OPERATOREN-BIBLIOTHEK

Die komplette Engine, ein Baustein nach dem anderen

Jeder Operator ist ein echter, versionierter Baustein. Suchen Sie, filtern Sie nach Kategorie und kopieren Sie jeden Baustein direkt auf Ihre Leinwand.

⌕

18 of 18 operatorsclick + Add to Canvas to copy

Retrieve

CORE UTILITY

Pulls data tables or stored models directly from repositories.

repository_entry (Path)

→ out (Data Table)

Read CSV / Excel

CORE UTILITY

Ingests external tabular structures.

file_encodingcolumn_separatorsfirst_row_names (Bool)

→ out (Data Table)

Read Database

HIGH RELEVANCE

Executes direct SQL queries against Snowflake, Postgres, etc.

connection_entryquery (SQL string)

→ out (Data Table)

Filter Examples

CORE UTILITY

Filters rows based on logical conditions.

condition_classparameter_string

→ ori (Original), fil (Filtered)

Select Attributes

CORE UTILITY

Retains or drops columns to reduce dimensions.

attribute_filter_type (subset, regex)

→ exa (Filtered Table)

Set Role

CORE UTILITY

Assigns special targets (e.g., label / prediction).

attribute_nametarget_role (label, id)

→ exa (Role-mapped Table)

Replace Missing Values

CORE UTILITY

Imputes empty cells natively.

default (average, minimum, zero, value)

→ exa (Imputed Table)

Join

CORE UTILITY

Horizontal table merges.

join_type (Inner, Left, Outer)key_attributes

→ join (Merged Table)

Random Forest

HIGH RELEVANCE

Ensemble model utilizing bagging decision trees.

number_of_treesmaximal_depthcriterion

→ mod (Trained Model)

Gradient Boosted Trees

HIGH RELEVANCE

Sequential loss-minimizing boosting trees.

number_of_treeslearning_rate

→ mod (Trained Model)

Apply Model

CORE UTILITY

Executes predictions on raw datasets using trained models.

None (auto-maps incoming columns)

→ lab (Predicted Table)

Cross Validation

HIGH RELEVANCE

Nested split-tester fold evaluator container.

number_of_folds (default: 10)sampling_type

→ mod, per (Metrics Table)

Performance (Classification)

CORE UTILITY

Scores categorical predictions.

accuracyprecisionrecallAUC (Bools)

→ per (Performance Metrics)

Execute Python

HIGH RELEVANCE

Runs embedded python scripting utilizing pandas DataFrames.

python_script (Code block)

→ out1, out2 (Pandas Outputs)

Segment Document

GENAI TREND

Splits massive unstructured documents into overlapping chunks.

segment_by (tokens, chars)chunk_sizechunk_overlap

→ doc (Segmented Chunks)

Embed & Index Documents

GENAI TREND

Generates numerical vector coordinates via selected models.

embedding_model_source (OpenAI, local)vector_dim

→ ind (Indexed Vector DB)

Retrieve Similar Docs

GENAI TREND

Semantic vector database querier.

top_ksimilarity_threshold

→ doc (Relevant Chunks)

Generate Text (LLM)

GENAI TREND

Passes dynamic context to LLM systems.

model_namesystem_prompttemperature

→ doc (LLM Generated Answer)