feat: support web search via ollama cloud web search for ChatGPT pro handler

Signed-off-by: Frost Ming <me@frostming.com>

README.md

@@ -63,6 +63,8 @@ Note, if you are using third party service, you need to `export ANTHROPIC_BASE_U
 3. use `gpt: ${message}` to ask
 
 Note, if you are using third party service, you need to `export OPENAI_API_BASE=${the_url}` to change the url.
+Optional web search support:
+- export `OLLAMA_WEB_SEARCH_API_KEY=${the_ollama_web_search_api_key}` (and `OLLAMA_WEB_SEARCH_MAX_RESULTS` as needed)
 
 ## Bot -> llama3
 

config.py

@@ -17,6 +17,9 @@ class Settings(BaseSettings):
     google_gemini_api_key: str | None = None
     anthropic_api_key: str | None = None
     telegra_ph_token: str | None = None
+    ollama_web_search_api_key: str | None = None
+    ollama_web_search_max_results: int = 5
+    ollama_web_search_timeout: int = 10
 
     @cached_property
     def openai_client(self) -> openai.OpenAI:

handlers/chatgpt.py

@@ -1,9 +1,12 @@
+import json
 import time
+import uuid
+from typing import Any
 
+import requests
 from expiringdict import ExpiringDict
 from telebot import TeleBot
 from telebot.types import Message
-from telegramify_markdown import markdownify
 
 from config import settings
 
@@ -15,7 +18,6 @@ from ._utils import (
     logger,
 )
 
-
 CHATGPT_MODEL = settings.openai_model
 CHATGPT_PRO_MODEL = settings.openai_model
 
@@ -23,11 +25,293 @@ CHATGPT_PRO_MODEL = settings.openai_model
 client = settings.openai_client
 
 
+# Web search / tool-calling configuration
+WEB_SEARCH_TOOL_NAME = "web_search"
+OLLAMA_WEB_SEARCH_URL = "https://ollama.com/api/web_search"
+WEB_SEARCH_TOOL = {
+    "type": "function",
+    "function": {
+        "name": WEB_SEARCH_TOOL_NAME,
+        "description": (
+            "Use the Ollama Cloud Web Search API to fetch recent information"
+            " from the public internet. Call this when you need up-to-date"
+            " facts, news, or citations."
+        ),
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": "Search keywords or question.",
+                },
+                "max_results": {
+                    "type": "integer",
+                    "description": (
+                        "Maximum number of search results to fetch; defaults"
+                        " to the bot configuration if omitted."
+                    ),
+                    "minimum": 1,
+                    "maximum": 10,
+                },
+            },
+            "required": ["query"],
+        },
+    },
+}
+STREAMING_UPDATE_INTERVAL = 1.2
+MAX_TOOL_ITERATIONS = 3
+
+
 # Global history cache
 chatgpt_player_dict = ExpiringDict(max_len=1000, max_age_seconds=600)
 chatgpt_pro_player_dict = ExpiringDict(max_len=1000, max_age_seconds=600)
 
