nirikshaai 0.0.1.dev5

NirikshaAI Python SDK — full-stack observability via OpenTelemetry

NirikshaAI Python SDK

The official Python SDK for NirikshaAI — AI-native observability for logs, metrics, traces, and LLM/agent telemetry.

Under the hood this is a thin wrapper around the OpenTelemetry Python SDK. It configures OTLP exporters, wires up auto-instrumentation, and exposes NirikshaAI-specific helpers (evals, prompt management). You can use the standard OTEL API at any time alongside it.

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Table of Contents

• Installation
• Quick Start
• LLM Applications
• Configuration Reference
• Auto-Instrumented Libraries
• Using the Standard OTEL API
• Logging Integration
• Eval Submission
• Prompt Management
• Framework Examples
• Environment Variables

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Installation

pip install nirikshaai

To include automatic instrumentation for common web frameworks and infrastructure libraries (Django, Flask, FastAPI, SQLAlchemy, Redis, etc.):

pip install "nirikshaai[all]"

To also include automatic instrumentation for LLM libraries (OpenAI, Anthropic, LangChain, etc.):

pip install "nirikshaai[all,llm]"

Minimum Python version: 3.9

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Quick Start

Two lines is all it takes to start sending traces, metrics, and logs to NirikshaAI from any Python service:

import nirikshaai

nirikshaai.init(
    endpoint="https://app.niriksha.ai",
    otlp_endpoint="grpc-ingest.niriksha.ai:443",  # SaaS: OTLP gateway is separate from the REST API
    api_key="nai_...",
    service_name="my-service",
)
# That's it — traces, metrics, and logs now flow to NirikshaAI

Your api_key is a project-scoped key (prefixed nai_). It encodes which org and project your telemetry belongs to — you do not need to pass org or project IDs separately.

---

LLM Applications

Enable LLM instrumentation with the enable_llm flag. When set, the SDK automatically patches supported LLM client libraries so that every API call creates a standard OpenTelemetry span with token counts, model name, and (optionally) prompt/completion content.

import nirikshaai

nirikshaai.init(
    endpoint="https://app.niriksha.ai",
    api_key="nai_...",
    service_name="my-ai-service",
    enable_llm=True,        # Auto-instruments OpenAI, Anthropic, LangChain, etc.
    capture_prompts=False,  # Set True only if PII controls are in place
)

import openai  # auto-instrumented — every call creates an OTEL span

client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Summarise this document."}],
)
# A LLM span is emitted automatically — no extra code needed

Privacy note: capture_prompts=False is the default. Setting it to True will capture the full text of prompts and completions as span attributes (llm.input.messages / llm.output.messages). Only enable this if you have appropriate data governance and PII controls in place.

---

Configuration Reference

All parameters are passed to nirikshaai.init().

Parameter	Type	Default	Description
endpoint	str	(required)	NirikshaAI REST/control-plane base URL. SaaS: https://app.niriksha.ai. Private Cloud: https://niriksha.internal
api_key	str	(required)	Project-scoped API key with nai_ prefix
service_name	str	"my-service"	Value of the service.name OTEL resource attribute
environment	str	"production"	Value of the deployment.environment OTEL resource attribute
enable_metrics	bool	True	Export OTLP metrics on a 60-second periodic interval
enable_logs	bool	True	Attach an OTLP log handler to the root Python logging logger
enable_llm	bool	False	Auto-instrument supported LLM client libraries (opt-in)
capture_prompts	bool	False	Capture llm.input/output.messages span attributes (PII risk)
otlp_port	int	4317	OTLP gRPC port. Ignored when otlp_endpoint is set.
otlp_endpoint	str	None	Override the gRPC OTLP address (host:port, no scheme). SaaS: grpc-ingest.niriksha.ai:443
insecure	bool	False	Send gRPC without TLS. Use when TLS is terminated at an ingress.
tls_skip_verify	bool	False	Use TLS but skip server certificate validation. Dev/staging only.
ca_cert_file	str	None	Path to a PEM CA certificate for verifying the gateway TLS cert.
disable_instrumentations	list[str]	[]	Library names to skip during auto-instrumentation (e.g. ["django"])

Private Cloud examples

# TLS with trusted certificate (system roots)
nirikshaai.init(
    endpoint="https://niriksha.internal",
    otlp_endpoint="niriksha.internal:4317",
    api_key="nai_...",
)

# Custom / self-signed CA
nirikshaai.init(
    endpoint="https://niriksha.internal",
    otlp_endpoint="niriksha.internal:4317",
    api_key="nai_...",
    ca_cert_file="/etc/ssl/niriksha-ca.crt",
)

# Skip TLS verification (dev/staging only)
nirikshaai.init(
    endpoint="https://niriksha.internal",
    otlp_endpoint="niriksha.internal:4317",
    api_key="nai_...",
    tls_skip_verify=True,
)

