canopy-sdk 0.9.0

Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone

Canopy

Canopy is an open-source Retrieval Augmented Generation (RAG) framework and context engine built on top of the Pinecone vector database. Canopy enables you to quickly and easily experiment with and build applications using RAG. Start chatting with your documents or text data with a few simple commands.

Canopy takes on the heavy lifting for building RAG applications: from chunking and embedding your text data to chat history management, query optimization, context retrieval (including prompt engineering), and augmented generation.

Canopy provides a configurable built-in server so you can effortlessly deploy a RAG-powered chat application to your existing chat UI or interface. Or you can build your own, custom RAG application using the Canopy library.

Canopy lets you evaluate your RAG workflow with a CLI based chat tool. With a simple command in the Canopy CLI you can interactively chat with your text data and compare RAG vs. non-RAG workflows side-by-side.

Check out our blog post to learn more, or see a quick tutorial here.

RAG with Canopy

Canopy implements the full RAG workflow to prevent hallucinations and augment your LLM with your own text data.

Canopy has two flows: knowledge base creation and chat. In the knowledge base creation flow, users upload their documents and transform them into meaningful representations stored in Pinecone's Vector Database. In the chat flow, incoming queries and chat history are optimized to retrieve the most relevant documents, the knowledge base is queried, and a meaningful context is generated for the LLM to answer.

What's inside the box?

1. Canopy Core Library - The library has 3 main classes that are responsible for different parts of the RAG workflow:
   • ChatEngine - Exposes a chat interface to interact with your data. Given the history of chat messages, the ChatEngine formulates relevant queries to the ContextEngine, then uses the LLM to generate a knowledgeable response.
   • ContextEngine - Performs the “retrieval” part of RAG. The ContextEngine utilizes the underlying KnowledgeBase to retrieve the most relevant documents, then formulates a coherent textual context to be used as a prompt for the LLM.
   • KnowledgeBase - Manages your data for the RAG workflow. It automatically chunks and transforms your text data into text embeddings, storing them in a Pinecone(Default)/Qdrant vector database. Given a text query - the knowledge base will retrieve the most relevant document chunks from the database.

More information about the Core Library usage can be found in the Library Documentation

2. Canopy Server - This is a webservice that wraps the Canopy Core library and exposes it as a REST API. The server is built on top of FastAPI, Uvicorn and Gunicorn and can be easily deployed in production. The server also comes with a built-in Swagger UI for easy testing and documentation. After you start the server, you can access the Swagger UI at http://host:port/docs (default: http://localhost:8000/docs)

3. Canopy CLI - A built-in development tool that allows users to swiftly set up their own Canopy server and test its configuration.
   With just three CLI commands, you can create a new Canopy server, upload your documents to it, and then interact with the Chatbot using a built-in chat application directly from the terminal. The built-in chatbot also enables comparison of RAG-infused responses against a native LLM chatbot.

Setup

0. set up a virtual environment (optional)

python3 -m venv canopy-env
source canopy-env/bin/activate

More information about virtual environments can be found here

1. install the package

pip install canopy-sdk

You can also install canopy-sdk with extras. CLICK HERE to see the available extras

Extras

Name	Description
grpc	To unlock some performance improvements by working with the GRPC version of the Pinecone Client
torch	To enable embeddings provided by sentence-transformers
transformers	If you are using Anyscale LLMs, it's recommended to use LLamaTokenizer tokenizer which requires transformers as dependency
cohere	To use Cohere reranker or/and Cohere LLM
qdrant	To use Qdrant as an alternate knowledge base

2. Set up the environment variables

export PINECONE_API_KEY="<PINECONE_API_KEY>"
export OPENAI_API_KEY="<OPENAI_API_KEY>"
export INDEX_NAME="<INDEX_NAME>"

CLICK HERE for more information about the environment variables

Mandatory Environment Variables

Name	Description	How to get it?
PINECONE_API_KEY	The API key for Pinecone. Used to authenticate to Pinecone services to create indexes and to insert, delete and search data	Register or log into your Pinecone account in the console. You can access your API key from the "API Keys" section in the sidebar of your dashboard
OPENAI_API_KEY	API key for OpenAI. Used to authenticate to OpenAI's services for embedding and chat API	You can find your OpenAI API key here. You might need to login or register to OpenAI services
INDEX_NAME	Name of the Pinecone index Canopy will underlying work with	You can choose any name as long as it follows Pinecone's restrictions
CANOPY_CONFIG_FILE	The path of a configuration yaml file to be used by the Canopy server.	Optional - if not provided, default configuration would be used

Optional Environment Variables

These optional environment variables are used to authenticate to other supported services for embeddings and LLMs. If you configure Canopy to use any of these providers - you would need to set the relevant environment variables.

