From 95f330c5ca6960c07c4ea7ac82005917378744bb Mon Sep 17 00:00:00 2001
From: unknown <syedshahmeerali196@gmail.com>
Date: Fri, 17 Apr 2026 18:05:18 +0500
Subject: [PATCH 1/5] feat: add Thunderbolt integration page

Closes #450
---
 integrations/thunderbolt.md | 103 ++++++++++++++++++++++++++++++++++++
 logos/thunderbolt.png       | Bin 0 -> 12147 bytes
 2 files changed, 103 insertions(+)
 create mode 100644 integrations/thunderbolt.md
 create mode 100644 logos/thunderbolt.png

diff --git a/integrations/thunderbolt.md b/integrations/thunderbolt.md
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index 0000000..b6256ba
--- /dev/null
+++ b/integrations/thunderbolt.md
@@ -0,0 +1,103 @@
+---
+layout: integration
+name: Thunderbolt
+description: Use Thunderbolt as a cross-platform AI client for your Haystack pipelines through Hayhooks
+authors:
+  - name: Thunderbird
+    socials:
+      github: thunderbird
+repo: https://github.com/thunderbird/thunderbolt
+type: UI
+report_issue: https://github.com/thunderbird/thunderbolt/issues
+logo: /logos/thunderbolt.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Setup](#setup)
+- [Usage](#usage)
+- [License](#license)
+
+## Overview
+
+[Thunderbolt](https://github.com/thunderbird/thunderbolt) is an open-source, cross-platform AI client developed by MZLA Technologies (Thunderbird). It runs on web, iOS, Android, Mac, Linux, and Windows, and works with any OpenAI-compatible model endpoint — including self-hosted ones.
+
+By exposing your Haystack pipeline through [Hayhooks](https://github.com/deepset-ai/hayhooks) as an OpenAI-compatible endpoint, you can connect Thunderbolt to your pipeline and interact with it from any device — without building a frontend yourself.
+
+Thunderbolt is designed for enterprise on-prem deployments but can be self-hosted locally for development and testing.
+
+## Setup
+
+### 1. Expose your Haystack pipeline with Hayhooks
+
+Install Hayhooks:
+
+```bash
+pip install hayhooks
+```
+
+Create a pipeline wrapper that implements `run_chat_completion`:
+
+```python
+# pipelines/my_rag/pipeline_wrapper.py
+from typing import Generator
+
+from haystack import Pipeline
+from haystack.components.builders import ChatPromptBuilder
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.dataclasses import ChatMessage
+
+from hayhooks import BasePipelineWrapper, streaming_generator
+
+
+class PipelineWrapper(BasePipelineWrapper):
+    def setup(self) -> None:
+        self.system_message = ChatMessage.from_system("You are a helpful assistant.")
+        prompt_builder = ChatPromptBuilder()
+        llm = OpenAIChatGenerator(model="gpt-4o-mini")
+
+        self.pipeline = Pipeline()
+        self.pipeline.add_component("prompt_builder", prompt_builder)
+        self.pipeline.add_component("llm", llm)
+        self.pipeline.connect("prompt_builder.prompt", "llm.messages")
+
+    def run_chat_completion(self, model: str, messages: list[dict], body: dict) -> Generator:
+        chat_messages = [self.system_message] + [
+            ChatMessage.from_openai_dict_format(msg) for msg in messages
+        ]
+        return streaming_generator(
+            pipeline=self.pipeline,
+            pipeline_run_args={"prompt_builder": {"template": chat_messages}},
+        )
+```
+
+Start Hayhooks:
+
+```bash
+hayhooks run --pipelines-dir ./pipelines
+```
+
+This exposes your pipeline at `http://localhost:1416/v1` as an OpenAI-compatible endpoint. See [Hayhooks OpenAI compatibility docs](https://deepset-ai.github.io/hayhooks/features/openai-compatibility) for details.
+
+### 2. Deploy Thunderbolt
+
+Follow the [Thunderbolt deployment guide](https://github.com/thunderbird/thunderbolt/blob/main/deploy/README.md) to self-host Thunderbolt with Docker Compose or Kubernetes, or run it locally for development. See the [development guide](https://github.com/thunderbird/thunderbolt/blob/main/docs/development.md) to get started quickly.
+
+## Usage
+
+Once Hayhooks is running and Thunderbolt is deployed:
+
+1. Open Thunderbolt and go to **Settings → Model Providers**.
+2. Add a new provider with a custom **OpenAI-compatible** base URL pointing to your Hayhooks server (e.g. `http://localhost:1416/v1`).
+3. Select your Haystack pipeline as the model.
+4. Start chatting — your messages are routed through Hayhooks to your Haystack pipeline.
+
+This gives you a polished, cross-platform chat interface backed by whatever Haystack pipeline you choose — RAG, agents, or a custom workflow.
+
+## License
+
+Thunderbolt is licensed under the [Mozilla Public License 2.0](https://github.com/thunderbird/thunderbolt/blob/main/LICENSE).
+Hayhooks is licensed under the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.
diff --git a/logos/thunderbolt.png b/logos/thunderbolt.png
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From c0ccdda9ba3e4342fd77f86ea8080eba641d7130 Mon Sep 17 00:00:00 2001
From: unknown <syedshahmeerali196@gmail.com>
Date: Tue, 21 Apr 2026 13:14:23 +0500
Subject: [PATCH 2/5] feat: add Presidio integration page

