Small fix

service_functions.py

@@ -130,7 +130,7 @@ async def compute_and_store_transcript_embeddings(audio_file_name, list_of_trans
     await store_document_embeddings_in_db(fake_upload_file, file_hash, combined_transcript_text.encode(), json.dumps(computed_embeddings).encode(), computed_embeddings, llm_model_name, ip_address, datetime.utcnow())
     return full_download_url
 
-async def compute_transcript_with_whisper_from_audio_func(audio_file_hash, audio_file_path, audio_file_name, audio_file_size_mb, ip_address, req: Request, compute_embeddings_for_resulting_transcript_document=True, llm_model_name=DEFAULT_MODEL_NAME, corpus_identifier_string: str):
+async def compute_transcript_with_whisper_from_audio_func(audio_file_hash, audio_file_path, audio_file_name, audio_file_size_mb, ip_address, req: Request, corpus_identifier_string: str, compute_embeddings_for_resulting_transcript_document=True, llm_model_name=DEFAULT_MODEL_NAME):
     model_size = "large-v2"
     logger.info(f"Loading Whisper model {model_size}...")
     num_workers = 1 if psutil.virtual_memory().total < 32 * (1024 ** 3) else min(4, max(1, int((psutil.virtual_memory().total - 32 * (1024 ** 3)) / (4 * (1024 ** 3))))) # Only use more than 1 worker if there is at least 32GB of RAM; then use 1 worker per additional 4GB of RAM up to 4 workers max
@@ -191,7 +191,7 @@ async def get_or_compute_transcript(file: UploadFile,
             if corpus_identifier_string is None:
                 corpus_identifier_string = audio_file_hash
 
-            segment_details, info, combined_transcript_text, combined_transcript_text_list_of_metadata_dicts, request_time, response_time, total_time, download_url = await compute_transcript_with_whisper_from_audio_func(audio_file_hash, audio_file_name, file.filename, audio_file_size_mb, ip_address, req, compute_embeddings_for_resulting_transcript_document, llm_model_name, corpus_identifier_string)
+            segment_details, info, combined_transcript_text, combined_transcript_text_list_of_metadata_dicts, request_time, response_time, total_time, download_url = await compute_transcript_with_whisper_from_audio_func(audio_file_hash, audio_file_name, file.filename, audio_file_size_mb, ip_address, req, corpus_identifier_string, compute_embeddings_for_resulting_transcript_document, llm_model_name)
             audio_transcript_response = {
                 "audio_file_hash": audio_file_hash,
                 "audio_file_name": file.filename,

swiss_army_llama.py

@@ -629,12 +629,10 @@ async def search_stored_embeddings_with_query_string_for_semantic_similarity(req
                 if faiss_index is None:
                     raise HTTPException(status_code=400, detail=f"No FAISS index found for model: {llm_model_name}")
                 logger.info("Searching for the most similar string in the FAISS index")
-
                 if request.corpus_identifier_string:
                     associated_texts_by_model = await get_texts_for_corpus_identifier(request.corpus_identifier_string)
                 else:
                     associated_texts_by_model = await get_texts_for_model(llm_model_name)
-
                 similarities, indices = faiss_index.search(input_embedding.reshape(1, -1), num_results)  # Search for num_results similar strings
                 results = []  # Create an empty list to store the results
                 for ii in range(num_results):
@@ -813,10 +811,8 @@ async def get_all_embedding_vectors_for_document(file: UploadFile = File(...),
             hash_obj.update(chunk)
     file_hash = hash_obj.hexdigest()
     logger.info(f"SHA3-256 hash of submitted file: {file_hash}")
-
     if corpus_identifier_string is None:
         corpus_identifier_string = file_hash
-
     unique_id = f"document_embedding_{file_hash}_{llm_model_name}"
     lock = await shared_resources.lock_manager.lock(unique_id)        
     if lock.valid: