationChunk
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from langchain_core.prompt_values import PromptValue
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from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
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from langchain_core.utils import (
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get_from_dict_or_env,
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get_pydantic_field_names,
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)
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from langchain_core.utils.utils import build_extra_kwargs, convert_to_secret_str
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class _AnthropicCommon(BaseLanguageModel):
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client: Any = None #: :meta private:
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async_client: Any = None #: :meta private:
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model: str = Field(default="claude-2", alias="model_name")
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"""Model name to use."""
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max_tokens_to_sample: int = Field(default=256, alias="max_tokens")
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"""Denotes the number of tokens to predict per generation."""
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temperature: Optional[float] = None
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"""A non-negative float that tunes the degree of randomness in generation."""
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top_k: Optional[int] = None
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"""Number of most likely tokens to consider at each step."""
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top_p: Optional[float] = None
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"""Total probability mass of tokens to consider at each step."""
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streaming: bool = False
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"""Whether to stream the results."""
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default_request_timeout: Optional[float] = None
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"""Timeout for requests to Anthropic Completion API. Default is 600 seconds."""
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max_retries: int = 2
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"""Number of retries allowed for requests sent to the Anthropic Completion API."""
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anthropic_api_url: Optional[str] = None
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anthropic_api_key: Optional[SecretStr] = None
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HUMAN_PROMPT: Optional[str] = None
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AI_PROMPT: Optional[str] = None
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count_tokens: Optional[Callable[[str], int]] = None
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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@root_validator(pre=True)
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def build_extra(cls, values: Dict) -> Dict:
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extra = values.get("model_kwargs", {})
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all_required_field_names = get_pydantic_field_names(cls)
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values["model_kwargs"] = build_extra_kwargs(
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extra, values, all_required_field_names
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)
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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values["anthropic_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "anthropic_api_key", "ANTHROPIC_API_KEY")
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)
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# Get custom api url from environment.
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values["anthropic_api_url"] = get_from_dict_or_env(
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values,
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"anthropic_api_url",
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"ANTHROPIC_API_URL",
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default="https://api.anthropic.com",
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)
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values["client"] = anthropic.Anthropic(
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base_url=values["anthropic_api_url"],
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api_key=values["anthropic_api_key"].get_secret_value(),
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timeout=values["default_request_timeout"],
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max_retries=values["max_retries"],
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)
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values["async_client"] = anthropic.AsyncAnthropic(
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base_url=values["anthropic_api_url"],
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api_key=values["anthropic_api_key"].get_secret_value(),
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timeout=values["default_request_timeout"],
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max_retries=values["max_retries"],
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)
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values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT
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values["AI_PROMPT"] = anthropic.AI_PROMPT
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values["count_tokens"] = values["client"].count_tokens
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return values
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@property
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def _default_params(self) -> Mapping[str, Any]:
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"""Get the default parameters for calling Anthropic API."""
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d = {
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"max_tokens_to_sample": self.max_tokens_to_sample,
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"model": self.model,
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}
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if self.temperature is not None:
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d["temperature"] = self.temperature
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if self.top_k is not None:
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d["top_k"] = self.top_k
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if self.top_p is not None:
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d["top_p"] = self.top_p
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return {**d, **self.model_kwargs}
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {**{}, **self._default_params}
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def _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]:
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if not self.HUMAN_PROMPT or not self.AI_PROMPT:
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raise NameError("Please ensure the anthropic package is loaded")
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if stop is None:
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stop = []
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# Never want model to invent new turns of Human / Assistant dialog.
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stop.extend([self.HUMAN_PROMPT])
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return stop
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class AnthropicLLM(LLM, _AnthropicCommon):
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"""Anthropic large language models.
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To use, you should have the ``anthropic`` python package installed, and the
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environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass
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it as a named parameter to the constructor.
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Example:
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.. code-block:: python
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import anthropic
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from langchain_community.llms import Anthropic
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model = Anthropic(model="<model_name>", anthropic_api_key="my-api-key")
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# Simplest invocation, automatically wrapped with HUMAN_PROMPT
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# and AI_PROMPT.
