About Pipecat
Pipecat is an open-source framework for building voice and multimodal agents. The pipecat-moss package provides MossRetrievalService, a pipeline processor that queries Moss on every user turn and injects context before the LLM generates a response. No tool calling needed. Sub-10ms retrieval keeps conversations flowing without dead air.
Why Use Moss with Pipecat
01
Native Pipecat pipeline processor
MossRetrievalService slots between STT and LLM in your pipeline
02
Context injection, no tool-calling overhead
Search runs automatically on every user utterance
03
Sub-10ms retrieval eliminates dead air
In the STT -> retrieval -> LLM -> TTS pipeline
04
Works with Deepgram, Cartesia, OpenAI, Anthropic
And other Pipecat plugins
05
Pre-load indexes at pipeline startup
`load_index()` for fastest first-query latency
Quick Start
import os
from pipecat_moss import MossRetrievalService
moss_service = MossRetrievalService(
project_id=os.getenv("MOSS_PROJECT_ID"),
project_key=os.getenv("MOSS_PROJECT_KEY"),
system_prompt="Relevant passages from the Moss knowledge base:\n\n",
)
await moss_service.load_index(os.getenv("MOSS_INDEX_NAME"))
pipeline = Pipeline([
transport.input(),
stt,
context_aggregator.user(),
moss_service.query(os.getenv("MOSS_INDEX_NAME"), top_k=5),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
])Get Started in 3 Steps
01
Install pipecat-moss
Run pip install pipecat-moss to install the Pipecat pipeline processor for Moss.
02
Create and load your index
Use the Moss SDK to create_index() with your knowledge base documents, then call moss_service.load_index() at pipeline startup.
03
Insert into your pipeline
Add moss_service.query() between your STT and LLM processors. Moss retrieves context on every user turn and injects it into the LLM prompt automatically.
Frequently asked questions
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