Adding search route

Author: pamelafox

.env.sample

@@ -13,7 +13,7 @@ OPENAI_EMBED_HOST=azure
 # You also need to `azd auth login` if running this locally
 AZURE_OPENAI_ENDPOINT=https://YOUR-AZURE-OPENAI-SERVICE-NAME.openai.azure.com
 AZURE_OPENAI_VERSION=2024-03-01-preview
-AZURE_OPENAI_CHAT_DEPLOYMENT=YOUR-AZURE-DEPLOYMENT-NAME
+AZURE_OPENAI_CHAT_DEPLOYMENT=chat
 AZURE_OPENAI_CHAT_MODEL=gpt-35-turbo
 AZURE_OPENAI_EMBED_DEPLOYMENT=embed
 AZURE_OPENAI_EMBED_MODEL=text-embedding-ada-002

README.md

@@ -51,7 +51,7 @@ You can run this template virtually by using GitHub Codespaces. The button will
     azd up
     ```
 
-    This project uses gpt-3.5-turbo and text-embedding-ada-002 which may not be available in all Azure regions. Check for [up-to-date region availability](https://learn.microsoft.com/azure/ai-services/openai/concepts/models#standard-deployment-model-availability) and select a region during deployment accordingly.
+    You will be asked to select two locations, first a region for most of the resources (Container Apps, PostgreSQL), then a region specifically for the Azure OpenAI models. This project uses the gpt-3.5-turbo (version 0125) and text-embedding-ada-002 models which may not be available in all Azure regions. Check for [up-to-date region availability](https://learn.microsoft.com/azure/ai-services/openai/concepts/models#standard-deployment-model-availability) and select a region accordingly.
 
 ### VS Code Dev Containers
 

src/fastapi_app/api_routes.py

@@ -36,32 +36,47 @@ async def similar_handler(id: int, n: int = 5):
         return [item.to_dict() | {"distance": round(distance, 2)} for item, distance in closest]
 
 
+@router.get("/search")
+async def search_handler(query: str, top: int = 5, enable_vector_search: bool = True, enable_text_search: bool = True):
+    """A search API to find items based on a query."""
+    searcher = PostgresSearcher(
+        global_storage.engine,
+        openai_embed_client=global_storage.openai_embed_client,
+        embed_deployment=global_storage.openai_embed_deployment,
+        embed_model=global_storage.openai_embed_model,
+        embed_dimensions=global_storage.openai_embed_dimensions,
+    )
+    results = await searcher.search_and_embed(
+        query, top=top, enable_vector_search=enable_vector_search, enable_text_search=enable_text_search
+    )
+    return [item.to_dict() for item in results]
+
+
 @router.post("/chat")
 async def chat_handler(chat_request: ChatRequest):
     messages = [message.model_dump() for message in chat_request.messages]
     overrides = chat_request.context.get("overrides", {})
 
+    searcher = PostgresSearcher(
+        global_storage.engine,
+        openai_embed_client=global_storage.openai_embed_client,
+        embed_deployment=global_storage.openai_embed_deployment,
+        embed_model=global_storage.openai_embed_model,
+        embed_dimensions=global_storage.openai_embed_dimensions,
+    )
     if overrides.get("use_advanced_flow"):
         ragchat = AdvancedRAGChat(
-            searcher=PostgresSearcher(global_storage.engine),
+            searcher=searcher,
             openai_chat_client=global_storage.openai_chat_client,
             chat_model=global_storage.openai_chat_model,
             chat_deployment=global_storage.openai_chat_deployment,
-            openai_embed_client=global_storage.openai_embed_client,
-            embed_deployment=global_storage.openai_embed_deployment,
-            embed_model=global_storage.openai_embed_model,
-            embed_dimensions=global_storage.openai_embed_dimensions,
         )
     else:
         ragchat = SimpleRAGChat(
-            searcher=PostgresSearcher(global_storage.engine),
+            searcher=searcher,
             openai_chat_client=global_storage.openai_chat_client,
             chat_model=global_storage.openai_chat_model,
             chat_deployment=global_storage.openai_chat_deployment,
-            openai_embed_client=global_storage.openai_embed_client,
-            embed_deployment=global_storage.openai_embed_deployment,
-            embed_model=global_storage.openai_embed_model,
-            embed_dimensions=global_storage.openai_embed_dimensions,
         )
 
     response = await ragchat.run(messages, overrides=overrides)

src/fastapi_app/postgres_searcher.py

@@ -1,13 +1,26 @@
+from openai import AsyncOpenAI
 from pgvector.utils import to_db
 from sqlalchemy import Float, Integer, select, text
 from sqlalchemy.ext.asyncio import async_sessionmaker
 
-from .postgres_models import Item
+from fastapi_app.embeddings import compute_text_embedding
+from fastapi_app.postgres_models import Item
 
 
 class PostgresSearcher:
-    def __init__(self, engine):
+    def __init__(
+        self,
+        engine,
+        openai_embed_client: AsyncOpenAI,
+        embed_deployment: str | None,  # Not needed for non-Azure OpenAI or for retrieval_mode="text"
+        embed_model: str,
+        embed_dimensions: int,
+    ):
         self.async_session_maker = async_sessionmaker(engine, expire_on_commit=False)
+        self.openai_embed_client = openai_embed_client
+        self.embed_model = embed_model
+        self.embed_deployment = embed_deployment
+        self.embed_dimensions = embed_dimensions
 
     def build_filter_clause(self, filters) -> tuple[str, str]:
         if filters is None:
@@ -26,7 +39,7 @@ async def search(
         self,
         query_text: str | None,
         query_vector: list[float] | list,
-        query_top: int = 5,
+        top: int = 5,
         filters: list[dict] | None = None,
     ):
         filter_clause_where, filter_clause_and = self.build_filter_clause(filters)
@@ -83,7 +96,32 @@ async def search(
 
