File size: 4,973 Bytes
bf1c7e8 1c43678 bf1c7e8 1d04b99 bf1c7e8 1d04b99 bf1c7e8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
import os
import streamlit as st
import pickle
import time
import langchain
#from langchain import OpenAI
#from langchain.chains import RetrievalQAWithSourcesChain
#from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import UnstructuredURLLoader
#from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
import requests
import pandas as pd
from langchain_community.llms import HuggingFaceEndpoint
from sentence_transformers import SentenceTransformer
from langchain.document_loaders import TextLoader
from sentence_transformers import SentenceTransformer, util
from langchain.schema import SystemMessage, HumanMessage, AIMessage
import faiss
from dotenv import load_dotenv
load_dotenv() # take environment variables from .env (especially openai api key)
def querypreprocess(query: str ):
vec = model.encode(query) #again embeddings of query by sentencetransformer and able to search the index vector.
#svec = np.array(vec).reshape(1,-1) # as 2D needed
distances, I = index.search(vec, k=2)
row_indices = I.tolist()[0]
list1 = [docs[i].page_content for i in row_indices]
str1 = " "
str1 = str1.join(list1)
#str1 = '\n'.join([str(message) for message in list1])
#results = ' '.join(map(str, list1)) #list to string convert
return str1
def augmented_prompt(query: str):
messages = querypreprocess(query)
source_knowledge =''.join([str(message) for message in messages])
#source_knowledge =results
augmented_prompt = f"""
using the contexts below, answer the query.
Contexts:
{source_knowledge}
Question: {query}
Answer:"""
return augmented_prompt
st.title("RockyBot: News Research Tool 📈")
st.sidebar.title("News Article URLs")
urls = []
for i in range(3):
url = st.sidebar.text_input(f"URL {i+1}")
urls.append(url)
process_url_clicked = st.sidebar.button("Process URLs")
file_path = "sentence_embeddings.pkl"
main_placeholder = st.empty()
#llm = OpenAI(temperature=0.9, max_tokens=500)
llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.2")
if process_url_clicked:
# load data
loader = UnstructuredURLLoader(urls=urls)
main_placeholder.text("Data Loading...Started...✅✅✅")
data = loader.load()
# split data
text_splitter = RecursiveCharacterTextSplitter(
separators=['\n\n', '\n', '.', ','],
chunk_size=1000,
chunk_overlap=0
)
main_placeholder.text("Text Splitter...Started...✅✅✅")
docs = text_splitter.split_documents(data)
# Create an array of text to embed
sentences = []
for i, row in enumerate(docs):
sentences.append(row.page_content)
# create embeddings and save it to FAISS index
#embeddings = OpenAIEmbeddings()
#vectorstore_openai = FAISS.from_documents(docs, embeddings)
# initialize sentence transformer model
model = SentenceTransformer('bert-base-nli-mean-tokens')
# create sentence embeddings
sentence_embeddings = model.encode(sentences)
main_placeholder.text("Embedding Vector Started Building...✅✅✅")
time.sleep(2)
# Save the FAISS index to a pickle file
with open(file_path, "wb") as f:
pickle.dump(sentence_embeddings, f)
query = main_placeholder.text_input("Question: ")
if query:
if os.path.exists(file_path):
with open(file_path, "rb") as f:
query = pickle.load(f)
import faiss
d = sentence_embeddings.shape[1]
index = faiss.IndexFlatL2(d) # build the index, d=size of vectors
# here we assume xb contains a n-by-d numpy matrix of type float32
index.add(sentence_embeddings) # add vectors to the index
#chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vectorstore.as_retriever())
#result = chain({"question": query}, return_only_outputs=True)
# result will be a dictionary of this format --> {"answer": "", "sources": [] }
#xq = model.encode([query])
#k=2
#D, I = index.search(xq, k=k)
#result1 = [f'{i}: {sentences[i]}' for i in I[0]]
messages = [
SystemMessage(content="You are a helpful assistant."),
HumanMessage(content=query),
AIMessage(content="I am Great, Thank You, How Can I Help You.")
]
prompt = augmented_prompt(query)
messages.append(prompt)
result = llm.invoke(messages)
st.header("Answer")
# Display sources, if available
sources = result.get("sources", "")
if sources:
st.subheader("Sources:")
sources_list = sources.split("\n") # Split the sources by newline
for source in sources_list:
st.write(source) |