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from langchain.vectorstores import FAISS
#from langchain.llms import GooglePalm, CTransformers
from langchain.document_loaders import PyPDFLoader, TextLoader, Docx2txtLoader
from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.text_splitter import RecursiveCharacterTextSplitter
from huggingface_hub import InferenceClient
import os
from dotenv import load_dotenv
vector_index_path = "assets/vectordb/faiss_index"
class LlmModel:
def __init__(self):
# load dot env variables
self.load_env_variables()
# load llm model
self.hf_embeddings = self.load_huggingface_embeddings()
def load_env_variables(self):
load_dotenv() # take environment variables from .env
def custom_prompt(self, question, history, context):
#RAG prompt template
prompt = "<s>"
for user_prompt, bot_response in history: # provide chat history
prompt += f"[INST] {user_prompt} [/INST]"
prompt += f" {bot_response}</s>"
message_prompt = f"""
You are a question answer agent and you must strictly follow below prompt template.
Given the following context and a question, generate an answer based on this context only.
Keep answers brief and well-structured. Do not give one word answers.
If the answer is not found in the context, kindly state "I don't know." Don't try to make up an answer.
CONTEXT: {context}
QUESTION: {question}
"""
prompt += f"[INST] {message_prompt} [/INST]"
return prompt
def format_sources(self, sources):
# format the document sources
source_results = []
for source in sources:
source_results.append(str(source.page_content) +
"\n Document: " + str(source.metadata['source']) +
" Page: " + str(source.metadata['page']))
return source_results
def mixtral_chat_inference(self, prompt, history, temperature, top_p, repetition_penalty, retriever):
context = retriever.get_relevant_documents(prompt)
sources = self.format_sources(context)
# use hugging face infrence api
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1",
token=os.environ["HF_TOKEN"]
)
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
generate_kwargs = dict(
temperature = temperature,
max_new_tokens = 512,
top_p = top_p,
repetition_penalty = repetition_penalty,
do_sample = True
)
formatted_prompt = self.custom_prompt(prompt, history, context)
return client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False), sources
def load_huggingface_embeddings(self):
# Initialize instructor embeddings using the Hugging Face model
#return HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-large")
return HuggingFaceEmbeddings(model_name = "sentence-transformers/all-MiniLM-L6-v2",
model_kwargs={'device': 'cpu'})
def create_vector_db(self, filename):
if filename.endswith(".pdf"):
loader = PyPDFLoader(file_path=filename)
elif filename.endswith(".doc") or filename.endswith(".docx"):
loader = Docx2txtLoader(filename)
elif filename.endswith("txt") or filename.endswith("TXT"):
loader = TextLoader(filename)
# Split documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
splits = text_splitter.split_documents(loader.load())
# Create a FAISS instance for vector database from 'data'
vectordb = FAISS.from_documents(documents = splits,
embedding = self.hf_embeddings)
# Save vector database locally
#vectordb.save_local(vector_index_path)
# set vectordb content
# Load the vector database from the local folder
#vectordb = FAISS.load_local(vector_index_path, self.hf_embeddings)
# Create a retriever for querying the vector database
return vectordb.as_retriever(search_type="similarity") |