The Oracle Analytics Cloud (OAC) "what's new" section is here:
https://docs.oracle.com/en/cloud/paas/analytics-cloud/acswn/index.html
It was updated with May release.
The 26ai database "New Features" doc has a May update as well
Wednesday, May 6, 2026
Thursday, March 19, 2026
Installing Oracle Analytics Server (OAS) 2026 on my Windows
This is a practice installation of OAS 2026 on my unsupported Windows 11 Laptop. Usually, I prefer a Linux installation for practical purposes, yet I also do a practice one on my laptop.
If you install on a laptop like me, you might want to check here before installation.
The download page is here, or the edelivery site here. If you use edelivery, don't forget the Java Developers Kit 21. In 21 the JRE is included in JDK.
After installing JDK, you should have at least the 2 jar files (3 on Linux):
The basic steps:
- Install JDK 21 if needed
- Install fmw 14.1.2.0.0
- Install Oracle Analytics Server 2026
- Configure Oracle Analytics Server 2026
The short version:
- Download and install JDK from here. I prefer installing in a folder without spaces in the name like c:\JAVA and not the default C:\Program Files\.... that has spaces.
- Install fmw 14.1.2.0.0
- java.exe -jar fmw_14.1.2.0.0_infrastructure.jar
- Install OAS 2026 in the same Oracle Home
- java.exe -jar Oracle_Analytics_Server_2026_Windows.jar
- Configure with RCU set up as separate step (as many prefer) or as part of the general Configure flow. (described here)
- RCU: ORACLE_HOME\oracle_common\bin\RCU.bat don't use the default option there (see below)
- Configure: ORACLE_HOME\bi\bin\config.cmd
- Download and configure the Remote Console from this Git
If you install the RCU on the supported Oracle 26ai, you might get "ERROR - RCU-6080 Global prerequisite check failed for the specified database". Check note KB876701 "Creating Repository Schemas for Oracle Analytics Server Errors With 'RCU-6080' " on support site for a patch or workaround.
Extra steps (not covered here):
Enable Users to Export to Excel: https://docs.oracle.com/en/middleware/bi/analytics-server/administer-oas/enable-users-export-excel.html
Enable the Workbook Email Scheduler: https://docs.oracle.com/en/middleware/bi/analytics-server/administer-oas/enable-workbook-email-scheduler.html
Enable Custom Java Script for Actions: https://docs.oracle.com/en/middleware/bi/analytics-server/administer-oas/enable-custom-javascript-actions.html
Configure Advanced System Settings: https://docs.oracle.com/en/middleware/bi/analytics-server/administer-oas/configure-advanced-system-settings.html
The Longer version:
After installing JDK 21, I can install FMW. On Windows run CMD as Administrator.
c:\JAVA\jdk-21.0.10\bin\java.exe -jar fmw_14.1.2.0.0_infrastructure.jar
Mine was C:\Oracle\Ora_Home1
Next I install the OAS software
c:\JAVA\jdk-21.0.10\bin\java.exe -jar Oracle_Analytics_Server_2026_Windows.jar
in the same home.
You will get Visual C++ installation request
"Setup Failed" usually means this is installed already.
The default option of "Oracle Database enabled for edition-based redefinition" doesn't let you select Analytics in the next screen
The regular Oracle database as Database Type, works fine:
ORACLE_HOME\bi\bin\config.cmd
You will see a welcome screen
If you used the RCU utility, select to use existing Schemas
Next is the port range
In the past I had few cases when the config timed out on the startup phase. What solved the issue for me then was to startup manually the OAS services without closing the UI window and let the config continue.
Don't forget to save all the link at the end:
When you try to Open Console, you will discover that you need to download and configure the Remote Console from this Git.
It looks a bit differant.
Let's do the basic task of adding users with the Remote Console.
On the left panel select the last option of Security Tree
Now you can create users
And see them in OAS:
Thanks to Roy Leeram, for the heads-up.
