Logic
Blocks for conditional logic, comparisons, calculations, data sampling, and control flow operations in workflows.
Calculator
What it is
Performs a mathematical operation on two numbers.
How it works
The Calculator block takes in two numbers and an operation choice. It then applies the chosen operation to the numbers and returns the result. If rounding is selected, it rounds the result to the nearest whole number.
Inputs
operation
Choose the math operation you want to perform
"Add" | "Subtract" | "Multiply" | "Divide" | "Power"
Yes
a
Enter the first number (A)
float
Yes
b
Enter the second number (B)
float
Yes
round_result
Do you want to round the result to a whole number?
bool
No
Outputs
error
Error message if the operation failed
str
result
The result of your calculation
float
Possible use case
A user wants to quickly perform a calculation, such as adding two numbers or calculating a percentage. They can input the numbers and operation into this block and receive the result instantly.
Condition
What it is
Handles conditional logic based on comparison operators
How it works
This block compares two values using standard operators (==, !=, >, <, >=, <=) and routes data based on the result. The comparison result determines which output receives data: yes_output for true conditions, no_output for false.
Optionally specify yes_value and no_value to output different data than the input values. If not specified, value1 is used as the output value.
Inputs
value1
Enter the first value for comparison
Value1
Yes
operator
Choose the comparison operator
"==" | "!=" | ">" | "<" | ">=" | "<="
Yes
value2
Enter the second value for comparison
Value2
Yes
yes_value
(Optional) Value to output if the condition is true. If not provided, value1 will be used.
Yes Value
No
no_value
(Optional) Value to output if the condition is false. If not provided, value1 will be used.
No Value
No
Outputs
error
Error message if the operation failed
str
result
The result of the condition evaluation (True or False)
bool
yes_output
The output value if the condition is true
Yes Output
no_output
The output value if the condition is false
No Output
Possible use case
Threshold Checks: Route workflow differently when values exceed limits (e.g., order total > $100 triggers approval).
Status Validation: Check if a status equals "complete" or "error" to branch workflow logic.
Numeric Comparisons: Compare scores, counts, or metrics to conditionally trigger actions.
Count Items
What it is
Counts the number of items in a collection.
How it works
The Count Items block receives a collection as input. It then determines the type of collection and uses the appropriate method to count the items. For most collections, it uses the length function. For other iterable objects, it counts the items one by one.
Inputs
collection
Enter the collection you want to count. This can be a list, dictionary, string, or any other iterable.
Collection
Yes
Outputs
error
Error message if the operation failed
str
count
The number of items in the collection
int
Possible use case
A user has a list of customer names and wants to quickly determine how many customers are in the list. They can input the list into this block and receive the total count immediately.
Data Sampling
What it is
This block samples data from a given dataset using various sampling methods.
How it works
This block extracts a sample from a dataset using various methods: random sampling, systematic sampling (every nth item), or top-N selection. Advanced options include stratified sampling by key, weighted sampling, and cluster sampling.
Configure sample_size to control how many items to select. Use random_seed for reproducible results. The accumulate option collects data before sampling, useful when processing streaming inputs.
Inputs
data
The dataset to sample from. Can be a single dictionary, a list of dictionaries, or a list of lists.
Dict[str, Any] | List[Dict[str, Any] | List[Any]]
Yes
sample_size
The number of samples to take from the dataset.
int
No
sampling_method
The method to use for sampling.
"random" | "systematic" | "top" | "bottom" | "stratified" | "weighted" | "reservoir" | "cluster"
No
accumulate
Whether to accumulate data before sampling.
bool
No
random_seed
Seed for random number generator (optional).
int
No
stratify_key
Key to use for stratified sampling (required for stratified sampling).
str
No
weight_key
Key to use for weighted sampling (required for weighted sampling).
str
No
cluster_key
Key to use for cluster sampling (required for cluster sampling).
str
No
Outputs
error
Error message if the operation failed
str
sampled_data
The sampled subset of the input data.
List[Dict[str, Any] | List[Any]]
sample_indices
The indices of the sampled data in the original dataset.
List[int]
Possible use case
A/B Testing: Randomly sample users or records for testing different workflow paths.
Representative Subsets: Extract stratified samples from large datasets for analysis or testing.
Performance Testing: Select a smaller sample from large data for workflow development and debugging.
If Input Matches
What it is
Handles conditional logic based on comparison operators
How it works
This block checks if an input matches a specified value and routes data accordingly. When the input equals the value, data flows to yes_output; otherwise, it goes to no_output.
