added env file

Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3.9
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+COPY ./requirements.txt ~/app/requirements.txt
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

README.md

@@ -1,5 +1,5 @@
 ---
-title: Airbnb Qabot
+title: Airbnb QA Bot
 emoji: 🚀
 colorFrom: green
 colorTo: pink

app.py

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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
+from langchain_community.document_loaders import PyPDFLoader
+# 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.get("HF_TOKEN")
+# ---- GLOBAL DECLARATIONS ---- #
+# -- RETRIEVAL -- #
+"""
+1. Load Documents
+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
+"""
+
+### 1. CREATE TEXT LOADER AND LOAD DOCUMENTS
+### NOTE: PAY ATTENTION TO THE PATH THEY ARE IN. 
+file_path = ("./data/airbnb.pdf")
+loader = PyPDFLoader(file_path)
+documents = loader.load()  # Load the documents
+
+### 2. CREATE TEXT SPLITTER AND SPLIT DOCUMENTS
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=30)
+split_documents = text_splitter.split_documents(documents)
+
+### 3. LOAD HUGGINGFACE EMBEDDINGS
+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)
+    ### 4. INDEX FILES
+    ### NOTE: REMEMBER TO BATCH THE DOCUMENTS WITH MAXIMUM BATCH SIZE = 32
+    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
+"""
+### 1. DEFINE STRING TEMPLATE
+RAG_PROMPT_TEMPLATE = """\
+<|start_header_id|>system<|end_header_id|>
+You are a helpful assistant. You answer user questions related to Airbnb based on provided context. If you can't answer the question related to Airbnb with the provided context, say you don't know. Otherwise, be conversational and informal in tone.<|eot_id|>
+<|start_header_id|>user<|end_header_id|>
+User Query:
+{query}
+Context:
+{context}<|eot_id|>
+<|start_header_id|>assistant<|end_header_id|>
+"""
+### 2. CREATE PROMPT TEMPLATE
+rag_prompt = PromptTemplate.from_template(RAG_PROMPT_TEMPLATE)
+# -- GENERATION -- #
+"""
+1. Create a HuggingFaceEndpoint for the LLM
+"""
+### 1. CREATE HUGGINGFACE ENDPOINT FOR LLM
+hf_llm = HuggingFaceEndpoint(
+    endpoint_url=HF_LLM_ENDPOINT,
+    max_new_tokens=512,
+    top_k=10,
+    top_p=0.95,
+    typical_p=0.95,
+    temperature=0.01,
+    repetition_penalty=1.03,
+    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 'Airbnb Filing Bot'.
+    """
+    rename_dict = {
+        "Assistant" : "Airbnb Filing 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.
+    """
+    ### BUILD LCEL RAG CHAIN THAT ONLY RETURNS TEXT
+    # Diagram https://docs.google.com/presentation/d/1P9ohPhMdDr9VdXT7qgROZNY93B4jw1xaAvNH6g0AIIQ/edit?usp=sharing 
+    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="")
+    async for chunk in lcel_rag_chain.astream(
+        {"query": message.content},
+        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
+    ):
+        await msg.stream_token(chunk)
+    await msg.send()

requirements.txt

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+chainlit==1.0.0
+langchain==0.2.5
+langchain_community==0.2.5
+langchain_core==0.2.9
+langchain_huggingface==0.0.3
+langchain_text_splitters==0.2.1
+python-dotenv==1.0.1
+faiss-cpu
+pypdf