Merge branch 'main' into dependabot/pip/src/backend/h11-0.16.0

Author: pamelafox

.devcontainer/devcontainer.json

@@ -29,11 +29,15 @@
 			"extensions": [
 				"ms-python.python",
 				"ms-python.vscode-pylance",
+				"ms-python.vscode-python-envs",
 				"charliermarsh.ruff",
 				"mtxr.sqltools",
 				"mtxr.sqltools-driver-pg",
+				"esbenp.prettier-vscode",
+				"mechatroner.rainbow-csv",
 				"ms-vscode.vscode-node-azure-pack",
-                "esbenp.prettier-vscode"
+				"teamsdevapp.vscode-ai-foundry",
+				"ms-windows-ai-studio.windows-ai-studio"
 			],
 			// Set *default* container specific settings.json values on container create.
 			"settings": {

.github/workflows/app-tests.yaml

@@ -123,7 +123,7 @@ jobs:
             key: mypy${{ matrix.os }}-${{ matrix.python_version }}-${{ hashFiles('requirements-dev.txt', 'src/backend/requirements.txt', 'src/backend/pyproject.toml') }}
 
         - name: Run MyPy
-          run: python3 -m mypy .
+          run: python3 -m mypy . --python-version ${{ matrix.python_version }}
 
         - name: Run Pytest
           run: python3 -m pytest -s -vv --cov --cov-fail-under=85

pyproject.toml

@@ -7,7 +7,6 @@ lint.isort.known-first-party = ["fastapi_app"]
 
 [tool.mypy]
 check_untyped_defs = true
-python_version = 3.9
 exclude = [".venv/*"]
 
 [tool.pytest.ini_options]

src/backend/fastapi_app/__init__.py

@@ -34,7 +34,13 @@ class State(TypedDict):
 @asynccontextmanager
 async def lifespan(app: fastapi.FastAPI) -> AsyncIterator[State]:
     context = await common_parameters()
-    azure_credential = await get_azure_credential()
+    azure_credential = None
+    if (
+        os.getenv("OPENAI_CHAT_HOST") == "azure"
+        or os.getenv("OPENAI_EMBED_HOST") == "azure"
+        or os.getenv("POSTGRES_HOST", "").endswith(".database.azure.com")
+    ):
+        azure_credential = await get_azure_credential()
     engine = await create_postgres_engine_from_env(azure_credential)
     sessionmaker = await create_async_sessionmaker(engine)
     chat_client = await create_openai_chat_client(azure_credential)
@@ -53,6 +59,7 @@ def create_app(testing: bool = False):
         if not testing:
             load_dotenv(override=True)
         logging.basicConfig(level=logging.INFO)
+
     # Turn off particularly noisy INFO level logs from Azure Core SDK:
     logging.getLogger("azure.core.pipeline.policies.http_logging_policy").setLevel(logging.WARNING)
     logging.getLogger("azure.identity").setLevel(logging.WARNING)

src/backend/fastapi_app/api_models.py

@@ -1,8 +1,8 @@
 from enum import Enum
 from typing import Any, Optional
 
-from openai.types.chat import ChatCompletionMessageParam
-from pydantic import BaseModel
+from openai.types.responses import ResponseInputItemParam
+from pydantic import BaseModel, Field
 
 
 class AIChatRoles(str, Enum):
@@ -36,19 +36,39 @@ class ChatRequestContext(BaseModel):
 
 
 class ChatRequest(BaseModel):
-    messages: list[ChatCompletionMessageParam]
+    messages: list[ResponseInputItemParam]
     context: ChatRequestContext
     sessionState: Optional[Any] = None
 
 
+class ItemPublic(BaseModel):
+    id: int
+    type: str
+    brand: str
+    name: str
+    description: str
+    price: float
+
+    def to_str_for_rag(self):
+        return f"Name:{self.name} Description:{self.description} Price:{self.price} Brand:{self.brand} Type:{self.type}"
+
+
+class ItemWithDistance(ItemPublic):
+    distance: float
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self.distance = round(self.distance, 2)
+
+
 class ThoughtStep(BaseModel):
     title: str
     description: Any
     props: dict = {}
 
 
 class RAGContext(BaseModel):
-    data_points: dict[int, dict[str, Any]]
+    data_points: dict[int, ItemPublic]
     thoughts: list[ThoughtStep]
     followup_questions: Optional[list[str]] = None
 
@@ -69,27 +89,39 @@ class RetrievalResponseDelta(BaseModel):
     sessionState: Optional[Any] = None
 
