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361eb254-8bc5-4bfc-8b11-63caacd9f468 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 25 | opea-semantic-v1 | 5ebf853c6a596c70 | ## Compatibility
EC-RAG megaservice and microservice are compatible with the existing OPEA
GenAIExamples and GenAIComps repos. The EC-RAG leverages the LLM microservice
and the VectorDB microservice from GenAIComps. | ai_ref_knowledge | OPEA Documentation | ## Compatibility
EC-RAG megaservice and microservice are compatible with the existing OPEA
GenAIExamples and GenAIComps repos. The EC-RAG leverages the LLM microservice
and the VectorDB microservice from GenAIComps. | ## Compatibility
EC-RAG megaservice and microservice are compatible with the existing OPEA
GenAIExamples and GenAIComps repos. The EC-RAG leverages the LLM microservice
and the VectorDB microservice from GenAIComps. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4a7ef585-67cd-40d4-96b2-33d20fdd766b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 3 | opea-semantic-v1 | b4cf021063b0ab81 | Core(TM) Processor + Intel(R) Iris(R) Xe Graphics - Intel(R) Core(TM) Processor + Intel(R) Arc(TM) A-Series Graphics - Intel(R) Xeon(R) Processor + Intel(R) Arc(TM) A-Series Graphics
The scenarios with these hardware options block the edge users from using large
parameter size LLMs on-prem as well as sophisticated RAG ... | ai_ref_knowledge | OPEA Documentation | Core(TM) Processor + Intel(R) Iris(R) Xe Graphics - Intel(R) Core(TM) Processor + Intel(R) Arc(TM) A-Series Graphics - Intel(R) Xeon(R) Processor + Intel(R) Arc(TM) A-Series Graphics
The scenarios with these hardware options block the edge users from using large
parameter size LLMs on-prem as well as sophisticated RAG ... | Core(TM) Processor + Intel(R) Iris(R) Xe Graphics - Intel(R) Core(TM) Processor + Intel(R) Arc(TM) A-Series Graphics - Intel(R) Xeon(R) Processor + Intel(R) Arc(TM) A-Series Graphics
The scenarios with these hardware options block the edge users from using large
parameter size LLMs on-prem as well as sophisticated RAG ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5f5d19d6-7fa2-4938-9a4a-78984e5135aa | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 13 | opea-semantic-v1 | 0edc5aec11fe7df3 | Below diagram illustrates the overall components of EC-RAG: 
The EC-RAG pipeline will expose 3 types of REST API endpoint:
- **/v1/data** for indexing
- **/v1/settings** for configuration
- **/v1/chatqna** for inferencing | ai_ref_knowledge | OPEA Documentation | Below diagram illustrates the overall components of EC-RAG: 
The EC-RAG pipeline will expose 3 types of REST API endpoint:
- **/v1/data** for indexing
- **/v1/settings** for configuration
- **/v1/chatqna** for inferencing | Below diagram illustrates the overall components of EC-RAG: 
The EC-RAG pipeline will expose 3 types of REST API endpoint:
- **/v1/data** for indexing
- **/v1/settings** for configuration
- **/v1/chatqna** for inferencing | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6e9e7387-ab80-45dd-914d-815fdd5a2524 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 16 | opea-semantic-v1 | 553b8c5791ec9bc5 | file | POST | /v1/data | FastAPI.UploadFile | | List files | GET | /v1/data | | | Remove | DELETE | /v1/data/{id} | |
### /v1/settings/pipelines | ai_ref_knowledge | OPEA Documentation | file | POST | /v1/data | FastAPI.UploadFile | | List files | GET | /v1/data | | | Remove | DELETE | /v1/data/{id} | |
### /v1/settings/pipelines | file | POST | /v1/data | FastAPI.UploadFile | | List files | GET | /v1/data | | | Remove | DELETE | /v1/data/{id} | |
### /v1/settings/pipelines | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6fd7b019-cadd-44d9-85a8-88d13a7f099c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 0 | opea-semantic-v1 | d7075181f9ded209 | ## Objective
Edge industry users are facing obstacles to build an "out-of-the-box" RAG
application to meet both quality and performance requirements. Total Cost of
Ownership(TCO) and pipeline optimization techniques are the two main reasons
to block this process. | ai_ref_knowledge | OPEA Documentation | ## Objective
Edge industry users are facing obstacles to build an "out-of-the-box" RAG
application to meet both quality and performance requirements. Total Cost of
Ownership(TCO) and pipeline optimization techniques are the two main reasons
to block this process. | ## Objective
Edge industry users are facing obstacles to build an "out-of-the-box" RAG
application to meet both quality and performance requirements. Total Cost of
Ownership(TCO) and pipeline optimization techniques are the two main reasons
to block this process. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
734779a4-1eb3-4476-be0a-2bd3d7a58393 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 22 | opea-semantic-v1 | 94f10c50f8411663 | "model_path": "./bge_ov_reranker", "device": "auto" } } ], "generator": { "model": { "model_id": "qwen2-7b-instruct", "model_path": "./qwen2-7b-instruct/INT4_compressed_weights", "device": "auto" }, "prompt_path" : "./data/default_prompt.txt" }, "active": "True" }
### UI | ai_ref_knowledge | OPEA Documentation | "model_path": "./bge_ov_reranker", "device": "auto" } } ], "generator": { "model": { "model_id": "qwen2-7b-instruct", "model_path": "./qwen2-7b-instruct/INT4_compressed_weights", "device": "auto" }, "prompt_path" : "./data/default_prompt.txt" }, "active": "True" }
### UI | "model_path": "./bge_ov_reranker", "device": "auto" } } ], "generator": { "model": { "model_id": "qwen2-7b-instruct", "model_path": "./qwen2-7b-instruct/INT4_compressed_weights", "device": "auto" }, "prompt_path" : "./data/default_prompt.txt" }, "active": "True" }
### UI | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
75bc6c20-85c7-4f70-9735-e87a526d5ebc | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 8 | opea-semantic-v1 | ca37ec473241f620 | Retrieval-Augmented Generation system for edge solutions. It is designed to curate the RAG pipeline to meet hardware requirements at edge with garanteed quality and performance.
From quality perspective, EC-RAG is tunable in the indexing, retrieving,
reranking and generation stages for particular edge use cases. From p... | ai_ref_knowledge | OPEA Documentation | Retrieval-Augmented Generation system for edge solutions. It is designed to curate the RAG pipeline to meet hardware requirements at edge with garanteed quality and performance.
From quality perspective, EC-RAG is tunable in the indexing, retrieving,
reranking and generation stages for particular edge use cases. From p... | Retrieval-Augmented Generation system for edge solutions. It is designed to curate the RAG pipeline to meet hardware requirements at edge with garanteed quality and performance.
From quality perspective, EC-RAG is tunable in the indexing, retrieving,
reranking and generation stages for particular edge use cases. From p... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7808f17a-6f97-4544-8437-0ab7c2575dfe | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 18 | opea-semantic-v1 | 41370a5566ec3984 | Pipeline object(s) | | | Update pipelines | PATCH | /v1/settings/pipelines/{id} | Pipeline object | | Remove a pipeline | DELETE | /v1/settings/pipelines/{id} | |
### /v1/settings/models | ai_ref_knowledge | OPEA Documentation | Pipeline object(s) | | | Update pipelines | PATCH | /v1/settings/pipelines/{id} | Pipeline object | | Remove a pipeline | DELETE | /v1/settings/pipelines/{id} | |
### /v1/settings/models | Pipeline object(s) | | | Update pipelines | PATCH | /v1/settings/pipelines/{id} | Pipeline object | | Remove a pipeline | DELETE | /v1/settings/pipelines/{id} | |
### /v1/settings/models | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
877832ec-1c57-4e9b-857b-2937e135843e | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 10 | opea-semantic-v1 | 46b6f74920d3dda5 | ## Design Proposal
EC-RAG is composed of the following components:
- UI for doc loading and interactive chatbot. - Gateway
- Mega-service with a single micro-services for the tunable* EC-RAG pipeline. - LLM serving microservice optimized for Intel(R) Iris(R) Xe Graphics and Intel(R) Arc(TM) A-Series
Graphics
- VectorDB... | ai_ref_knowledge | OPEA Documentation | ## Design Proposal
EC-RAG is composed of the following components:
- UI for doc loading and interactive chatbot. - Gateway
- Mega-service with a single micro-services for the tunable* EC-RAG pipeline. - LLM serving microservice optimized for Intel(R) Iris(R) Xe Graphics and Intel(R) Arc(TM) A-Series
Graphics
- VectorDB... | ## Design Proposal
EC-RAG is composed of the following components:
- UI for doc loading and interactive chatbot. - Gateway
- Mega-service with a single micro-services for the tunable* EC-RAG pipeline. - LLM serving microservice optimized for Intel(R) Iris(R) Xe Graphics and Intel(R) Arc(TM) A-Series
Graphics
- VectorDB... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9715b0aa-c400-4867-95ea-8485c2601f84 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 19 | opea-semantic-v1 | 7a4ebb8455d642ff | ### /v1/settings/models
| Description | Action | Endpoint | Data Schema |
| --------------- | ------ | -------------------------- | --------------- |
| Load models | POST | /v1/settings/models | Model object |
| Get/list models | GET | /v1/settings/models(/{id}) | Model object(s) |
| Update models | PATCH | /v1/setting... | ai_ref_knowledge | OPEA Documentation | ### /v1/settings/models
| Description | Action | Endpoint | Data Schema |
| --------------- | ------ | -------------------------- | --------------- |
| Load models | POST | /v1/settings/models | Model object |
| Get/list models | GET | /v1/settings/models(/{id}) | Model object(s) |
| Update models | PATCH | /v1/setting... | ### /v1/settings/models
| Description | Action | Endpoint | Data Schema |
| --------------- | ------ | -------------------------- | --------------- |
| Load models | POST | /v1/settings/models | Model object |
| Get/list models | GET | /v1/settings/models(/{id}) | Model object(s) |
| Update models | PATCH | /v1/setting... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9c36fdbd-c31e-4c88-9d32-e48826db3960 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 28 | opea-semantic-v1 | 11c755bb62e329cf | EC-RAG pipeline will be finished without Vector DB as persistent DB. Instead, FAISS will be used for vector search and keep vector store in memory.
In this phase, the LLM inferencing will happen in the pipeline until the LLM
serving microservice supports Intel(R) Iris(R) Xe Graphics and Intel(R) Arc(TM)
A-Series Graphi... | ai_ref_knowledge | OPEA Documentation | EC-RAG pipeline will be finished without Vector DB as persistent DB. Instead, FAISS will be used for vector search and keep vector store in memory.
In this phase, the LLM inferencing will happen in the pipeline until the LLM
serving microservice supports Intel(R) Iris(R) Xe Graphics and Intel(R) Arc(TM)
A-Series Graphi... | EC-RAG pipeline will be finished without Vector DB as persistent DB. Instead, FAISS will be used for vector search and keep vector store in memory.
