Retrieve the passage that meets every condition
Superlinked gives your agent one API to encode technical docs and regulations as token vectors, so retrieval checks every condition in a question.
create an index concurrently on a partitioned table
Concurrent builds for indexes on partitioned tables are currently not supported. However, you may concurrently build the index on each partition individually and then finally create the partitioned index non-concurrently in order to reduce the time where writes to the partitioned table will be locked out. In this case, building the partitioned index is a metadata only operation.
When CREATE INDEX is invoked on a partitioned table, the default behavior is to recurse to all partitions to ensure they all have matching indexes. Each partition is first checked to determine whether an equivalent index already exists, and if so, that index will become attached as a partition index to the index being created, which will become its parent index.
[CLS] [CLS] [CLS] [Q] [D] [D] create create CRE an the an index index DEX concurrently concurrently When on on on a the a partition partition partition ed ed ed table table table [SEP] is which Every ranking decision comes with the token scores behind it
can an airline take off with frost on the underside of the wing above the fuel tanks
(b) No person may take off an aircraft when frost, ice, or snow is adhering to the wings, control surfaces, propellers, engine inlets, or other critical surfaces of the aircraft or when the takeoff would not be in compliance with paragraph (c) of this section. Takeoffs with frost under the wing in the area of the fuel tanks may be authorized by the Administrator.
(d) A certificate holder may continue to operate under this section without a program as required in paragraph (c) of this section, if it includes in its operations specifications a requirement that, any time conditions are such that frost, ice, or snow may reasonably be expected to adhere to the aircraft, no aircraft will take off unless it has been checked to ensure that the wings, control surfaces, and other critical surfaces are free of frost, ice, and snow. The check must occur within five minutes prior to beginning takeoff. This check must be accomplished from outside the aircraft.
fuel fuel it tanks tanks it error a producer with acks=all gets when too few in-sync replicas acknowledge the write
When a producer sets acks to "all" (or "-1"), this configuration specifies the minimum number of replicas that must acknowledge a write for the write to be considered successful. If this minimum cannot be met, then the producer will raise an exception (either NotEnoughReplicas or NotEnoughReplicasAfterAppend).
acks=all This means the leader will wait for the full set of in-sync replicas to acknowledge the record. This guarantees that the record will not be lost as long as at least one in-sync replica remains alive. This is the strongest available guarantee. This is equivalent to the acks=-1 setting.
[Q] [D] [D] producer producer the in this in sync the sync write write be keep every browser and proxy cache from storing a response
The no-store response directive indicates that any caches of any kind (private or shared) should not store this response.
The private response directive indicates that the response can be stored only in a private cache (e.g., local caches in browsers).
browser this browsers The answer passage scored highest in 7 of the 8 recorded searches. This grid holds 3 of the 8 and the hero one more, and every search the answer passage lost is in this grid.
Score your own question against a passage, token by token
from sie_sdk import SIEClientclient = SIEClient( api_key="sk-sie-…", base_url="https://api.superlinked.com",)res = client.encode( "modellightonai/GTE-ModernColBERT-v1", [{"text": "querypartitioned table concurrently"}, {"text": "passageConcurrent builds for indexes on partitioned tables are currently not supported."}], output_types=["multivector"],)q = res[0]["multivector"] # [n_query_tokens, dim]d = res[1]["multivector"] # [n_doc_tokens, dim]# MaxSim late interaction: sum each query token's best doc matchscore = float(sum(max(qt @ dt for dt in d) for qt in q))print(score)import { SIEClient } from '@superlinked/sie-sdk';
const client = new SIEClient('https://api.superlinked.com', {
apiKey: 'sk-sie-…',
});
const res = await client.encode(
'lightonai/GTE-ModernColBERT-v1',
[{ text: "partitioned table concurrently" }, { text: "Concurrent builds for indexes on partitioned tables are currently not supported." }],
{ outputTypes: ['multivector'] },
);
const [q, d] = [res[0]!.multivector!, res[1]!.multivector!]; // per-token vectors
const dot = (a: Float32Array, b: Float32Array) => a.reduce((s, x, i) => s + x * b[i], 0);
const score = q.reduce((sum, qt) => sum + Math.max(...d.map((dt) => dot(qt, dt))), 0);
console.log(score);curl https://api.superlinked.com/v1/encode/lightonai%2FGTE-ModernColBERT-v1 \
-H "Authorization: Bearer sk-sie-…" \
-H "Content-Type: application/json" \
-d "{\"items\":[{\"text\":\"partitioned table concurrently\"},{\"text\":\"Concurrent builds for indexes on partitioned tables are currently not supported.\"}],\"params\":{\"output_types\":[\"multivector\"]}}"Build the "Multi-vector" capability into my app using the Superlinked Inference Engine (SIE).
Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: lightonai/GTE-ModernColBERT-v1 (SIE primitive: /encode). Keep the model id configurable.
Task
- Input: a query string plus a list of candidate documents.
- Behaviour: return the candidates ranked by late-interaction (ColBERT) relevance
- Encode the query and candidates in one batched /encode call, then rank by cosine similarity in the client. Do not call the API per candidate.
Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.- Matches at the token level with MaxSim
- Keeps detail a single vector blurs away
Multi-vector retrieval quality and latency
Deploy your way
Managed Cloud
Full compute toolkit for your agents with zero ops.
- No idle GPUs, pay for what you use
- Fits your stack: SDK, API, CLI, MCP
- Zero lock-in, self-host the same stack
- SOC 2 Type 2, US or EU data residency
no credit card required
Self-host with K8s
Easy & scalable deployment in your own cloud.
- Terraform to your cloud in minutes
- Apache-2.0, same engine as Cloud
- Scales to zero, no bill between jobs
- Per-tenant pools, no noisy neighbors
Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deploymentDeploy SIE to our GCP project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deploymentDeploy SIE to our Azure AKS cluster via helm install. Requirements: superlinked.com/docs/deployment Run locally
Run the same models on your own machine.
- Runs on NVIDIA GPU or Apple Silicon
- One command, no Docker or cluster
- All 100+ Cloud models, fully offline
- Same SDK and IDs, no code changes
pip install "sie-server[local]" && sie-server servepip install "sie-server[local]" && sie-server serve --device cuda