mirror of
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docs: update langchain-oracledb documentation (#32805)
`Oracle AI Vector Search` integrations for LangChain have been moved to a dedicated package, [langchain-oracledb ](https://pypi.org/project/langchain-oracledb/), and a new repository, [langchain-oracle ](https://github.com/oracle/langchain-oracle/tree/main/libs/oracledb). This PR updates the corresponding documentation, including installation instructions and import statements, to reflect these changes. This PR is complemented with: https://github.com/langchain-ai/langchain-community/pull/283 Co-authored-by: Mason Daugherty <mason@langchain.dev>
This commit is contained in:
Elif Sema Balcioglu
committed by
GitHub
GitHub
parent
3420ca1da2
commit
dc47c2c598
@@ -47,10 +47,12 @@
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"source": [
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"### Prerequisites\n",
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"\n",
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"Please install the Oracle Database [python-oracledb driver](https://pypi.org/project/oracledb/) to use LangChain with Oracle AI Vector Search:\n",
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"You'll need to install `langchain-oracledb` with `python -m pip install -U langchain-oracledb` to use this integration.\n",
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"\n",
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"The `python-oracledb` driver is installed automatically as a dependency of langchain-oracledb.\n",
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"\n",
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"```\n",
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"$ python -m pip install --upgrade oracledb\n",
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"$ python -m pip install -U langchain-oracledb\n",
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"```"
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]
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},
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@@ -217,7 +219,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.embeddings.oracleai import OracleEmbeddings\n",
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"from langchain_oracledb.embeddings.oracleai import OracleEmbeddings\n",
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"\n",
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"# please update with your related information\n",
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"# make sure that you have onnx file in the system\n",
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@@ -296,7 +298,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.document_loaders.oracleai import OracleDocLoader\n",
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"from langchain_oracledb.document_loaders.oracleai import OracleDocLoader\n",
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"from langchain_core.documents import Document\n",
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"\n",
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"# loading from Oracle Database table\n",
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@@ -354,7 +356,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.utilities.oracleai import OracleSummary\n",
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"from langchain_oracledb.utilities.oracleai import OracleSummary\n",
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"from langchain_core.documents import Document\n",
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"\n",
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"# using 'database' provider\n",
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@@ -395,7 +397,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.document_loaders.oracleai import OracleTextSplitter\n",
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"from langchain_oracledb.document_loaders.oracleai import OracleTextSplitter\n",
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"from langchain_core.documents import Document\n",
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"\n",
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"# split by default parameters\n",
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@@ -452,7 +454,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.embeddings.oracleai import OracleEmbeddings\n",
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"from langchain_oracledb.embeddings.oracleai import OracleEmbeddings\n",
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"from langchain_core.documents import Document\n",
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"\n",
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"# using ONNX model loaded to Oracle Database\n",
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@@ -498,14 +500,14 @@
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"import sys\n",
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"\n",
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"import oracledb\n",
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"from langchain_community.document_loaders.oracleai import (\n",
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"from langchain_oracledb.document_loaders.oracleai import (\n",
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" OracleDocLoader,\n",
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" OracleTextSplitter,\n",
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")\n",
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"from langchain_community.embeddings.oracleai import OracleEmbeddings\n",
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"from langchain_community.utilities.oracleai import OracleSummary\n",
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"from langchain_community.vectorstores import oraclevs\n",
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"from langchain_community.vectorstores.oraclevs import OracleVS\n",
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"from langchain_oracledb.embeddings.oracleai import OracleEmbeddings\n",
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"from langchain_oracledb.utilities.oracleai import OracleSummary\n",
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"from langchain_oracledb.vectorstores import oraclevs\n",
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"from langchain_oracledb.vectorstores.oraclevs import OracleVS\n",
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"from langchain_community.vectorstores.utils import DistanceStrategy\n",
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"from langchain_core.documents import Document"
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]
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@@ -677,19 +679,19 @@
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"outputs": [],
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"source": [
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"query = \"What is Oracle AI Vector Store?\"\n",
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"filter = {\"document_id\": [\"1\"]}\n",
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"db_filter = {\"document_id\": \"1\"}\n",
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"\n",
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"# Similarity search without a filter\n",
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"print(vectorstore.similarity_search(query, 1))\n",
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"\n",
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"# Similarity search with a filter\n",
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"print(vectorstore.similarity_search(query, 1, filter=filter))\n",
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"print(vectorstore.similarity_search(query, 1, filter=db_filter))\n",
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"\n",
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"# Similarity search with relevance score\n",
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"print(vectorstore.similarity_search_with_score(query, 1))\n",
