mirror of https://github.com/hwchase17/langchain
docs: update Google documentation (#18297)
**Description:** update Google documentation **Issue:** **Dependencies:**pull/18315/head
Averi Kitsch
4 months ago
committed by
GitHub
parent
1d865a7e86
commit
1b63530274
@ -0,0 +1,566 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "NKbPFu-GWFDV"
|
||||
},
|
||||
"source": [
|
||||
"# Google El Carro Oracle Operator\n",
|
||||
">\n",
|
||||
"Google [El Carro Oracle Operator](https://github.com/GoogleCloudPlatform/elcarro-oracle-operator)\n",
|
||||
"offers a way to run Oracle databases in Kubernetes as a portable, open source,\n",
|
||||
"community driven, no vendor lock-in container orchestration system. El Carro\n",
|
||||
"provides a powerful declarative API for comprehensive and consistent\n",
|
||||
"configuration and deployment as well as for real-time operations and\n",
|
||||
"monitoring..\n",
|
||||
"Extend your database application to build AI-powered experiences leveraging\n",
|
||||
"Oracle Langchain integrations.\n",
|
||||
"\n",
|
||||
"This guide goes over how to use El Carro Langchain integration to\n",
|
||||
"[save, load and delete langchain documents](https://python.langchain.com/docs/modules/data_connection/document_loaders/)\n",
|
||||
"with `ElCarroLoader` and `ElCarroDocumentSaver`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ZqONzXRcWMJg"
|
||||
},
|
||||
"source": [
|
||||
"## Before You Begin\n",
|
||||
"\n",
|
||||
"Please complete\n",
|
||||
"the [Getting Started](https://github.com/googleapis/langchain-google-el-carro-python/tree/main/README.md#getting-started)\n",
|
||||
"section of\n",
|
||||
"the README to set up your El Carro Oracle database."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "imbbHxKfWPso"
|
||||
},
|
||||
"source": [
|
||||
"### 🦜🔗 Library Installation\n",
|
||||
"\n",
|
||||
"The integration lives in its own `langchain-google-el-carro` package, so\n",
|
||||
"we need to install it."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "Su5BMP2zWRwM"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install --upgrade --quiet langchain-google-el-carro"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "azV0k45WWSVI"
|
||||
},
|
||||
"source": [
|
||||
"## Basic Usage\n",
|
||||
"\n",
|
||||
"### Set Up Oracle Database Connection\n",
|
||||
"\n",
|
||||
"ElCarroEngine configures a connection pool to your Oracle database,\n",
|
||||
"enabling successful connections from your application and following industry\n",
|
||||
"best practices.\n",
|
||||
"\n",
|
||||
"You can find the hostname and port values in the status of the El Carro\n",
|
||||
"Kubernetes instance.\n",
|
||||
"Use the user password you created for your PDB."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "xG1mYFkEWbkp"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_google_el_carro import ElCarroEngine\n",
|
||||
"\n",
|
||||
"elcarro_engine = ElCarroEngine.from_instance(\n",
|
||||
" db_host=\"127.0.0.1\",\n",
|
||||
" db_port=3307,\n",
|
||||
" db_name=\"PDB1\",\n",
|
||||
" db_user=\"scott\",\n",
|
||||
" db_password=\"tiger\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ICW3k_qUWgyv"
|
||||
},
|
||||
"source": [
|
||||
"### Initialize a table\n",
|
||||
"\n",
|
||||
"Initialize a table of default schema\n",
|
||||
"via `elcarro_engine.init_document_table(<TABLE_NAME>)`. Table Columns:\n",
|
||||
"\n",
|
||||
"- page_content (type: text)\n",
|
||||
"- langchain_metadata (type: JSON)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "JmlGLukoWdfS"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"TABLE_NAME = \"doc_table\"\n",
|
||||
"elcarro_engine.init_document_table(\n",
|
||||
" TABLE_NAME=TABLE_NAME,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "kaI3avj5Wn5O"
|
||||
},
|
||||
"source": [
|
||||
"### Save documents\n",
|
||||
"\n",
|
||||
"Save langchain documents with `ElCarroDocumentSaver.add_documents(<documents>)`.\n",
|
||||
"To initialize `ElCarroDocumentSaver` class you need to provide 2 things:\n",
|
||||
"\n",
|
||||
"1. `elcarro_engine` - An instance of a `ElCarroEngine` engine.\n",
|
||||
"2. `TABLE_NAME` - The name of the table within the Oracle database to store\n",