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response = model("What are the biggest risks facing humanity?")
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# Or if you want to use the chat mode, build a few-shot-prompt, or
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# put words in the Assistant's mouth, use HUMAN_PROMPT and AI_PROMPT:
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raw_prompt = "What are the biggest risks facing humanity?"
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prompt = f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}"
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response = model(prompt)
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"""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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arbitrary_types_allowed = True
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@root_validator()
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def raise_warning(cls, values: Dict) -> Dict:
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"""Raise warning that this class is deprecated."""
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warnings.warn(
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"This Anthropic LLM is deprecated. "
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"Please use `from langchain_community.chat_models import ChatAnthropic` "
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"instead"
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)
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return values
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "anthropic-llm"
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def _wrap_prompt(self, prompt: str) -> str:
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if not self.HUMAN_PROMPT or not self.AI_PROMPT:
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raise NameError("Please ensure the anthropic package is loaded")
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if prompt.startswith(self.HUMAN_PROMPT):
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return prompt # Already wrapped.
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# Guard against common errors in specifying wrong number of newlines.
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corrected_prompt, n_subs = re.subn(r"^\n*Human:", self.HUMAN_PROMPT, prompt)
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if n_subs == 1:
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return corrected_prompt
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# As a last resort, wrap the prompt ourselves to emulate instruct-style.
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return f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here you go:\n"
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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r"""Call out to Anthropic's completion endpoint.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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prompt = "What are the biggest risks facing humanity?"
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prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
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response = model(prompt)
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"""
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if self.streaming:
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completion = ""
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for chunk in self._stream(
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prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
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):
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completion += chunk.text
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return completion
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stop = self._get_anthropic_stop(stop)
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params = {**self._default_params, **kwargs}
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response = self.client.completions.create(
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prompt=self._wrap_prompt(prompt),
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stop_sequences=stop,
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**params,
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)
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return response.completion
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def convert_prompt(self, prompt: PromptValue) -> str:
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return self._wrap_prompt(prompt.to_string())
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async def _acall(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""Call out to Anthropic's completion endpoint asynchronously."""
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if self.streaming:
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completion = ""
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async for chunk in self._astream(
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prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
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):
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completion += chunk.text
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return completion
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stop = self._get_anthropic_stop(stop)
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params = {**self._default_params, **kwargs}
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response = await self.async_client.completions.create(
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prompt=self._wrap_prompt(prompt),
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stop_sequences=stop,
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**params,
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)
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return response.completion
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def _stream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[GenerationChunk]:
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r"""Call Anthropic completion_stream and return the resulting generator.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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A generator representing the stream of tokens from Anthropic.
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Example:
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.. code-block:: python
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prompt = "Write a poem about a stream."
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prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
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generator = anthropic.stream(prompt)
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for token in generator:
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yield token
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"""
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stop = self._get_anthropic_stop(stop)
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params = {**self._default_params, **kwargs}
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for token in self.client.completions.create(
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prompt=self._wrap_prompt(prompt), stop_sequences=stop, stream=True, **params
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):
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chunk = GenerationChunk(text=token.completion)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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async def _astream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[GenerationChunk]:
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r"""Call Anthropic completion_stream and return the resulting generator.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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A generator representing the stream of tokens from Anthropic.
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Example:
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.. code-block:: python
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prompt = "Write a poem about a stream."
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prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
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generator = anthropic.stream(prompt)
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for token in generator:
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yield token
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"""
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stop = self._get_anthropic_stop(stop)
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params = {**self._default_params, **kwargs}
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async for token in await self.async_client.completions.create(
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prompt=self._wrap_prompt(prompt),
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stop_sequences=stop,
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stream=True,
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**params,
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):
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chunk = GenerationChunk(text=token.completion)
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yield chunk
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if run_manager:
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await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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def get_num_tokens(self, text: str) -> int:
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"""Calculate number of tokens."""