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"\n",
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"# Similarity search with relevance score with filter\n",
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"print(vectorstore.similarity_search_with_score(query, 1, filter=filter))\n",
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"print(vectorstore.similarity_search_with_score(query, 1, filter=db_filter))\n",
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"\n",
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"# Max marginal relevance search\n",
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"print(vectorstore.max_marginal_relevance_search(query, 1, fetch_k=20, lambda_mult=0.5))\n",
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@@ -697,7 +699,7 @@
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"# Max marginal relevance search with filter\n",
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"print(\n",
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" vectorstore.max_marginal_relevance_search(\n",
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" query, 1, fetch_k=20, lambda_mult=0.5, filter=filter\n",
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" query, 1, fetch_k=20, lambda_mult=0.5, filter=db_filter\n",
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" )\n",
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")"
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]
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@@ -16,13 +16,7 @@
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"This notebook covers how to load documents from Oracle Autonomous Database.\n",
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"\n",
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"## Prerequisites\n",
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"1. Install python-oracledb:\n",
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"\n",
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" `pip install oracledb`\n",
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" \n",
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" See [Installing python-oracledb](https://python-oracledb.readthedocs.io/en/latest/user_guide/installation.html).\n",
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"\n",
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"2. A database that python-oracledb's default 'Thin' mode can connected to. This is true of Oracle Autonomous Database, see [python-oracledb Architecture](https://python-oracledb.readthedocs.io/en/latest/user_guide/introduction.html#architecture).\n"
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"1. A database that python-oracledb's default 'Thin' mode can connected to. This is true of Oracle Autonomous Database, see [python-oracledb Architecture](https://python-oracledb.readthedocs.io/en/latest/user_guide/introduction.html#architecture).\n"
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]
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},
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{
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@@ -38,17 +32,12 @@
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"pip install oracledb"
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"You'll need to install `langchain-oracledb` with `python -m pip install -U langchain-oracledb` to use this integration.\n",
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"\n",
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"The `python-oracledb` driver is installed automatically as a dependency of langchain-oracledb."
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]
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},
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{
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@@ -62,7 +51,21 @@
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},
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"outputs": [],
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"source": [
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"from langchain_community.document_loaders import OracleAutonomousDatabaseLoader\n",
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"# python -m pip install -U langchain-oracledb"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"from langchain_oracledb.document_loaders import OracleAutonomousDatabaseLoader\n",
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"from settings import s"
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]
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},
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@@ -99,7 +102,7 @@
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" config_dir=s.CONFIG_DIR,\n",
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" wallet_location=s.WALLET_LOCATION,\n",
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" wallet_password=s.PASSWORD,\n",
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" tns_name=s.TNS_NAME,\n",
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" dsn=s.DSN,\n",
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")\n",
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"doc_1 = doc_loader_1.load()\n",
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"\n",
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@@ -108,7 +111,7 @@
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" user=s.USERNAME,\n",
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" password=s.PASSWORD,\n",
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" schema=s.SCHEMA,\n",
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" connection_string=s.CONNECTION_STRING,\n",
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" dsn=s.DSN,\n",
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" wallet_location=s.WALLET_LOCATION,\n",
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" wallet_password=s.PASSWORD,\n",
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")\n",
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@@ -147,7 +150,7 @@
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" password=s.PASSWORD,\n",
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" schema=s.SCHEMA,\n",
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" config_dir=s.CONFIG_DIR,\n",
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" tns_name=s.TNS_NAME,\n",
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" dsn=s.DSN,\n",
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" parameters=[\"Direct Sales\"],\n",
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")\n",
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"doc_3 = doc_loader_3.load()\n",
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@@ -157,7 +160,7 @@
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" user=s.USERNAME,\n",
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" password=s.PASSWORD,\n",
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" schema=s.SCHEMA,\n",
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" connection_string=s.CONNECTION_STRING,\n",
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" dsn=s.DSN,\n",
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" parameters=[\"Direct Sales\"],\n",
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")\n",
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"doc_4 = doc_loader_4.load()"
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@@ -42,7 +42,9 @@
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"source": [
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"### Prerequisites\n",
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"\n",
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"Please install Oracle Python Client driver to use Langchain with Oracle AI Vector Search. "
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"You'll need to install `langchain-oracledb` with `python -m pip install -U langchain-oracledb` to use this integration.\n",
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"\n",
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"The `python-oracledb` driver is installed automatically as a dependency of langchain-oracledb."
