Moss - Real-time Semantic Search for Conversational AI

Use dspy-moss to add Moss semantic search to DSPy programs. The package provides MossRM, a DSPy retrieval model (RM) that plugs into DSPy’s retrieval interface for sub-10ms knowledge retrieval after the index is loaded locally.

Why use Moss with DSPy?

DSPy’s retrieval modules connect external knowledge sources to composable LLM programs. Moss provides a standard retriever backed by an in-memory semantic search runtime, so dspy.Retrieve, RAG modules, and ReAct agents can query your knowledge base without managing a vector database.

Required tools

Moss project credentials from the Moss Portal
Python 3.10+
A DSPy-compatible LLM provider

Integration guide

Install

pip install dspy-moss
# or
uv add dspy-moss

Configure credentials

MossRM can create its own MossClient from environment variables.

export MOSS_PROJECT_ID="your_project_id"
export MOSS_PROJECT_KEY="your_project_key"

Use as DSPy's default retriever

Create a MossRM, load the index into local memory, and register it with dspy.configure().

import dspy
from dspy_moss import MossRM

rm = MossRM(index_name="my-index")
rm.load_index()

dspy.configure(lm=dspy.LM("openai/gpt-4o"), rm=rm)

retrieve = dspy.Retrieve(k=3)
result = retrieve("What is the refund policy?")

for passage in result.passages:
    print(f"[{passage['score']:.3f}] {passage['long_text']}")

Use inside a RAG module

Any dspy.Retrieve() in your program now uses Moss.

import dspy
from dspy_moss import MossRM

rm = MossRM(index_name="support-kb", k=5, alpha=0.8)
rm.load_index()
dspy.configure(lm=dspy.LM("openai/gpt-4o"), rm=rm)

class RAG(dspy.Module):
    def __init__(self):
        self.retrieve = dspy.Retrieve(k=3)
        self.generate = dspy.ChainOfThought("context, question -> answer")

    def forward(self, question):
        context = self.retrieve(question).passages
        return self.generate(context=context, question=question)

rag = RAG()
print(rag("How long do refunds take?").answer)

Use as a ReAct tool

MossRM.forward() is synchronous, so you can pass the retriever instance directly to a DSPy ReAct agent.

import dspy
from dspy_moss import MossRM

rm = MossRM(index_name="support-kb", k=5)
rm.load_index()

agent = dspy.ReAct(signature="question -> answer", tools=[rm])
print(agent(question="What payment methods do you accept?").answer)

Configuration

MossRM

Parameter	Default	Description
`index_name`	Required	Name of the Moss index to query
`moss_client`	`None`	Existing `MossClient`. When omitted, one is created from credentials
`project_id`	`MOSS_PROJECT_ID` env var	Moss project ID
`project_key`	`MOSS_PROJECT_KEY` env var	Moss project key
`k`	`3`	Default number of passages per query
`alpha`	`0.8`	Search blend. `1.0` = semantic only, `0.0` = keyword only

Passage format

Each entry in result.passages is a dictionary:

Key	Type	Description
`long_text`	`str`	Document text in DSPy’s standard passage field
`id`	`str`	Document ID
`score`	`float`	Relevance score
`metadata`	`dict`	Metadata stored with the document

Mutable index helpers

MossRM also exposes helpers for agents that read or update the knowledge base:

# Read documents
objects = rm.get_objects(num_samples=10)

# Add or upsert documents
rm.insert([{"id": "doc-1", "text": "New fact.", "metadata": {"source": "agent"}}])

Voice & Real-time

AI Frameworks

Developer Tools

DSPy

Why use Moss with DSPy?

Required tools

Integration guide

Configuration

MossRM

Passage format

Mutable index helpers

​Why use Moss with DSPy?

​Required tools

​Integration guide

​Configuration

​MossRM

​Passage format

​Mutable index helpers

Why use Moss with DSPy?

Required tools

Integration guide

Configuration

MossRM

Passage format

Mutable index helpers