Upload 14 files

orchestrator.py

@@ -33,7 +33,7 @@ from tax_optimizer import TaxOptimizer
 RULES_PATH = "rules/rules_all.yaml"  # adjust if yours is different
 PDF_SOURCE = "data"                  # folder or a single PDF
 EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
-GROQ_MODEL = "llama-3.1-70b-versatile"
+GROQ_MODEL = "llama-3.3-70b-versatile"
 
 # Use /tmp for vector store in Hugging Face Spaces (writable directory)
 VECTOR_STORE_DIR = os.getenv('VECTOR_STORE_DIR', '/tmp/vector_store')

rag_pipeline.py

@@ -344,7 +344,7 @@ class RAGPipeline:
     def __init__(
         self,
         doc_store: DocumentStore,
-        model: str = "llama-3.1-70b-versatile",
+        model: str = "llama-3.3-70b-versatile",
         temperature: float = 0.1,
         max_tokens: int = 4096,
         top_k: int = 8,
@@ -1106,7 +1106,7 @@ def main():
     parser.add_argument(
         "--model",
         type=str,
-        default="llama-3.1-70b-versatile",
+        default="llama-3.3-70b-versatile",
         help="Groq model name"
     )
     parser.add_argument(

test_enhanced_responses.py

@@ -28,7 +28,7 @@ def test_student_question():
     # Initialize RAG
     rag = RAGPipeline(
         doc_store=doc_store,
-        model="llama-3.1-70b-versatile",
+        model="llama-3.3-70b-versatile",
         temperature=0.1,
         top_k=8
     )
@@ -74,7 +74,7 @@ def test_multiple_personas():
     
     rag = RAGPipeline(
         doc_store=doc_store,
-        model="llama-3.1-70b-versatile",
+        model="llama-3.3-70b-versatile",
         temperature=0.1,
         top_k=6
     )

test_optimizer.py

@@ -200,7 +200,7 @@ def test_with_rag():
         )
         pdfs = doc_store.discover_pdfs(pdf_source)
         doc_store.build_vector_store(pdfs, force_rebuild=False)
-        rag = RAGPipeline(doc_store=doc_store, model="llama-3.1-70b-versatile", temperature=0.1)
+        rag = RAGPipeline(doc_store=doc_store, model="llama-3.3-70b-versatile", temperature=0.1)
         
         # Initialize tax engine
         catalog = RuleCatalog.from_yaml_files(["rules/rules_all.yaml"])
@@ -293,7 +293,7 @@ def test_high_earner():
         )
         pdfs = doc_store.discover_pdfs(pdf_source)
         doc_store.build_vector_store(pdfs, force_rebuild=False)
-        rag = RAGPipeline(doc_store=doc_store, model="llama-3.1-70b-versatile", temperature=0.1)
+        rag = RAGPipeline(doc_store=doc_store, model="llama-3.3-70b-versatile", temperature=0.1)
         
         # Initialize tax engine
         catalog = RuleCatalog.from_yaml_files(["rules/rules_all.yaml"])