feat: add support for self hosted llm models

api/db/workflow_recording_client.py

@@ -64,7 +64,7 @@ class WorkflowRecordingClient(BaseDBClient):
                 storage_key=storage_key,
                 storage_backend=storage_backend,
                 created_by=created_by,
-                metadata=metadata or {},
+                recording_metadata=metadata or {},
             )
 
             session.add(recording)

api/services/configuration/check_validity.py

@@ -40,6 +40,7 @@ class UserConfigurationValidator:
             ServiceProviders.SPEECHMATICS.value: self._check_speechmatics_api_key,
             ServiceProviders.CAMB.value: self._check_camb_api_key,
             ServiceProviders.AWS_BEDROCK.value: self._check_aws_bedrock_api_key,
+            ServiceProviders.SELF_HOSTED.value: self._check_self_hosted_api_key,
         }
 
     async def validate(self, configuration: UserConfiguration) -> APIKeyStatusResponse:
@@ -74,6 +75,20 @@ class UserConfigurationValidator:
 
         provider = service_config.provider
 
+        # Self-hosted doesn't require an API key
+        if provider == ServiceProviders.SELF_HOSTED.value:
+            try:
+                if not self._check_self_hosted_api_key(provider, service_config):
+                    return [
+                        {
+                            "model": service_name,
+                            "message": f"Invalid {provider} configuration",
+                        }
+                    ]
+            except ValueError as e:
+                return [{"model": service_name, "message": str(e)}]
+            return []
+
         # AWS Bedrock uses AWS credentials instead of api_key
         if provider == ServiceProviders.AWS_BEDROCK.value:
             try:
@@ -163,7 +178,12 @@ class UserConfigurationValidator:
 
     def _check_camb_api_key(self, model: str, api_key: str) -> bool:
         return True
-      
+
+    def _check_self_hosted_api_key(self, model: str, service_config) -> bool:
+        if not getattr(service_config, "base_url", None):
+            raise ValueError("base_url is required for self-hosted LLM")
+        return True
+
     def _check_aws_bedrock_api_key(self, model: str, service_config) -> bool:
         if not service_config.aws_access_key or not service_config.aws_secret_key:
             raise ValueError("AWS access key and secret key are required for Bedrock")

api/services/configuration/registry.py

@@ -27,6 +27,7 @@ class ServiceProviders(str, Enum):
     SPEECHMATICS = "speechmatics"
     CAMB = "camb"
     AWS_BEDROCK = "aws_bedrock"
+    SELF_HOSTED = "self_hosted"
 
 
 class BaseServiceConfiguration(BaseModel):
@@ -40,6 +41,7 @@ class BaseServiceConfiguration(BaseModel):
         ServiceProviders.AZURE,
         ServiceProviders.DOGRAH,
         ServiceProviders.AWS_BEDROCK,
+        ServiceProviders.SELF_HOSTED,
         # ServiceProviders.SARVAM,
     ]
     api_key: str | list[str]
@@ -249,6 +251,22 @@ class AWSBedrockLLMConfiguration(BaseLLMConfiguration):
     api_key: str | list[str] | None = Field(default=None)
 
 
+SELF_HOSTED_LLM_MODELS = ["llama3", "mistral", "phi3", "qwen2", "gemma2", "deepseek-r1"]
+
+
+@register_llm
+class SelfHostedLLMConfiguration(BaseLLMConfiguration):
+    provider: Literal[ServiceProviders.SELF_HOSTED] = ServiceProviders.SELF_HOSTED
+    model: str = Field(
+        default="llama3", json_schema_extra={"examples": SELF_HOSTED_LLM_MODELS}
+    )
+    base_url: str = Field(
+        default="http://localhost:11434/v1",
+        description="OpenAI-compatible endpoint (Ollama, vLLM, etc.)",
+    )
+    api_key: str | list[str] | None = Field(default=None)
+
+
 LLMConfig = Annotated[
     Union[
         OpenAILLMService,
@@ -258,6 +276,7 @@ LLMConfig = Annotated[
         AzureLLMService,
         DograhLLMService,
         AWSBedrockLLMConfiguration,
+        SelfHostedLLMConfiguration,
     ],
     Field(discriminator="provider"),
 ]
@@ -334,6 +353,12 @@ class CartesiaTTSConfiguration(BaseTTSConfiguration):
     )
     voice: str = Field(default="3faa81ae-d3d8-4ab1-9e44-e50e46d33c30")
     speed: float = Field(default=1.0, ge=0.6, le=1.5, description="Speed of the voice")
+    volume: float = Field(
+        default=1.0,
+        ge=0.5,
+        le=2.0,
+        description="Volume multiplier for generated speech",
+    )
 
 
 SARVAM_TTS_MODELS = ["bulbul:v2", "bulbul:v3"]

api/services/pipecat/recording_router_processor.py

@@ -66,6 +66,7 @@ class RecordingRouterProcessor(FrameProcessor):
         self._frame_buffer: list[tuple[LLMTextFrame, FrameDirection]] = []
         self._mode: Optional[str] = None  # None = detecting, "tts", "recording"
         self._recording_id_buffer = ""
+        self._recording_playback_started = False
 
