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Glad you liked it!

Thanks, glad you found it helpful!

Glad you liked it, buddy!

Thanks a lot for your feedback! It matters a lot to me and encourages to keep working on the content 👍

You're welcome!

I’ll do my best to produce more videos in the future. As of now, I have to focus on a book about Postgres ;)

Glad it helped, my friend!

You’re welcome, my friend!

@@KayleighBarrow-z9b thanks for the feedback! I’m not aware of the QGIS capabilities but you can certainly run an instance of Postgres with both PostGIS and pgvector extensions. QGIS will be able to connect and then you can explore if the tool allows to write and send the prompts to Postgres

Thanks, such feedback matters a lot to me!

If you're talking about a database object that is created by the CREATE DATABASE command, then the access is controlled by the roles created for applications. For instance, if database_app1 and all its data can be accessed only by app1, then there needs to be a role that has required level of access to the database. All other roles and other apps will be prohibited from connecting and accessing database_app1. See these docs for details (if it's new to you): www.postgresql.org/docs/current/user-manag.html Shared memory acts transparently for the applications and the application layer has no access to it, it's just internal Postgres server's component. If the role used by app1 for database_app1 access is hacked (for instance, bad actors now know credentials of the role), then the bad actors can access database_app1 only and steal the data. But those actors won't have access to other database...unless the hacked role had superuser privileges. Overall, don't use the default "postgres" role at the application layer because the role has superuser access to the database server. Create application specific roles with fine-grained permissions for every app and you'll have a piece of mind in production.

@@savithweraniyagoda1297 thanks, glad you liked it!

Thanks buddy 👍 Glad you liked it

you're welcome, I'm glad you like the video!

Thanks! Glad you liked it my friend

Glad you found it helpful! Have fun building gen AI apps with pgvector!

The app from that course uses vector similarity search, but still the primary focus is scalability, high availability, and geo-distribution of databases. Check it out: th-cam.com/video/rqJBFQ-4Hgk/w-d-xo.htmlsi=7o-ARIbp09LHl_RR If you're interested in exploring Postgres as a vector database, then take a look at a collection of my videos about pgvector: th-cam.com/video/RFnZB76KVWk/w-d-xo.html

You’re more than welcome!

Could you confirm the database container started successfully in Docker? Check the preceding "Start PostgreSQL With pgvector" section that starts the database using a docker compose command and then tries to connect to it.

@@DevMastersDb i searched a bit online had tried docker ps in the terminal, which shows that the container is running, its also showing as running in docker desktop, then i thought maybe its because i have pgadmin installed on my device that this happens and tried changing the port to 5433 in dockerfile, but still the error persisted.

@@AI.ML.DataScientistWork does this command work for you? docker exec -it postgres-pgvector psql -U postgres Just want to make sure you can connect from within the Postgres container using the psql tool.

@@DevMastersDb I tried a lot of things but the issue was never resolved. Finally I uninstalled the local installation postgresql and then the docker instance connected without any hiccups both on pgadmin as well as in the code. Any Idea what could have been the reason?

@@AI.ML.DataScientistWork I have no idea, buddy, what was happening. Do you run on Windows? Might it have been something Widows specific? stackoverflow.com/questions/71984505/postgres-local-installed-instance-interfered-with-docker-instance

You're more than welcome! ;)

Hey, thanks a lot for sharing feedback! It means a lot to me.

Hey, sounds good. I’ll keep this in mind. Any specific use cases?

@@DevMastersDb sentiment analysis

Glad it was helpful!

Thanks, I'm glad you liked it! Teaching is one of the main reasons I work as a Developer Relations professional. In this role, I can spend a lot of time sharing my knowledge with others and being paid for that ;)

It depends on the target recall (search accuracy/relevancy) and performance for the similarity searches. The greater the `m`, the better recall, but the index build time increases, and query performance might also be impacted. Increasing the `ef_construction` can lead to better recall, but will also increase index build time. I would start with the defaults (m=16 and ef_construction=64) and increase/adjust them once you find the right balance between recall, query performance and index build time. Note, the ef_construction needs to be at least double of m.

Many thanks!

