oracle · mrDzurb · Jul 8, 2025 · Jul 8, 2025
diff --git a/README.md b/README.md
@@ -1,18 +1,154 @@
-# 🦜️🔗 LangChain 🤝 Oracle Cloud Infrastructure (OCI)
+# 🦜️🔗 LangChain 🤝 Oracle
 
-This repository provides LangChain components for various OCI services. It aims to replace and expand upon the existing LangChain OCI components found in the `langchain-community` package in the LangChain repository.
+Welcome to the official repository for LangChain integration with [Oracle Cloud Infrastructure (OCI)](https://cloud.oracle.com/). This library provides native LangChain components for interacting with Oracle's AI services—combining support for **OCI Generative AI** and **OCI Data Science**.
 
 ## Features
 
 - **LLMs**: Includes LLM classes for OCI services like [Generative AI](https://cloud.oracle.com/ai-services/generative-ai) and [ModelDeployment Endpoints](https://cloud.oracle.com/ai-services/model-deployment), allowing you to leverage their language models within LangChain.
 - **Agents**: Includes Runnables to support [Oracle Generative AI Agents](https://www.oracle.com/artificial-intelligence/generative-ai/agents/), allowing you to leverage Generative AI Agents within LangChain and LangGraph.
 - **More to come**: This repository will continue to expand and offer additional components for various OCI services as development progresses.
 
-**Note**: This repository will replace all OCI integrations currently present in the `langchain-community` package. Users are encouraged to migrate to this repository as soon as possible.
+> This project merges and replaces earlier OCI integrations from the `langchain-community` repository and unifies contributions from Oracle's GenAI and Data Science teams.
+> All integrations in this package assume that you have the credentials setup to connect with oci services.
+
+---
 
 ## Installation
 
-You can install the `langchain-oracle` package from PyPI.
+
+```bash
+pip install -U langchain-oci
+```
+
+---
+
+## Quick Start
+
+This repository includes two main integration categories:
+
+- [OCI Generative AI](#oci-generative-ai-examples)
+- [OCI Data Science (Model Deployment)](#oci-data-science-model-deployment-examples)
+
+
+---
+
+## OCI Generative AI Examples
+
+### 1. Use a Chat Model
+
+`ChatOCIGenAI` class exposes chat models from OCI Generative AI.
+
+```python
+from langchain_oci import ChatOCIGenAI
+
+llm = ChatOCIGenAI()
+llm.invoke("Sing a ballad of LangChain.")
+```
+
+### 2. Use a Completion Model
+`OCIGenAI` class exposes LLMs from OCI Generative AI.
+
+```python
+from langchain_oci import OCIGenAI
+
+llm = OCIGenAI()
+llm.invoke("The meaning of life is")
+```
+
+### 3. Use an Embedding Model
+`OCIGenAIEmbeddings` class exposes embeddings from OCI Generative AI.
+
+```python
+from langchain_oci import OCIGenAIEmbeddings
+
+embeddings = OCIGenAIEmbeddings()
+embeddings.embed_query("What is the meaning of life?")
+```
+
+
+## OCI Data Science Model Deployment Examples
+
+### 1. Use a Chat Model
+
+You may instantiate the OCI Data Science model with the generic `ChatOCIModelDeployment` or framework specific class like `ChatOCIModelDeploymentVLLM`.
+
+```python
+from langchain_oci.chat_models import ChatOCIModelDeployment, ChatOCIModelDeploymentVLLM
+
+# Create an instance of OCI Model Deployment Endpoint
+# Replace the endpoint uri with your own
+endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"
+
+messages = [
+    (
+        "system",
+        "You are a helpful assistant that translates English to French. Translate the user sentence.",
+    ),
+    ("human", "I love programming."),
+]
+
+chat = ChatOCIModelDeployment(
+    endpoint=endpoint,
+    streaming=True,
+    max_retries=1,
+    model_kwargs={
+        "temperature": 0.2,
+        "max_tokens": 512,
+    },  # other model params...
+    default_headers={
+        "route": "/v1/chat/completions",
+        # other request headers ...
+    },
+)
+chat.invoke(messages)
+
+chat_vllm = ChatOCIModelDeploymentVLLM(endpoint=endpoint)
+chat_vllm.invoke(messages)
+```
+
+### 2. Use a Completion Model
+You may instantiate the OCI Data Science model with `OCIModelDeploymentLLM` or `OCIModelDeploymentVLLM`.
+
+```python
+from langchain_oci.llms import OCIModelDeploymentLLM, OCIModelDeploymentVLLM
+
+# Create an instance of OCI Model Deployment Endpoint
+# Replace the endpoint uri and model name with your own
+endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"
+
+llm = OCIModelDeploymentLLM(
+    endpoint=endpoint,
+    model="odsc-llm",
+)
+llm.invoke("Who is the first president of United States?")
+
+vllm = OCIModelDeploymentVLLM(
+    endpoint=endpoint,
+)
+vllm.invoke("Who is the first president of United States?")
+```
+
+### 3. Use an Embedding Model
+You may instantiate the OCI Data Science model with the `OCIModelDeploymentEndpointEmbeddings`.
+
+```python
+from langchain_oci.embeddings import OCIModelDeploymentEndpointEmbeddings
+
+# Create an instance of OCI Model Deployment Endpoint
+# Replace the endpoint uri with your own
+endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"
+
+embeddings = OCIModelDeploymentEndpointEmbeddings(
+    endpoint=endpoint,
+)
+
+query = "Hello World!"
+embeddings.embed_query(query)
+
+documents = ["This is a sample document", "and here is another one"]
+embeddings.embed_documents(documents)
+```
+
 
 ## Contributing
 
@@ -27,8 +163,4 @@ Please consult the [security guide](./SECURITY.md) for our responsible security
 Copyright (c) 2025 Oracle and/or its affiliates.
 
