Merge pull request #101 from singlestore-labs/tim/add-lesson-areas

Add lesson areas

notebooks/atlas-and-kai/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="database"
 difficulty="beginner"
 tags=["starter", "mongo", "kai"]
+lesson_areas=["Kai"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/backup-database-s3/meta.toml

@@ -6,5 +6,6 @@ description = """\
 icon = "database"
 difficulty="beginner"
 tags = ["starter", "backup"]
+lesson_areas=["Data Management"]
 destinations = ["spaces"]
 minimum_tier="standard"

notebooks/building-a-multi-agent-ai-app-with-autogen/meta.toml

@@ -4,7 +4,8 @@ description="""\
     Learn how to build a multi-agent group chat with RAG using Autogen and SingleStore   \
     """
 icon="vector-circle"
-difficulty="beginner"
+difficulty="intermediate"
 tags=["starter", "autogen", "rag", "multiagent", "groupchat"]
+lesson_areas=["AI"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/cdc-mongodb-sql-commands/meta.toml

@@ -5,5 +5,6 @@ description = """
 icon = "database"
 difficulty="intermediate"
 tags =  ["mongo", "pipeline", "cdc", "ingest", "kai"]
+lesson_areas=["Data Management"]
 destinations = ["spaces"]
 minimum_tier="standard"

notebooks/confluent-cloud-integration/meta.toml

@@ -3,7 +3,8 @@ title = "Ingest data from Confluent Cloud (Kafka)"
 description = """
     A step-by-step guide on seamlessly ingesting data from Confluent Cloud (Kafka) into your SingleStoreDB database using a robust pipeline. """
 icon = "confluent-logo"
-difficulty="advanced"
+difficulty="intermediate"
 tags = ["advanced", "confluent", "kafka", "pipeline", "realtime"]
+lesson_areas=["Integrations"]
 destinations = ["spaces"]
 minimum_tier="standard"

notebooks/evaluating-llms-with-uptrain/meta.toml

@@ -5,6 +5,7 @@ Using Uptrain to evaluate LLMs built with SingleStore as the contextual store. T
     """
 difficulty="intermediate"
 tags=["openai", "llm", "vectordb", "langchain"]
+lesson_areas=["AI"]
 icon="vector-circle"
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/getting-started-with-dataframes/meta.toml

@@ -7,5 +7,6 @@ description="""\
 icon="browser"
 difficulty="beginner"
 tags=["starter", "dataframe", "python"]
+lesson_areas=["Python SDK"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/getting-started-with-fusion-sql/meta.toml

@@ -8,5 +8,6 @@ description="""\
 icon="browser"
 difficulty="beginner"
 tags=["starter", "fusion", "python"]
+lesson_areas=["Data Management", "Python SDK"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/getting-started-with-mongocdc/meta.toml

@@ -4,7 +4,8 @@ description="""\
     Setup Zero ETL data replication from MongoDB to SingleStore \
     """
 icon="pipeline"
-difficulty="intermediate"
+difficulty="beginner"
 tags=["cdc", "mongo", "kai"]
+lesson_areas=["Data Management"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/getting-started-with-notebooks/meta.toml

@@ -7,5 +7,6 @@ description="""\
 icon="notes"
 difficulty="beginner"
 tags=["starter", "python", "sqrl"]
+lesson_areas=["Python SDK"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/how-to-build-llm-apps-that-can-see-hear-speak/meta.toml

@@ -4,7 +4,8 @@ description="""\
     Using OpenAI to build an app that can take images, audio, and text data to generate output
     """
 icon="chart-network"
-difficulty="advanced"
+difficulty="intermediate"
 tags=["advanced", "openai", "genai", "vectordb"]
+lesson_areas=["AI"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/hybrid-full-text-vector-search/meta.toml

@@ -1,8 +1,9 @@
 [meta]
 title="Hybrid Full-text and Vector Search"
 description="Example of similarity search over vector data and a hybrid search that combines full-text search with an indexed vector search."
-difficulty="beginner"
+difficulty="intermediate"
 tags=["starter", "openai", "genai", "vectordb"]
+lesson_areas=["AI", "Query Performance"]
 destinations=["spaces"]
 icon="vector-circle"
 minimum_tier="free-shared"

notebooks/hybrid-search/meta.toml

@@ -6,6 +6,7 @@ description="""\
     """
 difficulty="beginner"
 tags=["starter", "openai", "genai", "vectordb"]
+lesson_areas=["AI"]
 icon="vector-circle"
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/image-matching-with-sql/meta.toml

@@ -4,7 +4,8 @@ description="""\
     Facial recognition using dot_product function on vectors stored in SingleStoreDB. \
     """
 icon="face-viewfinder"
-difficulty="beginner"
+difficulty="intermediate"
 tags=["starter", "vectordb", "genai", "facenet"]
+lesson_areas=["AI"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/ingest-pdfs-with-unstructured/meta.toml

