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Merge branch 'refs/heads/main' into mcp

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This commit is contained in:
liangxinbing 2025-03-19 13:27:24 +08:00
commit 167b1acd5c
6 changed files with 287 additions and 72 deletions
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2
app/config.py
View File

@ -25,7 +25,7 @@ class LLMSettings(BaseModel):
description="Maximum input tokens to use across all requests (None for unlimited)",
)
temperature: float = Field(1.0, description="Sampling temperature")
api_type: str = Field(..., description="AzureOpenai or Openai")
api_type: str = Field(..., description="Azure, Openai, or Ollama")
api_version: str = Field(..., description="Azure Openai version if AzureOpenai")

145
app/llm.py
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@ -30,6 +30,14 @@ from app.schema import (
REASONING_MODELS = ["o1", "o3-mini"]
MULTIMODAL_MODELS = [
"gpt-4-vision-preview",
"gpt-4o",
"gpt-4o-mini",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-haiku-20240307",
]
class TokenCounter:
@ -259,12 +267,15 @@ class LLM:
return "Token limit exceeded"
@staticmethod
def format_messages(messages: List[Union[dict, Message]]) -> List[dict]:
def format_messages(
messages: List[Union[dict, Message]], supports_images: bool = False
) -> List[dict]:
"""
Format messages for LLM by converting them to OpenAI message format.
Args:
messages: List of messages that can be either dict or Message objects
supports_images: Flag indicating if the target model supports image inputs
Returns:
List[dict]: List of formatted messages in OpenAI format
@ -288,54 +299,58 @@ class LLM:
if isinstance(message, Message):
message = message.to_dict()
if not isinstance(message, dict):
if isinstance(message, dict):
# If message is a dict, ensure it has required fields
if "role" not in message:
raise ValueError("Message dict must contain 'role' field")
# Process base64 images if present and model supports images
if supports_images and message.get("base64_image"):
# Initialize or convert content to appropriate format
if not message.get("content"):
message["content"] = []
elif isinstance(message["content"], str):
message["content"] = [
{"type": "text", "text": message["content"]}
]
elif isinstance(message["content"], list):
# Convert string items to proper text objects
message["content"] = [
(
{"type": "text", "text": item}
if isinstance(item, str)
else item
)
for item in message["content"]
]
# Add the image to content
message["content"].append(
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{message['base64_image']}"
},
}
)
# Remove the base64_image field
del message["base64_image"]
# If model doesn't support images but message has base64_image, handle gracefully
elif not supports_images and message.get("base64_image"):
# Just remove the base64_image field and keep the text content
del message["base64_image"]
if "content" in message or "tool_calls" in message:
formatted_messages.append(message)
# else: do not include the message
else:
raise TypeError(f"Unsupported message type: {type(message)}")
# Validate required fields
if "role" not in message:
raise ValueError("Message dict must contain 'role' field")
# Process base64 images if present
if message.get("base64_image"):
# Initialize or convert content to appropriate format
if not message.get("content"):
message["content"] = []
elif isinstance(message["content"], str):
message["content"] = [{"type": "text", "text": message["content"]}]
elif isinstance(message["content"], list):
# Convert string items to proper text objects
message["content"] = [
(
{"type": "text", "text": item}
if isinstance(item, str)
else item
)
for item in message["content"]
]
# Add the image to content
message["content"].append(
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{message['base64_image']}"
},
}
)
# Remove the base64_image field
del message["base64_image"]
# Only include messages with content or tool_calls
if "content" in message or "tool_calls" in message:
formatted_messages.append(message)
# Validate all roles
invalid_roles = [
msg for msg in formatted_messages if msg["role"] not in ROLE_VALUES
]
if invalid_roles:
raise ValueError(f"Invalid role: {invalid_roles[0]['role']}")
# Validate all messages have required fields
for msg in formatted_messages:
if msg["role"] not in ROLE_VALUES:
raise ValueError(f"Invalid role: {msg['role']}")
return formatted_messages
@ -372,12 +387,15 @@ class LLM:
Exception: For unexpected errors
"""
try:
# Format system and user messages
# Check if the model supports images
supports_images = self.model in MULTIMODAL_MODELS
# Format system and user messages with image support check
if system_msgs:
system_msgs = self.format_messages(system_msgs)
messages = system_msgs + self.format_messages(messages)
system_msgs = self.format_messages(system_msgs, supports_images)
messages = system_msgs + self.format_messages(messages, supports_images)
else:
messages = self.format_messages(messages)
messages = self.format_messages(messages, supports_images)
# Calculate input token count
input_tokens = self.count_message_tokens(messages)
@ -499,8 +517,15 @@ class LLM:
Exception: For unexpected errors
"""
try:
# Format messages
formatted_messages = self.format_messages(messages)
# For ask_with_images, we always set supports_images to True because
# this method should only be called with models that support images
if self.model not in MULTIMODAL_MODELS:
raise ValueError(
f"Model {self.model} does not support images. Use a model from {MULTIMODAL_MODELS}"
)
# Format messages with image support
formatted_messages = self.format_messages(messages, supports_images=True)
# Ensure the last message is from the user to attach images
if not formatted_messages or formatted_messages[-1]["role"] != "user":
@ -539,7 +564,10 @@ class LLM:
# Add system messages if provided
if system_msgs:
all_messages = self.format_messages(system_msgs) + formatted_messages
all_messages = (
self.format_messages(system_msgs, supports_images=True)
+ formatted_messages
)
else:
all_messages = formatted_messages
@ -653,12 +681,15 @@ class LLM:
if tool_choice not in TOOL_CHOICE_VALUES:
raise ValueError(f"Invalid tool_choice: {tool_choice}")
# Check if the model supports images
supports_images = self.model in MULTIMODAL_MODELS
# Format messages
if system_msgs:
system_msgs = self.format_messages(system_msgs)
messages = system_msgs + self.format_messages(messages)
system_msgs = self.format_messages(system_msgs, supports_images)
messages = system_msgs + self.format_messages(messages, supports_images)
else:
messages = self.format_messages(messages)
messages = self.format_messages(messages, supports_images)
# Calculate input token count
input_tokens = self.count_message_tokens(messages)

