OpenTelemetry instrumentation for Langchain
Project description
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
- Install traceAI LangChain
pip install traceAI-langchain
- 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()
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