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if not self.count_tokens:
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raise NameError("Please ensure the anthropic package is loaded")
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return self.count_tokens(text)
|
||||
|
||||
|
||||
@deprecated(since="0.1.0", removal="0.2.0", alternative="AnthropicLLM")
|
||||
class Anthropic(AnthropicLLM):
|
||||
pass
|
||||
74
libs/partners/anthropic/poetry.lock
generated
74
libs/partners/anthropic/poetry.lock
generated
@ -40,13 +40,13 @@ vertex = ["google-auth (>=2,<3)"]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.2.0"
|
||||
version = "4.3.0"
|
||||
description = "High level compatibility layer for multiple asynchronous event loop implementations"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
|
||||
{file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
|
||||
{file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
|
||||
{file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -301,13 +301,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "httpcore"
|
||||
version = "1.0.3"
|
||||
version = "1.0.4"
|
||||
description = "A minimal low-level HTTP client."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "httpcore-1.0.3-py3-none-any.whl", hash = "sha256:9a6a501c3099307d9fd76ac244e08503427679b1e81ceb1d922485e2f2462ad2"},
|
||||
{file = "httpcore-1.0.3.tar.gz", hash = "sha256:5c0f9546ad17dac4d0772b0808856eb616eb8b48ce94f49ed819fd6982a8a544"},
|
||||
{file = "httpcore-1.0.4-py3-none-any.whl", hash = "sha256:ac418c1db41bade2ad53ae2f3834a3a0f5ae76b56cf5aa497d2d033384fc7d73"},
|
||||
{file = "httpcore-1.0.4.tar.gz", hash = "sha256:cb2839ccfcba0d2d3c1131d3c3e26dfc327326fbe7a5dc0dbfe9f6c9151bb022"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -318,17 +318,17 @@ h11 = ">=0.13,<0.15"
|
||||
asyncio = ["anyio (>=4.0,<5.0)"]
|
||||
http2 = ["h2 (>=3,<5)"]
|
||||
socks = ["socksio (==1.*)"]
|
||||
trio = ["trio (>=0.22.0,<0.24.0)"]
|
||||
trio = ["trio (>=0.22.0,<0.25.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "httpx"
|
||||
version = "0.26.0"
|
||||
version = "0.27.0"
|
||||
description = "The next generation HTTP client."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "httpx-0.26.0-py3-none-any.whl", hash = "sha256:8915f5a3627c4d47b73e8202457cb28f1266982d1159bd5779d86a80c0eab1cd"},
|
||||
{file = "httpx-0.26.0.tar.gz", hash = "sha256:451b55c30d5185ea6b23c2c793abf9bb237d2a7dfb901ced6ff69ad37ec1dfaf"},
|
||||
{file = "httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5"},
|
||||
{file = "httpx-0.27.0.tar.gz", hash = "sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -425,7 +425,7 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.1.23"
|
||||
version = "0.1.25"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
@ -435,7 +435,7 @@ develop = true
|
||||
[package.dependencies]
|
||||
anyio = ">=3,<5"
|
||||
jsonpatch = "^1.33"
|
||||
langsmith = "^0.0.87"
|
||||
langsmith = "^0.1.0"
|
||||
packaging = "^23.2"
|
||||
pydantic = ">=1,<3"
|
||||
PyYAML = ">=5.3"
|
||||
@ -451,13 +451,13 @@ url = "../../core"
|
||||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.0.87"
|
||||
version = "0.1.5"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
files = [
|
||||
{file = "langsmith-0.0.87-py3-none-any.whl", hash = "sha256:8903d3811b9fc89eb18f5961c8e6935fbd2d0f119884fbf30dc70b8f8f4121fc"},
|
||||
{file = "langsmith-0.0.87.tar.gz", hash = "sha256:36c4cc47e5b54be57d038036a30fb19ce6e4c73048cd7a464b8f25b459694d34"},
|
||||
{file = "langsmith-0.1.5-py3-none-any.whl", hash = "sha256:a1811821a923d90e53bcbacdd0988c3c366aff8f4c120d8777e7af8ecda06268"},
|
||||