In this phase, the LLM inferencing will happen in the pipeline until the LLM
serving microservice supports Intel(R) Iris(R) Xe Graphics and Intel(R) Arc(TM)
A-Series Graphi... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9e048279-4b39-4b34-bb55-bf3a7d249dc8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 20 | opea-semantic-v1 | 091205d89b42fe62 | | Model object(s) | | Update models | PATCH | /v1/settings/models/{id} | Model object | | Remove a model | DELETE | /v1/settings/models/{id} | |
## Pipeline configuration example | ai_ref_knowledge | OPEA Documentation | | Model object(s) | | Update models | PATCH | /v1/settings/models/{id} | Model object | | Remove a model | DELETE | /v1/settings/models/{id} | |
## Pipeline configuration example | | Model object(s) | | Update models | PATCH | /v1/settings/models/{id} | Model object | | Remove a model | DELETE | /v1/settings/models/{id} | |
## Pipeline configuration example | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
adb2a677-db8f-42ce-8b2e-810d7757c359 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 15 | opea-semantic-v1 | 4059e65f259d9d7d | ### /v1/data
| Description | Action | Endpoint | Data Schema |
| ------------- | ------ | ------------- | ------------------ |
| Upload a file | POST | /v1/data | FastAPI.UploadFile |
| List files | GET | /v1/data | |
| Remove | DELETE | /v1/data/{id} | | | ai_ref_knowledge | OPEA Documentation | ### /v1/data
| Description | Action | Endpoint | Data Schema |
| ------------- | ------ | ------------- | ------------------ |
| Upload a file | POST | /v1/data | FastAPI.UploadFile |
| List files | GET | /v1/data | |
| Remove | DELETE | /v1/data/{id} | | | ### /v1/data
| Description | Action | Endpoint | Data Schema |
| ------------- | ------ | ------------- | ------------------ |
| Upload a file | POST | /v1/data | FastAPI.UploadFile |
| List files | GET | /v1/data | |
| Remove | DELETE | /v1/data/{id} | | | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b8ce8a19-fce9-4515-a3a3-614073945de1 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 4 | opea-semantic-v1 | db2212685326e6e7 | as sophisticated RAG pipeline for their data. Thus, the RAG pipeline at edge needs to be highly curated for underlying hardwares and suitable models accordingly.
### RAG Pipeline Optimization Techniques | ai_ref_knowledge | OPEA Documentation | as sophisticated RAG pipeline for their data. Thus, the RAG pipeline at edge needs to be highly curated for underlying hardwares and suitable models accordingly.
### RAG Pipeline Optimization Techniques | as sophisticated RAG pipeline for their data. Thus, the RAG pipeline at edge needs to be highly curated for underlying hardwares and suitable models accordingly.
### RAG Pipeline Optimization Techniques | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b8f7f653-760c-49d2-bc6d-292140bca1cf | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 24 | opea-semantic-v1 | 6221e8ce03c88ec6 | is able to select the models as well as input parameters in different stages for the pipeline. The chatbox is also integrated in the UI.
EC-RAG UI - Model Condiguration
 | ai_ref_knowledge | OPEA Documentation | is able to select the models as well as input parameters in different stages for the pipeline. The chatbox is also integrated in the UI.
EC-RAG UI - Model Condiguration
 | is able to select the models as well as input parameters in different stages for the pipeline. The chatbox is also integrated in the UI.
EC-RAG UI - Model Condiguration
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cb12684f-3455-40de-a178-e60c59dcf030 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 6 | opea-semantic-v1 | f1845ad68d0ada9f | the optimization techniques may not intuitively reflect to metrics improvements. E.g., recrusive retrieval may contribute to improving the recall and context relevancy, or may not.
## Motivation | ai_ref_knowledge | OPEA Documentation | the optimization techniques may not intuitively reflect to metrics improvements. E.g., recrusive retrieval may contribute to improving the recall and context relevancy, or may not.
## Motivation | the optimization techniques may not intuitively reflect to metrics improvements. E.g., recrusive retrieval may contribute to improving the recall and context relevancy, or may not.
## Motivation | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cfb2b050-ca4d-4235-a36e-8a4992af04a6 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 14 | opea-semantic-v1 | 762876d31bde6fd5 | The EC-RAG pipeline will expose 3 types of REST API endpoint: - **/v1/data** for indexing - **/v1/settings** for configuration - **/v1/chatqna** for inferencing
### /v1/data | ai_ref_knowledge | OPEA Documentation | The EC-RAG pipeline will expose 3 types of REST API endpoint: - **/v1/data** for indexing - **/v1/settings** for configuration - **/v1/chatqna** for inferencing
### /v1/data | The EC-RAG pipeline will expose 3 types of REST API endpoint: - **/v1/data** for indexing - **/v1/settings** for configuration - **/v1/chatqna** for inferencing
### /v1/data | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d1d52881-e745-42a7-8455-9e746c52d465 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 5 | opea-semantic-v1 | 5f918733ea3dd32a | ### RAG Pipeline Optimization Techniques
Tuning RAG pipeline is a systematic problem. First, the quality depends on the
result of each stage in the pipeline as well as the end-to-end outcome. Second,
optimization could be a trade-off among the metrics. It is difficult to decide one
answer is better than another if it i... | ai_ref_knowledge | OPEA Documentation | ### RAG Pipeline Optimization Techniques
Tuning RAG pipeline is a systematic problem. First, the quality depends on the
result of each stage in the pipeline as well as the end-to-end outcome. Second,
optimization could be a trade-off among the metrics. It is difficult to decide one
answer is better than another if it i... | ### RAG Pipeline Optimization Techniques
Tuning RAG pipeline is a systematic problem. First, the quality depends on the
result of each stage in the pipeline as well as the end-to-end outcome. Second,
optimization could be a trade-off among the metrics. It is difficult to decide one
answer is better than another if it i... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d3f7b0b4-4b42-4c2b-b9ee-88369efde8bc | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 23 | opea-semantic-v1 | e67578e1e3a76340 | ### UI
The EC-RAG UI is gradio. The user is able to select the models as well as input
parameters in different stages for the pipeline. The chatbox is also integrated
in the UI. | ai_ref_knowledge | OPEA Documentation | ### UI
The EC-RAG UI is gradio. The user is able to select the models as well as input
parameters in different stages for the pipeline. The chatbox is also integrated
in the UI. | ### UI
The EC-RAG UI is gradio. The user is able to select the models as well as input
parameters in different stages for the pipeline. The chatbox is also integrated
in the UI. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e2ccbce7-2588-44fb-8763-c7a6d0159f72 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 26 | opea-semantic-v1 | 965d15e764d8adf6 | megaservice and microservice are compatible with the existing OPEA GenAIExamples and GenAIComps repos. The EC-RAG leverages the LLM microservice and the VectorDB microservice from GenAIComps.
## Miscellaneous | ai_ref_knowledge | OPEA Documentation | megaservice and microservice are compatible with the existing OPEA GenAIExamples and GenAIComps repos. The EC-RAG leverages the LLM microservice and the VectorDB microservice from GenAIComps.
## Miscellaneous | megaservice and microservice are compatible with the existing OPEA GenAIExamples and GenAIComps repos. The EC-RAG leverages the LLM microservice and the VectorDB microservice from GenAIComps.
## Miscellaneous | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f556ff0c-61d9-4601-a221-2ca74beab427 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 11 | opea-semantic-v1 | 00ba7c3c04e1cb0e | Arc(TM) A-Series Graphics - VectorDB microservice optimized for Intel(R) Iris(R) Xe Graphics and/or Intel(R) Arc(TM) A-Series - Docker compose file to launch the UI, Mega/Micro-services
> [!NOTE]
> *Advanced tuning EC-RAG will need a tool co-piloting with the pipeline which will be described in
> a separate doc | ai_ref_knowledge | OPEA Documentation | Arc(TM) A-Series Graphics - VectorDB microservice optimized for Intel(R) Iris(R) Xe Graphics and/or Intel(R) Arc(TM) A-Series - Docker compose file to launch the UI, Mega/Micro-services
> [!NOTE]
> *Advanced tuning EC-RAG will need a tool co-piloting with the pipeline which will be described in
> a separate doc | Arc(TM) A-Series Graphics - VectorDB microservice optimized for Intel(R) Iris(R) Xe Graphics and/or Intel(R) Arc(TM) A-Series - Docker compose file to launch the UI, Mega/Micro-services
> [!NOTE]
> *Advanced tuning EC-RAG will need a tool co-piloting with the pipeline which will be described in
> a separate doc | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ff4da574-1445-402d-93f4-c40d9aa1dbbe | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-21-GenAIExample-002-Edge_Craft_RAG.md | unknown | 7861e580-da05-4949-bca5-c8d24607a6b9 | 17 | opea-semantic-v1 | b43e8ad7e85a7add | ### /v1/settings/pipelines
| Description | Action | Endpoint | Data Schema |
| ------------------ | ------ | ----------------------------- | ------------------ |
| Setup a pipeline | POST | /v1/settings/pipelines | Pipeline object |
| Get/list pipelines | GET | /v1/settings/pipelines(/{id}) | Pipeline object(s) | |
| U... | ai_ref_knowledge | OPEA Documentation | ### /v1/settings/pipelines
| Description | Action | Endpoint | Data Schema |
| ------------------ | ------ | ----------------------------- | ------------------ |
| Setup a pipeline | POST | /v1/settings/pipelines | Pipeline object |
| Get/list pipelines | GET | /v1/settings/pipelines(/{id}) | Pipeline object(s) | |
| U... | ### /v1/settings/pipelines
| Description | Action | Endpoint | Data Schema |
| ------------------ | ------ | ----------------------------- | ------------------ |
| Setup a pipeline | POST | /v1/settings/pipelines | Pipeline object |
| Get/list pipelines | GET | /v1/settings/pipelines(/{id}) | Pipeline object(s) | |
| U... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
02d5fee5-a26a-447d-b2e7-cd29b3937cd2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 21 | opea-semantic-v1 | 520ed646feb91fa6 | The response from the query is: * Text (already supported) * Video clip (already supported) * Single image frame (proposed) * Spoken audio file (proposed)
The [ASR microservice](https://github.com/opea-project/GenAIComps/blob/main/comps/asr/whisper/README.md) which uses the
whisper model, converts speech to text and pr... | ai_ref_knowledge | OPEA Documentation | The response from the query is: * Text (already supported) * Video clip (already supported) * Single image frame (proposed) * Spoken audio file (proposed)
The [ASR microservice](https://github.com/opea-project/GenAIComps/blob/main/comps/asr/whisper/README.md) which uses the
whisper model, converts speech to text and pr... | The response from the query is: * Text (already supported) * Video clip (already supported) * Single image frame (proposed) * Spoken audio file (proposed)
The [ASR microservice](https://github.com/opea-project/GenAIComps/blob/main/comps/asr/whisper/README.md) which uses the
whisper model, converts speech to text and pr... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
04d1e9f8-cd26-4248-b04b-3b473149dea3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 18 | opea-semantic-v1 | 9853dffde7b3a424 | `6007:/v1/dataprep/get_videos` becomes `6007:/v1/dataprep/get_files` | Multimodal | Lists names of uploaded files. | | `6007:/v1/dataprep/delete_videos` becomes `6007:/v1/dataprep/delete_files` | Multimodal | Deletes all the uploaded files. |
### User Query | ai_ref_knowledge | OPEA Documentation | `6007:/v1/dataprep/get_videos` becomes `6007:/v1/dataprep/get_files` | Multimodal | Lists names of uploaded files. | | `6007:/v1/dataprep/delete_videos` becomes `6007:/v1/dataprep/delete_files` | Multimodal | Deletes all the uploaded files. |
### User Query | `6007:/v1/dataprep/get_videos` becomes `6007:/v1/dataprep/get_files` | Multimodal | Lists names of uploaded files. | | `6007:/v1/dataprep/delete_videos` becomes `6007:/v1/dataprep/delete_files` | Multimodal | Deletes all the uploaded files. |
### User Query | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
05687e10-370f-4f9d-88ab-9fdcbe1bb931 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 46 | opea-semantic-v1 | d3ff54224e283a07 | in a longer page with multiple sections for the different file types and would benefit users who prefer having to scroll over having to click.
