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from langchain_openai import ChatOpenAI |
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from langchain_core.prompts import ChatPromptTemplate |
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from langchain_core.pydantic_v1 import BaseModel, Field |
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from langchain_core.output_parsers import PydanticToolsParser |
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import streamlit as st |
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import requests |
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import time |
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import json |
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import os |
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model_service = os.getenv("MODEL_ENDPOINT", |
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"http://localhost:8001") |
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model_service = f"{model_service}/v1" |
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@st.cache_resource(show_spinner=False) |
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def checking_model_service(): |
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start = time.time() |
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print("Checking Model Service Availability...") |
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ready = False |
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while not ready: |
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try: |
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request_cpp = requests.get(f'{model_service}/models') |
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request_ollama = requests.get(f'{model_service[:-2]}api/tags') |
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if request_cpp.status_code == 200: |
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server = "Llamacpp_Python" |
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ready = True |
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elif request_ollama.status_code == 200: |
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server = "Ollama" |
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ready = True |
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except: |
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pass |
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time.sleep(1) |
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print(f"{server} Model Service Available") |
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print(f"{time.time()-start} seconds") |
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return server |
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def get_models(): |
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try: |
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response = requests.get(f"{model_service[:-2]}api/tags") |
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return [i["name"].split(":")[0] for i in |
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json.loads(response.content)["models"]] |
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except: |
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return None |
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with st.spinner("Checking Model Service Availability..."): |
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server = checking_model_service() |
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def enableInput(): |
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st.session_state["input_disabled"] = False |
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def disableInput(): |
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st.session_state["input_disabled"] = True |
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st.title("💬 Function calling") |
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if "input_disabled" not in st.session_state: |
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enableInput() |
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model_name = os.getenv("MODEL_NAME", "") |
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if server == "Ollama": |
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models = get_models() |
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with st.sidebar: |
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model_name = st.radio(label="Select Model", |
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options=models) |
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class getWeather(BaseModel): |
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"""Get the current weather in a given latitude and longitude.""" |
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latitude: float = Field(description="The latitude of a place") |
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longitude: float = Field(description="The longitude of a place") |
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def retrieve(self): |
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return requests.get("https://api.open-meteo.com/v1/forecast", params={'latitude': self.latitude, 'longitude': self.longitude, 'hourly': 'temperature_2m'}).json(); |
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llm = ChatOpenAI(base_url=model_service, |
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api_key="sk-no-key-required", |
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model=model_name, |
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streaming=False, verbose=False).bind_tools(tools=[getWeather], tool_choice='auto') |
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SYSTEM_MESSAGE=""" |
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You are a helpful assistant. |
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You can call functions with appropriate input when necessary. |
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""" |
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prompt = ChatPromptTemplate.from_messages([ |
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("system", SYSTEM_MESSAGE), |
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("user", "What's the weather like in {input} ?") |
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]) |
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chain = prompt | llm | PydanticToolsParser(tools=[getWeather]) |
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st.markdown(""" |
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This demo application will ask the LLM for the weather in the city given in the input field and |
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specify a tool that can get weather information given a latitude and longitude. The weather information |
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retrieval is implemented using open-meteo.com. |
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""") |
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container = st.empty() |
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if prompt := st.chat_input(placeholder="Enter the city name:", disabled=not input): |
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with container: |
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st.write("Calling LLM") |
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response = chain.invoke(prompt) |
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with container: |
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st.write("Retrieving weather information") |
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temperatures = list(map(lambda r: r.retrieve(), response)) |
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print(temperatures[0]) |
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with container: |
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st.line_chart(temperatures[0]['hourly'], x='time', y='temperature_2m') |
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