Oracle × LangChain: A Plain-English Guide to Turning a World-Class Database into a Private AI Knowledge Base in Three Commands

“

If you already know LangChain but struggle to find a vector store that is both rock-solid and easy to scale
—or—
if you already run Oracle and want to try its brand-new AI Vector Search
then this article is for you.

We will walk through, step by step, how the official langchain-oci and langchain-oracledb packages let you:

• keep your data inside Oracle
• use cutting-edge language models
• search by meaning instead of keywords

All without leaving your laptop or your company firewall.

1. Why Oracle and LangChain Joined Forces

In short: Oracle keeps your data safe and fast; LangChain keeps your code short and portable.

2. What You Can Build Today

The README mentions three big blocks. Picture them like Lego:

langchain-oci   (cloud services)
├── LLMs – OCI Generative AI
├── LLMs – OCI Data Science ModelDeployment
└── Agents – Oracle Generative AI Agents

langchain-oracledb (database services)
├── OracleDocLoader   – read files
├── OracleTextSplitter– split text
├── OracleEmbeddings  – create vectors
├── OracleSummary     – summarize text
└── OracleVS          – store & search vectors

Any project you build will use one, two, or all three blocks above.

3. Ten-Minute Quick-Start: From Zero to Vector Query

3.1 What You Need First

• Python 3.9 or newer
• An OCI account and ~/.oci/config set up
• Oracle Database 23c (or the free Oracle 23c Free)
• 2 GB free RAM and 5 GB disk for the demo

3.2 Install the Packages

Open a terminal and install only what you need:

# If you only want cloud LLMs or Agents
python -m pip install -U langchain-oci

# If you also want the database features
python -m pip install -U langchain-oracledb

Both packages can live side-by-side; pip resolves the dependencies automatically.

3.3 Smallest Working Example

The code below does five things in one script: connect → load → split → embed → search.

import oracledb
from langchain_oracledb import (
    OracleDocLoader,
    OracleTextSplitter,
    OracleEmbeddings,
    OracleVS
)

# 1. Connect to the database
conn = oracledb.connect(
    user="vector_demo",
    password="W3lcome!",
    dsn="localhost/freepdb1"
)

# 2. Load a document (PDF, TXT, DOCX, HTML all work)
loader = OracleDocLoader(
    conn,
    table="demo_docs",
    file_path="whitepaper.pdf"
)
docs = loader.load()

# 3. Split into bite-size chunks
splitter = OracleTextSplitter(
    chunk_size=512,
    chunk_overlap=50
)
chunks = splitter.split_documents(docs)

# 4. Turn each chunk into a vector
embeddings = OracleEmbeddings(
    conn,
    provider="oci_generative_ai",
    model="cohere.embed-multilingual-v3"
)

# 5. Store vectors and run a search
vector_store = OracleVS.from_documents(
    chunks,
    embeddings,
    conn,
    table_name="demo_vs"
)
results = vector_store.similarity_search(
    "How does LangChain integrate with Oracle?",
    k=3
)
for r in results:
    print(r.page_content[:200], "\n---")

Run it. You should see three short paragraphs that answer the question, proving the pipeline works.

4. Deep Dive: Every Component Explained

4.1 OracleDocLoader – Put Any File into the Database

FAQ: Will big files crash my DB?
No. The loader streams the file in small pieces; RAM usage stays close to your chunk size.

4.2 OracleTextSplitter – Cut Text the Smart Way

• Token-based – good for English
• Sentence-based – uses Oracle Text under the hood to find sentence boundaries
• Fixed delimiter – for Chinese or logs: set separator="\n"

Example:

splitter = OracleTextSplitter(
    chunk_size=512,
    chunk_overlap=50,
    separator="\n",
    use_semantic_split=True  # turn on Oracle Text
)

4.3 OracleEmbeddings – One SQL Call, One Vector

Two modes:

Switching is one line:

embeddings = OracleEmbeddings(conn, provider="database", model="doc_model")

4.4 OracleSummary – Let the Database Write the TL;DR

OracleSummary uses the 23c package DBMS_SUMMARY.
Two styles:

