docs/examples/chat_store/CloudSQLPgChatStoreDemo.ipynb
PostgresChatStoreCloud SQL is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability. It offers MySQL, PostgreSQL, and SQL Server database engines. Extend your database application to build AI-powered experiences leveraging Cloud SQL's LlamaIndex integrations.
This notebook goes over how to use Cloud SQL for PostgreSQL to store chat history with PostgresChatStore class.
Learn more about the package on GitHub.
To run this notebook, you will need to do the following:
Install the integration library, llama-index-cloud-sql-pg, and the library for the embedding service, llama-index-embeddings-vertex.
%pip install --upgrade --quiet llama-index-cloud-sql-pg llama-index-llms-vertex llama-index
Colab only: Uncomment the following cell to restart the kernel or use the button to restart the kernel. For Vertex AI Workbench you can restart the terminal using the button on top.
# # Automatically restart kernel after installs so that your environment can access the new packages
# import IPython
# app = IPython.Application.instance()
# app.kernel.do_shutdown(True)
Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.
from google.colab import auth
auth.authenticate_user()
Set your Google Cloud project so that you can leverage Google Cloud resources within this notebook.
If you don't know your project ID, try the following:
gcloud config list.gcloud projects list.# @markdown Please fill in the value below with your Google Cloud project ID and then run the cell.
PROJECT_ID = "my-project-id" # @param {type:"string"}
# Set the project id
!gcloud config set project {PROJECT_ID}
Find your database values, in the Cloud SQL Instances page.
# @title Set Your Values Here { display-mode: "form" }
REGION = "us-central1" # @param {type: "string"}
INSTANCE = "my-primary" # @param {type: "string"}
DATABASE = "my-database" # @param {type: "string"}
TABLE_NAME = "chat_store" # @param {type: "string"}
USER = "postgres" # @param {type: "string"}
PASSWORD = "my-password" # @param {type: "string"}
One of the requirements and arguments to establish Cloud SQL as a chat store is a PostgresEngine object. The PostgresEngine configures a connection pool to your Cloud SQL database, enabling successful connections from your application and following industry best practices.
To create a PostgresEngine using PostgresEngine.from_instance() you need to provide only 4 things:
project_id : Project ID of the Google Cloud Project where the Cloud SQL instance is located.region : Region where the Cloud SQL instance is located.instance : The name of the Cloud SQL instance.database : The name of the database to connect to on the Cloud SQL instance.By default, IAM database authentication will be used as the method of database authentication. This library uses the IAM principal belonging to the Application Default Credentials (ADC) sourced from the envionment.
For more informatin on IAM database authentication please see:
Optionally, built-in database authentication using a username and password to access the Cloud SQL database can also be used. Just provide the optional user and password arguments to PostgresEngine.from_instance():
user : Database user to use for built-in database authentication and loginpassword : Database password to use for built-in database authentication and login.Note: This tutorial demonstrates the async interface. All async methods have corresponding sync methods.
from llama_index_cloud_sql_pg import PostgresEngine
engine = await PostgresEngine.afrom_instance(
project_id=PROJECT_ID,
region=REGION,
instance=INSTANCE,
database=DATABASE,
user=USER,
password=PASSWORD,
)
The PostgresChatStore class requires a database table. The PostgresEngine engine has a helper method ainit_chat_store_table() that can be used to create a table with the proper schema for you.
await engine.ainit_chat_store_table(table_name=TABLE_NAME)
You can also specify a schema name by passing schema_name wherever you pass table_name.
SCHEMA_NAME = "my_schema"
await engine.ainit_chat_store_table(
table_name=TABLE_NAME,
schema_name=SCHEMA_NAME,
)
from llama_index_cloud_sql_pg import PostgresChatStore
chat_store = await PostgresChatStore.create(
engine=engine,
table_name=TABLE_NAME,
# schema_name=SCHEMA_NAME
)
The ChatMemoryBuffer stores a history of recent chat messages, enabling the LLM to access relevant context from prior interactions.
By passing our chat store into the ChatMemoryBuffer, it can automatically retrieve and update messages associated with a specific session ID or chat_store_key.
from llama_index.core.memory import ChatMemoryBuffer
memory = ChatMemoryBuffer.from_defaults(
token_limit=3000,
chat_store=chat_store,
chat_store_key="user1",
)
You can use any of the LLMs compatible with LlamaIndex.
You may need to enable Vertex AI API to use Vertex.
from llama_index.llms.vertex import Vertex
llm = Vertex(model="gemini-1.5-flash-002", project=PROJECT_ID)
from llama_index.core.chat_engine import SimpleChatEngine
chat_engine = SimpleChatEngine(memory=memory, llm=llm, prefix_messages=[])
response = chat_engine.chat("Hello")
print(response)
IndexAn Index is allows us to quickly retrieve relevant context for a user query.
They are used to build QueryEngines and ChatEngines.
For a list of indexes that can be built in LlamaIndex, see Index Guide.
A VectorStoreIndex, can be built using the PostgresVectorStore. See the detailed guide on how to use the PostgresVectorStore to build an index here.
You can also use the PostgresDocumentStore and PostgresIndexStore to persist documents and index metadata.
These modules can be used to build other Indexes.
For a detailed python notebook on this, see LlamaIndex Doc Store Guide.
# Create an `index` here
chat_engine = index.as_chat_engine(llm=llm, chat_mode="context", memory=memory) # type: ignore
response = chat_engine.chat("What did the author do?")