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Merged
merged 2 commits into from
Jun 11, 2024
Merged

Various UI and backend improvements #43

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2d7378a
Various UI and backend improvements
pamelafox Jun 10, 2024
e6a0fda
Update to latest SDK
pamelafox Jun 11, 2024
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19 changes: 15 additions & 4 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ A related option is VS Code Dev Containers, which will open the project in your
1. Start Docker Desktop (install it if not already installed)
2. Open the project:

[![Open in Dev Containers](https://img.shields.io/static/v1?style=for-the-badge&label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](placeholder)
[![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)

3. In the VS Code window that opens, once the project files show up (this may take several minutes), open a terminal window.
4. Continue with the [deployment steps](#deployment)
Expand Down Expand Up @@ -135,15 +135,25 @@ Since the local app uses OpenAI models, you should first deploy it for the optim

If you opened the project in Codespaces or a Dev Container, these commands will already have been run for you.

2. Run the FastAPI backend:
2. Build the frontend:

```bash
cd src/frontend
npm install
npm run build
```

There must be an initial build of static assets before running the backend, since the backend serves static files from the `src/static` directory.

3. Run the FastAPI backend (with hot reloading):

```shell
python3 -m uvicorn fastapi_app:create_app --factory --reload
```

Or you can run "Backend" in the VS Code Run & Debug menu.

3. Run the frontend:
4. Run the frontend (with hot reloading):

```bash
cd src/frontend
Expand All @@ -152,7 +162,7 @@ Since the local app uses OpenAI models, you should first deploy it for the optim

Or you can run "Frontend" or "Frontend & Backend" in the VS Code Run & Debug menu.

4. Open the browser at `http://localhost:5173/` and you will see the frontend.
5. Open the browser at `http://localhost:5173/` and you will see the frontend.

## Costs

Expand All @@ -161,6 +171,7 @@ You may try the [Azure pricing calculator](https://azure.microsoft.com/pricing/c

* Azure Container Apps: Pay-as-you-go tier. Costs based on vCPU and memory used. [Pricing](https://azure.microsoft.com/pricing/details/container-apps/)
* 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/)
* 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/)
* Azure Monitor: Pay-as-you-go tier. Costs based on data ingested. [Pricing](https://azure.microsoft.com/pricing/details/monitor/)

## Security Guidelines
Expand Down
74 changes: 38 additions & 36 deletions src/fastapi_app/rag_advanced.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,42 +99,44 @@ async def run(
n=1,
stream=False,
)
chat_resp = chat_completion_response.model_dump()
chat_resp["choices"][0]["context"] = {
"data_points": {"text": sources_content},
"thoughts": [
ThoughtStep(
title="Prompt to generate search arguments",
description=[str(message) for message in query_messages],
props=(
{"model": self.chat_model, "deployment": self.chat_deployment}
if self.chat_deployment
else {"model": self.chat_model}
first_choice = chat_completion_response.model_dump()["choices"][0]
return {
"message": first_choice["message"],
"context": {
"data_points": {item.id: item.to_dict() for item in results},
"thoughts": [
ThoughtStep(
title="Prompt to generate search arguments",
description=[str(message) for message in query_messages],
props=(
{"model": self.chat_model, "deployment": self.chat_deployment}
if self.chat_deployment
else {"model": self.chat_model}
),
),
),
ThoughtStep(
title="Search using generated search arguments",
description=query_text,
props={
"top": top,
"vector_search": vector_search,
"text_search": text_search,
"filters": filters,
},
),
ThoughtStep(
title="Search results",
description=[result.to_dict() for result in results],
),
ThoughtStep(
title="Prompt to generate answer",
description=[str(message) for message in messages],
props=(
{"model": self.chat_model, "deployment": self.chat_deployment}
if self.chat_deployment
else {"model": self.chat_model}
ThoughtStep(
title="Search using generated search arguments",
description=query_text,
props={
"top": top,
"vector_search": vector_search,
"text_search": text_search,
"filters": filters,
},
),
),
],
ThoughtStep(
title="Search results",
description=[result.to_dict() for result in results],
),
ThoughtStep(
title="Prompt to generate answer",
description=[str(message) for message in messages],
props=(
{"model": self.chat_model, "deployment": self.chat_deployment}
if self.chat_deployment
else {"model": self.chat_model}
),
),
],
},
}
return chat_resp
56 changes: 29 additions & 27 deletions src/fastapi_app/rag_simple.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,32 +66,34 @@ async def run(
n=1,
stream=False,
)
chat_resp = chat_completion_response.model_dump()
chat_resp["choices"][0]["context"] = {
"data_points": {"text": sources_content},
"thoughts": [
ThoughtStep(
title="Search query for database",
description=original_user_query if text_search else None,
props={
"top": top,
"vector_search": vector_search,
"text_search": text_search,
},
),
ThoughtStep(
title="Search results",
description=[result.to_dict() for result in results],
),
ThoughtStep(
title="Prompt to generate answer",
description=[str(message) for message in messages],
props=(
{"model": self.chat_model, "deployment": self.chat_deployment}
if self.chat_deployment
else {"model": self.chat_model}
first_choice = chat_completion_response.model_dump()["choices"][0]
return {
"message": first_choice["message"],
"context": {
"data_points": {item.id: item.to_dict() for item in results},
"thoughts": [
ThoughtStep(
title="Search query for database",
description=original_user_query if text_search else None,
props={
"top": top,
"vector_search": vector_search,
"text_search": text_search,
},
),
),
],
ThoughtStep(
title="Search results",
description=[result.to_dict() for result in results],
),
ThoughtStep(
title="Prompt to generate answer",
description=[str(message) for message in messages],
props=(
{"model": self.chat_model, "deployment": self.chat_deployment}
if self.chat_deployment
else {"model": self.chat_model}
),
),
],
},
}
return chat_resp
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