 
-class ItemPublic(BaseModel):
-    id: int
-    type: str
-    brand: str
-    name: str
-    description: str
-    price: float
-
-
-class ItemWithDistance(ItemPublic):
-    distance: float
-
-    def __init__(self, **data):
-        super().__init__(**data)
-        self.distance = round(self.distance, 2)
-
-
 class ChatParams(ChatRequestOverrides):
     prompt_template: str
     response_token_limit: int = 1024
     enable_text_search: bool
     enable_vector_search: bool
     original_user_query: str
-    past_messages: list[ChatCompletionMessageParam]
+    past_messages: list[ResponseInputItemParam]
+
+
+class Filter(BaseModel):
+    column: str
+    comparison_operator: str
+    value: Any
+
+
+class PriceFilter(Filter):
+    column: str = Field(default="price", description="The column to filter on (always 'price' for this filter)")
+    comparison_operator: str = Field(description="The operator for price comparison ('>', '<', '>=', '<=', '=')")
+    value: float = Field(description="The price value to compare against (e.g., 30.00)")
+
+
+class BrandFilter(Filter):
+    column: str = Field(default="brand", description="The column to filter on (always 'brand' for this filter)")
+    comparison_operator: str = Field(description="The operator for brand comparison ('=' or '!=')")
+    value: str = Field(description="The brand name to compare against (e.g., 'AirStrider')")
+
+
+class SearchResults(BaseModel):
+    query: str
+    """The original search query"""
+
+    items: list[ItemPublic]
+    """List of items that match the search query and filters"""
+
+    filters: list[Filter]
+    """List of filters applied to the search results"""

src/backend/fastapi_app/openai_clients.py

@@ -9,12 +9,12 @@
 
 
 async def create_openai_chat_client(
-    azure_credential: Union[azure.identity.AzureDeveloperCliCredential, azure.identity.ManagedIdentityCredential],
+    azure_credential: Union[azure.identity.AzureDeveloperCliCredential, azure.identity.ManagedIdentityCredential, None],
 ) -> Union[openai.AsyncAzureOpenAI, openai.AsyncOpenAI]:
     openai_chat_client: Union[openai.AsyncAzureOpenAI, openai.AsyncOpenAI]
     OPENAI_CHAT_HOST = os.getenv("OPENAI_CHAT_HOST")
     if OPENAI_CHAT_HOST == "azure":
-        api_version = os.environ["AZURE_OPENAI_VERSION"] or "2024-03-01-preview"
+        api_version = os.environ["AZURE_OPENAI_VERSION"] or "2024-10-21"
         azure_endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
         azure_deployment = os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"]
         if api_key := os.getenv("AZURE_OPENAI_KEY"):
@@ -29,7 +29,7 @@ async def create_openai_chat_client(
                 azure_deployment=azure_deployment,
                 api_key=api_key,
             )
-        else:
+        elif azure_credential:
             logger.info(
                 "Setting up Azure OpenAI client for chat completions using Azure Identity, endpoint %s, deployment %s",
                 azure_endpoint,
@@ -44,6 +44,8 @@ async def create_openai_chat_client(
                 azure_deployment=azure_deployment,
                 azure_ad_token_provider=token_provider,
             )
+        else:
+            raise ValueError("Azure OpenAI client requires either an API key or Azure Identity credential.")
     elif OPENAI_CHAT_HOST == "ollama":
         logger.info("Setting up OpenAI client for chat completions using Ollama")
         openai_chat_client = openai.AsyncOpenAI(
@@ -67,7 +69,7 @@ async def create_openai_chat_client(
 
 
 async def create_openai_embed_client(
-    azure_credential: Union[azure.identity.AzureDeveloperCliCredential, azure.identity.ManagedIdentityCredential],
+    azure_credential: Union[azure.identity.AzureDeveloperCliCredential, azure.identity.ManagedIdentityCredential, None],
 ) -> Union[openai.AsyncAzureOpenAI, openai.AsyncOpenAI]:
     openai_embed_client: Union[openai.AsyncAzureOpenAI, openai.AsyncOpenAI]
     OPENAI_EMBED_HOST = os.getenv("OPENAI_EMBED_HOST")
@@ -87,7 +89,7 @@ async def create_openai_embed_client(
                 azure_deployment=azure_deployment,
                 api_key=api_key,
             )
-        else:
+        elif azure_credential:
             logger.info(
                 "Setting up Azure OpenAI client for embeddings using Azure Identity, endpoint %s, deployment %s",
                 azure_endpoint,
@@ -102,6 +104,8 @@ async def create_openai_embed_client(
                 azure_deployment=azure_deployment,
                 azure_ad_token_provider=token_provider,
             )
+        else:
+            raise ValueError("Azure OpenAI client requires either an API key or Azure Identity credential.")
     elif OPENAI_EMBED_HOST == "ollama":
         logger.info("Setting up OpenAI client for embeddings using Ollama")
         openai_embed_client = openai.AsyncOpenAI(

src/backend/fastapi_app/postgres_searcher.py

@@ -5,6 +5,7 @@
 from sqlalchemy import Float, Integer, column, select, text
 from sqlalchemy.ext.asyncio import AsyncSession
 