 
+def _web_search_available() -> bool:
+    return bool(settings.ollama_web_search_api_key)
+
+
+def _format_web_search_results(payload: dict[str, Any]) -> str:
+    results = payload.get("results") or payload.get("data") or []
+    if not isinstance(results, list):
+        results = []
+    formatted: list[str] = []
+    for idx, item in enumerate(results, start=1):
+        if not isinstance(item, dict):
+            continue
+        title = (
+            item.get("title") or item.get("name") or item.get("url") or f"Result {idx}"
+        )
+        url = item.get("url") or item.get("link") or item.get("source") or ""
+        snippet = (
+            item.get("snippet")
+            or item.get("summary")
+            or item.get("content")
+            or item.get("description")
+            or ""
+        ).strip()
+        snippet = snippet.replace("\n", " ")
+        if len(snippet) > 400:
+            snippet = snippet[:397].rstrip() + "..."
+        entry = f"[{idx}] {title}"
+        if url:
+            entry = f"{entry}\n{url}"
+        if snippet:
+            entry = f"{entry}\n{snippet}"
+        formatted.append(entry)
+    if formatted:
+        return "\n\n".join(formatted)
+    return json.dumps(payload, ensure_ascii=False)
+
+
+def _call_ollama_web_search(query: str, max_results: int | None = None) -> str:
+    if not _web_search_available():
+        return "Web search is not configured."
+    payload: dict[str, Any] = {"query": query.strip()}
+    limit = max_results if isinstance(max_results, int) else None
+    if limit is None or limit <= 0:
+        limit = settings.ollama_web_search_max_results
+    if limit:
+        payload["max_results"] = int(limit)
+    headers = {
+        "Authorization": f"Bearer {settings.ollama_web_search_api_key}",
+    }
+    try:
+        response = requests.post(
+            OLLAMA_WEB_SEARCH_URL,
+            json=payload,
+            headers=headers,
+            timeout=settings.ollama_web_search_timeout,
+        )
+        response.raise_for_status()
+        data = response.json()
+    except requests.RequestException as exc:
+        logger.exception("Ollama web search failed: %s", exc)
+        return f"Web search error: {exc}"
+    except ValueError:
+        logger.exception("Invalid JSON payload from Ollama web search")
+        return "Web search error: invalid payload."
+    return _format_web_search_results(data)
+
+
+def _available_tools() -> list[dict[str, Any]]:
+    if not _web_search_available():
+        return []
+    return [WEB_SEARCH_TOOL]
+
+
+def _accumulate_tool_call_deltas(
+    buffer: dict[int, dict[str, Any]],
+    deltas: list[Any],
+) -> None:
+    for delta in deltas:
+        idx = getattr(delta, "index", 0) or 0
+        entry = buffer.setdefault(
+            idx,
+            {
+                "id": getattr(delta, "id", None),
+                "type": getattr(delta, "type", "function") or "function",
+                "function": {"name": "", "arguments": ""},
+            },
+        )
+        if getattr(delta, "id", None):
+            entry["id"] = delta.id
+        if getattr(delta, "type", None):
+            entry["type"] = delta.type
+        func = getattr(delta, "function", None)
+        if func is not None:
+            if getattr(func, "name", None):
+                entry["function"]["name"] = func.name
+            if getattr(func, "arguments", None):
+                entry["function"]["arguments"] += func.arguments
+
+
+def _finalize_tool_calls(buffer: dict[int, dict[str, Any]]) -> list[dict[str, Any]]:
+    tool_calls: list[dict[str, Any]] = []
+    for idx in sorted(buffer):
+        entry = buffer[idx]
+        function_name = entry.get("function", {}).get("name")
+        if not function_name:
+            continue
+        arguments = entry.get("function", {}).get("arguments", "{}")
+        tool_calls.append(
+            {
+                "id": entry.get("id") or str(uuid.uuid4()),
+                "type": entry.get("type") or "function",
+                "function": {
+                    "name": function_name,
+                    "arguments": arguments,
+                },
+            }
+        )
+    return tool_calls
+
+
+def _execute_tool(function_name: str, arguments_json: str) -> str:
+    try:
+        arguments = json.loads(arguments_json or "{}")
+    except json.JSONDecodeError as exc:
+        logger.exception("Invalid tool arguments for %s: %s", function_name, exc)
+        return f"Invalid arguments for {function_name}: {exc}"
+
+    if function_name == WEB_SEARCH_TOOL_NAME:
+        query = (arguments.get("query") or "").strip()
+        if not query:
+            return "Web search error: no query provided."
+        max_results = arguments.get("max_results")
+        if isinstance(max_results, str):
+            max_results = int(max_results) if max_results.isdigit() else None
+        elif not isinstance(max_results, int):