{file = "langsmith-0.1.5.tar.gz", hash = "sha256:aa7a2861aa3d9ae563a077c622953533800466c4e2e539b0d567b84d5fd5b157"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -830,28 +830,28 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
|
||||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
version = "0.1.15"
|
||||
version = "0.2.2"
|
||||
description = "An extremely fast Python linter and code formatter, written in Rust."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "ruff-0.1.15-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:5fe8d54df166ecc24106db7dd6a68d44852d14eb0729ea4672bb4d96c320b7df"},
|
||||
{file = "ruff-0.1.15-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:6f0bfbb53c4b4de117ac4d6ddfd33aa5fc31beeaa21d23c45c6dd249faf9126f"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e0d432aec35bfc0d800d4f70eba26e23a352386be3a6cf157083d18f6f5881c8"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9405fa9ac0e97f35aaddf185a1be194a589424b8713e3b97b762336ec79ff807"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c66ec24fe36841636e814b8f90f572a8c0cb0e54d8b5c2d0e300d28a0d7bffec"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:6f8ad828f01e8dd32cc58bc28375150171d198491fc901f6f98d2a39ba8e3ff5"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:86811954eec63e9ea162af0ffa9f8d09088bab51b7438e8b6488b9401863c25e"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fd4025ac5e87d9b80e1f300207eb2fd099ff8200fa2320d7dc066a3f4622dc6b"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b17b93c02cdb6aeb696effecea1095ac93f3884a49a554a9afa76bb125c114c1"},
|
||||
{file = "ruff-0.1.15-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:ddb87643be40f034e97e97f5bc2ef7ce39de20e34608f3f829db727a93fb82c5"},
|
||||
{file = "ruff-0.1.15-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:abf4822129ed3a5ce54383d5f0e964e7fef74a41e48eb1dfad404151efc130a2"},
|
||||
{file = "ruff-0.1.15-py3-none-musllinux_1_2_i686.whl", hash = "sha256:6c629cf64bacfd136c07c78ac10a54578ec9d1bd2a9d395efbee0935868bf852"},
|
||||
{file = "ruff-0.1.15-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:1bab866aafb53da39c2cadfb8e1c4550ac5340bb40300083eb8967ba25481447"},
|
||||
{file = "ruff-0.1.15-py3-none-win32.whl", hash = "sha256:2417e1cb6e2068389b07e6fa74c306b2810fe3ee3476d5b8a96616633f40d14f"},
|
||||
{file = "ruff-0.1.15-py3-none-win_amd64.whl", hash = "sha256:3837ac73d869efc4182d9036b1405ef4c73d9b1f88da2413875e34e0d6919587"},
|
||||
{file = "ruff-0.1.15-py3-none-win_arm64.whl", hash = "sha256:9a933dfb1c14ec7a33cceb1e49ec4a16b51ce3c20fd42663198746efc0427360"},
|
||||
{file = "ruff-0.1.15.tar.gz", hash = "sha256:f6dfa8c1b21c913c326919056c390966648b680966febcb796cc9d1aaab8564e"},
|
||||
{file = "ruff-0.2.2-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:0a9efb032855ffb3c21f6405751d5e147b0c6b631e3ca3f6b20f917572b97eb6"},
|
||||
{file = "ruff-0.2.2-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:d450b7fbff85913f866a5384d8912710936e2b96da74541c82c1b458472ddb39"},
|
||||
{file = "ruff-0.2.2-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ecd46e3106850a5c26aee114e562c329f9a1fbe9e4821b008c4404f64ff9ce73"},
|
||||
{file = "ruff-0.2.2-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5e22676a5b875bd72acd3d11d5fa9075d3a5f53b877fe7b4793e4673499318ba"},
|
||||
{file = "ruff-0.2.2-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1695700d1e25a99d28f7a1636d85bafcc5030bba9d0578c0781ba1790dbcf51c"},
|
||||
{file = "ruff-0.2.2-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:b0c232af3d0bd8f521806223723456ffebf8e323bd1e4e82b0befb20ba18388e"},
|
||||
{file = "ruff-0.2.2-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f63d96494eeec2fc70d909393bcd76c69f35334cdbd9e20d089fb3f0640216ca"},
|
||||
