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import openai
import os
openai.api_key=os.getenv("OPENAI_API_KEY")
from dotenv import load_dotenv
load_dotenv()
from flask import Flask, jsonify, render_template, request
import requests, json
import PyPDF2
# import nltk
# nltk.download("punkt")
import shutil
from werkzeug.utils import secure_filename
from werkzeug.datastructures import FileStorage
import nltk
from datetime import datetime
import openai
from langchain.llms import OpenAI, Replicate
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.document_loaders import SeleniumURLLoader, PyPDFLoader
from langchain.docstore.document import Document
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import VectorDBQA
from langchain.document_loaders import UnstructuredFileLoader, TextLoader
from langchain import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferWindowMemory
from transformers import LlamaTokenizer, AutoTokenizer
import warnings
warnings.filterwarnings("ignore")
#app = Flask(__name__)
app = Flask(__name__, template_folder="./")
# Create a directory in a known location to save files to.
uploads_dir = os.path.join(app.root_path,'static', 'uploads')
os.makedirs(uploads_dir, exist_ok=True)
defaultEmbeddingModelID = 3
defaultLLMID=0
def pretty_print_docs(docs):
print(f"\n{'-' * 100}\n".join([f"Document {i + 1}:\n\n" + "Document Length>>>" + str(
len(d.page_content)) + "\n\nDocument Source>>> " + d.metadata['source'] + "\n\nContent>>> " + d.page_content for
i, d in enumerate(docs)]))
def getEmbeddingModel(embeddingId):
if (embeddingId == 1):
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
elif (embeddingId == 2):
model_name = "hkunlp/instructor-large"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
embeddings = HuggingFaceInstructEmbeddings(model_name=model_name,model_kwargs=model_kwargs,encode_kwargs=encode_kwargs)
elif (embeddingId == 3):
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(model_name=model_name,model_kwargs=model_kwargs,encode_kwargs=encode_kwargs)
else:
embeddings = OpenAIEmbeddings()
return OpenAIEmbeddings()
def getLLMModel(LLMID):
# else:
# llm = LlamaCpp(
if LLMID == 1:
# llm = Replicate(
# model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
# model_kwargs={"temperature": 0.2,"max_length": 2500})
llm = Replicate(
model="meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d",
model_kwargs={"temperature": 0.2,"max_new_tokens":2500})
print("LLAMA2 13B LLM Selected")
elif LLMID == 2:
# llm = Replicate(
# model="replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf",
# model_kwargs={"temperature": 0.2,"max_length": 2500})
llm = Replicate(
model="meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3",
model_kwargs={"temperature": 0.2,"max_new_tokens":2500})
print("LLAMA2 70B LLM Selected")
elif LLMID == 3:
llm = Replicate(model="meta/llama-2-7b-chat:8e6975e5ed6174911a6ff3d60540dfd4844201974602551e10e9e87ab143d81e",
model_kwargs={"temperature": 0.2,"max_new_tokens":2500})
print("LLAMA2 7B Chat LLM Selected")
elif LLMID == 4:
llm = Replicate(
model="a16z-infra/mistral-7b-instruct-v0.1:83b6a56e7c828e667f21fd596c338fd4f0039b46bcfa18d973e8e70e455fda70",
model_kwargs={"temperature": 0.2,"max_new_tokens":2500})
print("Mistral AI LLM Selected")
else:
llm = OpenAI(model_name="gpt-3.5-turbo-0125",temperature=0.0)
print("Open AI LLM Selected")
return llm
def clearKBUploadDirectory(uploads_dir):
for filename in os.listdir(uploads_dir):
file_path = os.path.join(uploads_dir, filename)
print("Clearing Doc Directory. Trying to delete" + file_path)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print('Failed to delete %s. Reason: %s' % (file_path, e))
def PDFChunkerWithSeparator(filepath, separator):
content = ""
if filepath.endswith(".pdf"):
# creating a pdf reader object
reader = PyPDF2.PdfReader(filepath)
# print the number of pages in pdf file
print(len(reader.pages))
for page in reader.pages:
content += page.extract_text()
elif filepath.endswith(".txt"):
with open(filepath) as f:
lines = f.readlines()
f.close()
for line in lines:
content+=line
splitted_content_list = content.split(separator)