## Compatibility | ai_ref_knowledge | OPEA Documentation | in a longer page with multiple sections for the different file types and would benefit users who prefer having to scroll over having to click.
## Compatibility | in a longer page with multiple sections for the different file types and would benefit users who prefer having to scroll over having to click.
## Compatibility | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0b1048bb-54ca-4cda-9467-58ea52236462 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 3 | opea-semantic-v1 | 68354ce6508d052c | explains, enterprises use multimodal data and the proposed enhancement will increase the variety of use cases that the MultimodalQnA example will be able to support.
Expanding on the types of supported data types will enable use cases such as:
1. **Voice Query and Response**: A user wants to query and chat with a multi... | ai_ref_knowledge | OPEA Documentation | explains, enterprises use multimodal data and the proposed enhancement will increase the variety of use cases that the MultimodalQnA example will be able to support.
Expanding on the types of supported data types will enable use cases such as:
1. **Voice Query and Response**: A user wants to query and chat with a multi... | explains, enterprises use multimodal data and the proposed enhancement will increase the variety of use cases that the MultimodalQnA example will be able to support.
Expanding on the types of supported data types will enable use cases such as:
1. **Voice Query and Response**: A user wants to query and chat with a multi... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0f30c11e-5552-4180-9079-3bca8f2dd1f7 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 33 | opea-semantic-v1 | a21b82904c2d0a3b | ### UI
The existing UI shows two modes of video upload capability - with transcripts and with captions, on different interface
tabs - and a main chat tab holding the text QnA conversation, a video clip area populated from the first response of a
chat session, a small text box for queries, a submit button, and a clear b... | ai_ref_knowledge | OPEA Documentation | ### UI
The existing UI shows two modes of video upload capability - with transcripts and with captions, on different interface
tabs - and a main chat tab holding the text QnA conversation, a video clip area populated from the first response of a
chat session, a small text box for queries, a submit button, and a clear b... | ### UI
The existing UI shows two modes of video upload capability - with transcripts and with captions, on different interface
tabs - and a main chat tab holding the text QnA conversation, a video clip area populated from the first response of a
chat session, a small text box for queries, a submit button, and a clear b... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
11c99ae0-3c89-40d7-8b79-354728c354e3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 32 | opea-semantic-v1 | 9c857ac37d264561 | the whisper model. After getting the text, the rest of the embedding microservice flow would work the same as it would for a text query.
### UI | ai_ref_knowledge | OPEA Documentation | the whisper model. After getting the text, the rest of the embedding microservice flow would work the same as it would for a text query.
### UI | the whisper model. After getting the text, the rest of the embedding microservice flow would work the same as it would for a text query.
### UI | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1258280c-b5f6-4dee-b6fa-400d8ae6515f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 20 | opea-semantic-v1 | b388640b67ae4784 | the MultimodalQnA megaservice. From the user's perspective, the query can be: * Text (already supported) * Spoken audio files (proposed) * Image and text (proposed)
The response from the query is:
* Text (already supported)
* Video clip (already supported)
* Single image frame (proposed)
* Spoken audio file (proposed) | ai_ref_knowledge | OPEA Documentation | the MultimodalQnA megaservice. From the user's perspective, the query can be: * Text (already supported) * Spoken audio files (proposed) * Image and text (proposed)
The response from the query is:
* Text (already supported)
* Video clip (already supported)
* Single image frame (proposed)
* Spoken audio file (proposed) | the MultimodalQnA megaservice. From the user's perspective, the query can be: * Text (already supported) * Spoken audio files (proposed) * Image and text (proposed)
The response from the query is:
* Text (already supported)
* Video clip (already supported)
* Single image frame (proposed)
* Spoken audio file (proposed) | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
148a157c-b462-4793-a4ed-044b1e275aaa | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 0 | opea-semantic-v1 | 26b756e1655f09b1 | ## Objective
The [MultimodalQnA](https://github.com/opea-project/GenAIExamples/tree/main/MultimodalQnA) megaservice in
[GenAIExamples](https://github.com/opea-project/GenAIExamples) currently supports text queries with a response based on
the context derived from a collection of videos. This RFC expands upon that and p... | ai_ref_knowledge | OPEA Documentation | ## Objective
The [MultimodalQnA](https://github.com/opea-project/GenAIExamples/tree/main/MultimodalQnA) megaservice in
[GenAIExamples](https://github.com/opea-project/GenAIExamples) currently supports text queries with a response based on
the context derived from a collection of videos. This RFC expands upon that and p... | ## Objective
The [MultimodalQnA](https://github.com/opea-project/GenAIExamples/tree/main/MultimodalQnA) megaservice in
[GenAIExamples](https://github.com/opea-project/GenAIExamples) currently supports text queries with a response based on
the context derived from a collection of videos. This RFC expands upon that and p... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1709efa9-36bc-489c-a15b-5c6bc78c4b39 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 35 | opea-semantic-v1 | 8ac3a7252311f235 | #### UI Changes
1. Modify the main chat screen with a dynamic media display area capable of supporting video, image, or audio results
and adjusting automatically when a new type is returned by the gateway. 1. Modify the query text box to allow multimodal file uploads in addition to text (likely with the Gradio
Multimod... | ai_ref_knowledge | OPEA Documentation | #### UI Changes
1. Modify the main chat screen with a dynamic media display area capable of supporting video, image, or audio results
and adjusting automatically when a new type is returned by the gateway. 1. Modify the query text box to allow multimodal file uploads in addition to text (likely with the Gradio
Multimod... | #### UI Changes
1. Modify the main chat screen with a dynamic media display area capable of supporting video, image, or audio results
and adjusting automatically when a new type is returned by the gateway. 1. Modify the query text box to allow multimodal file uploads in addition to text (likely with the Gradio
Multimod... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1be35518-83eb-4cdb-af56-bb8f158fd80e | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 15 | opea-semantic-v1 | 7c36edeba2c450e0 | The table below lists the endpoints for the multimodal redis langchain data prep microservice that will be changing with this proposal.
| Endpoint | Data type | Description |
|----------|-----------|-------------|
| `6007:/v1/videos_with_transcripts` becomes `6007:/v1/ingest_with_text` | Videos with transcripts and ima... | ai_ref_knowledge | OPEA Documentation | The table below lists the endpoints for the multimodal redis langchain data prep microservice that will be changing with this proposal.
| Endpoint | Data type | Description |
|----------|-----------|-------------|
| `6007:/v1/videos_with_transcripts` becomes `6007:/v1/ingest_with_text` | Videos with transcripts and ima... | The table below lists the endpoints for the multimodal redis langchain data prep microservice that will be changing with this proposal.
| Endpoint | Data type | Description |
|----------|-----------|-------------|
| `6007:/v1/videos_with_transcripts` becomes `6007:/v1/ingest_with_text` | Videos with transcripts and ima... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1db2e162-969b-4d1f-a56c-753cee125442 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 17 | opea-semantic-v1 | b2400aeb609851e6 | be used to generate a caption for the image. The data and metadata are prepared for ingestion and then added to the Redis vector store.
|
| `6007:/v1/ingest_pdf` | PDF files | Ingests a PDF and then uses these [utils](https://github.com/opea-project/GenAIComps/blob/main/comps/dataprep/utils.py) to extract chunks of tex... | ai_ref_knowledge | OPEA Documentation | be used to generate a caption for the image. The data and metadata are prepared for ingestion and then added to the Redis vector store.
|
| `6007:/v1/ingest_pdf` | PDF files | Ingests a PDF and then uses these [utils](https://github.com/opea-project/GenAIComps/blob/main/comps/dataprep/utils.py) to extract chunks of tex... | be used to generate a caption for the image. The data and metadata are prepared for ingestion and then added to the Redis vector store.
|
| `6007:/v1/ingest_pdf` | PDF files | Ingests a PDF and then uses these [utils](https://github.com/opea-project/GenAIComps/blob/main/comps/dataprep/utils.py) to extract chunks of tex... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1e2d3039-87b4-4002-819f-83c5e7a24a92 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 38 | opea-semantic-v1 | 759ab97a863d648b | #### UI Mockups
 and [user queries](#user-query) are... | ai_ref_knowledge | OPEA Documentation | There are two phases in the MultimodalQnA example that need to be considered: * Data ingestion and prep * User query
Both of these phases are affected by the enhancements in this RFC. The design for expanding the types of multimodal data
for [data ingestion](#data-ingestion-and-prep) and [user queries](#user-query) are... | There are two phases in the MultimodalQnA example that need to be considered: * Data ingestion and prep * User query
Both of these phases are affected by the enhancements in this RFC. The design for expanding the types of multimodal data
for [data ingestion](#data-ingestion-and-prep) and [user queries](#user-query) are... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
275f9916-5311-4852-aa1a-6ee87093dc09 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 8 | opea-semantic-v1 | 84d409cf86982530 | this RFC. The design for expanding the types of multimodal data for [data ingestion](#data-ingestion-and-prep) and [user queries](#user-query) are outlined in the next couple of sections.
There is also a Gradio user interface (UI) that allows the user to both upload data for ingestion and submit queries
based on the co... | ai_ref_knowledge | OPEA Documentation | this RFC. The design for expanding the types of multimodal data for [data ingestion](#data-ingestion-and-prep) and [user queries](#user-query) are outlined in the next couple of sections.
There is also a Gradio user interface (UI) that allows the user to both upload data for ingestion and submit queries
based on the co... | this RFC. The design for expanding the types of multimodal data for [data ingestion](#data-ingestion-and-prep) and [user queries](#user-query) are outlined in the next couple of sections.
