LlamaIndex Workflows with routing and multisource RAG (#222)

* Multisource + routing initial version

* Running black

* Removing unnecessary prompt details

* Updating starters

* Remove unnecessary styling

community/routing-multisource-rag/.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
+
+# Authorized origins
+allow_origins = ["*"]
+
+# Follow symlink for asset mount (see https://github.com/Chainlit/chainlit/issues/317)
+# follow_symlink = false
+
+[features]
+# Process and display HTML in messages. This can be a security risk (see https://stackoverflow.com/questions/19603097/why-is-it-dangerous-to-render-user-generated-html-or-javascript)
+unsafe_allow_html = false
+
+# Process and display mathematical expressions. This can clash with "$" characters in messages.
+latex = false
+
+# Automatically tag threads with the current chat profile (if a chat profile is used)
+auto_tag_thread = true
+
+# Allow users to edit their own messages
+edit_message = true
+
+# Authorize users to spontaneously upload files with messages
+[features.spontaneous_file_upload]
+    enabled = true
+    accept = ["*/*"]
+    max_files = 20
+    max_size_mb = 500
+
+[features.audio]
+    # Threshold for audio recording
+    min_decibels = -45
+    # Delay for the user to start speaking in MS
+    initial_silence_timeout = 3000
+    # Delay for the user to continue speaking in MS. If the user stops speaking for this duration, the recording will stop.
+    silence_timeout = 1500
+    # Above this duration (MS), the recording will forcefully stop.
+    max_duration = 15000
+    # Duration of the audio chunks in MS
+    chunk_duration = 1000
+    # Sample rate of the audio
+    sample_rate = 44100
+
+[UI]
+# Name of the assistant.
+name = "Assistant"
+
+# Description of the assistant. This is used for HTML tags.
+# description = ""
+
+# Large size content are by default collapsed for a cleaner ui
+default_collapse_content = true
+
+# Chain of Thought (CoT) display mode. Can be "hidden", "tool_call" or "full".
+cot = "full"
+
+# Link to your github repo. This will add a github button in the UI's header.
+# github = ""
+
+# Specify a CSS file that can be used to customize the user interface.
+# The CSS file can be served from the public directory or via an external link.
+# custom_css = "/public/test.css"
+
+# Specify a Javascript file that can be used to customize the user interface.
+# The Javascript file can be served from the public directory.
+# custom_js = "/public/test.js"
+
+# Specify a custom font url.
+# custom_font = "https://fonts.googleapis.com/css2?family=Inter:wght@400;500;700&display=swap"
+
+# Specify a custom meta image url.
+# custom_meta_image_url = "https://chainlit-cloud.s3.eu-west-3.amazonaws.com/logo/chainlit_banner.png"
+
+# Specify a custom build directory for the frontend.
+# This can be used to customize the frontend code.
+# Be careful: If this is a relative path, it should not start with a slash.
+# custom_build = "./public/build"
+
+[UI.theme]
+    default = "dark"
+    #layout = "wide"
+    #font_family = "Inter, sans-serif"
+# 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"
+    [UI.theme.light.text]
+        #primary = "#212121"
+        #secondary = "#616161"
+
+# 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"
+    [UI.theme.dark.text]
+        #primary = "#EEEEEE"
+        #secondary = "#BDBDBD"
+
+[meta]
+generated_by = "1.2.0"

community/routing-multisource-rag/.gitignore

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+.DS_Store
+*.db
+*.db.lock
+.venv
+.env
+
+.chainlit/translations

community/routing-multisource-rag/README.md

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+# LlamaIndex + NVIDIA
+This project shows how to use NVIDIA's APIs for large language models with LlamaIndex Workflow and ingestion functionality. Chainlit provides the chat UI. 
+
+### Project highlights:
+- Interfaces with chat completion and embedding models from [build.nvidia.com](https://build.nvidia.com)
+- Routs queries based on whether they require access to a document database
+- Answers queries using the [Perplexity API for web search](https://docs.perplexity.ai/home)
+- Performs vector lookup [using Milvus Lite](https://milvus.io/docs/milvus_lite.md) (WIP)
+- Stores user chat history with a local SQLite database (WIP)
+
+### Technologies used:
+- **Frontend**: Chainlit
+- **Web search**: Perplexity API
+- **LLM**: Llama 3.1 8b and Mistral Large 2
+- **Database**: Milvus Lite
+- **Chat application**: LlamaIndex Workflows
+
+![System architecture diagram](architecture.png)
+
+### Getting started
+To run this code, make sure you have environment variables set for the following:
+- `NVIDIA_API_KEY` for access to NVIDIA LLM APIs (required). You can set this by running `export NVIDIA_API_KEY="nvapi-*******************"`. If you don't have an API key, follow [these instructions](https://github.com/NVIDIA/GenerativeAIExamples/blob/main/docs/api-catalog.md#get-an-api-key-for-the-accessing-models-on-the-api-catalog) to sign up for an NVIDIA AI Foundation developer account and obtain access.
+
+
+- `PERPLEXITY_API_KEY` (optional) if you are interested in using Perplexity to answer queries using the web. 
+
+Then, clone this project and (optionally) create a new virtual environment in Python. Run `pip install -r requirements.txt` for the dependencies and begin the application using `chainlit run app.py` from this directory. The application should then be available at http://localhost:8000.
+
+### Design
+This project uses Chainlit to host a combined frontend and backend. The chat logic is implemented a LlamaIndex Workflow class, which runs the user's query through the following steps:
+- Decide whether user query warrants usage of LLM with or without RAG (`QueryFlow.route_query`)
+- If using RAG, the query is transformed (`QueryFlow.rewrite_query`)into a format better suited for web search and document retrieval and a vector embedding is produced (`QueryFlow.embed_query`)
+- Documents are retrieved from the document database (`QueryFlow.milvus_retrieve`)
+- An answer is solicited from the Perplexity API (`QueryFlow.pplx_retrieve`)
+- The results are combined and used for generating a final response which is streamed to the user (`QueryFlow.synthesize_response`)
+
+![Workflow diagram](diagram.png)

