Log, Trace, and Monitor

When building apps or agents using Langchain, you end up making multiple API calls to fulfill a single user request. However, these requests are not chained when you want to analyse them. With Portkey, all the embeddings, completion, and other requests from a single user request will get logged and traced to a common ID, enabling you to gain full visibility of user interactions.

This notebook serves as a step-by-step guide on how to log, trace, and monitor Langchain LLM calls using Portkey in your Langchain app.

First, let’s import Portkey, OpenAI, and Agent tools

import os

from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.llms import OpenAI
from langchain.utilities import Portkey

Paste your OpenAI API key below. (You can find it here)

os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"

Get Portkey API Key

1. Sign up for Portkey here
2. On your dashboard, click on the profile icon on the top left, then click on “Copy API Key”
3. Paste it below

PORTKEY_API_KEY = "<PORTKEY_API_KEY>"  # Paste your Portkey API Key here

Set Trace ID

1. Set the trace id for your request below
2. The Trace ID can be common for all API calls originating from a single request

TRACE_ID = "portkey_langchain_demo"  # Set trace id here

Generate Portkey Headers

headers = Portkey.Config(
    api_key=PORTKEY_API_KEY,
    trace_id=TRACE_ID,
)

Run your agent as usual. The only change is that we will include the above headers in the request now.

llm = OpenAI(temperature=0, headers=headers)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)

# Let's test it out!
agent.run(
    "What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?"
)

How Logging & Tracing Works on Portkey

Logging - Sending your request through Portkey ensures that all of the requests are logged by default - Each request log contains timestamp, model name, total cost, request time, request json, response json, and additional Portkey features

Tracing - Trace id is passed along with each request and is visibe on the logs on Portkey dashboard - You can also set a distinct trace id for each request if you want - You can append user feedback to a trace id as well. More info on this here

Advanced LLMOps Features - Caching, Tagging, Retries

In addition to logging and tracing, Portkey provides more features that add production capabilities to your existing workflows:

Caching

Respond to previously served customers queries from cache instead of sending them again to OpenAI. Match exact strings OR semantically similar strings. Cache can save costs and reduce latencies by 20x.

Retries

Automatically reprocess any unsuccessful API requests upto 5 times. Uses an exponential backoff strategy, which spaces out retry attempts to prevent network overload.

Feature	Config Key	Value (Type)
🔁 Automatic Retries	retry_count	integer [1,2,3,4,5]
🧠 Enabling Cache	cache	simple OR semantic

Tagging

Track and audit ach user interaction in high detail with predefined tags.

Tag	Config Key	Value (Type)
User Tag	user	string
Organisation Tag	organisation	string
Environment Tag	environment	string
Prompt Tag (version/id/string)	prompt	string

Code Example With All Features

headers = Portkey.Config(
    # Mandatory
    api_key="<PORTKEY_API_KEY>",
    # Cache Options
    cache="semantic",
    cache_force_refresh="True",
    cache_age=1729,
    # Advanced
    retry_count=5,
    trace_id="langchain_agent",
    # Metadata
    environment="production",
    user="john",
    organisation="acme",
    prompt="Frost",
)

llm = OpenAI(temperature=0.9, headers=headers)

print(llm("Two roads diverged in the yellow woods"))