|
||||
" langchain documents."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "skaXpthSWpeg"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.documents import Document\n",
|
||||
"from langchain_google_el_carro import ElCarroDocumentSaver\n",
|
||||
"\n",
|
||||
"doc = Document(\n",
|
||||
" page_content=\"Banana\",\n",
|
||||
" metadata={\"type\": \"fruit\", \"weight\": 100, \"organic\": 1},\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"saver = ElCarroDocumentSaver(\n",
|
||||
" elcarro_engine=elcarro_engine,\n",
|
||||
" TABLE_NAME=TABLE_NAME,\n",
|
||||
")\n",
|
||||
"saver.add_documents([doc])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "owTYQdNyWs9s"
|
||||
},
|
||||
"source": [
|
||||
"### Load documents\n",
|
||||
"\n",
|
||||
"Load langchain documents with `ElCarroLoader.load()`\n",
|
||||
"or `ElCarroLoader.lazy_load()`.\n",
|
||||
"`lazy_load` returns a generator that only queries database during the iteration.\n",
|
||||
"To initialize `ElCarroLoader` class you need to provide:\n",
|
||||
"\n",
|
||||
"1. `elcarro_engine` - An instance of a `ElCarroEngine` engine.\n",
|
||||
"2. `TABLE_NAME` - The name of the table within the Oracle database to store\n",
|
||||
" langchain documents.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "CM6p11amWvYp"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_google_el_carro import ElCarroLoader\n",
|
||||
"\n",
|
||||
"loader = ElCarroLoader(elcarro_engine=elcarro_engine, TABLE_NAME=TABLE_NAME)\n",
|
||||
"docs = loader.lazy_load()\n",
|
||||
"for doc in docs:\n",
|
||||
" print(\"Loaded documents:\", doc)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "OTIDGiZ8WyS3"
|
||||
},
|
||||
"source": [
|
||||
"### Load documents via query\n",
|
||||
"\n",
|
||||
"Other than loading documents from a table, we can also choose to load documents\n",
|
||||
"from a view generated from a SQL query. For example:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "p3OB9AwgWzrq"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_google_el_carro import ElCarroLoader\n",
|
||||
"\n",
|
||||
"loader = ElCarroLoader(\n",
|
||||
" elcarro_engine=elcarro_engine,\n",
|
||||
" query=f\"SELECT * FROM {TABLE_NAME} WHERE json_value(extra_json_metadata, '$.shape') = 'round'\",\n",
|
||||
")\n",
|
||||
"onedoc = loader.load()\n",
|
||||
"print(onedoc)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "E6Fl7YNvW3Ep"
|
||||
},
|
||||
"source": [
|
||||
"The view generated from SQL query can have different schema than default table.\n",
|
||||
"In such cases, the behavior of ElCarroLoader is the same as loading from table\n",
|
||||
"with non-default schema. Please refer to\n",
|
||||
"section [Load documents with customized document page content & metadata](#load-documents-with-customized-document-page-content--metadata)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "QgsP78MhW4wc"
|
||||
},
|
||||
"source": [
|
||||
"### Delete documents\n",
|
||||
"\n",
|
||||
"Delete a list of langchain documents from an Oracle table\n",
|
||||
"with `ElCarroDocumentSaver.delete(<documents>)`.\n",
|
||||
"\n",
|
||||
"For a table with a default schema (page_content, langchain_metadata), the\n",
|
||||
"deletion criteria is:\n",
|
||||
"\n",
|
||||
"A `row` should be deleted if there exists a `document` in the list, such that\n",
|
||||
"\n",
|
||||
"- `document.page_content` equals `row[page_content]`\n",
|
||||
"- `document.metadata` equals `row[langchain_metadata]`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "QSYRHGHXW6IN"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"docs = loader.load()\n",
|
||||
"print(\"Documents before delete:\", docs)\n",
|
||||
"saver.delete(onedoc)\n",
|
||||
"print(\"Documents after delete:\", loader.load())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "RerPkBRAW8yR"
|
||||
},
|
||||
"source": [
|
||||
"## Advanced Usage\n",
|
||||
"\n",
|
||||
"### Load documents with customized document page content & metadata\n",
|
||||
"\n",
|
||||
"First we prepare an example table with non-default schema, and populate it with\n",
|
||||
"some arbitrary data."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "u0Fd46aqW-8k"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sqlalchemy\n",