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]
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},
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{
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@@ -51,7 +53,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# pip install oracledb"
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"# python -m pip install -U langchain-oracledb"
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]
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},
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{
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@@ -154,7 +156,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.document_loaders.oracleai import OracleDocLoader\n",
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"from langchain_oracledb.document_loaders.oracleai import OracleDocLoader\n",
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"from langchain_core.documents import Document\n",
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"\n",
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"\"\"\"\n",
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@@ -199,7 +201,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.document_loaders.oracleai import OracleTextSplitter\n",
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"from langchain_oracledb.document_loaders.oracleai import OracleTextSplitter\n",
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"from langchain_core.documents import Document\n",
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"\n",
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"\"\"\"\n",
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@@ -42,7 +42,9 @@
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"source": [
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"### Prerequisites\n",
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"\n",
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"Ensure you have the Oracle Python Client driver installed to facilitate the integration of Langchain with Oracle AI Vector Search."
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"You'll need to install `langchain-oracledb` with `python -m pip install -U langchain-oracledb` to use this integration.\n",
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"\n",
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"The `python-oracledb` driver is installed automatically as a dependency of langchain-oracledb."
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]
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},
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{
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@@ -51,7 +53,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# pip install oracledb"
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"# python -m pip install -U langchain-oracledb"
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]
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},
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{
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@@ -113,7 +115,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.embeddings.oracleai import OracleEmbeddings\n",
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"from langchain_oracledb.embeddings.oracleai import OracleEmbeddings\n",
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"\n",
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"# Update the directory and file names for your ONNX model\n",
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"# make sure that you have onnx file in the system\n",
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@@ -223,7 +225,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.embeddings.oracleai import OracleEmbeddings\n",
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"from langchain_oracledb.embeddings.oracleai import OracleEmbeddings\n",
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"from langchain_core.documents import Document\n",
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"\n",
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"\"\"\"\n",
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@@ -237,10 +239,10 @@
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"\n",
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"# using huggingface\n",
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"embedder_params = {\n",
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" \"provider\": \"huggingface\", \n",
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" \"credential_name\": \"HF_CRED\", \n",
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" \"url\": \"https://api-inference.huggingface.co/pipeline/feature-extraction/\", \n",
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" \"model\": \"sentence-transformers/all-MiniLM-L6-v2\", \n",
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" \"provider\": \"huggingface\",\n",
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" \"credential_name\": \"HF_CRED\",\n",
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" \"url\": \"https://api-inference.huggingface.co/pipeline/feature-extraction/\",\n",
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" \"model\": \"sentence-transformers/all-MiniLM-L6-v2\",\n",
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" \"wait_for_model\": \"true\"\n",
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"}\n",
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"\"\"\"\n",
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@@ -42,7 +42,9 @@
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"source": [
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"### Prerequisites\n",
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"\n",
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"Please install Oracle Python Client driver to use Langchain with Oracle AI Vector Search. "
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"You'll need to install `langchain-oracledb` with `python -m pip install -U langchain-oracledb` to use this integration.\n",
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"\n",
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"The `python-oracledb` driver is installed automatically as a dependency of langchain-oracledb."
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]
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},
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{
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@@ -51,7 +53,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# pip install oracledb"
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"# python -m pip install -U langchain-oracledb"
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]
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},
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{
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@@ -123,7 +125,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.utilities.oracleai import OracleSummary\n",
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"from langchain_oracledb.utilities.oracleai import OracleSummary\n",
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"from langchain_core.documents import Document\n",
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"\n",
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"\"\"\"\n",
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@@ -43,9 +43,9 @@
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"source": [
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"### Prerequisites for using Langchain with Oracle AI Vector Search\n",
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"\n",
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"You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n",
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"You'll need to install `langchain-oracledb` with `python -m pip install -U langchain-oracledb` to use this integration.\n",
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"\n",
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"Please install Oracle Python Client driver to use Langchain with Oracle AI Vector Search. "
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"The `python-oracledb` driver is installed automatically as a dependency of langchain-oracledb."
