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ncorriveau · Oct 31, 2024 · 07595e6 · 07595e6
1 parent cc04880
commit 07595e6
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Showing 4 changed files with 2 additions and 100 deletions.
diff --git a/src/backend/app/api_v1/endpoints/appointments.py b/src/backend/app/api_v1/endpoints/appointments.py
@@ -11,7 +11,8 @@
 from dotenv import load_dotenv
 from fastapi import APIRouter, BackgroundTasks, File, Header, HTTPException, UploadFile
 from fastapi.responses import JSONResponse
-from llama_index.core import SimpleDirectoryReader, download_loader
+from llama_index.core import SimpleDirectoryReader
+from llama_index.readers.s3 import S3Reader
 from openai import AsyncOpenAI, OpenAI
 from psycopg2.extensions import connection
 from pydantic import BaseModel, Field
@@ -70,7 +71,6 @@
     aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
     region_name=AWS_REGION,
 )
-S3Reader = download_loader("S3Reader")
 
 
 class AppointmentAnalysis:

diff --git a/src/backend/app/db/nosql_db.py b/src/backend/app/db/nosql_db.py
@@ -152,60 +152,3 @@ def get_relevant_providers(
     # Perform the query
     results = provider_collection.find(query)
     return list(results)
-
-
-if __name__ == "__main__":
-
-    # Establish connection
-    client = MongoClient("mongodb://localhost:27017/")
-    db = client["wilson_ai"]
-    providers = db.providers
-
-    # Example document that will come in
-    # note that the document 'schema' will contain a list of locations and insurances but on any given
-    # update we will just be providing one from the incoming document
-    provider_info = {
-        # "npi": "1447331368",
-        "first_name": "Christine",
-        "last_name": "Corriveau",
-        "degree": "MD",
-        "email": None,
-        "phone_number": None,
-        "specialty": "PEDIATRIC",
-        # more complex info
-        "location": {
-            "name": "Children's National Hospital",
-            "street": "111 Michigan Ave NW",
-            "city": "WASHINGTON",
-            "state": "DC",
-            "zip_code": "20010-2978",
-            "coordinates": {
-                "lat": 40.766668,
-                "long": -73.9814608,
-            },
-        },
-    }
-
-    # # # Inserting a document
-    # # inserted_id = providers.insert_one(provider_info).inserted_id
-    # # print("Inserted physician with ID:", inserted_id)
-    # modified_count = upsert_provider(providers, provider_info)
-
-    patient_info = {"lat": 40.766668, "lng": -73.9814608, "insurance_id": 3}
-
-    # # Fetching a document
-    # # doc = providers.find_one({"specialties": 'PCP', "insurances.id": patient_info["insurance_id"]})  # , "insurances.insurance_id": patient_info["insurance_id"]
-    # # pprint.pprint(doc)
-    # results = get_relevant_providers(providers, patient_info, "PCP")
-    # print(results)
-    # for result in results:
-    # providers.update_one(
-    #     {"npi": "1609958305"},
-    #     {"$set": {"specialties": ["PCP"]}},
-    # )
-    pprint.pprint(
-        providers.find_one(
-            {"npi": "1609958305"},
-            {"first_name": 1, "last_name": 1, "specialties": 1, "_id": 0},
-        )
-    )
diff --git a/src/backend/app/db/relational_db.py b/src/backend/app/db/relational_db.py
@@ -547,20 +547,3 @@ def authenticate_user(conn: connection, email: str, password: str) -> Dict[str,
         return False
 
     return user
-
-
-if __name__ == "__main__":
-    params = {
-        "user_id": 1,
-        "provider_id": "1568424935",
-        "filename": "data",
-        "summary": "The patient has been experiencing a chronic cough for three years, associated with symptoms like throat clearing, intermittent dry cough, recurrent heartburn, postnasal drip, and chest tightness during physical activity. Despite previous treatments including Albuterol, Astelin spray, and Prevacid, the cough has persisted. Current recommendations include using Fluticasone spray, Nexium 40 mg daily for two months, and consultations with Allergy and Gastroenterology specialists.",
-        "appointment_datetime": "2023-09-26 11:30",
-        "follow_ups": '{"tasks": [{"task": "Consult Allergy and GI specialists."}, {"task": "AMB REF to Allergy & Clinical Immunology."}, {"task": "AMB REF to Gastroenterology."}]}',
-        "perscriptions": '{"drugs": [{"technical_name": "esomeprazole", "brand_name": "NEXIUM", "instructions": "Take 1 capsule by mouth every morning before breakfast."}, {"technical_name": "fluticasone propionate", "brand_name": "FLONASE", "instructions": "2 sprays in each nostril once daily."}, {"technical_name": "naproxen", "brand_name": "NAPROSYN", "instructions": "Take 1 tablet by mouth every 12 hours as needed. Stop if GI upset."}, {"technical_name": "azelastine", "brand_name": "ASTELIN", "instructions": "USE NASAL SPRAY AS DIRECTED."}, {"technical_name": "trazodone", "brand_name": "DESYREL", "instructions": "TAKE 1 TO 2 TABLETS BY MOUTH EVERY DAY AT BEDTIME AS NEEDED FOR INSOMNIA."}]}',
-    }
-    conn = create_connection()
-    try:
-        print(set_prescription_status(conn, 1, False))
-    except Exception as e:
-        raise e
diff --git a/src/backend/app/db/vector_db.py b/src/backend/app/db/vector_db.py
@@ -101,17 +101,6 @@ def structure_context(context: Dict[str, Any]) -> str:
     return "\n".join(df["formatted_row"].to_list())
 
 
-# def build_index(embed_model: Any) -> VectorStoreIndex:
-#     db = chromadb.PersistentClient(path=DB_PATH)
-#     chroma_collection = db.get_or_create_collection(COLLECTION)
-#     vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
-#     index = VectorStoreIndex.from_vector_store(
-#         vector_store,
-#         embed_model=embed_model,
-#     )
-#     return index
-
-
 def query_documents(query: str, user_id: int, index: VectorStoreIndex) -> Response:
     """query vector db filtering based off user_id in metadata"""
     query_engine = index.as_query_engine(
@@ -141,16 +130,3 @@ def query_documents(query: str, user_id: int, index: VectorStoreIndex) -> Respon
     chroma_collection = db.get_or_create_collection(
         COLLECTION, embedding_function=EMBED_MODEL
     )
-    # query = "What diagnosis would explain my symptoms?"
-    # context = get_context(
-    #     query, 1, chroma_collection
-    # )
-    # organized_context = structure_context(context)
-    # rqt = OAIRequest(
-    #     system_msg=CHAT_W_DATA_SYS_MSG,
-    #     user_msg=CHAT_W_DATA_USER_MSG.format(
-    #         query, organized_context
-    #     ),
-    # )
-    # response = asyncio.run(a_send_rqt_chat(client, rqt))
-    # pprint(response)