8000 fix: API reference doc pipeline by averikitsch · Pull Request #108 · googleapis/langchain-google-cloud-sql-pg-python · GitHub
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fix: API reference doc pipeline #108

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Merged
merged 3 commits into from
Apr 3, 2024
Merged

fix: API reference doc pipeline #108

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7a54001
fix: API reference doc pipeline
averikitsch Apr 3, 2024
9407718
use code blocks
averikitsch Apr 3, 2024
e7a2825
remove breaking changes
averikitsch Apr 3, 2024
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3 changes: 2 additions & 1 deletion .kokoro/publish-docs.sh
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Expand Up @@ -27,7 +27,8 @@ python3 -m nox --version
# build docs
nox -s docs

VERSION=$(python -c "import langchain_google_cloud_sql_pg;print(langchain_google_cloud_sql_pg.__version__)")
python3 -m pip install .
VERSION=$(python3 -c "import langchain_google_cloud_sql_pg;print(langchain_google_cloud_sql_pg.__version__)")

# create metadata
python3 -m docuploader create-metadata \
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70 changes: 34 additions & 36 deletions README.rst
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Expand Up @@ -4,7 +4,6 @@ Cloud SQL for PostgreSQL for LangChain
|preview| |pypi| |versions|

- `Client Library Documentation`_
- `How-to Guides`_
- `Product Documentation`_

.. |preview| image:: https://img.shields.io/badge/support-preview-orange.svg
Expand All @@ -14,7 +13,6 @@ Cloud SQL for PostgreSQL for LangChain
.. |versions| image:: https://img.shields.io/pypi/pyversions/langchain-google-cloud-sql-pg.svg
:target: https://pypi.org/project/langchain-google-cloud-sql-pg/
.. _Client Library Documentation: https://github.com/googleapis/langchain-google-cloud-sql-pg-python
.. _How-to Guides: https://github.com/googleapis/langchain-google-cloud-sql-pg-python/tree/main/samples
.. _Product Documentation: https://cloud.google.com/sql/docs

Quick Start
Expand Down Expand Up @@ -87,70 +85,70 @@ Vector Store Usage

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

.. code:: python
.. code-block:: python

from langchain_google_cloud_sql_pg import PostgresVectorstore, PostgresEngine
from langchain.embeddings import VertexAIEmbeddings
from langchain_google_cloud_sql_pg import PostgresVectorstore, PostgresEngine
from langchain.embeddings import VertexAIEmbeddings


engine = PostgresEngine.from_instance("project-id", "region", "my-instance", "my-database")
engine.init_vectorstore_table(
table_name="my-table",
vector_size=768, # Vector size for `VertexAIEmbeddings()`
)
embeddings_service = VertexAIEmbeddings()
vectorstore = PostgresVectorStore.create_sync(
engine,
table_name="my-table",
embeddings=embedding_service
)
engine = PostgresEngine.from_instance("project-id", "region", "my-instance", "my-database")
engine.init_vectorstore_table(
table_name="my-table",
vector_size=768, # Vector size for `VertexAIEmbeddings()`
)
embeddings_service = VertexAIEmbeddings()
vectorstore = PostgresVectorStore.create_sync(
engine,
table_name="my-table",
embeddings=embedding_service
)

See the full `Vector Store`_ tutorial.

.. _`Vector Store`: https://github.com/googleapis/langchain-google-cloud-sql-pg-python/tree/main/samples/vector_store.ipynb
.. _`Vector Store`: https://github.com/googleapis/langchain-google-cloud-sql-pg-python/tree/main/docs/vector_store.ipynb

Document Loader Usage
~~~~~~~~~~~~~~~~~~~~~

Use a document loader to load data as Documents.

.. code:: python
.. code-block:: python

from langchain_google_cloud_sql_pg import PostgresEngine, PostgresLoader
from langchain_google_cloud_sql_pg import PostgresEngine, PostgresLoader


engine = PostgresEngine.from_instance("project-id", "region", "my-instance", "my-database")
loader = PostgresSQLLoader.create_sync(
engine,
table_name="my-table-name"
)
docs = loader.lazy_load()
engine = PostgresEngine.from_instance("project-id", "region", "my-instance", "my-database")
loader = PostgresSQLLoader.create_sync(
engine,
table_name="my-table-name"
)
docs = loader.lazy_load()

See the full `Document Loader`_ tutorial.

.. _`Document Loader`: https://github.com/googleapis/langchain-google-cloud-sql-pg-python/tree/main/samples/document_loader.ipynb
.. _`Document Loader`: https://github.com/googleapis/langchain-google-cloud-sql-pg-python/tree/main/docs/document_loader.ipynb

Chat Message History Usage
~~~~~~~~~~~~~~~~~~~~~~~~~~~

Use Chat Message History to store messages and provide conversation history to LLMs.

.. code:: python
.. code-block:: python

from langchain_google_cloud_sql_pg import PostgresChatMessageHistory, PostgresEngine
from langchain_google_cloud_sql_pg import PostgresChatMessageHistory, PostgresEngine


engine = PostgresEngine.from_instance("project-id", "region", "my-instance", "my-database")
engine.init_chat_history_table(table_name="my-message-store")
history = PostgresChatMessageHistory.create_sync(
engine,
table_name="my-message-store",
session_id="my-session_id"
)
engine = PostgresEngine.from_instance("project-id", "region", "my-instance", "my-database")
engine.init_chat_history_table(table_name="my-message-store")
history = PostgresChatMessageHistory.create_sync(
engine,
table_name="my-message-store",
session_id="my-session_id"
)

See the full `Chat Message History`_ tutorial.

.. _`Chat Message History`: https://github.com/googleapis/langchain-google-cloud-sql-pg-python/tree/main/samples/chat_message_history.ipynb
.. _`Chat Message History`: https://github.com/googleapis/langchain-google-cloud-sql-pg-python/tree/main/docs/chat_message_history.ipynb

Contributing
~~~~~~~~~~~~
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2 changes: 1 addition & 1 deletion samples/chat_message_history.ipynb → docs/chat_message_history.ipynb
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Expand Up @@ -555,4 +555,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}
0 samples/document_loader.ipynb → docs/document_loader.ipynb
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0 samples/vector_store.ipynb → docs/vector_store.ipynb
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3 changes: 3 additions & 0 deletions noxfile.py
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Expand Up @@ -19,6 +19,8 @@
import os
import pathlib
import shutil
from pathlib import Path
from typing import Optional

import nox

Expand All @@ -28,6 +30,7 @@
# 'docfx' is excluded since it only needs to run in 'docs-presubmit'
nox.options.sessions = [
"docs",
"docfx",
]

# Error if a python version is missing
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2 changes: 1 addition & 1 deletion pyproject.toml
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Expand Up @@ -2,7 +2,7 @@
name = "langchain-google-cloud-sql-pg"
dynamic = ["version"]
description = "LangChain integrations for Google Cloud SQL for PostgreSQL"
readme = "README.md"
readme = "README.rst"
license = {file = "LICENSE"}
requires-python = ">=3.8"
authors = [
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2 changes: 1 addition & 1 deletion samples/langchain_quick_start.ipynb
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Expand Up @@ -1016,4 +1016,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}
0