mirror of
https://github.com/supermemoryai/supermemory.git
synced 2026-09-07 08:26:15 +00:00
mintify new doc pipecat changes
This commit is contained in:
Prasanna721
parent
5c4823ed80
commit
642744522f
2 changed files with 38 additions and 239 deletions
|
|
@ -28,7 +28,8 @@ You can obtain an API key from [console.supermemory.ai](https://console.supermem
|
|||
Supermemory integration is provided through the `SupermemoryPipecatService` class in Pipecat:
|
||||
|
||||
```python
|
||||
from supermemory_pipecat import SupermemoryPipecatService, InputParams
|
||||
from supermemory_pipecat import SupermemoryPipecatService
|
||||
from supermemory_pipecat.service import InputParams
|
||||
|
||||
memory = SupermemoryPipecatService(
|
||||
api_key=os.getenv("SUPERMEMORY_API_KEY"),
|
||||
|
|
@ -96,6 +97,7 @@ InputParams(
|
|||
search_limit=10, # Max memories to retrieve (default: 10)
|
||||
search_threshold=0.1, # Similarity threshold 0.0-1.0 (default: 0.1)
|
||||
system_prompt="Based on previous conversations:\n\n",
|
||||
inject_mode="auto", # "auto" | "system" | "user"
|
||||
)
|
||||
```
|
||||
|
||||
|
|
@ -105,6 +107,31 @@ InputParams(
|
|||
| `search_threshold` | float | 0.1 | Minimum similarity threshold for memory retrieval |
|
||||
| `mode` | str | "full" | Memory retrieval mode: `"profile"`, `"query"`, or `"full"` |
|
||||
| `system_prompt` | str | "Based on previous conversations:\n\n" | Prefix text for memory context |
|
||||
| `inject_mode` | str | "auto" | How memories are injected: `"auto"`, `"system"`, or `"user"` |
|
||||
|
||||
## Injection Modes
|
||||
|
||||
The `inject_mode` parameter controls how memories are added to the LLM context:
|
||||
|
||||
| Mode | Behavior |
|
||||
|------|----------|
|
||||
| `"auto"` | **Auto-detects** based on frame types. If audio frames detected → injects to system prompt (speech-to-speech). If only text frames → injects as user message (STT/TTS). |
|
||||
| `"system"` | Always injects memories into the system prompt |
|
||||
| `"user"` | Always injects memories as a user message |
|
||||
|
||||
## Speech-to-Speech Models (Gemini Live, etc.)
|
||||
|
||||
For speech-to-speech models like Gemini Live, the SDK **automatically detects** audio frames and injects memories into the system prompt. No configuration needed:
|
||||
|
||||
```python
|
||||
from supermemory_pipecat import SupermemoryPipecatService
|
||||
|
||||
# Auto-detection works out of the box
|
||||
memory = SupermemoryPipecatService(
|
||||
api_key=os.getenv("SUPERMEMORY_API_KEY"),
|
||||
user_id="unique_user_id",
|
||||
)
|
||||
```
|
||||
|
||||
## Example: Voice Agent with Memory
|
||||
|
||||
|
|
@ -130,7 +157,8 @@ from pipecat.transports.websocket.fastapi import (
|
|||
FastAPIWebsocketTransport,
|
||||
)
|
||||
|
||||
from supermemory_pipecat import SupermemoryPipecatService, InputParams
|
||||
from supermemory_pipecat import SupermemoryPipecatService
|
||||
from supermemory_pipecat.service import InputParams
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
|
|
@ -201,3 +229,11 @@ if __name__ == "__main__":
|
|||
import uvicorn
|
||||
uvicorn.run(app, host="0.0.0.0", port=8000)
|
||||
```
|
||||
|
||||
## Example: Gemini Live with Memory
|
||||
|
||||
For a complete example using Gemini Live speech-to-speech with Supermemory, check out the reference implementation:
|
||||
|
||||
<Card title="Pipecat Memory Example" icon="github" href="https://github.com/supermemoryai/pipecat-memory">
|
||||
Full working example with Gemini Live, including frontend and backend code.
