<|channel|>analysis
<think>
    The user asks: "Dude how do I get websearch functionality into llama.cpp?" They want to add websearch functionality to the llama.cpp project. The user uses casual tone. We should answer in a casual tone, with minimal lists (they asked to minimize lists). We need to not use tables unless asked. So we can give a textual description with bullet points maybe but keep them minimal. Provide steps to integrate websearch. Perhaps mention that llama.cpp itself is a C/C++ inference engine for LLaMA models; it doesn't have built-in websearch. To add websearch functionality, you need to interface the model with external tools. Could embed a websearch API like SerpAPI, Bing API, Google Custom Search, or use an open-source search engine. Use a tool like LangChain or LlamaIndex to orchestrate. Use a plugin architecture: run a wrapper that intercepts prompts, checks for search triggers, makes HTTP request to search API, and injects results back into the prompt. Could also use external scripts.

    Explain steps: 1) Get an API key for a search provider; 2) Write a small C++/Python script that queries the API; 3) Modify your inference code to parse a special token like "[SEARCH: query]" and call the script; 4) Append the search results to the context and generate further. Or use an external orchestrator (like using a shell script) that does the logic and calls llama.cpp with the appropriate context.

    Maybe mention using tools like "gpt-engineer" or "AutoGPT". Provide an example in pseudo code.

    Also mention that if you want a more integrated solution, you could add a function calling system akin to function calling in ChatGPT: define a function "web_search(query)" and have your program call it when the model outputs a certain tag.

    Explain that llama.cpp is single-threaded and low-level; you might want to run the search in a separate thread or process.

    Also mention that you can compile llama.cpp with extra libraries: libcurl for HTTP requests. Then you could embed a simple web search inside.

    Alternatively, use a python wrapper to call the search and then feed it to the model.

    Also mention that if you want a local search engine, you can run something like SearXNG or use a vector database with scraped content.

    We need to answer in casual tone. Provide enough detail, but not a huge list. Use minimal bullet points perhaps.

    Potential pitfalls: token limit, context size, need to filter results, maybe limit to short snippet.

    Also mention that you need to handle the model's output; you can add a detection for a "search:" marker.

    Ok.

    We can also mention that open-source projects like "text-generation-webui" already have websearch plugin support.

    But the user specifically asks for llama.cpp.

    We can suggest using "llama.cpp server" (the "llama-server" mode) and then have a separate backend for search.

    Alternatively, using "llama.cpp" with a custom prompt that includes search results.

    We can propose a simple pipeline: user input => check if search needed => call search API => feed results + original query to llama.cpp.

    We can also suggest using "LangChain" to orchestrate; but that is Python, not directly integrated into llama.cpp but you can use it as a wrapper.

    Alright.

    We need to avoid tables. Keep it casual.

    Let's write an answer with short paragraphs. Use bullet points sparingly.

    We can also mention that if you want to embed search directly in C++ code, you could add libcurl to query the API, parse JSON with nlohmann/json, and then construct a new prompt.

    Ok.

    Now let's produce final answer.
</think>

Alright, so you want your LLaMA model (running via llama.cpp) to actually go out and fetch the web when it needs more info. llama.cpp itself is just a bare‑bones inference engine – no built‑in “search” magic – but you can hook one up pretty easily. Below is the gist of how folks usually do it.

**1. Pick a search backend**
You need something that can return a snippet (or a few links) given a query. The most common routes are:

* A paid API (Google Custom Search, Bing Search API, SerpAPI, etc.) – you just send an HTTP GET and get JSON back.
* A free, self‑hosted option like SearXNG or MeiliSearch if you’ve already scraped a bunch of pages.

Get an API key (or spin up the local service) and test the endpoint with curl or Postman first – make sure you can pull a short text snippet.

**2. Wire a “search‑call” into your inference loop**
There are two popular ways to do this:

**A. Wrapper script (Python/Node/etc.)**
Run llama.cpp as a subprocess, but let a thin script mediate the conversation. The script looks for a special marker in the model’s output – something like `[SEARCH: what is the capital of Iceland?]`. When it sees that, it fires off the HTTP request, grabs the top result, and prepends it to the next prompt you feed back into the model.

