- chat vertex async
- vertex stream
- vertex full generation info
- vertex use server-side stopping
- model garden async
- update docs for all the above
in follow up will add
[] chat vertex full generation info
[] chat vertex retries
[] scheduled tests
"Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
"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(\n",
" [(\"system\", system), (\"human\", human)]\n",
")\n",
"messages = prompt.format_messages()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Sure, here is the translation of the sentence \"I love programming\" from English to French:\\n\\nJ\\'aime programmer.', additional_kwargs={}, example=False)"
"AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
]
},
"execution_count": 4,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"messages = [\n",
" SystemMessage(\n",
" content=\"You are a helpful assistant that translates English to French.\"\n",
" ),\n",
" HumanMessage(\n",
" content=\"Translate this sentence from English to French. I love programming.\"\n",
" ),\n",
"]\n",
"chat(messages)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.\n",
"\n",
"For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
"If we want to construct a simple chain that takes user specified parameters:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"template = (\n",
" \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
"system = \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
"human = \"{text}\"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [(\"system\", system), (\"human\", human)]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Sure, here is the translation of \"I love programming\" in French:\\n\\nJ\\'aime programmer.', additional_kwargs={}, example=False)"
"# 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\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Asynchronous calls\n",
"\n",
"We can make asynchronous calls via the `agenerate` and `ainvoke` methods."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"import asyncio\n",
"# import nest_asyncio\n",
"# nest_asyncio.apply()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='The following Python function can be used to identify all prime numbers up to a given integer:\\n\\n```\\ndef is_prime(n):\\n \"\"\"\\n Determines whether the given integer is prime.\\n\\n Args:\\n n: The integer to be tested for primality.\\n\\n Returns:\\n True if n is prime, False otherwise.\\n \"\"\"\\n\\n # Check if n is divisible by 2.\\n if n % 2 == 0:\\n return False\\n\\n # Check if n is divisible by any integer from 3 to the square root', additional_kwargs={}, example=False)"
"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": 4,
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"messages = [\n",
" HumanMessage(\n",
" content=\"How do I create a python function to identify all prime numbers?\"\n",
"**Note:** This is separate from the `Google PaLM` integration, it exposes [Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on `Google Cloud`. \n"
]
@ -41,12 +41,12 @@
},
"outputs": [],
"source": [
"#!pip install google-cloud-aiplatform"
"#!pip install langchain google-cloud-aiplatform"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@ -54,41 +54,55 @@
]
},
{
"cell_type": "markdown",
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Python is a widely used, interpreted, object-oriented, and high-level programming language with dynamic semantics, used for general-purpose programming. It is known for its readability, simplicity, and versatility. Here are some of the pros and cons of Python:\n",
"\n",
"**Pros:**\n",
"\n",
"- **Easy to learn:** Python is known for its simple and intuitive syntax, making it easy for beginners to learn. It has a relatively shallow learning curve compared to other programming languages.\n",
"\n",
"- **Versatile:** Python is a general-purpose programming language, meaning it can be used for a wide variety of tasks, including web development, data science, machine\n"
]
}
],
"source": [
"## Question-answering example"
"llm = VertexAI()\n",
"print(llm(\"What are some of the pros and cons of Python as a programming language?\"))"
"We can use the `generate` method to get back extra metadata like [safety attributes](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/responsible-ai#safety_attribute_confidence_scoring) and not just text completions"
"# If running in a Jupyter notebook you'll need to install nest_asyncio\n",
"\n",
"# !pip install nest_asyncio"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import asyncio\n",
"# import nest_asyncio\n",
"# nest_asyncio.apply()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'```python\\ndef is_prime(n):\\n \"\"\"\\n Determines if a number is prime.\\n\\n Args:\\n n: The number to be tested.\\n\\n Returns:\\n True if the number is prime, False otherwise.\\n \"\"\"\\n\\n # Check if the number is 1.\\n if n == 1:\\n return False\\n\\n # Check if the number is 2.\\n if n == 2:\\n return True\\n\\n'"
Bagatur
10 months ago
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