Integrate LangChain with FastAPI (#49)

Co-authored-by: Felix T.J. Dietrich <[email protected]>
Co-authored-by: Felix T.J. Dietrich <[email protected]>

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/Dockerfile

@@ -0,0 +1,17 @@
+FROM python:3.12 as requirements-stage
+
+WORKDIR /tmp
+
+RUN pip install poetry
+COPY ./pyproject.toml ./poetry.lock* /tmp/
+RUN poetry export -f requirements.txt --output requirements.txt --without-hashes
+
+FROM python:3.12
+
+WORKDIR /code
+
+COPY --from=requirements-stage /tmp/requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+COPY ./app /code/app
+
+CMD ["fastapi", "run", "app/main.py", "--port", "5000"]

server/intelligence-service/README.md

@@ -1,60 +1,41 @@
 # Intelligence Service
 
-This is a FastAPI service for interfacing with LangChain and other machine learning services.
+## Overview
 
-## Table of Contents
+A FastAPI service for interfacing with machine learning models.
 
-- [Installation](#installation)
-- [Usage](#usage)
-- [Project Structure](#project-structure)
-- [Testing](#testing)
+## Setup
 
-## Installation
+### Prerequisites
 
-To set up the project locally, follow these steps:
+- **Python 3.12**
+- **Poetry** for dependency management
+- **Docker** for containerization
 
-1. **Install dependencies:**
-    The project uses `poetry` for dependency management. Install the dependencies by running:
-    ```bash
-    poetry install
-    ```
+### Installation
 
-2. **Run the application:**
-    You can start the FastAPI application with Uvicorn:
-    ```bash
-    poetry run uvicorn src.main:app --reload
-    ```
+Install dependencies using Poetry:
 
-## Usage
+```bash
+pip install poetry
+poetry install
+```
 
-After running the application, you can access the FastAPI API documentation at `http://127.0.0.1:8000/docs` or `http://127.0.0.1:8000/redoc`.
+## Running the Service
 
-## Project Structure
+### Development
 
-The project is organized as follows:
-```
-intelligence-service/
-├── pyproject.toml     
-├── README.md            
-├── poetry.lock           
-├── .pytest_cache/        
-├── tests/                
-│   ├── __init__.py
-│   └── test_hello.py
-├── src/                  
-│   ├── config.py        
-│   ├── main.py          
-│   └── auth/            
-│       └── router.py    
-└── ...
+```bash
+fastapi dev
 ```
-## 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.
+### Production
 
-### Running Tests
+```bash
+fastapi run
+```
+
+## Usage
 
-To run all tests, use the following command:
+After running the application, you can access the FastAPI API documentation at `http://127.0.0.1:8000/docs` or `http://127.0.0.1:8000/redoc`.
 
-```bash
-poetry run pytest

server/intelligence-service/app/config.py

@@ -0,0 +1,19 @@
+from pydantic_settings import BaseSettings
+
+
+class Settings(BaseSettings):
+    OPENAI_API_KEY: str = ""
+
+    AZURE_OPENAI_API_KEY: str = ""
+    AZURE_OPENAI_ENDPOINT: str = ""
+    AZURE_OPENAI_API_VERSION: str = ""
+
+    @property
+    def is_openai_available(self):
+        return bool(self.OPENAI_API_KEY)
+
+    @property
+    def is_azure_openai_available(self):
+        return bool(self.AZURE_OPENAI_API_KEY) and bool(self.AZURE_OPENAI_ENDPOINT) and bool(self.AZURE_OPENAI_API_VERSION)
+
+settings = Settings()

server/intelligence-service/app/main.py

@@ -0,0 +1,18 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from .model import model
+
+app = FastAPI()
+
+
+class ChatRequest(BaseModel):
+    message: str
+
+
+@app.post("/chat", response_model=dict, summary="Chat with LLM")
+async def chat(request: ChatRequest):
+    try:
+        response = model.invoke(request.message)
+        return { "response": response.content }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

server/intelligence-service/app/model.py

@@ -0,0 +1,13 @@
+from langchain.chat_models.base import BaseChatModel
+from langchain_openai import ChatOpenAI, AzureChatOpenAI
+from .config import settings
+
+
+model: BaseChatModel
+
+if settings.is_openai_available:
+    model = ChatOpenAI()
+elif settings.is_azure_openai_available:
+    model = AzureChatOpenAI()
+else:
+    raise EnvironmentError("No LLM available")

server/intelligence-service/pyproject.toml

@@ -1,20 +1,23 @@
 [tool.poetry]
 name = "intelligence-service"
 version = "0.1.0"
-description = "A FastAPI service for interfacing with large language models (LLMs) and providing intelligent text-based responses"
-authors = ["milenasrb <[email protected]>"]
+description = "A FastAPI service for interfacing with machine learning models."
+authors = ["Felix T.J. Dietrich <[email protected]>", "milenasrb <[email protected]>"]
 readme = "README.md"
 package-mode = false
 
+
 [tool.poetry.dependencies]
 python = "^3.12"
-fastapi = "^0.112.0"
-uvicorn = "^0.30.5"
-
+fastapi = {extras = ["standard"], version = "0.112.1"}
+pydantic-settings = "2.4.0"
+langchain = "0.2.15"
+langchain-openai = "0.1.23"
 
-[tool.poetry.group.dev.dependencies]
-httpx = "^0.27.0"
-pytest = "^8.3.2"
+[virtualenvs]
+create = true
+in-project = true
+path = "./.venv"
 
 [build-system]
 requires = ["poetry-core"]

server/webhook-ingest/pyproject.toml

@@ -17,6 +17,7 @@ pydantic-settings = "2.4.0"
 [virtualenvs]
 create = true
 in-project = true
+path = "./.venv"
 
 [build-system]
 requires = ["poetry-core"]