Reusing the sample code from my previous demo, I build a table with 130 sentences describing AI Tools and then use SQL to run the sort of queries that you'd expect RAG to use against an "internal knowledge source".
Here's a preview of the sentences loaded :
SQL> select count(*) from my_data_source;
COUNT(*)
----------
130
SQL> select my_sentence from my_data_source fetch first 5 rows only;
MY_SENTENCE
----------------------------------------------------------------------------------------------------------------------------------------------------
Adobe Firefly: A family of generative AI models integrated into Adobe's Creative Cloud suite. It is used for professional image editing and graphic
design
AIVA (Artificial Intelligence Virtual Artist): An AI composer that generates music in various styles for content creators and brands
Amazon CodeWhisperer: An AI coding assistant from Amazon Web Services that provides contextual code recommendations
Canva Magic Media: An AI image generator integrated into the user friendly design platform. It is used for creating visuals for social media and pr
esentations
ChatGPT: An AI chatbot from OpenAI. It can engage in human like conversations, write code, summarize text, and create content such as stories and e
ssays
SQL> select my_sentence from my_data_source where id > 126 ;
MY_SENTENCE
----------------------------------------------------------------------------------------------------------------------------------------------------
Zubtitle.com captions for video
Cohere.ai large language models
Grok.com personal AI assistant
Claude.ai advanced AI chatbot
SQL>
The embeddings are then computed and loaded into the target table using the same query (the HuggingFace model ALL_MINILM_L12_V2_AUGMENTED has already been loaded with the ONNX RunTime engine with the same code as in the
previous blog post) :
insert into my_data_vectors
select ds.id as id, my_sentence, to_vector(et.embed_vector) sentence_vector
from
my_data_source ds,
dbms_vector_chain.utl_to_embeddings(
dbms_vector_chain.utl_to_chunks(dbms_vector_chain.utl_to_text(ds.my_sentence), json('{"normalize":"all"}')),
json('{"provider":"database", "model":"ALL_MINILM_L12_V2_AUGMENTED"}')) t,
JSON_TABLE(t.column_value, '$[*]' COLUMNS (embed_id NUMBER PATH '$.embed_id', embed_data VARCHAR2(4000) PATH '$.embed_data', embed_vector CLOB PATH '$.embed_vector')) et
/
I now test a few "Retrieval" queries thus : (Note how the queries return different results with smaller VECTORE_DISTANCE as I refine them)
SQL> ACCEPT text_input CHAR PROMPT 'Enter your query : '
Enter your query : Video
SQL> VARIABLE text_variable VARCHAR2(1000)
SQL> VARIABLE query_vector VECTOR
SQL> BEGIN
2 :text_variable := '&text_input';
3 SELECT vector_embedding(ALL_MINILM_L12_V2_AUGMENTED USING :text_variable as data) into :query_vector;
4 END;
5 /
old 2: :text_variable := '&text_input';
new 2: :text_variable := 'Video';
PL/SQL procedure successfully completed.
SQL>
SQL>
SQL> SELECT my_sentence, vector_distance(sentence_vector , :query_vector, COSINE) as Calc_Vector_Distance
2 FROM my_data_vectors
3 ORDER BY 2
4 FETCH FIRST 3 ROWS ONLY;
MY_SENTENCE
----------------------------------------------------------------------------------------------------------------------------------------------------
CALC_VECTOR_DISTANCE
--------------------
Clipchamp.com quick video creation
4.635E-001
Lumen5.com AI powered social video
5.277E-001
Synths.video convert blogs into videos
5.39E-001
SQL>
----------------------------------------------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------------------------
SQL> ACCEPT text_input CHAR PROMPT 'Enter your query : '
Enter your query : What AI Tool can I use to edit Videos ?
SQL> VARIABLE text_variable VARCHAR2(1000)
SQL> VARIABLE query_vector VECTOR
SQL> BEGIN
2 :text_variable := '&text_input';
3 SELECT vector_embedding(ALL_MINILM_L12_V2_AUGMENTED USING :text_variable as data) into :query_vector;
4 END;
5 /
old 2: :text_variable := '&text_input';
new 2: :text_variable := 'What AI Tool can I use to edit Videos ?';
PL/SQL procedure successfully completed.