Adds integration tile for presidio-haystack with usage examples for
PresidioDocumentCleaner, PresidioTextCleaner, and PresidioEntityExtractor.

Related: deepset-ai/haystack-core-integrations#3063
---
 integrations/presidio.md | 105 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 integrations/presidio.md

diff --git a/integrations/presidio.md b/integrations/presidio.md
new file mode 100644
index 0000000..5a93425
--- /dev/null
+++ b/integrations/presidio.md
@@ -0,0 +1,105 @@
+---
+layout: integration
+name: Presidio
+description: PII detection and anonymization for Haystack Documents and text strings, powered by Microsoft Presidio.
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: https://www.linkedin.com/company/deepset-ai/
+pypi: https://pypi.org/project/presidio-haystack/
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/presidio
+type: Custom Component
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/microsoft.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Document Cleaning](#document-cleaning)
+  - [Text Cleaning](#text-cleaning)
+  - [Entity Extraction](#entity-extraction)
+- [License](#license)
+
+## Overview
+
+[Microsoft Presidio](https://microsoft.github.io/presidio/) is an open-source library for PII detection and anonymization using NLP-based entity recognition.
+
+`presidio-haystack` provides three Haystack components:
+
+| Component | Input | Purpose |
+|-----------|-------|---------|
+| `PresidioDocumentCleaner` | `list[Document]` | Replace PII in document text with entity type placeholders |
+| `PresidioTextCleaner` | `list[str]` | Replace PII in plain strings — useful for sanitizing user queries |
+| `PresidioEntityExtractor` | `list[Document]` | Detect PII and store entities as structured document metadata |
+
+All components run locally — no external API required. Presidio uses spaCy NLP models under the hood.
+
+## Installation
+
+```bash
+pip install presidio-haystack
+python -m spacy download en_core_web_lg
+```
+
+## Usage
+
+### Document Cleaning
+
+Replace PII in document content before indexing:
+
+```python
+from haystack import Document
+from haystack_integrations.components.preprocessors.presidio import PresidioDocumentCleaner
+
+cleaner = PresidioDocumentCleaner()
+result = cleaner.run(documents=[
+    Document(content="Contact Alice Smith at alice@example.com or 212-555-1234.")
+])
+print(result["documents"][0].content)
+# Contact <PERSON> at <EMAIL_ADDRESS> or <PHONE_NUMBER>.
+```
+
+Original documents are not mutated. Documents with no text content pass through unchanged.
+
+### Text Cleaning
+
+Sanitize user queries before they reach your LLM:
+
+```python
+from haystack_integrations.components.preprocessors.presidio import PresidioTextCleaner
+
+cleaner = PresidioTextCleaner()
+result = cleaner.run(texts=["My name is John Doe, my SSN is 123-45-6789"])
+print(result["texts"][0])
+# My name is <PERSON>, my SSN is <US_SSN>
+```
+
+### Entity Extraction
+
+Detect PII and attach it as structured metadata without modifying the document text:
+
+```python
+from haystack import Document
+from haystack_integrations.components.preprocessors.presidio import PresidioEntityExtractor
+
+extractor = PresidioEntityExtractor()
+result = extractor.run(documents=[
+    Document(content="Contact Alice at alice@example.com")
+])
+print(result["documents"][0].meta["entities"])
+# [{"entity_type": "PERSON", "start": 8, "end": 13, "score": 0.85},
+#  {"entity_type": "EMAIL_ADDRESS", "start": 17, "end": 34, "score": 1.0}]
+```
+
+All three components accept `language`, `entities`, and `score_threshold` parameters at init time. See [Presidio supported entities](https://microsoft.github.io/presidio/supported_entities/) for the full list of detectable PII types.
+
+## License
+
+`presidio-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.

