Spaces:
Sleeping
Sleeping
File size: 3,200 Bytes
8ce02a3 |
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 |
import streamlit as st
from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
st.title(':blue[Langchain:] A Rag System on “Leave No Context Behind” Paper')
st.header("AI Chatbot :robot_face:")
os.environ["GOOGLE_API_KEY"] = os.getenv("k1")
# Creating a template
chat_template = ChatPromptTemplate.from_messages([
# System Message establishes bot's role and general behavior guidelines
SystemMessage(content="""You are a Helpful AI Bot.
You take the context and question from user. Your answer should be based on the specific context."""),
# Human Message Prompt Template
HumanMessagePromptTemplate.from_template("""Answer the question based on the given context.
Context:
{context}
Question:
{question}
Answer: """)
])
#user's question.
#how many results we want to print.
from langchain_google_genai import ChatGoogleGenerativeAI
chat_model = ChatGoogleGenerativeAI(google_api_key=KEY,
model="gemini-1.5-pro-latest")
from langchain_core.output_parsers import StrOutputParser
output_parser = StrOutputParser()
chain = chat_template | chat_model | output_parser
from langchain_community.document_loaders import PDFMinerLoader # type: ignore
dat = PDFMinerLoader(r"D:\Langchain\rag_system\2404.07143.pdf")
dat_nik =dat.load()
# Split the document into chunks
from langchain_text_splitters import NLTKTextSplitter
text_splitter = NLTKTextSplitter(chunk_size=500, chunk_overlap=100)
chunks = text_splitter.split_documents(dat_nik)
# Creating Chunks Embedding
# We are just loading OpenAIEmbeddings
from langchain_google_genai import GoogleGenerativeAIEmbeddings # type: ignore
embedding_model = GoogleGenerativeAIEmbeddings(google_api_key=KEY,
model="models/embedding-001")
# vectors = embeddings.embed_documents(chunks)
# Store the chunks in vector store
from langchain_community.vectorstores import Chroma # type: ignore
# Creating a New Chroma Database
db = Chroma.from_documents(chunks, embedding_model, persist_directory="./chroma_db_1")
# saving the database on drive
db.persist()
# Setting a Connection with the ChromaDB
db_connection = Chroma(persist_directory="./chroma_db_", embedding_function=embedding_model)
# Converting CHROMA db_connection to Retriever Object, which retrieves top 5 results
retriever = db_connection.as_retriever(search_kwargs={"k": 5})
from langchain_core.runnables import RunnablePassthrough #takes user's question.
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
# format chunks: takes the 5 results, combines all the chunks and displays one output.
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| chat_template
| chat_model
| output_parser
)
user_input = st.text_area("Ask Questions to AI")
if st.button("Submit"):
st.subheader(":green[Query:]")
st.subheader(user_input)
response = rag_chain.invoke(user_input)
st.subheader(":green[Response:-]")
st.write(response) |