forked from phoenix-oss/llama-stack-mirror
40f41af2f7
# What does this PR do? This PR makes it possible to switch between agentic and non-agentic RAG when running the respective Playground page. When non-agentic RAG is selected, user queries are answered by directly querying the vector DB, augmenting the prompt, and sending the extended prompt to the model via Inference API. ## Test Plan - Launch the Playground and go to the RAG page; - Select the vector DB ID; - Adjust other configuration parameters if necessary; - Set the radio button to Agent-based RAG; - Send a message to the chat; - The query will be answered by an agent using the knowledge search tool as indicated by the output; - Click the 'Clear Chat' button to make it possible to switch modes; - Send a message to the chat again; - This time, the query will be answered by the model directly as can be deduced from the reply.
294 lines
11 KiB
Python
294 lines
11 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import uuid
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import streamlit as st
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from llama_stack_client import Agent, AgentEventLogger, RAGDocument
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from llama_stack.apis.common.content_types import ToolCallDelta
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from llama_stack.distribution.ui.modules.api import llama_stack_api
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from llama_stack.distribution.ui.modules.utils import data_url_from_file
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def rag_chat_page():
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st.title("🦙 RAG")
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def reset_agent_and_chat():
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st.session_state.clear()
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st.cache_resource.clear()
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def should_disable_input():
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return "displayed_messages" in st.session_state and len(st.session_state.displayed_messages) > 0
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with st.sidebar:
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# File/Directory Upload Section
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st.subheader("Upload Documents", divider=True)
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uploaded_files = st.file_uploader(
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"Upload file(s) or directory",
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accept_multiple_files=True,
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type=["txt", "pdf", "doc", "docx"], # Add more file types as needed
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)
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# Process uploaded files
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if uploaded_files:
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st.success(f"Successfully uploaded {len(uploaded_files)} files")
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# Add memory bank name input field
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vector_db_name = st.text_input(
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"Document Collection Name",
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value="rag_vector_db",
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help="Enter a unique identifier for this document collection",
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)
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if st.button("Create Document Collection"):
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documents = [
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RAGDocument(
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document_id=uploaded_file.name,
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content=data_url_from_file(uploaded_file),
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)
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for i, uploaded_file in enumerate(uploaded_files)
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]
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providers = llama_stack_api.client.providers.list()
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vector_io_provider = None
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for x in providers:
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if x.api == "vector_io":
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vector_io_provider = x.provider_id
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llama_stack_api.client.vector_dbs.register(
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vector_db_id=vector_db_name, # Use the user-provided name
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embedding_dimension=384,
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embedding_model="all-MiniLM-L6-v2",
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provider_id=vector_io_provider,
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)
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# insert documents using the custom vector db name
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llama_stack_api.client.tool_runtime.rag_tool.insert(
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vector_db_id=vector_db_name, # Use the user-provided name
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documents=documents,
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chunk_size_in_tokens=512,
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)
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st.success("Vector database created successfully!")
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st.subheader("RAG Parameters", divider=True)
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rag_mode = st.radio(
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"RAG mode",
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["Direct", "Agent-based"],
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captions=[
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"RAG is performed by directly retrieving the information and augmenting the user query",
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"RAG is performed by an agent activating a dedicated knowledge search tool.",
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],
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on_change=reset_agent_and_chat,
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disabled=should_disable_input(),
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)
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# select memory banks
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vector_dbs = llama_stack_api.client.vector_dbs.list()
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vector_dbs = [vector_db.identifier for vector_db in vector_dbs]
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selected_vector_dbs = st.multiselect(
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label="Select Document Collections to use in RAG queries",
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options=vector_dbs,
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on_change=reset_agent_and_chat,
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disabled=should_disable_input(),
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)
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st.subheader("Inference Parameters", divider=True)
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available_models = llama_stack_api.client.models.list()
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available_models = [model.identifier for model in available_models if model.model_type == "llm"]
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selected_model = st.selectbox(
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label="Choose a model",
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options=available_models,
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index=0,
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on_change=reset_agent_and_chat,
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disabled=should_disable_input(),
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)
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system_prompt = st.text_area(
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"System Prompt",
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value="You are a helpful assistant. ",
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help="Initial instructions given to the AI to set its behavior and context",
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on_change=reset_agent_and_chat,
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disabled=should_disable_input(),
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)
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temperature = st.slider(
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"Temperature",
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min_value=0.0,
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max_value=1.0,
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value=0.0,
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step=0.1,
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help="Controls the randomness of the response. Higher values make the output more creative and unexpected, lower values make it more conservative and predictable",
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on_change=reset_agent_and_chat,
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disabled=should_disable_input(),
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)
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top_p = st.slider(
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"Top P",
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min_value=0.0,
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max_value=1.0,
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value=0.95,
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step=0.1,
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on_change=reset_agent_and_chat,
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disabled=should_disable_input(),
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)
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# Add clear chat button to sidebar
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if st.button("Clear Chat", use_container_width=True):
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reset_agent_and_chat()
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st.rerun()
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# Chat Interface
