MultimodalQnA audio features completion

MultimodalQnA/Dockerfile

@@ -20,7 +20,8 @@ WORKDIR $HOME
 FROM base AS git
 
 RUN apt-get update && apt-get install -y --no-install-recommends git
-RUN git clone --depth 1 https://github.com/opea-project/GenAIComps.git
+# RUN git clone --depth 1 https://github.com/opea-project/GenAIComps.git
+RUN git clone --single-branch --branch="mmqna-phase3" https://github.com/mhbuehler/GenAIComps.git
 
 # Stage 3: common layer shared by services using GenAIComps
 FROM base AS comps-base

MultimodalQnA/README.md

@@ -2,7 +2,7 @@
 
 Suppose you possess a set of videos, images, audio files, PDFs, or some combination thereof and wish to perform question-answering to extract insights from these documents. To respond to your questions, the system needs to comprehend a mix of textual, visual, and audio facts drawn from the document contents. The MultimodalQnA framework offers an optimal solution for this purpose.
 
-`MultimodalQnA` addresses your questions by dynamically fetching the most pertinent multimodal information (e.g. images, transcripts, and captions) from your collection of video, image, audio, and PDF files. For this purpose, MultimodalQnA utilizes [BridgeTower model](https://huggingface.co/BridgeTower/bridgetower-large-itm-mlm-gaudi), a multimodal encoding transformer model which merges visual and textual data into a unified semantic space. During the ingestion phase, the BridgeTower model embeds both visual cues and auditory facts as texts, and those embeddings are then stored in a vector database. When it comes to answering a question, the MultimodalQnA will fetch its most relevant multimodal content from the vector store and feed it into a downstream Large Vision-Language Model (LVM) as input context to generate a response for the user.
+`MultimodalQnA` addresses your questions by dynamically fetching the most pertinent multimodal information (e.g. images, transcripts, and captions) from your collection of video, image, audio, and PDF files. For this purpose, MultimodalQnA utilizes [BridgeTower model](https://huggingface.co/BridgeTower/bridgetower-large-itm-mlm-gaudi), a multimodal encoding transformer model which merges visual and textual data into a unified semantic space. During the ingestion phase, the BridgeTower model embeds both visual cues and auditory facts as texts, and those embeddings are then stored in a vector database. When it comes to answering a question, the MultimodalQnA will fetch its most relevant multimodal content from the vector store and feed it into a downstream Large Vision-Language Model (LVM) as input context to generate a response for the user, which can be text or audio.
 
 The MultimodalQnA architecture shows below:
 
@@ -41,12 +41,14 @@ flowchart LR
         UI([UI server<br>]):::orchid
     end
 
+    ASR{{Whisper service <br>}}
     TEI_EM{{Embedding service <br>}}
     VDB{{Vector DB<br><br>}}
     R_RET{{Retriever service <br>}}
     DP([Data Preparation<br>]):::blue
     LVM_gen{{LVM Service <br>}}
     GW([MultimodalQnA GateWay<br>]):::orange
+    TTS{{SpeechT5 service <br>}}
 
     %% Data Preparation flow
     %% Ingest data flow
@@ -74,25 +76,42 @@ flowchart LR
     R_RET <-.->VDB
     DP <-.->VDB
 
+    %% Audio speech recognition used for translating audio queries to text
+    GW <-.-> ASR
 
