Siddartha10
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app.py
ADDED
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1 |
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import os
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import streamlit as st
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from typing import List, Tuple
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import json
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import uvicorn
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from dotenv import load_dotenv
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load_dotenv()
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from fastapi import FastAPI
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from langchain.agents import AgentExecutor
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from langchain.agents.format_scratchpad import format_to_openai_function_messages
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from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
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from langchain.callbacks import FinalStreamingStdOutCallbackHandler
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain.pydantic_v1 import BaseModel, Field
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from langchain.schema.messages import AIMessage, HumanMessage
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from langchain.tools.render import format_tool_to_openai_function
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from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
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from langchain_core.runnables import ConfigurableField
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from langchain_core.tools import Tool
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from langserve import add_routes
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from langchain.prompts import PromptTemplate
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import requests
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.vectorstores import Qdrant
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from langchain.chains import RetrievalQA
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from langchain.agents import Tool, Agent, AgentType
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from langchain.agents import AgentExecutor
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from langchain_core.tools import Tool
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from langchain_openai import ChatOpenAI
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from langchain_openai import AzureChatOpenAI
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from langchain_community.document_loaders import JSONLoader
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embeddings = OpenAIEmbeddings()
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llm_1 = AzureChatOpenAI(openai_api_version=os.environ.get("AZURE_OPENAI_VERSION", "2023-07-01-preview"),
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azure_deployment=os.environ.get("AZURE_OPENAI_DEPLOYMENT", "gpt4chat"),
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azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT", "https://gpt-4-trails.openai.azure.com/"),
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api_key=os.environ.get("AZURE_OPENAI_KEY"))
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llm = ChatOpenAI(temperature=0.2,
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model="gpt-3.5-turbo-0125",
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streaming=True,
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callbacks=[FinalStreamingStdOutCallbackHandler()]).configurable_fields(
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temperature=ConfigurableField(
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id="llm_temperature",
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name="LLM Temperature",
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description="The temperature of the LLM"))
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assistant_system_message = """You are a helpful assistant. \
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Use tools (only if necessary) to best answer the users questions."""
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", assistant_system_message),
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MessagesPlaceholder(variable_name="chat_history"),
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("user", "{input}"),
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MessagesPlaceholder(variable_name="agent_scratchpad"),
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]
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)
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# Define the API call function for Ares API
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def api_call(text):
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url = "https://api-ares.traversaal.ai/live/predict"
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payload = { "query": [text]}
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headers = {
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"x-api-key": "ares_a0866ad7d71d2e895c5e05dce656704a9e29ad37860912ad6a45a4e3e6c399b5",
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"content-type": "application/json"
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}
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response = requests.post(url, json=payload, headers=headers)
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# here we will use the llm to summarize the response received from the ares api
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response_data = response.json()
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#print(response_data)
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try:
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response_text = response_data['data']['response_text']
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web_urls = response_data['data']['web_url']
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# Continue processing the data...
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except KeyError:
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print("Error: Unexpected response from the API. Please try again or contact the api owner.")
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# Optionally, you can log the error or perform other error handling actions.
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if len(response_text) > 10000:
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response_text = response_text[:8000]
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prompt = f"Summarize the following text in 500-100 0 words and jsut summarize what you see and do not add anythhing else: {response_text}"
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summary = llm_1.invoke(prompt)
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print(summary)
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else:
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summary = response_text
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result = "{} My list is: {}".format(response_text, web_urls)
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# Convert the result to a string
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result_str = str(result)
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return result_str
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def metadata_func(record: str, metadata: dict) -> dict:
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lines = record.split('\n')
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locality_line = lines[10]
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price_range_line = lines[12]
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locality = locality_line.split(': ')[1]
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price_range = price_range_line.split(': ')[1]
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metadata["location"] = locality
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metadata["price_range"] = price_range
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return metadata
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# Instantiate the JSONLoader with the metadata_func
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jq_schema = '.parser[] | to_entries | map("\(.key): \(.value)") | join("\n")'
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loader = JSONLoader(
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jq_schema=jq_schema,
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file_path='data.json',
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metadata_func=metadata_func,
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)
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# Load the JSON file and extract metadata
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documents = loader.load()
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from langchain.vectorstores import FAISS
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def get_vectorstore(text_chunks):
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# Check if the FAISS index file already exists
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if os.path.exists("faiss_index"):
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# Load the existing FAISS index
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vectorstore = FAISS.load_local("faiss_index")
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print("Loaded existing FAISS index.")
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else:
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# Create a new FAISS index
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embeddings = OpenAIEmbeddings()
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vectorstore = FAISS.from_documents(documents=text_chunks, embedding=embeddings)
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# Save the new FAISS index locally
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vectorstore.save_local("faiss_index")
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print("Created and saved new FAISS index.")
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return vectorstore
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#docs = new_db.similarity_search(query)
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vector = get_vectorstore(documents)
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template = """
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context:- I have low budget what is the best hotel in Instanbul?
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anser:- The other hotels in instanbul are costly and are not in your budget. so the best hotel in instanbul for you is hotel is xyz."
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Don’t give information not mentioned in the CONTEXT INFORMATION.
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+
The system should take into account various factors such as location, amenities, user reviews, and other relevant criteria to
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generate informative and personalized explanations.
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{context}
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+
Question: {question}
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Answer:"""
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def search():
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#llm = ChatOpenAI(model="gpt-3.5-turbo-1106", temperature=0)
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vector = vector
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prompt = PromptTemplate(template=template, input_variables=["context","question"])
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chain_type_kwargs = {"prompt": prompt}
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return RetrievalQA.from_chain_type(
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llm=llm,
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chain_type="stuff",
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retriever=vector.as_retriever(),
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chain_type_kwargs=chain_type_kwargs,
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)
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+
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# Initialize LangChain tools
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+
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api_tool = Tool(name="Ares_API",
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func=api_call,
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description="Integration with Traversaal AI Ares API for real-time internet searches."
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)
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chain_rag_tool = Tool(name="RAG_Chain",
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func=search,
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description="RAG chain for question answering."
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)
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app = FastAPI(
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title='Example',
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)
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tools = [chain_rag_tool, api_tool]
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llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
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def _format_chat_history(chat_history: List[Tuple[str, str]]):
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buffer = []
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for human, ai in chat_history:
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buffer.append(HumanMessage(content=human))
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buffer.append(AIMessage(content=ai))
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return buffer
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agent = (
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{
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"input": lambda x: x["input"],
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"chat_history": lambda x: _format_chat_history(x["chat_history"]),
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"agent_scratchpad": lambda x: format_to_openai_function_messages(
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x["intermediate_steps"]
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),
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}
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| prompt
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| llm_with_tools
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| OpenAIFunctionsAgentOutputParser()
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)
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class AgentInput(BaseModel):
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input: str
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chat_history: List[Tuple[str, str]] = Field(
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..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
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)
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types(
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input_type=AgentInput
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)
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def get_response(user_input):
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response = agent_executor.invoke({"input":user_input, "chat_history": _format_chat_history([])})
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return response
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def main():
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st.title("Travle Assistant Chatbot")
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st.write("Welcome to the Hotel Assistant Chatbot!")
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user_input = st.text_input("User Input:")
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if st.button("Submit"):
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response = get_response(user_input)
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st.text_area("Chatbot Response:", value=response)
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if st.button("Exit"):
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st.stop()
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if __name__ == "__main__":
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main()
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data.json
ADDED
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