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# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD 2-Clause License
"""NVIDIA Retrieval-Augmented Generation (RAG) service implementation.
Integrates with NVIDIA's Retrieval-Augmented Generation service to enhance responses
by incorporating knowledge from external documents. Features include:
- Document collection management
- Real-time retrieval and citation
- OpenAI-compatible LLM interface
- Configurable retrieval parameters
"""
import json
import httpx
from loguru import logger
from openai.types.chat import ChatCompletionMessageParam
from pipecat.frames.frames import (
CancelFrame,
EndFrame,
ErrorFrame,
Frame,
LLMFullResponseEndFrame,
LLMFullResponseStartFrame,
LLMMessagesFrame,
StartInterruptionFrame,
TextFrame,
VisionImageRawFrame,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext, OpenAILLMContextFrame
from pipecat.processors.frame_processor import FrameDirection
from pipecat.services.openai.llm import OpenAILLMService
from nvidia_pipecat.frames.nvidia_rag import NvidiaRAGCitation, NvidiaRAGCitationsFrame, NvidiaRAGSettingsFrame
class NvidiaRAGService(OpenAILLMService):
"""This is the base class for all services that use NVIDIA RAG/GenerativeAIExamples.
Requires deployed NVIDIA RAG server. For deployment instructions see:
https://github.com/NVIDIA-AI-Blueprints/rag/blob/main/docs/quickstart.md
Attributes:
collection_name: Document collection identifier.
rag_server_url: RAG API endpoint URL.
stop_words: Words that stop LLM generation.
temperature: Controls response randomness (0-1).
top_p: Token probability threshold (0-1).
max_tokens: Maximum response length.
use_knowledge_base: Whether to use RAG retrieval.
vdb_top_k: Number of chunks to retrieve.
reranker_top_k: Number of chunks to rerank.
enable_citations: Whether to return citations.
suffix_prompt: Text appended to last user message.
"""
_shared_session: httpx.AsyncClient | None = None
def __init__(
self,
collection_name: str,
rag_server_url: str = "http://localhost:8081",
stop_words: list | None = None,
temperature: float = 0.2,
top_p: float = 0.7,
max_tokens: int = 1000,
use_knowledge_base: bool = True,
vdb_top_k: int = 20,
reranker_top_k: int = 4,
enable_citations: bool = True,
suffix_prompt: str | None = None,
session: httpx.AsyncClient | None = None,
**kwargs,
):
"""Initialize the NVIDIA RAG service.
Args:
collection_name: Document collection identifier.
rag_server_url: RAG API endpoint URL.
stop_words: Words that stop LLM generation.
temperature: Controls response randomness (0-1).
top_p: Token probability threshold (0-1).
max_tokens: Maximum response length.
use_knowledge_base: Whether to use RAG retrieval.
vdb_top_k: Number of chunks to retrieve.
reranker_top_k: Number of chunks to rerank.
enable_citations: Whether to return citations.
suffix_prompt: Text appended to last user message.
session: Optional httpx.AsyncClient. Creates new if None.
**kwargs: Additional arguments passed to OpenAILLMService.
"""
super().__init__(api_key="", **kwargs)
self.collection_name = collection_name
self.rag_server_url = rag_server_url
if stop_words is None:
stop_words = []
self.stop_words = stop_words
self.temperature = temperature
self.top_p = top_p
self.max_tokens = max_tokens
self.use_knowledge_base = use_knowledge_base
self.vdb_top_k = vdb_top_k
self.reranker_top_k = reranker_top_k
self.enable_citations = enable_citations
self.suffix_prompt = suffix_prompt
self._external_client_session = None
self._current_task = None
if session is not None:
self._external_client_session = session
@property
def shared_session(self) -> httpx.AsyncClient:
"""Get the shared HTTP client session.
Returns:
httpx.AsyncClient: The shared session for making HTTP requests.
Creates a new session if none exists and no external session was provided.
"""
if self._external_client_session is not None:
return self._external_client_session
if NvidiaRAGService._shared_session is None:
NvidiaRAGService._shared_session = httpx.AsyncClient()
return NvidiaRAGService._shared_session
@shared_session.setter
def shared_session(self, shared_session: httpx.AsyncClient):
"""Set the shared HTTP client session.
Args:
shared_session: The httpx.AsyncClient to use for all instances.
"""
NvidiaRAGService._shared_session = shared_session
async def stop(self, frame: EndFrame):
"""Stop the NVIDIA RAG service and cleanup resources.
Args:
frame: The EndFrame that triggered the stop.
"""
await super().stop(frame)
if self._current_task:
await self.cancel_task(self._current_task)
async def cancel(self, frame: CancelFrame):
"""Cancel the NVIDIA RAG service and cleanup resources.
Args:
frame: The CancelFrame that triggered the cancellation.
"""
await super().cancel(frame)
if self._current_task:
await self.cancel_task(self._current_task)
async def cleanup(self):
"""Clean up resources used by the RAG service.
Closes the shared HTTP client session if it exists and performs parent cleanup.
"""
await super().cleanup()
await self._close_client_session()
async def _close_client_session(self):
"""Close the Client Session if it exists."""
if NvidiaRAGService._shared_session:
await NvidiaRAGService._shared_session.aclose()
NvidiaRAGService._shared_session = None
async def _get_rag_response(self, request_json: dict):
resp = await self.shared_session.post(f"{self.rag_server_url}/generate", json=request_json)
return resp
async def _process_context(self, context: OpenAILLMContext):
"""Processes LLM context through RAG pipeline.
