OCI Generative AI Integration for LangChain

Integrate with OCI Generative AI embeddings using LangChain Python.

This doc will help you get started with OCI Generative AI embedding models. Oracle Cloud Infrastructure (OCI) Generative AI provides state-of-the-art embedding models for text and images, enabling semantic search, RAG, clustering, and cross-modal applications.

For detailed documentation, see the OCI Generative AI documentation and API reference.

Overview

Integration details

Class	Package	Serializable	Caches at params	Async	Downloads	Version
OCIGenAIEmbeddings	langchain-oci	beta	✅	✅

Model features

Text embeddings	Image embeddings	Multimodal	Batch operations	Async
✅	✅	✅	✅	✅

Setup

pip

pip install -qU langchain-oci oci

Set up authentication:

oci setup config

Instantiation

from langchain_oci import OCIGenAIEmbeddings

embeddings = OCIGenAIEmbeddings(
    model_id="cohere.embed-english-v3.0",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.compartment.oc1..your-compartment-id",
)

Usage

Build semantic search over technical documentation:

# Index code documentation
docs = [
    "authenticate() validates JWT tokens and returns user object",
    "authorize() checks user permissions for resource access",
    "audit_log() records user actions for compliance tracking"
]
doc_vectors = embeddings.embed_documents(docs)

# Search with natural language query
query = "How do I verify user identity?"
query_vector = embeddings.embed_query(query)

# Find most relevant documentation
import numpy as np
similarities = [
    np.dot(query_vector, doc_vec) /
    (np.linalg.norm(query_vector) * np.linalg.norm(doc_vec))
    for doc_vec in doc_vectors
]
best_match = docs[np.argmax(similarities)]
# Returns: "authenticate() validates JWT tokens..."

Use cases: Code search, documentation Q&A, log analysis, duplicate detection

Image Embeddings

Search visual assets with text queries using multimodal embeddings:

import numpy as np
from langchain_oci import OCIGenAIEmbeddings

embeddings = OCIGenAIEmbeddings(
    model_id="cohere.embed-v4.0",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.compartment.oc1..your-compartment-id",
)

# Index architecture diagrams
diagrams = ["microservices.png", "database_schema.png", "network.png"]
image_vectors = embeddings.embed_image_batch(diagrams)

# Search with text query
query_vector = embeddings.embed_query("database relationships")

# Find best match
similarities = [np.dot(query_vector, v) / (np.linalg.norm(query_vector) * np.linalg.norm(v))
                for v in image_vectors]
print(diagrams[np.argmax(similarities)])  # "database_schema.png"

Use cases: Technical diagram search, asset management, visual documentation retrieval

Available Models

Model	Dimensions	Type
cohere.embed-english-v3.0	1024	Text only
cohere.embed-multilingual-v3.0	1024	Text only
cohere.embed-v4.0	256-1536	Text + Image

See the OCI model catalog for all models.

RAG Example

from langchain_community.vectorstores import FAISS

# Create vector store
vectorstore = FAISS.from_documents(documents, embeddings)

# Search
results = vectorstore.similarity_search("your query", k=3)

Async

Async operations for production use:

query_vector = await embeddings.aembed_query("What is AI?")
doc_vectors = await embeddings.aembed_documents(["Doc 1", "Doc 2"])

API Reference

For detailed documentation of all OCIGenAIEmbeddings features and configurations, head to the API reference.

Related

• OCI Provider Overview
• ChatOCIGenAI
• Embeddings Guide
• RAG Tutorial

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