@@ -2,7 +2,8 @@
 title="Ask questions of your PDFs with Unstructured"
 description="Ask questions of your unstructured PDFs. In this notebook, Unstructured.io ingests pdfs accurately, then Open AI is used to create embeddings, the vector data is stored in SingleStore and finally ask questions of your PDF data"
 icon="file-export"
-difficulty="intermediate"
+difficulty="beginner"
 tags=["ingest", "pdf","vector","unstructured"]
+lesson_areas=["AI", "Integrations"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/inserting-embeddings-from-multiple-models-into-singlestore-using-external-functions/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="vector-circle"
 difficulty="intermediate"
 tags=["vectordb", "genai", "openai", "rag", "huggingface"]
+lesson_areas=["AI", "Data Management"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/insure-gpt-demo/meta.toml

@@ -3,5 +3,6 @@ title="InsureGPT: OpenAI and SingleStore for Insurance"
 description="Creating an insurance analysis with OpenAI and vectorizing the results into SingleStore."
 difficulty="intermediate"
 tags=["openai", "langchain"]
+lesson_areas=["AI"]
 icon="database"
 minimum_tier="standard"

notebooks/integrating-with-pandas/meta.toml

@@ -7,5 +7,6 @@ description="""\
 icon="database"
 difficulty="beginner"
 tags=["starter", "dataframe", "python"]
+lesson_areas=["Python SDK"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/kebab-case/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="database"
 difficulty="advanced"
 tags=["advanced", "automation", "tuning"]
+lesson_areas=[]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/launch-open-source-apps-with-langchain/meta.toml

@@ -4,7 +4,8 @@ description="""\
     LangChain connector to use SingleStoreDB as your vector database for your apps. \
     """
 icon="vector-circle"
-difficulty="intermediate"
+difficulty="beginner"
 tags=["vectordb", "genai", "langchain"]
+lesson_areas=["AI"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/load-json-files-s3/meta.toml

@@ -4,7 +4,8 @@ description="""\
     This notebook will help you load JSON files from a public open AWS S3 bucket.
     """
 icon="chart-network"
-difficulty="advanced"
+difficulty="beginner"
 tags=["advanced", "pipeline", "json", "s3"]
+lesson_areas=["Ingest"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/load-s3-files-into-shared-tier/meta.toml

@@ -4,7 +4,8 @@ description="""\
     This notebook guides you through data ingestion of CSV files from an AWS S3 location into your shared tier workspace. \
     """
 icon="chart-network"
-difficulty="intermediate"
+difficulty="beginner"
 tags=["pipeline", "s3"]
+lesson_areas=["Ingest"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/managing-stage-files-with-fusion-sql/meta.toml

@@ -5,7 +5,8 @@ description="""\
     Cloud using Fusion SQL.
     """
 icon="files"
-difficulty="beginner"
+difficulty="intermediate"
 tags=["starter", "fusion", "python"]
+lesson_areas=["Python SDK", "Ingest"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/movie-recommendation/meta.toml

@@ -4,7 +4,8 @@ description="""\
     Movie recommendation engine using vectors stored in SingleStore to find your next watch. \
     """
 icon="camera-movie"
-difficulty="advanced"
+difficulty="intermediate"
 tags=["advanced", "vectordb", "genai", "openai"]
+lesson_areas=["AI"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/network-intrusion-detection-part-1/meta.toml

@@ -8,5 +8,6 @@ description="""
 icon="browser"
 difficulty="advanced"
 tags=["advanced", "cybersecurity", "vectordb", "iot", "ai"]
+lesson_areas=["AI", "Real-time"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/network-intrusion-detection-part-2/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="browser"
 difficulty="advanced"
 tags=["advanced", "cybersecurity", "vectordb", "iot", "ai"]
+lesson_areas=["AI", "Real-time"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/network-intrusion-detection-part-3/meta.toml

@@ -7,5 +7,6 @@ description="""
 icon="browser"
 difficulty="advanced"
 tags=["advanced", "cybersecurity", "vectordb", "iot", "ai"]
+lesson_areas=["AI", "Real-time"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/notebook-basics/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="notes"
 difficulty="beginner"
 tags=["starter", "python", "dataframes"]
+lesson_areas=["Python SDK"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/notebook-style-guide/meta.toml

@@ -3,5 +3,6 @@ title="Notebook Style Guide"
 description="Become a pro at formatting and organizing your notebooks."
 icon="link"
 tags=[]
+lesson_areas=[]
 destinations=[]
 minimum_tier="standard"

notebooks/optimize-performance-with-tpch-100/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="database"
 difficulty="advanced"
 tags=["advanced", "performance", "turning", "shardkey", "ingest", "tpch"]
+lesson_areas=["Query Performance"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/performance-troubleshooting/meta.toml

@@ -4,7 +4,8 @@ description="""\
     This notebook will help you perform database performance troubleshoot
 """
 icon="database"
-difficulty="beginner"
+difficulty="advanced"
 tags=["troubleshoot", "performance", "tuning", "starter"]
+lesson_areas=["Query Performance"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/pipelines-query-tuning/meta.toml