Pros: you stay in a high‑level language, easy to use `requests` or `httpx`, JSON parsing is trivial.
Cons: you have an extra process hop, a tiny bit more latency.

**B. Direct C++ integration**
If you’re comfortable hacking the llama.cpp code, you can compile it with libcurl (or any HTTP client you like) and a JSON parser (nlohmann/json is a good pick). Add a function like:

```cpp
std::string web_search(const std::string &query) {
    // build URL with your API key
    std::string url = "https://api.bing.microsoft.com/v7.0/search?q=" + curl_escape(query);
    // use libcurl to GET, store response in a string
    std::string json = http_get(url);
    // parse JSON, pull out snippet or first result
    return extract_snippet(json);
}
```

Then, right before you call `llama_eval`, scan the user prompt for a token like `<search>`; if it’s there, replace it with the snippet you just fetched. You can also have the model itself emit a token that you catch after each generation step.

Pros: everything stays in one binary, lower overhead.
Cons: you need to manage HTTP, JSON, and any rate‑limit handling yourself.

**3. Keep the context sane**
LLMs have a finite context window (usually 4‑8 k tokens for the LLaMA models people run locally). Don’t dump the whole HTML page back – strip it down to a sentence or two that actually answers the query. If you need more depth, you can chunk the result and feed it in multiple steps, but keep an eye on token budget.

**4. Prompt engineering**
Tell the model when it’s allowed to search. A classic pattern is:

```
You are a helpful assistant. If you don’t know something, you may request a web search by outputting:
[SEARCH: <your query>]

Otherwise, answer directly.
```

When the model outputs the `[SEARCH: …]` line, your wrapper intercepts it, runs the API call, and then re‑prompts:

```
User: Who won the 2023 Tour de France?
[SEARCH: 2023 Tour de France winner]
```

Your code fetches the answer (“Jonas Vingegaard”) and sends:

```
Search result: Jonas Vingegaard won the 2023 Tour de France.
User: Who won the 2023 Tour de France?
```

Now the model can answer confidently.

**5. Glue it together**
If you go the wrapper route, a minimal Python sketch looks like this:

```python
import subprocess, json, requests

API_KEY = "YOUR_BING_KEY"
SEARCH_URL = "https://api.bing.microsoft.com/v7.0/search"

def web_search(q):
    resp = requests.get(SEARCH_URL, params={"q": q}, headers={"Ocp-Apim-Subscription-Key": API_KEY})
    data = resp.json()
    return data["webPages"]["value"][0]["snippet"]

def ask_llama(prompt):
    proc = subprocess.Popen(["./llama-cli", "-p", prompt], stdout=subprocess.PIPE, text=True)
    out, _ = proc.communicate()
    return out.strip()

while True:
    user = input("You: ")
    resp = ask_llama(user)
    if resp.startswith("[SEARCH:"):
        query = resp[len("[SEARCH:"):].strip(" ]")
        result = web_search(query)
        # feed the result back into the model
        resp = ask_llama(f"Search result: {result}\nUser: {user}")
    print("LLM:", resp)
```

Swap the `subprocess` call for whatever CLI flags you use (e.g., `-n 512 -c 2048`), and you’ve got a tiny “search‑enabled” chatbot.

**6. Optional: Use LangChain or LlamaIndex**
If you’re already comfortable in Python, you can let those frameworks do the orchestration. You spin up a local llama.cpp server (`-s` mode), then write a LangChain agent that uses the `LLMChain` to talk to it and a `Tool` that calls your search API. That way you get tool‑calling semantics for free, at the cost of pulling in a heavier dependency stack.

**7. Things to watch out for**

* **Rate limits** – most search APIs cap you at a few hundred calls per month on the free tier. Cache results if you can.
* **Prompt length** – the search snippet plus the original user query can push you close to the context window, especially with bigger models. Trim aggressively.
* **Safety** – be mindful of injecting arbitrary web content into the prompt. Stripping HTML tags and limiting to plain text reduces the chance of weird tokens messing up the generation.

That’s the overall workflow. If you want a quick win, start with a tiny Python wrapper and the `[SEARCH:]` token trick – it’s just a few lines of code and you can see the benefit right away. Once you’re happy, you can migrate the logic into the C++ side for a tighter, single‑binary solution. Happy hacking! 🚀