mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
cab55e9bc1
- 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
441 lines
11 KiB
Plaintext
441 lines
11 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# GCP Vertex AI\n",
|
|
"\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"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Setting up"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) customer data to train its foundation models as part of Google Cloud's AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
|
|
"\n",
|
|
"To use `Vertex AI PaLM` you must have the `google-cloud-aiplatform` Python package installed and either:\n",
|
|
"- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
|
|
"- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
|
|
"\n",
|
|
"This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
|
|
"\n",
|
|
"For more information, see: \n",
|
|
"- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
|
|
"- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#!pip install langchain google-cloud-aiplatform"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.llms import VertexAI"
|
|
]
|
|
},
|
|
{
|
|
"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": [
|
|
"llm = VertexAI()\n",
|
|
"print(llm(\"What are some of the pros and cons of Python as a programming language?\"))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Using in a chain"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.prompts import PromptTemplate"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"template = \"\"\"Question: {question}\n",
|
|
"\n",
|
|
"Answer: Let's think step by step.\"\"\"\n",
|
|
"prompt = PromptTemplate.from_template(template)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"chain = prompt | llm"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" Justin Bieber was born on March 1, 1994. Bill Clinton was the president of the United States from January 20, 1993, to January 20, 2001.\n",
|
|
"The final answer is Bill Clinton\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"question = \"Who was the president in the year Justin Beiber was born?\"\n",
|
|
"print(chain.invoke({\"question\": question}))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Code generation example"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"You can now leverage the `Codey API` for code generation within `Vertex AI`. \n",
|
|
"\n",
|
|
"The model names are:\n",
|
|
"- `code-bison`: for code suggestion\n",
|
|
"- `code-gecko`: for code completion"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"llm = VertexAI(model_name=\"code-bison\", max_output_tokens=1000, temperature=0.3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"question = \"Write a python function that checks if a string is a valid email address\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"```python\n",
|
|
"import re\n",
|
|
"\n",
|
|
"def is_valid_email(email):\n",
|
|
" pattern = re.compile(r\"[^@]+@[^@]+\\.[^@]+\")\n",
|
|
" return pattern.match(email)\n",
|
|
"```\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(llm(question))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Full generation info\n",
|
|
"\n",
|
|
"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"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[[GenerationChunk(text='```python\\nimport re\\n\\ndef is_valid_email(email):\\n pattern = re.compile(r\"[^@]+@[^@]+\\\\.[^@]+\")\\n return pattern.match(email)\\n```', generation_info={'is_blocked': False, 'safety_attributes': {'Health': 0.1}})]]"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"result = llm.generate([question])\n",
|
|
"result.generations"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Asynchronous calls\n",
|
|
"\n",
|
|
"With `agenerate` we can make asynchronous calls"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# 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": [
|
|
"LLMResult(generations=[[GenerationChunk(text='```python\\nimport re\\n\\ndef is_valid_email(email):\\n pattern = re.compile(r\"[^@]+@[^@]+\\\\.[^@]+\")\\n return pattern.match(email)\\n```', generation_info={'is_blocked': False, 'safety_attributes': {'Health': 0.1}})]], llm_output=None, run=[RunInfo(run_id=UUID('caf74e91-aefb-48ac-8031-0c505fcbbcc6'))])"
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"asyncio.run(llm.agenerate([question]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Streaming calls\n",
|
|
"\n",
|
|
"With `stream` we can stream results from the model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import sys"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"```python\n",
|
|
"import re\n",
|
|
"\n",
|
|
"def is_valid_email(email):\n",
|
|
" \"\"\"\n",
|
|
" Checks if a string is a valid email address.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" email: The string to check.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" True if the string is a valid email address, False otherwise.\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" # Check for a valid email address format.\n",
|
|
" if not re.match(r\"^[A-Za-z0-9\\.\\+_-]+@[A-Za-z0-9\\._-]+\\.[a-zA-Z]*$\", email):\n",
|
|
" return False\n",
|
|
"\n",
|
|
" # Check if the domain name exists.\n",
|
|
" try:\n",
|
|
" domain = email.split(\"@\")[1]\n",
|
|
" socket.gethostbyname(domain)\n",
|
|
" except socket.gaierror:\n",
|
|
" return False\n",
|
|
"\n",
|
|
" return True\n",
|
|
"```"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for chunk in llm.stream(question):\n",
|
|
" sys.stdout.write(chunk)\n",
|
|
" sys.stdout.flush()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Vertex Model Garden"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Vertex Model Garden [exposes](https://cloud.google.com/vertex-ai/docs/start/explore-models) open-sourced models that can be deployed and served on Vertex AI. If you have successfully deployed a model from Vertex Model Garden, you can find a corresponding Vertex AI [endpoint](https://cloud.google.com/vertex-ai/docs/general/deployment#what_happens_when_you_deploy_a_model) in the console or via API."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.llms import VertexAIModelGarden"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"llm = VertexAIModelGarden(\n",
|
|
" project=\"YOUR PROJECT\",\n",
|
|
" endpoint_id=\"YOUR ENDPOINT_ID\"\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(llm(\"What is the meaning of life?\"))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Like all LLMs, we can then compose it with other components:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"prompt = PromptTemplate.from_template(\"What is the meaning of {thing}?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"chian = prompt | llm\n",
|
|
"print(chain.invoke({\"thing\": \"life\"}))"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "poetry-venv",
|
|
"language": "python",
|
|
"name": "poetry-venv"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.1"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "cc99336516f23363341912c6723b01ace86f02e26b4290be1efc0677e2e2ec24"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|