Tuesday, March 17, 2026
Finding Critical Patch Update (CPU) Advisor For Oracle Analytics Server and Oracle Business Intelligence on oracle support
If you are looking for Critical Patch Update (CPU) Advisor For Oracle Analytics Server and Oracle Business Intelligence (was Document ID 2832967.2) it is now named KA973 and is here now:
https://support.oracle.com/support/?kmContentId=11137874
It is always a good practice to check here https://www.oracle.com/security-alerts/
Monday, March 16, 2026
Using Hugging Face ONNX models in Oracle 26ai and Fixing the ONNX
To create Vector and use RAG in Oracle 23/26ai we can encode data using api or load ONNX formatted model and create datamining model in the DB using DBMS_VECTOR.LOAD_ONNX_MODEL (or with OML4Py).
The ONNX option, usually, has better performance.
*A little shorter syntax might have been using LOAD_ONNX_MODEL_CLOUD. It doesn't influence the core of this post.
I had problems loading few such onnx models from HuggingFace, so I tried to compare one to a default model that is provided by Oracle, all_MiniLM_L12 (see Now Available! Pre-built Embedding Generation model for Oracle Database 26ai).
I used Oracle Code Assist (with Cline and Codex) to compare Oracle provided version and Hugging Face version and write major portions of this blog.
Intro - How I started:
Uploaded relevant ONNX file to OCI bucket. Created PAR (Pre Authenticated Request) for the bucket (or the file).
Used DBMS_CLOUD.CREATE_CREDENTIAL to connect it.
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OCI_OBJ_CRED',
user_ocid => 'ocid1.user.oc1… ',
tenancy_ocid => 'ocid1.tenancy.oc1… ',
private_key => 'THEkeyString==',
fingerprint => '57:45:f…'
);
END;
/
Created a link to the file using DBMS_CLOUD.GET_OBJECT
BEGIN
DBMS_CLOUD.GET_OBJECT(
credential_name => 'OCI_OBJ_CRED',
object_uri => 'https://objectstorage.eu-frankfurt-1.oraclecloud.com/p …
/all_MiniLM_L12.onnx',
directory_name => 'DATA_PUMP_DIR',
file_name => 'all_MiniLM_L12.onnx'
);
END;
/
Loaded the model using DBMS_VECTOR.LOAD_ONNX_MODEL
BEGIN
DBMS_VECTOR.LOAD_ONNX_MODEL(
model_name => 'all_MiniLM_L12',
directory => 'DATA_PUMP_DIR',
file_name => 'all_MiniLM_L12.onnx'
);
END;
/
It worked well for Oracle’s version of all_MiniLM_L12.onnx but gave me error with the version I downloaded from Hugging Face.
Got:
ORA-54408: The "input.input_text"
field in the JSON metadata is not a model input.
ORA-06512: at "SYS.DBMS_VECTOR", line 3033
ORA-06512: at "SYS.DBMS_DATA_MINING", line 369
ORA-06512: at "SYS.DBMS_DATA_MINING", line 5795
ORA-06512: at "SYS.DBMS_VECTOR", line 3025
ORA-06512: at line 2
https://docs.oracle.com/error-help/db/ora-54408/
Error at Line: 1 Column: 1
After fixing the second onnx version as described below, this version of LOAD_ONNX_MODEL worked:
BEGIN
DBMS_VECTOR.LOAD_ONNX_MODEL(
directory => 'DATA_PUMP_DIR',
file_name => 'all_MiniLM_L12_v2.onnx',
model_name => 'all_MiniLM_L12_v2',
metadata => JSON('{
"function":"embedding",
"embeddingOutput": "sentence_embedding",
"input":{"input_text": ["DATA"]}
}')
);
END;
/
Core solution: ONNX + Oracle 23/26ai Embeddings: A Practical Field Guide
If you load custom ONNX embedding models with `DBMS_VECTOR.LOAD_ONNX_MODEL`, model validity alone is **not** enough.
You must align three things:
1. ONNX tensor contract (input/output names, dtypes, ranks)
2. Oracle metadata JSON schema (`embeddingOutput`, `input` mapping)
3. SQL alias mapping used in `VECTOR_EMBEDDING ... USING ... AS ...`
This guide summarizes a real troubleshooting path and the final repeatable pattern.
## 1) Why this path matters
In Oracle 23/26ai, you can generate embeddings either via:
- external APIs
- using OML4Py
- by loading ONNX models directly into DB (this post)
The ONNX route often has better control and performance but is strict about model contract compatibility.
## 2) Baseline sanity check (known-good sample)
Use Oracle’s provided MiniLM sample as your baseline:
- `all_MiniLM_L12_v2_GOOD.onnx`
If baseline works and your custom model fails, your environment is likely fine, and the issue is model contract/metadata.
