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from opik import track @track def llm_chain(user_question): context = get_context(user_question) response = call_llm(user_question, context) return response @track def get_context(user_question): # Logic that fetches the context, hard coded here return ["The dog chased the cat.", "The cat was called Luky."] @track def call_llm(user_question, context): # LLM call, can be combined with any Opik integration return "The dog chased the cat Luky." response = llm_chain("What did the dog do ?") print(response)

from llama_index.core import VectorStoreIndex, global_handler, set_global_handler from llama_index.core.schema import TextNode # Configure the Opik integration set_global_handler("opik") opik_callback_handler = global_handler node1 = TextNode(text="The cat sat on the mat.", id_="1") node2 = TextNode(text="The dog chased the cat.", id_="2") index = VectorStoreIndex([node1, node2]) # Create a LlamaIndex query engine query_engine = index.as_query_engine() # Query the documents response = query_engine.query("What did the dog do ?") print(response)

from langchain_openai import ChatOpenAI from opik.integrations.langchain import OpikTracer # Initialize the tracer opik_tracer = OpikTracer() # Create the LLM Chain using LangChain llm = ChatOpenAI(temperature=0) # Configure the Opik integration llm = llm.with_config({"callbacks": [opik_tracer]}) llm.invoke("Hello, how are you?")

from openai import OpenAI from opik.integrations.openai import track_openai openai_client = OpenAI() openai_client = track_openai(openai_client) response = openai_client.chat.completions.create( model="gpt-3.5-turbo", messages=[ {"role": "user", "content": "Hello, world!"} ] )

from comet_ml import Experiment import torch.nn as nn # 1. Define a new experiment experiment = Experiment(project_name="YOUR PROJECT") # 2. Create your model class class RNN(nn.Module): #... Define your Class # 3. Train and test your model while logging everything to Comet with experiment.train(): # ...Train your model and log metrics experiment.log_metric("accuracy", correct / total, step = step) # 4. View real-time metrics in Comet

from pytorch_lightning.loggers import CometLogger # 1. Create your Model # 2. Initialize CometLogger comet_logger = CometLogger() # 3. Train your model trainer = pl.Trainer( logger=[comet_logger], # ...configs ) trainer.fit(model) # 4. View real-time metrics in Comet

from comet_ml import Experiment from transformers import Trainer # 1. Define a new experiment experiment = Experiment(project_name="YOUR PROJECT") # 2. Train your model trainer = Trainer( model = model, # ...configs ) trainer.train() # 3. View real-time metrics in Comet

from comet_ml import Experiment from tensorflow import keras # 1. Define a new experiment experiment = Experiment(project_name="YOUR PROJECT") # 2. Define your model model = tf.keras.Model( # ...configs ) # 3. Train your model model.fit( x_train, y_train, validation_data=(x_test, y_test), ) # 4. Track real-time metrics in Comet

from comet_ml import Experiment import tensorflow as tf # 1. Define a new experiment experiment = Experiment(project_name="YOUR PROJECT") # 2. Define and train your model model.fit(...) # 3. Log additional model metrics and params experiment.log_parameters({'custom_params': True}) experiment.log_metric('custom_metric', 0.95) # 4. Track real-time metrics in Comet

from comet_ml import Experiment import tree from sklearn # 1. Define a new experiment experiment = Experiment(project_name="YOUR PROJECT") # 2. Build your model and fit clf = tree.DecisionTreeClassifier( # ...configs ) clf.fit(X_train_scaled, y_train) params = {...} metrics = {...} # 3. Log additional metrics and params experiment.log_parameters(params) experiment.log_metrics(metrics) # 4. Track model performance in Comet

from comet_ml import Experiment import xgboost as xgb # 1. Define a new experiment experiment = Experiment(project_name="YOUR PROJECT") # 2. Define your model and fit xg_reg = xgb.XGBRegressor( # ...configs ) xg_reg.fit( X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], eval_metric="rmse", ) # 3. Track model performance in Comet

# Utilize Comet in any environment from comet_ml import Experiment # 1. Define a new experiment experiment = Experiment(project_name="YOUR PROJECT") # 2. Model training here # 3. Log metrics or params over time experiment.log_metrics(metrics) #4. Track real-time metrics in Comet

# Utilize Comet in any environment from comet_mpm import CometMPM # 1. Create the MPM logger MPM = CometMPM() # 2. Add your inference logic here # 3. Log metrics or params over time MPM.log_event( prediction_id="...", input_features=input_features, output_value=prediction, output_probability=probability, )