[App Deployment] create chat with PDF app (#1049)

Co-authored-by: Deven Patel <deven298@yahoo.com>
Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>

examples/chat-pdf/README.md

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+# Embedchain Chat with PDF App
+
+You can easily create and deploy your own `Chat-with-PDF` App using Embedchain.
+
+Here are few simple steps for you to create and deploy your app:
+
+1. Fork the embedchain repo from [Github](https://github.com/embedchain/embedchain).
+
+If you run into problems with forking, please refer to [github docs](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo) for forking a repo.
+
+2. Navigate to `chat-pdf` example app from your forked repo:
+
+```bash
+cd <your_fork_repo>/examples/chat-pdf
+```
+
+3. Run your app in development environment with simple commands
+
+```bash
+pip install -r requirements.txt
+ec dev
+```
+
+Feel free to improve our simple `chat-pdf` streamlit app and create pull request to showcase your app [here](https://docs.embedchain.ai/examples/showcase)
+
+4. You can easily deploy your app using Streamlit interface
+
+Connect your Github account with Streamlit and refer this [guide](https://docs.streamlit.io/streamlit-community-cloud/deploy-your-app) to deploy your app.
+
+You can also use the deploy button from your streamlit website you see when running `ec dev` command.

examples/chat-pdf/app.py

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+import os
+import queue
+import re
+import tempfile
+import threading
+
+import streamlit as st
+
+from embedchain import Pipeline as App
+from embedchain.config import BaseLlmConfig
+from embedchain.helpers.callbacks import (StreamingStdOutCallbackHandlerYield,
+                                          generate)
+
+
+@st.cache_resource
+def embedchain_bot():
+    return App.from_config(
+        config={
+            "llm": {
+                "provider": "openai",
+                "config": {
+                    "model": "gpt-3.5-turbo-1106",
+                    "temperature": 0.5,
+                    "max_tokens": 1000,
+                    "top_p": 1,
+                    "stream": True,
+                },
+            },
+            "vectordb": {
+                "provider": "chroma",
+                "config": {"collection_name": "chat-pdf", "dir": "db", "allow_reset": True},
+            },
+            "chunker": {"chunk_size": 2000, "chunk_overlap": 0, "length_function": "len"},
+        }
+    )
+
+
+@st.cache_data
+def update_openai_key():
+    os.environ["OPENAI_API_KEY"] = st.session_state.chatbot_api_key
+
+
+with st.sidebar:
+    openai_access_token = st.text_input(
+        "OpenAI API Key", value=os.environ.get("OPENAI_API_KEY"), key="chatbot_api_key", type="password"
+    )  # noqa: E501
+    "WE DO NOT STORE YOUR OPENAI KEY."
+    "Just paste your OpenAI API key here and we'll use it to power the chatbot. [Get your OpenAI API key](https://platform.openai.com/api-keys)"  # noqa: E501
+
+    if openai_access_token:
+        update_openai_key()
+
+    pdf_files = st.file_uploader("Upload your PDF files", accept_multiple_files=True, type="pdf")
+    add_pdf_files = st.session_state.get("add_pdf_files", [])
+    for pdf_file in pdf_files:
+        file_name = pdf_file.name
+        if file_name in add_pdf_files:
+            continue
+        try:
+            if not os.environ.get("OPENAI_API_KEY"):
+                st.error("Please enter your OpenAI API Key")
+                st.stop()
+            app = embedchain_bot()
+            temp_file_name = None
+            with tempfile.NamedTemporaryFile(mode="wb", delete=False, prefix=file_name, suffix=".pdf") as f:
+                f.write(pdf_file.getvalue())
+                temp_file_name = f.name
+            if temp_file_name:
+                st.markdown(f"Adding {file_name} to knowledge base...")
+                app.add(temp_file_name, data_type="pdf_file")
+                st.markdown("")
+                add_pdf_files.append(file_name)
+                os.remove(temp_file_name)
+            st.session_state.messages.append({"role": "assistant", "content": f"Added {file_name} to knowledge base!"})
+        except Exception as e:
+            st.error(f"Error adding {file_name} to knowledge base: {e}")
+            st.stop()
+    st.session_state["add_pdf_files"] = add_pdf_files
+
+st.title("📄 Embedchain - Chat with PDF")
+styled_caption = '<p style="font-size: 17px; color: #aaa;">🚀 An <a href="https://github.com/embedchain/embedchain">Embedchain</a> app powered by OpenAI!</p>'  # noqa: E501
+st.markdown(styled_caption, unsafe_allow_html=True)
+
+if "messages" not in st.session_state:
+    st.session_state.messages = [
+        {
+            "role": "assistant",
+            "content": """
+                Hi! I'm chatbot powered by Embedchain, which can answer questions about your pdf documents.\n
+                Upload your pdf documents here and I'll answer your questions about them! 
+            """,
+        }
+    ]
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+if prompt := st.chat_input("Ask me anything!"):
+    if not os.environ.get("OPENAI_API_KEY"):
+        st.error("Please enter your OpenAI API Key", icon="🤖")
+        st.stop()
+
+    app = embedchain_bot()
+    with st.chat_message("user"):
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        msg_placeholder = st.empty()
+        msg_placeholder.markdown("Thinking...")
+        full_response = ""
+
+        q = queue.Queue()
+
+        def app_response(result):
+            llm_config = app.llm.config.as_dict()
+            llm_config["callbacks"] = [StreamingStdOutCallbackHandlerYield(q=q)]
+            config = BaseLlmConfig(**llm_config)
+            answer, citations = app.chat(prompt, config=config, citations=True)
+            result["answer"] = answer
+            result["citations"] = citations
+
+        results = {}
+        thread = threading.Thread(target=app_response, args=(results,))
+        thread.start()
+
+        for answer_chunk in generate(q):
+            full_response += answer_chunk
+            msg_placeholder.markdown(full_response)
+
+        thread.join()
+        answer, citations = results["answer"], results["citations"]
+        if citations:
+            full_response += "\n\n**Sources**:\n"
+            sources = []
+            for i, citation in enumerate(citations):
+                source = citation[1]
+                pattern = re.compile(r"([^/]+)\.[^\.]+\.pdf$")
+                match = pattern.search(source)
+                if match:
+                    source = match.group(1) + ".pdf"
+                sources.append(source)
+            sources = list(set(sources))
+            for source in sources:
+                full_response += f"- {source}\n"
+
+        msg_placeholder.markdown(full_response)
+        print("Answer: ", answer)
+        st.session_state.messages.append({"role": "assistant", "content": answer})

examples/chat-pdf/embedchain.json

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+{
+    "provider": "streamlit.io"
+}

examples/chat-pdf/requirements.txt

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+streamlit==1.29.0
+embedchain