From 07be7a87205da15e6cedb05658f76c0ad46d2207 Mon Sep 17 00:00:00 2001
From: unknown <syedshahmeerali196@gmail.com>
Date: Fri, 24 Apr 2026 16:29:11 +0500
Subject: [PATCH 3/5] docs(presidio): update PresidioEntityExtractor import
 path to extractors

---
 integrations/presidio.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/integrations/presidio.md b/integrations/presidio.md
index 5a93425..dd11d70 100644
--- a/integrations/presidio.md
+++ b/integrations/presidio.md
@@ -87,7 +87,7 @@ Detect PII and attach it as structured metadata without modifying the document t
 
 ```python
 from haystack import Document
-from haystack_integrations.components.preprocessors.presidio import PresidioEntityExtractor
+from haystack_integrations.components.extractors.presidio import PresidioEntityExtractor
 
 extractor = PresidioEntityExtractor()
 result = extractor.run(documents=[

From 4ec0a1b4e30a99a673ef28f7bd058ed71d9d64d1 Mon Sep 17 00:00:00 2001
From: unknown <syedshahmeerali196@gmail.com>
Date: Fri, 24 Apr 2026 18:29:43 +0500
Subject: [PATCH 4/5] docs(presidio): address review feedback

- Add Shahmeer Ali as co-author
- Remove unrelated thunderbolt files
- Expand installation section with spaCy model guidance, language support note, and sm vs lg clarification
---
 integrations/presidio.md    |  18 +++++++
 integrations/thunderbolt.md | 103 ------------------------------------
 logos/thunderbolt.png       | Bin 12147 -> 0 bytes
 3 files changed, 18 insertions(+), 103 deletions(-)
 delete mode 100644 integrations/thunderbolt.md
 delete mode 100644 logos/thunderbolt.png

diff --git a/integrations/presidio.md b/integrations/presidio.md
index dd11d70..8994cbf 100644
--- a/integrations/presidio.md
+++ b/integrations/presidio.md
@@ -8,6 +8,9 @@ authors:
         github: deepset-ai
         twitter: deepset_ai
         linkedin: https://www.linkedin.com/company/deepset-ai/
+    - name: Shahmeer Ali
+      socials:
+        github: SyedShahmeerAli12
 pypi: https://pypi.org/project/presidio-haystack/
 repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/presidio
 type: Custom Component
@@ -48,6 +51,21 @@ pip install presidio-haystack
 python -m spacy download en_core_web_lg
 ```
 
+`en_core_web_lg` is the recommended English model for best accuracy. For a lighter footprint, `en_core_web_sm` works too — see the [full list of spaCy models](https://spacy.io/models/en) for options.
+
+Each component accepts a `language` parameter (default `"en"`). To use a non-English language, download the corresponding spaCy model and pass the language code:
+
+```bash
+# Example: Spanish
+python -m spacy download es_core_news_md
+```
+
+```python
+cleaner = PresidioDocumentCleaner(language="es")
+```
+
+Note: each `language` value maps to one spaCy model at a time. You cannot select between `sm` and `lg` per component — whichever model is registered for that language in your environment will be used.
+
 ## Usage
 
