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langchain[patch]: fix ChatVertexAI streaming (#14369)
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@efriis
efriis authored Dec 7, 2023
1 parent db6bf8b commit 54040b0
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211 changes: 84 additions & 127 deletions docs/docs/integrations/chat/google_vertex_ai_palm.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -34,13 +34,13 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#!pip install langchain google-cloud-aiplatform"
"!pip install -U google-cloud-aiplatform"
]
},
{
Expand All @@ -57,41 +57,27 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"chat = ChatVertexAI()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"system = \"You are a helpful assistant who translate English to French\"\n",
"human = \"Translate this sentence from English to French. I love programming.\"\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
"messages = prompt.format_messages()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
"AIMessage(content=\" J'aime la programmation.\")"
]
},
"execution_count": 9,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat(messages)"
"system = \"You are a helpful assistant who translate English to French\"\n",
"human = \"Translate this sentence from English to French. I love programming.\"\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
"\n",
"chat = ChatVertexAI()\n",
"\n",
"chain = prompt | chat\n",
"chain.invoke({})"
]
},
{
Expand All @@ -103,35 +89,29 @@
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"system = (\n",
" \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
")\n",
"human = \"{text}\"\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' 私はプログラミングが大好きです。', additional_kwargs={}, example=False)"
"AIMessage(content=' プログラミングが大好きです')"
]
},
"execution_count": 13,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"system = (\n",
" \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
")\n",
"human = \"{text}\"\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
"\n",
"chain = prompt | chat\n",
"\n",
"chain.invoke(\n",
" {\n",
" \"input_language\": \"English\",\n",
Expand Down Expand Up @@ -162,20 +142,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"chat = ChatVertexAI(\n",
" model_name=\"codechat-bison\", max_output_tokens=1000, temperature=0.5\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 5,
"metadata": {
"tags": []
},
Expand All @@ -185,20 +152,39 @@
"output_type": "stream",
"text": [
" ```python\n",
"def is_prime(x): \n",
" if (x <= 1): \n",
"def is_prime(n):\n",
" if n <= 1:\n",
" return False\n",
" for i in range(2, x): \n",
" if (x % i == 0): \n",
" for i in range(2, n):\n",
" if n % i == 0:\n",
" return False\n",
" return True\n",
"\n",
"def find_prime_numbers(n):\n",
" prime_numbers = []\n",
" for i in range(2, n + 1):\n",
" if is_prime(i):\n",
" prime_numbers.append(i)\n",
" return prime_numbers\n",
"\n",
"print(find_prime_numbers(100))\n",
"```\n",
"\n",
"Output:\n",
"\n",
"```\n",
"[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]\n",
"```\n"
]
}
],
"source": [
"# For simple string in string out usage, we can use the `predict` method:\n",
"print(chat.predict(\"Write a Python function to identify all prime numbers\"))"
"chat = ChatVertexAI(\n",
" model_name=\"codechat-bison\", max_output_tokens=1000, temperature=0.5\n",
")\n",
"\n",
"message = chat.invoke(\"Write a Python function to identify all prime numbers\")\n",
"print(message.content)"
]
},
{
Expand All @@ -207,66 +193,42 @@
"source": [
"## Asynchronous calls\n",
"\n",
"We can make asynchronous calls via the `agenerate` and `ainvoke` methods."
"We can make asynchronous calls via the Runnables [Async Interface](/docs/expression_language/interface)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# for running these examples in the notebook:\n",
"import asyncio\n",
"\n",
"# import nest_asyncio\n",
"# nest_asyncio.apply()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LLMResult(generations=[[ChatGeneration(text=\" J'aime la programmation.\", generation_info=None, message=AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False))]], llm_output={}, run=[RunInfo(run_id=UUID('223599ef-38f8-4c79-ac6d-a5013060eb9d'))])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat = ChatVertexAI(\n",