# Plaintext gRPC (TLS terminated at ingress)
nirikshaai.init(
    endpoint="https://niriksha.internal",
    otlp_endpoint="niriksha.internal:4317",
    api_key="nai_...",
    insecure=True,
)

---

Auto-Instrumented Libraries

General (always enabled when the package is installed)

Library	What's captured
django	HTTP requests and responses, middleware timing, view names
flask	Routes, request timing, status codes
fastapi	Routes, request timing, async support, response codes
starlette	Routes, middleware, ASGI lifecycle
requests	Outbound HTTP calls, method, URL, status code
urllib3	Low-level HTTP connections and retries
aiohttp	Async HTTP client calls
grpc	gRPC server and client calls, method names
sqlalchemy	SQL queries and transaction timing (values are not captured)
psycopg2	PostgreSQL query timing and operation type
asyncpg	Async PostgreSQL query timing
pymongo	MongoDB operation type, collection, database
redis	Redis command name (values are not captured)
celery	Task name, queue, execution state, retry count

LLM (requires enable_llm=True)

Library	What's captured
openai	Chat completions, embeddings, model name, token usage (prompt + completion)
anthropic	Messages API calls, model name, token usage
langchain	Chain invocations, individual tool call spans, retrieval steps
llama_index	Query engine spans, retrieval spans, synthesiser spans
mistralai	Chat completions, model name, token usage
google-generativeai	Gemini API calls, model name, token usage

---

Using the Standard OTEL API

nirikshaai.init() configures the global OpenTelemetry providers. You can use the standard OTEL API directly at any point after calling init():

from opentelemetry import trace, metrics

tracer = trace.get_tracer("my-service")
meter  = metrics.get_meter("my-service")

# Custom span
with tracer.start_as_current_span("process-order") as span:
    span.set_attribute("order.id", order_id)
    span.set_attribute("order.total", total)
    span.set_attribute("order.currency", "USD")
    result = fulfil_order(order_id)
    span.set_attribute("order.status", result.status)

# Custom counter
order_counter = meter.create_counter(
    "orders.processed",
    description="Number of orders processed",
    unit="{order}",
)
order_counter.add(1, {"status": "success", "region": "us-east"})

# Custom histogram (useful for latency)
latency_histogram = meter.create_histogram(
    "order.processing.duration",
    description="Time to process an order",
    unit="ms",
)
latency_histogram.record(142.5, {"order_type": "express"})

---

Logging Integration

When enable_logs=True (the default), the SDK attaches an OTLP log handler to Python's root logger. All log records emitted through the standard logging module are automatically exported to NirikshaAI, with trace and span IDs attached so log lines are correlated to their parent trace.

import logging

logger = logging.getLogger("my-service")

# Structured extra fields are forwarded as log record attributes
logger.info("Order processed", extra={"order_id": "abc123", "amount": 99.99})
logger.warning("Inventory low", extra={"sku": "WIDGET-001", "remaining": 3})
logger.error("Payment failed", extra={"error_code": "CARD_DECLINED", "order_id": "abc123"})

# Exception info is captured automatically
try:
    process_payment(order)
except PaymentError as exc:
    logger.exception("Unhandled payment error", extra={"order_id": order.id})

Logs are exported in batches over OTLP gRPC to the same endpoint as traces and metrics.

---

Eval Submission

Submit evaluation results for LLM responses. Evals are linked to a specific trace so results appear in the NirikshaAI LLM Traces view alongside the originating span.

Single eval

import nirikshaai

nirikshaai.submit_eval(
    trace_id="your-otel-trace-id",   # hex trace ID from the OTEL span context
    metric_name="faithfulness",
    score=0.92,                       # float in [0, 1]
    label="pass",                     # "pass" | "fail" | any custom label
    explanation="Response accurately reflects the source documents",
    eval_type="llm_judge",            # "llm_judge" | "rule_based" | "human"
)

Batch eval

nirikshaai.submit_evals_batch([
    {
        "trace_id": "abc123...",
        "metric_name": "toxicity",
        "score": 0.02,
        "label": "pass",
        "eval_type": "rule_based",
    },
    {
        "trace_id": "abc123...",
        "metric_name": "relevance",
        "score": 0.87,
        "label": "pass",
        "eval_type": "llm_judge",
        "explanation": "Response addresses the user's question directly",
    },
])

Obtaining the trace ID from an active span

from opentelemetry import trace

with tracer.start_as_current_span("llm-call") as span:
    ctx = span.get_span_context()
    trace_id = format(ctx.trace_id, "032x")  # 32-character hex string

    response = client.chat.completions.create(...)

