Integrate LLM applications or inference services with ARMS

llm_app.py

import openai
from fastapi import FastAPI
import uvicorn
app = FastAPI()
@app.get("/")
def call_openai():
    client = openai.OpenAI(api_key="sk-xxx")
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Write a haiku."}],
        max_tokens=20,
    )
    return {"data": f"{response}"}
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)    

requirements.txt

fastapi
uvicorn
openai >= 1.0.0

llm_app.py

from http import HTTPStatus
import dashscope
from dashscope import Generation
from fastapi import FastAPI
import uvicorn
app = FastAPI()
@app.get("/")
def call_dashscope():
    dashscope.api_key = 'YOUR-DASHSCOPE-API-KEY'
    responses = Generation.call(model=Generation.Models.qwen_turbo,
                                prompt='How is the weather today?')
    resp = ""
    if responses.status_code == HTTPStatus.OK:
        resp = f"Result is: {responses.output}"
    else:
        resp = f"Failed request_id: {responses.request_id}, status_code: {responses.status_code}, code: {responses.code}, message: {responses.message}"
    return {"data": f"{resp}"}
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

fastapi
uvicorn
dashscope >= 1.0.0

llm_app.py

import time
from fastapi import FastAPI
import uvicorn
import aiohttp
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
from llama_index.embeddings.dashscope import DashScopeEmbedding
import chromadb
import dashscope
import os
from dotenv import load_dotenv
from llama_index.core.llms import ChatMessage
from llama_index.core import VectorStoreIndex, get_response_synthesizer
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.llms.dashscope import DashScope, DashScopeGenerationModels
import random
load_dotenv()
os.environ["DASHSCOPE_API_KEY"] = 'sk-xxxxxx'
dashscope.api_key = 'sk-xxxxxxx'
api_key = 'sk-xxxxxxxx'
llm = DashScope(model_name=DashScopeGenerationModels.QWEN_MAX,api_key=api_key)
# create client and a new collection
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("chapters")
# define embedding function
embed_model = DashScopeEmbedding(model_name="text-embedding-v1", api_key=api_key)
# load documents
filename_fn = lambda filename: {"file_name": filename}
# automatically sets the metadata of each document according to filename_fn
documents = SimpleDirectoryReader(
    "./data/", file_metadata=filename_fn
).load_data()
# set up ChromaVectorStore and load in data
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, embed_model=embed_model
)
retriever = VectorIndexRetriever(
    index=index,
    similarity_top_k=4,
    verbose=True
)
# configure response synthesizer
response_synthesizer = get_response_synthesizer(llm=llm, response_mode="refine")
# assemble query engine
query_engine = RetrieverQueryEngine(
    retriever=retriever,
    response_synthesizer=response_synthesizer,
)
SYSTEM_PROMPT = """
You are a general-knowledge chatbot for children. Your task is to answer user questions by combining the most relevant content found in the knowledge base. Do not answer subjective questions.
"""
# Initialize the conversation with a system message
messages = [ChatMessage(role="system", content=SYSTEM_PROMPT)]
app = FastAPI()
async def fetch(question):
    url = "https://www.aliyun.com"
    call_url = os.environ.get("LLM_INFRA_URL")
    if call_url is None or call_url == "":
        call_url = url
    else:
        call_url = f"{call_url}?question={question}"
    print(call_url)
    async with aiohttp.ClientSession() as session:
        async with session.get(call_url) as response:
            print(f"GET Status: {response.status}")
            data = await response.text()
            print(f"GET Response JSON: {data}")
            return data
@app.get("/heatbeat")
def heatbeat():
    return {"msg", "ok"}
cnt = 0
@app.get("/query")
async def call(question: str = None):
    global cnt
    cnt += 1
    if cnt == 20:
        cnt = 0
        raise BaseException("query is over limit,20 ", 401)
    # Add user message to the conversation history
    message = ChatMessage(role="user", content=question)
    # Convert messages into a string
    message_string = f"{message.role}:{message.content}"
    search = await fetch(question)
    print(f"search:{search}")
    resp = query_engine.query(message_string)
    print(resp)
    return {"data": f"{resp}".encode('utf-8').decode('utf-8')}
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

fastapi
uvicorn
numpy==1.23.5
llama-index==0.10.62
llama-index-core==0.10.28
llama-index-embeddings-dashscope==0.1.3
llama-index-llms-dashscope==0.1.2
llama-index-vector-stores-chroma==0.1.6
aiohttp

llm_app.py

from fastapi import FastAPI
from langchain.llms.fake import FakeListLLM
import uvicorn
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
app = FastAPI()
llm = FakeListLLM(responses=["I'll callback later.", "You 'console' them!"])
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
@app.get("/")
def call_langchain():
    res = llm_chain.run(question)
    return {"data": res}
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

fastapi
uvicorn
langchain
langchain_community