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Human-in-the-Loop через middleware: README.md

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# Human‑in‑the‑Loop Middleware # Human‑in‑the‑Loop Middleware Demo
Human‑in‑the‑Loop (HITL) middleware is a lightweight Python framework that lets you build conversational agents on top of **Qdrant** vector store and **Ollama** LLMs. A minimal Python project that demonstrates how to add a **Human‑in‑the‑Loop (HITL)** experience to a LangChain agent using the built‑in `HumanInTheLoopMiddleware`.
The project demonstrates how to: When the agent is about to invoke an external tool, it pauses and asks for user approval (`approve` or `reject`) before continuing.
* Store and retrieve documents with Qdrant. > **Why use middleware?**
* Embed text using the `nomic-embed-text` model from Ollama. > Unlike the older `interrupt_before=['tools']` approach, the middleware automatically builds the confirmation prompt, handles the response, and resumes execution via a `Command(resume={…})`. This keeps your agent logic clean and declarative.
* Generate responses with the `llama3` chat model via LangChain.
* Wrap everything in a simple HTTP client that can be used by external services or UI front‑ends.
The core logic lives in two files:
| File | Purpose |
|------|---------|
| **agent.py** | Implements the LangChain pipeline: embedding → vector search → LLM generation. |
| **client.py** | Exposes a minimal FastAPI server that accepts user queries and returns responses from `agent.py`. |
--- ---
## 📦 Prerequisites ## 📦 Project Structure
```
├── agent.py # Agent definition + HITL middleware
├── client.py # Simple CLI that runs the agent
├── requirements.txt
└── README.md
```
- `agent.py` – creates a LangChain agent with a single tool (`get_weather`) and attaches the HITL middleware.
- `client.py` – launches the agent in an interactive loop.
---
## ⚙️ Prerequisites
| Component | Minimum Version | Notes | | Component | Minimum Version | Notes |
|-----------|-----------------|-------| |-----------|-----------------|-------|
| Python | 3.10+ | Tested on 3.12 | | Python | 3.11+ | Tested on 3.12 |
| pip | – | Use the system package manager or `pipx` | | Ollama | latest | Local LLM (e.g., `llama3`) |
| **Ollama** | Latest | Install from https://ollama.ai/ | | LangChain | 0.2.x or newer | Provides agents & middleware |
| **Qdrant** | 1.7+ | Run locally (`docker run -p 6333:6333 qdrant/qdrant`) or use a managed instance | | LangGraph | 0.1.x or newer | For checkpointing (optional) |
> **Important:** > **Ollama** must be running locally and the model (`llama3` in this repo) should be pulled beforehand:
> * The Ollama image must expose the `llama3` and `nomic-embed-text` models. > ```bash
> ```bash > ollama pull llama3
> ollama pull llama3 > ```
> ollama pull nomic-embed-text
> ```
> * Qdrant should be reachable at `http://localhost:6333` (or set via the `QDRANT_URL` env var).
--- ---
## ⚙️ Installation ## 📦 Installation
```bash ```bash
# Clone the repo # Clone the repository
git clone https://github.com/your-org/hitl-middleware.git git clone https://github.com/your-username/hitl-middleware-demo.git
cd hitl-middleware cd hitl-middleware-demo
# Create a virtual environment (optional but recommended) # Create a virtual environment (recommended)
python -m venv .venv python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies # Install dependencies
pip install -r requirements.txt pip install -r requirements.txt
@@ -55,115 +57,76 @@ pip install -r requirements.txt
```text ```text
langchain==0.2.* langchain==0.2.*
langchain-ollama==0.1.* langgraph==0.1.*
langchain-qdrant==0.1.* langchain-ollama==0.2.*
fastapi==0.* # for client.py
uvicorn==0.* # ASGI server
python-dotenv==1.*
``` ```
--- ---
## 🚀 Running the Project ## 🚀 Running the Project
### 1️⃣ Start Qdrant (if not already running) ### 1️⃣ Start the Agent (CLI)
```bash ```bash
docker run -d --name qdrant \ python client.py
-p 6333:6333 \
qdrant/qdrant
``` ```
> Make sure the container is healthy before proceeding. You will see a prompt like:
### 2️⃣ Run the Agent
The agent can be executed as a script or imported into other code.