+from fastapi_app.api_models import Filter
 from fastapi_app.embeddings import compute_text_embedding
 from fastapi_app.postgres_models import Item
 
@@ -26,21 +27,24 @@ def __init__(
         self.embed_dimensions = embed_dimensions
         self.embedding_column = embedding_column
 
-    def build_filter_clause(self, filters) -> tuple[str, str]:
+    def build_filter_clause(self, filters: Optional[list[Filter]]) -> tuple[str, str]:
         if filters is None:
             return "", ""
         filter_clauses = []
         for filter in filters:
-            if isinstance(filter["value"], str):
-                filter["value"] = f"'{filter['value']}'"
-            filter_clauses.append(f"{filter['column']} {filter['comparison_operator']} {filter['value']}")
+            filter_value = f"'{filter.value}'" if isinstance(filter.value, str) else filter.value
+            filter_clauses.append(f"{filter.column} {filter.comparison_operator} {filter_value}")
         filter_clause = " AND ".join(filter_clauses)
         if len(filter_clause) > 0:
             return f"WHERE {filter_clause}", f"AND {filter_clause}"
         return "", ""
 
     async def search(
-        self, query_text: Optional[str], query_vector: list[float], top: int = 5, filters: Optional[list[dict]] = None
+        self,
+        query_text: Optional[str],
+        query_vector: list[float],
+        top: int = 5,
+        filters: Optional[list[Filter]] = None,
     ):
         filter_clause_where, filter_clause_and = self.build_filter_clause(filters)
         table_name = Item.__tablename__
@@ -106,7 +110,7 @@ async def search_and_embed(
         top: int = 5,
         enable_vector_search: bool = False,
         enable_text_search: bool = False,
-        filters: Optional[list[dict]] = None,
+        filters: Optional[list[Filter]] = None,
     ) -> list[Item]:
         """
         Search rows by query text. Optionally converts the query text to a vector if enable_vector_search is True.

src/backend/fastapi_app/prompts/query.txt

@@ -1,6 +1,5 @@
-Below is a history of the conversation so far, and a new question asked by the user that needs to be answered by searching database rows.
-You have access to an Azure PostgreSQL database with an items table that has columns for title, description, brand, price, and type.
-Generate a search query based on the conversation and the new question.
-If the question is not in English, translate the question to English before generating the search query.
-If you cannot generate a search query, return the original user question.
-DO NOT return anything besides the query.
+Your job is to find search results based off the user's question and past messages.
+You have access to only these tools:
+1. **search_database**: This tool allows you to search a table for items based on a query.
+  You can pass in a search query and optional filters.
+Once you get the search results, you're done.

src/backend/fastapi_app/prompts/query_fewshots.json

@@ -1,34 +1,36 @@
 [
-    {"role": "user", "content": "good options for climbing gear that can be used outside?"},
-    {"role": "assistant", "tool_calls": [
-        {
-            "id": "call_abc123",
-            "type": "function",
-            "function": {
-                "arguments": "{\"search_query\":\"climbing gear outside\"}",
-                "name": "search_database"
-            }
-        }
-    ]},
-    {
-        "role": "tool",
-        "tool_call_id": "call_abc123",
-        "content": "Search results for climbing gear that can be used outside: ..."
-    },
-    {"role": "user", "content": "are there any shoes less than $50?"},
-    {"role": "assistant", "tool_calls": [
-        {
-            "id": "call_abc456",
-            "type": "function",
-            "function": {
-                "arguments": "{\"search_query\":\"shoes\",\"price_filter\":{\"comparison_operator\":\"<\",\"value\":50}}",
-                "name": "search_database"
-            }
-        }
-    ]},
-    {
-        "role": "tool",
-        "tool_call_id": "call_abc456",
-        "content": "Search results for shoes cheaper than 50: ..."
-    }
+  {
+    "role": "user",
+    "content": "good options for climbing gear that can be used outside?"
+  },
+  {
+    "id": "madeup",
+    "call_id": "call_abc123",
+    "name": "search_database",
+    "arguments": "{\"search_query\":\"climbing gear outside\"}",
+    "type": "function_call"
+  },
+  {
+    "id": "madeupoutput",
+    "call_id": "call_abc123",
+    "output": "Search results for climbing gear that can be used outside: ...",
+    "type": "function_call_output"
+  },
+  {
+    "role": "user",
+    "content": "are there any shoes less than $50?"
+  },
+  {
+    "id": "madeup",
+    "call_id": "call_abc456",
+    "name": "search_database",
+    "arguments": "{\"search_query\":\"shoes\",\"price_filter\":{\"comparison_operator\":\"<\",\"value\":50}}",
+    "type": "function_call"
+  },
+  {
+    "id": "madeupoutput",
+    "call_id": "call_abc456",
+    "output": "Search results for shoes cheaper than 50: ...",
+    "type": "function_call_output"
+  }
 ]