chore: add faiss integrations page (#407)

Author: julian-risch

integrations/faiss.md

@@ -0,0 +1,119 @@
+---
+layout: integration
+name: FAISS
+description: A Document Store for vector search using FAISS
+authors:
+  - name: Guna Palanivel
+    socials:
+      github: GunaPalanivel
+  - name: deepset
+    socials:
+      github: deepset-ai
+      twitter: deepset_ai
+      linkedin: https://www.linkedin.com/company/deepset-ai/
+pypi: https://pypi.org/project/faiss-haystack
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/faiss
+type: Document Store
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/meta.png
+version: Haystack 2.0
+toc: true
+---
+
+The integration provides `FAISSDocumentStore`, which uses [FAISS](https://github.com/facebookresearch/faiss) (Facebook AI Similarity Search) for vector search and a simple JSON file for metadata storage. It is suitable for small to medium-sized datasets where simplicity is preferred over scalability, and supports optional persistence by saving the FAISS index to a `.faiss` file and documents to a `.json` file. Use `FAISSEmbeddingRetriever` for semantic retrieval in your pipelines.
+
+## Installation
+
+Install the package with pip:
+
+```bash
+pip install faiss-haystack
+```
+
+For GPU-accelerated FAISS, install `faiss-gpu` separately and use it in place of the default `faiss-cpu` dependency where applicable.
+
+The examples below use [Sentence Transformers](https://www.sbert.net/) for embeddings. Install with: `pip install "sentence-transformers>=5.0.0"`.
+
+## Usage
+
+### In-memory document store
+
+Create an in-memory document store (no persistence):
+
+```python
+from haystack_integrations.document_stores.faiss import FAISSDocumentStore
+
+document_store = FAISSDocumentStore(embedding_dim=768)
+```
+
+### Persisted document store
+
+To save and load the index and documents from disk, pass `index_path`:
+
+```python
+from haystack_integrations.document_stores.faiss import FAISSDocumentStore
+
+document_store = FAISSDocumentStore(
+    index_path="./my_faiss_index",
+    index_string="Flat",
+    embedding_dim=768,
+)
+# After writing documents, persist with:
+# document_store.save("./my_faiss_index")
+# Later, create the store with the same index_path to load from disk.
+```
+
+### Writing documents
+
+Use an indexing pipeline to write documents (with embeddings) to the store.
+This example uses Sentence Transformers (768 dimensions).
+
+```python
+from haystack import Pipeline
+from haystack.components.converters import TextFileToDocument
+from haystack.components.writers import DocumentWriter
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+from haystack_integrations.document_stores.faiss import FAISSDocumentStore
+
+document_store = FAISSDocumentStore(
+    index_path="./my_faiss_index",
+    index_string="Flat",
+    embedding_dim=768,
+)
+
+indexing = Pipeline()
+indexing.add_component("converter", TextFileToDocument())
+indexing.add_component("embedder", SentenceTransformersDocumentEmbedder())
+indexing.add_component("writer", DocumentWriter(document_store))
+indexing.connect("converter", "embedder")
+indexing.connect("embedder", "writer")
+indexing.run({"converter": {"sources": file_paths}})
+
+# If using persistence, save after indexing
+# document_store.save("./my_faiss_index")
+```
+
+### Retrieval with FAISSEmbeddingRetriever
+
+Build a query pipeline using `FAISSEmbeddingRetriever` for semantic search:
+
+```python
+from haystack import Pipeline
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack_integrations.components.retrievers.faiss import FAISSEmbeddingRetriever
+
+query_pipeline = Pipeline()
+query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
+query_pipeline.add_component(
+    "retriever",
+    FAISSEmbeddingRetriever(document_store=document_store, top_k=10),
+)
+query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+
+results = query_pipeline.run({"text_embedder": {"text": "your query"}})
+documents = results["retriever"]["documents"]
+```
+
+## License
+
+`faiss-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.