It will automatically load embeddings, connect to Qdrant, and expose a `process_query` function.
```bash
python agent.py
```
> The script prints a simple “Agent ready” message and waits for input if run directly.
### 3️⃣ Run the Client (FastAPI)
The client exposes an HTTP endpoint `/query`.
It forwards incoming requests to the agent and returns the LLM response.
```bash
uvicorn client:app --host 0.0.0.0 --port 8000
```
You should see:
``` ```
INFO: Started server process [12345] Agent: What would you like to know?
> Tell me the weather in Paris tomorrow.
... ...
INFO: Application startup complete. [HITL] The agent wants to call tool `get_weather` with arguments:
{
"city": "Paris",
"date": "2024-06-01"
}
Approve? (approve/reject):
``` ```
Now you can send requests to `http://localhost:8000/query`. Type **`approve`** or **`reject`** and press Enter.
If approved, the tool runs; if rejected, the agent will re‑think.
--- ### 2️⃣ Run a Single File Directly
## 📄 Example Usage You can also run each file individually:
### Using the HTTP API
```bash ```bash
curl -X POST http://localhost:8000/query \ # Agent definition (no output)
-H "Content-Type: application/json" \ python agent.py
-d '{"question": "What is the capital of France?"}'
```
**Response** # Client that uses the agent
python client.py
```json
{
"answer": "The capital of France is Paris."
}
```
### Using the Agent Directly (Python)
```python
from agent import process_query
response = process_query("Explain quantum computing in simple terms.")
print(response)
# Output: "Quantum computing uses qubits..."
``` ```
--- ---
## 🔧 Configuration ## 🔍 Example Interaction
All configuration values can be overridden via environment variables or a `.env` file placed at the project root.
| Variable | Default | Description |
|----------|---------|-------------|
| `QDRANT_URL` | `http://localhost:6333` | Qdrant endpoint |
| `OLLAMA_HOST` | `http://localhost:11434` | Ollama API host |
| `LLM_MODEL` | `llama3` | Chat model name |
| `EMBEDDING_MODEL` | `nomic-embed-text` | Embedding model name |
Example `.env`:
```dotenv
QDRANT_URL=http://qdrant:6333
OLLAMA_HOST=http://ollama:11434
LLM_MODEL=llama3
EMBEDDING_MODEL=nomic-embed-text
``` ```
Agent: Tell me the weather in Tokyo tomorrow.
[HITL] The agent wants to call tool `get_weather` with arguments:
{
"city": "Tokyo",
"date": "2024-06-01"
}
Approve? (approve/reject): approve
Tool output: "Tomorrow in Tokyo, expect a high of 28°C and light showers."
Agent: The weather in Tokyo tomorrow will be around 28°C with light showers.
```
If you type `reject`, the agent will ask for clarification or try a different approach.
--- ---
## 📚 Further Reading ## 📚 Further Reading
* [LangChain Docs](https://langchain.com/) - [Human‑in‑the‑Loop — LangChain](https://docs.langchain.com/docs/middleware/human_in_the_loop)
* [Ollama Quickstart](https://github.com/ollama/ollama) - [LangGraph Checkpoints](https://langgraph.org/docs/checkpointing)
* [Qdrant Documentation](https://qdrant.tech/documentation/)
Feel free to extend the middleware with custom prompts, retrieval strategies, or additional LLMs. Happy hacking! Feel free to extend the toolset or replace `ChatOllama` with an OpenAI model by swapping imports.
---
Happy hacking! 🚀