## 3) Error progression and what it usually means
### ORA-54446
`The embedding input tensor "input_ids" has an invalid element data type.`
**Meaning:** token-ID input model (`input_ids`, `attention_mask`, etc.) is being loaded into a text-input embedding path.
### ORA-54408
`The "input.<name>" field in the JSON metadata is not a model input.`
**Meaning:** metadata input mapping key doesn’t match real ONNX input tensor name.
### ORA-54435
`Invalid JSON field: "embedding"`
**Meaning:** wrong metadata key for your DB release. Use `embeddingOutput`.
### ORA-54442
`Incorrect batch size location in tensor "input_text"`
**Meaning:** input rank/shape is wrong for Oracle batch expectation.
Common failing shape: `input_text: STRING [1, num_sentences]`.
Common working shape: `input_text: STRING ['batch_size']`.
## 4) Working ONNX contract (MiniLM sample)
- input name: `input`
- input type/shape: `STRING ['batch_size']`
- output name: `embedding`
- output type/shape: `FLOAT ['batch_size', 384]`
## 5) Canonical metadata JSON (for this DB style)
```json
{
"function": "embedding",
"embeddingOutput": "embedding",
"input": {"input": ["DATA"]}
}
```
Notes:
- `embeddingOutput` must match a real ONNX output tensor name.
- `input` mapping key must match a real ONNX input tensor name.
- `DATA` is the SQL alias used in scoring (`USING ... AS DATA`).
## 6) General ORA-54442 remediation (Good -> Good2 patch)
If your converted model fails with ORA-54442 and has rank-2 text input, patch input to rank-1 batch.
```python
import onnx
src = r"C:\path\to\your_model_Good.onnx"
dst = r"C:\path\to\your_model_Good2.onnx"
m = onnx.load(src)
i = m.graph.input[0]
# Force rank-1 batch input for text tensor
del i.type.tensor_type.shape.dim[:]
d = i.type.tensor_type.shape.dim.add()
d.dim_param = "batch_size"
onnx.checker.check_model(m)
onnx.save(m, dst)
print("saved", dst)
```
Quick validation:
```python
import onnx
m = onnx.load(r"C:\path\to\your_model_Good2.onnx")
i = m.graph.input[0]
o = m.graph.output[0]
print("IN", i.name, [d.dim_param if d.dim_param else d.dim_value for d in i.type.tensor_type.shape.dim])
print("OUT", o.name, [d.dim_param if d.dim_param else d.dim_value for d in o.type.tensor_type.shape.dim])
```
## 7) ONNX pre-check script (before upload)
```python
# save as: check_oracle_adb_onnx.py
from pathlib import Path
import sys
import onnx
TYPES = {
1: "FLOAT", 2: "UINT8", 3: "INT8", 4: "UINT16", 5: "INT16",
6: "INT32", 7: "INT64", 8: "STRING", 9: "BOOL", 10: "FLOAT16",
11: "DOUBLE", 12: "UINT32", 13: "UINT64", 16: "BFLOAT16",
}
def shape_list(value_info):
t = value_info.type.tensor_type
return [d.dim_param if d.dim_param else d.dim_value for d in t.shape.dim]
def dtype_name(value_info):
return TYPES.get(value_info.type.tensor_type.elem_type, str(value_info.type.tensor_type.elem_type))
def check(path: Path) -> int:
m = onnx.load(str(path))
print(f"\n=== {path.name} ===")
print("Inputs:")
for i in m.graph.input:
print(f" - {i.name}: {dtype_name(i)}, shape={shape_list(i)}")
print("Outputs:")
for o in m.graph.output:
print(f" - {o.name}: {dtype_name(o)}, shape={shape_list(o)}")
errors = []
if len(m.graph.input) != 1:
errors.append("Expected exactly 1 model input.")
else:
inp = m.graph.input[0]
if dtype_name(inp) != "STRING":
errors.append(f"Input dtype should be STRING, got {dtype_name(inp)}.")
if len(shape_list(inp)) != 1:
errors.append(f"Input rank should be 1 [batch_size], got rank {len(shape_list(inp))}.")
if len(m.graph.output) < 1:
errors.append("Expected at least 1 model output.")
