> ## Documentation Index > Fetch the complete documentation index at: https://docs.withsplendor.com/llms.txt > Use this file to discover all available pages before exploring further. # Recipes > What people build with Splendor — three concrete patterns, end to end, with the calls that matter. A few patterns show up again and again. Each one below is a complete, real use of Splendor — what you get, and the handful of calls that get you there. They all build on the [Quickstart](/quickstart), so do that first if you haven't. ## Search your logs and events by meaning You have a stream of events — application logs, support tickets, audit trails — and you want to find things by *what they mean*, not the exact words. A user writes "I can't log in"; you want the auth-timeout errors that never say "log in." Push events to a source, then search semantically: ```bash theme={null} # Push a batch of events (ingest token; no tenant header) curl https://api.withsplendor.com/v1/ingest/sources/app-events/logs \ -H "Authorization: Bearer $SPLENDOR_INGEST_TOKEN" \ -H "Content-Type: application/json" \ -d '[{"level":"error","message":"auth token expired during checkout","service":"auth"}]' # Find it by meaning, not keywords curl https://api.withsplendor.com/v1/search \ -H "Authorization: Bearer $SPLENDOR_TOKEN" \ -H "X-Splendor-Tenant-Id: $SPLENDOR_TENANT_ID" \ -H "Content-Type: application/json" \ -d '{"text": "users can'\''t log in", "datasets": ["app-events"], "semantic": true, "limit": 10}' ``` The same dataset also answers keyword and `SQL` queries — so you can filter by `service`, range over time, and search by meaning, all against one set of records. → Full walkthrough: [Stream logs](/guides/stream-logs) · [Semantic search](/guides/semantic-search) ## Find records by what their image looks like You have records with images — products, listings, documents with photos — and you want to find a record by the *content of its image*, then filter it like any other record. Search "a brown wooden chair" and get back the product, which you can still filter by `price` or `in_stock`. Tell the source which field holds the image, then search the image modality: ```bash theme={null} # When you create the source, declare the image field # "semantic": { "image_embeddings": { "fields": ["photo_key"] } } curl https://api.withsplendor.com/v1/search \ -H "Authorization: Bearer $SPLENDOR_TOKEN" \ -H "X-Splendor-Tenant-Id: $SPLENDOR_TENANT_ID" \ -H "Content-Type: application/json" \ -d '{"text": "a brown wooden chair", "datasets": ["products"], "semantic": true, "content_filter": "images", "limit": 10}' ``` Splendor embeds each referenced image onto its record, so a visual match returns the **record** — metadata and all — not a bare image. One entry, found by what it says and what it looks like. → Full walkthrough: [Sources & images](/concepts/sources-and-assets) ## Load a large image corpus Your images live somewhere else — a bucket, or a dataset of image URLs — and you want to search them. Splendor embeds images from **your own storage** (not arbitrary web URLs), so bring the pixels into a source first. For many images, presign a **batch** in one call, PUT them in parallel, then point each record at the returned `image_reference`. ```bash theme={null} # 1. Presign up to 256 image objects in one call (one PUT URL each). No ingest # run is created per image — the objects are just stored for records to reference. curl https://api.withsplendor.com/v1/ingest/sources/$SOURCE_KEY/asset-uploads \ -H "Authorization: Bearer $SPLENDOR_INGEST_TOKEN" \ -H "Content-Type: application/json" \ -d '{"assets": [ {"content_type": "image/jpeg", "object_size_bytes": 84213, "filename": "a12.jpg"}, {"content_type": "image/png", "object_size_bytes": 12044, "filename": "b7.png"} ]}' # → { "assets": [ { "upload_url": "https://…", "required_headers": { … }, # "image_reference": "s3://…/sources/…/a12.jpg" }, … ] } # 2. PUT each image to its upload_url in parallel, sending required_headers. # 3. Ingest records whose declared image field holds the returned image_reference # (source config: "semantic": { "image_embeddings": { "fields": ["image"] } }): # { "id": "sku-a12", "image": "s3://…/sources/…/a12.jpg", "price": 249 } ``` Each record embeds its referenced image when it ingests; then search with `content_filter: "images"` as above. Use `image_reference` verbatim — a bare object key is read relative to the source prefix and would resolve to the wrong object. → [Sources & images](/concepts/sources-and-assets) ## Give every one of your customers their own search backend You're building a product *on* Splendor and each of your customers needs isolated search over their own data. You don't want to run a search cluster per customer, and their data must never mix. Provision a tenant per customer with one **platform key**, and act inside any of them by naming it in the header: ```bash theme={null} # Create a tenant for a customer curl https://api.withsplendor.com/v1/platform/tenants \ -H "Authorization: Bearer $SPLENDOR_PLATFORM_KEY" \ -H "Content-Type: application/json" \ -d '{"name": "Acme Corp", "external_id": "cus_8210"}' # Then do anything inside that tenant with the SAME key + its id curl https://api.withsplendor.com/v1/search \ -H "Authorization: Bearer $SPLENDOR_PLATFORM_KEY" \ -H "X-Splendor-Tenant-Id: t-3f9c2a1b..." \ -H "Content-Type: application/json" \ -d '{"text": "invoice", "datasets": ["documents"], "limit": 10}' ``` One credential creates and operates an unlimited fleet of tenants — each fully isolated — all over the API. → Full walkthrough: [Build a platform](/guides/platform-patterns) · [Provision tenants](/guides/provision-tenants) ## Keep going How text, SQL, and semantic search share one envelope. Push over HTTP, wire up a webhook, or read from your own S3 bucket. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.