+    %% Generate spoken responses with text-to-speech using the SpeechT5 model
+    GW <-.-> TTS
 
 ```
 
 This MultimodalQnA use case performs Multimodal-RAG using LangChain, Redis VectorDB and Text Generation Inference on [Intel Gaudi2](https://www.intel.com/content/www/us/en/products/details/processors/ai-accelerators/gaudi-overview.html) and [Intel Xeon Scalable Processors](https://www.intel.com/content/www/us/en/products/details/processors/xeon.html), and we invite contributions from other hardware vendors to expand the example.
 
+The [Whisper Service](https://github.com/opea-project/GenAIComps/blob/main/comps/asr/src/README.md)
+is used by MultimodalQnA for converting audio queries to text. If a spoken response is requested, the
+[SpeechT5 Service](https://github.com/opea-project/GenAIComps/blob/main/comps/tts/src/README.md) translates the text
+response from the LVM to a speech audio file.
+
 The Intel Gaudi2 accelerator supports both training and inference for deep learning models in particular for LLMs. Visit [Habana AI products](https://habana.ai/products) for more details.
 
 In the below, we provide a table that describes for each microservice component in the MultimodalQnA architecture, the default configuration of the open source project, hardware, port, and endpoint.
 
 <details>
-<summary><b>Gaudi default compose.yaml</b></summary>
-
-| MicroService | Open Source Project   | HW    | Port | Endpoint                                                    |
-| ------------ | --------------------- | ----- | ---- | ----------------------------------------------------------- |
-| Embedding    | Langchain             | Xeon  | 6000 | /v1/embeddings                                              |
-| Retriever    | Langchain, Redis      | Xeon  | 7000 | /v1/multimodal_retrieval                                    |
-| LVM          | Langchain, TGI        | Gaudi | 9399 | /v1/lvm                                                     |
-| Dataprep     | Redis, Langchain, TGI | Gaudi | 6007 | /v1/generate_transcripts, /v1/generate_captions, /v1/ingest |
+<summary><b>Gaudi and Xeon default compose.yaml settings</b></summary>
+
+| MicroService | Open Source Project     | HW    | Port | Endpoint                                                    |
+| ------------ | ----------------------- | ----- | ---- | ----------------------------------------------------------- |
+| Dataprep     | Redis, Langchain, TGI   | Xeon  | 6007 | /v1/generate_transcripts, /v1/generate_captions, /v1/ingest |
+| Embedding    | Langchain               | Xeon  | 6000 | /v1/embeddings                                              |
+| LVM          | Langchain, Transformers | Xeon  | 9399 | /v1/lvm                                                     |
+| Retriever    | Langchain, Redis        | Xeon  | 7000 | /v1/retrieval                                               |
+| SpeechT5     | Transformers            | Xeon  | 7055 | /v1/tts                                                     |
+| Whisper      | Transformers            | Xeon  | 7066 | /v1/asr                                                     |
+| Dataprep     | Redis, Langchain, TGI   | Gaudi | 6007 | /v1/generate_transcripts, /v1/generate_captions, /v1/ingest |
+| Embedding    | Langchain               | Gaudi | 6000 | /v1/embeddings                                              |
+| LVM          | Langchain, TGI          | Gaudi | 9399 | /v1/lvm                                                     |
+| Retriever    | Langchain, Redis        | Gaudi | 7000 | /v1/retrieval                                               |
+| SpeechT5     | Transformers            | Gaudi | 7055 | /v1/tts                                                     |
+| Whisper      | Transformers            | Gaudi | 7066 | /v1/asr                                                     |
 
 </details>
 
@@ -104,18 +123,41 @@ By default, the embedding and LVM models are set to a default value as listed be
 | --------- | ----- | ----------------------------------------- |
 | embedding | Xeon  | BridgeTower/bridgetower-large-itm-mlm-itc |
 | LVM       | Xeon  | llava-hf/llava-1.5-7b-hf                  |
+| SpeechT5  | Xeon  | microsoft/speecht5_tts                    |
+| Whisper   | Xeon  | openai/whisper-small                      |
 | embedding | Gaudi | BridgeTower/bridgetower-large-itm-mlm-itc |
 | LVM       | Gaudi | llava-hf/llava-v1.6-vicuna-13b-hf         |
+| SpeechT5  | Gaudi | microsoft/speecht5_tts                    |
+| Whisper   | Gaudi | openai/whisper-small                      |
 
 You can choose other LVM models, such as `llava-hf/llava-1.5-7b-hf ` and `llava-hf/llava-1.5-13b-hf`, as needed.
 
 ## Deploy MultimodalQnA Service
 
 The MultimodalQnA service can be effortlessly deployed on either Intel Gaudi2 or Intel XEON Scalable Processors.
 
-Currently we support deploying MultimodalQnA services with docker compose.
+Currently we support deploying MultimodalQnA services with docker compose. The [`docker_compose`](docker_compose)
+directory has folders which include `compose.yaml` files for different hardware types:
 
-### Setup Environment Variable
+```
+📂 docker_compose
+├── 📂 amd
+│   └── 📂 gpu
+│       └── 📂 rocm
+│           ├── 📄 compose.yaml
+│           └── ...
+└── 📂 intel
+    ├── 📂 cpu
+    │   └── 📂 xeon
+    │       ├── 📄 compose.yaml
+    │       └── ...
+    └── 📂 hpu
+        └── 📂 gaudi
+            ├── 📄 compose.yaml
+            └── ...
+```
+
+### Setup Environment Variables
 
 To set up environment variables for deploying MultimodalQnA services, follow these steps:
 
@@ -124,8 +166,10 @@ To set up environment variables for deploying MultimodalQnA services, follow the
    ```bash
    # Example: export host_ip=$(hostname -I | awk '{print $1}')
    export host_ip="External_Public_IP"
+
+   # Append the host_ip to the no_proxy list to allow container communication
    # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
-   export no_proxy="Your_No_Proxy"
+   export no_proxy="${no_proxy},${host_ip}"
    ```
 
 2. If you are in a proxy environment, also set the proxy-related environment variables:
@@ -137,36 +181,41 @@ To set up environment variables for deploying MultimodalQnA services, follow the
 
 3. Set up other environment variables:
 
-   > Notice that you can only choose **one** command below to set up envs according to your hardware. Other that the port numbers may be set incorrectly.
+   > Choose **one** command below to set env vars according to your hardware. Otherwise, the port numbers may be set incorrectly.
 
    ```bash
    # on Gaudi
-   source ./docker_compose/intel/hpu/gaudi/set_env.sh
+   cd docker_compose/intel/hpu/gaudi
+   source ./set_env.sh
+
    # on Xeon
-   source ./docker_compose/intel/cpu/xeon/set_env.sh
+   cd docker_compose/intel/cpu/xeon
+   source ./set_env.sh
    ```
 
 ### Deploy MultimodalQnA on Gaudi
 
-Refer to the [Gaudi Guide](./docker_compose/intel/hpu/gaudi/README.md) to build docker images from source.
+Refer to the [Gaudi Guide](./docker_compose/intel/hpu/gaudi/README.md) if you would like to build docker images from
+source, otherwise images will be pulled from Docker Hub.
 
 Find the corresponding [compose.yaml](./docker_compose/intel/hpu/gaudi/compose.yaml).
 
