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Merge pull request Azure-Samples#43 from Azure-Samples/improvements
Various UI and backend improvements
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‎README.md‎

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@@ -48,7 +48,7 @@ A related option is VS Code Dev Containers, which will open the project in your
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1. Start Docker Desktop (install it if not already installed)
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2. Open the project:
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[![Open in Dev Containers](https://img.shields.io/static/v1?style=for-the-badge&label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](placeholder)
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[![Open in Dev Containers](https://img.shields.io/static/v1?style=for-the-badge&label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/azure-samples/rag-postgres-openai-python)
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3. In the VS Code window that opens, once the project files show up (this may take several minutes), open a terminal window.
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4. Continue with the [deployment steps](#deployment)
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If you opened the project in Codespaces or a Dev Container, these commands will already have been run for you.
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2. Run the FastAPI backend:
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2. Build the frontend:
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```bash
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cd src/frontend
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npm install
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npm run build
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```
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There must be an initial build of static assets before running the backend, since the backend serves static files from the `src/static` directory.
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3. Run the FastAPI backend (with hot reloading):
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```shell
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python3 -m uvicorn fastapi_app:create_app --factory --reload
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```
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Or you can run "Backend" in the VS Code Run & Debug menu.
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3. Run the frontend:
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4. Run the frontend (with hot reloading):
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```bash
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cd src/frontend
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Or you can run "Frontend" or "Frontend & Backend" in the VS Code Run & Debug menu.
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4. Open the browser at `http://localhost:5173/` and you will see the frontend.
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5. Open the browser at `http://localhost:5173/` and you will see the frontend.
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## Costs
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@@ -161,6 +171,7 @@ You may try the [Azure pricing calculator](https://azure.microsoft.com/pricing/c
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* Azure Container Apps: Pay-as-you-go tier. Costs based on vCPU and memory used. [Pricing](https://azure.microsoft.com/pricing/details/container-apps/)
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* Azure OpenAI: Standard tier, GPT and Ada models. Pricing per 1K tokens used, and at least 1K tokens are used per question. [Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/)
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* Azure PostgreSQL Flexible Server: Burstable Tier with 1 CPU core, 32GB storage. Pricing is hourly. [Pricing](https://azure.microsoft.com/pricing/details/postgresql/flexible-server/)
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* Azure Monitor: Pay-as-you-go tier. Costs based on data ingested. [Pricing](https://azure.microsoft.com/pricing/details/monitor/)
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## Security Guidelines

‎src/fastapi_app/rag_advanced.py‎

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@@ -99,42 +99,44 @@ async def run(
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n=1,
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stream=False,
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)
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chat_resp = chat_completion_response.model_dump()
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chat_resp["choices"][0]["context"] = {
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"data_points": {"text": sources_content},
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"thoughts": [
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ThoughtStep(
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title="Prompt to generate search arguments",
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description=[str(message) for message in query_messages],
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props=(
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{"model": self.chat_model, "deployment": self.chat_deployment}
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if self.chat_deployment
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else {"model": self.chat_model}
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first_choice = chat_completion_response.model_dump()["choices"][0]
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return {
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"message": first_choice["message"],
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"context": {
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"data_points": {item.id: item.to_dict() for item in results},
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"thoughts": [
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ThoughtStep(
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title="Prompt to generate search arguments",
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description=[str(message) for message in query_messages],
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props=(
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{"model": self.chat_model, "deployment": self.chat_deployment}
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if self.chat_deployment
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else {"model": self.chat_model}
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),
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),
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),
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ThoughtStep(
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title="Search using generated search arguments",
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description=query_text,
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props={
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"top": top,
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"vector_search": vector_search,
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"text_search": text_search,
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"filters": filters,
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},
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),
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ThoughtStep(
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title="Search results",
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description=[result.to_dict() for result in results],
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),
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ThoughtStep(
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title="Prompt to generate answer",
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description=[str(message) for message in messages],
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props=(
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{"model": self.chat_model, "deployment": self.chat_deployment}
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if self.chat_deployment
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else {"model": self.chat_model}
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ThoughtStep(
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title="Search using generated search arguments",
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description=query_text,
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props={
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"top": top,
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"vector_search": vector_search,
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"text_search": text_search,
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"filters": filters,
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},
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),
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),
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],
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ThoughtStep(
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title="Search results",
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description=[result.to_dict() for result in results],
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),
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ThoughtStep(
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title="Prompt to generate answer",
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description=[str(message) for message in messages],
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props=(
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{"model": self.chat_model, "deployment": self.chat_deployment}
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if self.chat_deployment
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else {"model": self.chat_model}
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),
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),
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],
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},
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}
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return chat_resp

‎src/fastapi_app/rag_simple.py‎

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@@ -66,32 +66,34 @@ async def run(
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n=1,
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stream=False,
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)
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chat_resp = chat_completion_response.model_dump()
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chat_resp["choices"][0]["context"] = {
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"data_points": {"text": sources_content},
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"thoughts": [
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ThoughtStep(
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title="Search query for database",
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description=original_user_query if text_search else None,
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props={
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"top": top,
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"vector_search": vector_search,
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"text_search": text_search,
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},
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),
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ThoughtStep(
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title="Search results",
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description=[result.to_dict() for result in results],
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),
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ThoughtStep(
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title="Prompt to generate answer",
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description=[str(message) for message in messages],
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props=(
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{"model": self.chat_model, "deployment": self.chat_deployment}
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if self.chat_deployment
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else {"model": self.chat_model}
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first_choice = chat_completion_response.model_dump()["choices"][0]
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return {
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"message": first_choice["message"],
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"context": {
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"data_points": {item.id: item.to_dict() for item in results},
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"thoughts": [
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ThoughtStep(
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title="Search query for database",
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description=original_user_query if text_search else None,
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props={
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"top": top,
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"vector_search": vector_search,
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"text_search": text_search,
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},
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),
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),
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],
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ThoughtStep(
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title="Search results",
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description=[result.to_dict() for result in results],
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),
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ThoughtStep(
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title="Prompt to generate answer",
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description=[str(message) for message in messages],
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props=(
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{"model": self.chat_model, "deployment": self.chat_deployment}
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if self.chat_deployment
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else {"model": self.chat_model}
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),
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),
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],
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},
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}
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return chat_resp

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