vertexai: Add Intro to the Vertex AI library

libs/vertexai/langchain_google_vertexai/__init__.py

@@ -1,3 +1,155 @@
+"""
+## langchain-google-vertexai
+
+This module contains the LangChain integrations for Google Cloud generative models.
+
+## Installation
+
+```bash
+pip install -U langchain-google-vertexai
+```
+
+## Supported Models (MaaS: Model-as-a-Service)
+
+1. Llama 
+2. Mistral
+
+Integration on Google Cloud Vertex AI Model-as-a-Service.
+
+For more information, see:
+    https://cloud.google.com/blog/products/ai-machine-learning/llama-3-1-on-vertex-ai
+
+#### Setup
+
+You need to enable a corresponding MaaS model (Google Cloud UI console ->
+Vertex AI -> Model Garden -> search for a model you need and click enable)
+
+You must have the langchain-google-vertexai Python package installed
+.. code-block:: bash
+
+    pip install -U langchain-google-vertexai
+
+And either:
+    - Have credentials configured for your environment
+        (gcloud, workload identity, etc...)
+    - Store the path to a service account JSON file as the
+        GOOGLE_APPLICATION_CREDENTIALS environment variable
+
+This codebase uses the google.auth library which first looks for the application
+credentials variable mentioned above, and then looks for system-level auth.
+
+For more information, see:
+https://cloud.google.com/docs/authentication/application-default-credentials#GAC
+and 
+https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth.
+
+## Chat Models
+
+`ChatVertexAI` class exposes models such as `gemini-pro` and `chat-bison`.
+
+To use, you should have Google Cloud project with APIs enabled, and configured 
+credentials. Initialize the model as:
+
+```python
+from langchain_google_vertexai import ChatVertexAI
+
+llm = ChatVertexAI(model_name="gemini-pro")
+llm.invoke("Sing a ballad of LangChain.")
+```
+
+You can use other models, e.g. `chat-bison`:
+
+```python
+from langchain_google_vertexai import ChatVertexAI
+
+llm = ChatVertexAI(model_name="chat-bison", temperature=0.3)
+llm.invoke("Sing a ballad of LangChain.")
+```
+
+#### Multimodal inputs
+
+Gemini vision model supports image inputs when providing a single chat message. Example:
+
+```python
+from langchain_core.messages import HumanMessage
+from langchain_google_vertexai import ChatVertexAI
+
+llm = ChatVertexAI(model_name="gemini-pro-vision")
+# example
+message = HumanMessage(
+    content=[
+        {
+            "type": "text",
+            "text": "What's in this image?",
+        },  # You can optionally provide text parts
+        {"type": "image_url", "image_url": {"url": "https://picsum.photos/seed/picsum/200/300"}},
+    ]
+)
+llm.invoke([message])
+```
+
+The value of `image_url` can be any of the following:
+
+- A public image URL
+- An accessible gcs file (e.g., "gcs://path/to/file.png")
+- A base64 encoded image (e.g., `data:image/png;base64,abcd124`)
+
+## Embeddings
+
+You can use Google Cloud's embeddings models as:
+
+```python
+from langchain_google_vertexai import VertexAIEmbeddings
+
+embeddings = VertexAIEmbeddings()
+embeddings.embed_query("hello, world!")
+```
+
+## LLMs
+
+You can use Google Cloud's generative AI models as Langchain LLMs:
+
+```python
+from langchain_core.prompts import PromptTemplate
+from langchain_google_vertexai import ChatVertexAI
+
+template = \"""Question: {question}
+
+Answer: Let's think step by step.\"""
+prompt = PromptTemplate.from_template(template)
+
+llm = ChatVertexAI(model_name="gemini-pro")
+chain = prompt | llm
+
+question = "Who was the president of the USA in 1994?"
+print(chain.invoke({"question": question}))
+```
+
+You can use Gemini and Palm models, including code-generations ones:
+
+```python
+
+from langchain_google_vertexai import VertexAI
+
+llm = VertexAI(model_name="code-bison", max_output_tokens=1000, temperature=0.3)
+
+question = "Write a python function that checks if a string is a valid email address"
+
+output = llm(question)
+
+## Vector Stores
+
+#### Vector Search Vector Store GCS
+
+VertexAI VectorStore that handles the search and indexing using Vector Search 
+and stores the documents in Google Cloud Storage.
+
+#### Vector Search Vector Store Datastore
+
+VectorSearch with DatasTore document storage.
+```
+"""
+
 from google.cloud.aiplatform_v1beta1.types import (
     FunctionCallingConfig,
     FunctionDeclaration,