npm install --save @arizeai/openinference-core @opentelemetry/api

import { context } from "@opentelemetry/api"
import { setSession } from "@openinference-core"

context.with(
  setSession(context.active(), { sessionId: "session-id" }),
  () => {
      // Calls within this block will generate spans with the attributes:
      // "session.id" = "session-id"
  }
)

import { context } from "@opentelemetry/api"
import { setUser } from "@openinference-core"

context.with(
  setUser(context.active(), { userId: "user-id" }),
  () => {
      // Calls within this block will generate spans with the attributes:
      // "user.id" = "user-id"
  }
)

from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
from openinference.instrumentation import using_attributes

prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
llm = LLMChain(llm=OpenAI(), prompt=prompt, metadata={"category": "jokes"})

# Defining a Session
with using_attributes(session_id="my-session-id"):
    response = llm.predict(adjective="funny")

# Defining a User
with using_attributes(user_id="my-user-id"):
    response = llm.predict(adjective="funny")
    
# Defining a Session AND a User
with using_attributes(
    session_id="my-session-id",
    user_id="my-user-id",
):
    response = llm.predict(adjective="funny")

from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
from openinference.instrumentation import using_attributes

prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
llm = LLMChain(llm=OpenAI(), prompt=prompt, metadata={"category": "jokes"})

# Defining a Session
@using_attributes(session_id="my-session-id")
def call_fn(llm, *args, **kwargs):
    return llm.complete(*args, **kwargs)
    
# Defining a User
@using_attributes(user_id="my-user-id")
def call_fn(llm, *args, **kwargs):
    return llm.complete(*args, **kwargs)

# Defining a Session AND a User
@using_attributes(
    session_id="my-session-id",
    user_id="my-user-id",
)
def call_fn(llm, *args, **kwargs):
    return llm.complete(*args, **kwargs)

from llama_index.core.chat_engine import SimpleChatEngine
from openinference.instrumentation import using_attributes

chat_engine = SimpleChatEngine.from_defaults()

# Defining a Session
with using_attributes(session_id="my-session-id"):
    response = chat_engine.chat(
        "Say something profound and romantic about fourth of July"
    )

# Defining a User
with using_attributes(user_id="my-user-id"):
    response = chat_engine.chat(
        "Say something profound and romantic about fourth of July"
    )
    
# Defining a Session AND a User
with using_attributes(
    session_id="my-session-id",
    user_id="my-user-id",
):
    response = chat_engine.chat(
        "Say something profound and romantic about fourth of July"
    )

from llama_index.core.chat_engine import SimpleChatEngine
from openinference.instrumentation import using_attributes

chat_engine = SimpleChatEngine.from_defaults()

# Defining a Session
@using_attributes(session_id="my-session-id")
def call_fn(chat_engine, *args, **kwargs):
    return chat_engine.chat(
        "Say something profound and romantic about fourth of July"
    )
    
# Defining a User
@using_attributes(user_id="my-user-id")
def call_fn(chat_engine, *args, **kwargs):
    return chat_engine.chat(
        "Say something profound and romantic about fourth of July"
    )

# Defining a Session AND a User
@using_attributes(
    session_id="my-session-id",
    user_id="my-user-id",
)
def call_fn(chat_engine, *args, **kwargs):
    return chat_engine.chat(
        "Say something profound and romantic about fourth of July"
    )
    

import boto3
from openinference.instrumentation import using_attributes

session = boto3.session.Session()
client = session.client("bedrock-runtime", region_name="us-west-2")

# Defining a Session
with using_attributes(session_id="my-session-id"):
    response = client.invoke_model(
        modelId="anthropic.claude-v2",
        body=(
            b'{"prompt": "Human: Hello there, how are you? Assistant:", "max_tokens_to_sample": 1024}'
        )
    )

# Defining a User
with using_attributes(user_id="my-user-id"):
    response = client.invoke_model(
        modelId="anthropic.claude-v2",
        body=(
            b'{"prompt": "Human: Hello there, how are you? Assistant:", "max_tokens_to_sample": 1024}'
        )
    )
    