else:
out = m.graph.output[0]
if dtype_name(out) not in ("FLOAT", "FLOAT16", "BFLOAT16"):
errors.append(f"Output dtype should be float-like, got {dtype_name(out)}.")
if len(shape_list(out)) != 2:
errors.append(f"Output rank should be 2 [batch_size, dim], got rank {len(shape_list(out))}.")
if errors:
print("\nRESULT: FAIL")
for e in errors:
print(" *", e)
return 1
print("\nRESULT: PASS (heuristically compatible)")
return 0
if __name__ == "__main__":
if len(sys.argv) != 2:
print("Usage: py -3 check_oracle_adb_onnx.py <model.onnx>")
sys.exit(2)
sys.exit(check(Path(sys.argv[1])))
```
Run:
```bash
py -3 check_oracle_adb_onnx.py "C:\path\to\your_model.onnx"
```
## 8) Working SQL pattern (MiniLM sample)
```sql
BEGIN
DBMS_CLOUD.GET_OBJECT(
credential_name => 'OCI_OBJ_CRED',
object_uri => 'https://objectstorage.<region>.oraclecloud.com/n/<ns>/b/<bucket>/o/all_MiniLM_L12_v2_GOOD.onnx',
directory_name => 'DATA_PUMP_DIR',
file_name => 'all_MiniLM_L12_v2_GOOD.onnx'
);
END;
/
```
```sql
BEGIN
DBMS_VECTOR.LOAD_ONNX_MODEL(
directory => 'DATA_PUMP_DIR',
file_name => 'all_MiniLM_L12_v2_GOOD.onnx',
model_name => 'ALL_MINILM_L12_V2_GOOD',
metadata => JSON('{
"function": "embedding",
"embeddingOutput": "embedding",
"input": {"input": ["DATA"]}
}')
);
END;
/
```
```sql
SELECT VECTOR_EMBEDDING(ALL_MINILM_L12_V2_GOOD USING 'Hello World' AS DATA) AS emb
FROM dual;
```
## 9) Operational checklist
1. Inspect ONNX input/output names before load.
2. Prefer explicit metadata over defaults.
3. Ensure `embeddingOutput` key matches your DB release.
4. If ORA-54442 appears, patch rank-2 text input to rank-1 batch.
5. Treat ORA errors as contract diagnostics, not random failures.
## Final takeaway
Reliable in-DB embeddings depend on strict interface alignment:
**ONNX tensor contract + Oracle metadata contract + SQL alias mapping**
Once aligned, `LOAD_ONNX_MODEL` and `VECTOR_EMBEDDING` become repeatable and predictable.
Alternative approach of OML4Py: Running Hugging Face models inside Oracle 23ai with ONNX and OML4Py by fsarcosdb
Tuesday, March 10, 2026
Oracle Analytics Server 2026 is available
Oracle has released this year OAS version, OAS 2026, 26.01.0.0.0.
This is the official blog post about the release; it includes some new features too: https://blogs.oracle.com/analytics/announcing-the-general-availability-of-oracle-analytics-server-2026
The documentation is here.
The "what's new in OAS 2026" section is here. Note the long overdue "Share content using workbook email schedules". It seems the OAS 2026 is very similar to OAC Jan 2026, just without all the AI/LLM/AI Agents layer that is free in OAC.
While the download page is here, I recommend using the edelivery site here.
On edelivery, when you search for Analytics server, the proper Weblogic Server (Fusion Middleware Infrastructure) is suggested:
I will update the post when more docker images are available.
Gianni Ceresa has his first version available here https://github.com/gianniceresa/docker-images/tree/master/OracleAnalyticsServer/26.01.0.0.0
Please note the Weblogic Server (Fusion Middleware Infrastructure) is 14c now (14.2.1.0)
Sunday, February 22, 2026
Comparing Hebrew (or other non-English) strings in Oracle
Had 2 list of cities in Hebrew. Used utl_Match.jaro_winker_similarity to find the similarity.