{file = "ruff-0.2.2-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6a61ea0ff048e06de273b2e45bd72629f470f5da8f71daf09fe481278b175001"},
|
||||
{file = "ruff-0.2.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5e1439c8f407e4f356470e54cdecdca1bd5439a0673792dbe34a2b0a551a2fe3"},
|
||||
{file = "ruff-0.2.2-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:940de32dc8853eba0f67f7198b3e79bc6ba95c2edbfdfac2144c8235114d6726"},
|
||||
{file = "ruff-0.2.2-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:0c126da55c38dd917621552ab430213bdb3273bb10ddb67bc4b761989210eb6e"},
|
||||
{file = "ruff-0.2.2-py3-none-musllinux_1_2_i686.whl", hash = "sha256:3b65494f7e4bed2e74110dac1f0d17dc8e1f42faaa784e7c58a98e335ec83d7e"},
|
||||
{file = "ruff-0.2.2-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:1ec49be4fe6ddac0503833f3ed8930528e26d1e60ad35c2446da372d16651ce9"},
|
||||
{file = "ruff-0.2.2-py3-none-win32.whl", hash = "sha256:d920499b576f6c68295bc04e7b17b6544d9d05f196bb3aac4358792ef6f34325"},
|
||||
{file = "ruff-0.2.2-py3-none-win_amd64.whl", hash = "sha256:cc9a91ae137d687f43a44c900e5d95e9617cb37d4c989e462980ba27039d239d"},
|
||||
{file = "ruff-0.2.2-py3-none-win_arm64.whl", hash = "sha256:c9d15fc41e6054bfc7200478720570078f0b41c9ae4f010bcc16bd6f4d1aacdd"},
|
||||
{file = "ruff-0.2.2.tar.gz", hash = "sha256:e62ed7f36b3068a30ba39193a14274cd706bc486fad521276458022f7bccb31d"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -1075,13 +1075,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.2.0"
|
||||
version = "2.2.1"
|
||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
|
||||
{file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
|
||||
{file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
|
||||
{file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@ -1134,4 +1134,4 @@ watchmedo = ["PyYAML (>=3.10)"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "c4d03a1586b121b905ea4c0f86d04427cbb3e155e60d67c4b3351186de0d540a"
|
||||
content-hash = "e88b90ae60758bdab0fe844948c5b9a45bb7f3a96e9ab31ee6d56a7ebd24bfde"
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "langchain-anthropic"
|
||||
version = "0.0.2"
|
||||
version = "0.1.0"
|
||||
description = "An integration package connecting AnthropicMessages and LangChain"
|
||||
authors = []
|
||||
readme = "README.md"
|
||||
@ -37,7 +37,8 @@ codespell = "^2.2.0"
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.lint.dependencies]
|
||||
ruff = "^0.1.5"
|
||||
ruff = ">=0.2.2,<1"
|
||||
mypy = "^0.991"
|
||||
|
||||
[tool.poetry.group.typing.dependencies]
|
||||
mypy = "^0.991"
|
||||
|
||||
@ -1,7 +1,14 @@
|
||||
"""Test ChatAnthropicMessages chat model."""
|
||||
"""Test ChatAnthropic chat model."""
|
||||
|
||||
from typing import List
|
||||
|
||||
from langchain_core.callbacks import CallbackManager
|
||||
from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage, HumanMessage
|
||||
from langchain_core.outputs import ChatGeneration, LLMResult
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
|
||||
from langchain_anthropic.chat_models import ChatAnthropicMessages
|
||||
from langchain_anthropic import ChatAnthropic, ChatAnthropicMessages
|
||||
from tests.unit_tests._utils import FakeCallbackHandler
|
||||
|
||||
|
||||
def test_stream() -> None:
|
||||
@ -84,3 +91,72 @@ def test_system_invoke() -> None:
|
||||
|
||||
result = chain.invoke({})
|
||||
assert isinstance(result.content, str)
|
||||
|
||||
|
||||
def test_anthropic_call() -> None:
|
||||
"""Test valid call to anthropic."""
|
||||
chat = ChatAnthropic(model="test")
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
assert isinstance(response, AIMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
def test_anthropic_generate() -> None:
|
||||
"""Test generate method of anthropic."""