|
||||
</Card>
|
||||
|
|
|
|||
|
|
@ -1,237 +0,0 @@
|
|||
---
|
||||
title: "Pipecat"
|
||||
description: "Integrate Supermemory with Pipecat for conversational memory in voice AI agents"
|
||||
---
|
||||
|
||||
Supermemory integrates with [Pipecat](https://github.com/pipecat-ai/pipecat), providing long-term memory capabilities for voice AI agents. Your Pipecat applications will remember past conversations and provide personalized responses based on user history.
|
||||
|
||||
## Installation
|
||||
|
||||
To use Supermemory with Pipecat, install the required dependencies:
|
||||
|
||||
```bash
|
||||
pip install supermemory-pipecat
|
||||
```
|
||||
|
||||
Set up your API key as an environment variable:
|
||||
|
||||
```bash
|
||||
export SUPERMEMORY_API_KEY=your_supermemory_api_key
|
||||
```
|
||||
|
||||
You can obtain an API key from [console.supermemory.ai](https://console.supermemory.ai).
|
||||
|
||||
## Configuration
|
||||
|
||||
Supermemory integration is provided through the `SupermemoryPipecatService` class in Pipecat:
|
||||
|
||||
```python
|
||||
from supermemory_pipecat import SupermemoryPipecatService
|
||||
from supermemory_pipecat.service import InputParams
|
||||
|
||||
memory = SupermemoryPipecatService(
|
||||
api_key=os.getenv("SUPERMEMORY_API_KEY"),
|
||||
user_id="unique_user_id",
|
||||
session_id="session_123",
|
||||
params=InputParams(
|
||||
mode="full", # "profile" | "query" | "full"
|
||||
search_limit=10, # Max memories to retrieve
|
||||
search_threshold=0.1, # Relevance threshold (0.0-1.0)
|
||||
system_prompt="Based on previous conversations:\n\n",
|
||||
),
|
||||
)
|
||||
```
|
||||
|
||||
## Pipeline Integration
|
||||
|
||||
The `SupermemoryPipecatService` should be positioned between your context aggregator and LLM service in the Pipecat pipeline:
|
||||
|
||||
```python
|
||||
pipeline = Pipeline([
|
||||
transport.input(),
|
||||
stt, # Speech-to-text
|
||||
context_aggregator.user(),
|
||||
memory, # <- Supermemory memory service
|
||||
llm,
|
||||
tts, # Text-to-speech
|
||||
transport.output(),
|
||||
context_aggregator.assistant(),
|
||||
])
|
||||
```
|
||||
|
||||
## How It Works
|
||||
|
||||
When integrated with Pipecat, Supermemory provides two key functionalities:
|
||||
|
||||
### 1. Memory Retrieval
|
||||
|
||||
When a user message is detected, Supermemory retrieves relevant memories:
|
||||
|
||||
- **Static Profile**: Persistent facts about the user
|
||||
- **Dynamic Profile**: Recent context and preferences
|
||||
- **Search Results**: Semantically relevant past memories
|
||||
|
||||
### 2. Context Enhancement
|
||||
|
||||
Retrieved memories are formatted and injected into the LLM context before generation, giving the model awareness of past conversations.
|
||||
|
||||
## Memory Modes
|
||||
|
||||
| Mode | Static Profile | Dynamic Profile | Search Results | Use Case |
|
||||
|------|----------------|-----------------|----------------|----------|
|
||||
| `"profile"` | Yes | Yes | No | Personalization without search |
|
||||
| `"query"` | No | No | Yes | Finding relevant past context |
|
||||
| `"full"` | Yes | Yes | Yes | Complete memory (default) |
|
||||
|
||||
## Configuration Options
|
||||
|
||||
You can customize how memories are retrieved and used:
|
||||
|
||||
### InputParams
|
||||
|
||||
```python
|
||||
InputParams(
|
||||
mode="full", # Memory mode (default: "full")
|
||||
search_limit=10, # Max memories to retrieve (default: 10)
|
||||
search_threshold=0.1, # Similarity threshold 0.0-1.0 (default: 0.1)
|
||||
system_prompt="Based on previous conversations:\n\n",
|
||||
inject_mode="auto", # "auto" | "system" | "user"
|
||||
)
|
||||
```
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|-----------|------|---------|-------------|
|
||||
| `search_limit` | int | 10 | Maximum number of memories to retrieve per query |
|
||||
| `search_threshold` | float | 0.1 | Minimum similarity threshold for memory retrieval |
|
||||
| `mode` | str | "full" | Memory retrieval mode: `"profile"`, `"query"`, or `"full"` |
|
||||
| `system_prompt` | str | "Based on previous conversations:\n\n" | Prefix text for memory context |
|
||||
| `inject_mode` | str | "auto" | How memories are injected: `"auto"`, `"system"`, or `"user"` |
|
||||
|
||||
## Injection Modes
|
||||
|
||||
The `inject_mode` parameter controls how memories are added to the LLM context:
|
||||
|
||||
| Mode | Behavior |
|
||||
|------|----------|
|
||||
| `"auto"` | **Auto-detects** based on frame types. If audio frames detected → injects to system prompt (speech-to-speech). If only text frames → injects as user message (STT/TTS). |
