Skip to main content

Documentation Index

Fetch the complete documentation index at: https://developers.lighton.ai/llms.txt

Use this file to discover all available pages before exploring further.

Illustration of the Ask use case.
Ask is retrieval-augmented generation in a single call. It runs the same retrieval pipeline as Search, then passes the retrieved passages to an LLM that writes a natural-language answer grounded in them, and hands back both the answer and the sources it drew from. Use Ask when you want a direct answer to a question. Use Search when you want the raw passages and intend to do your own ranking, display, or generation on top of them.
Ask is intended for basic, single-turn question answering. It runs one retrieval and one generation with a fixed prompt and no tool use, memory, or follow-up reasoning. For advanced RAG (multi-step retrieval, query rewriting, conversational context, custom prompts, or your own choice of model), call POST /api/v3/search directly and drive generation from within your own agentic loop.
This tutorial walks through POST /api/v3/ask. For the full schema and every parameter, see the API reference. Each request costs one search-with-generation credit.

​
Your first question

import requests

response = requests.post(
    "https://api.lighton.ai/api/v3/ask",
    headers={"Authorization": "Bearer $CONSOLE_API_KEY"},
    json={"query": "What is our JWT token expiry policy?"},
)

body = response.json()
print(body["answer"])
for result in body["results"]:
    print(f"  ↳ {result['source']['filename']}, p.{result['source']['page_start']}")
By default this searches every document your API key can reach, retrieves the top 10 passages, and generates an answer with the flagship model. The query is capped at 1500 characters.

​
Reading the response

The response has two fields:
  • answer: the LLM-generated answer, grounded in the retrieved passages.
  • results: the ranked chunks used as context, in the same shape as a Search result (chunk_id, content, score, source, workspace). Use these to show citations or let users open the source document.
{
  "results": [
    {
      "chunk_id": "550e8400-e29b-41d4-a716-446655440000",
      "content": "JWT tokens are signed using RS256 and expire after 1 hour.",
      "score": { "retrieval": 0.92, "reranking": 0.95 },
      "source": {
        "file_id": 512,
        "filename": "auth-system.pdf",
        "title": "Authentication System Design",
        "mime_type": "pdf",
        "size_bytes": 482113,
        "page_start": 3,
        "page_end": 4,
        "total_pages": 12,
        "tags": [{"id": 7, "name": "security"}],
        "external_metadata": null
      },
      "workspace": {"id": 42, "name": "Engineering Docs"}
    }
  ],
  "answer": "JWT tokens are signed using RS256 and expire after 1 hour (auth-system.pdf, page 3)."
}

​
Scoping to a subset of documents

Ask uses the same scoping rules as Search. Pass workspace_id and/or tag_id to narrow the corpus, or file_id to target specific files. file_id is mutually exclusive with workspace_id and tag_id. 
json={
    "query": "What are the GDPR data retention requirements?",
    "tag_id": [7],
}
max_results (default 10, range 1 to 50) controls how many passages are retrieved and fed to the model as context. More context can improve answer quality on broad questions, at the cost of latency.

​
Choosing a model

Two models are supported. Any other value is rejected with a 422.
modelDescription
mistral-large-latest (default)Mistral Large 2, flagship, best answer quality.
alfred-ft5LightOn fine-tune, lighter and faster for straightforward questions.
json={
    "query": "Summarize the incident response playbook.",
    "model": "alfred-ft5",
}

​
Streaming the answer

For chat-style UIs where you want to show the answer as it’s written, set stream: true. The response is a stream of Server-Sent Events instead of a single JSON body:
EventPayload
sourcesThe retrieved chunks (same shape as results), emitted first.
tokenAn incremental piece of the answer. Many of these arrive in sequence.
doneThe stream is complete.
errorGeneration failed; the stream ends.
import json
import requests

with requests.post(
    "https://api.lighton.ai/api/v3/ask",
    headers={"Authorization": "Bearer $CONSOLE_API_KEY"},
    json={"query": "What is our JWT token expiry policy?", "stream": True},
    stream=True,
) as response:
    event = None
    for line in response.iter_lines(decode_unicode=True):
        if not line:
            continue
        if line.startswith("event:"):
            event = line[len("event:"):].strip()
        elif line.startswith("data:"):
            data = json.loads(line[len("data:"):].strip())
            if event == "sources":
                sources = data          # retrieved chunks, arrive once up front
            elif event == "token":
                print(data, end="", flush=True)   # stream answer to the UI
            elif event == "error":
                raise RuntimeError(data)
Sources arrive first so you can render citations before the answer starts streaming. Stop reading once you receive the done event.

​
Common errors

StatusCause
400Request body is not parsable JSON
403None of the provided filters resolve to authorized resources
422Validation error, e.g. unsupported model, or file_id combined with workspace_id/tag_id
429Rate limit exceeded
404A supported model is not currently available on the backend
503Model temporarily unavailable. Retry later
504Model did not respond in time. Retry later