initial commit

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"]

app.py

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+import chainlit as cl
+from llama_index.llms import MonsterLLM
+from llama_index import VectorStoreIndex,SimpleDirectoryReader, ServiceContext
+
+def indexing(llm,path):
+    documents = SimpleDirectoryReader(input_files=[path]).load_data()
+    print("loading done")
+    service_context = ServiceContext.from_defaults(
+        chunk_size=1024, llm=llm, embed_model="local:BAAI/bge-small-en-v1.5"
+    )
+    print("indexing")
+    index = VectorStoreIndex.from_documents(documents, service_context=service_context, use_async=True)
+    query_engine = index.as_query_engine()
+    print("all done")
+    print(query_engine)
+    return query_engine
+
+def qa(sp,engine,message):
+    message=message.content
+    ques=sp+" "+message
+    response=engine.query(ques)
+    return response
+
+@cl.on_chat_start
+async def factory():
+    url = await cl.AskUserMessage(author="Beast",content="Enter url").send()
+    print(url)
+    if url['output'][-1]=="/":
+        url['output']=url['output'].replace(".ai/",".ai")
+    auth = await cl.AskUserMessage(author="Beast",content="Enter auth token").send()
+    print(auth)
+    model = 'deploy-llm'
+    llm = MonsterLLM(model=model,base_url=url['output'],monster_api_key=auth['output'],temperature=0.75, context_window=1024)
+    files = None
+    while files is None:
+        files = await cl.AskFileMessage(author="Beast",
+            content="Please upload a PDF file to begin!",
+            accept=["application/pdf"],
+            max_size_mb=20,
+            timeout=180,
+        ).send()
+
+    pdf = files[0]
+    print(pdf)
+    msg = cl.Message(author="Beast",content=f"Processing `{pdf.name}`...")
+    await msg.send()    
+    query_engine = await cl.make_async(indexing)(llm,pdf.path)
+    msg.content = f"`{pdf.name}` processed."
+    await msg.update()
+    res = await cl.AskActionMessage(author="Beast",
+        content="Do you want to enter system prompt?",
+        actions=[
+            cl.Action(name="yes", value="yes", label="✅ Yes"),
+            cl.Action(name="no", value="no", label="❌ No"),
+        ],
+    ).send()
+
+    if res and res.get("value") == "yes":
+        sp = await cl.AskUserMessage(author="Beast",content="Enter system prompt").send()
+        await cl.Message(author="Beast",content="Noted. Go ahead as your questions!!").send()
+        cl.user_session.set("sp", sp["output"])
+    else:
+        await cl.Message(author="Beast",content="Okay, then you can start asking your questions!!").send()
+    cl.user_session.set("engine", query_engine)
+    
+    
+@cl.on_message
+async def main(message: cl.Message):
+    msg = cl.Message(author="Beast",content=f"Processing...", disable_feedback=False)
+    await msg.send()
+    engine = cl.user_session.get("engine")
+    sp=cl.user_session.get("sp")
+    if sp==None:
+        sp=""
+    response =await cl.make_async(qa)(sp,engine,message)
+    print(response)
+    msg.content = str(response)
+    await msg.update()
+   

requirements.txt

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+monsterapi
+git+https://github.com/Vikasqblocks/llama_index.git@f2f04654e9f2cbf1bf765b0d575a6af1f899b18e