Updated deprecated code

llm-complete-guide/steps/chunk_documents.py

@@ -21,7 +21,7 @@
 )
 from structures import Document
 from utils.llm_utils import split_documents
-from zenml import log_artifact_metadata, step
+from zenml import log_metadata, step
 from zenml.logger import get_logger
 
 logger = get_logger(__name__)
@@ -137,8 +137,9 @@ def chunk_documents(
     logger.info(
         f"Number of documents after chunking: {num_docs_after_chunking}"
     )
-    log_artifact_metadata(
+    log_metadata(
         artifact_name="chunked_documents",
+        infer_artifact=True,
         metadata={
             "before_chunking_count": num_docs_before_chunking,
             "after_chunking_count": num_docs_after_chunking,

llm-complete-guide/steps/eval_pii.py

@@ -6,7 +6,7 @@
 import matplotlib.pyplot as plt
 from datasets import Dataset
 from PIL import Image
-from zenml import log_artifact_metadata, step
+from zenml import log_metadata, step
 
 
 class PIIDetector:
@@ -305,8 +305,9 @@ def eval_pii(
         "dates_found": train_results["statistics"]["total_findings"]["dates"],
         "ips_found": train_results["statistics"]["total_findings"]["ips"],
     }
-    log_artifact_metadata(
-        metadata=train_metadata, artifact_name="train_pii_results"
+    log_metadata(
+        metadata=train_metadata, artifact_name="train_pii_results",
+        infer_artifact=True
     )
 
     test_metadata = {
@@ -320,8 +321,9 @@ def eval_pii(
         "dates_found": test_results["statistics"]["total_findings"]["dates"],
         "ips_found": test_results["statistics"]["total_findings"]["ips"],
     }
-    log_artifact_metadata(
-        metadata=test_metadata, artifact_name="test_pii_results"
+    log_metadata(
+        metadata=test_metadata, artifact_name="test_pii_results", 
+        infer_artifact=True
     )
 
     pii_chart = plot_pii_results(train_results, test_results)

llm-complete-guide/steps/finetune_embeddings.py

@@ -47,7 +47,7 @@
 )
 from sentence_transformers.training_args import BatchSamplers
 from sentence_transformers.util import cos_sim
-from zenml import ArtifactConfig, log_model_metadata, step
+from zenml import ArtifactConfig, log_metadata, step
 from zenml.client import Client
 from zenml.utils.cuda_utils import cleanup_gpu_memory
 
@@ -168,8 +168,8 @@ def evaluate_base_model(
         for dim in EMBEDDINGS_MODEL_MATRYOSHKA_DIMS
     }
 
-    log_model_metadata(
-        metadata={"base_model_eval": base_model_eval},
+    log_metadata(
+        metadata={"base_model_eval": base_model_eval}, infer_model=True
     )
 
     return results
@@ -201,8 +201,8 @@ def evaluate_finetuned_model(
         for dim in EMBEDDINGS_MODEL_MATRYOSHKA_DIMS
     }
 
-    log_model_metadata(
-        metadata={"finetuned_model_eval": finetuned_model_eval},
+    log_metadata(
+        metadata={"finetuned_model_eval": finetuned_model_eval}, infer_model=True
     )
 
     return results
@@ -298,7 +298,8 @@ def finetune(
         token=zenml_client.get_secret(SECRET_NAME).secret_values["hf_token"],
     )
 
-    log_model_metadata(
+    log_metadata( 
+        infer_model=True,                
         metadata={
             "training_params": {
                 "num_train_epochs": epochs,

llm-complete-guide/steps/finetune_embeddings_legacy.py

@@ -25,7 +25,7 @@
 from torch.nn import CosineSimilarity
 from torch.utils.data import DataLoader
 from utils.visualization_utils import create_comparison_chart
-from zenml import log_artifact_metadata, step
+from zenml import log_metadata, step
 from zenml.logger import get_logger
 
 logger = get_logger(__name__)
@@ -79,12 +79,14 @@ def load_datasets(
     print("train_dataset_length_raw", len(train_dataset))
     print("test_dataset_length_raw", len(test_dataset))
 
-    log_artifact_metadata(
+    log_metadata(
         artifact_name="train_dataset",
+        infer_artifact=True,
         metadata={"row_count": len(train_dataset)},
     )
-    log_artifact_metadata(
+    log_metadata(
         artifact_name="test_dataset",
+        infer_artifact=True,
         metadata={"row_count": len(test_dataset)},
     )
 
@@ -187,8 +189,9 @@ def train_model(
         warmup_steps=warmup_steps,
     )
 
-    log_artifact_metadata(
+    log_metadata(
         artifact_name="trained_model",
+        infer_artifact=True,
         metadata={
             "model_path": model_path,
             "num_epochs": num_epochs,
@@ -280,8 +283,9 @@ def evaluate_model(
         finetuned_similarity=finetuned_avg_sim,
     )
 