 ### Document Cleaning
diff --git a/integrations/thunderbolt.md b/integrations/thunderbolt.md
deleted file mode 100644
index b6256ba..0000000
--- a/integrations/thunderbolt.md
+++ /dev/null
@@ -1,103 +0,0 @@
----
-layout: integration
-name: Thunderbolt
-description: Use Thunderbolt as a cross-platform AI client for your Haystack pipelines through Hayhooks
-authors:
-  - name: Thunderbird
-    socials:
-      github: thunderbird
-repo: https://github.com/thunderbird/thunderbolt
-type: UI
-report_issue: https://github.com/thunderbird/thunderbolt/issues
-logo: /logos/thunderbolt.png
-version: Haystack 2.0
-toc: true
----
-
-### Table of Contents
-
-- [Overview](#overview)
-- [Setup](#setup)
-- [Usage](#usage)
-- [License](#license)
-
-## Overview
-
-[Thunderbolt](https://github.com/thunderbird/thunderbolt) is an open-source, cross-platform AI client developed by MZLA Technologies (Thunderbird). It runs on web, iOS, Android, Mac, Linux, and Windows, and works with any OpenAI-compatible model endpoint — including self-hosted ones.
-
-By exposing your Haystack pipeline through [Hayhooks](https://github.com/deepset-ai/hayhooks) as an OpenAI-compatible endpoint, you can connect Thunderbolt to your pipeline and interact with it from any device — without building a frontend yourself.
-
-Thunderbolt is designed for enterprise on-prem deployments but can be self-hosted locally for development and testing.
-
-## Setup
-
-### 1. Expose your Haystack pipeline with Hayhooks
-
-Install Hayhooks:
-
-```bash
-pip install hayhooks
-```
-
-Create a pipeline wrapper that implements `run_chat_completion`:
-
-```python
-# pipelines/my_rag/pipeline_wrapper.py
-from typing import Generator
-
-from haystack import Pipeline
-from haystack.components.builders import ChatPromptBuilder
-from haystack.components.generators.chat import OpenAIChatGenerator
-from haystack.dataclasses import ChatMessage
-
-from hayhooks import BasePipelineWrapper, streaming_generator
-
-
-class PipelineWrapper(BasePipelineWrapper):
-    def setup(self) -> None:
-        self.system_message = ChatMessage.from_system("You are a helpful assistant.")
-        prompt_builder = ChatPromptBuilder()
-        llm = OpenAIChatGenerator(model="gpt-4o-mini")
-
-        self.pipeline = Pipeline()
-        self.pipeline.add_component("prompt_builder", prompt_builder)
-        self.pipeline.add_component("llm", llm)
-        self.pipeline.connect("prompt_builder.prompt", "llm.messages")
-
-    def run_chat_completion(self, model: str, messages: list[dict], body: dict) -> Generator:
-        chat_messages = [self.system_message] + [
-            ChatMessage.from_openai_dict_format(msg) for msg in messages
-        ]
-        return streaming_generator(
-            pipeline=self.pipeline,
-            pipeline_run_args={"prompt_builder": {"template": chat_messages}},
-        )
-```
-
-Start Hayhooks:
-
-```bash
-hayhooks run --pipelines-dir ./pipelines
-```
-
-This exposes your pipeline at `http://localhost:1416/v1` as an OpenAI-compatible endpoint. See [Hayhooks OpenAI compatibility docs](https://deepset-ai.github.io/hayhooks/features/openai-compatibility) for details.
-
-### 2. Deploy Thunderbolt
-
-Follow the [Thunderbolt deployment guide](https://github.com/thunderbird/thunderbolt/blob/main/deploy/README.md) to self-host Thunderbolt with Docker Compose or Kubernetes, or run it locally for development. See the [development guide](https://github.com/thunderbird/thunderbolt/blob/main/docs/development.md) to get started quickly.
-
-## Usage
-
-Once Hayhooks is running and Thunderbolt is deployed:
-
-1. Open Thunderbolt and go to **Settings → Model Providers**.
-2. Add a new provider with a custom **OpenAI-compatible** base URL pointing to your Hayhooks server (e.g. `http://localhost:1416/v1`).
-3. Select your Haystack pipeline as the model.
-4. Start chatting — your messages are routed through Hayhooks to your Haystack pipeline.
-
-This gives you a polished, cross-platform chat interface backed by whatever Haystack pipeline you choose — RAG, agents, or a custom workflow.
-
-## License
-
-Thunderbolt is licensed under the [Mozilla Public License 2.0](https://github.com/thunderbird/thunderbolt/blob/main/LICENSE).
-Hayhooks is licensed under the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.
diff --git a/logos/thunderbolt.png b/logos/thunderbolt.png
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From 7225d098e2c7c336340f71d98e73c77d10e731d0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Kacper=20=C5=81ukawski?=
 <kacperlukawski@users.noreply.github.com>
Date: Fri, 24 Apr 2026 17:36:34 +0200
Subject: [PATCH 5/5] Apply suggestions from code review
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Kacper Łukawski <kacperlukawski@users.noreply.github.com>
---
 integrations/presidio.md | 13 +------------
 1 file changed, 1 insertion(+), 12 deletions(-)

diff --git a/integrations/presidio.md b/integrations/presidio.md
index 8994cbf..6a80d30 100644
--- a/integrations/presidio.md
+++ b/integrations/presidio.md
@@ -48,23 +48,12 @@ All components run locally — no external API required. Presidio uses spaCy NLP
 
 ```bash
 pip install presidio-haystack
-python -m spacy download en_core_web_lg
 ```
 
 `en_core_web_lg` is the recommended English model for best accuracy. For a lighter footprint, `en_core_web_sm` works too — see the [full list of spaCy models](https://spacy.io/models/en) for options.
 
-Each component accepts a `language` parameter (default `"en"`). To use a non-English language, download the corresponding spaCy model and pass the language code:
+Each component accepts a `language` parameter (default `"en"`). To use a non-English language, specify the language code, and provide a model mapping, unless you want to use the large one.
 
-```bash
-# Example: Spanish
-python -m spacy download es_core_news_md
-```
-
-```python
-cleaner = PresidioDocumentCleaner(language="es")
-```
-
-Note: each `language` value maps to one spaCy model at a time. You cannot select between `sm` and `lg` per component — whichever model is registered for that language in your environment will be used.
 
 ## Usage