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]
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},
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{
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@@ -55,7 +55,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# pip install oracledb"
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"# python -m pip install -U langchain-oracledb"
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]
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},
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{
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@@ -103,8 +103,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.vectorstores import oraclevs\n",
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"from langchain_community.vectorstores.oraclevs import OracleVS\n",
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"from langchain_oracledb.vectorstores import oraclevs\n",
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"from langchain_oracledb.vectorstores.oraclevs import OracleVS\n",
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"from langchain_community.vectorstores.utils import DistanceStrategy\n",
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"from langchain_core.documents import Document\n",
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"from langchain_huggingface import HuggingFaceEmbeddings"
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@@ -400,7 +400,111 @@
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"id": "7223d048-5c0b-4e91-a91b-a7daa9f86758",
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"metadata": {},
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"source": [
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"### Demonstrate advanced searches on all six vector stores, with and without attribute filtering – with filtering, we only select the document id 101 and nothing else"
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"### Demonstrate advanced searches on all six vector stores, with and without attribute filtering – with filtering, we only select the document id 101 and nothing else.\n",
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"\n",
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"Oracle Database 23ai supports pre-filtering, in-filtering, and post-filtering to enhance AI Vector Search capabilities. These filtering mechanisms allow users to apply constraints before, during, and after performing vector similarity searches, improving search performance and accuracy.\n",
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"\n",
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"Key Points about Filtering in Oracle 23ai:\n",
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"1. Pre-filtering\n",
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" Applies traditional SQL filters to reduce the dataset before performing the vector similarity search.\n",
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" Helps improve efficiency by limiting the amount of data processed by AI algorithms.\n",
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"2. In-filtering\n",
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" Utilizes AI Vector Search to perform similarity searches directly on vector embeddings, using optimized indexes and algorithms.\n",
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" Efficiently filters results based on vector similarity without requiring full dataset scans.\n",
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"3. Post-filtering\n",
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" Applies additional SQL filtering to refine the results after the vector similarity search.\n",
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" Allows further refinement based on business logic or additional metadata conditions.\n",
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"\n",
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"\n",
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"**Why is this Important?**\n",
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"- Performance Optimization: Pre-filtering significantly reduces query execution time, making searches on massive datasets more efficient.\n",
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"- Accuracy Enhancement: In-filtering ensures that vector searches are semantically meaningful, improving the quality of search results.\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "71406bf9",
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"metadata": {},
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"source": [
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"#### Filter Details\n",
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"\n",
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"`OracleVS` supports a set of filters that can be applied to `metadata` fields using `filter` parameter. These filters allow you to select and refine data based on various criteria. \n",
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"\n",
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"**Available Filter Operators:**\n",
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"\n",
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||||
"| Operator | Description |\n",
|
||||
"|--------------------------|--------------------------------------------------------------------------------------------------|\n",
|
||||
"| \\$exists | Field exists. |\n",
|
||||
"| \\$eq | Field value equals the operand value (`=`). |\n",
|
||||