There is also a Gradio user interface (UI) that allows the user to both upload data for ingestion and submit queries
based on the co... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2eb43b6a-ff67-48fd-aedf-5a0fb09c9387 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 39 | opea-semantic-v1 | 40730f5024396bd9 |             
explains, enterprises use multimodal data and the proposed enhancement will increase the variety of use cases that the
MultimodalQnA example will be able to support. | ai_ref_knowledge | OPEA Documentation | ## Motivation
As the [Multimodal RAG RFC](https://github.com/opea-project/docs/blob/01597aabeaf4c5d171bdc8cd9f7bccdd9e64f697/community/rfcs/MM-RAG-RFG.md)
explains, enterprises use multimodal data and the proposed enhancement will increase the variety of use cases that the
MultimodalQnA example will be able to support. | ## Motivation
As the [Multimodal RAG RFC](https://github.com/opea-project/docs/blob/01597aabeaf4c5d171bdc8cd9f7bccdd9e64f697/community/rfcs/MM-RAG-RFG.md)
explains, enterprises use multimodal data and the proposed enhancement will increase the variety of use cases that the
MultimodalQnA example will be able to support. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3d423747-9325-4755-b373-9cde354a145e | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 4 | opea-semantic-v1 | c458bd3729a98be9 | labels, such as "normal" and “abnormal” radiology images, or user-provided captions, like radiologist's notes, and then query with a new image to find similar ones.
After retrieving the most similar image, the system could predict the new image's label (i.e. assist with diagnosis). 1. **QnA with Multimodal PDFs**: A us... | ai_ref_knowledge | OPEA Documentation | labels, such as "normal" and “abnormal” radiology images, or user-provided captions, like radiologist's notes, and then query with a new image to find similar ones.
After retrieving the most similar image, the system could predict the new image's label (i.e. assist with diagnosis). 1. **QnA with Multimodal PDFs**: A us... | labels, such as "normal" and “abnormal” radiology images, or user-provided captions, like radiologist's notes, and then query with a new image to find similar ones.
After retrieving the most similar image, the system could predict the new image's label (i.e. assist with diagnosis). 1. **QnA with Multimodal PDFs**: A us... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
415c2d74-50d2-494d-86e3-f27a3c734a40 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 50 | opea-semantic-v1 | 0327e8cb6f23b597 | ### Development Phases
We have planned the following development phases based on the priority of the features and their development effort: | ai_ref_knowledge | OPEA Documentation | ### Development Phases
We have planned the following development phases based on the priority of the features and their development effort: | ### Development Phases
We have planned the following development phases based on the priority of the features and their development effort: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4314af2e-ef7f-4b18-b106-0351addc823b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 43 | opea-semantic-v1 | 6dafcb565ce06b33 | ## Alternatives Considered
The following alternatives can be considered:
* We are proposing to use the ASR microservice, which would add two more containers (`opea/asr` and `opea/whisper`/`opea/whisper-gaudi`)
to the `compose.yaml` file, and when using Gaudi, the whisper service container would use one HPU. Instead of... | ai_ref_knowledge | OPEA Documentation | ## Alternatives Considered
The following alternatives can be considered:
* We are proposing to use the ASR microservice, which would add two more containers (`opea/asr` and `opea/whisper`/`opea/whisper-gaudi`)
to the `compose.yaml` file, and when using Gaudi, the whisper service container would use one HPU. Instead of... | ## Alternatives Considered
The following alternatives can be considered:
* We are proposing to use the ASR microservice, which would add two more containers (`opea/asr` and `opea/whisper`/`opea/whisper-gaudi`)
to the `compose.yaml` file, and when using Gaudi, the whisper service container would use one HPU. Instead of... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
45efe249-597c-4c36-87fc-8779d6ac68e2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 36 | opea-semantic-v1 | 3f2f3621225b4d74 | the two modes of video upload into one tab with radio buttons that enable users to choose the correct ingestion endpoint for their videos. 1.
Add a new tab for image uploads with radio buttons allowing users to choose between caption generation and custom
label or caption, as well as a text box for uploading a custom l... | ai_ref_knowledge | OPEA Documentation | the two modes of video upload into one tab with radio buttons that enable users to choose the correct ingestion endpoint for their videos. 1.
Add a new tab for image uploads with radio buttons allowing users to choose between caption generation and custom
label or caption, as well as a text box for uploading a custom l... | the two modes of video upload into one tab with radio buttons that enable users to choose the correct ingestion endpoint for their videos. 1.
Add a new tab for image uploads with radio buttons allowing users to choose between caption generation and custom
label or caption, as well as a text box for uploading a custom l... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4c593878-0277-43d0-be1f-f725f6436a78 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 19 | opea-semantic-v1 | f6862e1ec952eb1a | ### User Query
After the vector database has been populated, the user can then submit a query to the MultimodalQnA megaservice. From
the user's perspective, the query can be:
* Text (already supported)
* Spoken audio files (proposed)
* Image and text (proposed) | ai_ref_knowledge | OPEA Documentation | ### User Query
After the vector database has been populated, the user can then submit a query to the MultimodalQnA megaservice. From
the user's perspective, the query can be:
* Text (already supported)
* Spoken audio files (proposed)
* Image and text (proposed) | ### User Query
After the vector database has been populated, the user can then submit a query to the MultimodalQnA megaservice. From
the user's perspective, the query can be:
* Text (already supported)
* Spoken audio files (proposed)
* Image and text (proposed) | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
57c45952-e55a-4711-9979-abc0708ade47 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 40 | opea-semantic-v1 | 81c12f12caefdfc4 | #### Model Selection
We are proposing an enhancement that allows the user to select the LVM model and embedding model. To do this,
we will enable the functionality for users to specify the container's entry point in such a way that enables a
user to pass in and change values of default script arguments for the respec... | ai_ref_knowledge | OPEA Documentation | #### Model Selection
We are proposing an enhancement that allows the user to select the LVM model and embedding model. To do this,
we will enable the functionality for users to specify the container's entry point in such a way that enables a
user to pass in and change values of default script arguments for the respec... | #### Model Selection
We are proposing an enhancement that allows the user to select the LVM model and embedding model. To do this,
we will enable the functionality for users to specify the container's entry point in such a way that enables a
user to pass in and change values of default script arguments for the respec... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
65ba7ac6-62ae-42cd-bce5-f315f950ba27 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 27 | opea-semantic-v1 | 7c70b9bf62478041 | #### MultimodalQnAGateway
Currently, the [MultimodalQnAGateway](https://github.com/opea-project/GenAIComps/blob/main/comps/cores/mega/gateway.py#L688)
class analyzes the input message from the request coming in to determine if it's a first query or a follow up query. Initial queries have a single prompt string, whereas... | ai_ref_knowledge | OPEA Documentation | #### MultimodalQnAGateway
Currently, the [MultimodalQnAGateway](https://github.com/opea-project/GenAIComps/blob/main/comps/cores/mega/gateway.py#L688)
class analyzes the input message from the request coming in to determine if it's a first query or a follow up query. Initial queries have a single prompt string, whereas... | #### MultimodalQnAGateway
Currently, the [MultimodalQnAGateway](https://github.com/opea-project/GenAIComps/blob/main/comps/cores/mega/gateway.py#L688)
class analyzes the input message from the request coming in to determine if it's a first query or a follow up query. Initial queries have a single prompt string, whereas... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
65c5305e-0d5f-4c1e-a922-9efb53b88441 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 51 | opea-semantic-v1 | 7640963a10723bbf | We have planned the following development phases based on the priority of the features and their development effort:
* Phase 1
* Data prep and ingestion:
* Accept image only
* Accept image and text
* Accept speech audio only
* Query enhancements:
* Accept speech audio only
* Other enhancements:
* Allow the user... | ai_ref_knowledge | OPEA Documentation | We have planned the following development phases based on the priority of the features and their development effort:
* Phase 1
* Data prep and ingestion:
* Accept image only
* Accept image and text
* Accept speech audio only
* Query enhancements:
* Accept speech audio only
* Other enhancements:
* Allow the user... | We have planned the following development phases based on the priority of the features and their development effort:
* Phase 1
* Data prep and ingestion:
* Accept image only
* Accept image and text
* Accept speech audio only
* Query enhancements:
* Accept speech audio only
* Other enhancements:
* Allow the user... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6d9f8a0c-bc41-4c06-8356-f7ad1414f25a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 47 | opea-semantic-v1 | f6faece97c735808 | ## Compatibility
Interface changes are being made to the following components:
* MultimodalQnA gateway
* Embeddings multimodal langchain
* Dataprep multimodal redis langchain | ai_ref_knowledge | OPEA Documentation | ## Compatibility
Interface changes are being made to the following components:
* MultimodalQnA gateway
* Embeddings multimodal langchain
* Dataprep multimodal redis langchain | ## Compatibility
Interface changes are being made to the following components:
* MultimodalQnA gateway
* Embeddings multimodal langchain
* Dataprep multimodal redis langchain | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
71acf562-0b95-4957-80fe-a0aca6724d3f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 6 | opea-semantic-v1 | 1b80034d707bbe2b | ## Design Proposal
There are two phases in the MultimodalQnA example that need to be considered:
* Data ingestion and prep
* User query | ai_ref_knowledge | OPEA Documentation | ## Design Proposal
There are two phases in the MultimodalQnA example that need to be considered:
* Data ingestion and prep
* User query | ## Design Proposal
There are two phases in the MultimodalQnA example that need to be considered:
* Data ingestion and prep
* User query | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7590aee8-c1b7-476d-bb86-ac30f8be6f6b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 34 | opea-semantic-v1 | e63e05181d8bef1d | Visually organize and emphasize the enhanced multimodal options for file upload, query input, and query response * Streamline some of the titles and text headings
We list each proposed change in detail below and then provide mockups of the new screens. | ai_ref_knowledge | OPEA Documentation | Visually organize and emphasize the enhanced multimodal options for file upload, query input, and query response * Streamline some of the titles and text headings
We list each proposed change in detail below and then provide mockups of the new screens. | Visually organize and emphasize the enhanced multimodal options for file upload, query input, and query response * Streamline some of the titles and text headings
We list each proposed change in detail below and then provide mockups of the new screens. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
776da5b5-c52f-44e1-9594-94ba08afb952 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 41 | opea-semantic-v1 | a8dc69a429f34ae8 | arguments in the `set_env.sh` with environment variables such as `LVM_MODEL` before being officially passed in and overwritten into the [dockerfile](https://github.com/opea-project/GenAIComps/blob/main/comps/lvms/llava/dependency/Dockerfile#L22) entry point when building compose.yaml.
The purpose of this change is to a... | ai_ref_knowledge | OPEA Documentation | arguments in the `set_env.sh` with environment variables such as `LVM_MODEL` before being officially passed in and overwritten into the [dockerfile](https://github.com/opea-project/GenAIComps/blob/main/comps/lvms/llava/dependency/Dockerfile#L22) entry point when building compose.yaml.
The purpose of this change is to a... | arguments in the `set_env.sh` with environment variables such as `LVM_MODEL` before being officially passed in and overwritten into the [dockerfile](https://github.com/opea-project/GenAIComps/blob/main/comps/lvms/llava/dependency/Dockerfile#L22) entry point when building compose.yaml.
