Fiddler is the pioneer in enterprise Generative and Predictive system ops, offering a unified platform that enables Data Science, MLOps, Risk, Compliance, Analytics, and other LOB teams to monitor, explain, analyze, and improve ML deployments at enterprise scale.

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1. Installation and Setup

#!pip install langchain langchain-community langchain-openai fiddler-client

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2. Fiddler connection details

Before you can add information about your model with Fiddler
  1. The URL you’re using to connect to Fiddler
  2. Your organization ID
  3. Your authorization token
These can be found by navigating to the Settings page of your Fiddler environment. 
URL = ""  # Your Fiddler instance URL, Make sure to include the full URL (including https://). For example: https://demo.fiddler.ai
ORG_NAME = ""
AUTH_TOKEN = ""  # Your Fiddler instance auth token

# Fiddler project and model names, used for model registration
PROJECT_NAME = ""
MODEL_NAME = ""  # Model name in Fiddler

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3. Create a fiddler callback handler instance

from langchain_community.callbacks.fiddler_callback import FiddlerCallbackHandler

fiddler_handler = FiddlerCallbackHandler(
    url=URL,
    org=ORG_NAME,
    project=PROJECT_NAME,
    model=MODEL_NAME,
    api_key=AUTH_TOKEN,
)

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Example 1 : Basic Chain

from langchain_core.output_parsers import StrOutputParser
from langchain_openai import OpenAI

# Note : Make sure openai API key is set in the environment variable OPENAI_API_KEY
llm = OpenAI(temperature=0, streaming=True, callbacks=[fiddler_handler])
output_parser = StrOutputParser()

chain = llm | output_parser

# Invoke the chain. Invocation will be logged to Fiddler, and metrics automatically generated
chain.invoke("How far is moon from earth?")

# Few more invocations
chain.invoke("What is the temperature on Mars?")
chain.invoke("How much is 2 + 200000?")
chain.invoke("Which movie won the oscars this year?")
chain.invoke("Can you write me a poem about insomnia?")
chain.invoke("How are you doing today?")
chain.invoke("What is the meaning of life?")

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Example 2 : Chain with prompt templates

from langchain_core.prompts import (
    ChatPromptTemplate,
    FewShotChatMessagePromptTemplate,
)

examples = [
    {"input": "2+2", "output": "4"},
    {"input": "2+3", "output": "5"},
]

example_prompt = ChatPromptTemplate.from_messages(
    [
        ("human", "{input}"),
        ("ai", "{output}"),
    ]
)

few_shot_prompt = FewShotChatMessagePromptTemplate(
    example_prompt=example_prompt,
    examples=examples,
)

final_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a wondrous wizard of math."),
        few_shot_prompt,
        ("human", "{input}"),
    ]
)

# Note : Make sure openai API key is set in the environment variable OPENAI_API_KEY
llm = OpenAI(temperature=0, streaming=True, callbacks=[fiddler_handler])

chain = final_prompt | llm

# Invoke the chain. Invocation will be logged to Fiddler, and metrics automatically generated
chain.invoke({"input": "What's the square of a triangle?"})