Merge testing into prod

src/infrastructure/docker-compose.yml

@@ -16,7 +16,7 @@ services:
       - server
 
   server:
-    build:
+    build: 
       context: ../server
       dockerfile: ../infrastructure/Dockerfile.server
     ports: 

src/server/controllers/example_controller.py

@@ -1,6 +1,5 @@
-import os
 from schemas.schema import SampleRequest
-from services.service import sample_service,AppError
+from services.example_service import sample_service,AppError
 from fastapi import HTTPException
 
 
@@ -18,57 +17,3 @@ def sample_controller(request:SampleRequest)->type:
         )
 
 
-import hashlib
-from langchain_community.document_loaders import DirectoryLoader, JSONLoader
-import json
-from langchain_community.vectorstores.upstash import UpstashVectorStore
-import getpass
-from langchain_openai import OpenAIEmbeddings
-
-#input should be a folder with json or a singular json file
-def update_Database(path:str)->str: #return number of added docs
-
-    os.environ["UPSTASH_VECTOR_REST_URL"] = "https://loving-kingfish-56853-us1-vector.upstash.io"
-    if not os.environ.get("UPSTASH_VECTOR_REST_TOKEN"):
-        os.environ["UPSTASH_VECTOR_REST_TOKEN"] = getpass.getpass("Enter API key for Upstash: ")
-
-    if not os.environ.get("OPENAI_API_KEY"):
-        os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")
-    loader_kwargs = {
-        "jq_schema": ".[]", #iterate over question objects.
-        "text_content": False
-    }
-    loader = DirectoryLoader(
-        path=path, 
-        glob="**/*.json", #ensures json
-        loader_cls=JSONLoader,
-        loader_kwargs=loader_kwargs
-    )
-    docs = loader.load()
-    sanitized_docs = []
-    doc_ids = [] # Standardized name
-
-    for doc in docs:
-        raw_data = json.loads(doc.page_content)
-        question_text = raw_data.get("question")
-        unique_id = hashlib.md5(question_text.encode('utf-8')).hexdigest()
-
-        doc.metadata = {
-            "choices": raw_data.get("choices"),
-            "category": raw_data.get("category"),
-            "answer": raw_data.get("answer"),
-            "difficulty": raw_data.get("difficulty"),
-        }
-        doc.page_content = question_text
-        doc_ids.append(unique_id)
-        sanitized_docs.append(doc)
-
-    embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
-    store = UpstashVectorStore(
-        embedding=embeddings
-    )
-    store.add_documents(documents=sanitized_docs, ids=doc_ids)
-    return f"Successfully processed {len(sanitized_docs)} documents."
-
-
-

src/server/server.py

@@ -1,6 +1,6 @@
 from fastapi import FastAPI
-from jwt_auth.services.auth_services import router as auth_router
-from routes.example_route import router as business_router
+from jwt_auth.auth_routes import router as auth_router
+from routes.example_router import router as business_router
 from fastapi.middleware.cors import CORSMiddleware
 
 app = FastAPI()

src/server/services/services.py

@@ -0,0 +1,63 @@
+'''IMPLEMENT SERVICES HERE'''
+
+
+
+class AppError(Exception):
+    def __init__(self, message: str, status_code: int = 400):
+        self.message = message
+        self.status_code = status_code
+        super().__init__(message)
+
+import hashlib,os,json,getpass
+from langchain_community.document_loaders import DirectoryLoader, JSONLoader
+from langchain_community.vectorstores.upstash import UpstashVectorStore
+from langchain_openai import OpenAIEmbeddings
+
+#input should be a folder with json or a singular json file
+def update_Database(path:str)->str: #return number of added docs
+
+    os.environ["UPSTASH_VECTOR_REST_URL"] = "https://loving-kingfish-56853-us1-vector.upstash.io"
+    if not os.environ.get("UPSTASH_VECTOR_REST_TOKEN"):
+        os.environ["UPSTASH_VECTOR_REST_TOKEN"] = getpass.getpass("Enter API key for Upstash: ")
+
+    if not os.environ.get("OPENAI_API_KEY"):
+        os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")
+    loader_kwargs = {
+        "jq_schema": ".[]", #iterate over question objects.
+        "text_content": False
+    }
+    loader = DirectoryLoader(
+        path=path, 
+        glob="**/*.json", #ensures json
+        loader_cls=JSONLoader,
+        loader_kwargs=loader_kwargs
+    )
+    docs = loader.load()
+    sanitized_docs = []
+    doc_ids = [] # Standardized name
+
+    for doc in docs:
+        raw_data = json.loads(doc.page_content)
+        question_text = raw_data.get("question")
+        unique_id = hashlib.md5(question_text.encode('utf-8')).hexdigest()
+
+        doc.metadata = {
+            "choices": raw_data.get("choices"),
+            "category": raw_data.get("category"),
+            "answer": raw_data.get("answer"),
+            "difficulty": raw_data.get("difficulty"),
+        }
+        doc.page_content = question_text
+        doc_ids.append(unique_id)
+        sanitized_docs.append(doc)
+
+    embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+    store = UpstashVectorStore(
+        embedding=embeddings
+    )
+    store.add_documents(documents=sanitized_docs, ids=doc_ids)
+    return f"Successfully processed {len(sanitized_docs)} documents."
+
+
+
+