Args:
context: Contains conversation history and settings.
Raises:
Exception: If invalid message role or empty query.
"""
try:
messages: list[ChatCompletionMessageParam] = context.get_messages()
chat_details = []
for msg in messages:
if msg["role"] != "system" and msg["role"] != "user" and msg["role"] != "assistant":
raise Exception(f"Unexpected role {msg['role']} found!")
chat_details.append({"role": msg["role"], "content": msg["content"]})
if self.suffix_prompt:
for i in range(len(chat_details) - 1, -1, -1):
if chat_details[i]["role"] == "user":
chat_details[i]["content"] += f" {self.suffix_prompt}"
break
logger.debug(f"Chat details: {chat_details}")
if len(chat_details) == 0 or all(msg["content"] == "" for msg in chat_details) or not self.collection_name:
raise Exception("No query or collection name is provided..")
"""
Call the RAG chain server and return the streaming response.
"""
request_json = {
"messages": chat_details,
"use_knowledge_base": self.use_knowledge_base,
"temperature": self.temperature,
"top_p": self.top_p,
"max_tokens": self.max_tokens,
"vdb_top_k": self.vdb_top_k,
"reranker_top_k": self.reranker_top_k,
"collection_name": self.collection_name,
"stop": self.stop_words,
"enable_citations": self.enable_citations,
}
await self.start_ttfb_metrics()
full_response = ""
resp = await self._get_rag_response(request_json)
try:
async for chunk in resp.aiter_lines():
await self.stop_ttfb_metrics()
citations = []
try:
chunk = chunk.strip("\n")
try:
if len(chunk) > 6:
parsed = json.loads(chunk[6:])
message = parsed["choices"][0]["message"]["content"]
if "citations" in parsed:
for citation in parsed["citations"]["results"]:
citations.append(
NvidiaRAGCitation(
document_type=str(citation["document_type"]),
document_id=str(citation["document_id"]),
document_name=str(citation["document_name"]),
content=str(citation["content"]).encode(),
metadata=str(citation["metadata"]),
score=float(citation["score"]),
)
)
else:
logger.warning(f"Received empty RAG response chunk '{chunk}'.")
message = ""
except Exception as e:
logger.debug(f"Parsing RAG response chunk failed. Error: {e}")
message = ""
if not message and not citations:
continue
full_response += message
if citations:
scores = [citation.score for citation in citations]
types = [citation.document_type for citation in citations]
logger.debug(f"Received total {len(citations)} RAG citations")
logger.debug(f"Received RAG citation types: {types}")
logger.debug(f"Received RAG citation scores: {scores}")
await self.push_frame(NvidiaRAGCitationsFrame(citations=citations))
if message:
await self.push_frame(TextFrame(message))
except Exception as e:
await self.push_error(ErrorFrame("Internal error in RAG stream: " + str(e)))
finally:
await resp.aclose()
logger.debug(f"Full RAG response: {full_response}")
except Exception as e:
logger.error(f"An error occurred in http request to RAG endpoint, Error: {e}")
await self.push_error(ErrorFrame("An error occurred in http request to RAG endpoint, Error: " + str(e)))
async def _update_settings(self, settings):
"""Updates service settings.
Args:
settings: Dictionary of setting name-value pairs.
"""
for setting, value in settings.items():
logger.debug(f"Updating {setting} to {value} via NvidiaRAGSettingsFrame")
match setting:
case "collection_name":
self.collection_name = value
case "rag_server_url":
self.rag_server_url = value
case "stop_words":
self.stop_words = value
case "temperature":
self.temperature = value
case "top_p":
self.top_p = value
case "max_tokens":
self.max_tokens = value
case "use_knowledge_base":
self.use_knowledge_base = value
case "vdb_top_k":
self.vdb_top_k = value
case "reranker_top_k":
self.reranker_top_k = value
case "enable_citations":
self.enable_citations = value
case _:
logger.warning(f"Unknown setting for NvidiaRAG service: {setting}")
async def _process_context_and_frames(self, context: OpenAILLMContext):
"""Process context and handle start/end frames with metrics."""
await self.push_frame(LLMFullResponseStartFrame())
await self.start_processing_metrics()
await self._process_context(context)
await self.stop_processing_metrics()
await self.push_frame(LLMFullResponseEndFrame())
async def process_frame(self, frame: Frame, direction: FrameDirection):
"""Processes pipeline frames.
Handles settings updates and parent frame processing.
Args:
frame: Input frame to process.
direction: Frame processing direction.
"""
context = None
if isinstance(frame, NvidiaRAGSettingsFrame):
await self._update_settings(frame.settings)
if isinstance(frame, OpenAILLMContextFrame):
context: OpenAILLMContext = frame.context
elif isinstance(frame, LLMMessagesFrame):
context = OpenAILLMContext.from_messages(frame.messages)
elif isinstance(frame, VisionImageRawFrame):
context = OpenAILLMContext()
context.add_image_frame_message(format=frame.format, size=frame.size, image=frame.image, text=frame.text)
elif isinstance(frame, StartInterruptionFrame):
if self._current_task is not None:
await self.cancel_task(self._current_task)
await self._start_interruption()
await self.stop_all_metrics()
await self.push_frame(frame)
else:
await super().process_frame(frame, direction)
if context:
new_task = self.create_task(self._process_context_and_frames(context))
if self._current_task is not None:
await self.cancel_task(self._current_task)
self._current_task = new_task
self._current_task.add_done_callback(lambda _: setattr(self, "_current_task", None))
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