Misty Waters

from langchain.agents import initialize_agent
from langchain.llms import OpenAI

llm = OpenAI(temperature=0.7)
agent = initialize_agent(
    llm=llm,
    tools=[],
    verbose=True
)
result = agent.run(
    "Summarize this article in 3 bullet points."
)

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

embeddings = OpenAIEmbeddings()
texts = ["AI is transforming healthcare",
         "Machine learning enables automation"]

vectorstore = FAISS.from_texts(
    texts, embeddings
)
docs = vectorstore.similarity_search(
    "healthcare technology", k=2
)

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

qa_chain = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    chain_type="stuff",
    retriever=vectorstore.as_retriever(),
    return_source_documents=True
)

response = qa_chain(
    "What are the benefits of AI in healthcare?"
)

from web3 import Web3
from langchain.tools import tool

@tool
def get_balance(address: str) -> str:
    """Get ETH balance for an address"""
    w3 = Web3(Web3.HTTPProvider('infura_url'))
    balance = w3.eth.get_balance(address)
    return w3.from_wei(balance, 'ether')

agent = initialize_agent(
    tools=[get_balance],
    llm=OpenAI(temperature=0)
)

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Load and preprocess data
df = pd.read_csv('data.csv')
df = df.dropna()

# Feature engineering
X = df.drop('target', axis=1)
y = df['target']

# Train model
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Query(BaseModel):
    text: str
    max_tokens: int = 100

@app.post("/generate")
async def generate_response(query: Query):
    response = llm.generate(
        prompt=query.text,
        max_tokens=query.max_tokens
    )
    return {"response": response}

@app.get("/health")
async def health_check():
    return {"status": "healthy"}