Package: canopy-sdk
CanopyCanopyis 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 ourblog postto learn more, or see a quicktutorial here.RAG with CanopyCanopy 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?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, theChatEngineformulates relevant queries to theContextEngine, then uses the LLM to generate a knowledgeable response.ContextEngine- Performs the “retrieval” part of RAG. TheContextEngineutilizes the underlyingKnowledgeBaseto 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 vector database. Given a text query - theKnowledgeBasewill retrieve the most relevant document chunks from the database.More information about the Core Library usage can be found in theLibrary DocumentationCanopy Server- This is a webservice that wraps theCanopy Corelibrary 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 youstart the server, you can access the Swagger UI athttp://host:port/docs(default:http://localhost:8000/docs)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.Setupset up a virtual environment (optional)python3-mvenvcanopy-envsourcecanopy-env/bin/activateMore information about virtual environments can be foundhereinstall the packagepipinstallcanopy-sdkYou can also install canopy-sdk with extras.CLICK HEREto see the available extrasExtrasNameDescriptiongrpcTo unlock some performance improvements by working with the GRPC version of thePinecone ClienttorchTo enable embeddings provided bysentence-transformerstransformersIf you are using Anyscale LLMs, it's recommended to useLLamaTokenizertokenizer which requires transformers as dependencycohereTo use Cohere reranker or/and Cohere LLMSet up the environment variablesexportPINECONE_API_KEY="<PINECONE_API_KEY>"exportOPENAI_API_KEY="<OPENAI_API_KEY>"exportJINA_API_KEY="<JINA_API_KEY>"exportINDEX_NAME="<INDEX_NAME>"CLICK HEREfor more information about the environment variablesMandatory Environment VariablesNameDescriptionHow to get it?PINECONE_API_KEYThe API key for Pinecone. Used to authenticate to Pinecone services to create indexes and to insert, delete and search dataRegister or log into your Pinecone account in theconsole. You can access your API key from the "API Keys" section in the sidebar of your dashboardOPENAI_API_KEYAPI key for OpenAI. Used to authenticate to OpenAI's services for embedding and chat APIYou can find your OpenAI API keyhere. You might need to login or register to OpenAI servicesINDEX_NAMEName of the Pinecone index Canopy will underlying work withYou can choose any name as long as it follows Pinecone'srestrictionsCANOPY_CONFIG_FILEThe path of a configuration yaml file to be used by the Canopy server.Optional - if not provided, default configuration would be usedOptional Environment VariablesThese 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.NameDescriptionHow to get it?ANYSCALE_API_KEYAPI key for Anyscale. Used to authenticate to Anyscale Endpoints for open source LLMsYou can register Anyscale Endpoints and find your API keyhereCO_API_KEYAPI key for Cohere. Used to authenticate to Cohere services for embeddingYou can find more information on registering to CoherehereJINA_API_KEYAPI key for Jina AI. Used to authenticate to JinaAI's services for embedding and chat APIYou can find your OpenAI API keyhere. You might need to login or register to OpenAI servicesAZURE_OPENAI_ENDOINTThe URL of the Azure OpenAI endpoint you deployed.You can find this in the Azure OpenAI portal under _Keys and Endpoints`AZURE_OPENAI_API_KEYThe API key to use for your Azure OpenAI models.You can find this in the Azure OpenAI portal under _Keys and Endpoints`Check that installation is successful and environment is set, run:canopyOutput should be similar to this:Canopy:Ready

Usage:canopy[OPTIONS]COMMAND[ARGS]...# rest of the help messageQuickstartIn this quickstart, we will show you how to use theCanopyto build a simple question answering system using RAG (retrieval augmented generation).1. Create a newCanopyIndexAs a one-time setup, Canopy needs to create a new Pinecone index that is configured to work with Canopy, just run:canopynewAnd follow the CLI instructions. The index that will be created will have a prefixcanopy--<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 indexes2. Uploading dataYou can load data into your Canopy index using the command:canopyupsert/path/to/data_directory# orcanopyupsert/path/to/data_directory/file.parquet# orcanopyupsert/path/to/data_directory/file.jsonl# orcanopyupsert/path/to/directory_of_txt_files/# ...Canopy supports files injsonl,parquetandcsvformats. Additionally, you can load plaintext data files in.txtformat. 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 notebookshows 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 intonamespaces,
you can use the--namespaceoption or theINDEX_NAMESPACEenvironment variable.3. Start the Canopy serverThe 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.completionendpoint that can be easily integrated with any chat application.
To start the server, run:canopystartNow, 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 theCanopyserver with any chat application that supports a/chat.completionendpoint.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 serverTo stop the server, simply pressCTRL+Cin the terminal where you started it.Evaluation chat toolCanopy'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 resultsIn a new terminal window, set therequired environment variablesthen run:canopychatThis 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-ragflagNote: This method is only supported with OpenAI at the moment.canopychat--no-ragThis will open a similar chat interface window, but will show both the RAG and non-RAG responses side-by-side.ConsiderationsRate 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.ContributingThank you for considering contributing to Canopy! Please see ourcontributing guidelinesfor more information.Advanced usageMigrating an existing OpenAI application toCanopyIf you already have an application that uses the OpenAI API, you can migrate it toCanopyby simply changing the API endpoint tohttp://host:port/v1, for example with the default configuration:fromopenaiimportOpenAIclient=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:fromopenaiimportOpenAIclient=OpenAI(base_url="http://localhost:8000/v1/my-namespace")Running Canopy server in productionCanopy 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 onGitHub Packages,
for your production needs.For guidance on deploying Canopy on the Google Cloud Platform (GCP), refer to the example provided in theDeployment to GCPdocumentation.Alternatively, you can use Gunicorn as production-grade WSGI, more detailshere.Set your desiredPORTandWORKER_COUNTenvrionment variables, and start the server with:gunicorncanopy_server.app:app--worker-classuvicorn.workers.UvicornWorker--bind0.0.0.0:$PORT--workers$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.