-    log_artifact_metadata(
+    log_metadata(
         artifact_name="evaluation_results",
+        infer_artifact=True,
         metadata={
             "pretrained_average_similarity": {
                 "value": pretrained_avg_sim,

llm-complete-guide/steps/generate_questions.py

@@ -21,7 +21,7 @@
 from rich import print
 from structures import Document
 from utils.openai_utils import get_openai_api_key
-from zenml import log_artifact_metadata, step
+from zenml import log_metadata, step
 from zenml.logger import get_logger
 
 logger = get_logger(__name__)
@@ -160,8 +160,9 @@ def generate_questions(
         f"Generated {len(final_df)} questions for {len(documents)} documents."
     )
 
-    log_artifact_metadata(
+    log_metadata(
         artifact_name="generated_questions",
+        infer_artifact=True,
         metadata={
             "num_documents": len(documents),
             "num_questions_generated": len(final_df),

llm-complete-guide/steps/markdown_loader.py

@@ -18,7 +18,7 @@
 
 import polars as pl
 from constants import FILES_TO_IGNORE
-from zenml import log_artifact_metadata, step
+from zenml import log_metadata, step
 from zenml.logger import get_logger
 
 logger = get_logger(__name__)
@@ -61,8 +61,9 @@ def load_markdown_files(
                 f"Subfolder '{subfolder}' not found in the cloned repository."
             )
 
-    log_artifact_metadata(
+    log_metadata(
         artifact_name="markdown_files",
+        infer_artifact=True,
         metadata={
             "num_markdown_files": len(markdown_files),
             "columns": "filename, page_content",

llm-complete-guide/steps/populate_index.py

@@ -39,7 +39,7 @@
 from sentence_transformers import SentenceTransformer
 from structures import Document
 from utils.llm_utils import get_db_conn, get_es_client, split_documents
-from zenml import ArtifactConfig, log_artifact_metadata, step, log_model_metadata
+from zenml import ArtifactConfig, log_metadata, step, log_metadata
 from zenml.metadata.metadata_types import Uri
 from zenml.client import Client
 from constants import SECRET_NAME
@@ -515,8 +515,9 @@ def preprocess_documents(
         Exception: If an error occurs during preprocessing.
     """
     try:
-        log_artifact_metadata(
+        log_metadata(
             artifact_name="split_chunks",
+        infer_artifact=True,
             metadata={
                 "chunk_size": CHUNK_SIZE,
                 "chunk_overlap": CHUNK_OVERLAP,
@@ -536,8 +537,9 @@ def preprocess_documents(
         histogram_chart: Image.Image = create_histogram(stats)
         bar_chart: Image.Image = create_bar_chart(stats)
 
-        log_artifact_metadata(
+        log_metadata(
             artifact_name="split_chunks",
+        infer_artifact=True,
             metadata=stats,
         )
 
@@ -568,8 +570,9 @@ def generate_embeddings(
     try:
         model = SentenceTransformer(EMBEDDINGS_MODEL)
 
-        log_artifact_metadata(
+        log_metadata(
             artifact_name="documents_with_embeddings",
+        infer_artifact=True,
             metadata={
                 "embedding_type": EMBEDDINGS_MODEL,
                 "embedding_dimensionality": EMBEDDING_DIMENSIONALITY,
@@ -828,7 +831,8 @@ def _log_metadata(index_type: IndexType) -> None:
             "dbname": "postgres",
         }
 
-    log_model_metadata(
+    log_metadata(
+        infer_model=True,
         metadata={
             "embeddings": {
                 "model": EMBEDDINGS_MODEL,

llm-complete-guide/steps/url_scraper.py

@@ -16,7 +16,7 @@
 import json
 
 from typing_extensions import Annotated
-from zenml import ArtifactConfig, log_artifact_metadata, step
+from zenml import ArtifactConfig, log_metadata, step
 
 from steps.url_scraping_utils import get_all_pages
 
@@ -58,8 +58,9 @@ def url_scraper(
     # website_urls = get_all_pages(website_url)
     # all_urls = docs_urls + website_urls + examples_readme_urls
     all_urls = docs_urls
-    log_artifact_metadata(
+    log_metadata(
         artifact_name="urls",
+        infer_artifact=True,
         metadata={
             "count": len(all_urls),
         },

llm-complete-guide/steps/visualize_chat.py

@@ -1,6 +1,6 @@
 from typing import Optional, Dict, Any
 from typing_extensions import Annotated
-from zenml import log_artifact_metadata, pipeline, step
+from zenml import log_metadata, pipeline, step
 from zenml.types import HTMLString
 
 @step(enable_cache=False)

llm-complete-guide/utils/llm_utils.py

@@ -441,7 +441,7 @@ def find_vectorstore_name() -> str:
     client = Client()
     model = client.get_model_version(ZENML_CHATBOT_MODEL, model_version_name_or_number_or_id="v0.68.1-dev")
 
-    return model.run_metadata["vector_store"].value["name"]
+    return model.run_metadata["vector_store"]["name"]
 
 
 def rerank_documents(