lint markdown, representative image

README.md

@@ -1,6 +1,6 @@
-# Gamayun -
+# Gamayun
 
-# Forensic Semantic Search System
+![alt text](Gemini_Generated_Image_wv24iuwv24iuwv24.png)
 
 ## Concept
 
@@ -13,7 +13,6 @@ This enables the combination of semantic understanding with context and temporal
 
 An investigator can ask "show me what happened after they discussed the payment" and the system will find relevant communication about payments, then correlate subsequent activities (file access, application launches, network traffic) in a temporal sequence, regardless of the specific applications or messaging platforms involved.
 
-
 ## System Overview
 
 ### Core Concept
@@ -32,8 +31,6 @@ Client-Server Separation: Compute-intensive operations (embedding generation, LL
 - Deployment in air-gapped labs
 - Efficient resource utilization (centralized compute nodes can serve multiple investigators)
 
-
-
 ## Data Flow
 
 ### Ingestion Pipeline
@@ -162,7 +159,6 @@ Natural Language Query
 | NLP                  | spaCy                     | TBD       | MIT                            | Entity extraction              |
 | Programming Language | Python                    | 3.13+     | PSF                            | Application logic              |
 
-
 ### Infrastructure Requirements
 
 #### Remote Processing
@@ -202,11 +198,11 @@ TBD
 
 ### Secondary: Conversion Engine (algorithmic)
 
-Example: 
+Example:
+
 - SQLite Parser for browser History -> Special Data Object
 - Converter for TSK artifacts -> Metadata in Special Data Object (TBD)
 
-
 ## Use Case Scenarios
 
 ### Scenario 1: Drug Transaction Investigation
@@ -246,5 +242,6 @@ Query: "What happened between receiving the threatening email and deleting files
 3. Returns all artifacts between these timestamps
 4. Visualizes complete timeline Evidence: Establishes sequence of events and potential motive
 
-# License
-BSD 3-Clause (subject to change during development)
+## License
+
+BSD 3-Clause (subject to change during development)