AutoGPT/docs/integrations/block-integrations/logic.md

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

Input	Description	Type	Required
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

Output	Description	Type
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.

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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

Input	Description	Type	Required
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

Output	Description	Type
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.

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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

Input	Description	Type	Required
collection	Enter the collection you want to count. This can be a list, dictionary, string, or any other iterable.	Collection	Yes

Outputs

Output	Description	Type
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.

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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

Input	Description	Type	Required
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

Output	Description	Type
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.

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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	Description	Type	Required
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

Output	Description	Type
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".

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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

Input	Description	Type	Required
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

Output	Description	Type
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.

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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

Input	Description	Type	Required
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

Output	Description	Type
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.

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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

Input	Description	Type	Required
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

Output	Description	Type
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.

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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

Input	Description	Type	Required
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

Output	Description	Type
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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