Merge branch 'main' into test-pull-request-2

Including the repository name in the README will undoubtedly facilitate easier onboarding for future users, minimizing the need for modifications.

README.md

@@ -34,8 +34,11 @@ To run this proxy locally on your own machine, follow these steps:
 4. Set your Groq API token key as an environment variable:
 ```export GROQ_API_KEY=your-api-key```
 
-5. Run the FastAPI server:
-```uvicorn main:app --reload```
+5. Create uvicorn logs folder:
+```mkdir .logs```
+
+6. Run the FastAPI server:
+```uvicorn --app-dir app/ main:app --reload```
 
 
 ## Using the Pre-built Server 🌐
@@ -49,7 +52,7 @@ To use the pre-built server, simply make requests to the following base URL:
 This README is organized into three main sections, each showcasing different aspects of FunckyCall.ai:
 
 - **Sending POST Requests**: Here, I explore the functionality of sending direct POST requests to LLMs using FunckyCall.ai. This section highlights the flexibility and control offered by the library when interacting with LLMs.
-- **FunckyHub**: The third section introduces the concept of FunckyHub, a usefull feature that simplifies the process of executing functions. With FunckyHub, there is no need to send the function JSON schema explicitly, as the functions are already hosted on the proxy server. This approach streamlines the workflow, allowing developers to obtain results with a single call without having to handle function call is production server.
+- **FunckyHub**: The second section introduces the concept of FunckyHub, a useful feature that simplifies the process of executing functions. With FunckyHub, there is no need to send the function JSON schema explicitly, as the functions are already hosted on the proxy server. This approach streamlines the workflow, allowing developers to obtain results with a single call without having to handle function call is production server.
 - **Using FunckyCall with PhiData**: In this section, I demonstrate how FunckyCall.ai can be seamlessly integrated with other libraries such as my favorite one, the PhiData library, leveraging its built-in tools to connect to LLMs and perform external tool requests.
 
 

app/libs/chains.py

@@ -5,9 +5,9 @@
 import uuid
 from fastapi import Request
 from fastapi.responses import JSONResponse
-from app.providers import BaseProvider
-from app.prompts import SYSTEM_MESSAGE, SUFFIX, get_func_result_guide
-from app.providers import GroqProvider
+from providers import BaseProvider
+from prompts import SYSTEM_MESSAGE, SUFFIX, get_func_result_guide
+from providers import GroqProvider
 import importlib
 
 

app/main.py

@@ -3,11 +3,11 @@
 from fastapi.templating import Jinja2Templates
 from fastapi.staticfiles import StaticFiles
 from starlette.requests import Request
-from app.routes import proxy
-from app.routes import examples
+from routes import proxy
+from routes import examples
 
 
-from app.utils import create_logger
+from utils import create_logger
 import os
 from dotenv import load_dotenv
 load_dotenv()

app/routes/proxy.py

@@ -1,6 +1,6 @@
 from fastapi import APIRouter, Response, Request, Path, Query
 from fastapi.responses import JSONResponse
-from app.libs.chains import (
+from libs.chains import (
     Context,
     ProviderSelectionHandler,
     ToolExtractionHandler,