aboutsummaryrefslogtreecommitdiff
diff options
context:
space:
mode:
Diffstat
-rw-r--r--apps/docs/integrations/pipecat.mdx40
-rw-r--r--apps/docs/voice-realtime/pipecat.mdx237
2 files changed, 38 insertions, 239 deletions
diff --git a/apps/docs/integrations/pipecat.mdx b/apps/docs/integrations/pipecat.mdx
index c96f27e1..5ba772b2 100644
--- a/apps/docs/integrations/pipecat.mdx
+++ b/apps/docs/integrations/pipecat.mdx
@@ -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>
diff --git a/apps/docs/voice-realtime/pipecat.mdx b/apps/docs/voice-realtime/pipecat.mdx
deleted file mode 100644
index 61508593..00000000
--- a/apps/docs/voice-realtime/pipecat.mdx
+++ /dev/null
@@ -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)
-
-
[email protected]("/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>
generated by cgit v1.2.3 (git 2.25.1) at 2026-03-11 14:12:55 +0000