The purpose of this change is to a... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
78c506e6-2a8f-4ea3-b41c-c84c335c3f26 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 25 | opea-semantic-v1 | b2b7e4c5fc17de24 | The details explaining the specific changes to these components are given in the sections below.
The option for providing a spoken response could be provided as a flag when starting the megaservice, simliar to how
ChatQnA is able to start with or without reranking, or with or without guardrails. If the MultimodalQnA me... | ai_ref_knowledge | OPEA Documentation | The details explaining the specific changes to these components are given in the sections below.
The option for providing a spoken response could be provided as a flag when starting the megaservice, simliar to how
ChatQnA is able to start with or without reranking, or with or without guardrails. If the MultimodalQnA me... | The details explaining the specific changes to these components are given in the sections below.
The option for providing a spoken response could be provided as a flag when starting the megaservice, simliar to how
ChatQnA is able to start with or without reranking, or with or without guardrails. If the MultimodalQnA me... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
78ecc09a-88e4-4fe0-9cec-d4cfdf643ff2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 10 | opea-semantic-v1 | 4d96c88d818f65ea | ### Data Ingestion and Prep
In the data ingestion and prep phase, a collection of multimodal data is uploaded to a vector database to be retrieved
and used as context for the subsequent queries. From a user's perspective, they will be able to upload:
* Videos with spoken audio (already supported)
* Videos without spoke... | ai_ref_knowledge | OPEA Documentation | ### Data Ingestion and Prep
In the data ingestion and prep phase, a collection of multimodal data is uploaded to a vector database to be retrieved
and used as context for the subsequent queries. From a user's perspective, they will be able to upload:
* Videos with spoken audio (already supported)
* Videos without spoke... | ### Data Ingestion and Prep
In the data ingestion and prep phase, a collection of multimodal data is uploaded to a vector database to be retrieved
and used as context for the subsequent queries. From a user's perspective, they will be able to upload:
* Videos with spoken audio (already supported)
* Videos without spoke... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
79bb95cf-b928-4298-9e0d-05de2e060b54 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 1 | opea-semantic-v1 | 2544e9de7a058be8 | expands upon that and proposes the addition of images, images with text, and audio data types for both the ingested data and the user query.
## Motivation | ai_ref_knowledge | OPEA Documentation | expands upon that and proposes the addition of images, images with text, and audio data types for both the ingested data and the user query.
## Motivation | expands upon that and proposes the addition of images, images with text, and audio data types for both the ingested data and the user query.
## Motivation | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7b134961-a58b-4fb8-b209-0fd59f6383e0 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 30 | opea-semantic-v1 | 64284ea1840f71a9 | #### Embedding Microservice
The [embedding microservice endpoint](https://github.com/opea-project/GenAIComps/blob/main/comps/embeddings/multimodal/multimodal_langchain/mm_embedding_mmei.py#L41)
gets input as a [`MultimodalDoc`](https://github.com/opea-project/GenAIComps/blob/main/comps/cores/proto/docarray.py#L66-L70).... | ai_ref_knowledge | OPEA Documentation | #### Embedding Microservice
The [embedding microservice endpoint](https://github.com/opea-project/GenAIComps/blob/main/comps/embeddings/multimodal/multimodal_langchain/mm_embedding_mmei.py#L41)
gets input as a [`MultimodalDoc`](https://github.com/opea-project/GenAIComps/blob/main/comps/cores/proto/docarray.py#L66-L70).... | #### Embedding Microservice
The [embedding microservice endpoint](https://github.com/opea-project/GenAIComps/blob/main/comps/embeddings/multimodal/multimodal_langchain/mm_embedding_mmei.py#L41)
gets input as a [`MultimodalDoc`](https://github.com/opea-project/GenAIComps/blob/main/comps/cores/proto/docarray.py#L66-L70).... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
81bf1b79-5a34-4112-b94a-107f0c7cebf2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 31 | opea-semantic-v1 | f322e5c17dd18d66 | a [`MultimodalDoc`](https://github.com/opea-project/GenAIComps/blob/main/comps/cores/proto/docarray.py#L66-L70). The `MultimodalDoc` is a union of: `TextDoc`, `ImageDoc`, and `TextImageDoc`. In order to accomodate audio input, we will add `Base64ByteStrDoc` to the union.
If the embedding service gets a `Base64ByteStrDo... | ai_ref_knowledge | OPEA Documentation | a [`MultimodalDoc`](https://github.com/opea-project/GenAIComps/blob/main/comps/cores/proto/docarray.py#L66-L70). The `MultimodalDoc` is a union of: `TextDoc`, `ImageDoc`, and `TextImageDoc`. In order to accomodate audio input, we will add `Base64ByteStrDoc` to the union.
If the embedding service gets a `Base64ByteStrDo... | a [`MultimodalDoc`](https://github.com/opea-project/GenAIComps/blob/main/comps/cores/proto/docarray.py#L66-L70). The `MultimodalDoc` is a union of: `TextDoc`, `ImageDoc`, and `TextImageDoc`. In order to accomodate audio input, we will add `Base64ByteStrDoc` to the union.
If the embedding service gets a `Base64ByteStrDo... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8614f568-ebae-4414-a570-f72e8d2adab3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 29 | opea-semantic-v1 | 1729f9ba13677520 | need to change the inital query from a string to a dictionary in order to comprehend data type and handle multiple items (image, text, audio).
#### Embedding Microservice | ai_ref_knowledge | OPEA Documentation | need to change the inital query from a string to a dictionary in order to comprehend data type and handle multiple items (image, text, audio).
#### Embedding Microservice | need to change the inital query from a string to a dictionary in order to comprehend data type and handle multiple items (image, text, audio).
#### Embedding Microservice | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8a2200a8-dd1a-4cc1-ab75-4fb7b829c818 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 13 | opea-semantic-v1 | 9c8b7c7fbdba013d | processed with a library such as [PyMuPDF (fitz)](https://pymupdf.readthedocs.io/). There is already an example of such PDF processing in [this dataprep microservice](https://github.com/opea-project/GenAIComps/tree/main/comps/dataprep/milvus/langchain) which may be reusable.
Spoken audio files can be translated to text... | ai_ref_knowledge | OPEA Documentation | processed with a library such as [PyMuPDF (fitz)](https://pymupdf.readthedocs.io/). There is already an example of such PDF processing in [this dataprep microservice](https://github.com/opea-project/GenAIComps/tree/main/comps/dataprep/milvus/langchain) which may be reusable.
Spoken audio files can be translated to text... | processed with a library such as [PyMuPDF (fitz)](https://pymupdf.readthedocs.io/). There is already an example of such PDF processing in [this dataprep microservice](https://github.com/opea-project/GenAIComps/tree/main/comps/dataprep/milvus/langchain) which may be reusable.
Spoken audio files can be translated to text... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9f4296fe-d18b-4a29-96d8-c50dd7c8e79e | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 37 | opea-semantic-v1 | 90ea1148f57ddef5 | labels or captions in the future. 1. Add a new tab for PDF uploads, which currently is envisioned as one endpoint without any input options.
#### UI Mockups | ai_ref_knowledge | OPEA Documentation | labels or captions in the future. 1. Add a new tab for PDF uploads, which currently is envisioned as one endpoint without any input options.
#### UI Mockups | labels or captions in the future. 1. Add a new tab for PDF uploads, which currently is envisioned as one endpoint without any input options.
#### UI Mockups | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9fad6a3b-2fbb-4cec-b308-b3f1739623a9 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 9 | opea-semantic-v1 | 4ea2ec876e995fdd | context in the database. The introduction of different data types will affect the UI design, and the proposed changes are discussed in the [UI section](#ui).
 | ai_ref_knowledge | OPEA Documentation | context in the database. The introduction of different data types will affect the UI design, and the proposed changes are discussed in the [UI section](#ui).
 | context in the database. The introduction of different data types will affect the UI design, and the proposed changes are discussed in the [UI section](#ui).
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a0fdd1de-067d-4d45-a2fe-d7e46b37c41a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 11 | opea-semantic-v1 | 1362384dc95597ed | * Videos with transcriptions (already supported) * Images with text (proposed) * Images without text (proposed) * Spoken audio files (proposed) * PDF files (proposed)
The [BridgeTower model](https://huggingface.co/BridgeTower/bridgetower-large-itm-mlm-gaudi) which is already utilized
by MultimodalQnA merges visual and ... | ai_ref_knowledge | OPEA Documentation | * Videos with transcriptions (already supported) * Images with text (proposed) * Images without text (proposed) * Spoken audio files (proposed) * PDF files (proposed)
The [BridgeTower model](https://huggingface.co/BridgeTower/bridgetower-large-itm-mlm-gaudi) which is already utilized
by MultimodalQnA merges visual and ... | * Videos with transcriptions (already supported) * Images with text (proposed) * Images without text (proposed) * Spoken audio files (proposed) * PDF files (proposed)
The [BridgeTower model](https://huggingface.co/BridgeTower/bridgetower-large-itm-mlm-gaudi) which is already utilized
by MultimodalQnA merges visual and ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bb60dc7b-38bf-4fd5-85b5-c9c03ca2019f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 48 | opea-semantic-v1 | 0501472c080b0cdb | Interface changes are being made to the following components: * MultimodalQnA gateway * Embeddings multimodal langchain * Dataprep multimodal redis langchain
At the time that this RFC is written, there aren't any other megaservices in GenAIExamples that are using the
[Embeddings multimodal langchain](https://github.com... | ai_ref_knowledge | OPEA Documentation | Interface changes are being made to the following components: * MultimodalQnA gateway * Embeddings multimodal langchain * Dataprep multimodal redis langchain
At the time that this RFC is written, there aren't any other megaservices in GenAIExamples that are using the
[Embeddings multimodal langchain](https://github.com... | Interface changes are being made to the following components: * MultimodalQnA gateway * Embeddings multimodal langchain * Dataprep multimodal redis langchain
At the time that this RFC is written, there aren't any other megaservices in GenAIExamples that are using the
[Embeddings multimodal langchain](https://github.com... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bef7c740-b4b1-4a5c-b522-4f5c192043d1 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 49 | opea-semantic-v1 | 82f82ad4b7267ce5 | search of the repo to make sure that other megaservices are not affected by our changes, and if they are we will make changes accordingly.
## Miscellaneous | ai_ref_knowledge | OPEA Documentation | search of the repo to make sure that other megaservices are not affected by our changes, and if they are we will make changes accordingly.
## Miscellaneous | search of the repo to make sure that other megaservices are not affected by our changes, and if they are we will make changes accordingly.
## Miscellaneous | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bf9b49e9-089d-4d90-9e2d-164b6fba5f35 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 42 | opea-semantic-v1 | 559693fa0b4edf95 | this change is to allow users to utilize more script arguments that currently are hard set to their default values when a container is built.
## Alternatives Considered | ai_ref_knowledge | OPEA Documentation | this change is to allow users to utilize more script arguments that currently are hard set to their default values when a container is built.