• Extractive – pick key sentences
• Abstractive – call OCI Generative AI for a fresh paragraph

from langchain_oracledb import OracleSummary

summarizer = OracleSummary(
    conn,
    style="abstractive",
    max_tokens=150
)
summary = summarizer.summarize("demo_docs", filter="id=1")
print(summary)

4.5 OracleVS – Vector Store + Hybrid Search

OracleVS stores vectors in a column of type VECTOR.
Three index types:

Creating the index is automatic:

vector_store.create_index(
    index_type="HNSW",
    M=64,
    ef_construction=400
)

5. End-to-End Example: Build a Private Q&A Bot for Your Team

5.1 Scenario

• Data: 3 000 internal PDFs on a NAS
• Goal: employees ask questions in plain English and get answers plus page numbers
• Constraint: no data may leave the local network

5.2 Architecture

NAS ──► OracleDocLoader ──► OracleTextSplitter ──► OracleEmbeddings
                                    ▲
                                    │ SQL query
                                    ▼
User question ──► LangChain Retriever ──► OracleVS ──► LLM (OCI endpoint) ──► Answer

5.3 Skeleton Code

from langchain.chains import RetrievalQA
from langchain_oci import OCIGenerativeAI

# 1. Retriever
retriever = vector_store.as_retriever(search_kwargs={"k": 5})

# 2. Large language model
llm = OCIGenerativeAI(
    compartment_id="ocid1.compartment.oc1..xxx",
    endpoint="https://generativeai.us-ashburn-1.oci.oraclecloud.com",
    model="cohere.command-r-plus"
)

# 3. Question-answering chain
qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
answer = qa.run("How do I request a test server?")
print(answer)

5.4 Performance Checklist

6. Frequently Asked Questions

Q1: How is Oracle AI Vector Search different from other vector databases?

The key difference is hybrid search.
In one SQL statement you can mix vector similarity with traditional filters:

SELECT id, title,
       VECTOR_DISTANCE(embedding, :query_vec, COSINE) AS dist
FROM   demo_vs
WHERE  publish_date >= DATE '2024-01-01'
ORDER  BY dist
FETCH FIRST 10 ROWS ONLY;

Q2: I don’t have Oracle 23c yet—what now?

• Option A: use the free Oracle 23c Free, runs on Windows/Mac/Linux
• Option B: spin up an Always-Free ATP 23c in OCI (three clicks in the web console)

Q3: Will the new packages clash with the old langchain-community ones?

No. The old integrations have been archived.
Uninstall them to keep things clean:

pip uninstall langchain-community-oci

Q4: How do I swap the embedding model?

Change one argument:

embeddings = OracleEmbeddings(
    conn,
    provider="openai",
    model="text-embedding-3-small"
)

No table rebuild needed; the VECTOR column accepts any dimension size.

7. Troubleshooting in Seven Steps

1. Can Python reach the DB?
   python -c "import oracledb; oracledb.connect(...)"

2. Enough tablespace?
   SELECT * FROM user_ts_quotas;

3. Index built?
   SELECT index_name FROM user_indexes WHERE table_name='DEMO_VS';

4. Dimension mismatch?
   DESCRIBE demo_vs ➜ check the VECTOR column type

5. OCI 401 Unauthorized?
   oci iam region list ➜ verify your config file

6. Slow queries?
   EXPLAIN PLAN FOR SELECT ... ➜ look for VECTOR index usage

7. Out of memory?
   Lower chunk_size or fetch_size

8. What’s Next on the Roadmap

According to the official README, coming soon:

• Graph extensions – add relationship reasoning on top of vectors
• Persistent memory – store chat history directly in Oracle tables
• More agent blueprints – HR, ERP, CRM starter kits maintained by Oracle

9. Recap in Three Sentences

1. Install two packages: langchain-oci for models, langchain-oracledb for data.
2. Run five steps: connect → load → split → embed → query.
3. When something breaks, go to Section 7 and check the list.

Happy building—and feel free to open an issue in the official repository if you hit a wall.