I have 2 tables: Bagrut with column LOC and Israel_all_loc with columns SETTLEMENT_CODE, SETTLEMENT_NAME. There are small differences in some of the names such as:
תל אביב-יפו vs. תל אביב - יפו
or
כנרת )קבוצה( vs. כנרת (קבוצה)
or
נהרייה vs. נהריה
The list are few hundreds each, so did a cartesian join with a similarity ranking:
select * From
(
select SETTLEMENT_CODE, SETTLEMENT_NAME, LOC,
utl_MATCH.jaro_winkler_similarity(SETTLEMENT_NAME, LOC) similarity
from
(select distinct SETTLEMENT_CODE, SETTLEMENT_NAME from ISRAEL_ALL_LOC) a,
(select distinct LOC from BAGRUT) B
)
where similarity>95
The 95 similarity was selected after some manual examinations.
Next checked each LOC with more than 1 line and selected the max(similarity).
Still had to delete 4 lines manually.
Thursday, February 12, 2026
Using Autonomous DB - Data Pump
The Data Pump tool in Autonomous DB is designed to import DMP files as an object from a bucket. By default, the database does not have access to the buckets, that is why we need to do the following steps. Similar steps are required for external tables from buckets (not covered here).
Few relevant links:
Oracle Doc - importing data using data pump
CarlosAL - how to import dump files into oci autonomous database using database actions
Stuart Coggins (Coggs) - cloud credentials with oci
If you try using Data Pump with no setup, you should expect an error:
In most cases in Autonomous DB things are rather autonomous or one click away. This is not one of those cases.
Part of the setup is done in the DB and part in the OCI itself. Since I use it only once, I'm going to use the Admin user in DB and my user in OCI. If you plan using Data Pump, as repeating process, you might want to consider otherwise, especially regarding the OCI user, so you are not dependent on a specific person.
As Admin user in SQL I run:
EXEC DBMS_CLOUD_ADMIN.ENABLE_RESOURCE_PRINCIPAL();
This creates the credential "OCI$RESOURCE_PRINCIPAL" in the Database.
Don't bother the Data Pump yet... You wouldn't get the previous error and will be able to select Credential Name (OCI$RESOURCE_PRINCIPAL) but will not be able to select Compartment or a Bucket.
- Create a Dynamic Group & relevant Policy in OCI
- Create API keys for my user in OCI
- Use them in a relevant SQL
Only then, I can use the Data Pump.
Create a Dynamic Group & relevant Policy in OCI
In OCI go to your Autonomous AI DB, click the ... (3 dots) and copy OCID. We will use it in the dynamic group setting
- You are a member of the Administrators group.
- You are granted the Identity Domain Administrator role or the Security Administrator role.
- You are a member of a group that is granted manage identity-domains or manage dynamic-groups.
Under OCI console: Identity and Security, Domains
In the domain (I used the default) click Dynamic Groups and create Dynamic group.
Set the Rule in the format
any { resource.id = 'Copied DB OCID'}
Press Create
Under OCI console: Identity and Security, Policies
Press Create Policy
The documentation named the policy ObjectStorageReadersPolicy, I will do the same.
Select the relevant compartment level.
In policy builder switch to manual and enter
Allow dynamic-group YOUR_DYNAMIC_GROUP_NAME to read buckets in tenancy
Create and add a second statement
Allow dynamic-group YOUR_DYNAMIC_GROUP_NAME to read objects in tenancy
The flow is: the Dynamic Group is connected to the DB and the Policy is connected to the Dynamic Group.
Create API keys for my user in OCI
The documentation advised to create a user for this task. I used my own.
Click the profile icon on the Right and click your username.
Select Tokens and Keys and press Add API key.
Download the Private key. Now we can press Add.
Copy and save the Configurate data from next screen. Close.
Use them in a relevant SQL
Return to the SQL in Autonomous AI databases (in your database select the SQL Database Action).
Use the value from previous section of API keys to in the code. All cove from the data you saved, except the private key (only the text between -----BEGIN PRIVATE KEY----- and -----END PRIVATE KEY-----):
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL (
credential_name => 'API_KEY',
user_ocid => 'YOUR User OCID',
tenancy_ocid => 'YOUR Tenancy OCID',
private_key => 'M. . .T=',
fingerprint => 'YOUR fingerprint');
END;
(It might also work with AUH_TOKEN as described in Stuart Coggins (Coggs) - cloud credentials with oci, didn't try it myself.)
Now it's time go back to the Data Pump tool in the Database, and press Import. In my case it took a minute or 2 for data Pump to be able to actually see the bucket.
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