Add Anthropic ChatModel to langchain (#2293)

* Adds an Anthropic ChatModel
* Factors out common code in our LLMModel and ChatModel
* Supports streaming llm-tokens to the callbacks on a delta basis (until
a future V2 API does that for us)
* Some fixes

tests/integration_tests/chat_models/test_anthropic.py

@@ -0,0 +1,81 @@
+"""Test Anthropic API wrapper."""
+from typing import List
+
+import pytest
+
+from langchain.callbacks.base import CallbackManager
+from langchain.chat_models.anthropic import ChatAnthropic
+from langchain.schema import (
+    AIMessage,
+    BaseMessage,
+    ChatGeneration,
+    HumanMessage,
+    LLMResult,
+)
+from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
+
+
+def test_anthropic_call() -> None:
+    """Test valid call to anthropic."""
+    chat = ChatAnthropic(model="bare-nano-0")
+    message = HumanMessage(content="Hello")
+    response = chat([message])
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+
+def test_anthropic_streaming() -> None:
+    """Test streaming tokens from anthropic."""
+    chat = ChatAnthropic(model="bare-nano-0", streaming=True)
+    message = HumanMessage(content="Hello")
+    response = chat([message])
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.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(
+        streaming=True,
+        callback_manager=callback_manager,
+        verbose=True,
+    )
+    message = HumanMessage(content="Write me a sentence with 100 words.")
+    chat([message])
+    assert callback_handler.llm_streams > 1
+
+
+@pytest.mark.asyncio
+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(
+        streaming=True,
+        callback_manager=callback_manager,
+        verbose=True,
+    )
+    chat_messages: List[BaseMessage] = [
+        HumanMessage(content="How many toes do dogs have?")
+    ]
+    result: LLMResult = await chat.agenerate([chat_messages])
+    assert callback_handler.llm_streams > 1
+    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
+
+
+def test_formatting() -> None:
+    chat = ChatAnthropic()
+
+    chat_messages: List[BaseMessage] = [HumanMessage(content="Hello")]
+    result = chat._convert_messages_to_prompt(chat_messages)
+    assert result == "\n\nHuman: Hello\n\nAssistant:"
+
+    chat_messages = [HumanMessage(content="Hello"), AIMessage(content="Answer:")]
+    result = chat._convert_messages_to_prompt(chat_messages)
+    assert result == "\n\nHuman: Hello\n\nAssistant: Answer:"

tests/integration_tests/llms/test_anthropic.py

@@ -32,7 +32,6 @@ def test_anthropic_streaming_callback() -> None:
     callback_handler = FakeCallbackHandler()
     callback_manager = CallbackManager([callback_handler])
     llm = Anthropic(
-        model="claude-v1",
         streaming=True,
         callback_manager=callback_manager,
         verbose=True,
@@ -55,7 +54,6 @@ async def test_anthropic_async_streaming_callback() -> None:
     callback_handler = FakeCallbackHandler()
     callback_manager = CallbackManager([callback_handler])
     llm = Anthropic(
-        model="claude-v1",
         streaming=True,
         callback_manager=callback_manager,
         verbose=True,