Oracle Study Notes by Denis

Wednesday, July 01, 2026

Using Oracle SQLcl MCP with Claude Code CLI tool

While SQL Developer for VS Code provides a built-in "one-click" MCP for Copilot, Claude Code requires you to explicitly add the database MCP server (like SQLcl for Oracle or generic SQL servers) using its CLI.

Here are the steps I used to configure it with CC:

(1) Download SQLcl

You may download the latest Oracle SQLcl tool which supports MCP at https://www.oracle.com/database/sqldeveloper/technologies/sqlcl/download/ , current version 26.1.

In my case, it was downloaded last year with version 25.2 it is at my local path: C:\users\dsun\sqlcl-latest\sqlcl\bin

(2) Add the SQL MCP Server In my project directory C:\Users\dsun\ClaudeCode_DBA_Assistant:

PS C:\Users\dsun\ClaudeCode_DBA_Assistant> claude mcp add --transport stdio sqlcl -- C:\users\dsun\sqlcl-latest\sqlcl\bin\sql -mcp
Added stdio MCP server sqlcl with command: C:\users\dsun\sqlcl-latest\sqlcl\bin\sql -mcp to local config
File modified: C:\Users\dsun\.claude.json [project: C:\Users\dsun\ClaudeCode_DBA_Assistant]

(3) Verify the Connection

 
PS C:\Users\dsun\ClaudeCode_DBA_Assistant> claude mcp list
sqlcl: C:\users\dsun\sqlcl-latest\sqlcl\bin\sql -mcp - v Connected
PS C:\Users\dsun\ClaudeCode_DBA_Assistant>

(4) Create a saved connection Note: using -thin is necessary in my environment to avoid error from " thick client "

PS C:\users\dsun\sqlcl-latest\sqlcl\bin> .\sql -thin 'test/"xxzzzzzz"@dbhost1.mycompany.com:1521/testdb'


SQLcl: Release 25.2 Production on Wed Jul 01 07:07:37 2026

Copyright (c) 1982, 2026, Oracle.  All rights reserved.

Last Successful login time: Wed Jul 01 2026 07:07:42 -04:00

Connected to:
Oracle Database 19c Enterprise Edition Release 19.0.0.0.0 - Production
Version 19.28.0.0.0

SQL> conn -save testdb_cc -savepwd
Name: testdb_cc
Connect String: dbhost1.mycompany.com:1521/testdb
User: test
Password: ******

(5) Interact with database in CC CLI Using natural language e.g. sqlcl show 10 largest tables in testdb_cc

PS C:\Users\dsun\ClaudeCode_DBA_Assistant> claude
 ¦?¦¦¦?¦   Claude Code v2.1.181
??¦¦¦¦¦??  Haiku 4.5 · API Usage Billing
  ?? ??    ~\ClaudeCode_DBA_Assistant


> sqlcl show 10 largest tables in testdb_cc

  Thought for 3s, called MCP (ctrl+o to expand)

? I'll connect to testdb_cc and query the 10 largest tables using sqlcl.

  Called sqlcl 2 times (ctrl+o to expand)

? Now I'll query the 10 largest tables by size.

  Called sqlcl (ctrl+o to expand)

? Let me check the available columns in DBA_TABLES and use the correct approach.

  Called sqlcl 2 times (ctrl+o to expand)

? Let me use DBA_SEGMENTS instead, which contains the actual allocated space:

  Called sqlcl (ctrl+o to expand)

? Here are the 10 largest tables in testdb_cc:

  +-------------------------------------------------------------------+
  ¦ Owner ¦    Table Name     ¦ Size (GB) ¦ Segment Type ¦ Tablespace ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ TEST  ¦ LOGFILE_SYNC_TEST ¦ 57.30     ¦ TABLE        ¦ USERS      ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER1 ¦ CF1               ¦ 2.06      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER3 ¦ CF1               ¦ 2.01      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER5 ¦ CF1               ¦ 2.00      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER4 ¦ CF1               ¦ 2.00      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER7 ¦ CF1               ¦ 2.00      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER6 ¦ CF1               ¦ 2.00      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER2 ¦ CF1               ¦ 1.99      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ USER8 ¦ CF1               ¦ 1.98      ¦ TABLE        ¦ IOPS       ¦
  +-------+-------------------+-----------+--------------+------------¦
  ¦ TPCC  ¦ STOCK             ¦ 0.18      ¦ TABLE        ¦ TPCC_DATA  ¦
  +-------------------------------------------------------------------+