+            max_results = None
+        return _call_ollama_web_search(query, max_results)
+
+    return f"Function {function_name} is not implemented."
+
+
+def _append_tool_messages(
+    conversation: list[dict[str, Any]], tool_calls: list[dict[str, Any]]
+) -> None:
+    if not tool_calls:
+        return
+    conversation.append(
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": tool_calls,
+        }
+    )
+    for call in tool_calls:
+        result = _execute_tool(
+            call["function"]["name"], call["function"].get("arguments", "{}")
+        )
+        conversation.append(
+            {
+                "role": "tool",
+                "tool_call_id": call["id"],
+                "content": result,
+            }
+        )
+
+
+def _stream_chatgpt_pro_response(
+    conversation: list[dict[str, Any]],
+    reply_id: Message,
+    who: str,
+    bot: TeleBot,
+) -> str:
+    tools = _available_tools()
+    tool_loops_remaining = MAX_TOOL_ITERATIONS if tools else 0
+    final_response = ""
+    while True:
+        request_payload: dict[str, Any] = {
+            "messages": conversation,
+            "model": CHATGPT_PRO_MODEL,
+            "stream": True,
+        }
+        if tools:
+            request_payload["tools"] = tools
+
+        stream = client.chat.completions.create(**request_payload)
+        buffer = ""
+        pending_tool_call = False
+        tool_buffer: dict[int, dict[str, Any]] = {}
+        last_update = time.time()
+
+        for chunk in stream:
+            if not chunk.choices:
+                continue
+            delta = chunk.choices[0].delta
+            if delta is None:
+                continue
+            if delta.tool_calls:
+                pending_tool_call = True
+                _accumulate_tool_call_deltas(tool_buffer, delta.tool_calls)
+                continue
+            content_piece = delta.content
+            if isinstance(content_piece, list):
+                content_piece = "".join(
+                    getattr(part, "text", "") for part in content_piece
+                )
+            if not content_piece:
+                continue
+            buffer += content_piece
+            now = time.time()
+            if not pending_tool_call and now - last_update > STREAMING_UPDATE_INTERVAL:
+                last_update = now
+                bot_reply_markdown(reply_id, who, buffer, bot, split_text=False)
+
+        if pending_tool_call and tools:
+            if tool_loops_remaining <= 0:
+                logger.warning(
+                    "chatgpt_pro_handler reached the maximum number of tool calls"
+                )
+                final_response = buffer or "Unable to finish after calling tools."
+                break
+            tool_calls = _finalize_tool_calls(tool_buffer)
+            if any(
+                call["function"]["name"] == WEB_SEARCH_TOOL_NAME for call in tool_calls
+            ):
+                bot_reply_markdown(
+                    reply_id,
+                    who,
+                    "Searching the web for up-to-date information…",
+                    bot,
+                    split_text=False,
+                    disable_web_page_preview=True,
+                )
+            _append_tool_messages(conversation, tool_calls)
+            tool_loops_remaining -= 1
+            continue
+
+        final_response = buffer
+        break
+
+    if not final_response:
+        final_response = "I could not generate a response."
+    bot_reply_markdown(reply_id, who, final_response, bot, split_text=True)
+    return final_response
+
+
 def chatgpt_handler(message: Message, bot: TeleBot) -> None:
     """gpt : /gpt <question>"""
     logger.debug(message)
@@ -125,32 +409,11 @@ def chatgpt_pro_handler(message: Message, bot: TeleBot) -> None:
         player_message = player_message[2:]
 
     try:
-        r = client.chat.completions.create(
+        reply_text = _stream_chatgpt_pro_response(player_message, reply_id, who, bot)
-            messages=player_message,
-            model=CHATGPT_PRO_MODEL,
-            stream=True,
-        )
-        s = ""
-        start = time.time()
-        for chunk in r:
-            logger.debug(chunk)
-            if chunk.choices:
-                if chunk.choices[0].delta.content is None:
-                    break
-                s += chunk.choices[0].delta.content
-                if time.time() - start > 1.2:
-                    start = time.time()
-                    bot_reply_markdown(reply_id, who, s, bot, split_text=False)
-        # maybe not complete
-        try:
-            bot_reply_markdown(reply_id, who, s, bot, split_text=True)
-        except Exception:
-            pass
-
         player_message.append(
             {
                 "role": "assistant",
-                "content": markdownify(s),
+                "content": reply_text,
             }
         )