Fiddler는 엔터프라이즈 Generative 및 Predictive 시스템 운영의 선구자로, Data Science, MLOps, Risk, Compliance, Analytics 및 기타 LOB 팀이 엔터프라이즈 규모에서 ML 배포를 모니터링, 설명, 분석 및 개선할 수 있는 통합 플랫폼을 제공합니다.

​
1. Installation and Setup

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

​
2. Fiddler connection details

Fiddler에 모델 정보를 추가하기 전에
  1. Fiddler에 연결하는 데 사용하는 URL
  2. 조직 ID
  3. 인증 토큰
이러한 정보는 Fiddler 환경의 Settings 페이지로 이동하여 찾을 수 있습니다. 
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

​
3. fiddler callback handler instance 생성

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,
)

​
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?")

​
Example 2 : prompt template이 있는 Chain

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?"})
Edit the source of this page on GitHub.
Connect these docs programmatically to Claude, VSCode, and more via MCP for real-time answers.
⌘I