👍🏼 Update Demo with Ingestion Retrival and also Query - Adithya S K

demo.py

@@ -10,7 +10,7 @@
 import time
 from collections import namedtuple
 import pandas as pd
-
+import concurrent.futures
 from varag.rag import SimpleRAG, VisionRAG, ColpaliRAG, HybridColpaliRAG
 from varag.vlms import OpenAI
 from varag.llms import OpenAI as OpenAILLM
@@ -20,7 +20,7 @@
 load_dotenv()
 
 # Initialize shared database
-shared_db = lancedb.connect("~/demo_rag_db")
+shared_db = lancedb.connect("~/rag_db")
 
 # Initialize embedding models
 text_embedding_model = SentenceTransformer("all-MiniLM-L6-v2", trust_remote_code=True)
@@ -62,8 +62,6 @@ def ingest_data(pdf_files, use_ocr, chunk_size, progress=gr.Progress()):
     total_start_time = time.time()
     progress_data = []
 
-    progress(0, desc="Starting ingestion process")
-
     # SimpleRAG
     yield IngestResult(
         status_text="Starting SimpleRAG ingestion...\n",
@@ -148,27 +146,80 @@ def ingest_data(pdf_files, use_ocr, chunk_size, progress=gr.Progress()):
     )
 
 
-def retrieve_data(query, top_k):
+def retrieve_data(query, top_k, sequential=False):
+    results = {}
+    timings = {}
+
+    def retrieve_simple():
+        start_time = time.time()
+        simple_results = simple_rag.search(query, k=top_k)
+
+        print(simple_results)
+
+        simple_context = "\n".join([r["text"] for r in simple_results])
+        end_time = time.time()
+        return "SimpleRAG", simple_context, end_time - start_time
+
+    def retrieve_vision():
+        start_time = time.time()
+        vision_results = vision_rag.search(query, k=top_k)
+        vision_images = [r["image"] for r in vision_results]
+        end_time = time.time()
+        return "VisionRAG", vision_images, end_time - start_time
+
+    def retrieve_colpali():
+        start_time = time.time()
+        colpali_results = colpali_rag.search(query, k=top_k)
+        colpali_images = [r["image"] for r in colpali_results]
+        end_time = time.time()
+        return "ColpaliRAG", colpali_images, end_time - start_time
+
+    def retrieve_hybrid():
+        start_time = time.time()
+        hybrid_results = hybrid_rag.search(query, k=top_k, use_image_search=True)
+        hybrid_images = [r["image"] for r in hybrid_results]
+        end_time = time.time()
+        return "HybridColpaliRAG", hybrid_images, end_time - start_time
+
+    retrieval_functions = [
+        retrieve_simple,
+        retrieve_vision,
+        retrieve_colpali,
+        retrieve_hybrid,
+    ]
+
+    if sequential:
+        for func in retrieval_functions:
+            rag_type, content, timing = func()
+            results[rag_type] = content
+            timings[rag_type] = timing
+    else:
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            future_results = [executor.submit(func) for func in retrieval_functions]
+            for future in concurrent.futures.as_completed(future_results):
+                rag_type, content, timing = future.result()
+                results[rag_type] = content
+                timings[rag_type] = timing
+
+    return results, timings
+
+
+def query_data(query, retrieved_results):
     results = {}
 
     # SimpleRAG
-    simple_results = simple_rag.search(query, k=top_k)
-    simple_context = "\n".join([r["text"] for r in simple_results])
-    simple_response = vlm.query(
+    simple_context = retrieved_results["SimpleRAG"]
+    simple_response = llm.query(
         context=simple_context,
         system_prompt="Given the below information answer the questions",
         query=query,
     )
     results["SimpleRAG"] = {"response": simple_response, "context": simple_context}
 
     # VisionRAG
-    vision_results = vision_rag.search(query, k=top_k)
-    vision_images = [r["image"] for r in vision_results]
+    vision_images = retrieved_results["VisionRAG"]
     vision_context = f"Query: {query}\n\nRelevant image information:\n" + "\n".join(
-        [
-            f"Image {i+1}: From document '{r['document_name']}', page {r['page_number']}"
-            for i, r in enumerate(vision_results)
-        ]
+        [f"Image {i+1}" for i in range(len(vision_images))]
     )
     vision_response = vlm.query(vision_context, vision_images, max_tokens=500)
     results["VisionRAG"] = {
@@ -178,13 +229,9 @@ def retrieve_data(query, top_k):
     }
 
     # ColpaliRAG
-    colpali_results = colpali_rag.search(query, k=top_k)
-    colpali_images = [r["image"] for r in colpali_results]
+    colpali_images = retrieved_results["ColpaliRAG"]
     colpali_context = f"Query: {query}\n\nRelevant image information:\n" + "\n".join(
-        [
-            f"Image {i+1}: From document '{r['name']}', page {r['page_number']}\nText: {r['page_text'][:500]}..."
-            for i, r in enumerate(colpali_results)
-        ]
+        [f"Image {i+1}" for i in range(len(colpali_images))]
     )
     colpali_response = vlm.query(colpali_context, colpali_images, max_tokens=500)
     results["ColpaliRAG"] = {
@@ -194,13 +241,9 @@ def retrieve_data(query, top_k):
     }
 