  Key observations:
  - The TEST.LOGFILE_SYNC_TEST table dominates storage at 57.3 GB (likely a test/load table)
  - Multiple user schemas (USER1-USER8) have similarly-sized CF1 tables (~2 GB each) in the IOPS tablespace
  - The TPCC.STOCK table is relatively small at 0.18 GB in the TPCC_DATA tablespace

  Would you like to analyze space usage patterns, identify candidates for cleanup, or investigate the purposes of these large tables?

? Worked for 25s

? recap: Identified 10 largest tables in testdb_cc: TEST.LOGFILE_SYNC_TEST dominates at 57.3GB, followed by USER schemas' CF1 tables at ~2GB each. Awaiting next diagnostic step. (disable recaps in /config)

In summary, this post demonstrates how to connect Oracle SQLcl to the Claude Code CLI tool using the Model Context Protocol (MCP). By registering SQLcl as an MCP server and saving database credentials locally, users can query Oracle databases using natural language commands directly within the Claude Code interface. In the provided walkthrough, Claude successfully identifies the 10 largest tables in an Oracle 19c database by dynamically inspecting data dictionary views like DBA_SEGMENTS.

Monday, September 29, 2025

AI coding agent : a toy project helps me understand the agentic AI concept a lot

Following the following course :

I have been able to complete the AI coding agent project , which really helped me understand agentic AI from a practical point of view. To put the agent to the test, I introduced an intentional bug in a simple Calculator class by setting the operator precedence incorrectly:



class Calculator:
    def __init__(self):
        self.operators = {
            "+": lambda a, b: a + b,
            "-": lambda a, b: a - b,
            "*": lambda a, b: a * b,
            "/": lambda a, b: a / b,
        }
        self.precedence = {
            "+": 3,           # should be 1, set to 3 so 3 + 5 * 2 = 16
            "-": 1,
            "*": 2,
            "/": 2,
        }

As expected, this flaw caused the calculator to fail basic math. For instance, the expression 3 + 5 * 2 returned 16 instead of the correct value of 13:


(venv313) C:\Users\dsun\Denis_files\python_proj\ai_apps\aicodeagent>python calculator\main.py " 3 + 5 * 2 "
{
  "expression": " 3 + 5 * 2 ",
  "result": 16
}

What defines an "agent" is its ability to autonomously use tools and iterate based on its findings. In this setup, the agent was equipped with four primary functions:



available_functions = types.Tool(
    function_declarations=[
        schema_get_files_info,
        schema_get_file_content,
        schema_run_python_file,
        schema_write_file,
    ]
)

The following "conversation" log illustrates the agent's decision-making process as it navigated the codebase and applied the fix automatically: (venv313) C:\Users\dsun\Denis_files\python_proj\ai_apps\aicodeagent>python main.py " 3 + 5 * 2 should not be 16 please fix the code problem under calculator folder"

User: " 3 + 5 * 2 should not be 16 please fix the code problem under calculator folder"
Model: "I want to call get_files_info..."
 - Calling function: get_files_info
Tool: "Here's the result of get_files_info..."
Model: "I want to call get_files_info..."
 - Calling function: get_files_info
Tool: "Here's the result of get_files_info..."
Model: "I want to call get_file_content..."
 - Calling function: get_file_content
Tool: "Here's the result of get_file_content..."
Model: "I want to call get_files_info..."
 - Calling function: get_files_info
Tool: "Here's the result of get_files_info..."
Model: "I want to call get_file_content..."
 - Calling function: get_file_content
Tool: "Here's the result of get_file_content..."
Model: "I want to call write_file..."
 - Calling function: write_file
Tool: "Here's the result of write_file..."
Model: "I want to call run_python_file..."
 - Calling function: run_python_file
Tool: "Here's the result of run_python_file..."
Model: "Great! The output is 13, which means the fix was successful. The calculator now correctly evaluates the expression "3 + 5 * 2".
"