|
||||
chat = ChatAnthropic(model="test")
|
||||
chat_messages: List[List[BaseMessage]] = [
|
||||
[HumanMessage(content="How many toes do dogs have?")]
|
||||
]
|
||||
messages_copy = [messages.copy() for messages in chat_messages]
|
||||
result: LLMResult = chat.generate(chat_messages)
|
||||
assert isinstance(result, LLMResult)
|
||||
for response in result.generations[0]:
|
||||
assert isinstance(response, ChatGeneration)
|
||||
assert isinstance(response.text, str)
|
||||
assert response.text == response.message.content
|
||||
assert chat_messages == messages_copy
|
||||
|
||||
|
||||
def test_anthropic_streaming() -> None:
|
||||
"""Test streaming tokens from anthropic."""
|
||||
chat = ChatAnthropic(model="test")
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat.stream([message])
|
||||
for token in response:
|
||||
assert isinstance(token, AIMessageChunk)
|
||||
assert isinstance(token.content, str)
|
||||
|
||||
|
||||
def test_anthropic_streaming_callback() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
chat = ChatAnthropic(
|
||||
model="test",
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
message = HumanMessage(content="Write me a sentence with 10 words.")
|
||||
for token in chat.stream([message]):
|
||||
assert isinstance(token, AIMessageChunk)
|
||||
assert isinstance(token.content, str)
|
||||
assert callback_handler.llm_streams > 1
|
||||
|
||||
|
||||
async def test_anthropic_async_streaming_callback() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
chat = ChatAnthropic(
|
||||
model="test",
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
chat_messages: List[BaseMessage] = [
|
||||
HumanMessage(content="How many toes do dogs have?")
|
||||
]
|
||||
async for token in chat.astream(chat_messages):
|
||||
assert isinstance(token, AIMessageChunk)
|
||||
assert isinstance(token.content, str)
|
||||
assert callback_handler.llm_streams > 1
|
||||
|
||||
74
libs/partners/anthropic/tests/integration_tests/test_llms.py
Normal file
74
libs/partners/anthropic/tests/integration_tests/test_llms.py
Normal file
@ -0,0 +1,74 @@
|
||||
"""Test Anthropic API wrapper."""
|
||||
|
||||
from typing import Generator
|
||||
|
||||
import pytest
|
||||
from langchain_core.callbacks import CallbackManager
|
||||
from langchain_core.outputs import LLMResult
|
||||
|
||||
from langchain_anthropic import Anthropic
|
||||
from tests.unit_tests._utils import FakeCallbackHandler
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_model_name_param() -> None:
|
||||
llm = Anthropic(model_name="foo")
|
||||
assert llm.model == "foo"
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_model_param() -> None:
|
||||
llm = Anthropic(model="foo")
|
||||
assert llm.model == "foo"
|
||||
|
||||
|
||||
def test_anthropic_call() -> None:
|
||||
"""Test valid call to anthropic."""
|
||||
llm = Anthropic(model="claude-instant-1")
|
||||
output = llm("Say foo:")
|
||||
assert isinstance(output, str)
|
||||
|
||||
|
||||
def test_anthropic_streaming() -> None:
|
||||
"""Test streaming tokens from anthropic."""
|
||||
llm = Anthropic(model="claude-instant-1")
|
||||
generator = llm.stream("I'm Pickle Rick")
|
||||
|
||||
assert isinstance(generator, Generator)
|
||||
|
||||
for token in generator:
|
||||
assert isinstance(token, str)
|
||||
|
||||
|
||||
def test_anthropic_streaming_callback() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
llm = Anthropic(
|
||||
streaming=True,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
llm("Write me a sentence with 100 words.")
|
||||
assert callback_handler.llm_streams > 1
|
||||
|
||||
|
||||
async def test_anthropic_async_generate() -> None:
|
||||
"""Test async generate."""
|
||||
llm = Anthropic()
|
||||
output = await llm.agenerate(["How many toes do dogs have?"])
