File size: 4,509 Bytes
11acc44 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | from langchain.agents import initialize_agent, Tool
from langchain_groq import ChatGroq
from langchain.utilities import SerpAPIWrapper, WikipediaAPIWrapper
from langchain_community.tools import WikipediaQueryRun
from langchain_experimental.tools import PythonREPLTool
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.tools import ArxivQueryRun
from langchain_community.tools import PubmedQueryRun
from langchain_community.tools import ShellTool
from langchain_community.utilities.requests import RequestsWrapper
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from fastapi import FastAPI
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
import os
from dotenv import load_dotenv
load_dotenv()
app=FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class Chat_input(BaseModel):
input: str
os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
# os.environ["SERPAPI_API_KEY"] = os.getenv("SERPAPI_API_KEY")
llm = ChatGroq(model="llama3-70b-8192",
temperature=0.5,
max_tokens=None,
timeout=60,
max_retries=2,
api_key=os.getenv("GROQ_API_KEY"),)
search = SerpAPIWrapper(serpapi_api_key=os.getenv("SERPAPI_API_KEY"),search_engine="google")
youtube = SerpAPIWrapper(serpapi_api_key=os.getenv("SERPAPI_API_KEY"), search_engine="youtube")
wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
duckduckgo_search = DuckDuckGoSearchRun()
arxiv = ArxivQueryRun()
pubmed = PubmedQueryRun()
requests_tool = RequestsWrapper()
def calculate_bmi(input_string):
try:
height_cm, weight_kg = map(float, input_string.split(','))
height_m = height_cm / 100
bmi = weight_kg / (height_m ** 2)
return f"The BMI is {bmi:.2f}"
except ValueError:
return "Error: Please provide input in the format 'height,weight'."
tools = [
Tool(
name="BMI Calculator",
func=calculate_bmi,
description="Calculates BMI when given height in cm and weight in kg. Input should be two numbers separated by a comma: height,weight"
),
Tool(
name="Youtube",
func=youtube.run,
description="Search YouTube for videos. Input should be a query string."
),
Tool(
name="Search",
func=search.run,
description="Useful for when you need to answer questions youtube links, any usefull links."
),
Tool(
name="Wikipedia",
func=wikipedia.run,
description="Useful for when you need detailed information on a topic. Use this for historical facts, definitions, or in-depth knowledge on a subject."
),
Tool(
name="Python REPL",
func=PythonREPLTool().run,
description="Useful for when you need to execute Python code, especially for calculations or data processing."
),
Tool(
name="DuckDuckGo Search",
func=duckduckgo_search.run,
description="Useful for searching the internet for current information, used it for google search, past information and dark world or hacking related data. Ip address and other information."
),
Tool(
name="ArXiv",
func=arxiv.run,
description="Useful for searching and retrieving scientific papers from arXiv."
),
Tool(
name="PubMed",
func=pubmed.run,
description="Useful for searching and retrieving biomedical literature from PubMed."
),
Tool(
name="Requests",
func=requests_tool.get,
description="Useful for making HTTP requests to websites and APIs."
),
Tool(
name="Shell",
func=ShellTool().run,
description="Useful for running shell commands. Use with caution!"
)
]
memory=ConversationBufferWindowMemory(k=20, memory_key="chat_history", return_messages=True)
agent = initialize_agent(tools=tools,
llm=llm,
memory=memory,
max_iterations=3,
early_stopping_method="generate",
agent="zero-shot-react-description",
verbose=True,
min_tokens=1000,
handle_parsing_errors=True)
@app.post("/search")
async def search(input: Chat_input):
return agent.run(input.input)
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