doclist = []
for splitted_content in splitted_content_list:
new_doc = Document(page_content=splitted_content, metadata={"source": filepath})
# print(type(new_doc))
doclist.append(new_doc)
if len(doclist)>3:
print(doclist[len(doclist) - 3])
return doclist
def loadKB(fileprovided, urlProvided, uploads_dir, request):
documents = []
global tokenizer
BASE_MODEL = "LLAMA-TOKENIZER"
savedModelPath = "./model/" + BASE_MODEL
#tokenizer = LlamaTokenizer.from_pretrained(savedModelPath)
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
separator = "</Q>"
if fileprovided:
# Delete Files
clearKBUploadDirectory(uploads_dir)
# Read and Embed New Files provided
for file in request.files.getlist('files[]'):
print("File Received>>>" + file.filename)
file.save(os.path.join(uploads_dir, secure_filename(file.filename)))
#loader = PyPDFLoader(os.path.join(uploads_dir, secure_filename(file.filename)))
#documents.extend(loader.load())
documents.extend(PDFChunkerWithSeparator(os.path.join(uploads_dir, secure_filename(file.filename)),separator))
else:
#loader = TextLoader('Jio.txt')
#documents.extend(loader.load())
documents.extend(PDFChunkerWithSeparator('JTest.txt',separator))
if urlProvided:
weburl = request.form.getlist('weburl')
print(weburl)
urlList = weburl[0].split(';')
print(urlList)
print("Selenium Started", datetime.now().strftime("%H:%M:%S"))
# urlLoader=RecursiveUrlLoader(urlList[0])
urlLoader = SeleniumURLLoader(urlList)
print("Selenium Completed", datetime.now().strftime("%H:%M:%S"))
documents.extend(urlLoader.load())
print("inside selenium loader:")
print(documents)
return documents
def getRAGChain(customerName, customerDistrict, custDetailsPresent, vectordb,llmID):
chain = RetrievalQA.from_chain_type(
llm=getLLMModel(llmID),
chain_type='stuff',
retriever=getRetriever(vectordb),
#retriever=vectordb.as_retriever(),
memory = ConversationBufferWindowMemory(k=3, memory_key="history", input_key="question"),
verbose=False,
chain_type_kwargs={
"verbose": False,
"prompt": createPrompt(customerName, customerDistrict, custDetailsPresent),
"memory": ConversationBufferWindowMemory(
k=3,
memory_key="history",
input_key="question"),
}
)
return chain
def getRetriever(vectordb):
return vectordb.as_retriever(search_type="mmr", search_kwargs={'k': 2})
def createVectorDB(documents,embeddingModelID):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)
texts = []
for document in documents:
tokenized_input = tokenizer.tokenize(document.page_content)
print("Token Count::::::::::" + str(len(tokenized_input)))
if (len(tokenized_input) > 1000):
print("Splitting Content using RTS")
splitted_doc = text_splitter.split_documents([document])
texts.extend(splitted_doc)
# for text in texts:
# print("splitted content:"+str(len(text.page_content)))
# print(text.page_content)
elif (len(tokenized_input) < 1000 and len(tokenized_input) > 1):
texts.append(document)
# texts = text_splitter.split_documents(documents)
print("All chunk List START ***********************\n\n")
pretty_print_docs(texts)
print("All chunk List END ***********************\n\n")
embeddings = getEmbeddingModel(embeddingModelID)
print("Embedding Started >>>>>>>>>>>>>>>>>>", datetime.now().strftime("%H:%M:%S"))
vectordb = Chroma.from_documents(texts, embeddings, collection_metadata={"hnsw:space": "cosine"})
print("Vector Store Creation Completed*********************************\n\n")
return vectordb
# texts = text_splitter.split_documents(documents)
# print("All chunk List START ***********************\n\n")
# pretty_print_docs(texts)
# print("All chunk List END ***********************\n\n")
# embeddings = getEmbeddingModel(0)
# vectordb = Chroma.from_documents(texts, embeddings)
# return vectordb
def createPrompt(cName, cCity, custDetailsPresent):
cProfile = "Customer's Name is " + cName + "\nCustomer's lives in or customer's Resident State or Customer's place is " + cCity + "\n"
print(cProfile)
template1 = """You role is of a Professional Customer Support Executive and your name is Jio AIAssist.
You are talking to the below customer whose information is provided in block delimited by <cp></cp>.
