Research assistant

This example builds a research assistant that searches multiple related queries, deduplicates and aggregates the results. It uses the default auto search mode, which gives multi-query research calls deep treatment automatically — you can pin searchMode=deep or exhaustive for guaranteed research-grade coverage.

Complete example

# Multi-query search with a JSON array
curl -G -s "https://api.andiai.com/api/v1/search" \
  --data-urlencode 'q=["quantum computing applications", "quantum computing challenges", "quantum computing outlook"]' \
  -d "limit=10" \
  -H "x-api-key: $ANDI_API_KEY" | jq '{
    correctedQuery,
    result_count: (.results | length),
    results: [.results[] | {title, link, source}]
  }'

import os
import requests

api_key = os.environ["ANDI_API_KEY"]


def research(queries: list[str], search_mode: str = "auto", limit: int = 10) -> dict:
    """Run multiple search queries and aggregate results."""
    all_results = []
    seen_urls = set()
    corrected_queries = {}

    for query in queries:
        response = requests.get(
            "https://api.andiai.com/api/v1/search",
            params={
                "q": query,
                "searchMode": search_mode,
                "limit": limit,
                "extracts": "true",
            },
            headers={"x-api-key": api_key},
        )
        response.raise_for_status()
        data = response.json()

        # Track spell corrections
        if data.get("correctedQuery"):
            corrected_queries[query] = data["correctedQuery"]

        # Deduplicate by URL
        for result in data["results"]:
            if result["link"] not in seen_urls:
                seen_urls.add(result["link"])
                result["_query"] = data.get("correctedQuery", query)
                all_results.append(result)

    return {
        "results": all_results,
        "total_unique": len(all_results),
        "corrected_queries": corrected_queries,
    }


# Research a topic from multiple angles
queries = [
    "quantum computing applications",
    "quantum computing challenges limitations",
    "recent quantum computing breakthroughs",
]

research_data = research(queries)
print(f"Found {research_data['total_unique']} unique results across {len(queries)} queries")

if research_data["corrected_queries"]:
    print("\nSpell corrections:")
    for original, corrected in research_data["corrected_queries"].items():
        print(f"  {original} -> {corrected}")

print("\nTop results:")
for result in research_data["results"][:10]:
    print(f"  [{result['source']}] {result['title']}")

const apiKey = process.env.ANDI_API_KEY;

async function research(queries, searchMode = "auto", limit = 10) {
  const allResults = [];
  const seenUrls = new Set();
  const correctedQueries = {};

  for (const query of queries) {
    const url = new URL("https://api.andiai.com/api/v1/search");
    url.searchParams.set("q", query);
    url.searchParams.set("searchMode", searchMode);
    url.searchParams.set("limit", String(limit));
    url.searchParams.set("extracts", "true");

    const response = await fetch(url, {
      headers: { "x-api-key": apiKey },
    });

    if (!response.ok) throw new Error(`Search failed: ${response.status}`);

    const data = await response.json();

    if (data.correctedQuery) {
      correctedQueries[query] = data.correctedQuery;
    }

    for (const result of data.results) {
      if (!seenUrls.has(result.link)) {
        seenUrls.add(result.link);
        result._query = data.correctedQuery || query;
        allResults.push(result);
      }
    }
  }

  return {
    results: allResults,
    totalUnique: allResults.length,
    correctedQueries,
  };
}

// Research a topic from multiple angles
const queries = [
  "quantum computing applications",
  "quantum computing challenges limitations",
  "recent quantum computing breakthroughs",
];

const data = await research(queries);
console.log(
  `Found ${data.totalUnique} unique results across ${queries.length} queries`
);

if (Object.keys(data.correctedQueries).length > 0) {
  console.log("\nSpell corrections:");
  for (const [original, corrected] of Object.entries(data.correctedQueries)) {
    console.log(`  ${original} -> ${corrected}`);
  }
}

console.log("\nTop results:");
data.results.slice(0, 10).forEach((r) => {
  console.log(`  [${r.source}] ${r.title}`);
});

How it works

Multiple queries — search the same topic from different angles to get broader coverage
Search mode — the default auto mode gives multi-query research calls deep treatment automatically; pin searchMode=deep or exhaustive for guaranteed research-grade coverage and spell correction
Deduplication — track URLs already seen to avoid duplicate results across queries
Spell correction tracking — correctedQuery shows when deep search fixed a typo

Using multi-query in a single request

The API also supports passing a JSON array of up to 5 queries in the q parameter:

import json

response = requests.get(
    "https://api.andiai.com/api/v1/search",
    params={
        "q": json.dumps([
            "quantum computing applications",
            "quantum computing challenges",
            "quantum computing outlook",
        ]),
        "limit": 10,
    },
    headers={"x-api-key": api_key},
)

Multi-query via JSON array returns combined results in a single response. The sequential approach above gives you per-query control and deduplication, but uses more API calls.

Variations

With source filtering

Focus research on academic or authoritative sources:

research_data = research(
    queries=["quantum computing applications"],
    limit=20,
)

# Post-filter by domain
academic_results = [
    r for r in research_data["results"]
    if any(d in r["source"] for d in ["arxiv.org", "nature.com", "ieee.org", "acm.org"])
]

Or use includeDomains to restrict at the API level:

response = requests.get(
    "https://api.andiai.com/api/v1/search",
    params={
        "q": "quantum computing",
        "includeDomains": "arxiv.org,nature.com,ieee.org",
    },
    headers={"x-api-key": api_key},
)

Generating a research summary

Combine results with an LLM for a synthesized report:

# Gather context from research
context_parts = []
for result in research_data["results"][:15]:
    text = result["desc"]
    if result.get("extracts"):
        text = " ".join(result["extracts"])
    context_parts.append(f"[{result['title']}]({result['link']})\n{text}")

context = "\n\n".join(context_parts)

prompt = f"""Write a research summary based on these search results.
Organize by theme. Cite sources with URLs.

{context}"""

# Send to your LLM of choice

Next steps

Deep search

Spell correction and extended source coverage.

RAG pipeline

Use search results as LLM context.

Query parameters

Full parameter reference.

Filtering

Domain, date, and content filtering.

Getting started

Use cases

Complete example

How it works

Using multi-query in a single request

Variations

With source filtering

Generating a research summary

Next steps

Deep search

RAG pipeline

Query parameters

Filtering

​Complete example

​How it works

​Using multi-query in a single request

​Variations

​With source filtering

​Generating a research summary

​Next steps

Deep search

RAG pipeline

Query parameters

Filtering

Complete example

How it works

Using multi-query in a single request

Variations

With source filtering

Generating a research summary

Next steps