Upload Index.py

Index.py

@@ -181,7 +181,8 @@ def _check_if_db_exists(db_path: str) -> bool:
 def _load_embeddings_from_db(
     db_present: bool,
     domain: str,
-    path: str = "sentence-transformers/all-MiniLM-L6-v2",
+    #path: str = "sentence-transformers/all-MiniLM-L6-v2",
+    path: str = "sentence-transformers/nli-mpnet-base-v2",
 ):
     # Create embeddings model with content support
     embeddings = Embeddings({"path": path, "content": True})
@@ -218,20 +219,27 @@ def _search(query, extractor, question=None):
     # llm_chain = LLMChain(prompt=prompt, llm=extractor)
 
     # return {"question": question, "answer": llm_chain.run(question)}
+    print(extractor([("answer", query, _prompt(question), False)])[0][1])
     return extractor([("answer", query, _prompt(question), False)])[0][1]
 
 
 @app.get("/rag")
 def rag(domain: str, question: str):
+    print()
     db_exists = _check_if_db_exists(db_path=f"{os.getcwd()}/index/{domain}/documents")
     print(db_exists)
+
+    bool_value = _check_if_db_exists(db_path=f"{os.getcwd()}/index/{domain}/documents")
+    print(bool_value)
+
+
     # if db_exists:
     embeddings = _load_embeddings_from_db(db_exists, domain)
     # Create extractor instance
     #extractor = Extractor(embeddings, "google/flan-t5-base")
     #extractor = Extractor(embeddings, "TheBloke/Llama-2-7B-GGUF")
     print("before calling extractor")
-    extractor = Extractor(embeddings, "google/flan-t5-base")
+    extractor = Extractor(embeddings, "distilbert-base-cased-distilled-squad")
     # llm = HuggingFaceHub(
     #     repo_id="google/flan-t5-xxl",
     #     model_kwargs={"temperature": 1, "max_length": 1000000},