Use yes_value and no_value to specify what data to output in each case. This provides a simple equality check for branching workflow logic.
Inputs
input
The input to match against
Input
Yes
value
The value to output if the input matches
Value
Yes
yes_value
The value to output if the input matches
Yes Value
No
no_value
The value to output if the input does not match
No Value
No
Outputs
error
Error message if the operation failed
str
result
The result of the condition evaluation (True or False)
bool
yes_output
The output value if the condition is true
Yes Output
no_output
The output value if the condition is false
No Output
Possible use case
Category Routing: Route items to different processing paths based on their category or type.
Feature Flags: Check if a feature flag equals "enabled" to conditionally execute new functionality.
Status Handling: Branch workflow based on specific status values like "pending", "approved", or "rejected".
Pinecone Init
What it is
Initializes a Pinecone index
How it works
This block initializes or connects to a Pinecone vector database index. Specify the index name, vector dimension, and distance metric (cosine, euclidean, or dot product) for new indexes.
For serverless deployment, configure the cloud provider and region. The block returns the initialized index name for use with insert and query operations.
Inputs
index_name
Name of the Pinecone index
str
Yes
dimension
Dimension of the vectors
int
No
metric
Distance metric for the index
str
No
cloud
Cloud provider for serverless
str
No
region
Region for serverless
str
No
Outputs
error
Error message if the operation failed
str
index
Name of the initialized Pinecone index
str
message
Status message
str
Possible use case
RAG Pipeline Setup: Initialize a vector index for storing document embeddings in retrieval-augmented generation.
Semantic Search: Set up a vector database for similarity search across products, documents, or media.
Knowledge Base: Create a searchable vector store for FAQ answers or support documentation.
Pinecone Insert
What it is
Upload data to a Pinecone index
How it works
This block uploads vectors and associated text chunks to a Pinecone index. Each chunk is paired with its embedding vector, and optional metadata can be attached for filtering during queries.
Use namespaces to organize vectors into logical groups within the same index. The upsert operation adds new vectors or updates existing ones with matching IDs.
Inputs
index
Initialized Pinecone index
str
Yes
chunks
List of text chunks to ingest
List[Any]
Yes
embeddings
List of embeddings corresponding to the chunks
List[Any]
Yes
namespace
Namespace to use in Pinecone
str
No
metadata
Additional metadata to store with each vector
Dict[str, Any]
No
Outputs
error
Error message if the operation failed
str
upsert_response
Response from Pinecone upsert operation
str
Possible use case
Document Indexing: Store document chunks with their embeddings for later semantic search.
Knowledge Ingestion: Add FAQ entries, product descriptions, or support articles to a searchable index.
Memory Storage: Store conversation history or agent memories as searchable vectors.
Pinecone Query
What it is
Queries a Pinecone index
How it works
This block searches a Pinecone index for vectors similar to a query vector. Specify top_k to control how many results to return, and use namespace to search within a specific partition.
Results include similarity scores and optionally the vector values and metadata. Combined results aggregate the text chunks for easy use in RAG pipelines.
Inputs
query_vector
Query vector
List[Any]
Yes
namespace
Namespace to query in Pinecone
str
No
top_k
Number of top results to return
int
No
include_values
Whether to include vector values in the response
bool
No
include_metadata
Whether to include metadata in the response
bool
No
host
Host for pinecone
str
No
idx_name
Index name for pinecone
str
Yes
Outputs
error
Error message if the operation failed
str
results
Query results from Pinecone
Results
combined_results
Combined results from Pinecone
Combined Results
Possible use case
Semantic Search: Find the most relevant documents or answers based on meaning, not just keywords.
RAG Context: Retrieve relevant context passages to augment LLM prompts with domain-specific knowledge.
Similar Content: Find products, articles, or media similar to a reference item.
Step Through Items
What it is
Iterates over a list or dictionary and outputs each item.
How it works
When given a list or dictionary, the block processes each item individually. For lists, it keeps track of the item's position (index). For dictionaries, it focuses on the values, using the value as both the item and the key in the output.
Inputs
items
The list or dictionary of items to iterate over
List[Any]
No
items_object
The list or dictionary of items to iterate over
Dict[str, Any]
No
items_str
The list or dictionary of items to iterate over
str
No
Outputs
error
Error message if the operation failed
str
item
The current item in the iteration
Item
key
The key or index of the current item in the iteration
Key
Possible use case
Imagine you have a list of customer names and you want to perform a specific action for each customer, like sending a personalized email. This block could help you go through the list one by one, allowing you to process each customer individually.
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