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if "messages" not in st.session_state:
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st.session_state.messages = []
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if "displayed_messages" not in st.session_state:
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st.session_state.displayed_messages = []
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# Display chat history
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for message in st.session_state.displayed_messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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if temperature > 0.0:
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strategy = {
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"type": "top_p",
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"temperature": temperature,
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"top_p": top_p,
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}
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else:
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strategy = {"type": "greedy"}
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@st.cache_resource
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def create_agent():
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return Agent(
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llama_stack_api.client,
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model=selected_model,
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instructions=system_prompt,
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sampling_params={
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"strategy": strategy,
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},
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tools=[
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dict(
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name="builtin::rag/knowledge_search",
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args={
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"vector_db_ids": list(selected_vector_dbs),
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},
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)
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],
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)
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if rag_mode == "Agent-based":
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agent = create_agent()
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if "agent_session_id" not in st.session_state:
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st.session_state["agent_session_id"] = agent.create_session(session_name=f"rag_demo_{uuid.uuid4()}")
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session_id = st.session_state["agent_session_id"]
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def agent_process_prompt(prompt):
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content": prompt})
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# Send the prompt to the agent
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response = agent.create_turn(
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messages=[
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{
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"role": "user",
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"content": prompt,
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}
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],
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session_id=session_id,
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)
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# Display assistant response
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with st.chat_message("assistant"):
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retrieval_message_placeholder = st.empty()
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message_placeholder = st.empty()
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full_response = ""
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retrieval_response = ""
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for log in AgentEventLogger().log(response):
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log.print()
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if log.role == "tool_execution":
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retrieval_response += log.content.replace("====", "").strip()
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retrieval_message_placeholder.info(retrieval_response)
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else:
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full_response += log.content
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message_placeholder.markdown(full_response + "▌")
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message_placeholder.markdown(full_response)
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st.session_state.messages.append({"role": "assistant", "content": full_response})
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st.session_state.displayed_messages.append({"role": "assistant", "content": full_response})
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def direct_process_prompt(prompt):
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# Add the system prompt in the beginning of the conversation
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if len(st.session_state.messages) == 0:
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st.session_state.messages.append({"role": "system", "content": system_prompt})
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# Query the vector DB
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rag_response = llama_stack_api.client.tool_runtime.rag_tool.query(
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content=prompt, vector_db_ids=list(selected_vector_dbs)
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)
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prompt_context = rag_response.content
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with st.chat_message("assistant"):
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retrieval_message_placeholder = st.empty()
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message_placeholder = st.empty()
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full_response = ""
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retrieval_response = ""
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# Display the retrieved content
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retrieval_response += str(prompt_context)
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retrieval_message_placeholder.info(retrieval_response)
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# Construct the extended prompt
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extended_prompt = f"Please answer the following query using the context below.\n\nCONTEXT:\n{prompt_context}\n\nQUERY:\n{prompt}"
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# Run inference directly
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st.session_state.messages.append({"role": "user", "content": extended_prompt})
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response = llama_stack_api.client.inference.chat_completion(
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messages=st.session_state.messages,
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model_id=selected_model,
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sampling_params={
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"strategy": strategy,
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},
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stream=True,
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)
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# Display assistant response
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for chunk in response:
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response_delta = chunk.event.delta
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if isinstance(response_delta, ToolCallDelta):
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retrieval_response += response_delta.tool_call.replace("====", "").strip()
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retrieval_message_placeholder.info(retrieval_response)
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else:
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full_response += chunk.event.delta.text
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message_placeholder.markdown(full_response + "▌")
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message_placeholder.markdown(full_response)
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response_dict = {"role": "assistant", "content": full_response, "stop_reason": "end_of_message"}
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st.session_state.messages.append(response_dict)
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st.session_state.displayed_messages.append(response_dict)
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# Chat input
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if prompt := st.chat_input("Ask a question about your documents"):
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# Add user message to chat history
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st.session_state.displayed_messages.append({"role": "user", "content": prompt})
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# Display user message
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with st.chat_message("user"):
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st.markdown(prompt)
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# store the prompt to process it after page refresh
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st.session_state.prompt = prompt
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# force page refresh to disable the settings widgets
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st.rerun()
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if "prompt" in st.session_state and st.session_state.prompt is not None:
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if rag_mode == "Agent-based":
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agent_process_prompt(st.session_state.prompt)
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else: # rag_mode == "Direct"
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direct_process_prompt(st.session_state.prompt)
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st.session_state.prompt = None
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rag_chat_page()
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