             # Convert results to Item models
             items = []
-            for id, _ in results[:query_top]:
+            for id, _ in results[:top]:
                 item = await session.execute(select(Item).where(Item.id == id))
                 items.append(item.scalar())
             return items
+
+    async def search_and_embed(
+        self,
+        query_text: str,
+        top: int = 5,
+        enable_vector_search: bool = False,
+        enable_text_search: bool = False,
+        filters: list[dict] | None = None,
+    ) -> list[Item]:
+        """
+        Search items by query text. Optionally converts the query text to a vector if enable_vector_search is True.
+        """
+        vector: list[float] = []
+        if enable_vector_search:
+            vector = await compute_text_embedding(
+                query_text,
+                self.openai_embed_client,
+                self.embed_model,
+                self.embed_deployment,
+                self.embed_dimensions,
+            )
+        if not enable_text_search:
+            query_text = None
+
+        return await self.search(query_text, vector, top, filters)

src/fastapi_app/rag_advanced.py

@@ -11,7 +11,6 @@
 from openai_messages_token_helper import build_messages, get_token_limit
 
 from .api_models import ThoughtStep
-from .embeddings import compute_text_embedding
 from .postgres_searcher import PostgresSearcher
 from .query_rewriter import build_search_function, extract_search_arguments
 
@@ -24,19 +23,11 @@ def __init__(
         openai_chat_client: AsyncOpenAI,
         chat_model: str,
         chat_deployment: str | None,  # Not needed for non-Azure OpenAI
-        openai_embed_client: AsyncOpenAI,
-        embed_deployment: str | None,  # Not needed for non-Azure OpenAI or for retrieval_mode="text"
-        embed_model: str,
-        embed_dimensions: int,
     ):
         self.searcher = searcher
         self.openai_chat_client = openai_chat_client
         self.chat_model = chat_model
         self.chat_deployment = chat_deployment
-        self.openai_embed_client = openai_embed_client
-        self.embed_deployment = embed_deployment
-        self.embed_model = embed_model
-        self.embed_dimensions = embed_dimensions
         self.chat_token_limit = get_token_limit(chat_model, default_to_minimum=True)
         current_dir = pathlib.Path(__file__).parent
         self.query_prompt_template = open(current_dir / "prompts/query.txt").read()
@@ -77,19 +68,13 @@ async def run(
         query_text, filters = extract_search_arguments(chat_completion)
 
         # Retrieve relevant items from the database with the GPT optimized query
-        vector: list[float] = []
-        if vector_search:
-            vector = await compute_text_embedding(
-                original_user_query,
-                self.openai_embed_client,
-                self.embed_model,
-                self.embed_deployment,
-                self.embed_dimensions,
-            )
-        if not text_search:
-            query_text = None
-
-        results = await self.searcher.search(query_text, vector, top, filters)
+        results = await self.searcher.search_and_embed(
+            query_text,
+            top=top,
+            enable_vector_search=vector_search,
+            enable_text_search=text_search,
+            filters=filters,
+        )
 
         sources_content = [f"[{(item.id)}]:{item.to_str_for_rag()}\n\n" for item in results]
         content = "\n".join(sources_content)

src/fastapi_app/rag_simple.py

@@ -8,7 +8,6 @@
 from openai_messages_token_helper import build_messages, get_token_limit
 
 from .api_models import ThoughtStep
-from .embeddings import compute_text_embedding
 from .postgres_searcher import PostgresSearcher
 
 
@@ -20,19 +19,11 @@ def __init__(
         openai_chat_client: AsyncOpenAI,
         chat_model: str,
         chat_deployment: str | None,  # Not needed for non-Azure OpenAI
-        openai_embed_client: AsyncOpenAI,
-        embed_deployment: str | None,  # Not needed for non-Azure OpenAI or for retrieval_mode="text"
-        embed_model: str,
-        embed_dimensions: int,
     ):
         self.searcher = searcher
         self.openai_chat_client = openai_chat_client
         self.chat_model = chat_model
         self.chat_deployment = chat_deployment
-        self.openai_embed_client = openai_embed_client
-        self.embed_deployment = embed_deployment
-        self.embed_model = embed_model
-        self.embed_dimensions = embed_dimensions
         self.chat_token_limit = get_token_limit(chat_model, default_to_minimum=True)
         current_dir = pathlib.Path(__file__).parent
         self.answer_prompt_template = open(current_dir / "prompts/answer.txt").read()
@@ -48,20 +39,9 @@ async def run(
         past_messages = messages[:-1]
 
         # Retrieve relevant items from the database
-        vector: list[float] = []
-        query_text = None
-        if vector_search:
-            vector = await compute_text_embedding(
-                original_user_query,
-                self.openai_embed_client,
-                self.embed_model,
-                self.embed_deployment,
-                self.embed_dimensions,
-            )
-        if text_search:
-            query_text = original_user_query
-
-        results = await self.searcher.search(query_text, vector, top)
+        results = await self.searcher.search_and_embed(
+            original_user_query, top=top, enable_vector_search=vector_search, enable_text_search=text_search
+        )
 
         sources_content = [f"[{(item.id)}]:{item.to_str_for_rag()}\n\n" for item in results]
         content = "\n".join(sources_content)
@@ -92,7 +72,7 @@ async def run(
             "thoughts": [
                 ThoughtStep(
                     title="Search query for database",
-                    description=query_text,
+                    description=original_user_query if text_search else None,
                     props={
                         "top": top,
                         "vector_search": vector_search,