> For the complete documentation index, see [llms.txt](https://docs.fusion.vectra.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fusion.vectra.ai/api-recipes/recipes.md). # Recipes - [curl: Authenticate to API using NETOSECRET](https://docs.fusion.vectra.ai/api-recipes/recipes/curl-authenticate-to-api-using-netosecret.md): Shell script that takes a NETOSECRET API key, builds a JWT request token, authenticates to the Fusion API, and output the bearer token to use in subsequent API calls. - [NetoAPI Python class to create traffic sources in Fusion](https://docs.fusion.vectra.ai/api-recipes/recipes/netoapi-python-class-to-create-traffic-sources-in-fusion.md) - [Retrieve a list of source IP addresses from the blocklist with the API](https://docs.fusion.vectra.ai/api-recipes/recipes/retrieve-a-list-of-source-ip-addresses-from-the-blocklist-with-the-api.md): An example of how to authenticate and then use the API to retrieve values from the blocklist. - [Bulk add IP labels (php)](https://docs.fusion.vectra.ai/api-recipes/recipes/bulk-add-ip-labels-php.md) - [Authenticate to the API](https://docs.fusion.vectra.ai/api-recipes/recipes/authenticate-to-the-api.md): Create a JWT request token and authenticate to the API with it, returning a JWT bearer token. Store the bearer token to a file. - [Create a JWT request token](https://docs.fusion.vectra.ai/api-recipes/recipes/create-a-jwt-request-token.md): This simple recipe demonstrates how to encode a JWT request token and output it. The output can be used as the string to pass in the jwt params in the HTTP POST to /auth/token - [Sanitize context label values](https://docs.fusion.vectra.ai/api-recipes/recipes/sanitize-context-label-values.md): Python code example of how to ensure invalid characters are not part of a context label value being sent to the context labels API. - [netosecret.py - Python class and CLI](https://docs.fusion.vectra.ai/api-recipes/recipes/netosecret.py-python-class-and-cli.md): Python containing the NetoSecret class to encode and decode a netosecret string and a CLI to interact with the secret on command line. - [netosecret.sh - bash script CLI](https://docs.fusion.vectra.ai/api-recipes/recipes/netosecret.sh-bash-script-cli.md): Bash shell script to encode and decode netosecret - [Create a Traffic Source in Python](https://docs.fusion.vectra.ai/api-recipes/recipes/create-a-traffic-source-in-python.md) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.fusion.vectra.ai/api-recipes/recipes.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.