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Fix ChatAnthropic stop_sequences error (#2919) (#2920)

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Note to self: Always run integration tests, even on "that last minute
change you thought would be safe" :)

---------

Co-authored-by: Mike Lambert <mike.lambert@anthropic.com>
fix_agent_callbacks
Ankush Gola 1 year ago committed by GitHub
parent 13a0ed064b
commit ec59e9d886
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GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 189 additions and 21 deletions
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171
docs/modules/models/chat/integrations/anthropic.ipynb
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@ -0,0 +1,171 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "bf733a38-db84-4363-89e2-de6735c37230",
"metadata": {},
"source": [
"# Anthropic\n",
"\n",
"This notebook covers how to get started with Anthropic chat models."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chat_models import ChatAnthropic\n",
"from langchain.prompts.chat import (\n",
" ChatPromptTemplate,\n",
" SystemMessagePromptTemplate,\n",
" AIMessagePromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
")\n",
"from langchain.schema import (\n",
" AIMessage,\n",
" HumanMessage,\n",
" SystemMessage\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"chat = ChatAnthropic()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\" J'adore programmer.\", additional_kwargs={})"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"messages = [\n",
" HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
"]\n",
"chat(messages)"
]
},
{
"cell_type": "markdown",
"id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
"metadata": {},
"source": [
"## `ChatAnthropic` also supports async and streaming functionality:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.callbacks.base import CallbackManager\n",
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"LLMResult(generations=[[ChatGeneration(text=\" J'aime programmer.\", generation_info=None, message=AIMessage(content=\" J'aime programmer.\", additional_kwargs={}))]], llm_output={})"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"await chat.agenerate([messages])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" J'aime la programmation."
]
},
{
"data": {
"text/plain": [
"AIMessage(content=\" J'aime la programmation.\", additional_kwargs={})"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat = ChatAnthropic(streaming=True, verbose=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))\n",
"chat(messages)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

3
langchain/chat_models/__init__.py
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@ -1,5 +1,6 @@
from langchain.chat_models.anthropic import ChatAnthropic
from langchain.chat_models.azure_openai import AzureChatOpenAI
from langchain.chat_models.openai import ChatOpenAI
from langchain.chat_models.promptlayer_openai import PromptLayerChatOpenAI
__all__ = ["ChatOpenAI", "AzureChatOpenAI", "PromptLayerChatOpenAI"]
__all__ = ["ChatOpenAI", "AzureChatOpenAI", "PromptLayerChatOpenAI", "ChatAnthropic"]

22
langchain/chat_models/anthropic.py
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@ -1,4 +1,4 @@
from typing import List, Optional
from typing import Any, Dict, List, Optional
from pydantic import Extra
@ -26,17 +26,7 @@ class ChatAnthropic(BaseChatModel, _AnthropicCommon):
.. code-block:: python
import anthropic
from langchain.llms import Anthropic
model = Anthropic(model="<model_name>", anthropic_api_key="my-api-key")
# Simplest invocation, automatically wrapped with HUMAN_PROMPT
# and AI_PROMPT.
response = model("What are the biggest risks facing humanity?")
# Or if you want to use the chat mode, build a few-shot-prompt, or
# put words in the Assistant's mouth, use HUMAN_PROMPT and AI_PROMPT:
raw_prompt = "What are the biggest risks facing humanity?"
prompt = f"{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}"
response = model(prompt)
model = ChatAnthropic(model="<model_name>", anthropic_api_key="my-api-key")
"""
class Config:
@ -98,7 +88,9 @@ class ChatAnthropic(BaseChatModel, _AnthropicCommon):
self, messages: List[BaseMessage], stop: Optional[List[str]] = None
) -> ChatResult:
prompt = self._convert_messages_to_prompt(messages)
params = {"prompt": prompt, "stop_sequences": stop, **self._default_params}
params: Dict[str, Any] = {"prompt": prompt, **self._default_params}
if stop:
params["stop_sequences"] = stop
if self.streaming:
completion = ""
@ -120,7 +112,9 @@ class ChatAnthropic(BaseChatModel, _AnthropicCommon):
self, messages: List[BaseMessage], stop: Optional[List[str]] = None
) -> ChatResult:
prompt = self._convert_messages_to_prompt(messages)
params = {"prompt": prompt, "stop_sequences": stop, **self._default_params}
params: Dict[str, Any] = {"prompt": prompt, **self._default_params}
if stop:
params["stop_sequences"] = stop
if self.streaming:
completion = ""

2
langchain/llms/anthropic.py
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@ -10,7 +10,7 @@ from langchain.utils import get_from_dict_or_env
class _AnthropicCommon(BaseModel):
client: Any = None #: :meta private:
model: str = "claude-latest"
model: str = "claude-v1"
"""Model name to use."""
max_tokens_to_sample: int = 256

2
poetry.lock generated
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@ -9035,4 +9035,4 @@ qdrant = ["qdrant-client"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "373f68ef16e7f3d5d9cde8b81c5f261096cc537ddca4f6a36711d7215b63f226"
content-hash = "7e343fa8e31d8fcf1023cbda592f64c05e80015c4e0e23c1d387d2e9671ce995"

2
pyproject.toml
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@ -36,7 +36,7 @@ pinecone-text = {version = "^0.4.2", optional = true}
weaviate-client = {version = "^3", optional = true}
google-api-python-client = {version = "2.70.0", optional = true}
wolframalpha = {version = "5.0.0", optional = true}
anthropic = {version = "^0.2.4", optional = true}
anthropic = {version = "^0.2.6", optional = true}
qdrant-client = {version = "^1.1.2", optional = true, python = ">=3.8.1,<3.12"}
dataclasses-json = "^0.5.7"
tensorflow-text = {version = "^2.11.0", optional = true, python = "^3.10, <3.12"}

8
tests/integration_tests/chat_models/test_anthropic.py
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@ -17,7 +17,7 @@ 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")
chat = ChatAnthropic(model="test")
message = HumanMessage(content="Hello")
response = chat([message])
assert isinstance(response, AIMessage)
@ -26,7 +26,7 @@ def test_anthropic_call() -> None:
def test_anthropic_streaming() -> None:
"""Test streaming tokens from anthropic."""
chat = ChatAnthropic(model="bare-nano-0", streaming=True)
chat = ChatAnthropic(model="test", streaming=True)
message = HumanMessage(content="Hello")
response = chat([message])
assert isinstance(response, AIMessage)
@ -38,11 +38,12 @@ def test_anthropic_streaming_callback() -> None:
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
chat = ChatAnthropic(
model="test",
streaming=True,
callback_manager=callback_manager,
verbose=True,
)
message = HumanMessage(content="Write me a sentence with 100 words.")
message = HumanMessage(content="Write me a sentence with 10 words.")
chat([message])
assert callback_handler.llm_streams > 1
@ -53,6 +54,7 @@ async def test_anthropic_async_streaming_callback() -> None:
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
chat = ChatAnthropic(
model="test",
streaming=True,
callback_manager=callback_manager,
verbose=True,

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