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import os
import chainlit as cl
from dotenv import load_dotenv
from operator import itemgetter
from langchain_huggingface import HuggingFaceEndpoint
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEndpointEmbeddings
from langchain_core.prompts import PromptTemplate
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.runnable.config import RunnableConfig
# GLOBAL SCOPE - ENTIRE APPLICATION HAS ACCESS TO VALUES SET IN THIS SCOPE #
# ---- ENV VARIABLES ---- #
"""
This function will load our environment file (.env) if it is present.
NOTE: Make sure that .env is in your .gitignore file - it is by default, but please ensure it remains there.
"""
load_dotenv()
"""
We will load our environment variables here.
"""
HF_LLM_ENDPOINT = os.environ["HF_LLM_ENDPOINT"]
HF_EMBED_ENDPOINT = os.environ["HF_EMBED_ENDPOINT"]
HF_TOKEN = os.environ["HF_TOKEN"]
# ---- GLOBAL DECLARATIONS ---- #
# -- RETRIEVAL -- #
"""
1. Load Documents from Text File
2. Split Documents into Chunks
3. Load HuggingFace Embeddings (remember to use the URL we set above)
4. Index Files if they do not exist, otherwise load the vectorstore
"""
document_loader = TextLoader("./data/paul_graham_essays.txt")
documents = document_loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=30)
split_documents = text_splitter.split_documents(documents)
hf_embeddings = HuggingFaceEndpointEmbeddings(
model=HF_EMBED_ENDPOINT,
task="feature-extraction",
huggingfacehub_api_token=HF_TOKEN,
)
if os.path.exists("./data/vectorstore"):
vectorstore = FAISS.load_local(
"./data/vectorstore",
hf_embeddings,
allow_dangerous_deserialization=True # this is necessary to load the vectorstore from disk as it's stored as a `.pkl` file.
)
hf_retriever = vectorstore.as_retriever()
print("Loaded Vectorstore")
else:
print("Indexing Files")
os.makedirs("./data/vectorstore", exist_ok=True)
for i in range(0, len(split_documents), 32):
if i == 0:
vectorstore = FAISS.from_documents(split_documents[i:i+32], hf_embeddings)
continue
vectorstore.add_documents(split_documents[i:i+32])
vectorstore.save_local("./data/vectorstore")
hf_retriever = vectorstore.as_retriever()
# -- AUGMENTED -- #
"""
1. Define a String Template
2. Create a Prompt Template from the String Template
"""
RAG_PROMPT_TEMPLATE = """\
<|start_header_id|>system<|end_header_id|>
You are a helpful assistant. You answer user questions based on provided context. If you can't answer the question with the provided context, say you don't know.<|eot_id|>
<|start_header_id|>user<|end_header_id|>
User Query:
{query}
Context:
{context}
<|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""
rag_prompt = PromptTemplate.from_template(RAG_PROMPT_TEMPLATE)
# -- GENERATION -- #
"""
1. Create a HuggingFaceEndpoint for the LLM
"""
hf_llm = HuggingFaceEndpoint(
endpoint_url=HF_LLM_ENDPOINT,
max_new_tokens=512,
top_k=10,
top_p=0.95,
temperature=0.3,
repetition_penalty=1.15,
huggingfacehub_api_token=HF_TOKEN,
)
@cl.author_rename
def rename(original_author: str):
"""
This function can be used to rename the 'author' of a message.
In this case, we're overriding the 'Assistant' author to be 'Paul Graham Essay Bot'.
"""
rename_dict = {
"Assistant" : "Paul Graham Essay Bot"
}
return rename_dict.get(original_author, original_author)
@cl.on_chat_start
async def start_chat():
"""
This function will be called at the start of every user session.
We will build our LCEL RAG chain here, and store it in the user session.
The user session is a dictionary that is unique to each user session, and is stored in the memory of the server.
"""
lcel_rag_chain = (
{"context": itemgetter("query") | hf_retriever, "query": itemgetter("query")}
| rag_prompt | hf_llm
)
cl.user_session.set("lcel_rag_chain", lcel_rag_chain)
@cl.on_message
async def main(message: cl.Message):
"""
This function will be called every time a message is recieved from a session.
We will use the LCEL RAG chain to generate a response to the user query.
The LCEL RAG chain is stored in the user session, and is unique to each user session - this is why we can access it here.
"""
lcel_rag_chain = cl.user_session.get("lcel_rag_chain")
msg = cl.Message(content="")
for chunk in await cl.make_async(lcel_rag_chain.stream)(
{"query": message.content},
config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
):
await msg.stream_token(chunk)
await msg.send() |