62
app/tool/browser_use_tool.py
View File

@ -418,17 +418,7 @@ class BrowserUseTool(BaseTool, Generic[Context]):
# Create prompt for LLM
prompt_text = """
Your task is to extract the content of the page. You will be given a page and a goal, and you should extract all relevant information around this goal from the page.
Examples of extraction goals:
- Extract all company names
- Extract specific descriptions
- Extract all information about a topic
- Extract links with companies in structured format
- Extract all links
If the goal is vague, summarize the page. Respond in JSON format.
Your task is to extract the content of the page. You will be given a page and a goal, and you should extract all relevant information around this goal from the page. If the goal is vague, summarize the page. Respond in json format.
Extraction goal: {goal}
Page content:
@ -445,10 +435,54 @@ Page content:
messages = [Message.user_message(formatted_prompt)]
# Use LLM to extract content based on the goal
response = await self.llm.ask(messages)
# Define extraction function for the tool
extraction_function = {
"type": "function",
"function": {
"name": "extract_content",
"description": "Extract specific information from a webpage based on a goal",
"parameters": {
"type": "object",
"properties": {
"extracted_content": {
"type": "object",
"description": "The content extracted from the page according to the goal",
}
},
"required": ["extracted_content"],
},
},
}
# Use LLM to extract content with required function calling
response = await self.llm.ask_tool(
messages,
tools=[extraction_function],
tool_choice="required",
)
# Extract content from function call response
if (
response
and response.tool_calls
and len(response.tool_calls) > 0
):
# Get the first tool call arguments
tool_call = response.tool_calls[0]
# Parse the JSON arguments
try:
args = json.loads(tool_call.function.arguments)
extracted_content = args.get("extracted_content", {})
# Format extracted content as JSON string
content_json = json.dumps(
extracted_content, indent=2, ensure_ascii=False
)
msg = f"Extracted from page:\n{content_json}\n"
except Exception as e:
msg = f"Error parsing extraction result: {str(e)}\nRaw response: {tool_call.function.arguments}"
else:
msg = "No content was extracted from the page."
msg = f"Extracted from page:\n{response}\n"
return ToolResult(output=msg)
except Exception as e:
# Provide a more helpful error message

2
app/tool/search/__init__.py
View File

@ -1,5 +1,6 @@
from app.tool.search.baidu_search import BaiduSearchEngine
from app.tool.search.base import WebSearchEngine
from app.tool.search.bing_search import BingSearchEngine
from app.tool.search.duckduckgo_search import DuckDuckGoSearchEngine
from app.tool.search.google_search import GoogleSearchEngine
@ -9,4 +10,5 @@ __all__ = [
"BaiduSearchEngine",
"DuckDuckGoSearchEngine",
"GoogleSearchEngine",
"BingSearchEngine",
]