 Released under the Universal Permissive License v1.0 as shown at
-<https://oss.oracle.com/licenses/upl/>.
-
-release:
-/github subscribe langchain-ai/langchain-oci releases workflows:{name:"release"}
-/github unsubscribe langchain-ai/langchain-oci issues pulls commits deployments
+<https://oss.oracle.com/licenses/upl/>
diff --git a/libs/oci/README.md b/libs/oci/README.md
@@ -9,9 +9,21 @@ pip install -U langchain-oci
 ```
 All integrations in this package assume that you have the credentials setup to connect with oci services.
 
-## Chat Models
+---
 
-### OCI Generative AI
+## Quick Start
+
+This repository includes two main integration categories:
+
+- [OCI Generative AI](#oci-generative-ai-examples)
+- [OCI Data Science (Model Deployment)](#oci-data-science-model-deployment-examples)
+
+
+---
+
+## OCI Generative AI Examples
+
+### 1. Use a Chat Model
 
 `ChatOCIGenAI` class exposes chat models from OCI Generative AI.
 
@@ -22,9 +34,32 @@ llm = ChatOCIGenAI()
 llm.invoke("Sing a ballad of LangChain.")
 ```
 
-### OCI Data Science
+### 2. Use a Completion Model
+`OCIGenAI` class exposes LLMs from OCI Generative AI.
+
+```python
+from langchain_oci import OCIGenAI
+
+llm = OCIGenAI()
+llm.invoke("The meaning of life is")
+```
 
-You may also instantiate the OCI Data Science model with the generic `ChatOCIModelDeployment` or framework specific class like `ChatOCIModelDeploymentVLLM`.
+### 3. Use an Embedding Model
+`OCIGenAIEmbeddings` class exposes embeddings from OCI Generative AI.
+
+```python
+from langchain_oci import OCIGenAIEmbeddings
+
+embeddings = OCIGenAIEmbeddings()
+embeddings.embed_query("What is the meaning of life?")
+```
+
+
+## OCI Data Science Model Deployment Examples
+
+### 1. Use a Chat Model
+
+You may instantiate the OCI Data Science model with the generic `ChatOCIModelDeployment` or framework specific class like `ChatOCIModelDeploymentVLLM`.
 
 ```python
 from langchain_oci.chat_models import ChatOCIModelDeployment, ChatOCIModelDeploymentVLLM
@@ -60,22 +95,30 @@ chat_vllm = ChatOCIModelDeploymentVLLM(endpoint=endpoint)
 chat_vllm.invoke(messages)
 ```
 
-## Embeddings
+### 2. Use a Completion Model
+You may instantiate the OCI Data Science model with `OCIModelDeploymentLLM` or `OCIModelDeploymentVLLM`.
 
-### OCI Generative AI
+```python
+from langchain_oci.llms import OCIModelDeploymentLLM, OCIModelDeploymentVLLM
 
-`OCIGenAIEmbeddings` class exposes embeddings from OCI Generative AI.
+# Create an instance of OCI Model Deployment Endpoint
+# Replace the endpoint uri and model name with your own
+endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"
 
-```python
-from langchain_oci import OCIGenAIEmbeddings
+llm = OCIModelDeploymentLLM(
+    endpoint=endpoint,
+    model="odsc-llm",
+)
+llm.invoke("Who is the first president of United States?")
 
-embeddings = OCIGenAIEmbeddings()
-embeddings.embed_query("What is the meaning of life?")
+vllm = OCIModelDeploymentVLLM(
+    endpoint=endpoint,
+)
+vllm.invoke("Who is the first president of United States?")
 ```
 
-### OCI Data Science
-
-You may also instantiate the OCI Data Science model with the `OCIModelDeploymentEndpointEmbeddings`.
+### 3. Use an Embedding Model
+You may instantiate the OCI Data Science model with the `OCIModelDeploymentEndpointEmbeddings`.
 
 ```python
 from langchain_oci.embeddings import OCIModelDeploymentEndpointEmbeddings
@@ -94,39 +137,3 @@ embeddings.embed_query(query)
 documents = ["This is a sample document", "and here is another one"]
 embeddings.embed_documents(documents)
 ```
-
-## LLMs
-
-### OCI Generative AI
-
-`OCIGenAI` class exposes LLMs from OCI Generative AI.
-
-```python
-from langchain_oci import OCIGenAI
-
-llm = OCIGenAI()
-llm.invoke("The meaning of life is")
-```
-
-### OCI Data Science
-
-You may also instantiate the OCI Data Science model with `OCIModelDeploymentLLM` or `OCIModelDeploymentVLLM`.
-
-```python
-from langchain_oci.llms import OCIModelDeploymentLLM, OCIModelDeploymentVLLM
-
-# Create an instance of OCI Model Deployment Endpoint
-# Replace the endpoint uri and model name with your own
-endpoint = "https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict"
-
-llm = OCIModelDeploymentLLM(
-    endpoint=endpoint,
-    model="odsc-llm",
-)
-llm.invoke("Who is the first president of United States?")
-
-vllm = OCIModelDeploymentVLLM(
-    endpoint=endpoint,
-)
-vllm.invoke("Who is the first president of United States?")
-```