SQL>
SQL>
SQL> SELECT my_sentence, vector_distance(sentence_vector , :query_vector, COSINE) as Calc_Vector_Distance
2 FROM my_data_vectors
3 ORDER BY 2
4 FETCH FIRST 3 ROWS ONLY;
MY_SENTENCE
----------------------------------------------------------------------------------------------------------------------------------------------------
CALC_VECTOR_DISTANCE
--------------------
Wisecut.video auto edit with AI
2.62E-001
Runwayml.com AI video editing
2.777E-001
Gling.ai YouTube video editor
3.413E-001
SQL>
SQL>
----------------------------------------------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------------------------
SQL> ACCEPT text_input CHAR PROMPT 'Enter your query : '
Enter your query : image
SQL> VARIABLE text_variable VARCHAR2(1000)
SQL> VARIABLE query_vector VECTOR
SQL> BEGIN
2 :text_variable := '&text_input';
3 SELECT vector_embedding(ALL_MINILM_L12_V2_AUGMENTED USING :text_variable as data) into :query_vector;
4 END;
5 /
old 2: :text_variable := '&text_input';
new 2: :text_variable := 'image';
PL/SQL procedure successfully completed.
SQL>
SQL>
SQL> SELECT my_sentence, vector_distance(sentence_vector , :query_vector, COSINE) as Calc_Vector_Distance
2 FROM my_data_vectors
3 ORDER BY 2
4 FETCH FIRST 3 ROWS ONLY;
MY_SENTENCE
----------------------------------------------------------------------------------------------------------------------------------------------------
CALC_VECTOR_DISTANCE
--------------------
VanceAI.com image enhancement
5.268E-001
Canva Magic Media: An AI image generator integrated into the user friendly design platform. It is used for creating visuals for social media and pr
esentations
5.459E-001
PicWish.com photo editing
5.696E-001
SQL>
SQL>
----------------------------------------------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------------------------
SQL> ACCEPT text_input CHAR PROMPT 'Enter your query : '
Enter your query : How can I use AI to edit Images ?
SQL> VARIABLE text_variable VARCHAR2(1000)
SQL> VARIABLE query_vector VECTOR
SQL> BEGIN
2 :text_variable := '&text_input';
3 SELECT vector_embedding(ALL_MINILM_L12_V2_AUGMENTED USING :text_variable as data) into :query_vector;
4 END;
5 /
old 2: :text_variable := '&text_input';
new 2: :text_variable := 'How can I use AI to edit Images ?';
PL/SQL procedure successfully completed.
SQL>
SQL>
SQL> SELECT my_sentence, vector_distance(sentence_vector , :query_vector, COSINE) as Calc_Vector_Distance
2 FROM my_data_vectors
3 ORDER BY 2
4 FETCH FIRST 3 ROWS ONLY;
MY_SENTENCE
----------------------------------------------------------------------------------------------------------------------------------------------------
CALC_VECTOR_DISTANCE
--------------------
Hotpot.ai AI image editing
2.805E-001
Runwayml.com AI video editing
3.711E-001
Wisecut.video auto edit with AI
3.921E-001
SQL>
SQL>
----------------------------------------------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------------------------
SQL> ACCEPT text_input CHAR PROMPT 'Enter your query : '
Enter your query : Can I chat with an AI ?
SQL> VARIABLE text_variable VARCHAR2(1000)
SQL> VARIABLE query_vector VECTOR
SQL> BEGIN
2 :text_variable := '&text_input';
3 SELECT vector_embedding(ALL_MINILM_L12_V2_AUGMENTED USING :text_variable as data) into :query_vector;
4 END;
5 /
old 2: :text_variable := '&text_input';
new 2: :text_variable := 'Can I chat with an AI ?';
PL/SQL procedure successfully completed.
