piighost
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piighost replaces personal data with placeholders before the LLM sees it, then restores the real values in the reply and in tool calls. This Python library de-identifies confidential data, meaning personal data (PII) and secrets, through a reversible pseudonymization often called anonymization. LangChain, Pydantic AI, LlamaIndex and Claude Code integrations are provided, together with an OpenAI and Anthropic API connector.
This de-identification spots confidential data with pluggable detectors (regex, NER, LLM) and replaces each value with a placeholder, the token that takes its place. For example:
This placeholder stays the same from one message to the next with the conversational pipeline, which keeps the mapping between a value and its placeholder across the whole conversation. If [email protected] reappears three messages later, the placeholder is still <<EMAIL:1>>, which lets the LLM follow the thread.
The LLM therefore only receives de-identified text. When it returns placeholders, for example by answering Hello <<PERSON:1>>, piighost replaces them with the real values. The user sees John Doe and never sees the de-identification.
The same mechanism protects agents that call tools. With the LangChain middleware, a tool that needs the real email address receives it in clear, while the LLM that supplies it only writes <<EMAIL:1>>.
The LLM only sees placeholders. The tool receives the real address, the user gets a clear-text reply, and your agent code stays the same.
[!NOTE]
This retained mapping makes the de-identification a pseudonymization under the GDPR, not a definitive anonymization. With the conversational pipeline, the real values stay stored for the duration of the conversation and must be protected accordingly.
Quickstart
pip install piighost # or: uv add piighost
ExactMatchDetector de-identifies a dictionary of known values, with no model to download.
import asyncio
from piighost.components.detector import ExactMatchDetector
from piighost.pipeline import AnonymizationPipeline
detector = ExactMatchDetector({"John Doe": "PERSON", "[email protected]": "EMAIL"})
pipeline = AnonymizationPipeline(detector)
result = asyncio.run(pipeline.anonymize("Write to John Doe at [email protected]."))
print(result.text) # Write to <<PERSON:1>> at <<EMAIL:1>>.
The Quickstart goes on with a real detector and a conversation.
Go further
- Start: installation, first pipeline, conversational pipeline
- Configure: a pipeline in a TOML file, pattern groups from the catalog, every config key
- Integrate: LangChain, Pydantic AI, LlamaIndex, Claude Code, a remote piighost-api
- Deploy: an API server from a catalog configuration, a production thread pipeline, several instances
- Understand: why de-identify, architecture, , , , ,
Ecosystem
- piighost.dev: the project website, with the ecosystem and its philosophy
- piighost catalog: reviewed regex groups and ready-made pipeline configurations, pulled by reference
- piighost-api: a server hosting one pipeline behind HTTP, with OpenAI- and Anthropic-compatible proxies
- piighost-chat: an example chat interface with human-in-the-loop
- caviardage: a browser app that redacts a Word deed, with piighost running in Pyodide. Nothing of the document leaves the tab
Project
If piighost is useful to you, a star on GitHub helps other developers find it.