 ```bash
-cd GenAIExamples/MultimodalQnA/docker_compose/intel/hpu/gaudi/
+# While still in the docker_compose/intel/hpu/gaudi directory, use docker compose to bring up the services
 docker compose -f compose.yaml up -d
 ```
 
-> Notice: Currently only the **Habana Driver 1.17.x** is supported for Gaudi.
+> Notice: Currently only the **Habana Driver 1.18.x** is supported for Gaudi.
 
 ### Deploy MultimodalQnA on Xeon
 
-Refer to the [Xeon Guide](./docker_compose/intel/cpu/xeon/README.md) for more instructions on building docker images from source.
+Refer to the [Xeon Guide](./docker_compose/intel/cpu/xeon/README.md) if you would like to build docker images from
+source, otherwise images will be pulled from Docker Hub.
 
 Find the corresponding [compose.yaml](./docker_compose/intel/cpu/xeon/compose.yaml).
 
 ```bash
-cd GenAIExamples/MultimodalQnA/docker_compose/intel/cpu/xeon/
+# While still in the docker_compose/intel/cpu/xeon directory, use docker compose to bring up the services
 docker compose -f compose.yaml up -d
 ```
 

MultimodalQnA/docker_compose/amd/gpu/rocm/README.md

@@ -178,7 +178,7 @@ curl http://${host_ip}:$MM_EMBEDDING_PORT_MICROSERVICE/v1/embeddings \
 
 ```bash
 export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
-curl http://${host_ip}:7000/v1/multimodal_retrieval \
+curl http://${host_ip}:7000/v1/retrieval \
     -X POST \
     -H "Content-Type: application/json" \
     -d "{\"text\":\"test\",\"embedding\":${your_embedding}}"

MultimodalQnA/docker_compose/amd/gpu/rocm/compose.yaml

@@ -175,6 +175,8 @@ services:
       - DATAPREP_INGEST_SERVICE_ENDPOINT=${DATAPREP_INGEST_SERVICE_ENDPOINT}
       - DATAPREP_GEN_TRANSCRIPT_SERVICE_ENDPOINT=${DATAPREP_GEN_TRANSCRIPT_SERVICE_ENDPOINT}
       - DATAPREP_GEN_CAPTION_SERVICE_ENDPOINT=${DATAPREP_GEN_CAPTION_SERVICE_ENDPOINT}
+      - DATAPREP_GET_FILE_ENDPOINT=${DATAPREP_GET_FILE_ENDPOINT}
+      - DATAPREP_DELETE_FILE_ENDPOINT=${DATAPREP_DELETE_FILE_ENDPOINT}
     ipc: host
     restart: always