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import gradio as gr
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain.vectorstores import DocArrayInMemorySearch
from langchain.chains import RetrievalQA, ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import HuggingFaceEmbeddings
from langchain import HuggingFaceHub
from langchain.llms import LlamaCpp
from huggingface_hub import hf_hub_download
import param
import os
import torch
from langchain.document_loaders import (
EverNoteLoader,
TextLoader,
UnstructuredEPubLoader,
UnstructuredHTMLLoader,
UnstructuredMarkdownLoader,
UnstructuredODTLoader,
UnstructuredPowerPointLoader,
UnstructuredWordDocumentLoader,
PyPDFLoader,
)
#YOUR_HF_TOKEN = os.getenv("My_hf_token")
llm_api=HuggingFaceHub(
huggingfacehub_api_token=os.getenv("My_hf_token"),
repo_id="tiiuae/falcon-7b-instruct",
model_kwargs={
"temperature":0.2,
"max_new_tokens":500,
"top_k":50,
"top_p":0.95,
"repetition_penalty":1.2,
},), #ChatOpenAI(model_name=llm_name, temperature=0)
#alter
def load_db(files, chain_type, k, llm):
EXTENSIONS = {
".txt": (TextLoader, {"encoding": "utf8"}),
".pdf": (PyPDFLoader, {}),
".doc": (UnstructuredWordDocumentLoader, {}),
".docx": (UnstructuredWordDocumentLoader, {}),
".enex": (EverNoteLoader, {}),
".epub": (UnstructuredEPubLoader, {}),
".html": (UnstructuredHTMLLoader, {}),
".md": (UnstructuredMarkdownLoader, {}),
".odt": (UnstructuredODTLoader, {}),
".ppt": (UnstructuredPowerPointLoader, {}),
".pptx": (UnstructuredPowerPointLoader, {}),
}
# select extensions loader
documents = []
for file in files:
ext = "." + file.rsplit(".", 1)[-1]
if ext in EXTENSIONS:
loader_class, loader_args = EXTENSIONS[ext]
loader = loader_class(file, **loader_args)
documents.extend(loader.load())
else:
pass
# load documents
if documents == []:
loader_class, loader_args = EXTENSIONS['.txt']
loader = loader_class('demo_docs/demo.txt', **loader_args)
documents = loader.load()
# split documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
docs = text_splitter.split_documents(documents)
# define embedding
embeddings = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2') # all-mpnet-base-v2 #embeddings = OpenAIEmbeddings()
# create vector database from data
db = DocArrayInMemorySearch.from_documents(docs, embeddings)
# define retriever
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k})
# create a chatbot chain. Memory is managed externally.
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
chain_type=chain_type,
retriever=retriever,
return_source_documents=True,
return_generated_question=True,
)
return qa
class DocChat(param.Parameterized):
chat_history = param.List([])
answer = param.String("")
db_query = param.String("")
db_response = param.List([])
llm = llm_api[0]
k_value = param.Integer(3)
def __init__(self, **params):
super(DocChat, self).__init__( **params)
self.loaded_file = "demo_docs/demo.txt"
self.qa = load_db(self.loaded_file,"stuff", self.k_value, self.llm)
def call_load_db(self, path_file, k):
if not os.path.exists(path_file[0]): # init or no file specified
return "No file loaded"
else:
try:
self.qa = load_db(path_file, "stuff", k, self.llm)
self.loaded_file = path_file
except:
return f'No valid file'
self.clr_history()
return f"New DB created | Loaded File: {self.loaded_file}"
# chat
def convchain(self, query, k_max):
if k_max != self.k_value:
print("Maximum querys changed, reloading DB")
self.qa = load_db(self.loaded_file,"stuff", k_max, self.llm)
self.k_value = k_max
result = self.qa({"question": query, "chat_history": self.chat_history})
self.chat_history.extend([(query, result["answer"])])
self.db_query = result["generated_question"]
self.db_response = result["source_documents"]
self.answer = result['answer']
return self.answer
def change_llm(self, repo_, file_, max_tokens=16, temperature=0.2, top_p=0.95, top_k=50, repeat_penalty=1.2, k=3):
if torch.cuda.is_available():
try:
model_path = hf_hub_download(repo_id=repo_, filename=file_)
self.llm = LlamaCpp(
model_path=model_path,
n_ctx=1000,
n_batch=512,
n_gpu_layers=35,
max_tokens=max_tokens,
verbose=False,
temperature=temperature,
top_p=top_p,
top_k=top_k,
repeat_penalty=repeat_penalty,
)
self.qa = load_db(self.loaded_file,"stuff", k, self.llm)
self.k_value = k
return f"Loaded {file_}"
except:
return "No valid model"
else:
try:
model_path = hf_hub_download(repo_id=repo_, filename=file_)
self.llm = LlamaCpp(
model_path=model_path,
n_ctx=1000,
n_batch=8,
max_tokens=max_tokens,
verbose=False,
temperature=temperature,
top_p=top_p,
top_k=top_k,
repeat_penalty=repeat_penalty,
)
self.qa = load_db(self.loaded_file,"stuff", k, self.llm)
self.k_value = k
return f"Loaded {file_}"
except:
return "No valid model"
def default_falcon_model(self):
self.llm = llm_api[0]
self.qa = load_db(self.loaded_file,"stuff", self.k_value, self.llm)
return "Loaded model Falcon 7B-instruct"
@param.depends('db_query ', )
def get_lquest(self):
if not self.db_query :
return print("Last question to DB: no DB accesses so far")
return self.db_query
@param.depends('db_response', )
def get_sources(self):
if not self.db_response:
return
#rlist=[f"Result of DB lookup:"]
rlist=[]
for doc in self.db_response:
for element in doc:
rlist.append(element)
return rlist
@param.depends('convchain', 'clr_history')
def get_chats(self):
if not self.chat_history:
return "No History Yet"
#rlist=[f"Current Chat History variable"]
rlist=[]
for exchange in self.chat_history:
rlist.append(exchange)
return rlist
def clr_history(self,count=0):
self.chat_history = []
return "HISTORY CLEARED"
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