anthropic[minor]: package move

libs/partners/anthropic/langchain_anthropic/__init__.py

@@ -1,3 +1,4 @@
-from langchain_anthropic.chat_models import ChatAnthropicMessages
+from langchain_anthropic.chat_models import ChatAnthropic, ChatAnthropicMessages
+from langchain_anthropic.llms import Anthropic, AnthropicLLM
 
-__all__ = ["ChatAnthropicMessages"]
+__all__ = ["ChatAnthropicMessages", "ChatAnthropic", "Anthropic", "AnthropicLLM"]

libs/partners/anthropic/langchain_anthropic/chat_models.py

@@ -2,6 +2,7 @@
 from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Tuple
 
 import anthropic
+from langchain_core._api.deprecation import deprecated
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
@@ -14,7 +15,11 @@
 )
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
-from langchain_core.utils import convert_to_secret_str
+from langchain_core.utils import (
+    build_extra_kwargs,
+    convert_to_secret_str,
+    get_pydantic_field_names,
+)
 
 _message_type_lookups = {"human": "user", "ai": "assistant"}
 
@@ -50,7 +55,7 @@ def _format_messages(messages: List[BaseMessage]) -> Tuple[Optional[str], List[D
     return system, formatted_messages
 
 
-class ChatAnthropicMessages(BaseChatModel):
+class ChatAnthropic(BaseChatModel):
     """ChatAnthropicMessages chat model.
 
     Example:
@@ -61,13 +66,18 @@ class ChatAnthropicMessages(BaseChatModel):
             model = ChatAnthropicMessages()
     """
 
-    _client: anthropic.Client = Field(default_factory=anthropic.Client)
-    _async_client: anthropic.AsyncClient = Field(default_factory=anthropic.AsyncClient)
+    class Config:
+        """Configuration for this pydantic object."""
+
+        allow_population_by_field_name = True
+
+    _client: anthropic.Client = Field(default=None)
+    _async_client: anthropic.AsyncClient = Field(default=None)
 
     model: str = Field(alias="model_name")
     """Model name to use."""
 
-    max_tokens: int = Field(default=256)
+    max_tokens: int = Field(default=256, alias="max_tokens_to_sample")
     """Denotes the number of tokens to predict per generation."""
 
     temperature: Optional[float] = None
@@ -88,16 +98,20 @@ class ChatAnthropicMessages(BaseChatModel):
 
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
 
-    class Config:
-        """Configuration for this pydantic object."""
-
-        allow_population_by_field_name = True
-
     @property
     def _llm_type(self) -> str:
         """Return type of chat model."""
         return "chat-anthropic-messages"
 
+    @root_validator(pre=True)
+    def build_extra(cls, values: Dict) -> Dict:
+        extra = values.get("model_kwargs", {})
+        all_required_field_names = get_pydantic_field_names(cls)
+        values["model_kwargs"] = build_extra_kwargs(
+            extra, values, all_required_field_names
+        )
+        return values
+
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         anthropic_api_key = convert_to_secret_str(
@@ -130,6 +144,7 @@ def _format_params(
             "top_p": self.top_p,
             "stop_sequences": stop,
             "system": system,
+            **self.model_kwargs,
         }
         rtn = {k: v for k, v in rtn.items() if v is not None}
 
@@ -145,7 +160,10 @@ def _stream(
         params = self._format_params(messages=messages, stop=stop, **kwargs)
         with self._client.messages.stream(**params) as stream:
             for text in stream.text_stream:
-                yield ChatGenerationChunk(message=AIMessageChunk(content=text))
+                chunk = ChatGenerationChunk(message=AIMessageChunk(content=text))
+                if run_manager:
+                    run_manager.on_llm_new_token(text, chunk=chunk)
+                yield chunk
 
     async def _astream(
         self,
@@ -157,7 +175,10 @@ async def _astream(
         params = self._format_params(messages=messages, stop=stop, **kwargs)
         async with self._async_client.messages.stream(**params) as stream:
             async for text in stream.text_stream:
-                yield ChatGenerationChunk(message=AIMessageChunk(content=text))
+                chunk = ChatGenerationChunk(message=AIMessageChunk(content=text))
+                if run_manager:
+                    await run_manager.on_llm_new_token(text, chunk=chunk)
+                yield chunk
 
     def _generate(
         self,
@@ -190,3 +211,8 @@ async def _agenerate(
             ],
             llm_output=data,
         )
+
+
+@deprecated(since="0.1.0", removal="0.2.0", alternative="ChatAnthropic")
+class ChatAnthropicMessages(ChatAnthropic):
+    pass