first push

Dockerfile

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+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"]

app.py

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+# flake8: noqa ignore
+
+from langchain_community.vectorstores import FAISS
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_core.prompts import PromptTemplate
+from langchain_huggingface.embeddings import HuggingFaceEndpointEmbeddings
+import numpy as np
+from numpy.linalg import norm
+from langchain_community.document_loaders import TextLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from operator import itemgetter
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough
+from langchain_core.runnables.passthrough import RunnablePassthrough
+from langchain_core.runnables.config import RunnableConfig
+from dotenv import load_dotenv
+import chainlit as cl
+import os
+import uuid
+
+load_dotenv()
+
+HF_LLM_ENDPOINT = os.environ["HF_LLM_ENDPOINT"]
+HF_EMBED_ENDPOINT = os.environ["HF_EMBED_ENDPOINT"]
+HF_TOKEN = os.environ["HF_TOKEN"]
+
+document_loader = TextLoader("data/paul-graham-to-kindle/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/index.faiss"):
+    vectorstore = FAISS.load_local(
+        "./data/vectorstore",
+        hf_embeddings,
+    )
+    hf_retriever = vectorstore.as_retriever()
+    print("Loaded Vectorstore")
+else:
+    print("Indexing Files")
+    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])
+
+hf_retriever = vectorstore.as_retriever()
+
+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)
+
+hf_llm = HuggingFaceEndpoint(
+    endpoint_url=f"{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.on_chat_start
+async def on_chat_start():
+    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)
+    await cl.Message(
+        content="Hi! What questions do you have about Paul Graham's essays?"
+    ).send()
+
+
+@cl.author_rename
+def rename(orig_author: str):
+    rename_dict = {
+        "ChatOpenAI": "the Generator...",
+        "VectorStoreRetriever": "the Retriever...",
+    }
+    return rename_dict.get(orig_author, orig_author)
+
+
+@cl.on_message
+async def main(message: cl.Message):
+    runnable = cl.user_session.get("lcel_rag_chain")
+
+    msg = cl.Message(content="")
+
+    async for chunk in runnable.astream(
+        {"query": message.content},
+        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
+    ):
+        await msg.stream_token(chunk)
+
+    await msg.send()

chainlit.md

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+# AIM Endpoint Assignment
+
+Check out this cool app I made to chat with Paul Graham's essays!