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AI Chatbot using LangChain, OpenAI and Custom Data ( Excel )
# -*- coding: utf-8 -*-
Automatically generated by Colaboratory.
Original file is located at
!pip install langchain
!pip install unstructured
!pip install openai
!pip install python-dotenv
!pip install faiss-cpu
!pip install tiktoken pyngrok==4.1.1 flask_ngrok requests
from dotenv import load_dotenv
import os
import openai
!ngrok authtoken '<YOUR-NGROK_TOKEN>'
API_KEY = os.environ.get("API_KEY")
"""## 3: Loading your custom data
To use data with an LLM, documents must first be loaded into a vector database.
The first step is to load them into memory via a loader
from langchain.document_loaders import TextLoader , UnstructuredExcelLoader
loader = UnstructuredExcelLoader(
docs = loader.load()
"""### Text splitter
Split the loaded data and put it in chunks to the vector db
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
documents = text_splitter.split_documents(docs)
# documents
"""## Embeddings
Texts are not stored as text in the database, but as vector representations.
Embeddings are a type of word representation that represents the semantic meaning of words in a vector space.
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(openai_api_key=API_KEY)
"""## Loading Vectors into VectorDB (FAISS)
As created by OpenAIEmbeddings vectors can now be stored in the database. The DB can be stored as .pkl file
from langchain.vectorstores.faiss import FAISS
import pickle
vectorstore = FAISS.from_documents(documents, embeddings)
with open("vectorstore.pkl", "wb") as f:
pickle.dump(vectorstore, f)
"""## Loading the database
Before using the database, it must of course be loaded again.
with open("vectorstore.pkl", "rb") as f:
vectorstore = pickle.load(f)
"""## Prompts
With an LLM you have the possibility to give it an identity before a conversation or to define how question and answer should look like.
from langchain.prompts import PromptTemplate
basePrompt = """
Put your prompt here
Question: {question}
Answer here:
PROMPT = PromptTemplate(
template=basePrompt, input_variables=["context", "question"]
"""## Chains
With chain classes you can easily influence the behavior of the LLM
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
chain_type_kwargs = {"prompt": PROMPT}
llm = OpenAI(openai_api_key=API_KEY)
"""## Memory
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(
memory_key="chat_history", return_messages=True, output_key="answer"
"""## Using Memory in Chains
from langchain.chains import ConversationalRetrievalChain
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
qa = ConversationalRetrievalChain.from_llm(
llm=OpenAI(model_name="gpt-3.5-turbo", temperature=0, openai_api_key=API_KEY),
combine_docs_chain_kwargs={"prompt": PROMPT},
"""# Python Web Server"""
from flask import Flask, render_template, render_template_string, request, jsonify
from flask_ngrok import run_with_ngrok
# ===== Web Server with NgRok ===
app = Flask(__name__)
# Once the application is runs successfully you can call the API inside your chatbot
@app.route('/submit-prompt', methods=['POST'])
def generate():
data = request.get_json()
prompt = data.get('prompt', '')
query = prompt
print("Question Asked: ", query);
response = qa({"question": query})
print("Sending Response...")
data = {
"response": response["answer"]
return jsonify(data)
if __name__ == '__main__':
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