Pull Request for Issue #461: Introducing /apps/voice_rag/ - A Demo Application for RAG in Vocode OpenSource

apps/voice_rag/.env.template

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+OPENAI_API_KEY=
+DEEPGRAM_API_KEY=
+AZURE_SPEECH_KEY=
+AZURE_SPEECH_REGION=
+
+PINECONE_API_KEY=
+PINECONE_ENVIRONMENT=
+PINECONE_INDEX_NAME=

apps/voice_rag/Dockerfile

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+# Use the official micromamba image as a base
+FROM docker.io/mambaorg/micromamba:1.5-jammy
+
+# Create a new user '$MAMBA_USER' and set the working directory
+COPY --chown=$MAMBA_USER:$MAMBA_USER environment.docker.yml /tmp/environment.yml
+
+# Install the specified packages using micromamba
+RUN micromamba install -y -n base -f /tmp/environment.yml && \
+    micromamba clean --all --yes    
+
+USER root
+WORKDIR /usr/local/src
+
+ARG VOCODE_USER=vocode
+ARG VOCODE_UID=8476
+ARG VOCODE_GID=8476
+
+RUN groupadd --gid $VOCODE_GID $VOCODE_USER && \
+    useradd --uid $VOCODE_UID --gid $VOCODE_GID --shell /bin/bash --create-home $VOCODE_USER
+
+# COPY --chown=$VOCODE_USER:$VOCODE_USER ../../../ /vocode-python
+# WORKDIR /usr/local/src/vocode
+# RUN poetry install -E all
+
+# Copy the rest of your application files into the Docker image
+COPY --chown=$VOCODE_USER:$VOCODE_USER . /vocode
+WORKDIR /vocode
+
+#USER vocode
+USER root
+
+ENV DOCKER_ENV="docker"
+
+# # Expose the port your FastAPI app will run on
+EXPOSE 19002
+
+# Set build arguments
+ARG BUILD_DATE
+ARG VCS_REF
+ARG VERSION
+
+# Set labels
+LABEL org.label-schema.build-date=$BUILD_DATE \
+      org.label-schema.name="vocode" \
+      org.label-schema.description="Vocode Docker Image" \
+      org.label-schema.url="https://vocode.dev/" \
+      org.label-schema.vcs-url="https://github.com/vocodedev" \
+      org.label-schema.maintainer="[email protected]" \
+      org.label-schema.vcs-ref=$VCS_REF \
+      org.label-schema.vendor="Vocode" \
+      org.label-schema.version=$VERSION
+
+# Start the FastAPI app using Uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "19002"]

apps/voice_rag/README.md

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+# voice_rag
+
+## Docker
+
+1. Set up the configuration for your agent in `main.py`.
+2. Set up an .env file using the template
+
+```
+cp .env.template .env
+```
+
+Fill in your API keys into .env
+
+3. Build the Docker image
+
+```bash
+docker build --build-arg BUILD_DATE=$(date -u +'%Y-%m-%dT%H:%M:%SZ') \
+             --build-arg VCS_REF=$(git rev-parse --short HEAD) \
+             --build-arg VERSION=0.1.0 \
+             -t vocode/vocode-voice-rag:0.1.0 .
+```
+
+4. Run the image and forward the port.
+
+```bash
+docker run --env-file=.env -p 3000:3000 -t vocode/vocode-voice-rag
+```
+
+Now you have a client backend hosted at localhost:3000 to pass into the Vocode React SDK. You'll likely need to tunnel port 3000 to ngrok / host your server in order to use it in the React SDK.
+
+## Non-docker setup
+
+`main.py` just sets up a FastAPI server, so you can just run it with uvicorn:
+
+```
+uvicorn main:app
+```

apps/voice_rag/environment.docker.yml

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+name: vocode-rag
+channels:
+  - conda-forge
+  - pytorch
+dependencies:
+  - python=3.10
+  - openssl=1.1.1w
+  - portaudio
+  - ffmpeg
+  - git
+  - pip
+  - pip:
+    # Installing vocode from the git repository
+    - git+https://github.com/ArtisanLabs/vocode-python/@461-VectorDB-OpenSource-Documentation#egg=vocode
+    - azure-cognitiveservices-speech==1.31.0
+    - python-dotenv
+    - ipython
+    - deepgram-sdk
+    - uvicorn
+    - pinecone-client
+    - poetry

apps/voice_rag/main.py

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+import os
+import logging
+from fastapi import FastAPI
+
+from vocode.streaming.models.agent import ChatGPTAgentConfig
+from vocode.streaming.models.synthesizer import AzureSynthesizerConfig
+from vocode.streaming.synthesizer.azure_synthesizer import AzureSynthesizer
+
+from vocode.streaming.agent.chat_gpt_agent import ChatGPTAgent
+from vocode.streaming.client_backend.conversation import ConversationRouter
+from vocode.streaming.models.message import BaseMessage
+from vocode.streaming.vector_db.factory import VectorDBFactory
+from vocode.streaming.vector_db.pinecone import PineconeConfig
+from vocode.streaming.transcriber.deepgram_transcriber import DeepgramTranscriber
+
+
+from vocode.streaming.models.transcriber import (
+    DeepgramTranscriberConfig,
+    TimeEndpointingConfig
+)
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+app = FastAPI(docs_url=None)
+
+logging.basicConfig()
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+vector_db_config = PineconeConfig(
+    index=os.getenv('PINECONE_INDEX_NAME')
+)
+
+INITIAL_MESSAGE="Hello!"
+PROMPT_PREAMBLE='''
+I want you to act as an IT Architect. 
+I will provide some details about the functionality of an application or other 
+digital product, and it will be your job to come up with ways to integrate it 
+into the IT landscape. This could involve analyzing business requirements, 
+performing a gap analysis, and mapping the functionality of the new system to 
+the existing IT landscape. The next steps are to create a solution design. 
+
+You are an expert in these technologies: 
+- Langchain
+- Supabase
+- Next.js
+- Fastapi
+- Vocode.
+'''
+
+TIME_ENDPOINTING_CONFIG = TimeEndpointingConfig()
+TIME_ENDPOINTING_CONFIG.time_cutoff_seconds = 2
+
+AZURE_SYNTHESIZER_THUNK = lambda output_audio_config: AzureSynthesizer(
+  AzureSynthesizerConfig.from_output_audio_config(output_audio_config, ),
+  logger=logger
+)
+
+DEEPGRAM_TRANSCRIBER_THUNK = lambda input_audio_config: DeepgramTranscriber(
+    DeepgramTranscriberConfig.from_input_audio_config(
+        input_audio_config=input_audio_config,
+        endpointing_config=TIME_ENDPOINTING_CONFIG,
+        min_interrupt_confidence=0.9,
+    ),
+    logger=logger
+)
+
+conversation_router = ConversationRouter(
+    agent_thunk=lambda: ChatGPTAgent(
+        ChatGPTAgentConfig(
+            initial_message=BaseMessage(text=INITIAL_MESSAGE),
+            prompt_preamble=PROMPT_PREAMBLE,
+            vector_db_config=vector_db_config,
+            logger=logger,
+        ),
+        logger=logger
+    ),
+    synthesizer_thunk=AZURE_SYNTHESIZER_THUNK,
+    transcriber_thunk=DEEPGRAM_TRANSCRIBER_THUNK,
+    logger=logger,
+)
+
+app.include_router(conversation_router.get_router())