llama-index-cloud-sql-pg 0.3.1

Installation

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it’s possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install llama-index-cloud-sql-pg

pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install llama-index-cloud-sql-pg

Vector Store Usage

Use a vector store to store embedded data and perform vector search.

import google.auth
from llama_index.core import Settings
from llama_index.embeddings.vertex import VertexTextEmbedding
from llama_index_cloud_sql_pg import PostgresEngine, PostgresVectorStore


credentials, project_id = google.auth.default()
engine = await PostgresEngine.afrom_instance(
   "project-id", "region", "my-instance", "my-database"
)
Settings.embed_model = VertexTextEmbedding(
   model_name="textembedding-gecko@003",
   project="project-id",
   credentials=credentials,
)

vector_store = await PostgresVectorStore.create(
   engine=engine, table_name="vector_store"
)

Chat Store Usage

A chat store serves as a centralized interface to store your chat history.

from llama_index.core.memory import ChatMemoryBuffer
from llama_index_cloud_sql_pg import PostgresChatStore, PostgresEngine


engine = await PostgresEngine.afrom_instance(
   "project-id", "region", "my-instance", "my-database"
)
chat_store = await PostgresChatStore.create(
   engine=engine, table_name="chat_store"
)
memory = ChatMemoryBuffer.from_defaults(
   token_limit=3000,
   chat_store=chat_store,
   chat_store_key="user1",
)

Document Reader Usage

A Reader ingest data from different data sources and data formats into a simple Document representation.

from llama_index.core.memory import ChatMemoryBuffer
from llama_index_cloud_sql_pg import PostgresReader, PostgresEngine


engine = await PostgresEngine.afrom_instance(
   "project-id", "region", "my-instance", "my-database"
)
reader = await PostgresReader.create(
   engine=engine, table_name="my-db-table"
)
documents = reader.load_data()

Document Store Usage

Use a document store to make storage and maintenance of data easier.

from llama_index_cloud_sql_pg import PostgresEngine, PostgresDocumentStore


engine = await PostgresEngine.afrom_instance(
   "project-id", "region", "my-instance", "my-database"
)
doc_store = await PostgresDocumentStore.create(
   engine=engine, table_name="doc_store"
)

Index Store Usage

Use an index store to keep track of indexes built on documents.

from llama_index_cloud_sql_pg import PostgresIndexStore, PostgresEngine


engine = await PostgresEngine.from_instance(
   "project-id", "region", "my-instance", "my-database"
)
index_store = await PostgresIndexStore.create(
   engine=engine, table_name="index_store"
)

Contributions

Contributions to this library are always welcome and highly encouraged.

See CONTRIBUTING for more information how to get started.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. See Code of Conduct for more information.