Documentation Index
Fetch the complete documentation index at: https://docs.rotavision.com/llms.txt
Use this file to discover all available pages before exploring further.
Overview
Integrate Rotavision with LangChain to add fairness monitoring, explainability, and reliability tracking to your LLM applications.
Installation
pip install rotavision langchain
Sankalp as LangChain LLM
Use Sankalp as your LangChain LLM for unified routing and monitoring:
from langchain.llms import BaseLLM
from rotavision.integrations.langchain import SankalpLLM
# Create Sankalp-backed LLM
llm = SankalpLLM(
api_key="rv_live_...",
model="gpt-5-mini",
routing={
"optimize": "cost",
"data_residency": "india"
}
)
# Use with LangChain
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
prompt = PromptTemplate(
input_variables=["topic"],
template="Write a brief summary about {topic}"
)
chain = LLMChain(llm=llm, prompt=prompt)
result = chain.run("AI adoption in India")
Callback Handler for Monitoring
Add Guardian monitoring to any LangChain app:
from langchain.callbacks import BaseCallbackHandler
from rotavision.integrations.langchain import GuardianCallbackHandler
# Create callback handler
guardian_callback = GuardianCallbackHandler(
api_key="rv_live_...",
monitor_id="mon_abc123"
)
# Use with any LangChain component
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(
callbacks=[guardian_callback]
)
# All LLM calls are automatically logged to Guardian
response = llm.predict("Hello, world!")
RAG with Fairness Monitoring
Monitor your RAG pipeline for fairness:
from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma
from rotavision.integrations.langchain import FairnessMonitor
# Create fairness monitor
fairness_monitor = FairnessMonitor(
api_key="rv_live_...",
protected_attributes=["language", "region"]
)
# Wrap your retriever
monitored_retriever = fairness_monitor.wrap_retriever(
retriever=vectorstore.as_retriever()
)
# Use in RAG chain
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=monitored_retriever
)
# Queries are analyzed for fairness across protected groups
result = qa_chain.run("What are the loan eligibility criteria?")
Agent Monitoring
Monitor LangChain agents:
from langchain.agents import initialize_agent, Tool
from rotavision.integrations.langchain import AgentMonitor
agent_monitor = AgentMonitor(
api_key="rv_live_...",
log_thoughts=True,
log_actions=True
)
agent = initialize_agent(
tools=tools,
llm=llm,
agent="zero-shot-react-description",
callbacks=[agent_monitor]
)
# Agent reasoning and actions are logged
result = agent.run("Research the latest EV sales in India")
LCEL Integration
Works with LangChain Expression Language:
from langchain.schema.runnable import RunnablePassthrough
from rotavision.integrations.langchain import rotavision_middleware
# Add Rotavision middleware to any chain
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| rotavision_middleware(api_key="rv_live_...", monitor_id="mon_123")
| prompt
| llm
| output_parser
)
