Integrate LangChain with FastAPI

server/intelligence-service/.env.example

@@ -0,0 +1,7 @@
+# For Non-Azure OpenAI
+OPENAI_API_KEY=your-openai-secret-key
+
+# For Azure OpenAI
+AZURE_OPENAI_API_KEY=your-azure-api-key
+AZURE_OPENAI_ENDPOINT=your-azure-base-url
+AZURE_OPENAI_API_VERSION=your-api-version

server/intelligence-service/.gitignore

@@ -0,0 +1 @@
+.env

server/intelligence-service/README.md

@@ -15,12 +15,14 @@ To set up the project locally, follow these steps:
 
 1. **Install dependencies:**
     The project uses `poetry` for dependency management. Install the dependencies by running:
+
     ```bash
     poetry install
     ```
 
 2. **Run the application:**
     You can start the FastAPI application with Uvicorn:
+
     ```bash
     poetry run uvicorn src.main:app --reload
     ```
@@ -32,6 +34,7 @@ After running the application, you can access the FastAPI API documentation at `
 ## Project Structure
 
 The project is organized as follows:
+
 ```
 intelligence-service/
 ├── pyproject.toml     
@@ -41,13 +44,16 @@ intelligence-service/
 ├── tests/                
 │   ├── __init__.py
 │   └── test_hello.py
-├── src/                  
-│   ├── config.py        
+├── src/               
+│   ├── __init__.py   
+│   ├── config.py   
+│   ├── langchain_client.py
 │   ├── main.py          
 │   └── auth/            
 │       └── router.py    
 └── ...
 ```
+
 ## Testing
 
 The project includes a set of unit tests to ensure that the core functionalities work as expected. These tests are located in the `tests/` directory.
@@ -57,4 +63,4 @@ The project includes a set of unit tests to ensure that the core functionalities
 To run all tests, use the following command:
 
 ```bash
-poetry run pytest
+poetry run pytest

server/intelligence-service/pyproject.toml

@@ -10,6 +10,11 @@ package-mode = false
 python = "^3.12"
 fastapi = "^0.112.0"
 uvicorn = "^0.30.5"
+python-dotenv = "^1.0.1"
+langchain-community = "^0.2.11"
+openai = "^1.40.3"
+langchain = "^0.2.12"
+langchain-openai = "^0.1.21"
 
 
 [tool.poetry.group.dev.dependencies]

server/intelligence-service/src/config.py

@@ -1,5 +1,17 @@
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
 class Settings:
     APP_NAME: str = "Intelligence Service"
+    # Non-Azure OpenAI Configuration
+    OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+    # Azure OpenAI Configuration
+    AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
+    AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
+    AZURE_OPENAI_API_VERSION = os.getenv("AZURE_OPENAI_API_VERSION")
 
 
 settings = Settings()

server/intelligence-service/src/main.py

@@ -1,8 +1,23 @@
-from fastapi import FastAPI
-
+from fastapi import FastAPI, HTTPException
 from .auth.router import router
 from .config import settings
+from .services.langchain_client import get_openai_client
+from pydantic import BaseModel
 
 app = FastAPI(title=settings.APP_NAME)
 
 app.include_router(router)
+
+
+class ChatRequest(BaseModel):
+    message: str
+
+
+@app.post("/chat", response_model=dict, summary="Chat with LLM")
+async def chat(request: ChatRequest):
+    try:
+        client = get_openai_client()
+        response = client.invoke(request.message)
+        return {"response": response}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

server/intelligence-service/src/services/langchain_client.py

@@ -0,0 +1,15 @@
+from langchain_openai import OpenAI
+from langchain_community.chat_models import AzureChatOpenAI
+from ..config import settings
+
+def get_openai_client():
+    if settings.OPENAI_API_KEY:
+        return OpenAI(api_key=settings.OPENAI_API_KEY)
+    elif settings.AZURE_OPENAI_API_KEY:
+        return AzureChatOpenAI(
+            api_key=settings.AZURE_OPENAI_API_KEY,
+            endpoint=settings.AZURE_OPENAI_ENDPOINT,
+            api_version=settings.AZURE_OPENAI_API_VERSION
+        )
+    else:
+        raise ValueError("No valid OpenAI configuration found.")

server/intelligence-service/tests/test_langchain.py

@@ -0,0 +1,9 @@
+from fastapi.testclient import TestClient
+from src.main import app
+
+client = TestClient(app)
+
+def test_chat_endpoint():
+    response = client.post("/chat", json={"message": "Hello, how are you?"})
+    assert response.status_code == 200
+    assert "response" in response.json()