With the agent's intervention complete, the bug was resolved without any manual code editing:

(venv313) C:\Users\dsun\Denis_files\python_proj\ai_apps\aicodeagent>python calculator\main.py " 3 + 5 * 2 "
{
  "expression": " 3 + 5 * 2 ",
  "result": 13
}

In summary,this experiment highlighted the most critical aspect of agentic AI: the feedback loop. By allowing the agent to run code, analyze errors, and rewrite files, we move beyond simple text generation into the realm of autonomous problem-solving. To me, this project was a vital stepping stone in understanding how AI can act as a true collaborator in the development process.

Monday, June 16, 2025

SnowSQL config password needs to be enclosed in double quotes

Today I tested how to use snowsql to connect to SnowFlake. I put the connection credential in the config file. But I encountered the following error message in the intial attempt:

(venv) c:\Users\Yu\.snowsql>snowsql

250001 (08001): Failed to connect to DB: abcdefg-hij01234.snowflakecomputing.com:443. Incorrect username or password was specified.

If the error message is unclear, enable logging using -o log_level=DEBUG and see the log to find out the cause. Contact support for further help.

Goodbye!

It turned out I need to enclose the password with double quotes in the config file.

for example, in the c:\Users\Yu\.snowsql\.config file:

accountname =abcdefg-hij01234

username =denissun

# password =Ab0cdE725##FG0 -- not working

password = "Ab0cdE725##FG0" -- works!

After put double quotes, I tested again and it succeed:

(venv) c:\Users\Yu\.snowsql>snowsql

* SnowSQL * v1.4.1

Type SQL statements or !help

denissun9883#COMPUTE_WH@(no database).(no schema)>!help

+------------+-------------------------------------------+-------------+--------------------------------------------------------------------------------------------+

|------------+-------------------------------------------+-------------+--------------------------------------------------------------------------------------------|

| !queries | !queries help, <filter>=<value>, <filter> | | Lists queries matching the specified filters. Write <!queries> help for a list of filters. |

+------------+-------------------------------------------+-------------+--------------------------------------------------------------------------------------------+

It could be due to my password has special characters.

Wednesday, May 14, 2025

AI Engineer - Embedding Generation from Germini API and Storing in PostgreSQL

In this post, I first demonstrate that sample Python programs have been developed and tested to do the following two tasks:

1. Create embeddings from sample source data using the Germini API

2. Use PostgreSQL as a vector database and store embeddings data in it using pgvector.

Then I demonstrate similarity search in PostgreSQL with pgvector.

1. Source data preparation:

From the DBAETS application `events` table in the PostgreSQL, dumping ` event_id` and `title` data into a csv file. Total 2021 records.

\copy ( select event_id, title from events order by 1 ) to 'events_title.csv' with csv

The `events` table stores data about database administration events, each entry represents a certain kind of DBA task or activity. The `title` of the `event` table is trivial, but the purpose of using it as the source of content is to make me familiar with the embedding creation and storing process. The end goal is to use the `description` column of the `events` table, which contains a detailed description of the event, therefore providing the useful domain knowledge for DBAs.

2. Embedding generation

The python program emb_to_csv.py is developed to generate embedding from source csv file and generate a csv file including embeddings of `title`. Gemini API is used with model: ‘text-embedding-004’, which generates vectors with 768 dimensions .

==============  screenshot run the program ==============
(.venv) C:\Users\dsun\Denis_files\python_proj\ai_apps\dbaets_pgvector>python emb_to_csv_2.py
   event_id                                              title
0         3  PREP CALL for the 2pm actual call to discuss r...
1         4                   Query Certification / 04-11-2012
2         5        [DemoApp] [Migration] -  RD175935 - Tonight
3         6  RE: myhostpd3:testprod3 ALERT_LOG Error: [SR 3-...
4         7                    space add in mrosple/mxdsacdd02
total batches : 22 batch size: 100 total records: 2120
process batch no: 0
loop: 0 event_id: 3
loop: 10 event_id: 13
loop: 20 event_id: 24
loop: 30 event_id: 34
loop: 40 event_id: 44
loop: 50 event_id: 54
loop: 60 event_id: 64
loop: 70 event_id: 74
loop: 80 event_id: 84
loop: 90 event_id: 94
process batch no: 1
loop: 100 event_id: 104
loop: 110 event_id: 114
loop: 120 event_id: 124
loop: 130 event_id: 134
loop: 140 event_id: 144
loop: 150 event_id: 155
loop: 160 event_id: 165
loop: 170 event_id: 175
loop: 180 event_id: 185
loop: 190 event_id: 195
process batch no: 2
loop: 200 event_id: 205
…
(ommitting ..)

