From 05383715cd64e471bae7a0bb646167fdf4a4b183 Mon Sep 17 00:00:00 2001
From: Quentin Fuxa <quentin.fuxa@sia-partners.com>
Date: Sun, 15 Dec 2024 14:00:04 +0100
Subject: [PATCH] 0.1.0 release

---
 README.md                                    | 17 +++--------------
 rag_handler/__init__.py                      |  2 ++
 rag_handler.py => rag_handler/rag_handler.py |  0
 setup.py                                     | 17 +++++++++++++++++
 4 files changed, 22 insertions(+), 14 deletions(-)
 create mode 100644 rag_handler/__init__.py
 rename rag_handler.py => rag_handler/rag_handler.py (100%)
 create mode 100644 setup.py

diff --git a/README.md b/README.md
index 4450497..4119148 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# RAGHandler for PostgreSQL
+# RAGHandler for PostgreSQL with pgvector
 
 A **Retrieval-Augmented Generation (RAG)** handler class built around a PostgreSQL database and OpenAI’s API. This code allows you to query a database, generate vector embeddings for columns, and integrate these operations with a conversational Large Language Model (LLM), such as GPT.
 
@@ -6,20 +6,9 @@ A **Retrieval-Augmented Generation (RAG)** handler class built around a PostgreS
 
 - **System Prompt Generation:** Automates the creation of a system prompt that describes your PostgreSQL table schema and sample rows.
 
+- **Sophisticated SQL Execution**:  Runs multi-step queries with pgvector embeddings for semantic search and filtering.
 
-- **SQL Execution & Vector Replacement**: Executes SQL queries directly against the database. If the SQL query contains <vector>TEXT<vector/> placeholders, those placeholders are replaced with vector embeddings generated via OpenAI.
-
-- **Chat-Based Interaction:** Supports a chat-based workflow by combining user messages, system prompts, and LLM responses. Integrates function calls for:
-
- 	- Executing SQL queries (execute_sql_query),
- 	- Structuring objects for potential insertion (structure_object_from_draft),
-	-	Generating Plotly graphs (plot_graph).
-
-- **Schema Summarization**: Summarizes a database table’s schema by prompting the LLM, providing a quick overview of column purposes and example values.
-
-- **Object Structuring**: Helps transform free-form text “drafts” into structured objects (e.g., JSON) for database insertion.
-
-- **Embedding Integration**: Automatically generates and stores vector embeddings for specified text columns when creating or replacing tables.
+- **Plotly Graph Generation**: Helps transform free-form text “drafts” into structured objects (e.g., JSON) for database insertion.
 
 
 ## Installation
diff --git a/rag_handler/__init__.py b/rag_handler/__init__.py
new file mode 100644
index 0000000..97cc37c
--- /dev/null
+++ b/rag_handler/__init__.py
@@ -0,0 +1,2 @@
+# __init__.py
+from .rag_handler import RAGHandler, VECTOR
\ No newline at end of file
diff --git a/rag_handler.py b/rag_handler/rag_handler.py
similarity index 100%
rename from rag_handler.py
rename to rag_handler/rag_handler.py
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..948d606
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,17 @@
+from setuptools import setup, find_packages
+
+setup(
+    name='rag-handler',
+    version='0.1.0',
+    description='Retrieval-Augmented Generation Handler using PostgreSQL and OpenAI',
+    author='Quentin Fuxa',
+    packages=find_packages(),
+    install_requires=[
+        'openai',
+        'pandas',
+        'psycopg2',
+        'sqlalchemy',
+        'plotly'
+    ],
+    python_requires='>=3.9'
+)
\ No newline at end of file