> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mixpeek.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Migrate from Weaviate
> Upgrade from vector database to multimodal data warehouse
Weaviate is a vector database with built-in vectorization modules. Mixpeek is a multimodal data warehouse that goes further: decomposing files into layered features, storing them across cost tiers, and reassembling answers through multi-stage retrieval pipelines.
This guide walks you through migrating your search workload from Weaviate to Mixpeek.
## Why Migrate
Weaviate introduced multimodal capabilities with modules like `multi2vec-clip` and `img2vec-neural`. Mixpeek builds on this direction but takes a fundamentally different approach.
Where Weaviate adds vector search to a database, Mixpeek starts from the file itself. A single video becomes transcripts, visual embeddings, scene descriptions, and detected entities, each independently searchable, all stored across cost tiers, and reassembled through configurable pipelines.
| Weaviate | Mixpeek |
| --------------------------------------- | --------------------------------------------------------------------- |
| Vectorization modules bolt onto storage | Feature extraction is the core of the pipeline |
| GraphQL queries with vector search | Multi-stage retrieval pipelines: search, filter, rerank, enrich |
| Single storage tier | Tiered storage: hot, cold, archive, up to 90% savings |
| Schema-per-class design | Namespace-level organization with collections per processing pipeline |
| Module-based multimodal support | Native decomposition: one file becomes many searchable layers |
## Concept Mapping
| Weaviate | Mixpeek | Notes |
| -------------------------------- | ------------------------ | ------------------------------------------------------------------------------- |
| Class | Collection | Defines processing pipeline and feature extraction for a data type |
| Property | Document field | Documents contain extracted features, metadata, and source lineage |
| Object (with vector) | Document (with features) | Documents hold multiple feature types, not just one vector |
| Module (e.g., `text2vec-openai`) | Feature Extractor | Built-in extractors: multimodal, image, text, face-identity, document, and more |
| GraphQL query | Retriever execution | Retrievers are multi-stage pipelines, not single queries |
| `nearText` / `nearVector` | Semantic search stage | One stage in a larger pipeline |
| `where` filter | Attribute filter stage | Filters compose as pipeline stages alongside search and ranking |
| Cross-reference | Semantic JOIN / Taxonomy | Connect documents across collections using vector similarity |
## Migration Steps
Replace your Weaviate instance with a Mixpeek namespace.
```bash theme={null}
curl -X POST https://api.mixpeek.com/v1/namespaces \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"namespace_name": "media-library"
}'
```
Each Weaviate class becomes a Mixpeek collection with a feature extractor. Instead of choosing a vectorization module, you choose an extractor that matches your content type.
```python Weaviate (before) theme={null}
# Weaviate class definition
client.schema.create_class({
"class": "Article",
"vectorizer": "text2vec-openai",
"properties": [
{"name": "title", "dataType": ["text"]},
{"name": "body", "dataType": ["text"]},
{"name": "category", "dataType": ["text"]}
]
})
```
```python Mixpeek (after) theme={null}
from mixpeek import Mixpeek
client = Mixpeek(api_key="YOUR_API_KEY")
# Collection with automatic feature extraction
collection = client.collections.create(
collection_name="articles",
feature_extractor={
"feature_extractor_name": "multimodal",
"version": "v1"
},
namespace="media-library"
)
```
Upload source files through the Mixpeek pipeline instead of importing Weaviate objects. The pipeline extracts richer features than a single vectorization module.
Do not export vectors from Weaviate and import them into Mixpeek. Re-ingest your source files so the pipeline can extract multi-layered features, build lineage, and index across modalities.
```python theme={null}
# Upload objects to a bucket for processing
client.objects.create(
bucket_id="your-bucket-id",
key_prefix="/articles",
blobs=[
{"property": "content", "data": "s3://your-bucket/article-001.pdf"}
],
namespace="media-library"
)
```
Weaviate's GraphQL queries map to Mixpeek retrievers. The difference: retrievers chain multiple stages together.
```graphql Weaviate (before) theme={null}
{
Get {
Article(
nearText: { concepts: ["machine learning trends"] }
where: {
path: ["category"]
operator: Equal
valueText: "technology"
}
limit: 10
) {
title
body
_additional { score }
}
}
}
```
```python Mixpeek (after) theme={null}
results = client.retrievers.execute(
retriever_id="article-search",
inputs={
"query": "machine learning trends",
"category": "technology"
},
limit=10,
namespace="media-library"
)
```
Go beyond what Weaviate's query language supports. Chain semantic search with attribute filters, reranking, and enrichment in a single retriever.
```bash theme={null}
curl -X POST https://api.mixpeek.com/v1/retrievers \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "X-Namespace: media-library" \
-H "Content-Type: application/json" \
-d '{
"retriever_name": "article-search",
"stages": [
{
"stage_name": "search",
"stage_type": "filter",
"config": {
"stage_id": "feature_search",
"parameters": {
"final_top_k": 50,
"searches": [
{
"feature_uri": "mixpeek://multimodal_extractor@v1/vertex_multimodal_embedding",
"query": { "input_mode": "text", "value": "{{INPUT.query}}" },
"top_k": 50
}
]
}
}
},
{
"stage_name": "filter_category",
"stage_type": "filter",
"config": {
"stage_id": "attribute_filter",
"parameters": {
"field": "category",
"operator": "eq",
"value": "{{INPUT.category}}"
}
}
},
{
"stage_name": "rerank",
"stage_type": "sort",
"config": {
"stage_id": "rerank",
"parameters": {
"top_k": 10
}
}
}
]
}'
```
Execute retrievers and validate results against your Weaviate baseline.
```bash theme={null}
curl -X POST https://api.mixpeek.com/v1/retrievers/{retriever_id}/execute \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "X-Namespace: media-library" \
-H "Content-Type: application/json" \
-d '{
"inputs": {
"query": "machine learning trends",
"category": "technology"
},
"limit": 10
}'
```
## What You Gain
| Capability | Weaviate | Mixpeek |
| ------------------------- | -------------------------------------- | --------------------------------------------------------------------------------- |
| **File decomposition** | Vectorize one property at a time | Decompose a file into multiple searchable layers automatically |
| **Multi-stage retrieval** | Single query with optional filters | Chain search, filter, rerank, and enrich stages in one pipeline |
| **Tiered storage** | All data at one storage tier | Hot, cold, and archive tiers, up to 90% savings |
| **Cross-modal search** | Modules per class, limited cross-modal | Native cross-modal: text query finds video moments, audio segments, image regions |
| **No infrastructure** | Self-hosted or managed cluster | Fully managed API, no clusters, no module configuration |
| **Complete lineage** | Objects in a class | Trace results back through document, object, and source file |
If you are using Weaviate's `multi2vec-clip` for image-text search, Mixpeek's multimodal extractor handles the same use case and adds video, audio, and document support in the same namespace.
## Next Steps
Get Mixpeek running in 10 minutes
Learn about automatic feature extraction
Build multi-stage retrieval pipelines
Understand the data model