@@ -3,8 +3,9 @@ title="Kafka Pipelines and Query Tuning"
 description="""\
     Create a SingleStore pipeline to track the International Space Station and adjust queries & schema to optimize performance.
     """
-difficulty="intermediate"
+difficulty="advanced"
 tags=["beginner", "kafka", "pipeline", "querytuning"]
+lesson_areas=["Query Performance", "Ingest"]
 icon="database"
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/rag-with-bedrock/meta.toml

@@ -4,7 +4,8 @@ description="""\
     Infuse domain-specific knowledge from SingleStoreDB into generative AI models on Amazon Bedrock to showcase Question & Answering using RAG (retrieval-agumented generation) to improve the quality of responses.\
     """
 icon="crystal-ball"
-difficulty="beginner"
+difficulty="intermediate"
 tags=["vectordb", "genai", "bedrock", "rag", "starter"]
+lesson_areas=["AI", "Integrations"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/real-time-anomaly-detection/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="chart-scatter"
 difficulty="intermediate"
 tags=["vectordb", "realtime"]
+lesson_areas=["AI", "Real-time"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/real-time-recommendation-engine/meta.toml

@@ -4,7 +4,8 @@ description="""\
 We demonstrate how to build and host a real-time recommendation engine for free with SingleStore. The notebook also leverages our new SingleStore Job Service to ensure that the latest data is ingested and used in providing recommendations.\
     """
 icon="crystal-ball"
-difficulty="beginner"
+difficulty="intermediate"
 tags=["starter", "openai", "vercel", "realtime", "vectordb"]
+lesson_areas=["AI", "Real-time"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/representing-unstructured-data-as-vectors/meta.toml

@@ -1,7 +1,8 @@
 [meta]
 title="Representing Unstructured Data as Vectors"
 description="Transforming unstructured data into vectors and representing them in a 3-dimensional space to find the distance between them using various techniques"
-difficulty="intermediate"
+difficulty="beginner"
 tags=["vectors", "unstructureddata"]
+lesson_areas=["Python SDK", "AI"]
 icon="vector-circle"
 minimum_tier="standard"

notebooks/restore-database-from-s3/meta.toml

@@ -6,5 +6,6 @@ description = """\
 icon = "database"
 difficulty="beginner"
 tags = ["starter", "admin", "restore"]
+lesson_areas=["Data Management"]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/resume-evaluator-ANN-index-search/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="arrows-spin"
 difficulty="intermediate"
 tags=["vectordb", "genai", "openai", "rag"]
+lesson_areas=["AI"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/resume-evaluator/meta.toml

@@ -4,7 +4,8 @@ description="""\
     Resume Evaluator using vectors stored in SingleStore to find sutiable resume for your job description. \
     """
 icon="arrows-spin"
-difficulty="beginner"
+difficulty="intermediate"
 tags=["starter", "vectordb", "genai", "openai"]
+lesson_areas=["AI"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/searching-all-of-wikipedia/meta.toml

@@ -4,7 +4,8 @@ description = """\
     Simulate searching through 10 million paragraphs in Wikipedia for information about video games, and generate answers using RAG! \
     """
 icon = "book-open-cover"
-difficulty="advanced"
+difficulty="intermediate"
 tags = ["vector", "advanced", "vectordb", "hybridsearch", "rag", "ann"]
+lesson_areas=["AI"]
 destinations = ["spaces"]
 minimum_tier="standard"

notebooks/semantic-search-with-hugging-face/meta.toml

@@ -7,5 +7,6 @@ description="""\
 icon="chart-network"
 difficulty="beginner"
 tags=["starter", "vectordb", "huggingface"]
+lesson_areas=["AI"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/semantic-search-with-openai-embedding-creation/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="chart-network"
 difficulty="beginner"
 tags=["starter", "openai", "vectordb", "genai"]
+lesson_areas=["AI"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/semantic-search-with-openai-qa/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="crystal-ball"
 difficulty="beginner"
 tags=["starter", "openai", "vectordb", "genai"]
+lesson_areas=["AI"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/semantic-visualization-and-vector-datatype/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="chart-network"
 difficulty="beginner"
 tags=["starter", "vectordb", "genai", "huggingface", "semanticsearch"]
+lesson_areas=["AI", "Python SDK"]
 destinations=["spaces"]
 minimum_tier="free-shared"

notebooks/similarity-search-on-vector-data/meta.toml

@@ -3,6 +3,7 @@ title="Similarity Search on Vector Data"
 description="Example of similarity searches over vector data."
 difficulty="beginner"
 tags=["starter", "openai", "genai", "vectordb"]
+lesson_areas=["AI"]
 destinations=["spaces"]
 icon="vector-circle"
 minimum_tier="free-shared"

notebooks/singlestore-april-challenge-haiku-ascii/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="chart-network"
 difficulty="intermediate"
 tags=["challenge", "haiku", "ascii"]
+lesson_areas=[]
 destinations=["spaces"]
 minimum_tier="standard"

notebooks/unified-data-analysis-sql-nosql-kai/meta.toml

@@ -6,5 +6,6 @@ description="""\
 icon="database"
 difficulty="intermediate"
 tags=["cdc", "mongo", "sql", "nosql", "kai"]
+lesson_areas=["Kai"]
 destinations=["spaces"]
 minimum_tier="standard"