|
||||
"\n",
|
||||
"create_table_query = f\"\"\"CREATE TABLE {TABLE_NAME} (\n",
|
||||
" fruit_id NUMBER GENERATED BY DEFAULT AS IDENTITY (START WITH 1),\n",
|
||||
" fruit_name VARCHAR2(100) NOT NULL,\n",
|
||||
" variety VARCHAR2(50),\n",
|
||||
" quantity_in_stock NUMBER(10) NOT NULL,\n",
|
||||
" price_per_unit NUMBER(6,2) NOT NULL,\n",
|
||||
" organic NUMBER(3) NOT NULL\n",
|
||||
")\"\"\"\n",
|
||||
"\n",
|
||||
"with elcarro_engine.connect() as conn:\n",
|
||||
" conn.execute(sqlalchemy.text(create_table_query))\n",
|
||||
" conn.commit()\n",
|
||||
" conn.execute(\n",
|
||||
" sqlalchemy.text(\n",
|
||||
" f\"\"\"\n",
|
||||
" INSERT INTO {TABLE_NAME} (fruit_name, variety, quantity_in_stock, price_per_unit, organic)\n",
|
||||
" VALUES ('Apple', 'Granny Smith', 150, 0.99, 1)\n",
|
||||
" \"\"\"\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" conn.execute(\n",
|
||||
" sqlalchemy.text(\n",
|
||||
" f\"\"\"\n",
|
||||
" INSERT INTO {TABLE_NAME} (fruit_name, variety, quantity_in_stock, price_per_unit, organic)\n",
|
||||
" VALUES ('Banana', 'Cavendish', 200, 0.59, 0)\n",
|
||||
" \"\"\"\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" conn.execute(\n",
|
||||
" sqlalchemy.text(\n",
|
||||
" f\"\"\"\n",
|
||||
" INSERT INTO {TABLE_NAME} (fruit_name, variety, quantity_in_stock, price_per_unit, organic)\n",
|
||||
" VALUES ('Orange', 'Navel', 80, 1.29, 1)\n",
|
||||
" \"\"\"\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" conn.commit()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "hGPYiTu7XBh3"
|
||||
},
|
||||
"source": [
|
||||
"If we still load langchain documents with default parameters of `ElCarroLoader`\n",
|
||||
"from this example table, the `page_content` of loaded documents will be the\n",
|
||||
"first column of the table, and `metadata` will be consisting of key-value pairs\n",
|
||||
"of all the other columns."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "eQbRapM_XC1S"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader = ElCarroLoader(\n",
|
||||
" elcarro_engine=elcarro_engine,\n",
|
||||
" TABLE_NAME=TABLE_NAME,\n",
|
||||
")\n",
|
||||
"loader.load()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "tOH6i2jWXFqz"
|
||||
},
|
||||
"source": [
|
||||
"We can specify the content and metadata we want to load by setting\n",
|
||||
"the `content_columns` and `metadata_columns` when initializing\n",
|
||||
"the `ElCarroLoader`.\n",
|
||||
"\n",
|
||||
"1. `content_columns`: The columns to write into the `page_content` of the\n",
|
||||
" document.\n",
|
||||
"2. `metadata_columns`: The columns to write into the `metadata` of the document.\n",
|
||||
"\n",
|
||||
"For example here, the values of columns in `content_columns` will be joined\n",
|
||||
"together into a space-separated string, as `page_content` of loaded documents,\n",
|
||||
"and `metadata` of loaded documents will only contain key-value pairs of columns\n",
|
||||
"specified in `metadata_columns`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "9gCFWqgGXHD3"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader = ElCarroLoader(\n",
|
||||
" elcarro_engine=elcarro_engine,\n",
|
||||
" TABLE_NAME=TABLE_NAME,\n",
|
||||
" content_columns=[\n",
|
||||
" \"variety\",\n",
|
||||
" \"quantity_in_stock\",\n",
|
||||
" \"price_per_unit\",\n",
|
||||
" \"organic\",\n",
|
||||
" ],\n",
|
||||
" metadata_columns=[\"fruit_id\", \"fruit_name\"],\n",
|
||||
")\n",
|
||||
"loaded_docs = loader.load()\n",
|
||||
"print(f\"Loaded Documents: [{loaded_docs}]\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "4KlSfvPJXKgM"
|
||||
},
|
||||
"source": [
|
||||
"### Save document with customized page content & metadata\n",
|
||||
"\n",
|
||||
"In order to save langchain document into table with customized metadata fields.\n",
|
||||
"We need first create such a table via `ElCarroEngine.init_document_table()`, and\n",
|
||||
"specify the list of `metadata_columns` we want it to have. In this example, the\n",
|
||||
"created table will have table columns:\n",
|
||||
"\n",
|
||||
"- description (type: text): for storing fruit description.\n",
|