## Alternatives Considered | this change is to allow users to utilize more script arguments that currently are hard set to their default values when a container is built.
## Alternatives Considered | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c4075e44-8f2c-4b7a-89fe-e90d6dc53b8f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 23 | opea-semantic-v1 | 292553bf6da979a0 | The [TTS microservice](https://github.com/opea-project/GenAIComps/tree/main/comps/tts/speecht5) provides the capability to translate text to speech, which would allow the megaservice to return a spoken audio file response.
Changes to the user query flow will involve the following components:
* The [MultimodalQnA gatewa... | ai_ref_knowledge | OPEA Documentation | The [TTS microservice](https://github.com/opea-project/GenAIComps/tree/main/comps/tts/speecht5) provides the capability to translate text to speech, which would allow the megaservice to return a spoken audio file response.
Changes to the user query flow will involve the following components:
* The [MultimodalQnA gatewa... | The [TTS microservice](https://github.com/opea-project/GenAIComps/tree/main/comps/tts/speecht5) provides the capability to translate text to speech, which would allow the megaservice to return a spoken audio file response.
Changes to the user query flow will involve the following components:
* The [MultimodalQnA gatewa... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ca476a0c-2801-46cd-b08f-0901bce0fced | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 28 | opea-semantic-v1 | 8c215d06ffe0b9e4 | first query or a follow up query. Initial queries have a single prompt string, whereas follow up queries have a list of prompts and images.
When introducing different types of data for user queries, we will need to change the inital query from a string to a
dictionary in order to comprehend data type and handle multipl... | ai_ref_knowledge | OPEA Documentation | first query or a follow up query. Initial queries have a single prompt string, whereas follow up queries have a list of prompts and images.
When introducing different types of data for user queries, we will need to change the inital query from a string to a
dictionary in order to comprehend data type and handle multipl... | first query or a follow up query. Initial queries have a single prompt string, whereas follow up queries have a list of prompts and images.
When introducing different types of data for user queries, we will need to change the inital query from a string to a
dictionary in order to comprehend data type and handle multipl... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d242d8f7-ee8c-4c31-b60f-1344023e93fb | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 5 | opea-semantic-v1 | 8c293b1985b5bb51 | like books, journal articles, business reports, or travel brochures. The PDFs could contain images with or without captions and charts that include titles and descriptions.
## Design Proposal | ai_ref_knowledge | OPEA Documentation | like books, journal articles, business reports, or travel brochures. The PDFs could contain images with or without captions and charts that include titles and descriptions.
## Design Proposal | like books, journal articles, business reports, or travel brochures. The PDFs could contain images with or without captions and charts that include titles and descriptions.
## Design Proposal | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d485d9c2-b430-4b09-a3ea-8e717c36cc45 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 16 | opea-semantic-v1 | 811270565edfa253 | only | For videos with spoken audio, data prep extracts the audio from the video and then generates a transcript (.vtt) using the whisper model.
For audio only, the transcript would also be generated using the whisper model. The data and metadata are prepared for ingestion and then added to the Redis vector store. |
| ... | ai_ref_knowledge | OPEA Documentation | only | For videos with spoken audio, data prep extracts the audio from the video and then generates a transcript (.vtt) using the whisper model.
For audio only, the transcript would also be generated using the whisper model. The data and metadata are prepared for ingestion and then added to the Redis vector store. |
| ... | only | For videos with spoken audio, data prep extracts the audio from the video and then generates a transcript (.vtt) using the whisper model.
For audio only, the transcript would also be generated using the whisper model. The data and metadata are prepared for ingestion and then added to the Redis vector store. |
| ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
de369731-efd6-4c45-a7b0-3012b801b273 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 26 | opea-semantic-v1 | d0c5b61b4d26b030 | a second Dockerfile that uses that speech response flag in its entrypoint, and another docker compose yaml file that starts the `tts-service` and `speecht5-service` containers.
#### MultimodalQnAGateway | ai_ref_knowledge | OPEA Documentation | a second Dockerfile that uses that speech response flag in its entrypoint, and another docker compose yaml file that starts the `tts-service` and `speecht5-service` containers.
#### MultimodalQnAGateway | a second Dockerfile that uses that speech response flag in its entrypoint, and another docker compose yaml file that starts the `tts-service` and `speecht5-service` containers.
#### MultimodalQnAGateway | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ea00df3c-0d3a-4095-a1cb-99fb20f3a4be | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 12 | opea-semantic-v1 | c496fcc965c67f4c | video. Those frames and their metadata are stored in the vector store, which is used in a RAG pipeline as context for the user's queries.
The addition of image and text are analogous to the video frames and transcripts, and the
[CV2 VideoCapture](https://docs.opencv.org/3.4/d8/dfe/classcv_1_1VideoCapture.html#a949d90b7... | ai_ref_knowledge | OPEA Documentation | video. Those frames and their metadata are stored in the vector store, which is used in a RAG pipeline as context for the user's queries.
The addition of image and text are analogous to the video frames and transcripts, and the
[CV2 VideoCapture](https://docs.opencv.org/3.4/d8/dfe/classcv_1_1VideoCapture.html#a949d90b7... | video. Those frames and their metadata are stored in the vector store, which is used in a RAG pipeline as context for the user's queries.
The addition of image and text are analogous to the video frames and transcripts, and the
[CV2 VideoCapture](https://docs.opencv.org/3.4/d8/dfe/classcv_1_1VideoCapture.html#a949d90b7... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
eee124f1-b4b9-4936-9215-f8a0a32ca377 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 44 | opea-semantic-v1 | 579b9fc7b5e008e0 | performance benefits of Gaudi when converting speech-to-text with the whisper model. * In data prep, we could have separate endpoints for different types of media.
For example, instead of having
`/v1/ingest_with_text`, we could break that out into `/v1/videos_with_transcript` and `/v1/images_with_text`
separately. * ... | ai_ref_knowledge | OPEA Documentation | performance benefits of Gaudi when converting speech-to-text with the whisper model. * In data prep, we could have separate endpoints for different types of media.
For example, instead of having
`/v1/ingest_with_text`, we could break that out into `/v1/videos_with_transcript` and `/v1/images_with_text`
separately. * ... | performance benefits of Gaudi when converting speech-to-text with the whisper model. * In data prep, we could have separate endpoints for different types of media.
For example, instead of having
`/v1/ingest_with_text`, we could break that out into `/v1/videos_with_transcript` and `/v1/images_with_text`
separately. * ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f4679763-2345-4a68-aaeb-60f4a97f4c95 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 24 | opea-semantic-v1 | 7dadda9a91eae4cd | Changes to the user query flow will involve the following components: * The [MultimodalQnA gateway](#multimodalqnagateway) * The [embedding microservice](#embedding-microservice)
The details explaining the specific changes to these components are given in the sections below. | ai_ref_knowledge | OPEA Documentation | Changes to the user query flow will involve the following components: * The [MultimodalQnA gateway](#multimodalqnagateway) * The [embedding microservice](#embedding-microservice)
The details explaining the specific changes to these components are given in the sections below. | Changes to the user query flow will involve the following components: * The [MultimodalQnA gateway](#multimodalqnagateway) * The [embedding microservice](#embedding-microservice)
The details explaining the specific changes to these components are given in the sections below. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f584b2c5-8e41-474f-83a9-a1500133dee7 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 22 | opea-semantic-v1 | f17e8f4c462e48d9 | spoken audio queries. Once the audio has been converted to text, submitting the query would be no different than how the text queries work today.
The [TTS microservice](https://github.com/opea-project/GenAIComps/tree/main/comps/tts/speecht5) provides the capability
to translate text to speech, which would allow the meg... | ai_ref_knowledge | OPEA Documentation | spoken audio queries. Once the audio has been converted to text, submitting the query would be no different than how the text queries work today.
The [TTS microservice](https://github.com/opea-project/GenAIComps/tree/main/comps/tts/speecht5) provides the capability
to translate text to speech, which would allow the meg... | spoken audio queries. Once the audio has been converted to text, submitting the query would be no different than how the text queries work today.
The [TTS microservice](https://github.com/opea-project/GenAIComps/tree/main/comps/tts/speecht5) provides the capability
to translate text to speech, which would allow the meg... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fbc91e02-b716-4fd4-b866-c73e50fa89cd | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-02-GenAIExamples-001-Image_and_Audio_Support_in_MultimodalQnA.md | unknown | 3c14bcd7-ab0b-4cc7-b561-ac4c644ee8e4 | 45 | opea-semantic-v1 | 61f4e79bf4b65497 | add on `get_files` and `delete_files`. This would help to preserve some backwards compatibility for any applications outside of GenAIExamples who may be using those endpoints.
If we decide
to do it this way, we could add comments in the code and documentation about the eventual deprecation of
`delete_videos` and `get... | ai_ref_knowledge | OPEA Documentation | add on `get_files` and `delete_files`. This would help to preserve some backwards compatibility for any applications outside of GenAIExamples who may be using those endpoints.
If we decide
to do it this way, we could add comments in the code and documentation about the eventual deprecation of
`delete_videos` and `get... | add on `get_files` and `delete_files`. This would help to preserve some backwards compatibility for any applications outside of GenAIExamples who may be using those endpoints.
If we decide
to do it this way, we could add comments in the code and documentation about the eventual deprecation of
`delete_videos` and `get... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0439ae93-b1e6-41ec-acf5-17e9f1f2a9ce | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-20-OPEA-001-Haystack-Integration.md | unknown | 2feb1ab4-9df6-4dc3-bda6-f78e8e3d5503 | 7 | opea-semantic-v1 | e8a4019f786dc8e3 | its capabilities. To support this, several component wrappers need to be implemented in the first version of the integration (other wrappers will be added gradually):
1. OPEA Document Embedder | ai_ref_knowledge | OPEA Documentation | its capabilities. To support this, several component wrappers need to be implemented in the first version of the integration (other wrappers will be added gradually):
1. OPEA Document Embedder | its capabilities. To support this, several component wrappers need to be implemented in the first version of the integration (other wrappers will be added gradually):
1. OPEA Document Embedder | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
22caf5e5-6a4d-4b44-b5b5-93360be554b4 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-20-OPEA-001-Haystack-Integration.md | unknown | 2feb1ab4-9df6-4dc3-bda6-f78e8e3d5503 | 9 | opea-semantic-v1 | 8af81c95d2b1f691 | This component will receive an embedding and retrieve documents with similar emebddings using an OPEA retrieval microservice.
## Alternatives Considered | ai_ref_knowledge | OPEA Documentation | This component will receive an embedding and retrieve documents with similar emebddings using an OPEA retrieval microservice.
## Alternatives Considered | This component will receive an embedding and retrieve documents with similar emebddings using an OPEA retrieval microservice.
