Releasing ChatWithMyPDF

.chainlit/config.toml

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+[project]
+# Whether to enable telemetry (default: true). No personal data is collected.
+enable_telemetry = true
+
+# List of environment variables to be provided by each user to use the app.
+user_env = []
+
+# Duration (in seconds) during which the session is saved when the connection is lost
+session_timeout = 3600
+
+# Enable third parties caching (e.g LangChain cache)
+cache = false
+
+# Follow symlink for asset mount (see https://github.com/Chainlit/chainlit/issues/317)
+# follow_symlink = false
+
+[features]
+# Show the prompt playground
+prompt_playground = true
+
+[UI]
+# Name of the app and chatbot.
+name = "Chatbot"
+
+# Description of the app and chatbot. This is used for HTML tags.
+# description = ""
+
+# Large size content are by default collapsed for a cleaner ui
+default_collapse_content = true
+
+# The default value for the expand messages settings.
+default_expand_messages = false
+
+# Hide the chain of thought details from the user in the UI.
+hide_cot = false
+
+# Link to your github repo. This will add a github button in the UI's header.
+# github = ""
+
+# Override default MUI light theme. (Check theme.ts)
+[UI.theme.light]
+    #background = "#FAFAFA"
+    #paper = "#FFFFFF"
+
+    [UI.theme.light.primary]
+        #main = "#F80061"
+        #dark = "#980039"
+        #light = "#FFE7EB"
+
+# Override default MUI dark theme. (Check theme.ts)
+[UI.theme.dark]
+    #background = "#FAFAFA"
+    #paper = "#FFFFFF"
+
+    [UI.theme.dark.primary]
+        #main = "#F80061"
+        #dark = "#980039"
+        #light = "#FFE7EB"
+
+
+[meta]
+generated_by = "0.7.0"

app.py

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+import os
+from typing import List
+
+from langchain.embeddings.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
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+
+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 process_file(file: AskFileResponse):
+    import tempfile
+
+    with tempfile.NamedTemporaryFile(mode="w", delete=False) as tempfile:
+        with open(tempfile.name, "wb") as f:
+            f.write(file.content)
+
+    pypdf_loader = PyPDFLoader(tempfile.name)
+    texts = pypdf_loader.load_and_split()
+    texts = [text.page_content for text in texts]
+    return texts
+
+
+@cl.on_chat_start
+async def on_chat_start():
+    files = None
+
+    # Wait for the user to upload a file
+    while files == None:
+        files = await cl.AskFileMessage(
+            content="Please upload a PDF file to begin!",
+            accept=["application/pdf"],
+            max_size_mb=20,
+            timeout=180,
+        ).send()
+
+    file = files[0]
+
+    msg = cl.Message(
+        content=f"Processing `{file.name}`...", disable_human_feedback=True
+    )
+    await msg.send()
+
+    # load the file
+    texts = process_file(file)
+
+    print(texts[0])
+
+    # Create a metadata for each chunk
+    metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
+
+    # Create a Chroma vector store
+    embeddings = OpenAIEmbeddings()
+    docsearch = await cl.make_async(Chroma.from_texts)(
+        texts, embeddings, metadatas=metadatas
+    )
+
+    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,
+    )
+
+    # Let the user know that the system is ready
+    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
+    await msg.update()
+
+    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, 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).send()

chainlit.md

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+# Welcome to Chainlit! 🚀🤖
+
+Hi there, Developer! 👋 We're excited to have you on board. Chainlit is a powerful tool designed to help you prototype, debug and share applications built on top of LLMs.
+
+## Useful Links 🔗
+
+- **Documentation:** Get started with our comprehensive [Chainlit Documentation](https://docs.chainlit.io) 📚
+- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/ZThrUxbAYw) to ask questions, share your projects, and connect with other developers! 💬
+
+We can't wait to see what you create with Chainlit! Happy coding! 💻😊
+
+## Welcome screen
+
+To modify the welcome screen, edit the `chainlit.md` file at the root of your project. If you do not want a welcome screen, just leave this file empty.

requirements.txt

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+langchain
+chromadb
+tiktoken
+pypdf
+chainlit
+openai