    # Submit eval referencing this trace
    nirikshaai.submit_eval(
        trace_id=trace_id,
        metric_name="faithfulness",
        score=judge(response),
        label="pass",
    )

---

Prompt Management

Fetch versioned prompt templates from the NirikshaAI prompt vault. Variable substitution is performed server-side before the rendered text is returned.

Fetch the latest deployed version

prompt = nirikshaai.get_prompt("customer-support-system")
print(prompt["text"])           # Rendered prompt text
print(prompt["version"])        # Active version number
print(prompt["name"])

Fetch a specific version with variable substitution

prompt = nirikshaai.get_prompt(
    "product-description",
    version=3,
    variables={
        "product_name": "Widget Pro",
        "category": "Electronics",
        "price": "$49.99",
    },
)
print(prompt["text"])  # All {{variables}} replaced server-side

List all available prompts

prompts = nirikshaai.list_prompts()
for p in prompts:
    print(f"{p['name']} (v{p['version']}) — {p['description']}")

---

Framework Examples

Django

Call nirikshaai.init() in your settings.py (or in an AppConfig.ready() method). Auto-instrumentation is applied globally once at startup.

# myproject/settings.py
import nirikshaai

nirikshaai.init(
    endpoint="https://app.niriksha.ai",
    api_key="nai_...",
    service_name="django-app",
    environment="production",
)

INSTALLED_APPS = [
    # ... your apps
]

Flask

from flask import Flask
import nirikshaai

# init() before creating the Flask app
nirikshaai.init(
    endpoint="https://app.niriksha.ai",
    api_key="nai_...",
    service_name="flask-app",
)

app = Flask(__name__)

@app.route("/orders/<order_id>")
def get_order(order_id):
    # This route is automatically traced — no extra code needed
    order = db.get_order(order_id)
    return {"id": order_id, "status": order.status}

FastAPI

from fastapi import FastAPI
import nirikshaai

nirikshaai.init(
    endpoint="https://app.niriksha.ai",
    api_key="nai_...",
    service_name="fastapi-app",
)

app = FastAPI()

@app.get("/orders/{order_id}")
async def get_order(order_id: str):
    # Async routes are fully supported — traced automatically
    order = await db.get_order(order_id)
    return {"id": order_id, "status": order.status}

Celery

from celery import Celery
import nirikshaai

nirikshaai.init(
    endpoint="https://app.niriksha.ai",
    api_key="nai_...",
    service_name="celery-worker",
)

app = Celery("tasks", broker="redis://localhost:6379/0")

@app.task
def send_notification(user_id: str, message: str):
    # Every task invocation is automatically traced
    # The task name, queue, and retry count appear as span attributes
    notify(user_id, message)

LLM application with eval feedback loop

import nirikshaai
from opentelemetry import trace

nirikshaai.init(
    endpoint="https://app.niriksha.ai",
    api_key="nai_...",
    service_name="rag-service",
    enable_llm=True,
)

import openai

client = openai.OpenAI()
tracer = trace.get_tracer("rag-service")

def answer_question(question: str) -> str:
    with tracer.start_as_current_span("answer-question") as span:
        span.set_attribute("question.length", len(question))

        # LLM call is auto-instrumented — a child span is created
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": question},
            ],
        )
        answer = response.choices[0].message.content

        # Record trace ID for eval submission
        ctx = span.get_span_context()
        trace_id = format(ctx.trace_id, "032x")

    # Async eval — run your judge independently and post the result
    score = run_faithfulness_judge(question, answer)
    nirikshaai.submit_eval(
        trace_id=trace_id,
        metric_name="faithfulness",
        score=score,
        label="pass" if score >= 0.8 else "fail",
        eval_type="llm_judge",
    )

    return answer

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Environment Variables

You can configure the SDK through standard OpenTelemetry environment variables instead of (or in addition to) passing arguments to init(). Environment variables take lower precedence than explicit arguments.

Variable	Equivalent init() parameter
OTEL_SERVICE_NAME	service_name
OTEL_EXPORTER_OTLP_ENDPOINT	Derived from endpoint + otlp_port
OTEL_EXPORTER_OTLP_HEADERS	Set to X-API-Key=nai_your_key
OTEL_RESOURCE_ATTRIBUTES	Use to set deployment.environment and other attributes

Example shell setup:

export OTEL_SERVICE_NAME=my-service
export OTEL_EXPORTER_OTLP_ENDPOINT=http://your-nirikshaai:4317
export OTEL_EXPORTER_OTLP_HEADERS="X-API-Key=nai_your_key"
export OTEL_RESOURCE_ATTRIBUTES="deployment.environment=production"

Then call init() with no arguments (the SDK will read the environment):

import nirikshaai
nirikshaai.init()

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License

Apache 2.0 — see LICENSE.