## Alternatives Considered | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
43db16a9-ee37-4e0c-b318-63bce24b3f65 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-20-OPEA-001-Haystack-Integration.md | unknown | 2feb1ab4-9df6-4dc3-bda6-f78e8e3d5503 | 4 | opea-semantic-v1 | 3f708f301aea02bb | ## Design Proposal
The idea is to create thin wrappers for OPEA components that will enable communicating with them using the existing REST API. The wrappers will match Haystack's API so that they could be used within Haystack pipelines. This will allow developers to seamlessly use OPEA components alongside other Hayst... | ai_ref_knowledge | OPEA Documentation | ## Design Proposal
The idea is to create thin wrappers for OPEA components that will enable communicating with them using the existing REST API. The wrappers will match Haystack's API so that they could be used within Haystack pipelines. This will allow developers to seamlessly use OPEA components alongside other Hayst... | ## Design Proposal
The idea is to create thin wrappers for OPEA components that will enable communicating with them using the existing REST API. The wrappers will match Haystack's API so that they could be used within Haystack pipelines. This will allow developers to seamlessly use OPEA components alongside other Hayst... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
51536b85-9fbd-4ba8-9453-af56a9b8f430 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-20-OPEA-001-Haystack-Integration.md | unknown | 2feb1ab4-9df6-4dc3-bda6-f78e8e3d5503 | 6 | opea-semantic-v1 | 0df9a86435c1d6fa | be hosted in OPEA's GenAIComps repo under a new directory called Integrations. The package itself will be uploaded to [PyPi](https://pypi.org/) to allow for easy installation.
Following a discussion with Haystack's technical team, it was agreed that a ChatQnA example, using this OPEA integration, would be a good way to... | ai_ref_knowledge | OPEA Documentation | be hosted in OPEA's GenAIComps repo under a new directory called Integrations. The package itself will be uploaded to [PyPi](https://pypi.org/) to allow for easy installation.
Following a discussion with Haystack's technical team, it was agreed that a ChatQnA example, using this OPEA integration, would be a good way to... | be hosted in OPEA's GenAIComps repo under a new directory called Integrations. The package itself will be uploaded to [PyPi](https://pypi.org/) to allow for easy installation.
Following a discussion with Haystack's technical team, it was agreed that a ChatQnA example, using this OPEA integration, would be a good way to... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
66e1ab64-3d59-4ef9-b000-76c50b74aa95 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-20-OPEA-001-Haystack-Integration.md | unknown | 2feb1ab4-9df6-4dc3-bda6-f78e8e3d5503 | 10 | opea-semantic-v1 | 82448189034704f8 | ## Miscs
Once implemented, the Haystack team list the OPEA integration on their [integrations page](https://haystack.deepset.ai/integrations) which will allow for easier discovery. Haystack, in collaboration with Intel, will also publish a technical blog post showcasing a ChatQnA example using this integration (similar... | ai_ref_knowledge | OPEA Documentation | ## Miscs
Once implemented, the Haystack team list the OPEA integration on their [integrations page](https://haystack.deepset.ai/integrations) which will allow for easier discovery. Haystack, in collaboration with Intel, will also publish a technical blog post showcasing a ChatQnA example using this integration (similar... | ## Miscs
Once implemented, the Haystack team list the OPEA integration on their [integrations page](https://haystack.deepset.ai/integrations) which will allow for easier discovery. Haystack, in collaboration with Intel, will also publish a technical blog post showcasing a ChatQnA example using this integration (similar... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8c36de43-cbe5-4247-9e0d-d26076950a39 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-20-OPEA-001-Haystack-Integration.md | unknown | 2feb1ab4-9df6-4dc3-bda6-f78e8e3d5503 | 5 | opea-semantic-v1 | d914dcd9c086ffce | match Haystack's API so that they could be used within Haystack pipelines. This will allow developers to seamlessly use OPEA components alongside other Haystack components.
The integration will be implemented as a Python package (similar to other Haystack integrations). The source code will be hosted in OPEA's GenAICom... | ai_ref_knowledge | OPEA Documentation | match Haystack's API so that they could be used within Haystack pipelines. This will allow developers to seamlessly use OPEA components alongside other Haystack components.
The integration will be implemented as a Python package (similar to other Haystack integrations). The source code will be hosted in OPEA's GenAICom... | match Haystack's API so that they could be used within Haystack pipelines. This will allow developers to seamlessly use OPEA components alongside other Haystack components.
The integration will be implemented as a Python package (similar to other Haystack integrations). The source code will be hosted in OPEA's GenAICom... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9ffb0d9f-a7f6-4826-a7ca-d33929e91059 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-20-OPEA-001-Haystack-Integration.md | unknown | 2feb1ab4-9df6-4dc3-bda6-f78e8e3d5503 | 8 | opea-semantic-v1 | 5ef879d6c582d000 | 4. OPEA Retriever
This component will receive an embedding and retrieve documents with similar emebddings using an OPEA retrieval microservice. | ai_ref_knowledge | OPEA Documentation | 4. OPEA Retriever
This component will receive an embedding and retrieve documents with similar emebddings using an OPEA retrieval microservice. | 4. OPEA Retriever
This component will receive an embedding and retrieve documents with similar emebddings using an OPEA retrieval microservice. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a1560324-76de-4056-a6ac-6c24e6512480 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-20-OPEA-001-Haystack-Integration.md | unknown | 2feb1ab4-9df6-4dc3-bda6-f78e8e3d5503 | 0 | opea-semantic-v1 | 57cabb829f01b6c6 | ## Objective
Create a Haystack integration for OPEA that will enable the use of OPEA components within a Haystack pipeline. | ai_ref_knowledge | OPEA Documentation | ## Objective
Create a Haystack integration for OPEA that will enable the use of OPEA components within a Haystack pipeline. | ## Objective
Create a Haystack integration for OPEA that will enable the use of OPEA components within a Haystack pipeline. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
aab7274b-d421-4941-ace3-c874aecee397 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-20-OPEA-001-Haystack-Integration.md | unknown | 2feb1ab4-9df6-4dc3-bda6-f78e8e3d5503 | 2 | opea-semantic-v1 | ed3cc59a77825087 | ## Motivation
Haystack is a production-ready open source AI framework that is used by many AI practitioners. It has over 70 integrations with various GenAI components such as document stores, model providers and evaluation frameworks from companies such as Amazon, Microsoft, Nvidia and more. Creating an integration for... | ai_ref_knowledge | OPEA Documentation | ## Motivation
Haystack is a production-ready open source AI framework that is used by many AI practitioners. It has over 70 integrations with various GenAI components such as document stores, model providers and evaluation frameworks from companies such as Amazon, Microsoft, Nvidia and more. Creating an integration for... | ## Motivation
Haystack is a production-ready open source AI framework that is used by many AI practitioners. It has over 70 integrations with various GenAI components such as document stores, model providers and evaluation frameworks from companies such as Amazon, Microsoft, Nvidia and more. Creating an integration for... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c1973899-c51c-42f7-9c68-bd734a7f205e | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-20-OPEA-001-Haystack-Integration.md | unknown | 2feb1ab4-9df6-4dc3-bda6-f78e8e3d5503 | 3 | opea-semantic-v1 | 61c51ac646ab0ba8 | OPEA will allow Haystack customers to use OPEA components in their pipelines. This RFC is used to present a high-level overview of the Haystack integration.
## Design Proposal | ai_ref_knowledge | OPEA Documentation | OPEA will allow Haystack customers to use OPEA components in their pipelines. This RFC is used to present a high-level overview of the Haystack integration.
## Design Proposal | OPEA will allow Haystack customers to use OPEA components in their pipelines. This RFC is used to present a high-level overview of the Haystack integration.
## Design Proposal | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f163d0f9-dd57-4ecc-af35-133ecd5da06c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-10-20-OPEA-001-Haystack-Integration.md | unknown | 2feb1ab4-9df6-4dc3-bda6-f78e8e3d5503 | 1 | opea-semantic-v1 | 0d0babea647b2368 | Create a Haystack integration for OPEA that will enable the use of OPEA components within a Haystack pipeline.
## Motivation | ai_ref_knowledge | OPEA Documentation | Create a Haystack integration for OPEA that will enable the use of OPEA components within a Haystack pipeline.
## Motivation | Create a Haystack integration for OPEA that will enable the use of OPEA components within a Haystack pipeline.
## Motivation | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0052ca65-4462-4f80-90c9-753e2794cc83 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 33 | opea-semantic-v1 | 05e6a5ea28268ac7 | organization can run LLM agents locally, ensuring that sensitive patient data remains within their secure infrastructure. This preserves privacy and complies with data protection regulations.
2. **Cost Efficiency**:
- **Scenario**: A startup is developing an AI-driven customer support system but has limited budget for... | ai_ref_knowledge | OPEA Documentation | organization can run LLM agents locally, ensuring that sensitive patient data remains within their secure infrastructure. This preserves privacy and complies with data protection regulations.
2. **Cost Efficiency**:
- **Scenario**: A startup is developing an AI-driven customer support system but has limited budget for... | organization can run LLM agents locally, ensuring that sensitive patient data remains within their secure infrastructure. This preserves privacy and complies with data protection regulations.
2. **Cost Efficiency**:
- **Scenario**: A startup is developing an AI-driven customer support system but has limited budget for... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
036b2aa6-23cb-4725-9655-b47c1844b941 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 3 | opea-semantic-v1 | b5f027429a4c40b6 | Eliminates the need for paid cloud-based API services by running open-source SLMs locally on-prem CPUs. - **Data Privacy**: Ensures data privacy by processing data locally.
- **Compute Efficiency**: Leverages the computational power of x86 CPU servers for efficient LLM execution. - **Lower Network Latency and Bandwidth... | ai_ref_knowledge | OPEA Documentation | Eliminates the need for paid cloud-based API services by running open-source SLMs locally on-prem CPUs. - **Data Privacy**: Ensures data privacy by processing data locally.
- **Compute Efficiency**: Leverages the computational power of x86 CPU servers for efficient LLM execution. - **Lower Network Latency and Bandwidth... | Eliminates the need for paid cloud-based API services by running open-source SLMs locally on-prem CPUs. - **Data Privacy**: Ensures data privacy by processing data locally.