146
app/tool/search/bing_search.py Normal file
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@ -0,0 +1,146 @@
from typing import List
import requests
from bs4 import BeautifulSoup
from app.logger import logger
from app.tool.search.base import WebSearchEngine
ABSTRACT_MAX_LENGTH = 300
USER_AGENTS = [
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36",
"Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)",
"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/49.0.2623.108 Chrome/49.0.2623.108 Safari/537.36",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; pt-BR) AppleWebKit/533.3 (KHTML, like Gecko) QtWeb Internet Browser/3.7 http://www.QtWeb.net",
"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US) AppleWebKit/532.2 (KHTML, like Gecko) ChromePlus/4.0.222.3 Chrome/4.0.222.3 Safari/532.2",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.4pre) Gecko/20070404 K-Ninja/2.1.3",
"Mozilla/5.0 (Future Star Technologies Corp.; Star-Blade OS; x86_64; U; en-US) iNet Browser 4.7",
"Mozilla/5.0 (Windows; U; Windows NT 6.1; rv:2.2) Gecko/20110201",
"Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.8.1.13) Gecko/20080414 Firefox/2.0.0.13 Pogo/2.0.0.13.6866",
]
HEADERS = {
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8",
"Content-Type": "application/x-www-form-urlencoded",
"User-Agent": USER_AGENTS[0],
"Referer": "https://www.bing.com/",
"Accept-Encoding": "gzip, deflate",
"Accept-Language": "zh-CN,zh;q=0.9",
}
BING_HOST_URL = "https://www.bing.com"
BING_SEARCH_URL = "https://www.bing.com/search?q="
class BingSearchEngine(WebSearchEngine):
session: requests.Session = None
def __init__(self, **data):
"""Initialize the BingSearch tool with a requests session."""
super().__init__(**data)
self.session = requests.Session()
self.session.headers.update(HEADERS)
def _search_sync(self, query: str, num_results: int = 10) -> List[str]:
"""
Synchronous Bing search implementation to retrieve a list of URLs matching a query.
Args:
query (str): The search query to submit to Bing. Must not be empty.
num_results (int, optional): The maximum number of URLs to return. Defaults to 10.
Returns:
List[str]: A list of URLs from the search results, capped at `num_results`.
Returns an empty list if the query is empty or no results are found.
Notes:
- Pagination is handled by incrementing the `first` parameter and following `next_url` links.
- If fewer results than `num_results` are available, all found URLs are returned.
"""
if not query:
return []
list_result = []
first = 1
next_url = BING_SEARCH_URL + query
while len(list_result) < num_results:
data, next_url = self._parse_html(
next_url, rank_start=len(list_result), first=first
)
if data:
list_result.extend([item["url"] for item in data])
if not next_url:
break
first += 10
return list_result[:num_results]
def _parse_html(self, url: str, rank_start: int = 0, first: int = 1) -> tuple:
"""
Parse Bing search result HTML synchronously to extract search results and the next page URL.
Args:
url (str): The URL of the Bing search results page to parse.
rank_start (int, optional): The starting rank for numbering the search results. Defaults to 0.
first (int, optional): Unused parameter (possibly legacy). Defaults to 1.
Returns:
tuple: A tuple containing:
- list: A list of dictionaries with keys 'title', 'abstract', 'url', and 'rank' for each result.
- str or None: The URL of the next results page, or None if there is no next page.
"""
try:
res = self.session.get(url=url)
res.encoding = "utf-8"
root = BeautifulSoup(res.text, "lxml")
list_data = []
ol_results = root.find("ol", id="b_results")
if not ol_results:
return [], None
for li in ol_results.find_all("li", class_="b_algo"):
title = ""
url = ""
abstract = ""
try:
h2 = li.find("h2")
if h2:
title = h2.text.strip()
url = h2.a["href"].strip()
p = li.find("p")
if p:
abstract = p.text.strip()
if ABSTRACT_MAX_LENGTH and len(abstract) > ABSTRACT_MAX_LENGTH:
abstract = abstract[:ABSTRACT_MAX_LENGTH]
rank_start += 1
list_data.append(
{
"title": title,
"abstract": abstract,
"url": url,
"rank": rank_start,
}
)
except Exception:
continue
next_btn = root.find("a", title="Next page")
if not next_btn:
return list_data, None
next_url = BING_HOST_URL + next_btn["href"]
return list_data, next_url
except Exception as e:
logger.warning(f"Error parsing HTML: {e}")
return [], None
def perform_search(self, query, num_results=10, *args, **kwargs):
"""Bing search engine."""
return self._search_sync(query, num_results=num_results)

2
app/tool/web_search.py
View File

@ -7,6 +7,7 @@ from app.config import config
from app.tool.base import BaseTool
from app.tool.search import (
BaiduSearchEngine,
BingSearchEngine,
DuckDuckGoSearchEngine,
GoogleSearchEngine,
WebSearchEngine,
@ -37,6 +38,7 @@ class WebSearch(BaseTool):
"google": GoogleSearchEngine(),
"baidu": BaiduSearchEngine(),
"duckduckgo": DuckDuckGoSearchEngine(),
"bing": BingSearchEngine(),
}
async def execute(self, query: str, num_results: int = 10) -> List[str]: