haystack

Haystack + Moss Cookbook Example

Use Moss as realtime semantic search in Haystack RAG pipelines. Moss provides sub-10ms semantic search, Haystack orchestrates the retrieval-to-generation pipeline.

Note: This is a cookbook example, not a packaged integration. moss_haystack.py is a self-contained module you can adapt into your own project.

Installation

pip install haystack-ai moss python-dotenv

Setup

Set your credentials in a .env file (see .env.example):

MOSS_PROJECT_ID=your-project-id
MOSS_PROJECT_KEY=your-project-key
GEMINI_API_KEY=your-gemini-key

Quick Start

from haystack import Document
from moss_haystack import MossDocumentStore, MossRetriever

store = MossDocumentStore(index_name="knowledge-base")
store.write_documents([
    Document(id="1", content="I wake up at 6:30 AM on weekdays."),
    Document(id="2", content="Cold showers improve circulation and alertness."),
])

retriever = MossRetriever(document_store=store, top_k=3)
retriever.load_index()
result = retriever.run(query="when do I wake up?")

for doc in result["documents"]:
    print(f"[{doc.score:.2f}] {doc.content}")

Demo: Multi-Index Life Assistant

The included example_usage.py runs an interactive CLI life assistant with keyword-based routing across two Moss indexes:

User Question
     |
     v
Keyword Router
     |
     +-- personal ("my", "I", "me") --> MossRetriever (life-personal)
     |                                        |
     +-- general ("how to", "tips")  --> MossRetriever (life-general)
     |                                        |
     +-- combined (both or neither)  --> Both retrievers → DocumentJoiner
                                              |
                                              v
                                     PromptBuilder → Gemini LLM
                                              |
                                              v
                                        Final Answer

How it works

1. Two Moss indexes with synthetic data:

   • life-personal (15 docs) — daily routines, fitness schedule, diet, sleep habits
   • life-general (15 docs) — tips, research, and advice on health, fitness, productivity

2. Keyword router classifies queries:

   • Personal pronouns ("my", "I", "me") → search personal index
   • General keywords ("how to", "benefits", "tips") → search general index
   • Both or neither → search both indexes and join results

3. Haystack RAG pipeline retrieves docs → builds prompt → generates answer via Gemini

Run the demo

cd examples/cookbook/haystack
python example_usage.py

=== Life Assistant (Haystack + Moss) ===
Ask about your habits or get general advice.
Type 'quit' to exit.

You: What is my gym routine?
  [Routed to: personal]
Assistant: You go to the gym Monday, Wednesday, and Friday...

You: What are the benefits of cold showers?
  [Routed to: general]
Assistant: Cold exposure therapy benefits include improved circulation...

You: Should I change my morning routine?
  [Routed to: combined]
Assistant: Your current morning routine includes yoga and lemon water...

Components

MossDocumentStore

Implements Haystack's DocumentStore protocol. Creates its own MossClient from credentials.

Method	Description
write_documents(docs, policy)	Write documents. First call creates the index, subsequent calls upsert.
count_documents()	Return document count
delete_documents(ids)	Delete documents by ID
load_index()	Download index for fast local queries

MossRetriever

Haystack @component for semantic search.

Parameter	Default	Description
document_store	required	MossDocumentStore instance
top_k	5	Number of results
alpha	0.8	Hybrid search balance (0=keyword, 1=semantic)

Method	Description
load_index()	Load Moss index for fast local queries
run(query, top_k)	Search and return {"documents": list[Document]}

Files

File	Description
moss_haystack.py	MossDocumentStore + MossRetriever implementation
example_usage.py	Multi-index life assistant with keyword routing
data/	Synthetic data: personal_habits.json, general_knowledge.json
test_live.py	Live platform tests
.env.example	Template for required environment variables