||||
"- fruit_name (type text): for storing fruit name.\n",
|
||||
"- organic (type tinyint(1)): to tell if the fruit is organic.\n",
|
||||
"- other_metadata (type: JSON): for storing other metadata information of the\n",
|
||||
" fruit.\n",
|
||||
"\n",
|
||||
"We can use the following parameters\n",
|
||||
"with `elcarro_engine.init_document_table()` to create the table:\n",
|
||||
"\n",
|
||||
"1. `TABLE_NAME`: The name of the table within the Oracle database to store\n",
|
||||
" langchain documents.\n",
|
||||
"2. `metadata_columns`: A list of `sqlalchemy.Column` indicating the list of\n",
|
||||
" metadata columns we need.\n",
|
||||
"3. `content_column`: column name to store `page_content` of langchain\n",
|
||||
" document. Default: `\"page_content\", \"VARCHAR2(4000)\"`\n",
|
||||
"4. `metadata_json_column`: column name to store extra\n",
|
||||
" JSON `metadata` of langchain document.\n",
|
||||
" Default: `\"langchain_metadata\", \"VARCHAR2(4000)\"`.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "1Wqs05gpXMW9"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"elcarro_engine.init_document_table(\n",
|
||||
" TABLE_NAME=TABLE_NAME,\n",
|
||||
" metadata_columns=[\n",
|
||||
" sqlalchemy.Column(\"type\", sqlalchemy.dialects.oracle.VARCHAR2(200)),\n",
|
||||
" sqlalchemy.Column(\"weight\", sqlalchemy.INT),\n",
|
||||
" ],\n",
|
||||
" content_column=\"content\",\n",
|
||||
" metadata_json_column=\"extra_json_metadata\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "bVEWHYU-XPFt"
|
||||
},
|
||||
"source": [
|
||||
"Save documents with `ElCarroDocumentSaver.add_documents(<documents>)`. As you\n",
|
||||
"can see in this example,\n",
|
||||
"\n",
|
||||
"- `document.page_content` will be saved into `page_content` column.\n",
|
||||
"- `document.metadata.type` will be saved into `type` column.\n",
|
||||
"- `document.metadata.weight` will be saved into `weight` column.\n",
|
||||
"- `document.metadata.organic` will be saved into `extra_json_metadata` column in\n",
|
||||
" JSON format.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "Iy4wRZLPXQn5"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"doc = Document(\n",
|
||||
" page_content=\"Banana\",\n",
|
||||
" metadata={\"type\": \"fruit\", \"weight\": 100, \"organic\": 1},\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(f\"Original Document: [{doc}]\")\n",
|
||||
"\n",
|
||||
"saver = ElCarroDocumentSaver(\n",
|
||||
" elcarro_engine=elcarro_engine,\n",
|
||||
" TABLE_NAME=TABLE_NAME,\n",
|
||||
" content_column=\"content\",\n",
|
||||
" metadata_json_column=\"extra_json_metadata\",\n",
|
||||
")\n",
|
||||
"saver.add_documents([doc])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "x0vkL7PKXUmU"
|
||||
},
|
||||
"source": [
|
||||
"### Delete documents with customized page content & metadata\n",
|
||||
"\n",
|
||||
"We can also delete documents from table with customized metadata columns\n",
|
||||
"via `ElCarroDocumentSaver.delete(<documents>)`. The deletion criteria is:\n",
|
||||
"\n",
|
||||
"A `row` should be deleted if there exists a `document` in the list, such that\n",
|
||||
"\n",
|
||||
"- `document.page_content` equals `row[page_content]`\n",
|
||||
"- For every metadata field `k` in `document.metadata`\n",
|
||||
" - `document.metadata[k]` equals `row[k]` or `document.metadata[k]`\n",
|
||||
" equals `row[langchain_metadata][k]`\n",
|
||||
"- There is no extra metadata field present in `row` but not\n",
|
||||
" in `document.metadata`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "OcJPeCuKXWSa"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"saver.delete(loaded_docs)\n",
|
||||
"print(f\"Documents left: {len(loader.load())}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "S4SxUoY-XsPN"
|
||||
},
|
||||
"source": [
|
||||
"## More examples\n",
|
||||
"\n",
|
||||
"Please look\n",
|
||||
"at [demo_doc_loader_basic.py](https://github.com/googleapis/langchain-google-el-carro-python/tree/main/samples/demo_doc_loader_basic.py)\n",
|
||||
"and [demo_doc_loader_advanced.py](https://github.com/googleapis/langchain-google-el-carro-python/tree/main/samples/demo_doc_loader_advanced.py)\n",
|
||||
"for\n",
|
||||