|
||||
assert isinstance(output, LLMResult)
|
||||
|
||||
|
||||
async def test_anthropic_async_streaming_callback() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
llm = Anthropic(
|
||||
streaming=True,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
result = await llm.agenerate(["How many toes do dogs have?"])
|
||||
assert callback_handler.llm_streams > 1
|
||||
assert isinstance(result, LLMResult)
|
||||
255
libs/partners/anthropic/tests/unit_tests/_utils.py
Normal file
255
libs/partners/anthropic/tests/unit_tests/_utils.py
Normal file
@ -0,0 +1,255 @@
|
||||
"""A fake callback handler for testing purposes."""
|
||||
|
||||
from typing import Any, Union
|
||||
|
||||
from langchain_core.callbacks import BaseCallbackHandler
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
|
||||
|
||||
class BaseFakeCallbackHandler(BaseModel):
|
||||
"""Base fake callback handler for testing."""
|
||||
|
||||
starts: int = 0
|
||||
ends: int = 0
|
||||
errors: int = 0
|
||||
text: int = 0
|
||||
ignore_llm_: bool = False
|
||||
ignore_chain_: bool = False
|
||||
ignore_agent_: bool = False
|
||||
ignore_retriever_: bool = False
|
||||
ignore_chat_model_: bool = False
|
||||
|
||||
# to allow for similar callback handlers that are not technicall equal
|
||||
fake_id: Union[str, None] = None
|
||||
|
||||
# add finer-grained counters for easier debugging of failing tests
|
||||
chain_starts: int = 0
|
||||
chain_ends: int = 0
|
||||
llm_starts: int = 0
|
||||
llm_ends: int = 0
|
||||
llm_streams: int = 0
|
||||
tool_starts: int = 0
|
||||
tool_ends: int = 0
|
||||
agent_actions: int = 0
|
||||
agent_ends: int = 0
|
||||
chat_model_starts: int = 0
|
||||
retriever_starts: int = 0
|
||||
retriever_ends: int = 0
|
||||
retriever_errors: int = 0
|
||||
retries: int = 0
|
||||
|
||||
|
||||
class BaseFakeCallbackHandlerMixin(BaseFakeCallbackHandler):
|
||||
"""Base fake callback handler mixin for testing."""
|
||||
|
||||
def on_llm_start_common(self) -> None:
|
||||
self.llm_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_llm_end_common(self) -> None:
|
||||
self.llm_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_llm_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
|
||||
def on_llm_new_token_common(self) -> None:
|
||||
self.llm_streams += 1
|
||||
|
||||
def on_retry_common(self) -> None:
|
||||
self.retries += 1
|
||||
|
||||
def on_chain_start_common(self) -> None:
|
||||
self.chain_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_chain_end_common(self) -> None:
|
||||
self.chain_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_chain_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
|
||||
def on_tool_start_common(self) -> None:
|
||||
self.tool_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_tool_end_common(self) -> None:
|
||||
self.tool_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_tool_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
|
||||
def on_agent_action_common(self) -> None:
|
||||
self.agent_actions += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_agent_finish_common(self) -> None:
|
||||
self.agent_ends += 1
|
||||
self.ends += 1
|
||||
|
||||
def on_chat_model_start_common(self) -> None:
|
||||
self.chat_model_starts += 1
|
||||
self.starts += 1
|
||||
|
||||
def on_text_common(self) -> None:
|
||||
self.text += 1
|
||||
|
||||
def on_retriever_start_common(self) -> None:
|
||||
self.starts += 1
|
||||
self.retriever_starts += 1
|
||||
|
||||
def on_retriever_end_common(self) -> None:
|
||||
self.ends += 1
|
||||
self.retriever_ends += 1
|
||||
|
||||
def on_retriever_error_common(self) -> None:
|
||||
self.errors += 1
|
||||
self.retriever_errors += 1
|
||||
|
||||
|
||||
class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
|
||||
"""Fake callback handler for testing."""
|
||||
|
||||
@property
|
||||
def ignore_llm(self) -> bool:
|
||||
"""Whether to ignore LLM callbacks."""
