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Dockerfile

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+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"]

README.md

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+title: ChatWithYourLegalPDF

app.py

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+import os
+from typing import List
+from langchain_community.document_loaders import PyMuPDFLoader
+import uuid
+
+from langchain_openai import OpenAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.chains import (
+    ConversationalRetrievalChain,
+)
+from langchain.document_loaders import PyPDFLoader
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.docstore.document import Document
+from langchain.memory import ChatMessageHistory, ConversationBufferMemory
+from chainlit.types import AskFileResponse
+
+import chainlit as cl
+from langchain_qdrant import QdrantVectorStore
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams
+
+system_template = """Use the following pieces of context to answer the users question.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+ALWAYS return a "SOURCES" part in your answer.
+The "SOURCES" part should be a reference to the source of the document from which you got your answer.
+And if the user greets with greetings like Hi, hello, How are you, etc reply accordingly as well.
+Example of your response should be:
+The answer is foo
+SOURCES: xyz
+Begin!
+----------------
+{summaries}"""
+messages = [
+    SystemMessagePromptTemplate.from_template(system_template),
+    HumanMessagePromptTemplate.from_template("{question}"),
+]
+prompt = ChatPromptTemplate.from_messages(messages)
+chain_type_kwargs = {"prompt": prompt}
+
+
+def generate_vdb(chunks=None):
+    EMBEDDING_MODEL = "text-embedding-3-small"
+    embeddings = OpenAIEmbeddings(model=EMBEDDING_MODEL)
+    PERSIST_PATH = "./qdrant_vector_db"  # Directory to store Qdrant collection
+    COLLECTION_NAME = "legal_data"
+    VECTOR_SIZE = 1536
+
+    # Check if the vector database already exists
+    if os.path.exists(PERSIST_PATH):
+        print(f"Loading existing Qdrant database from {PERSIST_PATH}")
+        qdrant_client = QdrantClient(path=PERSIST_PATH)  # Load the existing DB
+        qdrant_vector_store = QdrantVectorStore(
+            client=qdrant_client,
+            collection_name=COLLECTION_NAME,
+            embedding=embeddings,
+        )
+    else:
+        print(f"Creating new Qdrant database at {PERSIST_PATH}")
+        qdrant_client = QdrantClient(path=PERSIST_PATH)  # Create a new DB
+        qdrant_client.create_collection(
+            collection_name=COLLECTION_NAME,
+            vectors_config=VectorParams(size=VECTOR_SIZE, distance=Distance.COSINE),
+        )
+        qdrant_vector_store = QdrantVectorStore(
+            client=qdrant_client,
+            collection_name=COLLECTION_NAME,
+            embedding=embeddings,
+        )
+        qdrant_vector_store.add_documents(chunks)
+    return qdrant_vector_store
+
+
+@cl.on_chat_start
+async def on_chat_start():
+    await cl.Avatar(
+        name="Chat Legal AI",
+        path="./chat_logo.jpg",
+    ).send()
+
+    pdf_links = [
+    "https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf",
+    "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf"]
+
+    if not os.path.exists("./qdrant_vector_db"):
+        documents = []
+        for pdf_link in pdf_links:
+            loader = PyMuPDFLoader(pdf_link)
+            loaded_docs = loader.load()
+            documents.extend(loaded_docs)
+
+        CHUNK_SIZE = 1000
+        CHUNK_OVERLAP = 200
+
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=CHUNK_SIZE,
+            chunk_overlap=CHUNK_OVERLAP,
+            length_function=len,
+        )
+        split_chunks = text_splitter.split_documents(documents)
+
+        docsearch = generate_vdb(split_chunks)
+    else:
+        docsearch = generate_vdb()
+
+    # Let the user know that the system is ready
+    msg = cl.Message(
+        content=f"Welcome to the AI Legal Chatbot! Ask me anything about the AI policy", disable_human_feedback=True, author="Chat Legal AI"
+        )
+    await msg.send()
+
+    message_history = ChatMessageHistory()
+
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key="answer",
+        chat_memory=message_history,
+        return_messages=True,
+    )
+
+    # Create a chain that uses the Chroma vector store
+    chain = ConversationalRetrievalChain.from_llm(
+        ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True),
+        chain_type="stuff",
+        retriever=docsearch.as_retriever(),
+        memory=memory,
+        return_source_documents=True,
+    )
+    cl.user_session.set("chain", chain)
+
+
+@cl.on_message
+async def main(message):
+    chain = cl.user_session.get("chain")  # type: ConversationalRetrievalChain
+    cb = cl.AsyncLangchainCallbackHandler()
+
+    res = await chain.acall(message.content, callbacks=[cb])
+    answer = res["answer"]
+    source_documents = res["source_documents"]  # type: List[Document]
+
+    text_elements = []  # type: List[cl.Text]
+
+    if source_documents:
+        for source_idx, source_doc in enumerate(source_documents):
+            source_name = f"source_{source_idx}"
+            # Create the text element referenced in the message
+            text_elements.append(
+                cl.Text(content=source_doc.page_content, name=source_name)
+            )
+        source_names = [text_el.name for text_el in text_elements]
+
+        if source_names:
+            answer += f"\nSources: {', '.join(source_names)}"
+        else:
+            answer += "\nNo sources found"
+
+    await cl.Message(content=answer, elements=text_elements,author="Chat Legal AI").send()

chainlit.md

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+# Chat with Legal PDF
+
+This Chainlit app was created following instructions from [this repository!](https://github.com/AI-Maker-Space/Beyond-ChatGPT)