"complete code examples.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,368 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Google El Carro Oracle Operator\n",
|
||||
"\n",
|
||||
"> Google [El Carro Oracle Operator](https://github.com/GoogleCloudPlatform/elcarro-oracle-operator) offers a way to run Oracle databases in Kubernetes as a portable, open source, community driven, no vendor lock-in container orchestration system. El Carro provides a powerful declarative API for comprehensive and consistent configuration and deployment as well as for real-time operations and monitoring. Extend your database application to build AI-powered experiences leveraging Oracle Langchain integrations.\n",
|
||||
"\n",
|
||||
"This guide goes over how to use the El Carro Langchain integration to store chat message history with the `ElCarroChatMessageHistory` class.\n",
|
||||
"\n",
|
||||
"[](https://colab.research.google.com/github/googleapis/langchain-google-el-carro-python/blob/main/docs/chat_message_history.ipynb)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Before You Begin\n",
|
||||
"\n",
|
||||
"To run this notebook, you will need to do the following:\n",
|
||||
" * Complete the [Getting Started](https://github.com/googleapis/langchain-google-el-carro-python/tree/main/README.md#getting-started) section"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 🦜🔗 Library Installation\n",
|
||||
"The integration lives in its own `langchain-google-el-carro` package, so we need to install it."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install --upgrade --quiet langchain-google-el-carro langchain-google-vertexai langchain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# # Automatically restart kernel after installs so that your environment can access the new packages\n",
|
||||
"# import IPython\n",
|
||||
"\n",
|
||||
"# app = IPython.Application.instance()\n",
|
||||
"# app.kernel.do_shutdown(True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 🔐 Authentication\n",
|
||||
"Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.\n",
|
||||
"\n",
|
||||
"* If you are using Colab to run this notebook, use the cell below and continue.\n",
|
||||
"* If you are using Vertex AI Workbench, check out the setup instructions [here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from google.colab import auth\n",
|
||||
"\n",
|
||||
"auth.authenticate_user()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### ☁ Set Your Google Cloud Project\n",
|
||||
"Set your Google Cloud project so that you can leverage Google Cloud resources within this notebook.\n",
|
||||
"\n",
|
||||
"If you don't know your project ID, try the following:\n",
|
||||
"\n",
|
||||
"* Run `gcloud config list`.\n",
|
||||
"* Run `gcloud projects list`.\n",
|
||||
"* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @markdown Please fill in the value below with your Google Cloud project ID and then run the cell.\n",
|
||||
"\n",
|
||||
"PROJECT_ID = \"my-project-id\" # @param {type:\"string\"}\n",
|
||||
"\n",
|
||||
"# Set the project id\n",
|
||||
"!gcloud config set project {PROJECT_ID}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Basic Usage"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Set Up Oracle Database Connection\n",
|
||||
"You can find the hostname and port values in the status of the El Carro\n",
|
||||
"Kubernetes instance. Use the user password you created for your PDB."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title Set Your Values Here { display-mode: \"form\" }\n",
|
||||
"HOST = \"127.0.0.1\" # @param {type: \"string\"}\n",
|
||||
"PORT = 3307 # @param {type: \"integer\"}\n",
|
||||
"DATABASE = \"my-database\" # @param {type: \"string\"}\n",
|
||||
"TABLE_NAME = \"message_store\" # @param {type: \"string\"}\n",
|
||||
"USER = \"my-user\" # @param {type: \"string\"}\n",
|
||||
"PASSWORD = input(\"Please provide a password to be used for the database user: \")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### ElCarroEngine Connection Pool\n",
|
||||
"\n",
|
||||
"`ElCarroEngine` configures a connection pool to your Oracle database, enabling successful connections from your application and following industry best practices."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_google_el_carro import ElCarroEngine\n",
