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docs: update langchain-oracledb documentation (#32805)

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`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
2025-09-12 22:47:10 +02:00
committed by GitHub
parent 3420ca1da2
commit dc47c2c598
6 changed files with 195 additions and 64 deletions
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34
cookbook/oracleai_demo.ipynb
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@@ -47,10 +47,12 @@
"source": [
"### Prerequisites\n",
"\n",
"Please install the Oracle Database [python-oracledb driver](https://pypi.org/project/oracledb/) to use LangChain with Oracle AI Vector Search:\n",
"You'll need to install `langchain-oracledb` with `python -m pip install -U langchain-oracledb` to use this integration.\n",
"\n",
"The `python-oracledb` driver is installed automatically as a dependency of langchain-oracledb.\n",
"\n",
"```\n",
"$ python -m pip install --upgrade oracledb\n",
"$ python -m pip install -U langchain-oracledb\n",
"```"
]
},
@@ -217,7 +219,7 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.embeddings.oracleai import OracleEmbeddings\n",
"from langchain_oracledb.embeddings.oracleai import OracleEmbeddings\n",
"\n",
"# please update with your related information\n",
"# make sure that you have onnx file in the system\n",
@@ -296,7 +298,7 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.document_loaders.oracleai import OracleDocLoader\n",
"from langchain_oracledb.document_loaders.oracleai import OracleDocLoader\n",
"from langchain_core.documents import Document\n",
"\n",
"# loading from Oracle Database table\n",
@@ -354,7 +356,7 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.utilities.oracleai import OracleSummary\n",
"from langchain_oracledb.utilities.oracleai import OracleSummary\n",
"from langchain_core.documents import Document\n",
"\n",
"# using 'database' provider\n",
@@ -395,7 +397,7 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.document_loaders.oracleai import OracleTextSplitter\n",
"from langchain_oracledb.document_loaders.oracleai import OracleTextSplitter\n",
"from langchain_core.documents import Document\n",
"\n",
"# split by default parameters\n",
@@ -452,7 +454,7 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.embeddings.oracleai import OracleEmbeddings\n",
"from langchain_oracledb.embeddings.oracleai import OracleEmbeddings\n",
"from langchain_core.documents import Document\n",
"\n",
"# using ONNX model loaded to Oracle Database\n",
@@ -498,14 +500,14 @@
"import sys\n",
"\n",
"import oracledb\n",
"from langchain_community.document_loaders.oracleai import (\n",
"from langchain_oracledb.document_loaders.oracleai import (\n",
" OracleDocLoader,\n",
" OracleTextSplitter,\n",
")\n",
"from langchain_community.embeddings.oracleai import OracleEmbeddings\n",
"from langchain_community.utilities.oracleai import OracleSummary\n",
"from langchain_community.vectorstores import oraclevs\n",
"from langchain_community.vectorstores.oraclevs import OracleVS\n",
"from langchain_oracledb.embeddings.oracleai import OracleEmbeddings\n",
"from langchain_oracledb.utilities.oracleai import OracleSummary\n",
"from langchain_oracledb.vectorstores import oraclevs\n",
"from langchain_oracledb.vectorstores.oraclevs import OracleVS\n",
"from langchain_community.vectorstores.utils import DistanceStrategy\n",
"from langchain_core.documents import Document"
]
@@ -677,19 +679,19 @@
"outputs": [],
"source": [
"query = \"What is Oracle AI Vector Store?\"\n",
"filter = {\"document_id\": [\"1\"]}\n",
"db_filter = {\"document_id\": \"1\"}\n",
"\n",
"# Similarity search without a filter\n",
"print(vectorstore.similarity_search(query, 1))\n",
"\n",
"# Similarity search with a filter\n",
"print(vectorstore.similarity_search(query, 1, filter=filter))\n",
"print(vectorstore.similarity_search(query, 1, filter=db_filter))\n",
"\n",
"# Similarity search with relevance score\n",
"print(vectorstore.similarity_search_with_score(query, 1))\n",
"\n",
"# Similarity search with relevance score with filter\n",
"print(vectorstore.similarity_search_with_score(query, 1, filter=filter))\n",
"print(vectorstore.similarity_search_with_score(query, 1, filter=db_filter))\n",
"\n",
"# Max marginal relevance search\n",
"print(vectorstore.max_marginal_relevance_search(query, 1, fetch_k=20, lambda_mult=0.5))\n",
@@ -697,7 +699,7 @@
"# Max marginal relevance search with filter\n",
"print(\n",
" vectorstore.max_marginal_relevance_search(\n",
" query, 1, fetch_k=20, lambda_mult=0.5, filter=filter\n",
" query, 1, fetch_k=20, lambda_mult=0.5, filter=db_filter\n",
" )\n",
")"
]