Excellent! glad you managed to get it working on your end 👍 Try to instruct the LLM to retrieve that context dynamically. For instance, you can say to execute the query "select name, price from products where id = {id}" setting the id as a parameter. Then, the LLM can perform this request over the database and pull a user-specific context. Also, LangChain support various tools/actions that let LLM pull info from other sources or perform various actions: python.langchain.com/v0.1/docs/modules/tools/

@@DevMastersDb That makes sense, the issue is (unless I'm misunderstanding), passing entire tables to the LLM, regardless of whether the LLM knows that it should filter down to a subset of data, seems to take a lot of tokens. i.e. We're providing a lot of context, and then asking the LLM to disregard most of it (as opposed to providing the narrow context in the first place). As a result, if I add more than two tables, I get the error: {'error': {'message': "This model's maximum context length is 4097 tokens, however you requested 10494 tokens. Please reduce your prompt; or completion length.", 'type': 'invalid_request_error', 'param': None, 'code': None}} I'm extremely new to this stuff (just a day or two), so I could totally be missing something! One thing I'm going to try next is to create a View with the data after I've applied some joins and filters to it and then pass the view in include_tables instead, so I'm providing just the minimum context the model would need.. not sure if that'll work, or is even the right way of thinking about it 🤔

@@anuragangara2619 how many tables do you folks have? First things, yes, ask the agent to look into those which are necessary by defining them in the "include_tables". The LLM will pull only the metadata of those tables and not the actual data. So, it should fit in the context window. And then in your system message for the LLM you also say that use this query to pull the actual data for a selected user. If that data set doesn't fit into the context, then try to filter by the user_id/product_id and some time frame. Anyway, take it slowly and learn LangChain capabilities by playing with a few tutorials. You'll definitely get it figured!

@@DevMastersDb Hmm, odd -- only 5 or 6 tables; personal project, so just me. I'll look into that then! Thanks so much for the great video, really enjoyed this!

@@anuragangara2619 make sure that LLM doesn’t pull data, enable the “verbose” mode to see what happens. Also, start with 1-2 tables and then add more, see when it breaks. It might have to discover the root cause. Glad you liked the video, thanks for feedback!

Yes, it should be doable. You need to use an Azure OpenAI SDK (or another framework that supports Azure embedding models) to generate embeddings. Here is a quick tutorial by Microsoft for Python: learn.microsoft.com/en-us/azure/ai-services/openai/tutorials/embeddings Btw, what's your programming language?

@@DevMastersDb thank you for your reply. Using Python. Let me go through link. Thanks again man. Really appreciate it.

@@debarghaya excellent!

@@DevMastersDb I have multiple tables and these tables have different columns. I would like to generate vector for multiple tables. What is your recommendation for vector storage. Same table with new vector column or new table specifically for the vector?

@@debarghaya Postgres stores vectors with a large number of dimensions in the internal TOAST (The Oversized-Attribute Storage Technique) tables. Thus, from the storage perspective the vector columns can be either in your existing tables with other data or in separate tables. I would store the vector columns in the original tables if I need to filter data by the vector columns and other columns frequently. For instance, if most of the times your queries look like "select * from table1 where vector_column <=> ... and other_column = 5 or another_column = 4" then it makes sense to store vectors in the original tables with other data. Otherwise you would need to join the original tables with the tables dedicated for the vectors.

Try to add the "rejectUnauthorized:false" flag to the SSL settings of the database connection parameteres. ssl: { rejectUnauthorized: false, ca: fs.readFileSync('path_to_your_root_certificate').toString()}, But get a proper certificate before you go in prod :)

Thanks! Let’s learn it slow and gradually:)

Hey, sure, even though Spring AI doesn't support PostgresML natively (and the PostgresML team doesn't have a client library for Java yet), you still can use Spring JDBC Client (or another driver/framework) to work with PostgresML using the SQL APIs. Btw, this is a PostgresML video that I shot in the past: th-cam.com/video/Gtz883daf1Y/w-d-xo.html

@@DevMastersDb Thanks I figured out that langchain is a better option for me. As I can convert natural language to SQL. However I need to figure how to convert python to GPT. Then how to use GPT

@@ifeolu8501 check out LangChain4j if Java is better option for you

You're welcome!

Hey, thanks for sharing your feedback and picking my session among the others! Staying in touch.

So glad! Thanks for support!

Yes, agree. I always use a dedicated network when need containerized applications talk to a Postgres instance in Docker.

Thanks a ton, my friend! I’ll do my best to get the good stuff coming 👍

Hey, thanks for sharing your feedback! Does the client just want to ensure his data is not visible to other clients, or has the client explicitly asked to segregate his data into separate database tables? Overall, consider these options: 1. Shared tables for clients. All customer data is stored in the same tables, but there is a special column such as "tenant_id" that lets you filter out one client's data from another's. You need to set up row-level security to ensure that when an application requests data belonging to tenant_id=5, the data of other tenants won't be visible. 2.Schema per client. Each client has his own set of tables stored in a client-specific schema (CREATE SCHEMA client1, CREATE SCHEMA client2, etc.). The application needs to specify the schema while querying or updating data of a specific client - "select balance from client5.account" or "select balance from client100.account". Distributed Postgres is useful for multi-tenant use cases if you expect to have many tenants and will need to scale. It's also useful if you need to pin each tenant's/client's data to a specific location (zone, region, database server).

I used OpenAI GPT 4. Absolutely, you can use other models including Azure OpenAI