|
||||
"\n",
|
||||
"elcarro_engine = ElCarroEngine.from_instance(\n",
|
||||
" db_host=HOST,\n",
|
||||
" db_port=PORT,\n",
|
||||
" db_name=DATABASE,\n",
|
||||
" db_user=USER,\n",
|
||||
" db_password=PASSWORD,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Initialize a table\n",
|
||||
"The `ElCarroChatMessageHistory` class requires a database table with a specific\n",
|
||||
"schema in order to store the chat message history.\n",
|
||||
"\n",
|
||||
"The `ElCarroEngine` class has a\n",
|
||||
"method `init_chat_history_table()` that can be used to create a table with the\n",
|
||||
"proper schema for you."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"elcarro_engine.init_chat_history_table(table_name=TABLE_NAME)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### ElCarroChatMessageHistory\n",
|
||||
"\n",
|
||||
"To initialize the `ElCarroChatMessageHistory` class you need to provide only 3\n",
|
||||
"things:\n",
|
||||
"\n",
|
||||
"1. `elcarro_engine` - An instance of an `ElCarroEngine` engine.\n",
|
||||
"1. `session_id` - A unique identifier string that specifies an id for the\n",
|
||||
" session.\n",
|
||||
"1. `table_name` : The name of the table within the Oracle database to store the\n",
|
||||
" chat message history."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_google_el_carro import ElCarroChatMessageHistory\n",
|
||||
"\n",
|
||||
"history = ElCarroChatMessageHistory(\n",
|
||||
" elcarro_engine=elcarro_engine, session_id=\"test_session\", table_name=TABLE_NAME\n",
|
||||
")\n",
|
||||
"history.add_user_message(\"hi!\")\n",
|
||||
"history.add_ai_message(\"whats up?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"history.messages"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Cleaning up\n",
|
||||
"When the history of a specific session is obsolete and can be deleted, it can be done the following way.\n",
|
||||
"\n",
|
||||
"**Note:** Once deleted, the data is no longer stored in AlloyDB and is gone forever."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"history.clear()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 🔗 Chaining\n",
|
||||
"\n",
|
||||
"We can easily combine this message history class with [LCEL Runnables](/docs/expression_language/how_to/message_history)\n",
|
||||
"\n",
|
||||
"To do this we will use one of [Google's Vertex AI chat models](https://python.langchain.com/docs/integrations/chat/google_vertex_ai_palm) which requires that you [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com) in your Google Cloud Project.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# enable Vertex AI API\n",
|
||||
"!gcloud services enable aiplatform.googleapis.com"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
|
||||
"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
|
||||
"from langchain_google_vertexai import ChatVertexAI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [\n",
|
||||
" (\"system\", \"You are a helpful assistant.\"),\n",
|
||||
" MessagesPlaceholder(variable_name=\"history\"),\n",
|
||||
" (\"human\", \"{question}\"),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"chain = prompt | ChatVertexAI(project=PROJECT_ID)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chain_with_history = RunnableWithMessageHistory(\n",
|
||||
" chain,\n",
|
||||
" lambda session_id: ElCarroChatMessageHistory(\n",
|
||||
" elcarro_engine,\n",
|
||||
" session_id=session_id,\n",
|
||||
" table_name=TABLE_NAME,\n",
|
||||
" ),\n",
|
||||
" input_messages_key=\"question\",\n",
|
||||
" history_messages_key=\"history\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# This is where we configure the session id\n",
|
||||
"config = {\"configurable\": {\"session_id\": \"test_session\"}}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chain_with_history.invoke({\"question\": \"Hi! I'm bob\"}, config=config)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chain_with_history.invoke({\"question\": \"Whats my name\"}, config=config)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Reference in New Issue