I intentionally used some small batch size and sleep 200 seconds between batches to avoid Gemini API free tier rate limit, if I don't do that, I could end up with errors "RATE_LIMIT_EXCEEDED".

3. Embedding loacding

The python program emb_csv_to_pgvector.py is developed to load the embeddings from csv to a PostgreSQL table.

4. Similarity search using SQL

The following query calculates vector distances using the <#> negative inner product operator to compare event titles against a target event (event_id = 2690). It retrieves the target event's self-similarity score alongside the top 10 most similar distinct events, returning their event IDs, titles, and similarity scores in a single combined result.

etsdb=> -- top event titles that are mostly similar to first title
etsdb=> (
etsdb(> select event_id, title,  embedding <#> embedding as similarity
etsdb(> from event_title_embeddings
etsdb(> where event_id=2690
etsdb(> )
etsdb-> union all
etsdb-> (
etsdb(> select t.event_id , t.title , (t.embedding <#> i.embedding ) as similarity
etsdb(> from event_title_embeddings t join event_title_embeddings i
etsdb(> on t.event_id != i.event_id
etsdb(> and i.event_id=2690
etsdb(> order by similarity
etsdb(> limit 10
etsdb(> )
etsdb-> ;
 event_id |                              title                               |     similarity
----------+------------------------------------------------------------------+---------------------
     2690 | Master note for operation support - mydb2                        | -0.9999982118606567
     2431 | Master note for operation support - mydb1 BVC                    | -0.8375328183174133
     2629 | Master note for operation support - omrtppdb OEM repostory       | -0.8193813562393188
     2444 | Master note for operation support - iexdb                        | -0.7917341589927673
     2450 | mydb4 database support master notes                              | -0.7660208344459534
     2459 | master note for operation support - mydb12sc APP1                | -0.766010582447052
     2876 | Operations Master notes for EV6V- mmypos on dbhost41/42/43       | -0.6405361294746399
     2953 | Master Notes -APP2                                               | -0.6208126544952393
     2937 | SCM master notes                                                 | -0.6061583161354065
     2275 | mydb1 database related doc                                       | -0.6051017045974731
     2879 | Master Notes - APP1 - mydb3                                      | -0.6019692420959473
(11 rows)

In summary, I successfully established an end-to-end embedding pipeline using the Gemini text-embedding-004 model and PostgreSQL with pgvector. By generating 768-dimensional vector embeddings from DBA event titles and querying them using inner product distance (<#>), the setup demonstrated accurate semantic similarity retrieval across database task records. This foundational work sets the stage for scaling up to full event descriptions and implementing a robust domain-specific vector search system.

Tuesday, February 25, 2025

AI Engineer - Starting My Journey

Recently, I have come across a youtube video that is quite inspiring. It explains what AI Engineers do and how to develop AI applications in PostgreSQL. It made me believe I can develop something useful as if I am an AI Engineer! At least I know Python and PostgreSQL reasonably well.

One of the functionalities of the DBAETS application I developed is it is a knowledge base for DBAs. From time to time, I post notes there about database related activities, such as troubleshooting, performance tuning and so on and so forth. My ambition is to develop a Q&A RAG application for DBAs to ask questions and get answers utilizing the knowledge-base data in the DBAETS.

What is RAG?

"In the field of AI, RAG stands for Retrieval-Augmented Generation. It's a framework that combines the strengths of traditional information retrieval systems with generative large language models (LLMs). RAG enhances AI responses by retrieving relevant information from external sources, such as databases or web pages, and using that information to augment the LLM's generation process. This approach helps create more accurate, up-to-date, and relevant text outputs. "

Let the journey begin! And I will post any meaningful progress along the way.

