MCP with Langchain Sample Setup

Okay, here's a sample setup for an MCP (presumably referring to a **Multi-Client Processing** or **Message Communication Protocol**) server and client, designed to be compatible with LangChain. This example focuses on a simple request-response pattern, suitable for offloading LangChain tasks to a separate process or machine. **Important Considerations:** * **Serialization:** LangChain objects can be complex. You'll need a robust serialization/deserialization method (e.g., `pickle`, `json`, `cloudpickle`) to send data between the server and client. `cloudpickle` is often preferred for its ability to handle more complex Python objects, including closures and functions. * **Error Handling:** Implement comprehensive error handling on both the server and client to gracefully manage exceptions and network issues. * **Security:** If you're transmitting data over a network, consider security measures like encryption (e.g., TLS/SSL) to protect sensitive information. * **Asynchronous Operations:** For better performance, especially with LangChain tasks that might be I/O bound, consider using asynchronous programming (e.g., `asyncio`). This example shows a basic synchronous version for clarity. * **Message Format:** Define a clear message format (e.g., JSON with specific keys) for requests and responses. * **LangChain Compatibility:** The key is to serialize the *input* to a LangChain component (like a Chain or LLM) on the client, send it to the server, deserialize it, run the LangChain component on the server, serialize the *output*, and send it back to the client. **Python Code (using `socket` module for simplicity):** **1. Server (server.py):** ```python import socket import pickle # Or json, cloudpickle import langchain import os # Example LangChain setup (replace with your actual chain) from langchain.llms import OpenAI from langchain.chains import LLMChain from langchain.prompts import PromptTemplate os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY" # Replace with your actual API key llm = OpenAI(temperature=0.7) prompt = PromptTemplate( input_variables=["product"], template="What is a good name for a company that makes {product}?", ) chain = LLMChain(llm=llm, prompt=prompt) HOST = '127.0.0.1' # Standard loopback interface address (localhost) PORT = 65432 # Port to listen on (non-privileged ports are > 1023) def process_langchain_request(data): """ Processes a LangChain request. This is the core logic on the server. """ try: # Deserialize the input (assuming it's a dictionary) input_data = pickle.loads(data) # Or json.loads(data) if using JSON # **Crucially, ensure the input_data matches what your LangChain component expects.** # For example, if your chain expects a dictionary with a "text" key: # input_text = input_data["text"] # Run the LangChain component result = chain.run(input_data["product"]) # Replace with your actual LangChain call # Serialize the result serialized_result = pickle.dumps(result) # Or json.dumps(result) return serialized_result except Exception as e: print(f"Error processing request: {e}") return pickle.dumps({"error": str(e)}) # Serialize the error message with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Server listening on {HOST}:{PORT}") conn, addr = s.accept() with conn: print(f"Connected by {addr}") while True: data = conn.recv(4096) # Adjust buffer size as needed if not data: break response = process_langchain_request(data) conn.sendall(response) ``` **2. Client (client.py):** ```python import socket import pickle # Or json, cloudpickle HOST = '127.0.0.1' # The server's hostname or IP address PORT = 65432 # The port used by the server def send_langchain_request(input_data): """ Sends a LangChain request to the server and returns the response. """ try: with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.connect((HOST, PORT)) # Serialize the input data serialized_data = pickle.dumps(input_data) # Or json.dumps(input_data) s.sendall(serialized_data) received = s.recv(4096) # Adjust buffer size as needed # Deserialize the response deserialized_response = pickle.loads(received) # Or json.loads(received) return deserialized_response except Exception as e: print(f"Error sending request: {e}") return {"error": str(e)} if __name__ == "__main__": # Example usage input_data = {"product": "eco-friendly cleaning products"} # Replace with your actual input response = send_langchain_request(input_data) if "error" in response: print(f"Error from server: {response['error']}") else: print(f"Server response: {response}") ``` **How to Run:** 1. **Install LangChain:** `pip install langchain openai` 2. **Set your OpenAI API Key:** Replace `"YOUR_API_KEY"` in `server.py` with your