Use the following customer related information (delimited by <cp></cp>) and context (delimited by <ctx></ctx>) to answer the question at the end by thinking step by step alongwith reaonsing steps:
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Use the customer information to replace entities in the question before answering\n
\n"""
template2 = """
<ctx>
{context}
</ctx>
<hs>
{history}
</hs>
Question: {question}
Answer: """
prompt_template = template1 + "<cp>\n" + cProfile + "\n</cp>\n" + template2
PROMPT = PromptTemplate(template=prompt_template, input_variables=["history", "context", "question"])
return PROMPT
vectordb = createVectorDB(loadKB(False, False, uploads_dir, None),defaultEmbeddingModelID)
@app.route('/', methods=['GET'])
def test():
return "Docker hello"
@app.route('/KBUploader')
def KBUpload():
return render_template("KBTrain.html")
@app.route('/aiassist')
def aiassist():
return render_template("index.html")
@app.route('/aisearch')
def aisearch():
return render_template("aisearch.html")
@app.route('/agent/chat/suggestion', methods=['POST'])
def process_json():
print(f"\n{'*' * 100}\n")
print("Request Received >>>>>>>>>>>>>>>>>>", datetime.now().strftime("%H:%M:%S"))
content_type = request.headers.get('Content-Type')
if content_type == 'application/json':
requestQuery = request.get_json()
print(type(requestQuery))
custDetailsPresent = False
customerName = ""
customerDistrict = ""
if "custDetails" in requestQuery:
custDetailsPresent = True
customerName = requestQuery['custDetails']['cName']
customerDistrict = requestQuery['custDetails']['cDistrict']
selectedLLMID=defaultLLMID
if "llmID" in requestQuery:
selectedLLMID=(int) (requestQuery['llmID'])
print("chain initiation")
chainRAG = getRAGChain(customerName, customerDistrict, custDetailsPresent, vectordb,selectedLLMID)
print("chain created")
suggestionArray = []
searchResultArray = []
for index, query in enumerate(requestQuery['message']):
# message = answering(query)
relevantDoc = vectordb.similarity_search_with_score(query, distance_metric="cos", k=3)
print("Printing Retriever Docs")
for doc in getRetriever(vectordb).get_relevant_documents(query):
searchResult = {}
print(f"\n{'-' * 100}\n")
searchResult['documentSource'] = doc.metadata['source']
searchResult['pageContent'] = doc.page_content
print(doc)
print("Document Source>>>>>> " + searchResult['documentSource'] + "\n\n")
print("Page Content>>>>>> " + searchResult['pageContent'] + "\n\n")
print(f"\n{'-' * 100}\n")
searchResultArray.append(searchResult)
print("Printing Retriever Docs Ended")
print("Chain Run Started >>>>>>>>>>>>>>>>>>", datetime.now().strftime("%H:%M:%S"))
message = chainRAG.run({"query": query})
print("Chain Run Completed >>>>>>>>>>>>>>>>>>", datetime.now().strftime("%H:%M:%S"))
print("query:", query)
print("Response:", message)
if "I don't know" in message:
message = "Dear Sir/ Ma'am, Could you please ask questions relevant to Jio?"
responseJSON = {"message": message, "id": index}
suggestionArray.append(responseJSON)
print("Response Sent >>>>>>>>>>>>>>>>>>", datetime.now().strftime("%H:%M:%S"))
return jsonify(suggestions=suggestionArray, searchResult=searchResultArray)
else:
return 'Content-Type not supported!'
@app.route('/file_upload', methods=['POST'])
def file_Upload():
fileprovided = not request.files.getlist('files[]')[0].filename == ''
urlProvided = not request.form.getlist('weburl')[0] == ''
embeddingModelProvided = not request.form.getlist('embeddingModelID')[0] == ''
print("*******")
print("File Provided:" + str(fileprovided))
print("URL Provided:" + str(urlProvided))
print("Embedding Model Provided:" + str(embeddingModelProvided))
print("*******")
print(uploads_dir)
documents = loadKB(fileprovided, urlProvided, uploads_dir, request)
embeddingModelID = defaultEmbeddingModelID
if embeddingModelProvided:
embeddingModelID = int(request.form.getlist('embeddingModelID')[0])
global vectordb
vectordb = createVectorDB(documents, embeddingModelID)
#vectordb=createVectorDB(documents)
return render_template("aisearch.html")
if __name__ == '__main__':
app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 7860)))
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