As of today, I have achieved three things as described in the following:

I turned my PostgreSQL db as a vector database by installing the pgvector extension

– installation of pgvector
pgdbhost003.mycompany.com, as root
cd /u01/stage
git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git
cd pgvector
make
make install
– enable the extension
postgres=# \c etsdb
You are now connected to database "etsdb" as user "postgres".
etsdb=# CREATE EXTENSION vector;
CREATE EXTENSION
etsdb=#
– create a table with `vector` data type
etsdb=# create table items(id bigserial primary key, embedding vector(3));
CREATE TABLE
– insert
etsdb=# INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');
INSERT 0 2
– query
etsdb=# SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;
id | embedding
----+-----------
1 | [1,2,3]
2 | [4,5,6]

As you can see I have successfully turned my PostgreSQL database into a vector store.

I am able to call Gemini API

In the following example script, I ask a LLM model what an AI Engineer do through an API



(venv311_prac) C:\Users\dsun\Denis_files\python_proj\ragtest\ragtest>type test_googleapi.py
# ref : https://github.com/google-gemini/cookbook
# ref : https://github.com/googleapis/python-genai
#
# RAG:
#
# https://medium.com/@saurabhgssingh/understanding-rag-building-a-rag-system-from-scratch-with-gemini-api-b11ad9fc1bf7
#
#
# type of services
#   ** Gemini Developer API
#   ** Vertex AI
# pip install google-genai
#
import os
from google import genai
from google.genai import types
client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
response = client.models.generate_content(
    model="gemini-2.0-flash-001",
    contents="Explain what an AI Enigeer do?",
)
print(response.text)

Run the program:

(venv311_prac) C:\Users\dsun\Denis_files\python_proj\ragtest\ragtest>python test_googleapi.py
An AI Engineer is a professional who focuses on **applying artificial intelligence (AI) and machine learning (ML) models to solve real-world problems.** They bridge the gap between theoretical AI research and practical implementation. Think of them as the builders and architects of AI systems.
Here's a breakdown of what an AI Engineer typically does:
**1. Data Handling and Preparation:**
* **Data Collection:** Gathering data from various sources, including databases, APIs, and other data stores.
* **Data Cleaning and Preprocessing:** Cleaning messy data (handling missing values, outliers, and inconsistencies) to make it suitable for training ML models. This is a crucial step as the quality of the data directly impacts the model's performance.
* **Data Transformation:** Converting data into a format that can be easily processed by ML algorithms. This might involve feature scaling, encoding categorical variables, and creating new features.
* **Data Augmentation:** Generating synthetic data to increase the size and diversity of the training dataset, which can improve model robustness.
**2. Model Development and Training:**
* **Model Selection:** Choosing the appropriate AI/ML model based on the problem, data characteristics, and desired outcome (e.g., regression, classification, clustering, or a specific deep learning architecture).
* **Model Training:** Using the prepared data to train the selected model. This involves tuning the model's parameters to optimize its performance.
* **Model Evaluation:** Assessing the trained model's performance using various metrics and techniques (e.g., accuracy, precision, recall, F1-score, ROC curves).
* **Hyperparameter Tuning:** Optimizing the model's hyperparameters (parameters that are not learned during training) to improve its performance. This often involves using techniques like grid search or Bayesian optimization.
**3. Model Deployment and Maintenance:**
* **Model Deployment:** Putting the trained model into a production environment where it can be used to make predictions on new data. This could involve deploying the model to a cloud platform, a server, or an embedded device.
* **Model Monitoring:** Tracking the model's performance in production to ensure it continues to perform as expected.
* **Model Retraining:** Retraining the model with new data to keep it up-to-date and prevent performance degradation (model drift).
* **Infrastructure Management:** Managing the infrastructure required to run the AI system, including servers, databases, and cloud resources.
**4. Collaboration and Communication:**
* **Collaboration:** Working closely with data scientists, software engineers, and other stakeholders to build and deploy AI solutions.
* **Communication:** Effectively communicating technical concepts and findings to both technical and non-technical audiences.
* **Documentation:** Creating and maintaining documentation for the AI system, including the model architecture, training process, and deployment instructions.
**Key Skills for AI Engineers:**
* **Programming:** Strong proficiency in programming languages like Python (most common), Java, or C++.
* **Machine Learning:** A solid understanding of machine learning algorithms and techniques.
* **Deep Learning:** Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras.
* **Data Science Fundamentals:** Knowledge of data analysis, data visualization, and statistical modeling.
* **Data Engineering:** Skills in data wrangling, data cleaning, and data pipelining.
* **Cloud Computing:** Experience with cloud platforms like AWS, Azure, or Google Cloud.
* **DevOps:** Familiarity with DevOps practices like continuous integration and continuous delivery (CI/CD).
* **Software Engineering:** Understanding of software development principles, design patterns, and testing methodologies.
* **Problem-Solving:** Strong analytical and problem-solving skills.
* **Communication:** Excellent communication and collaboration skills.
**In summary, an AI Engineer is a hands-on role that requires a blend of programming, machine learning, and software engineering skills. They are responsible for building, deploying, and maintaining AI systems that solve real-world problems.** They are not just theorists; they are builders who bring AI models to life.

