feat: add Upstash Vector DocumentStore integration

integrations/upstash.md

@@ -0,0 +1,166 @@
+---
+layout: integration
+name: Upstash Vector
+description: Use Upstash Vector as a serverless document store in Haystack pipelines — zero infrastructure, pay-as-you-go, with native hybrid search via Reciprocal Rank Fusion.
+authors:
+    - name: Avish Sinha
+      socials:
+        github: avish006
+        linkedin: https://www.linkedin.com/in/avish-sinha
+pypi: https://pypi.org/project/upstash-haystack
+repo: https://github.com/avish006/upstash_haystack
+report_issue: https://github.com/avish006/upstash_haystack/issues
+type: Document Store
+version: Haystack 2.0
+logo: /logos/upstash.png
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+
+## Overview
+
+[Upstash Vector](https://upstash.com/vector) is a serverless, pay-as-you-go vector database that you can use in Haystack pipelines with the `UpstashDocumentStore`. It requires zero infrastructure — no Docker containers, no servers, no clusters to manage.
+
+This integration provides three components:
+
+| Component | Description |
+|---|---|
+| `UpstashDocumentStore` | Full-featured document store backed by Upstash Vector |
+| `UpstashEmbeddingRetriever` | Dense retrieval using cosine/dot-product similarity |
+| `UpstashHybridRetriever` | Dense + sparse hybrid search via native Reciprocal Rank Fusion (RRF) |
+
+## Installation
+
+```bash
+pip install upstash-haystack
+```
+
+## Usage
+
+To use Upstash Vector as your data storage for Haystack LLM pipelines, you must have an [Upstash account](https://console.upstash.com/) and a Vector index. Once you have those, set your credentials as environment variables:
+
+```bash
+export UPSTASH_VECTOR_REST_URL="https://your-endpoint.upstash.io"
+export UPSTASH_VECTOR_REST_TOKEN="your-token"
+```
+
+Then initialize an `UpstashDocumentStore`:
+
+```python
+from haystack_integrations.document_stores.upstash import UpstashDocumentStore
+
+# Reads UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN from env
+document_store = UpstashDocumentStore()
+```
+
+### Indexing Pipeline
+
+```python
+from haystack import Pipeline
+from haystack.components.converters import MarkdownToDocument
+from haystack.components.preprocessors import DocumentSplitter
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder
+from haystack.components.writers import DocumentWriter
+from haystack_integrations.document_stores.upstash import UpstashDocumentStore
+
+document_store = UpstashDocumentStore()
+
+indexing = Pipeline()
+indexing.add_component("converter", MarkdownToDocument())
+indexing.add_component("splitter", DocumentSplitter(split_by="sentence", split_length=2))
+indexing.add_component("embedder", SentenceTransformersDocumentEmbedder())
+indexing.add_component("writer", DocumentWriter(document_store))
+indexing.connect("converter", "splitter")
+indexing.connect("splitter", "embedder")
+indexing.connect("embedder", "writer")
+
+indexing.run({"converter": {"sources": ["filename.md"]}})
+```
+
+### RAG Query Pipeline
+
+Once documents are indexed, use `UpstashEmbeddingRetriever` to retrieve them in a RAG pipeline:
+
+```python
+from haystack import Pipeline
+from haystack.components.embedders import SentenceTransformersTextEmbedder
+from haystack.components.builders import PromptBuilder
+from haystack.components.generators import OpenAIChatGenerator
+from haystack.utils import Secret
+from haystack_integrations.document_stores.upstash import UpstashDocumentStore
+from haystack_integrations.components.retrievers.upstash import UpstashEmbeddingRetriever
+
+document_store = UpstashDocumentStore()
+
+prompt_template = """Answer the following query based on the provided context. If the context does
+                     not include an answer, reply with 'I don't know'.
+                     Query: {{query}}
+                     Documents:
+                     {% for doc in documents %}
+                        {{ doc.content }}
+                     {% endfor %}
+                     Answer:
+                  """
+
+query_pipeline = Pipeline()
+query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
+query_pipeline.add_component("retriever", UpstashEmbeddingRetriever(document_store=document_store))
+query_pipeline.add_component("prompt_builder", PromptBuilder(template=prompt_template))
+query_pipeline.add_component("generator", OpenAIChatGenerator(model="gpt-4o-mini"))
+query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+query_pipeline.connect("retriever.documents", "prompt_builder.documents")
+query_pipeline.connect("prompt_builder", "generator")
+
+query = "What is Upstash Vector?"
+results = query_pipeline.run(
+    {
+        "text_embedder": {"text": query},
+        "prompt_builder": {"query": query},
+    }
+)
+```
+
+### Hybrid Retrieval (Dense + Sparse)
+
+`UpstashHybridRetriever` uses Upstash Vector's native Reciprocal Rank Fusion (RRF) to combine dense and sparse signals without any custom fusion logic:
+
+```python
+from haystack.dataclasses import SparseEmbedding
+from haystack_integrations.document_stores.upstash import UpstashDocumentStore
+from haystack_integrations.components.retrievers.upstash import UpstashHybridRetriever
+
+document_store = UpstashDocumentStore()
+retriever = UpstashHybridRetriever(document_store=document_store)
+
+result = retriever.run(
+    query_embedding=[0.1, 0.2, 0.3],
+    query_sparse_embedding=SparseEmbedding(indices=[0, 5, 12], values=[0.9, 0.4, 0.2]),
+    top_k=5,
+)
+print(result["documents"])
+```
+
+### Filtering
+
+```python
+# Equality filter
+docs = document_store.filter_documents(
+    filters={"field": "meta.category", "operator": "==", "value": "science"}
+)
+
+# Compound AND filter
+docs = document_store.filter_documents(
+    filters={
+        "operator": "AND",
+        "conditions": [
+            {"field": "meta.category", "operator": "==", "value": "science"},
+            {"field": "meta.year", "operator": ">", "value": 2020},
+        ],
+    }
+)
+```