actual OpenAI API key. 3. **Run the server:** `python server.py` 4. **Run the client:** `python client.py` **Explanation:** * **Server (`server.py`):** * Creates a socket and listens for incoming connections. * When a client connects, it receives data, deserializes it (using `pickle`), processes it using a LangChain component (in this case, a simple `LLMChain`), serializes the result, and sends it back to the client. * Includes basic error handling. * **Client (`client.py`):** * Creates a socket and connects to the server. * Serializes the input data (using `pickle`), sends it to the server, receives the response, deserializes it, and prints the result. * Includes basic error handling. * **Serialization:** `pickle` (or `json`, `cloudpickle`) is used to convert Python objects into a byte stream that can be sent over the network. The same method must be used for both serialization and deserialization. * **LangChain Integration:** The `process_langchain_request` function on the server is where the LangChain logic resides. It receives the serialized input, deserializes it, runs the LangChain component, and serializes the output. **Key Improvements and Considerations for Production:** * **Asynchronous Communication (using `asyncio`):** Use `asyncio` for non-blocking I/O, allowing the server to handle multiple clients concurrently. This significantly improves performance. * **Message Queues (e.g., RabbitMQ, Redis):** Instead of direct socket connections, use a message queue for more robust and scalable communication. This decouples the client and server and allows for asynchronous processing. * **gRPC:** Consider using gRPC for efficient and type-safe communication between the client and server. gRPC uses Protocol Buffers for serialization, which is generally faster and more compact than `pickle` or `json`. * **Authentication and Authorization:** Implement authentication and authorization to secure the server and prevent unauthorized access. * **Logging:** Use a logging library (e.g., `logging`) to record events and errors for debugging and monitoring. * **Configuration:** Use a configuration file (e.g., YAML, JSON) to store settings like the server address, port, and API keys. * **Monitoring:** Monitor the server's performance and resource usage to identify bottlenecks and potential issues. * **Data Validation:** Validate the input data on both the client and server to prevent errors and security vulnerabilities. * **Retry Logic:** Implement retry logic on the client to handle transient network errors. * **Heartbeat Mechanism:** Implement a heartbeat mechanism to detect and handle server failures. * **Cloudpickle:** For complex LangChain objects, especially those involving custom functions or classes, `cloudpickle` is often necessary to ensure proper serialization and deserialization. Install it with `pip install cloudpickle`. **Example using `cloudpickle`:** ```python # Server (server.py) import cloudpickle def process_langchain_request(data): try: input_data = cloudpickle.loads(data) result = chain.run(input_data["product"]) serialized_result = cloudpickle.dumps(result) return serialized_result except Exception as e: print(f"Error processing request: {e}") return cloudpickle.dumps({"error": str(e)}) # Client (client.py) import cloudpickle def send_langchain_request(input_data): try: with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.connect((HOST, PORT)) serialized_data = cloudpickle.dumps(input_data) s.sendall(serialized_data) received = s.recv(4096) deserialized_response = cloudpickle.loads(received) return deserialized_response except Exception as e: print(f"Error sending request: {e}") return {"error": str(e)} ``` This more complete example provides a solid foundation for building a distributed LangChain application. Remember to adapt the code to your specific needs and consider the production-level improvements mentioned above. **Chinese Translation of Key Concepts:** * **MCP (Multi-Client Processing/Message Communication Protocol):** 多客户端处理/消息通信协议 (Duō kèhùduān chǔlǐ/Xiāoxī tōngxìn xiéyì) * **Serialization:** 序列化 (Xùlièhuà) * **Deserialization:** 反序列化 (Fǎn xùlièhuà) * **LangChain:** LangChain (No direct translation, use the English name) * **Socket:** 套接字 (Tàojiēzì) * **Asynchronous:** 异步 (Yìbù) * **Message Queue:** 消息队列 (Xiāoxī duìliè) * **gRPC:** gRPC (No direct translation, use the English name) * **Protocol Buffers:** 协议缓冲区 (Xiéyì huǎnchōngqū) * **Authentication:** 身份验证 (Shēnfèn yànzhèng) * **Authorization:** 授权 (Shòuquán) * **Logging:** 日志记录 (Rìzhì jìlù) * **Cloudpickle:** Cloudpickle (No direct translation, use the English name) This should give you a good starting point. Let me know if you have any more specific questions.