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new f3fa16ba data-extract: fix README and schema
f3fa16ba is described below
commit f3fa16ba5482731d99e34f2c757b4093d56a03c8
Author: aldettinger <[email protected]>
AuthorDate: Tue Jan 6 15:34:52 2026 +0100
data-extract: fix README and schema
---
data-extract-langchain4j/README.adoc | 20 ++++++++++----------
data-extract-langchain4j/schema.png | Bin 96255 -> 99984 bytes
data-extract-langchain4j/schemas-source.odp | Bin 36144 -> 37250 bytes
3 files changed, 10 insertions(+), 10 deletions(-)
diff --git a/data-extract-langchain4j/README.adoc
b/data-extract-langchain4j/README.adoc
index b72a898f..02721a07 100644
--- a/data-extract-langchain4j/README.adoc
+++ b/data-extract-langchain4j/README.adoc
@@ -31,7 +31,7 @@ After a moment, a log like below should be output:
[source,shell]
----
-time=2024-10-08T12:43:43.329Z level=INFO source=types.go:107 msg="inference
compute" id=0 library=cpu variant=avx2 compute="" driver=0.0 name=""
total="62.5 GiB" available="52.4 GiB"
+time=2026-01-06T14:21:55.578Z level=INFO source=types.go:130 msg="inference
compute" id=0 library=cpu variant="" compute="" driver=0.0 name="" total="62.2
GiB" available="49.9 GiB"
----
Then, download the codellama model, in a second shell type:
@@ -83,7 +83,7 @@ The Camel route should output a log as below:
[source,shell]
----
-024-09-03 10:14:34,757 INFO [route1] (Camel (camel-1) thread #1 -
file://target/transcripts) A document has been received by the
camel-quarkus-file extension: {
+2026-01-06 15:24:48,298 INFO [route1] (Camel (camel-1) thread #1 -
file://target/transcripts) A document has been received by the
camel-quarkus-file extension: {
"id": 1,
"content": "Operator: Hello, how may I help you ?\nCustomer: Hello, I'm
calling because I need to declare an accident on my main vehicle.\nOperator:
Ok, can you please give me your name ?\nCustomer: My name is Sarah
London.\nOperator: Could you please give me your birth date ?\nCustomer: 1986,
July the 10th.\nOperator: Ok, I've got your contract and I'm happy to share
with you that we'll be able to reimburse all expenses linked to this
accident.\nCustomer: Oh great, many thanks."
}
@@ -99,20 +99,20 @@ At the end, we are provided with a Plain Old Java Object
(POJO) handling the ext
[source,shell]
----
-2024-09-03 10:14:51,284 INFO [org.acm.ext.CustomPojoStore] (Camel (camel-1)
thread #1 - file://target/transcripts) An extracted POJO has been added to the
store:
+2026-01-06 15:24:56,889 INFO [org.acme.extraction.CustomPojoStore] (Camel
(camel-1) thread #1 - file://target/transcripts) An extracted POJO has been
added to the store:
{
"customerSatisfied": "true",
"customerName": "Sarah London",
"customerBirthday": "10 July 1986",
- "summary": "The customer, Sarah London, called to declare an accident on
her main vehicle and was informed that all expenses related to the accident
would be reimbursed."
+ "summary": "The customer, Sarah London, is calling to declare an accident
and seek reimbursement for related expenses."
}
----
-See how the LLM shows its capacity to:
- * Extract a human friendly sentiment like `customerSatisfied`
- * Exhibits
https://nlp.stanford.edu/projects/coref.shtml#:~:text=Overview,question%20answering%2C%20and%20information%20extraction.[coreference
resolution], like `customerName` that is deduced from information spread in
the whole conversation
- * Manage issues related to date format, like the field `customerBirthday`
- * Mixed structured and unstructured data (semi-structured data) with the
field `summary`.
+.See how the LLM shows its capacity to:
+* Extract a human friendly sentiment like `customerSatisfied`
+* Exhibits
https://nlp.stanford.edu/projects/coref.shtml#:~:text=Overview,question%20answering%2C%20and%20information%20extraction.[coreference
resolution], like `customerName` that is deduced from information spread in
the whole conversation
+* Manage issues related to date format, like the field `customerBirthday`
+* Mixed structured and unstructured data (semi-structured data) with the field
`summary`.
Notice how all of this is computed simultaneously during a single LLM
inference.
@@ -121,7 +121,7 @@ For each of them, it could be interesting to compare the
unstructured input text
Details of the LangChain4j `AiService` setup can be found in class
`CustomPojoExtractionService`.
-Details of the custom data extract `camel-langchain4j-agent` AI agent
implementation can be found in classes `DataExtractAgent` and
`DataExtractConfiguration`.
+Details of the custom data extract `camel-langchain4j-agent` AI agent
implementation can be found in classes `DataExtractAgent` and
`DataExtractAgentConfiguration`.
==== Native mode
diff --git a/data-extract-langchain4j/schema.png
b/data-extract-langchain4j/schema.png
index 4a8b105b..797c4daa 100644
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diff --git a/data-extract-langchain4j/schemas-source.odp
b/data-extract-langchain4j/schemas-source.odp
index ef421397..f8e8b1fe 100644
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