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import requests
from typing import Any, Dict, List, Optional
from langchain.llms.base import LLM
from langchain.agents import initialize_agent, AgentType, Tool
from pydantic import Field, BaseModel
import os
from dotenv import load_dotenv
from datetime import datetime
import wikipedia
from asteval import Interpreter # For a safer calculator
import logging
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Load environment variables from .env file if present
load_dotenv()
# Constants for LM Studio API
LM_STUDIO_API_URL = os.getenv("LM_STUDIO_API_URL", "http://192.168.0.104:1234/v1/chat/completions")
MODEL_NAME = os.getenv("LM_STUDIO_MODEL", "lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf")
CONTENT_TYPE = "application/json"
class LMStudioLLM(LLM):
"""
Custom LangChain LLM to interface with LM Studio API.
"""
api_url: str = Field(default=LM_STUDIO_API_URL, description="The API endpoint for LM Studio.")
model: str = Field(default=MODEL_NAME, description="The model path/name.")
temperature: float = Field(default=0.7, description="Sampling temperature.")
max_tokens: Optional[int] = Field(default=4096, description="Maximum number of tokens to generate.")
streaming: bool = Field(default=False, alias="stream", description="Whether to use streaming responses.")
class Config:
populate_by_name = True
@property
def _llm_type(self) -> str:
return "lmstudio"
@property
def identifying_params(self) -> Dict[str, Any]:
return {
"api_url": self.api_url,
"model": self.model,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
"stream": self.streaming,
}
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""
Generate a response from the LM Studio model.
Args:
prompt (str): The input prompt.
stop (Optional[List[str]]): Stop sequences.
Returns:
str: The generated response.
"""
headers = {
"Content-Type": CONTENT_TYPE,
}
payload = {
"model": self.model,
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt},
],
"temperature": self.temperature,
"max_tokens": self.max_tokens if self.max_tokens is not None else -1,
"stream": self.streaming, # Uses alias 'stream'
}
logger.info(f"Payload: {payload}")
try:
response = requests.post(
self.api_url,
headers=headers,
json=payload,
timeout=60,
stream=self.streaming
)
response.raise_for_status()
logger.info(f"Response content: {response.text}")
except requests.RequestException as e:
logger.error(f"Failed to connect to LM Studio API: {e}")
raise RuntimeError(f"Failed to connect to LM Studio API: {e}")
if self.streaming:
return self._handle_stream(response)
else:
try:
response_json = response.json()
choices = response_json.get("choices", [])
if not choices:
raise ValueError("No choices found in the response.")
# Extract the first response's content
content = choices[0].get("message", {}).get("content", "")
return content.strip()
except (ValueError, KeyError) as e:
logger.error(f"Invalid response format: {e}")
raise RuntimeError(f"Invalid response format: {e}")
def _handle_stream(self, response: requests.Response) -> str:
"""
Process streaming responses from the LM Studio API.
Args:
response (requests.Response): The streaming response object.
Returns:
str: The concatenated content from the stream.
"""
content = ""
try:
for line in response.iter_lines():
if line:
decoded_line = line.decode('utf-8')
if decoded_line.startswith("data: "):
data = decoded_line[6:]
if data == "[DONE]":
break
try:
json_data = requests.utils.json.loads(data)
choices = json_data.get("choices", [])
for chunk in choices:
delta = chunk.get("delta", {})
piece = delta.get("content", "")
content += piece
except requests.utils.json.JSONDecodeError:
continue
return content.strip()
except Exception as e:
logger.error(f"Error processing streaming response: {e}")
raise RuntimeError(f"Error processing streaming response: {e}")
def calculator(input: str) -> str:
"""
A simple calculator tool that safely evaluates mathematical expressions.
Args:
input (str): The mathematical expression to evaluate.
Returns:
str: The result of the evaluation.
"""
try:
aeval = Interpreter()
result = aeval(input)
return str(result)
except Exception as e:
logger.error(f"Calculator error: {e}")
return f"Error evaluating expression: {e}"
def wikipedia_search(query: str) -> str:
"""
Search Wikipedia for a given query and return a summary.
Args:
query (str): The search query.
Returns:
str: A summary from Wikipedia.
"""
try:
summary = wikipedia.summary(query, sentences=2)
return summary
except wikipedia.exceptions.DisambiguationError as e:
return f"Disambiguation error. Options include: {e.options}"
except wikipedia.exceptions.PageError:
return "No page found for the query."
except Exception as e:
logger.error(f"Wikipedia search error: {e}")
return f"An error occurred: {e}"
def current_time(_input: str) -> str:
"""
Returns the current system time.
Args:
_input (str): Ignored input.
Returns:
str: The current time as a string.
"""
now = datetime.now()
return now.strftime("%Y-%m-%d %H:%M:%S")
# Initialize the custom LLM
llm = LMStudioLLM()
# Define Tools
tools = [
Tool(
name="Calculator",
func=calculator,
description="Useful for performing mathematical calculations. Input should be a valid mathematical expression.",
),
Tool(
name="WikipediaSearch",
func=wikipedia_search,
description="Useful for fetching summaries from Wikipedia. Input should be a search query.",
),
Tool(
name="CurrentTime",
func=current_time,
description="Returns the current system time.",
),
]
# Initialize the Agent
agent = initialize_agent(
tools=tools,
llm=llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True,
)
# Example Usage
if __name__ == "__main__":
user_input = "What is the capital of France and what is 15 multiplied by 3?"
try:
response = agent({"input": user_input})
print(response["output"])
except Exception as e:
logger.error(f"Agent invocation error: {e}")
print(f"An error occurred: {e}")