     # ------------------------------------------------------------------
     # Frame dispatch
@@ -99,9 +100,15 @@ class RecordingRouterProcessor(FrameProcessor):
             await self.push_frame(frame, direction)
             return
 
-        # --- Recording mode: accumulate recording_id silently ---
+        # --- Recording mode: accumulate text and start playback ASAP ---
         if self._mode == "recording":
             self._recording_id_buffer += frame.text
+            if not self._recording_playback_started:
+                buf = self._recording_id_buffer.lstrip()
+                if " " in buf:
+                    recording_id = buf.split()[0]
+                    self._recording_playback_started = True
+                    await self._play_recording(recording_id)
             return
 
         # --- Detection mode: buffer until marker found ---
@@ -178,16 +185,21 @@ class RecordingRouterProcessor(FrameProcessor):
         self, frame: LLMFullResponseEndFrame, direction: FrameDirection
     ):
         if self._mode == "recording":
-            recording_id = self._recording_id_buffer.strip()
-            if recording_id:
-                # Push accumulated text as TTSTextFrame for UI feedback via observer
+            full_text = self._recording_id_buffer.strip()
+            if full_text:
+                recording_id = full_text.split()[0]
+
+                # Push full text (marker + id + transcript) for assistant context
                 await self.push_frame(
                     TTSTextFrame(
                         text=RECORDING_MARKER + self._recording_id_buffer,
                         aggregated_by="recording_router",
                     )
                 )
-                await self._play_recording(recording_id)
+
+                # Fallback: if response ended before a space arrived (no transcript)
+                if not self._recording_playback_started:
+                    await self._play_recording(recording_id)
             else:
                 logger.warning(
                     "RecordingRouterProcessor: recording mode but empty recording_id"
@@ -256,3 +268,4 @@ class RecordingRouterProcessor(FrameProcessor):
         self._frame_buffer = []
         self._mode = None
         self._recording_id_buffer = ""
+        self._recording_playback_started = False

api/services/pipecat/service_factory.py

@@ -8,7 +8,11 @@ from api.services.configuration.registry import ServiceProviders
 from pipecat.services.aws.llm import AWSBedrockLLMService, AWSBedrockLLMSettings
 from pipecat.services.azure.llm import AzureLLMService, AzureLLMSettings
 from pipecat.services.cartesia.stt import CartesiaSTTService
-from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings, GenerationConfig
+from pipecat.services.cartesia.tts import (
+    CartesiaTTSService,
+    CartesiaTTSSettings,
+    GenerationConfig,
+)
 from pipecat.services.deepgram.flux.stt import (
     DeepgramFluxSTTService,
     DeepgramFluxSTTSettings,
@@ -212,13 +216,19 @@ def create_tts_service(user_config, audio_config: "AudioConfig"):
         )
     elif user_config.tts.provider == ServiceProviders.CARTESIA.value:
         speed = getattr(user_config.tts, "speed", None)
-        generation_config = GenerationConfig(speed=speed) if speed and speed != 1.0 else None
+        generation_config = (
+            GenerationConfig(speed=speed) if speed and speed != 1.0 else None
+        )
         return CartesiaTTSService(
             api_key=user_config.tts.api_key,
             settings=CartesiaTTSSettings(
                 voice=user_config.tts.voice,
                 model=user_config.tts.model,
-                **({"generation_config": generation_config} if generation_config else {}),
+                **(
+                    {"generation_config": generation_config}
+                    if generation_config
+                    else {}
+                ),
             ),
             text_filters=[xml_function_tag_filter],
             silence_time_s=1.0,
@@ -353,6 +363,12 @@ def create_llm_service_from_provider(
             aws_region=aws_region,
             settings=AWSBedrockLLMSettings(model=model),
         )
+    elif provider == ServiceProviders.SELF_HOSTED.value:
+        return OpenAILLMService(
+            base_url=base_url or "http://localhost:11434/v1",
+            api_key=api_key or "none",
+            settings=OpenAILLMSettings(model=model),
+        )
     else:
         raise HTTPException(status_code=400, detail=f"Invalid LLM provider {provider}")
 