" model_name=\"chat-bison\",\n",
" max_output_tokens=1000,\n",
" temperature=0.7,\n",
" top_p=0.95,\n",
" top_k=40,\n",
")\n",
"import nest_asyncio\n",
"\n",
"asyncio.run(chat.agenerate([messages]))"
"nest_asyncio.apply()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' अहं प्रोग्रामिंग प्रेमामि', additional_kwargs={}, example=False)"
"AIMessage(content=' Why do you love programming?')"
]
},
"execution_count": 36,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = prompt | chat\n",
"\n",
"asyncio.run(\n",
" chain.ainvoke(\n",
" {\n",
Expand All @@ -289,56 +251,51 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys"
]
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1. China (1,444,216,107)\n",
"2. India (1,393,409,038)\n",
"3. United States (332,403,650)\n",
"4. Indonesia (273,523,615)\n",
"5. Pakistan (220,892,340)\n",
"6. Brazil (212,559,409)\n",
"7. Nigeria (206,139,589)\n",
"8. Bangladesh (164,689,383)\n",
"9. Russia (145,934,462)\n",
"10. Mexico (128,932,488)\n",
"11. Japan (126,476,461)\n",
"12. Ethiopia (115,063,982)\n",
"13. Philippines (109,581,078)\n",
"14. Egypt (102,334,404)\n",
"15. Vietnam (97,338,589)"
" The five most populous countries in the world are:\n",
"1. China (1.4 billion)\n",
"2. India (1.3 billion)\n",
"3. United States (331 million)\n",
"4. Indonesia (273 million)\n",
"5. Pakistan (220 million)"
]
}
],
"source": [
"import sys\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [(\"human\", \"List out the 15 most populous countries in the world\")]\n",
" [(\"human\", \"List out the 5 most populous countries in the world\")]\n",
")\n",
"messages = prompt.format_messages()\n",
"for chunk in chat.stream(messages):\n",
"\n",
"chat = ChatVertexAI()\n",
"\n",
"chain = prompt | chat\n",
"\n",
"for chunk in chain.stream({}):\n",
" sys.stdout.write(chunk.content)\n",
" sys.stdout.flush()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "poetry-venv",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "poetry-venv"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -350,7 +307,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.11.4"
},
"vscode": {
"interpreter": {
Expand Down
2 changes: 1 addition & 1 deletion libs/langchain/langchain/chat_models/vertexai.py
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Original file line number Diff line number Diff line change
Expand Up @@ -242,7 +242,7 @@ def _stream(
) -> Iterator[ChatGenerationChunk]:
question = _get_question(messages)
history = _parse_chat_history(messages[:-1])
params = self._prepare_params(stop=stop, **kwargs)
params = self._prepare_params(stop=stop, stream=True, **kwargs)
examples = kwargs.get("examples", None)
if examples:
params["examples"] = _parse_examples(examples)
Expand Down
18 changes: 17 additions & 1 deletion libs/langchain/tests/integration_tests/chat_models/test_vertexai.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,12 @@
from unittest.mock import MagicMock, Mock, patch

import pytest
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import LLMResult

from langchain.chat_models import ChatVertexAI
Expand Down Expand Up @@ -41,6 +46,7 @@ def test_vertexai_single_call(model_name: str) -> None:
assert isinstance(response.content, str)


@pytest.mark.scheduled
def test_candidates() -> None:
model = ChatVertexAI(model_name="chat-bison@001", temperature=0.3, n=2)
message = HumanMessage(content="Hello")
Expand All @@ -62,6 +68,16 @@ async def test_vertexai_agenerate() -> None:
assert response.generations[0][0] == sync_response.generations[0][0]


@pytest.mark.scheduled
async def test_vertexai_stream() -> None:
model = ChatVertexAI(temperature=0)
message = HumanMessage(content="Hello")

sync_response = model.stream([message])
for chunk in sync_response:
assert isinstance(chunk, AIMessageChunk)


@pytest.mark.scheduled
def test_vertexai_single_call_with_context() -> None:
model = ChatVertexAI()
Expand Down

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