Logging Prompt Templates & Variables

Install the core instrumentation package and any framework instrumentors you need.

pip install fi-instrumentation-otel traceai_openai openai

Register your tracer provider, instrument your LLM clients, and wrap your LLM calls with using_attributes to attach the prompt template to spans.

import os
from fi_instrumentation import register, Transport, using_attributes
from traceai_openai import OpenAIInstrumentor
from fi_instrumentation.fi_types import ProjectType
from traceai_langchain import LangChainInstrumentor


from fi_instrumentation import register
from fi_instrumentation.fi_types import (
    ProjectType,

)
from traceai_langchain import LangChainInstrumentor



os.environ["GOOGLE_API_KEY"] = "google_api_key"
print(os.environ.get("GOOGLE_API_KEY"))


os.environ["OPENAI_API_KEY"] = "futureagi_openai_api_key"
os.environ["FI_API_KEY"] = "futureagi_api_key"
os.environ["FI_SECRET_KEY"] = "futureagi_secret_key"



# Setup OTel via our register function
trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="<project_name>",            # Your project name
    transport=Transport.HTTP,           # Transport mechanism for your traces
)


OpenAIInstrumentor().instrument(tracer_provider=trace_provider)
LangChainInstrumentor().instrument(tracer_provider=trace_provider)


from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

with using_attributes(
    prompt_template="<prompt_template_name>",
    prompt_template_label="<prompt_template_label>",

):

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

    result = chain.invoke({"y": "sky"})

    print(f"Response: {result}")