community/routing-multisource-rag/app.py

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+# SPDX-FileCopyrightText: Copyright (c) 2023-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import time
+
+import chainlit as cl
+from dotenv import load_dotenv
+from llama_index.core import Settings
+
+from workflow import QueryFlow
+
+load_dotenv()
+
+workflow = QueryFlow(timeout=45, verbose=False)
+
+
+@cl.on_chat_start
+async def on_chat_start():
+
+    cl.user_session.set("message_history", [])
+
+    workflow = QueryFlow(timeout=90, verbose=False)
+
+    cl.user_session.set("workflow", workflow)
+
+
+@cl.set_starters
+async def set_starters():
+    return [
+        cl.Starter(
+            label="Write a haiku about CPUs",
+            message="Write a haiku about CPUs.",
+            icon="/avatars/servers",
+        ),
+        cl.Starter(
+            label="Write Docker Compose",
+            message="Write a Docker Compose file for deploying a web app with a Redis cache and Postgres database",
+            icon="/avatars/screen",
+        ),
+        cl.Starter(
+            label="What NIMs are available?",
+            message="Summarize the different large language models that have NVIDIA inference microservices (NIMs) available for them. List as many as you can.",
+            icon="/avatars/container",
+        ),
+        cl.Starter(
+            label="Summarize BioNemo use cases",
+            message="Write a table summarizing how customers are using bionemo. Use one sentence per customer and include columns for customer, industry, and use case. Make the table between 5 to 10 rows and relatively narrow.",
+            icon="/avatars/dna",
+        ),
+    ]
+
+
+@cl.on_chat_end
+def end():
+    logging.info("Chat ended.")
+
+
+@cl.on_message
+async def main(user_message: cl.Message, count_tokens: bool = True):
+    """
+    Executes when a user sends a message. We send the message off to the LlamaIndex chat engine
+    for a streaming answer. When the answer is done streaming, we go back over the response
+    to identify the sources used, and then add a block of text about the sources.
+    """
+
+    msg_start_time = time.time()
+    logging.info(f"Received message: <{user_message.content[0:50]}...> ")
+    message_history = cl.user_session.get("message_history", [])
+
+    # In case the chat workflow needs extra time to start up,
+    # we block until it's ready.
+
+    assistant_message = cl.Message(content="")
+
+    token_count = 0
+    with cl.Step(name="Mistral Large 2", type="tool"):
+
+        response, source_nodes = await workflow.run(
+            query=user_message.content,
+            chat_messages=message_history,
+        )
+
+        async for chunk in response:
+            token_count += 1
+            chars = chunk.delta
+            await assistant_message.stream_token(chars)
+
+        msg_time = time.time() - msg_start_time
+        logging.info(f"Message generated in {msg_time:.1f} seconds.")
+
+    message_history += [
+        {"role": "user", "content": user_message.content},
+        {"role": "assistant", "content": assistant_message.content},
+    ]
+
+    cl.user_session.set("message_history", message_history)
+
+    await assistant_message.send()

community/routing-multisource-rag/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/k73SQ3FyUh) 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.

community/routing-multisource-rag/config.py

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+# SPDX-FileCopyrightText: Copyright (c) 2023-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+from pydantic import BaseModel, Field
+
+
+class WorkflowConfig(BaseModel):
+    perplexity_timeout: int = Field(
+        default=20, description="Timeout in seconds for Perplexity API call"
+    )
+    source_field_name: str = Field(
+        default="source_uri",
+        description="Field name for source URI in document metadata",
+    )
+    display_field_name: str = Field(
+        default="display_name",
+        description="Field name for display name in document metadata",
+    )
+    n_messages_in_history: int = Field(
+        default=6, description="Number of messages to include in chat history"
+    )
+    max_tokens_generated: int = Field(
+        default=1024, description="Maximum number of tokens to generate in response"
+    )
+    context_window: int = Field(
+        default=128_000, description="Size of the context window for the LLM"
+    )
+
+    chat_model_name: str = Field(
+        default="mistralai/mistral-large-2-instruct",
+        description="Model for final response synthesis. ",
+    )
+    routing_model_name: str = Field(
+        default="meta/llama-3.1-8b-instruct",
+        description="Model for performing query routing. Can be a bit dumber.",
+    )
+    perplexity_model_name: str = Field(
+        default="llama-3.1-sonar-large-128k-online",
+        description="Name of the Perplexity model; alternatives are huge and small.",
+    )
+    embedding_model_name: str = Field(
+        default="nvidia/nv-embed-v1", description="Name of the embedding model"
+    )
+    embedding_model_dim: int = Field(
+        default=4096, description="Dimension of the embedding model"
+    )
+    similarity_top_k: int = Field(
+        default=5,
+        description="Number of similar documents to return from vector search",
+    )
+
+    nvidia_api_key: str = Field(
+        default=os.getenv("NVIDIA_API_KEY"), description="NVIDIA API key"
+    )
+    perplexity_api_key: str = Field(
+        default=os.getenv("PERPLEXITY_API_KEY"),
+        description="Perplexity API key (optional)",
+    )
+
+    data_dir: str = Field(
+        default="data", description="Directory containing the documents to be indexed"
+    )
+    milvus_path: str = Field(
+        default="db/milvus_lite.db", description="Path to the Milvus database"
+    )
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        if not self.nvidia_api_key:
+            raise ValueError("NVIDIA_API_KEY is required and must not be null")
+
+
+config = WorkflowConfig()