> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neum.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# PipelineCollection

> PipelineCollection object

<Warning>These capabilities are currently in beta. Please contact [founders@tryneum.com](mailto:founders@tryneum.com) with any questions or asks.</Warning>

## Overview

Pipeline collections are a higher level abstraction that joins multiple pipelines as a single entity. This means that you can configure pipelines individually for different data source, pre-processing steps, etc. but still treat them as a group when it comes to triggering them to run as well as when searching them.

When it comes to search, pipeline collections support three search options:

* Unified search where results from the pipelines are collected and re-ranked into a single response.
* Separate search where results for each pipeline are returned raw with an assignment to what pipeline they came from.
* (Coming soon) Routed search where the system uses the pipeline description to decide what pipelines to search given a query and re-rank the results.

## Example

For example, there are three pipelines that I have:

* The first connects to S3 where I have files from customers
* The second connects to Postgres where I am querying real-time metrics
* The third connects to some static websites with content

I can programatically build pipeline collections that have all three of my pipelines or only a subset. If I am exposing an experience to a customer, maybe I will only have a collection with customer data and static content vs if I am building an internal experience I can re-use the same pipelines I have but this time add my Postgress pipeline that has internal metrics.

## Intialize a pipeline collection

```python
from neumai.Pipelines.Pipeline import Pipeline
from neumai_tools.PipelineCollection.PipelineCollection import PipelineCollection

pipeline1 = Pipeline(...)
pipeline2 = Pipeline(...)
pipeline3 = Pipeline(...)

collection = PipelineCollection(pipelines = [pipeline1, pipeline2, pipeline3])
```

## Run pipeline collection

```python
collection.run()
```

## Search pipeline collection

```python
# Unified
collection.search_unified(query="", number_of_results=3)

# Separate
collection.search_separate(query="", number_of_results=3)

# Router (Coming Soon)
collection.search_routed(query="", number_of_results=3)

```