"| \\$ne | Field exists and value does not equal the operand value (`!=`). |\n",
|
||||
"| \\$gt | Field value is greater than the operand value (`>`). |\n",
|
||||
"| \\$lt | Field value is less than the operand value (`<`). |\n",
|
||||
"| \\$gte | Field value is greater than or equal to the operand value (`>=`). |\n",
|
||||
"| \\$lte | Field value is less than or equal to the operand value (`<=`). |\n",
|
||||
"| \\$between | Field value is between (or equal to) two values in the operand array. |\n",
|
||||
"| \\$startsWith | Field value starts with the operand value. |\n",
|
||||
"| \\$hasSubstring | Field value contains the operand as a substring. |\n",
|
||||
"| \\$instr | Field value contains the operand as a substring. |\n",
|
||||
"| \\$regex | Field value matches the given regular expression pattern. |\n",
|
||||
"| \\$like | Field value matches the operand pattern (using SQL-like syntax). |\n",
|
||||
"| \\$in | Field value equals at least one value in the operand array. |\n",
|
||||
"| \\$nin | Field exists, but its value is not equal to any in the operand array, or the field does not exist.|\n",
|
||||
"| \\$all | Field value is an array containing all items from the operand array, or a scalar matching a single operand. |\n",
|
||||
"\n",
|
||||
"- You can combine these filters using logical operators:\n",
|
||||
"\n",
|
||||
"| Logical Operator | Description |\n",
|
||||
"|------------------|----------------------|\n",
|
||||
"| \\$and | Logical AND |\n",
|
||||
"| \\$or | Logical OR |\n",
|
||||
"| \\$nor | Logical NOR |\n",
|
||||
"\n",
|
||||
"**Example Filter:**\n",
|
||||
"```json\n",
|
||||
"{\n",
|
||||
" \"age\": 65,\n",
|
||||
" \"name\": {\"$regex\": \"*rk\"},\n",
|
||||
" \"$or\": [\n",
|
||||
" {\n",
|
||||
" \"$and\": [\n",
|
||||
" {\"name\": \"Jason\"},\n",
|
||||
" {\"drinks\": {\"$in\": [\"tea\", \"soda\"]}}\n",
|
||||
" ]\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" \"$nor\": [\n",
|
||||
" {\"age\": {\"$lt\": 65}},\n",
|
||||
" {\"name\": \"Jason\"}\n",
|
||||
" ]\n",
|
||||
" }\n",
|
||||
" ]\n",
|
||||
"}\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"**Additional Usage Tips:**\n",
|
||||
"- You can omit `$and` when all filters in an object must be satisfied. These two are equivalent:\n",
|
||||
"```json\n",
|
||||
"{ \"$and\": [\n",
|
||||
" { \"name\": { \"$startsWith\": \"Fred\" } },\n",
|
||||
" { \"salary\": { \"$gt\": 10000, \"$lte\": 20000 } }\n",
|
||||
"]}\n",
|
||||
"```\n",
|
||||
"```json\n",
|
||||
"{\n",
|
||||
" \"name\": { \"$startsWith\": \"Fred\" },\n",
|
||||
" \"salary\": { \"$gt\": 10000, \"$lte\": 20000 }\n",
|
||||
"}\n",
|
||||
"```\n",
|
||||
"- The `$not` clause can negate a comparison operator:\n",
|
||||
"```json\n",
|
||||
"{ \"address.zip\": { \"$not\": { \"$eq\": \"90001\" } } }\n",
|
||||
"```\n",
|
||||
"- Using `field: scalar` is equivalent to `field: { \"$eq\": scalar }`:\n",
|
||||
"```json\n",
|
||||
"{ \"animal\": \"cat\" }\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"For more filter examples, refer to the [test specification](https://github.com/oracle/langchain-oracle/blob/main/libs/oracledb/tests/integration_tests/vectorstores/test_oraclevs.py)."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -415,7 +519,23 @@
|
||||
" query = \"How are LOBS stored in Oracle Database\"\n",
|
||||
" # Constructing a filter for direct comparison against document metadata\n",
|
||||
" # This filter aims to include documents whose metadata 'id' is exactly '2'\n",
|
||||
" filter_criteria = {\"id\": [\"101\"]} # Direct comparison filter\n",
|
||||
" db_filter = {\n",
|
||||
" \"$and\": [\n",
|
||||
" {\"id\": \"101\"}, # FilterCondition\n",
|
||||
" {\n",
|
||||
" \"$or\": [ # FilterGroup\n",
|
||||
" {\"status\": \"approved\"},\n",
|
||||
" {\"link\": \"Document Example Test 2\"},\n",
|
||||
" {\n",
|
||||
" \"$and\": [ # Nested FilterGroup\n",
|
||||
" {\"status\": \"approved\"},\n",
|
||||
" {\"link\": \"Document Example Test 2\"},\n",
|
||||
" ]\n",
|
||||
" },\n",
|
||||
" ]\n",
|
||||
" },\n",
|
||||
" ]\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" for i, vs in enumerate(vector_stores, start=1):\n",
|
||||
" print(f\"\\n--- Vector Store {i} Advanced Searches ---\")\n",
|
||||
@@ -425,7 +545,7 @@
|
||||
"\n",
|
||||
" # Similarity search with a filter\n",
|
||||
" print(\"\\nSimilarity search results with filter:\")\n",
|
||||
" print(vs.similarity_search(query, 2, filter=filter_criteria))\n",
|
||||
" print(vs.similarity_search(query, 2, filter=db_filter))\n",
|
||||
"\n",
|
||||
" # Similarity search with relevance score\n",
|
||||
" print(\"\\nSimilarity search with relevance score:\")\n",
|
||||
@@ -433,7 +553,7 @@
|
||||
"\n",
|
||||
" # Similarity search with relevance score with filter\n",
|
||||
" print(\"\\nSimilarity search with relevance score with filter:\")\n",
|
||||
" print(vs.similarity_search_with_score(query, 2, filter=filter_criteria))\n",
|
||||
" print(vs.similarity_search_with_score(query, 2, filter=db_filter))\n",
|
||||
"\n",
|
||||
" # Max marginal relevance search\n",
|
||||
" print(\"\\nMax marginal relevance search results:\")\n",
|
||||
@@ -443,7 +563,7 @@
|
||||
" print(\"\\nMax marginal relevance search results with filter:\")\n",
|
||||
" print(\n",
|
||||
" vs.max_marginal_relevance_search(\n",
|
||||
" query, 2, fetch_k=20, lambda_mult=0.5, filter=filter_criteria\n",
|
||||
" query, 2, fetch_k=20, lambda_mult=0.5, filter=db_filter\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
@@ -477,7 +597,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.9"
|
||||
"version": "3.13.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Reference in New Issue
Block a user