fix minor bug in demo - Adithya S K

Author: adithya-s-k

demo.py

@@ -281,7 +281,9 @@ def gradio_interface():
                 50, 5000, value=200, step=10, label="Chunk Size (for SimpleRAG)"
             )
             ingest_button = gr.Button("Ingest PDFs")
-            ingest_output = gr.Markdown(label="Ingestion Status", lines=10)
+            ingest_output = gr.Markdown(
+                label="Ingestion Status :",
+            )
             progress_table = gr.DataFrame(
                 label="Ingestion Progress", headers=["Technique", "Time Taken (s)"]
             )

varag/rag/_simpleRAG.py

@@ -271,62 +271,3 @@ def change_table(self, new_table_name: str):
             self.table_name, schema=self.schema, exist_ok=True
         )
         print(f"Switched to new table: {self.table_name}")
-
-
-# Usage example:
-if __name__ == "__main__":
-    from sentence_transformers import SentenceTransformer
-
-    # Initialize embedding model
-    embedding_model = SentenceTransformer("all-MiniLM-L6-v2", trust_remote_code=True)
-
-    # Initialize TextRAG
-    text_rag = SimpleRAG(
-        text_embedding_model=embedding_model,
-        db_path="~/visionrag_db",
-        table_name="default_table",
-    )
-
-    # Initialize OpenAI client
-    llm = OpenAI()
-
-    # Index PDFs
-    pdf_paths = ["path/to/pdf1.pdf", "path/to/pdf2.pdf"]
-    result = text_rag.index(
-        pdf_paths,
-        recursive=True,
-        chunking_strategy=FixedTokenChunker(chunk_size=500),
-        metadata={"source": "example_data"},
-        overwrite=True,
-        verbose=True,
-        ocr=True,
-    )
-    print(result)
-
-    # Search for relevant chunks
-    query = "What is the main topic of the documents?"
-    search_results = text_rag.search(query, k=5)
-    print("Search results:", search_results)
-
-    # Generate response using OpenAI
-    context = "\n".join([r["text"] for r in search_results])
-    response = llm.chat.completions.create(
-        model="gpt-4",
-        messages=[
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": f"Context: {context}\n\nQuestion: {query}"},
-        ],
-    )
-    print("Generated response:", response.choices[0].message.content)
-
-    # Add new documents to the index
-    new_pdf_paths = ["path/to/new_pdf.pdf"]
-    result = text_rag.add_to_index(
-        new_pdf_paths,
-        recursive=False,
-        chunking_strategy=FixedTokenChunker(chunk_size=500),
-        metadata={"project": "new_project"},
-        verbose=True,
-        ocr=False,
-    )
-    print(result)