@@ -368,6 +384,8 @@ def create_llm_service(user_config):
         kwargs["base_url"] = user_config.llm.base_url
     elif provider == ServiceProviders.AZURE.value:
         kwargs["endpoint"] = user_config.llm.endpoint
+    elif provider == ServiceProviders.SELF_HOSTED.value:
+        kwargs["base_url"] = user_config.llm.base_url
     elif provider == ServiceProviders.AWS_BEDROCK.value:
         kwargs["aws_access_key"] = user_config.llm.aws_access_key
         kwargs["aws_secret_key"] = user_config.llm.aws_secret_key

api/services/workflow/pipecat_engine.py

@@ -437,9 +437,7 @@ class PipecatEngine:
 
         async def _do_extraction():
             try:
-                logger.debug(
-                    f"Starting variable extraction for node: {node.name}"
-                )
+                logger.debug(f"Starting variable extraction for node: {node.name}")
                 extracted_data = (
                     await self._variable_extraction_manager._perform_extraction(
                         extraction_variables, parent_context, extraction_prompt
@@ -454,7 +452,9 @@ class PipecatEngine:
                     f"Variable extraction completed for node: {node.name}. Extracted: {extracted_data}"
                 )
             except Exception as e:
-                logger.error(f"Error during variable extraction for node {node.name}: {str(e)}")
+                logger.error(
+                    f"Error during variable extraction for node {node.name}: {str(e)}"
+                )
 
         if run_in_background:
             logger.debug(
@@ -497,9 +497,7 @@ class PipecatEngine:
                     logger.error(
                         f"Pending extraction task '{task_name}' failed: {result}"
                     )
-            logger.debug(
-                f"All pending extraction tasks completed in {elapsed:.2f}s"
-            )
+            logger.debug(f"All pending extraction tasks completed in {elapsed:.2f}s")
         except asyncio.TimeoutError:
             incomplete = [
                 t.get_name() for t in self._pending_extraction_tasks if not t.done()

api/services/workflow/pipecat_engine_context_composer.py

@@ -34,13 +34,13 @@ You have two modes for responding:
    Example: ▸ Hello! How can I help you today?
 
 2. PRE-RECORDED AUDIO (●): Play a pre-recorded audio message.
-   Format: `●` followed by a space and ONLY the recording_id. Nothing else.
-   Example: ● rec_greeting_01
+   Format: `●` followed by a space followed by recording_id followed by provided transcript. Nothing else.
+   Example: ● rec_greeting_01 [ Provided Transcript ]
 
 RULES:
 - Your response MUST start with either `▸` or `●` as the very first character.
 - For `▸` (dynamic speech): Follow with a space and your full response text.
-- For `●` (pre-recorded audio): Follow with a space and ONLY the recording_id. No other text.
+- For `●` (pre-recorded audio): Follow with a space and the recording_id and the provided transcript. No other text.
 - Use `●` when a pre-recorded message matches the situation well.
 - Use `▸` when you need to generate a dynamic, contextual response.
 - NEVER mix modes in a single response. Choose one."""

api/services/workflow/qa/analysis.py

@@ -28,7 +28,9 @@ from api.utils.template_renderer import render_template
 from pipecat.processors.aggregators.llm_context import LLMContext
 
 
-async def _run_llm_inference(llm, messages: list[dict], system_prompt: str) -> str | None:
+async def _run_llm_inference(
+    llm, messages: list[dict], system_prompt: str
+) -> str | None:
     """Run a one-shot LLM inference using the pipecat service."""
     context = LLMContext()
     context.set_messages(messages)
@@ -51,7 +53,10 @@ async def _generate_conversation_summary(
     ]
 
     try:
-        summary = await _run_llm_inference(llm, messages, CONVERSATION_SUMMARY_SYSTEM_PROMPT) or ""
+        summary = (
+            await _run_llm_inference(llm, messages, CONVERSATION_SUMMARY_SYSTEM_PROMPT)
+            or ""
+        )
 
         span_name = f"conversation-summary-before-{node_name}"
         add_qa_span_to_trace(parent_ctx, model, messages, summary, span_name)

api/services/workflow/qa/node_summary.py

@@ -154,7 +154,12 @@ async def ensure_node_summaries(
         try:
             context = LLMContext()
             context.set_messages(messages)
-            summary_text = await llm.run_inference(context, system_instruction=NODE_SUMMARY_SYSTEM_PROMPT) or ""
+            summary_text = (
+                await llm.run_inference(
+                    context, system_instruction=NODE_SUMMARY_SYSTEM_PROMPT
+                )
+                or ""
+            )
         except Exception as e:
             logger.warning(f"Failed to generate summary for node {node_id}: {e}")
             updated_summaries[node_id] = {"summary": ""}

api/tests/test_camb_tts_integration.py

@@ -9,7 +9,7 @@ Covers:
 """
 
 from types import SimpleNamespace
-from unittest.mock import AsyncMock, MagicMock, patch
+from unittest.mock import MagicMock, patch
 
 import pytest
 from pydantic import ValidationError
@@ -17,13 +17,12 @@ from pydantic import ValidationError
 from api.services.configuration.check_validity import UserConfigurationValidator
 from api.services.configuration.registry import (
     CAMB_TTS_MODELS,
-    CambTTSConfiguration,
     REGISTRY,
+    CambTTSConfiguration,
     ServiceProviders,
     ServiceType,
 )
 
-
 # ---------------------------------------------------------------------------
 # 1. CambTTSConfiguration model tests
 # ---------------------------------------------------------------------------