This repo provides a simple example of memory service you can build and deploy using LanGraph.
Inspired by papers like MemGPT and distilled from our own works on long-term memory, the graph extracts memories from chat interactions and persists them to a database. This information can later be read or queried semantically to provide personalized context when your bot is responding to a particular user.
The memory graph handles thread process deduplication and supports continuous updates to a single "memory schema" as well as "event-based" memories that can be queried semantically.
├── langgraph.json # LangGraph Cloud Configuration
├── memory_service
│ ├── __init__.py
│ └── graph.py # Define the memory service
├── poetry.lock
├── pyproject.toml # Project dependencies
└── tests # Add testing + evaluation logic
└── evals
└── test_memories.pyThis quick start will get your memory service deployed on LangGraph Cloud. Once created, you can interact with it from any API.
This example defaults to using Pinecone for its memory database, and nomic-ai/nomic-embed-text-v1.5 as the text encoder (hosted on Fireworks).
- Create an index with a dimension size of
768. Note down your Pinecone API key, index name, and namespace for the next step. - Create an API Key to use for the LLM & embeddings models served on Fireworks.
Note: (Closed Beta) LangGraph Cloud is a managed service for deploying and hosting LangGraph applications. It is currently (as of 26 June, 2024) in closed beta. If you are interested in applying for access, please fill out this form.
To deploy this example on LangGraph, fork the repo.
Next, navigate to the 🚀 deployments tab on LangSmith.
If you have not deployed to LangGraph Cloud before: there will be a button that shows up saying Import from GitHub. You’ll need to follow that flow to connect LangGraph Cloud to GitHub.
Once you have set up your GitHub connection, select +New Deployment. Fill out the required information, including:
- Your GitHub username (or organization) and the name of the repo you just forked.
- You can leave the defaults for the config file (
langgraph.config) and branch (main) - Environment variables (see below)
The default required environment variables can be found in .env.example and are copied below:
# .env
PINECONE_API_KEY=...
PINECONE_INDEX_NAME=...
PINECONE_NAMESPACE=...
FIREWORKS_API_KEY=...
# You can add other keys as appropriate, depending on
# the services you are using.You can fill these out locally, copy the .env file contents, and paste them in the first Name argument.
Assuming you've followed the steps above, in just a couple of minutes, you should have a working memory service deployed!
Now let's try it out.
Check out the example notebook to show how to connect your chat bot (in this case a second graph) to your new memory service.
This chat bot reads from the same memory DB as your memory service to easily query from "recall memory".
Connecting to this type of memory service typically follows an interaction pattern similar to the one outlined below:
A typical user-facing application you'd build to connect with this service would have 3 or more nodes. The first node queries the DB for useful memories. The second node, which contains the LLM, generates the response. The third node posts the new messages to the service.
The service waits for a pre-determined interval before it considers the thread "complete". If the user queries a second time within that interval, the memory run is rolled-back to avoid duplicate processing of a thread.
Memory management can be challenging to get right. To make sure your schemas suit your applications' needs, we recommend starting from an evaluation set, adding to it over time as you find and address common errors in your service.
We have provided a few example evaluation cases in the test file here. As you can see, the metrics themselves don't have to be terribly complicated, especially not at the outset.
We use LangSmith's @test decorator to sync all the evalutions to LangSmith so you can better optimize your system and identify the root cause of any issues that may arise.