|
||||
| `"system"` | Always injects memories into the system prompt |
|
||||
| `"user"` | Always injects memories as a user message |
|
||||
|
||||
## Speech-to-Speech Models (Gemini Live, etc.)
|
||||
|
||||
For speech-to-speech models like Gemini Live, the SDK **automatically detects** audio frames and injects memories into the system prompt. No configuration needed:
|
||||
|
||||
```python
|
||||
from supermemory_pipecat import SupermemoryPipecatService
|
||||
|
||||
# Auto-detection works out of the box
|
||||
memory = SupermemoryPipecatService(
|
||||
api_key=os.getenv("SUPERMEMORY_API_KEY"),
|
||||
user_id="unique_user_id",
|
||||
)
|
||||
```
|
||||
|
||||
## Example: Voice Agent with Memory
|
||||
|
||||
Here's a complete example of a Pipecat voice agent with Supermemory integration:
|
||||
|
||||
```python
|
||||
import os
|
||||
from fastapi import FastAPI, WebSocket
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMMessagesFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.serializers.protobuf import ProtobufFrameSerializer
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.openai.tts import OpenAITTSService
|
||||
from pipecat.services.openai.stt import OpenAISTTService
|
||||
from pipecat.transports.websocket.fastapi import (
|
||||
FastAPIWebsocketParams,
|
||||
FastAPIWebsocketTransport,
|
||||
)
|
||||
|
||||
from supermemory_pipecat import SupermemoryPipecatService
|
||||
from supermemory_pipecat.service import InputParams
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
SYSTEM_PROMPT = """You are a helpful voice assistant with memory capabilities.
|
||||
You remember information from past conversations and use it to provide personalized responses.
|
||||
Keep responses brief and conversational."""
|
||||
|
||||
|
||||
async def run_bot(websocket_client, user_id: str, session_id: str):
|
||||
transport = FastAPIWebsocketTransport(
|
||||
websocket=websocket_client,
|
||||
params=FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
serializer=ProtobufFrameSerializer(),
|
||||
),
|
||||
)
|
||||
|
||||
stt = OpenAISTTService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-5-mini")
|
||||
tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy")
|
||||
|
||||
# Supermemory memory service
|
||||
memory = SupermemoryPipecatService(
|
||||
user_id=user_id,
|
||||
session_id=session_id,
|
||||
params=InputParams(
|
||||
mode="full",
|
||||
search_limit=10,
|
||||
search_threshold=0.1,
|
||||
),
|
||||
)
|
||||
|
||||
context = OpenAILLMContext([{"role": "system", "content": SYSTEM_PROMPT}])
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline([
|
||||
transport.input(),
|
||||
stt,
|
||||
context_aggregator.user(),
|
||||
memory,
|
||||
llm,
|
||||
tts,
|
||||
transport.output(),
|
||||
context_aggregator.assistant(),
|
||||
])
|
||||
|
||||
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False)
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
@app.websocket("/ws")
|
||||
async def websocket_endpoint(websocket: WebSocket):
|
||||
await websocket.accept()
|
||||
await run_bot(websocket, user_id="alice", session_id="session-123")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
uvicorn.run(app, host="0.0.0.0", port=8000)
|
||||
```
|
||||
|
||||
## Example: Gemini Live with Memory
|
||||
|
||||
For a complete example using Gemini Live speech-to-speech with Supermemory, check out the reference implementation:
|
||||
|
||||
<Card title="Pipecat Memory Example" icon="github" href="https://github.com/supermemoryai/pipecat-memory">
|
||||
Full working example with Gemini Live, including frontend and backend code.
|
||||
</Card>
|
||||
Loading…
Add table
Reference in a new issue