I can generate embeddings:

(venv311_prac) C:\Users\dsun\Denis_files\python_proj\ragtest\ragtest>type gen_emb.py
from google import genai
import os
client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
result = client.models.embed_content(
model="gemini-embedding-exp-03-07",
contents="What is the meaning of life?")
print(result.embeddings)
(venv311_prac) C:\Users\dsun\Denis_files\python_proj\ragtest\ragtest>python gen_emb.py
[ContentEmbedding(values=[-0.022372285, -0.004451784, 0.013473644, -0.053762246, -0.020569915, 0.011864573, 0.015185799, 0.006950965, 0.03180835, 0.007074574, 0.027503368, -0.00600613, -0.014889315, 0.03269886, 0.12054204, 0.019322146, 0.000517173, 0.0045754807, -0.00856155, -0.01532448, 0.015616342, -0.008661197, -0.017454486, 0.0099245, -0.015551475, 0.012284064, 0.020809751, -0.0037114064, 0.025106275, 0.008105811, 0.020252233, 0.0019548477, -0.010780675, 0.027334962, -0.017213175, -0.011735542, 0.009507163, -0.015499457, -0.013591795, 0.0138707, -0.022853972, -0.009638755, -0.0034423112, -0.018855078, 0.018475225, -0.010515843, 0.015031793, -0.042978574, -0.013993226, 0.007916359, -0.012274015, 0.011758872, -0.010251529, -0.15881006, 0.016281825, 0.0103672175, -0.006364179, -0.009997806, -0.025991082, -0.027687864, -0.008586312, -0.014933809, -0.007584574, -0.021427568, 0.008805106, -0.009369353, -0.02012641, 0.011695516, 0.0020037016, 0.012606018, -0.01629937, 0.015133599, -0.005364407, -0.013935832, 0.0065723164, 0.014414399, 0.016648233, -0.009122886, 0.0006896179, -0.0042646583, 0.0001541545, -0.01034019, -0.025387881, -0.01852225, -0.005335359, 0.013288918, -0.008168338, -0.005782173, 0.017077502, -0.0015941358, 0.027414756, 0.00015515718, 0.019269329, 0.026197737, -0.015433854, -0.016303977, -0.0048079626, -0.013850489, 0.018751703, 0.0037166988, -0.029702129, -0.018824589, 0.0037240938, -0.00461113, 0.0010804693, 0.0029576228, 0.020787966, 0.014922842, 0.010893234, -0.00839888, 0.016467841, -0.011249123, -0.014664661, -9.935649e-06, -0.016037786, -0.17255014, 0.011858983, -0.0148698045, -0.019271476, 0.02731455, 0.029812986, 0.0046130605, -0.012905329, 0.010795695, -0.0144493915, 0.0009820901, 0.02241333 ..... ]

In summary, I have successfully enabled vector capabilities in a PostgreSQL database and demonstrated the ability to call the Gemini API. This blog post serves as a starting point of my exploration toward developing practical AI solutions.

Bridging Database and AI Engineering