LangChain

Use AJ STUDIOZ Cloud Infra with LangChain for AI pipelines and agents

Overview

LangChain works seamlessly with AJ STUDIOZ Cloud Infra via the OpenAI-compatible API. All 32+ models are hosted in our cloud — no local installation needed. Use ChatOpenAI and OpenAIEmbeddings by simply pointing base_url to https://api.ajstudioz.co.in/v1.

Installation

pip install langchain langchain-openai

Chat Models

Basic Chat

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gemma3:27b",
    base_url="https://api.ajstudioz.co.in/v1",
    api_key="YOUR_API_KEY",
    temperature=0.7
)

response = llm.invoke("What is the capital of Japan?")
print(response.content)

Streaming

Streaming

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="deepseek-v3.2",
    base_url="https://api.ajstudioz.co.in/v1",
    api_key="YOUR_API_KEY",
    streaming=True
)

for chunk in llm.stream("Explain transformers in machine learning"):
    print(chunk.content, end="", flush=True)

---

Chat Chains with Prompts

Chat Chain

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(
    model="gemma3:27b",
    base_url="https://api.ajstudioz.co.in/v1",
    api_key="YOUR_API_KEY"
)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an expert in {domain}. Answer concisely."),
    ("user", "{question}")
])

chain = prompt | llm | StrOutputParser()

result = chain.invoke({
    "domain": "machine learning",
    "question": "What is gradient descent?"
})
print(result)

---

Embeddings

All embedding models are cloud-hosted. No local GPU required.

Embeddings

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="gemma3:4b",
    base_url="https://api.ajstudioz.co.in/v1",
    api_key="YOUR_API_KEY"
)

vector = embeddings.embed_query("Hello from AJ STUDIOZ!")
print(f"Dimensions: {len(vector)}")

docs = ["Document one", "Document two", "Document three"]
vectors = embeddings.embed_documents(docs)
print(f"Embedded {len(vectors)} documents")

---

RAG Pipeline

Full RAG Pipeline

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

BASE_URL = "https://api.ajstudioz.co.in/v1"
API_KEY = "YOUR_API_KEY"

llm = ChatOpenAI(model="gemma3:27b", base_url=BASE_URL, api_key=API_KEY)
embeddings = OpenAIEmbeddings(model="gemma3:4b", base_url=BASE_URL, api_key=API_KEY)

docs = [
    "AJ STUDIOZ Cloud Infra hosts 32+ AI models in the cloud.",
    "Available models include Gemma, Qwen, Kimi, DeepSeek, GLM, and Mistral families.",
    "Authenticate using a Bearer token in the Authorization header.",
    "Ollama-compatible base URL: https://api.ajstudioz.co.in",
    "OpenAI-compatible base URL: https://api.ajstudioz.co.in/v1",
]

vectorstore = FAISS.from_texts(docs, embedding=embeddings)
retriever = vectorstore.as_retriever()

prompt = ChatPromptTemplate.from_template("""
Answer based only on the following context:
{context}

Question: {question}
""")

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

answer = chain.invoke("What APIs does AJ STUDIOZ support?")
print(answer)

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Next Steps

LlamaIndex Integration

Use LlamaIndex with AJ STUDIOZ

Function Calling Guide

Deep dive into tool calling