SQL>
SQL>
SQL> SELECT my_sentence, vector_distance(sentence_vector , :query_vector, COSINE) as Calc_Vector_Distance
2 FROM my_data_vectors
3 ORDER BY 2
4 FETCH FIRST 3 ROWS ONLY;
MY_SENTENCE
----------------------------------------------------------------------------------------------------------------------------------------------------
CALC_VECTOR_DISTANCE
--------------------
Claude.ai advanced AI chatbot
2.989E-001
Claude.ai conversational assistant
3.828E-001
Grok.com personal AI assistant
4.025E-001
SQL>
SQL>
----------------------------------------------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------------------------
SQL> ACCEPT text_input CHAR PROMPT 'Enter your query : '
Enter your query : coding
SQL> VARIABLE text_variable VARCHAR2(1000)
SQL> VARIABLE query_vector VECTOR
SQL> BEGIN
2 :text_variable := '&text_input';
3 SELECT vector_embedding(ALL_MINILM_L12_V2_AUGMENTED USING :text_variable as data) into :query_vector;
4 END;
5 /
old 2: :text_variable := '&text_input';
new 2: :text_variable := 'coding';
PL/SQL procedure successfully completed.
SQL>
SQL>
SQL> SELECT my_sentence, vector_distance(sentence_vector , :query_vector, COSINE) as Calc_Vector_Distance
2 FROM my_data_vectors
3 ORDER BY 2
4 FETCH FIRST 3 ROWS ONLY;
MY_SENTENCE
----------------------------------------------------------------------------------------------------------------------------------------------------
CALC_VECTOR_DISTANCE
--------------------
Amazon CodeWhisperer: An AI coding assistant from Amazon Web Services that provides contextual code recommendations
5.543E-001
Replit.com write and run code
5.548E-001
GitHub Copilot: An AI coding assistant that provides code suggestions and autocompletion within an IDE
5.677E-001
SQL>
SQL>
----------------------------------------------------------------------------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------------------------------------------------------------
SQL> ACCEPT text_input CHAR PROMPT 'Enter your query : '
Enter your query : Tell me of a Coding Assistant
SQL> VARIABLE text_variable VARCHAR2(1000)
SQL> VARIABLE query_vector VECTOR
SQL> BEGIN
2 :text_variable := '&text_input';
3 SELECT vector_embedding(ALL_MINILM_L12_V2_AUGMENTED USING :text_variable as data) into :query_vector;
4 END;
5 /
old 2: :text_variable := '&text_input';
new 2: :text_variable := 'Tell me of a Coding Assistant';
PL/SQL procedure successfully completed.
SQL>
SQL>
SQL> SELECT my_sentence, vector_distance(sentence_vector , :query_vector, COSINE) as Calc_Vector_Distance
2 FROM my_data_vectors
3 ORDER BY 2
4 FETCH FIRST 3 ROWS ONLY;
MY_SENTENCE
----------------------------------------------------------------------------------------------------------------------------------------------------
CALC_VECTOR_DISTANCE
--------------------
GitHub Copilot: An AI coding assistant that provides code suggestions and autocompletion within an IDE
4.306E-001
Amazon CodeWhisperer: An AI coding assistant from Amazon Web Services that provides contextual code recommendations
4.702E-001
Replit: An online IDE with an integrated AI assistant that helps with code generation, debugging, and project creation
4.734E-001
SQL>
SQL>
Thus, in the first pair of examples, if I refine (aka "prompt engineering") my query from "Video" to "What AI Tool can I use to edit Videos ?", I get a better set of responses with a smaller VECTOR_DISTANCE from my query.
In the second pair, I change my query from "image" to "How can I use AI to edit Images ?" I get an improved set of responses (again with a smaller VECTOR_DISTANCE).
In the last pair, when I change my query from "coding" to "Tell me of a Coding Assistant", I get responses with a better match to my query.
As noted in my
previous blog post, the "LLM" is actually a "MinLM" called "all-MiniLM-L12-v2" from HuggingFace.