|
||||
return self.ignore_llm_
|
||||
|
||||
@property
|
||||
def ignore_chain(self) -> bool:
|
||||
"""Whether to ignore chain callbacks."""
|
||||
return self.ignore_chain_
|
||||
|
||||
@property
|
||||
def ignore_agent(self) -> bool:
|
||||
"""Whether to ignore agent callbacks."""
|
||||
return self.ignore_agent_
|
||||
|
||||
@property
|
||||
def ignore_retriever(self) -> bool:
|
||||
"""Whether to ignore retriever callbacks."""
|
||||
return self.ignore_retriever_
|
||||
|
||||
def on_llm_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_start_common()
|
||||
|
||||
def on_llm_new_token(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_new_token_common()
|
||||
|
||||
def on_llm_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_end_common()
|
||||
|
||||
def on_llm_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_llm_error_common()
|
||||
|
||||
def on_retry(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retry_common()
|
||||
|
||||
def on_chain_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_start_common()
|
||||
|
||||
def on_chain_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_end_common()
|
||||
|
||||
def on_chain_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_chain_error_common()
|
||||
|
||||
def on_tool_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_start_common()
|
||||
|
||||
def on_tool_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_end_common()
|
||||
|
||||
def on_tool_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_tool_error_common()
|
||||
|
||||
def on_agent_action(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_agent_action_common()
|
||||
|
||||
def on_agent_finish(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_agent_finish_common()
|
||||
|
||||
def on_text(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_text_common()
|
||||
|
||||
def on_retriever_start(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_start_common()
|
||||
|
||||
def on_retriever_end(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_end_common()
|
||||
|
||||
def on_retriever_error(
|
||||
self,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
self.on_retriever_error_common()
|
||||
|
||||
def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler":
|
||||
return self
|
||||
@ -1,10 +1,54 @@
|
||||
"""Test chat model integration."""
|
||||
|
||||
import os
|
||||
|
||||
from langchain_anthropic.chat_models import ChatAnthropicMessages
|
||||
import pytest
|
||||
|
||||
from langchain_anthropic import ChatAnthropic, ChatAnthropicMessages
|
||||
|
||||
os.environ["ANTHROPIC_API_KEY"] = "foo"
|
||||
|
||||
|
||||
def test_initialization() -> None:
|
||||
"""Test chat model initialization."""
|
||||
ChatAnthropicMessages(model_name="claude-instant-1.2", anthropic_api_key="xyz")
|
||||
ChatAnthropicMessages(model="claude-instant-1.2", anthropic_api_key="xyz")
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_model_name_param() -> None:
|
||||
llm = ChatAnthropic(model_name="foo")
|
||||
assert llm.model == "foo"
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_model_param() -> None:
|
||||
llm = ChatAnthropic(model="foo")
|
||||
assert llm.model == "foo"
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_model_kwargs() -> None:
|
||||
llm = ChatAnthropic(model_name="foo", model_kwargs={"foo": "bar"})
|
||||
assert llm.model_kwargs == {"foo": "bar"}
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_invalid_model_kwargs() -> None:
|
||||
with pytest.raises(ValueError):
|
||||
ChatAnthropic(model="foo", model_kwargs={"max_tokens_to_sample": 5})
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_incorrect_field() -> None:
|
||||
with pytest.warns(match="not default parameter"):
|
||||
llm = ChatAnthropic(model="foo", foo="bar")
|
||||
assert llm.model_kwargs == {"foo": "bar"}
|
||||
|
||||
|
||||
@pytest.mark.requires("anthropic")
|
||||
def test_anthropic_initialization() -> None:
|
||||
"""Test anthropic initialization."""
|
||||
# Verify that chat anthropic can be initialized using a secret key provided
|
||||
# as a parameter rather than an environment variable.
|
||||
ChatAnthropic(model="test", anthropic_api_key="test")
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
from langchain_anthropic import __all__
|
||||
|
||||
EXPECTED_ALL = ["ChatAnthropicMessages"]
|
||||
EXPECTED_ALL = ["ChatAnthropicMessages", "ChatAnthropic", "Anthropic", "AnthropicLLM"]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user