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Great Video. Can you please share end to end solution for that? How to setup lambda and all.

Great video! Currently using RAG and wondering how fine-tuning will perform!

Thanks for the video! I would love to test this out - so hope I get time this evening/tomorrow. Qq is there a reason the citations (documents) are capped at 2? Quickly looking at the code it does not look like it's capped? also does not look like there is any history in this - i..e. if I wanted to make the chat session look stateful to the user (so they could ask follow up questions with context) presumably I would need to add in a conversation buffer?

very good session

Brief and to -the -point explanation! Thank you, Denys! Way2go!

Thanks a lot

can this be accessed by other aws users ? or is it available only to the particular user ?

Great overview. Exactly what i was looking for. Thanks!

Hello, Thanks for your tutorial which is soooo helpful ! I finally used EC2 (OS: Debian) to create docker and ran this program. However, when I asked a question on the UI interface, an error message appeared. Part of it is as follows. I would like to know how to solve it. Thanks so much ಥ⌣ಥ File "/usr/local/lib/python3.12/site-packages/botocore/client.py", line 629, in resolve resolved = self.endpoint_resolver.construct_endpoint( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/botocore/regions.py", line 230, in construct_endpoint result = self._endpoint_for_partition( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/botocore/regions.py", line 278, in _endpoint_for_partition raise NoRegionError()

Thanks. Waiting for similar videos.

Hello, it was really helpful but I got stock in one part, when I try to sync the knowledge base it do not trigger anything

Great video! I really enjoy this architectural approach before actually facing the problems. Please continue with your nice work!

Hi, did you encounter a validationException: Knowledge base role arn:aws:iam::xxxxxx:role/service-role/AmazonBedrockExecutionRoleFor_xx is not able to call specified bedrock embedding model arn:aws:bedrock:us-east-1::foundation-model/cohere.embed-english-v3: You don't have access to the model with the specified model ID. I've been stuck on this for the past hour and I dont know how to resolve this

Can we not use RAG within bedrock and use the default OpenSearch vector db for this. Does that also do chunking and creating vector store similar to llama index?

Thanks man

Thank you for this tutorial!

You're using service context in vectorstoreindex in the lambda function but not using that in the notebook. Why is that? Will the output in the two cases be different. Sorry for asking many questions, but the video is really interesting, and I am trying to learn llama_index

Really good illustration Denys! Just one question, will this architecture still function well when you have too many tables with bad naming? I only see some products like AskYourDatabase work well with this situation. How should the solution fit in this architecture?

Just a question about the action group: Did you build a simple lambda function from scratch or create one from a container? I am asking this question to understand how you installed the dependencies specified in the requirements file

I don't think openai key is required since you're uisng aws bedrock models, right?

thank you just discovered your channel

really good video. thank you!

Thanks Denys for putting this together - can you elaborate on what goes into the "prompt template"?

Great video. Many thanks for sharing this

Hello Denis. Thanks for the video. I am wondering if it’s possible to add implementation details in tech stack and tools for RAG type of architecture. What framework was used to load DB schema - if Langchain, what loader and how it was vectorized, which Vector DB is good for this type of cases and Foundational modes kids from your experience for both: vectors as well as generation. Maybe some examples of code for loader, retrieval and connectors if possible. I have the case in mind to implement and puzzling on how to load structured data into vector DB as well as retrieve it for generations. Thank you in advance. ❤

Many thanks for your prompt answers. Can't wait to see the next video

it seems like RAG (knowledge base) has not been used in the architecture, llama indes is used instead, so the llm model (foundation model) is building the query with the help of user NLP input + few shots examples and tables metadata, right?

I would be also greatful if you can share a walkthrough of how to create few shot examples

Great video. Could you please explain here or in a separate video the glu and metadata data extraction part

Good day! Thank you for all the kind words 💞 Unfortunately, I don’t have the capacity to answer specific technical questions here. If you need support with a specific problem, please consider joining my Patreon private chat (link in bio). There, I can help you with your issues and we can also schedule private sessions to address more complex problems.

InvokeAgent operation: Failed to retrieve resource because it doesn't exist. Retry the request with a different resource identifier - this is the error I am getting .. Any thoughts on this ?

hello sir!!! I have a issue in creating the knowledge base. When I create it shows failed to create open search serverless collection.eventhough I gave the full access access for bedrock and opensearch service for the user and made the s3 bucket to be accessed by the opensearch service, the issues is not fixed. can you help me to clear that issue? I'm struggling with that issue !!! please help me !!!!

i've down all 3 of architectures you've mentioned, but still not getting the ideal results. The main issues i've encountered: 1. lack of text2sql pairs, i've collected all of the sql queries succeed in our database, but it's incredibly hard to inference back to the original query in human language. 2. it's almost impossible to help llm understand the relation between business info(usually used in human language) to actual data structure. 3. the information dense is quite low when export database scheme, table structure, we used lots of nested json stored in single column, also enums with no detailed discription. but it was done months ago, today i might have some new ideas on issue 1&3, but 2 remains to be seemingly impossible.

Oh... I was hoping you would cover a bit more, like if I have the source in the Documents metadata, how can I get that to be used for citations

I agree with what you were saying about langchain hiding things lol. Even with Debug and verbose on, with everything set to "with_config(run_name="blah blah")", while also reading through the source code, it's hard to really trace what's going on in langchain :')

how to we coming to this page the first is how we registry to get user and password

Hey Denys, great video! Is it possible to query a postgreSQL database?

Just one question. How about the database which has around 1000 table? how it will handle prompt and tokens? will it send 1000 table schema each time the query is passed by the user? will appreciate your prompt reply. thank you.

Excellent. Thank you for this tutorial.

I would never trust the LLm to write the sql query. I would create all the sql queries behind a JDBC and write restful api with search parameters. Can langchain do that?

thank you

thank you

how about context memory? the uer asks question A and then a follow up question that needs to use question A to answer?

thank you !!

How dobi add memory? For example Question 1. Who how many salesmen do we have Answer 3 Question2. Can i get their names? As you can see, the second question rely on the first .. How do we achieve that? Thanks

have you been able to get this working with Llama through langchain?

Finally someone who is not indian, thank you Denys!!!

Is there anything simlar for no-sql?

does it still work with the current updates? been having trouble downloading mine, this tutorial just made it all possible.

In 1:57 i try to save but i got an error "Unable to connect to server: connection failed: FATAL : password authentication failed for user "postgres"