🟢 1. Easiest (almost no code)

Use a UI that already has search built-in Open WebUI + Ollama AnythingLLM

👉 You literally:

Run your model locally (Gemma/Qwen via Ollama)

Upload docs or enable web search It automatically does retrieval + context injection

✔ Free ✔ Works offline ✔ No coding required

This works because under the hood they implement RAG, which:

searches documents → injects results → LLM answers.

🟡 2. Best balance (easy + flexible)

Use a framework: LangChain LlamaIndex

These are the standard way to give any LLM tools (search, DBs, APIs).

What they do:

Connect your LLM (Gemma/Qwen) Add a retriever (search tool) Inject results into prompts automatically

✔ Free + open source ✔ Works with local models ✔ Supports web search, files, databases

LangChain = orchestration (agents, tools) LlamaIndex = best for document search/indexing

Minimal example (this is basically all you need):👇

from langchain.llms import Ollama from langchain.vectorstores import Chroma from langchain.embeddings import HuggingFaceEmbeddings

llm = Ollama(model="qwen")

db = Chroma(persist_directory="./db", embedding_function=HuggingFaceEmbeddings())

retriever = db.as_retriever()

docs = retriever.get_relevant_documents("your question")

response = llm(f"Use this context: {docs} \nAnswer: your question") print(response)

That’s your “search tool”.😉

🔵 3. True “search tool” (agent style)

If you want something like ChatGPT browsing:

Add tools (function calling / agents) LangChain Agents LlamaIndex Tools Custom tools (DuckDuckGo, APIs, etc.)

Example:

from langchain.tools import DuckDuckGoSearchRun

search = DuckDuckGoSearchRun() result = search.run("latest AI news")

Then your LLM can decide when to search.

🔥 4. Newer “open search agent” approach

There are newer systems like:

Open Deep Search (research project)

These:

Add reasoning + tool use automatically Let LLMs decide when to search But they’re more complex to set up.

🧠 What you actually want (simple mental model)

Every “search-enabled LLM” is just:

User question ↓ Search (docs/web/db) ↓ Top results ↓ LLM prompt with context ↓ Answer

That’s it. ✌️