Name	Description	How to get it?
ANYSCALE_API_KEY	API key for Anyscale. Used to authenticate to Anyscale Endpoints for open source LLMs	You can register Anyscale Endpoints and find your API key here
CO_API_KEY	API key for Cohere. Used to authenticate to Cohere services for embedding	You can find more information on registering to Cohere here
JINA_API_KEY	API key for Jina AI. Used to authenticate to JinaAI's services for embedding and chat API	You can find your OpenAI API key here. You might need to login or register to OpenAI services
AZURE_OPENAI_ENDOINT	The URL of the Azure OpenAI endpoint you deployed.	You can find this in the Azure OpenAI portal under _Keys and Endpoints`
AZURE_OPENAI_API_KEY	The API key to use for your Azure OpenAI models.	You can find this in the Azure OpenAI portal under _Keys and Endpoints`
OCTOAI_API_KEY	API key for OctoAI. Used to authenticate for open source LLMs served in OctoAI	You can sign up for OctoAI and find your API key here

3. Check that installation is successful and environment is set, run:

canopy

Output should be similar to this:

Canopy: Ready

Usage: canopy [OPTIONS] COMMAND [ARGS]...
# rest of the help message

Quickstart

In this quickstart, we will show you how to use the Canopy to build a simple question answering system using RAG (retrieval augmented generation).

1. Create a new Canopy Index

As a one-time setup, Canopy needs to create a new Pinecone index that is configured to work with Canopy, just run:

canopy new

And follow the CLI instructions. The index that will be created will have a prefix canopy--<INDEX_NAME>.
You only have to do this process once for every Canopy index you want to create.

To learn more about Pinecone indexes and how to manage them, please refer to the following guide: Understanding indexes

2. Uploading data

You can load data into your Canopy index using the command:

canopy upsert /path/to/data_directory
# or
canopy upsert /path/to/data_directory/file.parquet
# or
canopy upsert /path/to/data_directory/file.jsonl
# or
canopy upsert /path/to/directory_of_txt_files/
# ...

Canopy supports files in jsonl, parquet and csv formats. Additionally, you can load plaintext data files in .txt format. In this case, each file will be treated as a single document. The document id will be the filename, and the source will be the full path of the file.

Note: Document fields are used in the RAG flow and should comply with the following schema:

+----------+--------------+--------------+---------------+
| id(str)  | text(str)    | source       | metadata      |
|          |              | Optional[str]| Optional[dict]|
|----------+--------------+--------------+---------------|
| "id1"    | "some text"  | "some source"| {"key": "val"}|
+----------+--------------+--------------+---------------+

# id       - unique identifier for the document
#
# text     - the text of the document, in utf-8 encoding.
#
# source   - the source of the document, can be any string, or null.
#            ** this will be used as a reference in the generated context. **
#
# metadata - optional metadata for the document, for filtering or additional context.
#            Dict[str, Union[str, int, float, List[str]]]

This notebook shows how you create a dataset in this format, Follow the instructions in the CLI when you upload your data.

[!TIP] If you would like to separate your data into namespaces, you can use the --namespace option or the INDEX_NAMESPACE environment variable.

3. Start the Canopy server

The Canopy server exposes Canopy's functionality via a REST API. Namely, it allows you to upload documents, retrieve relevant docs for a given query, and chat with your data. The server exposes a /chat.completion endpoint that can be easily integrated with any chat application. To start the server, run:

canopy start

Now, you should be prompted with the following standard Uvicorn message:

...

INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

That's it! you can now start using the Canopy server with any chat application that supports a /chat.completion endpoint.

The canopy start command will keep the terminal occupied (recommended use). If you want to run the server in the background, you can use the following command - nohup canopy start &

Stopping the server

To stop the server, simply press CTRL+C in the terminal where you started it.

Evaluation chat tool

Canopy's CLI comes with a built-in chat app that allows you to interactively chat with your text data and compare RAG vs. non-RAG workflows side-by-side to evaluate the results

In a new terminal window, set the required environment variables then run:

canopy chat

This will open a chat interface in your terminal. You can ask questions and the RAG-infused chatbot will try to answer them using the data you uploaded.

To compare the chat response with and without RAG use the --no-rag flag

Note: This method is only supported with OpenAI at the moment.

canopy chat --no-rag

This will open a similar chat interface window, but will show both the RAG and non-RAG responses side-by-side.

Considerations

• Rate limits and pricing set by model providers apply to Canopy usage. Canopy currently works with OpenAI, Azure OpenAI, Anyscale, and Cohere models.
• More integrations will be supported in the near future.

Contributing

Thank you for considering contributing to Canopy! Please see our contributing guidelines for more information.

Advanced usage

Migrating an existing OpenAI application to Canopy

If you already have an application that uses the OpenAI API, you can migrate it to Canopy by simply changing the API endpoint to http://host:port/v1, for example with the default configuration:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1")

If you would like to use a specific index namespace for chatting, you can just append the namespace to the API endpoint:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1/my-namespace")

Running Canopy server in production

Canopy is using FastAPI as the web framework and Uvicorn as the ASGI server.
To use Canopy in production, it is recommended to utilize Canopy's docker image, available on GitHub Packages, for your production needs.
For guidance on deploying Canopy on the Google Cloud Platform (GCP), refer to the example provided in the Deployment to GCP documentation.

Alternatively, you can use Gunicorn as production-grade WSGI, more details here.
Set your desired PORT and WORKER_COUNT envrionment variables, and start the server with:

gunicorn canopy_server.app:app --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:$PORT --workers $WORKER_COUNT

[!IMPORTANT] The server interacts with services like Pinecone and OpenAI using your own authentication credentials. When deploying the server on a public web hosting provider, it is recommended to enable an authentication mechanism, so that your server would only take requests from authenticated users.