It would be nice to have a video that goes into more detail about the quality of results in vector vs hybrid vs E-KNN. Also, why use Langchain? I'm unclear what it's doing that is better than makign the API calls directly.
Ans 1 : Great suggestion! Ans 2 : For Langchain used ConversationalMemory which can be coded also but having a good tested framework helps to implement ConversationalMemory easier
The sequential API calls to retrieve and then generate the response will result in a higher turn-around time. Langchain runs on LCEL that allows "runnables" or chains to run parallelly, achieving the same functionality but improving time. Also, it'll be easier to implement context-aware retrieving via Agent Tools using the framework. The only downside I see right now is a bug in the community integration for Azure AI Search that doesn't allow Hybrid or Semantic-Hybrid searches on a custom schema.
I have created an index for a CSV file and i had few custom fields and also i have enabled Semantic reranker for the AI Search. Now i want to use this in my code but unable to get any fruitfl results. Can you please help ? And I am using Langchain as orchestrator
![Agents [ Langchain] with Math Calculation Tools,SQL database, Azure AI Vector Search in 17 mins](http://i.ytimg.com/vi/Rl63WI5H7lk/mqdefault.jpg)
![Agents [ Langchain] with Math Calculation Tools,SQL database, Azure AI Vector Search in 17 mins](/img/tr.png)
![Multimodal Search [ Image and Text ] using Azure AI Search and Azure Computer Vision](http://i.ytimg.com/vi/zs2iKch_pBo/mqdefault.jpg)
Thank you for this lovely video. I am interested in how you created those indexes within Azure AI Search. What if I have data in json format?
It would be nice to have a video that goes into more detail about the quality of results in vector vs hybrid vs E-KNN. Also, why use Langchain? I'm unclear what it's doing that is better than makign the API calls directly.
Ans 1 : Great suggestion!
Ans 2 : For Langchain used ConversationalMemory which can be coded also but having a good tested framework helps to implement ConversationalMemory easier
The sequential API calls to retrieve and then generate the response will result in a higher turn-around time.
Langchain runs on LCEL that allows "runnables" or chains to run parallelly, achieving the same functionality but improving time. Also, it'll be easier to implement context-aware retrieving via Agent Tools using the framework. The only downside I see right now is a bug in the community integration for Azure AI Search that doesn't allow Hybrid or Semantic-Hybrid searches on a custom schema.
Is it acceptable to perform all these tasks directly from the portal, using the no-code option?
can you do one between llama-index and azure ai search?
I have created an index for a CSV file and i had few custom fields and also i have enabled Semantic reranker for the AI Search. Now i want to use this in my code but unable to get any fruitfl results. Can you please help ? And I am using Langchain as orchestrator
Excel doesn't work well I believe
Do you mind sharing the repo for this talk ?
github.com/ambarishg/AZURE-AI-VECTOR-SEARCH