LlamaIndex + AI Gateway
Use LlamaIndex for retrieval context and a gateway for inference control, preserving both evidence trails from source to selected model route.
Published: March 20, 2026 · By the QVeris Team
TL;DR
Keep documents, chunks, filters, scores and citations.
Keep candidates, policy, selected model, attempts and native IDs.
External APIs and actions need separate credentials and side-effect controls.
Measure retrieval relevance, context completeness, route fit and answer quality.
Integration boundaries
Connectors, indexes, retrievers and query engines produce a context package with citations. The LLM interface sends that package and required capabilities to the gateway.
The boundary should be explicit. LlamaIndex owns source ingestion, document metadata, chunk selection, retrieval filters, reranking and citation assembly. The gateway owns model eligibility, region and policy checks, rate-limit handling, retries, fallbacks and usage accounting. If either layer silently changes the other's output, the team can no longer explain whether a weak answer came from poor retrieval or a poor inference route.
Define a context contract before integration. It should state the maximum context size, required citation fields, freshness timestamp, tenant or data-classification labels, and behavior when evidence is incomplete. The gateway may choose a model that can accept that contract, but it should not drop citations or compress away qualifiers merely to fit a smaller context window.
The gateway selects and executes a model route without rewriting retrieval provenance. Tool and live-data calls stay independently governed, because retrieving a document is not equivalent to authorizing an external action or accepting a live-data result.
Evidence and control ownership
| Layer | Best fit | Verify before choosing |
|---|---|---|
| Retrieval | LlamaIndex data and query pipeline. | Version sources, filters, chunks, citations and freshness. |
| Inference | AI gateway model path. | Record capability match, route, adapter, attempts and usage. |
| Tools | Application capability layer. | Scope keys, validate arguments, enforce idempotency and trace actions. |
| Evaluation | Joined offline and online evidence. | Score retrieval and model behavior separately before end-to-end acceptance. |
Setup checklist
Define citation, freshness, size and privacy rules.
Declare tools, schema, context and streaming requirements.
Propagate one workflow trace across retrieval, route and tools.
Keep the prior model route and index configuration ready.
Evaluate before rollout
Build the evaluation set from real query classes rather than a handful of polished demos. Include direct factual questions, multi-document synthesis, ambiguous requests, time-sensitive questions and queries that should return “insufficient evidence.” Each test case needs an expected source set or relevance judgment as well as an answer-quality rubric.
- Replay labeled queries and verify retrieved sources plus citations.
- Contract-test each eligible model route with real context sizes.
- Inject stale indexes, missing sources, provider failures and tool errors.
- Canary by query class and retain independent retrieval and route rollback.
Measure the layers separately before judging the final answer. Useful retrieval metrics include source recall, chunk precision, citation completeness and freshness violations. Route metrics include policy eligibility, time to first token, total latency, fallback rate, cost and structured-output validity. End-to-end answer quality matters, but it cannot identify which layer needs repair on its own.
For rollout, compare the candidate stack against the current production baseline. A new gateway route should not ship merely because it is cheaper or faster; it must remain within the accepted retrieval-grounding and answer-quality thresholds. Canary traffic by query class, not only by random user percentage, because long-context research and short factual lookup often fail in different ways.
Join two evidence planes
LlamaIndex emits a cited context package. The gateway receives that package, workload alias and capability flags, then chooses and executes a model route. Retrieval and route records keep separate schemas under one trace.
A practical trace joins, but does not merge, the two records. The retrieval record can include index version, query transformation, filters, selected node IDs, scores, document versions and citation IDs. The route record can include policy version, candidate models, selected provider, attempt sequence, native request IDs, token usage and validation outcome. A shared workflow trace ID is enough to correlate them during debugging.
Keep raw evidence long enough to investigate failures, while applying retention and redaction rules to sensitive document content. Operational dashboards may aggregate latency and cost, but an incident reviewer still needs the source and route identifiers behind a specific answer. Without that link, observability shows that something failed without explaining why.
Production rule: a model route must never erase or invent retrieval provenance.
Use QVeris for external capabilities and live data
QVeris complements retrieval and inference with Discover → Inspect → Call access to external APIs, tools, services and live data. Attach source freshness and tool evidence to the same workflow trace.
This separation is useful when a RAG answer needs information that should not be embedded into the index. A financial research assistant may retrieve internal policy documents with LlamaIndex, then request a current market price, filing or exchange rate through QVeris. The indexed documents remain the source of policy truth; the tool result remains a time-stamped observation with its own provider and schema evidence.
Inspect the capability before calling it. Confirm required arguments, coverage, update frequency, expected output schema, estimated cost and permission requirements. After execution, validate timestamps, units, identifiers and provider-reported errors before adding the result to the final prompt. This prevents a fresh-looking tool response from overriding better retrieval evidence simply because it arrived later.
Use OpenAILike as a narrow LlamaIndex adapter
LlamaIndex provides an OpenAILike integration for OpenAI-compatible endpoints whose model names are not known to its native OpenAI class. Configure exact context and output limits from the tested endpoint instead of copying values from a model-family name.
from llama_index.llms.openai_like import OpenAILike
import os
llm = OpenAILike(
model=os.environ["LLM_MODEL_ID"],
api_base=os.environ["LLM_GATEWAY_BASE_URL"],
api_key=os.environ["LLM_GATEWAY_API_KEY"],
is_chat_model=True,
context_window=int(os.environ["LLM_CONTEXT_WINDOW"]),
max_tokens=64,
)
response = llm.complete("Reply with LLAMAINDEX_OK")
print(str(response))- Install and pin llama-index-llms-openai-like in the deployment lockfile.
- Test completion, chat, streaming, embeddings, and tool use as separate capabilities.
- Store the gateway trace ID beside the LlamaIndex callback or event record.
Verified implementation reference: LlamaIndex OpenAILike.
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Use these adjacent guides to compare the next architectural decision, implementation boundary, or operating control without starting the research again.
FAQ
No. LlamaIndex handles data and retrieval; the gateway handles model access.
Retrieval provenance and model routing decisions.
In a governed capability layer with separate credentials and traces.