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This repository contains a collection of Jupyter notebooks demonstrating how to use the Writer Python SDK for various AI-related tasks. These notebooks provide practical examples and tutorials for working with our models and services, including model retrieval, text and chat completion, knowledge graph manipulation, and more.
This repository contains a collection of Jupyter notebooks demonstrating how to use the Writer Python SDK for various AI-related tasks. These notebooks provide practical examples and tutorials for working with our models and services, including model retrieval, text and chat completion, knowledge graph manipulation, and more.
You can run the cookbooks locally, or, if you prefer, copy and paste the code snippets from the notebooks into your own Python files.
To run the cookbooks locally, follow these setup steps:
Clone this repository:
git clone https://github.com/writer/cookbooks.git
cd cookbooks
Create and activate a virtual environment:
python -m venv my_env
source my_env/bin/activate # On Windows, use `my_env\Scripts\activate`
Once you have an API key, we recommend that you store it as an environment variable in a .env file like so:
WRITER_API_KEY="{Your Writer API key goes here}"
Ensure your virtual environment is activated.
Start Jupyter Notebook or JupyterLab:
jupyter notebook
or
jupyter lab
Navigate to the notebook you want to run and open it.
Follow the instructions within each notebook to execute the cells and interact with the Writer SDK.
/applications)
application_basic_usage.ipynb: Invoke no-code applications using the Writer SDK.application_graph_management.ipynb: Attach a Knowledge Graph to a no-code application via the Writer SDK.application_jobs_utilization.ipynb: Run no-code applications asynchronously and manage the async jobs./completion)
text_completion.ipynb: Use the Writer SDK for text completion tasks.chat_completion.ipynb: Explores chat-based interactions.structured_output_cookbook.ipynb: Shows how to use structured output in Writer AI Studio with JSON Schema and Pydantic, for streaming and non-streaming responses, using chat.chat and chat.parse.multimodal_chat_x5.ipynb: Demonstrates passing images and text to Palmyra X5 chat completions, using local or hosted images./knowledge_graph)
knowledge_graph.ipynb: Introduces the basics of working with files and Knowledge Graphs./integrations)
instructor/Instructor.ipynb: Extract structured data from text, CSV, or PDF files using Instructor with Writer and Pydantic models.instructor/Advanced.ipynb: Work with complex nested Pydantic models, validation with automatic retry/repair patterns, and streaming extraction using Instructor.langchain/LangChain.ipynb: Use Writer with LangChain, including chat model usage, LangGraph agents with tool calling, and DeepAgents with web search.langchain/Advanced.ipynb: Build agents with advanced LangChain patterns including middleware for PII detection, context engineering for personalized responses, and Writer MCP integration.OpenLLMetry.ipynb: Monitor and debug Writer API calls using OpenLLMetry and Traceloop, including workflow annotations and observability integration.Strands.ipynb: Build agents using the AWS Strands Agents SDK with Writer models through direct integration or Amazon Bedrock./models)
Amazon_Bedrock.ipynb: Use third-party Amazon Bedrock models through the Writer SDK, including chat completion, multimodal requests, and tool calling.model_retrieval.ipynb: Retrieve and list available Palmyra models.Writer is the full-stack generative AI platform for enterprises. Quickly and easily build and deploy AI apps with a suite of developer tools fully integrated with our LLMs, graph-based RAG, AI guardrails, and more. To learn more, visit our website.
If you encounter any issues or have questions, please file an issue on this repository.
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