- **Compute Efficiency**: Leverages the computational power of x86 CPU servers for efficient LLM execution. - **Lower Network Latency and Bandwidth... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
060ffda9-55d7-4228-b86e-80d336364084 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 5 | opea-semantic-v1 | 1009dafdd992d315 | ### Goals
- **Local Deployment**: Enable local deployment of open-source SLMs on-prem x86 CPU servers. - **Integration with Ollama**: Seamless integration of Ollama framework to access open-source SLMs. - **Maintain Functionality**: Ensure the AgentQnA workflow continues to function effectively with the new setup. - **... | ai_ref_knowledge | OPEA Documentation | ### Goals
- **Local Deployment**: Enable local deployment of open-source SLMs on-prem x86 CPU servers. - **Integration with Ollama**: Seamless integration of Ollama framework to access open-source SLMs. - **Maintain Functionality**: Ensure the AgentQnA workflow continues to function effectively with the new setup. - **... | ### Goals
- **Local Deployment**: Enable local deployment of open-source SLMs on-prem x86 CPU servers. - **Integration with Ollama**: Seamless integration of Ollama framework to access open-source SLMs. - **Maintain Functionality**: Ensure the AgentQnA workflow continues to function effectively with the new setup. - **... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
06a99dd3-2665-4a19-ba7e-6a231223fad3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 1 | opea-semantic-v1 | b3010ec55579db2e | language models (SLMs) locally deployed on x86 CPU servers using Ollama, thereby enabling LLM computation locally on on-prem CPUs and reducing operational expenses. ## Author(s)
[Pratool Bharti](https://github.com/pbharti0831/) | ai_ref_knowledge | OPEA Documentation | language models (SLMs) locally deployed on x86 CPU servers using Ollama, thereby enabling LLM computation locally on on-prem CPUs and reducing operational expenses. ## Author(s)
[Pratool Bharti](https://github.com/pbharti0831/) | language models (SLMs) locally deployed on x86 CPU servers using Ollama, thereby enabling LLM computation locally on on-prem CPUs and reducing operational expenses. ## Author(s)
[Pratool Bharti](https://github.com/pbharti0831/) | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0899f8d1-7dfa-4273-b47c-444d12898c21 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 10 | opea-semantic-v1 | 78f33c0c8553deba | tasks within the AgentQnA workflow. Given the right prompt, smaller Llama models are fairly accurate in tool calling which is an essential feature for agents.
### Ollama Popularity and Wide Range of Models
Ollama provides a comprehensive set of libraries and tools to facilitate the deployment and management of open-sou... | ai_ref_knowledge | OPEA Documentation | tasks within the AgentQnA workflow. Given the right prompt, smaller Llama models are fairly accurate in tool calling which is an essential feature for agents.
### Ollama Popularity and Wide Range of Models
Ollama provides a comprehensive set of libraries and tools to facilitate the deployment and management of open-sou... | tasks within the AgentQnA workflow. Given the right prompt, smaller Llama models are fairly accurate in tool calling which is an essential feature for agents.
### Ollama Popularity and Wide Range of Models
Ollama provides a comprehensive set of libraries and tools to facilitate the deployment and management of open-sou... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0adac313-627d-4a4a-8836-c1328d823592 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 29 | opea-semantic-v1 | fd6e860d0ce1dd90 | models (SLMs) from the Llama 3.1 and 3.2 model families, as well as the DeepSeek-R1 model, will be added and validated for the AgentQnA workflow.
### 3. Compatibility | ai_ref_knowledge | OPEA Documentation | models (SLMs) from the Llama 3.1 and 3.2 model families, as well as the DeepSeek-R1 model, will be added and validated for the AgentQnA workflow.
### 3. Compatibility | models (SLMs) from the Llama 3.1 and 3.2 model families, as well as the DeepSeek-R1 model, will be added and validated for the AgentQnA workflow.
### 3. Compatibility | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0dbff1b9-d2d0-4bb5-b2fc-bd08d793caff | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 35 | opea-semantic-v1 | 4ac83dda1e17c252 | power locally, enabling the institution to achieve low latency and high performance. This ensures timely and accurate analysis without the delays associated with cloud-based services.
4. **Scalability and Control**:
- **Scenario**: An enterprise wants to scale its AI capabilities across multiple departments while main... | ai_ref_knowledge | OPEA Documentation | power locally, enabling the institution to achieve low latency and high performance. This ensures timely and accurate analysis without the delays associated with cloud-based services.
4. **Scalability and Control**:
- **Scenario**: An enterprise wants to scale its AI capabilities across multiple departments while main... | power locally, enabling the institution to achieve low latency and high performance. This ensures timely and accurate analysis without the delays associated with cloud-based services.
4. **Scalability and Control**:
- **Scenario**: An enterprise wants to scale its AI capabilities across multiple departments while main... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1258266f-115a-4619-907b-1d87f18c6788 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 23 | opea-semantic-v1 | 5b0e63e7974ee08f | config: flowchart: nodeSpacing: 200 rankSpacing: 50 curve: linear themeVariables: fontSize: 30px
flowchart LR
%% Colors %%
classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orchid fill:#C26DBC,stroke:#AD... | ai_ref_knowledge | OPEA Documentation | config: flowchart: nodeSpacing: 200 rankSpacing: 50 curve: linear themeVariables: fontSize: 30px
flowchart LR
%% Colors %%
classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orchid fill:#C26DBC,stroke:#AD... | config: flowchart: nodeSpacing: 200 rankSpacing: 50 curve: linear themeVariables: fontSize: 30px
flowchart LR
%% Colors %%
classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orchid fill:#C26DBC,stroke:#AD... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
14358bb6-3440-4024-8180-31f52ebbb35c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 0 | opea-semantic-v1 | 5ddfb85081901503 | # 24-11-25-GenAIExamples-Ollama_Support_for_CPU_Server
The AgentQnA workflow in GenAIExamples leverages large language models (LLMs) as agents to intelligently manage control flow within the pipeline. Currently, it depends on cloud-hosted, paid APIs for LLM services on the CPU server platform, which incurs significant ... | ai_ref_knowledge | OPEA Documentation | # 24-11-25-GenAIExamples-Ollama_Support_for_CPU_Server
The AgentQnA workflow in GenAIExamples leverages large language models (LLMs) as agents to intelligently manage control flow within the pipeline. Currently, it depends on cloud-hosted, paid APIs for LLM services on the CPU server platform, which incurs significant ... | # 24-11-25-GenAIExamples-Ollama_Support_for_CPU_Server
The AgentQnA workflow in GenAIExamples leverages large language models (LLMs) as agents to intelligently manage control flow within the pipeline. Currently, it depends on cloud-hosted, paid APIs for LLM services on the CPU server platform, which incurs significant ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1cd40883-d2e5-4b8c-b814-421662a610ed | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 8 | opea-semantic-v1 | f99c80662151a840 | local SLMs as an agent. - **Support for Non-Xeon Platforms**: This RFC is specific to x86 CPU servers and does not cover other hardware platforms.
## Motivation | ai_ref_knowledge | OPEA Documentation | local SLMs as an agent. - **Support for Non-Xeon Platforms**: This RFC is specific to x86 CPU servers and does not cover other hardware platforms.
## Motivation | local SLMs as an agent. - **Support for Non-Xeon Platforms**: This RFC is specific to x86 CPU servers and does not cover other hardware platforms.
## Motivation | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1f54f9d6-5d6f-4f27-8b38-c1dc509e7cff | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 9 | opea-semantic-v1 | 4800559032619b0a | ## Motivation
### SLMs Performance on CPU
Open-source small language models (SLMs) are optimized to run efficiently on CPU servers, including Intel Xeon processors. These models are designed to balance performance and resource usage, making them suitable for deployment in environments where GPU resources are limited or... | ai_ref_knowledge | OPEA Documentation | ## Motivation
### SLMs Performance on CPU
Open-source small language models (SLMs) are optimized to run efficiently on CPU servers, including Intel Xeon processors. These models are designed to balance performance and resource usage, making them suitable for deployment in environments where GPU resources are limited or... | ## Motivation
### SLMs Performance on CPU
Open-source small language models (SLMs) are optimized to run efficiently on CPU servers, including Intel Xeon processors. These models are designed to balance performance and resource usage, making them suitable for deployment in environments where GPU resources are limited or... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
25403117-4283-4723-a54c-2c4922417404 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 27 | opea-semantic-v1 | e5f0cf3864b81000 | The proposed design for Ollama serving support entails following changes:
### 1. Ollama serving container:
- **Models hosted in Ollama container**: Build and run a container on Xeon platform that hosts Ollama models as an alternative LLM service engine. Hosted models can be accessed by Agent microservice at a given hos... | ai_ref_knowledge | OPEA Documentation | The proposed design for Ollama serving support entails following changes:
### 1. Ollama serving container:
- **Models hosted in Ollama container**: Build and run a container on Xeon platform that hosts Ollama models as an alternative LLM service engine. Hosted models can be accessed by Agent microservice at a given hos... | The proposed design for Ollama serving support entails following changes:
### 1. Ollama serving container:
- **Models hosted in Ollama container**: Build and run a container on Xeon platform that hosts Ollama models as an alternative LLM service engine. Hosted models can be accessed by Agent microservice at a given hos... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2856a75d-4700-4108-9998-822f3045ff41 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 16 | opea-semantic-v1 | 5125320c9c889e1f | By incorporating Ollama into the AgentQnA workflow, the project can leverage these benefits to enhance the overall performance, security, and cost-efficiency of the system.
### Open-source Models are Getting Better
The landscape of open-source language models is rapidly evolving, with continuous improvements in model a... | ai_ref_knowledge | OPEA Documentation | By incorporating Ollama into the AgentQnA workflow, the project can leverage these benefits to enhance the overall performance, security, and cost-efficiency of the system.
### Open-source Models are Getting Better
The landscape of open-source language models is rapidly evolving, with continuous improvements in model a... | By incorporating Ollama into the AgentQnA workflow, the project can leverage these benefits to enhance the overall performance, security, and cost-efficiency of the system.
### Open-source Models are Getting Better
The landscape of open-source language models is rapidly evolving, with continuous improvements in model a... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
40707d12-8c36-4a4d-8caf-dadf56307b7c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 38 | opea-semantic-v1 | 57bd057909a3b8e0 | regulatory compliance and maintaining client confidentiality. This setup ensures that neither prompts nor proprietary data inserted to a vector database need to leave the enterprise.
The proposed design for Ollama serving support on-prem x86 CPU servers integrates Ollama as an additional LLM service alongside existing ... | ai_ref_knowledge | OPEA Documentation | regulatory compliance and maintaining client confidentiality. This setup ensures that neither prompts nor proprietary data inserted to a vector database need to leave the enterprise.
The proposed design for Ollama serving support on-prem x86 CPU servers integrates Ollama as an additional LLM service alongside existing ... | regulatory compliance and maintaining client confidentiality. This setup ensures that neither prompts nor proprietary data inserted to a vector database need to leave the enterprise.
The proposed design for Ollama serving support on-prem x86 CPU servers integrates Ollama as an additional LLM service alongside existing ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4e758fce-f8c8-4857-8093-d0cf379cd38d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 7 | opea-semantic-v1 | a9d4a9da217d2a56 | ### Non-Goals
- **New Features**: No new features will be added to the AgentQnA workflow beyond the support for local SLMs as an agent. - **Support for Non-Xeon Platforms**: This RFC is specific to x86 CPU servers and does not cover other hardware platforms. | ai_ref_knowledge | OPEA Documentation | ### Non-Goals
- **New Features**: No new features will be added to the AgentQnA workflow beyond the support for local SLMs as an agent. - **Support for Non-Xeon Platforms**: This RFC is specific to x86 CPU servers and does not cover other hardware platforms. | ### Non-Goals
- **New Features**: No new features will be added to the AgentQnA workflow beyond the support for local SLMs as an agent. - **Support for Non-Xeon Platforms**: This RFC is specific to x86 CPU servers and does not cover other hardware platforms. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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