initial version

Dockerfile

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+
+# reference: https://huggingface.co/spaces/key2xanadu/hello-world-docker
+
+# working on localhost --
+#    
+# FROM python:3.11
+# RUN useradd -m -u 1000 user
+# USER user
+# ENV HOME=/home/user \
+#     PATH=/home/user/.local/bin:$PATH
+# WORKDIR $HOME/app
+# COPY --chown=user . $HOME/app
+# COPY ./requirements.txt ~/app/requirements.txt
+# RUN pip install -r requirements.txt
+# COPY . .
+# CMD ["chainlit", "run", "app.py", "--port", "7860"]
+
+FROM python:3.11
+
+RUN useradd -m -u 1000 user
+USER user
+
+ENV HOME=/code/user \
+    PATH=/code/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+
+COPY ./requirements.txt $HOME/app/requirements.txt
+
+
+RUN pip install --no-cache-dir --upgrade -r $HOME/app/requirements.txt
+
+COPY . .
+
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

app.py

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+import chainlit as cl
+from datasets import load_dataset
+from langchain_community.document_loaders import CSVLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_openai import OpenAIEmbeddings
+from langchain.embeddings import CacheBackedEmbeddings
+from langchain.storage import LocalFileStore
+from langchain_community.vectorstores import FAISS
+from langchain_core.runnables.base import RunnableSequence
+from langchain_core.runnables.passthrough import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langchain.schema.runnable import Runnable, RunnablePassthrough, RunnableConfig
+from langchain.callbacks.base import BaseCallbackHandler
+
+def setup_data():
+    dataset = load_dataset("ShubhamChoksi/IMDB_Movies")
+    dataset_dict = dataset
+    dataset_dict["train"].to_csv("imdb.csv")
+
+    loader = CSVLoader(file_path="imdb.csv")
+    data = loader.load()
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=100
+    )
+
+    chunked_documents = text_splitter.split_documents(data)
+    embedding_model = OpenAIEmbeddings()
+
+    store = LocalFileStore("./cache/")
+    embedder = CacheBackedEmbeddings.from_bytes_store(embedding_model, store, namespace=embedding_model.model)
+
+    vector_store = FAISS.from_documents(chunked_documents, embedder)
+    vector_store.save_local("faiss_index")
+
+    return vector_store
+
+
+doc_search = setup_data()
+model = ChatOpenAI(model_name="gpt-4o", temperature=0, streaming=True)
+
+
+@cl.on_chat_start
+async def on_chat_start():
+    template = """Answer the question based only on the following context:
+
+    {context}
+
+    Question: {question}
+    """
+    prompt = ChatPromptTemplate.from_template(template)
+
+    def format_docs(docs):
+        return "\n\n".join([d.page_content for d in docs])
+
+    retriever = doc_search.as_retriever()
+
+    runnable = (
+        {"context": retriever | format_docs, "question": RunnablePassthrough()}
+        | prompt
+        | model
+        | StrOutputParser()
+    )
+
+    cl.user_session.set("runnable", runnable)
+
+
+@cl.on_message
+async def on_message(message: cl.Message):
+    runnable = cl.user_session.get("runnable")  # type: Runnable
+    msg = cl.Message(content="")
+
+    class PostMessageHandler(BaseCallbackHandler):
+        """
+        Callback handler for handling the retriever and LLM processes.
+        Used to post the sources of the retrieved documents as a Chainlit element.
+        """
+
+        def __init__(self, msg: cl.Message):
+            BaseCallbackHandler.__init__(self)
+            self.msg = msg
+            self.sources = set()  # To store unique pairs
+
+        def on_retriever_end(self, documents, *, run_id, parent_run_id, **kwargs):
+            for d in documents:
+                source_page_pair = (d.metadata['source'], d.metadata['page'])
+                self.sources.add(source_page_pair)  # Add unique pairs to the set
+
+        def on_llm_end(self, response, *, run_id, parent_run_id, **kwargs):
+            if len(self.sources):
+                sources_text = "\n".join([f"{source}#page={page}" for source, page in self.sources])
+                self.msg.elements.append(
+                    cl.Text(name="Sources", content=sources_text, display="inline")
+                )
+
+    async with cl.Step(type="run", name="QA Assistant"):
+        async for chunk in runnable.astream(
+            message.content,
+            config=RunnableConfig(callbacks=[
+                cl.LangchainCallbackHandler(),
+                PostMessageHandler(msg)
+            ]),
+        ):
+            await msg.stream_token(chunk)
+
+    await msg.send()

requirements.txt

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+datasets
+langchain
+langchain-community
+langchain_openai
+faiss-cpu
+tiktoken
+chainlit
+