first commit

README.md

@@ -64,15 +64,23 @@ Each question is parsed into a structured requirement:
 Facts are extracted **only when explicitly stated** in documentation and stored as graph triples:
 
 ```
-REQUIREMENT -[HAS_METRIC]-> messages per hour
-REQUIREMENT -[HAS_VALUE]-> < 1 hour
-REQUIREMENT -[SUPPORTED_BY]-> Document section
+    SERVICE -[SUPPORTS_CAPABILITY]-> CAPABILITY
+    SERVICE -[DOES_NOT_SUPPORT]-> CAPABILITY
+    SERVICE -[HAS_LIMITATION]-> LIMITATION
+    SERVICE -[HAS_SLA]-> SLA_VALUE
 ```
 
-There are three types of information:
-- What metric: HAS_METRIC 
-- Value of metric: HAS_VALUE
-- Font of information: SUPPORTED_BY
+There are four types of structured relationships extracted explicitly from documentation:
+* Capability support: SERVICE -[SUPPORTS_CAPABILITY]-> CAPABILITY
+* Capability exclusion: SERVICE -[DOES_NOT_SUPPORT]-> CAPABILITY
+* Technical limitation: SERVICE -[HAS_LIMITATION]-> LIMITATION
+* Service level definition: SERVICE -[HAS_SLA]-> SLA_VALUE
+
+Each relationship is:
+* Extracted strictly from explicit documentary evidence
+* Linked to a specific document chunk (CHUNK_HASH)
+* Associated with structured JSON node properties
+* Backed by an evidence table for full auditability
 
 This ensures:
 - No hallucination
@@ -178,7 +186,7 @@ POST /chat
 
 This code implements a **GraphRAG-based pipeline focused on RFP (Request for Proposal) validation**, not generic Q&A.
 
->**Download** the code [graphrag_rerank.py](./files/graphrag_rerank.py)
+>**Download** here the [Source Code](./files/source_code.zip)
 
 The main goal is to:
 - Extract **explicit, verifiable facts** from large PDF contracts and datasheets
@@ -212,7 +220,7 @@ This represents a **strategic shift** from concept-based LLM answers to **compli
         - `REQUIREMENT -[HAS_VALUE]-> 1 hour`
     - Stored in Oracle Property Graph tables
 
-![img_1.png](img_1.png)
+![img_3.png](img_3.png)
 
 5. **RFP Requirement Parsing**
     - Each user question is converted into a structured requirement:
@@ -294,8 +302,7 @@ FIrst of all, you need to run the code to prepare the Vector and Graph database.
 
 ![img.png](img.png)
 
-![img_1.png](img_1.png)
-
+![img_3.png](img_3.png)
 
 After the execution, the code will chat with you to test. You can give some questions like:
 
@@ -368,13 +375,13 @@ root
 
 Call the http://localhost:8100 in your browser.
 
-![img_2.png](img_2.png)
+![img_4.png](img_4.png)
 
 There is also a REST Service implemented in the code, so you can automatize a RFP list calling item by item and obtain the responses you want: YES/NO
 
-    curl -X POST http://localhost:8100/chat \
-    -H "Content-Type: application/json" \
-    -d '{"question": "What is the RTO of Oracle Application?"}'
+    curl -X POST http://demo-orcl-api-ai.hoshikawa.com.br:8100/rest/chat \
+    -H "Content-Type: application/json" -u app_user:app_password \
+    -d '{ "question": "Does Oracle Cloud Infrastructure (OCI) Compute support online resizing of memory for running virtual machine instances?" }'
 
 ---