# Defining a Session AND a User
with using_attributes(
    session_id="my-session-id",
    user_id="my-user-id",
):
    response = client.invoke_model(
        modelId="anthropic.claude-v2",
        body=(
            b'{"prompt": "Human: Hello there, how are you? Assistant:", "max_tokens_to_sample": 1024}'
        )
    )

import boto3
from openinference.instrumentation import using_attributes

session = boto3.session.Session()
client = session.client("bedrock-runtime", region_name="us-west-2")

# Defining a Session
@using_attributes(session_id="my-session-id")
def call_fn(client, *args, **kwargs):
    return client.invoke_model(*args, **kwargs)
    
# Defining a User
@using_attributes(user_id="my-user-id")
def call_fn(client, *args, **kwargs):
    return client.invoke_model(*args, **kwargs)

# Defining a Session AND a User
@using_attributes(
    session_id="my-session-id",
    user_id="my-user-id",
)
def call_fn(client, *args, **kwargs):
    return client.invoke_model(*args, **kwargs)

from mistralai.client import MistralClient
from openinference.instrumentation import using_attributes

client = MistralClient()

# Defining a Session
with using_attributes(session_id="my-session-id"):
    response = client.chat(
        model="mistral-large-latest",
        messages=[
            ChatMessage(
                content="Who won the World Cup in 2018?",
                role="user",
            )
        ],
    )

# Defining a User
with using_attributes(user_id="my-user-id"):
    response = client.chat(
        model="mistral-large-latest",
        messages=[
            ChatMessage(
                content="Who won the World Cup in 2018?",
                role="user",
            )
        ],
    )
    
# Defining a Session AND a User
with using_attributes(
    session_id="my-session-id",
    user_id="my-user-id",
):
    response = client.chat(
        model="mistral-large-latest",
        messages=[
            ChatMessage(
                content="Who won the World Cup in 2018?",
                role="user",
            )
        ],
    )

from mistralai.client import MistralClient
from openinference.instrumentation import using_attributes

client = MistralClient()

# Defining a Session
@using_attributes(session_id="my-session-id")
def call_fn(client, *args, **kwargs):
    return client.chat(*args, **kwargs)
    
# Defining a User
@using_attributes(user_id="my-user-id")
def call_fn(client, *args, **kwargs):
    return client.chat(*args, **kwargs)

# Defining a Session AND a User
@using_attributes(
    session_id="my-session-id",
    user_id="my-user-id",
)
def call_fn(client, *args, **kwargs):
    return client.chat(*args, **kwargs)

import dspy
from openinference.instrumentation import using_attributes

class BasicQA(dspy.Signature):
    """Answer questions with short factoid answers."""

    question = dspy.InputField()
    answer = dspy.OutputField(desc="often between 1 and 5 words")

turbo = dspy.OpenAI(model="gpt-3.5-turbo")
dspy.settings.configure(lm=turbo)
predictor = dspy.Predict(BasicQA) # Define the predictor.

# Defining a Session
with using_attributes(session_id="my-session-id"):
    response = predictor(
        question="What is the capital of the united states?"
    )  

# Defining a User
with using_attributes(user_id="my-user-id"):
    response = predictor(
        question="What is the capital of the united states?"
    )  
    
# Defining a Session AND a User
with using_attributes(
    session_id="my-session-id",
    user_id="my-user-id",
):
    response = predictor(
        question="What is the capital of the united states?"
    )  

import dspy
from openinference.instrumentation import using_attributes

# Defining a Session
@using_attributes(session_id="my-session-id")
def call_fn(predictor, *args, **kwargs):
    return predictor(*args,**kwargs)  
    
# Defining a User
@using_attributes(user_id="my-user-id")
def call_fn(predictor, *args, **kwargs):
    return predictor(*args,**kwargs)  

# Defining a Session AND a User
@using_attributes(
    session_id="my-session-id",
    user_id="my-user-id",
)
def call_fn(predictor, *args, **kwargs):
    return predictor(*args,**kwargs)