traceAI-langchain 0.1.9

OpenTelemetry instrumentation for Langchain

LangChain OpenTelemetry Integration

Overview

This integration provides support for using OpenTelemetry with the LangChain framework. It enables tracing and monitoring of applications built with LangChain.

Installation

1. Install traceAI LangChain

pip install traceAI-langchain

2. Install LangChain OpenAI

pip install langchain-openai

Set Environment Variables

Set up your environment variables to authenticate with FutureAGI.

import os

os.environ["FI_API_KEY"] = FI_API_KEY
os.environ["FI_SECRET_KEY"] = FI_SECRET_KEY
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

Quickstart

Register Tracer Provider

Set up the trace provider to establish the observability pipeline. The trace provider:

from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="langchain_app",
    project_version_name="v1",  
    session_name="chat-bot"
)

Configure LangChain Instrumentation

Instrument the LangChain client to enable telemetry collection. This step ensures that all interactions with the LangChain SDK are tracked and monitored.

from traceai_langchain import LangChainInstrumentor

LangChainInstrumentor().instrument(tracer_provider=trace_provider)

Create LangChain Components

Set up your LangChain client with built-in observability.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_template("{x} {y} {z}?").partial(x="why is", z="blue")
chain = prompt | ChatOpenAI(model_name="gpt-3.5-turbo")

def run_chain():
    try:
        result = chain.invoke({"y": "sky"})
        print(f"Response: {result}")
    except Exception as e:
        print(f"Error executing chain: {e}")

if __name__ == "__main__":
    run_chain()