     # HybridColpaliRAG
-    hybrid_results = hybrid_rag.search(query, k=top_k, use_image_search=True)
-    hybrid_images = [r["image"] for r in hybrid_results]
+    hybrid_images = retrieved_results["HybridColpaliRAG"]
     hybrid_context = f"Query: {query}\n\nRelevant image information:\n" + "\n".join(
-        [
-            f"Image {i+1}: From document '{r['name']}', page {r['page_number']}\nText: {r['page_text'][:500]}..."
-            for i, r in enumerate(hybrid_results)
-        ]
+        [f"Image {i+1}" for i in range(len(hybrid_images))]
     )
     hybrid_response = vlm.query(hybrid_context, hybrid_images, max_tokens=500)
     results["HybridColpaliRAG"] = {
@@ -238,42 +281,34 @@ def gradio_interface():
                 50, 5000, value=200, step=10, label="Chunk Size (for SimpleRAG)"
             )
             ingest_button = gr.Button("Ingest PDFs")
-            ingest_output = gr.Textbox(label="Ingestion Status", lines=10)
+            ingest_output = gr.Markdown(label="Ingestion Status", lines=10)
             progress_table = gr.DataFrame(
                 label="Ingestion Progress", headers=["Technique", "Time Taken (s)"]
             )
 
-        with gr.Tab("Retrieve Data"):
+        with gr.Tab("Retrieve and Query Data"):
             query_input = gr.Textbox(label="Enter your query")
             top_k_slider = gr.Slider(1, 10, value=3, step=1, label="Top K Results")
-            search_button = gr.Button("Search and Analyze")
+            sequential_checkbox = gr.Checkbox(label="Sequential Retrieval", value=False)
+            retrieve_button = gr.Button("Retrieve")
+            query_button = gr.Button("Query")
+
+            retrieval_timing = gr.DataFrame(
+                label="Retrieval Timings", headers=["RAG Type", "Time (s)"]
+            )
+
+            with gr.Row():
+                simple_content = gr.Markdown(label="SimpleRAG Content")
+                vision_gallery = gr.Gallery(label="VisionRAG Images")
+                colpali_gallery = gr.Gallery(label="ColpaliRAG Images")
+                hybrid_gallery = gr.Gallery(label="HybridColpaliRAG Images")
 
             with gr.Row():
                 simple_response = gr.Markdown(label="SimpleRAG Response")
                 vision_response = gr.Markdown(label="VisionRAG Response")
                 colpali_response = gr.Markdown(label="ColpaliRAG Response")
                 hybrid_response = gr.Markdown(label="HybridColpaliRAG Response")
 
-            with gr.Row():
-                simple_context = gr.Accordion("SimpleRAG Context", open=False)
-                with simple_context:
-                    gr.Markdown(elem_id="simple_context")
-
-                vision_context = gr.Accordion("VisionRAG Context", open=False)
-                with vision_context:
-                    gr.Markdown(elem_id="vision_context")
-                    gr.Gallery(label="VisionRAG Images")
-
-                colpali_context = gr.Accordion("ColpaliRAG Context", open=False)
-                with colpali_context:
-                    gr.Markdown(elem_id="colpali_context")
-                    gr.Gallery(label="ColpaliRAG Images")
-
-                hybrid_context = gr.Accordion("HybridColpaliRAG Context", open=False)
-                with hybrid_context:
-                    gr.Markdown(elem_id="hybrid_context")
-                    gr.Gallery(label="HybridColpaliRAG Images")
-
         with gr.Tab("Settings"):
             api_key_input = gr.Textbox(label="OpenAI API Key", type="password")
             update_api_button = gr.Button("Update API Key")
@@ -294,27 +329,61 @@ def gradio_interface():
             update_table_button = gr.Button("Update Table Names")
             table_update_status = gr.Textbox(label="Table Update Status")
 
-        ingest_button.click(
-            ingest_data,
-            inputs=[pdf_input, use_ocr, chunk_size],
-            outputs=[ingest_output, progress_table],
+        retrieved_results = gr.State({})
+
+        def update_retrieval_results(query, top_k, sequential):
+            results, timings = retrieve_data(query, top_k, sequential)
+            timing_df = pd.DataFrame(
+                list(timings.items()), columns=["RAG Type", "Time (s)"]
+            )
+            return (
+                results["SimpleRAG"],
+                results["VisionRAG"],
+                results["ColpaliRAG"],
+                results["HybridColpaliRAG"],
+                timing_df,
+                results,
+            )
+
+        retrieve_button.click(
+            update_retrieval_results,
+            inputs=[query_input, top_k_slider, sequential_checkbox],
+            outputs=[
+                simple_content,
+                vision_gallery,
+                colpali_gallery,
+                hybrid_gallery,
+                retrieval_timing,
+                retrieved_results,
+            ],
         )
 
-        search_button.click(
-            retrieve_data,
-            inputs=[query_input, top_k_slider],
+        def update_query_results(query, retrieved_results):
+            results = query_data(query, retrieved_results)
+            return (
+                results["SimpleRAG"]["response"],
+                results["VisionRAG"]["response"],
+                results["ColpaliRAG"]["response"],
+                results["HybridColpaliRAG"]["response"],
+            )
+
+        query_button.click(
+            update_query_results,
+            inputs=[query_input, retrieved_results],
             outputs=[
                 simple_response,
                 vision_response,
                 colpali_response,
                 hybrid_response,
-                simple_context,
-                vision_context,
-                colpali_context,
-                hybrid_context,
             ],
         )
 
+        ingest_button.click(
+            ingest_data,
+            inputs=[pdf_input, use_ocr, chunk_size],
+            outputs=[ingest_output, progress_table],
+        )
+
         update_api_button.click(
             update_api_key, inputs=[api_key_input], outputs=api_update_status
         )