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with mlflow.start_run(run_name="databricks-docs-bot"):
logged_chain_info = mlflow.langchain.log_model(
lc_model=os.path.join(
os.getcwd(),
f"{QUICK_START_REPO_SAVE_FOLDER}/quick_start_demo/sample_rag_chain",
), # Chain code file from the quick start repo
model_config=chain_config, # Chain configuration set above
artifact_path="chain", # Required by MLflow
input_example=input_example, # Save the chain's input schema. MLflow will execute the chain before logging and capturing its output schema.
)
langchain 0.2.1
langchain-community 0.2.4
langchain-core 0.2.5
langchain-text-splitters 0.2.1
mlflow 2.15.1
mlflow-skinny 2.15.1
import mlflow
import os
Log the model to MLflow
with mlflow.start_run(run_name="databricks-docs-bot"):
logged_chain_info = mlflow.langchain.log_model(
lc_model=os.path.join(
os.getcwd(),
f"{QUICK_START_REPO_SAVE_FOLDER}/quick_start_demo/sample_rag_chain",
), # Chain code file from the quick start repo
model_config=chain_config, # Chain configuration set above
artifact_path="chain", # Required by MLflow
input_example=input_example, # Save the chain's input schema. MLflow will execute the chain before logging and capturing its output schema.
)
Test the chain locally to see the MLflow Trace
chain = mlflow.langchain.load_model(logged_chain_info.model_uri)
chain.invoke(input_example)
I assume this is a version issue but I can't find doc on the log_model parameters.
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