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INTEGRATIONS · LLAMAINDEX

LlamaIndex search tool integration

Use Annolux as a search tool or external retrieval reader in LlamaIndex Python & TS for temporal RAG pipelines.

Python FunctionTool setup

Wrap the Annolux Search API as a LlamaIndex FunctionTool for use in AgentRunner, FunctionCallingAgent, and RAG QueryEngine.

import os
import requests
from llama_index.core.tools import FunctionTool

def search_web(query: str, limit: int = 10) -> str:
    """Search curated English and Chinese technical web pages with timestamps."""
    api_key = os.environ.get("ANNOLUX_API_KEY", "")
    res = requests.post(
        "https://api.annolux.com/api/v1/search",
        headers={"Authorization": f"Bearer {api_key}"},
        json={"query": query, "limit": limit, "deduplicate": True},
        timeout=30,
    )
    return res.text if res.ok else f"Error: {res.status_code}"

annolux_tool = FunctionTool.from_defaults(
    fn=search_web,
    name="annolux_search",
    description="Search curated English and Chinese technical web pages with verified fetched_at timestamps."
)

Using search in RAG pipelines

In dynamic RAG pipelines, the agent invokes search_web when internal vector embeddings return low similarity. Results carry fetched_at timestamps so answers reflect verified doc dates.

Boundaries and credits

search_web queries a curated bilingual index. Exactly 1 credit per successful 2xx query; errors and timeouts cost zero. New accounts receive 1,000 free permanent credits.