Redis Chat Message History

Redis (Remote Dictionary Server) is an open-source in-memory storage, used as a distributed, in-memory key–value database, cache and message broker, with optional durability. Redis offers low-latency reads and writes. Redis is the most popular NoSQL database, and one of the most popular databases overall.

This notebook demonstrates how to use the RedisChatMessageHistory class from the langchain-redis package to store and manage chat message history using Redis.

Setup

First, we need to install the required dependencies and ensure we have a Redis instance running.

%pip install -qU langchain-redis langchain-openai redis

Make sure you have a Redis server running. You can start one using Docker with the following command:

docker run -d -p 6379:6379 redis:latest

Or install and run Redis locally according to the instructions for your operating system.

import os

# Use the environment variable if set, otherwise default to localhost
REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379")
print(f"Connecting to Redis at: {REDIS_URL}")

Connecting to Redis at: redis://redis:6379

Importing Required Libraries

from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI
from langchain_redis import RedisChatMessageHistory

API Reference:BaseChatMessageHistory | AIMessage | HumanMessage | ChatPromptTemplate | MessagesPlaceholder | RunnableWithMessageHistory | ChatOpenAI

Basic Usage of RedisChatMessageHistory

# Initialize RedisChatMessageHistory
history = RedisChatMessageHistory(session_id="user_123", redis_url=REDIS_URL)

# Add messages to the history
history.add_user_message("Hello, AI assistant!")
history.add_ai_message("Hello! How can I assist you today?")

# Retrieve messages
print("Chat History:")
for message in history.messages:
    print(f"{type(message).__name__}: {message.content}")

Chat History:
HumanMessage: Hello, AI assistant!
AIMessage: Hello! How can I assist you today?

Using RedisChatMessageHistory with Language Models

Set OpenAI API key

from getpass import getpass

# Check if OPENAI_API_KEY is already set in the environment
openai_api_key = os.getenv("OPENAI_API_KEY")

if not openai_api_key:
    print("OpenAI API key not found in environment variables.")
    openai_api_key = getpass("Please enter your OpenAI API key: ")

    # Set the API key for the current session
    os.environ["OPENAI_API_KEY"] = openai_api_key
    print("OpenAI API key has been set for this session.")
else:
    print("OpenAI API key found in environment variables.")

OpenAI API key not found in environment variables.
``````output
Please enter your OpenAI API key:  ········
``````output
OpenAI API key has been set for this session.

# Create a prompt template
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful AI assistant."),
        MessagesPlaceholder(variable_name="history"),
        ("human", "{input}"),
    ]
)

# Initialize the language model
llm = ChatOpenAI()

# Create the conversational chain
chain = prompt | llm


# Function to get or create a RedisChatMessageHistory instance
def get_redis_history(session_id: str) -> BaseChatMessageHistory:
    return RedisChatMessageHistory(session_id, redis_url=REDIS_URL)


# Create a runnable with message history
chain_with_history = RunnableWithMessageHistory(
    chain, get_redis_history, input_messages_key="input", history_messages_key="history"
)

# Use the chain in a conversation
response1 = chain_with_history.invoke(
    {"input": "Hi, my name is Alice."},
    config={"configurable": {"session_id": "alice_123"}},
)
print("AI Response 1:", response1.content)

response2 = chain_with_history.invoke(
    {"input": "What's my name?"}, config={"configurable": {"session_id": "alice_123"}}
)
print("AI Response 2:", response2.content)

AI Response 1: Hello Alice! How can I assist you today?
AI Response 2: Your name is Alice.

Advanced Features

Custom Redis Configuration

# Initialize with custom Redis configuration
custom_history = RedisChatMessageHistory(
    "user_456",
    redis_url=REDIS_URL,
    key_prefix="custom_prefix:",
    ttl=3600,  # Set TTL to 1 hour
    index_name="custom_index",
)

custom_history.add_user_message("This is a message with custom configuration.")
print("Custom History:", custom_history.messages)

Custom History: [HumanMessage(content='This is a message with custom configuration.')]

Searching Messages

# Add more messages
history.add_user_message("Tell me about artificial intelligence.")
history.add_ai_message(
    "Artificial Intelligence (AI) is a branch of computer science..."
)

# Search for messages containing a specific term
search_results = history.search_messages("artificial intelligence")
print("Search Results:")
for result in search_results:
    print(f"{result['type']}: {result['content'][:50]}...")

Search Results:
human: Tell me about artificial intelligence....
ai: Artificial Intelligence (AI) is a branch of comput...

Clearing History

# Clear the chat history
history.clear()
print("Messages after clearing:", history.messages)

Messages after clearing: []

Conclusion

This notebook demonstrated the key features of RedisChatMessageHistory from the langchain-redis package. It showed how to initialize and use the chat history, integrate it with language models, and utilize advanced features like custom configurations and message searching. Redis provides a fast and scalable solution for managing chat history in AI applications.