From ec57b196e3047cc86f6ad38534d7d2d30608cb64 Mon Sep 17 00:00:00 2001 From: Lance Martin Date: Fri, 4 Oct 2024 12:37:29 -0700 Subject: [PATCH 1/7] Add comments --- src/memory_graph/graph.py | 69 ++++++++++++++++++++++++++++++++++----- 1 file changed, 60 insertions(+), 9 deletions(-) diff --git a/src/memory_graph/graph.py b/src/memory_graph/graph.py index 9509698..9f5b960 100644 --- a/src/memory_graph/graph.py +++ b/src/memory_graph/graph.py @@ -23,31 +23,50 @@ async def handle_patch_memory( state: ProcessorState, config: RunnableConfig, *, store: BaseStore ) -> dict: """Extract the user's state from the conversation and update the memory.""" + + # Get the overall configuration configurable = configuration.Configuration.from_runnable_config(config) + + # Namespace for memory events, where function_name is the name of the memory schema namespace = (configurable.user_id, "user_states", state.function_name) + + # Fetch existing memories from the store for this (patch) memory schema existing_item = await store.aget(namespace, "memory") existing = {existing_item.key: existing_item.value} if existing_item else None + + # Get the configuration for this memory schema (identified by function_name) memory_config = next( conf for conf in configurable.memory_types if conf.name == state.function_name ) + + # This is what we use to generate new memories extractor = create_extractor( utils.init_model(configurable.model), + # We pass the specified (patch) memory schema as a tool tools=[ { + # Tool name "name": memory_config.name, + # Tool description "description": memory_config.description, + # Schema for patch memory "parameters": memory_config.parameters, } ], tool_choice=memory_config.name, ) + + # Prepare the messages prepared_messages = utils.prepare_messages( state.messages, memory_config.system_prompt ) + + # Pass messages and existing patch to the extractor inputs = {"messages": prepared_messages, "existing": existing} + # Update the patch memory result = await extractor.ainvoke(inputs, config) extracted = result["responses"][0].model_dump(mode="json") - # Upsert the memory to storage + # Save to storage await store.aput(namespace, "memory", extracted) return {"messages": []} @@ -55,30 +74,50 @@ async def handle_patch_memory( async def handle_insertion_memory( state: ProcessorState, config: RunnableConfig, *, store: BaseStore ) -> dict[str, list]: - """Upsert memory events.""" + + """Handle insertion memory events.""" + + # Get the overall configuration configurable = configuration.Configuration.from_runnable_config(config) + + # Namespace for memory events, where function_name is the name of the memory schema namespace = (configurable.user_id, "events", state.function_name) + + # Fetch existing memories from the store (5 most recent ones) for the this (insert) memory schema existing_items = await store.asearch(namespace, limit=5) + + # Get the configuration for this memory schema (identified by function_name) memory_config = next( conf for conf in configurable.memory_types if conf.name == state.function_name ) + + # This is what we use to generate new memories extractor = create_extractor( utils.init_model(configurable.model), + # We pass the specified (insert) memory schema as a tool tools=[ { + # Tool name "name": memory_config.name, + # Tool description "description": memory_config.description, + # Schema for insert memory "parameters": memory_config.parameters, } ], tool_choice="any", + # This allows the extractor to insert new memories enable_inserts=True, ) + + # Generate new memories or update existing memories extracted = await extractor.ainvoke( { + # Prepare the messages "messages": utils.prepare_messages( state.messages, memory_config.system_prompt ), + # Prepare the existing memories "existing": ( [ (existing_item.key, state.function_name, existing_item.value) @@ -90,6 +129,8 @@ async def handle_insertion_memory( }, config, ) + + # Add the memories to storage await asyncio.gather( *( store.aput( @@ -103,31 +144,41 @@ async def handle_insertion_memory( return {"messages": []} -# Create the graph + all nodes +# Create the graph and all nodes builder = StateGraph(State, config_schema=configuration.Configuration) - builder.add_node(handle_patch_memory, input=ProcessorState) builder.add_node(handle_insertion_memory, input=ProcessorState) - def scatter_schemas(state: State, config: RunnableConfig) -> list[Send]: - """Route the memory_types for the memory assistant. + """The entry point to the graph will will iterate over all memory types in the configuration. - These will be executed in parallel. + It will route each memory type from configuration to the corresponding memory update node. + + The memory update nodes will be executed in parallel. """ + + # Get the configuration configurable = configuration.Configuration.from_runnable_config(config) sends = [] current_state = asdict(state) + + # Loop over all memory types specified in the configuration for v in configurable.memory_types: update_mode = v.update_mode + + # This specifies the type of memory update to perform from the configuration match update_mode: case "patch": + # This is the corresponding node in the graph for the patch-based memory update target = "handle_patch_memory" case "insert": + # This is the corresponding node in the graph for the insert-based memory update target = "handle_insertion_memory" case _: raise ValueError(f"Unknown update mode: {update_mode}") + # Use Send API to route to the target node and pass the name of the memory schema as function_name + # Send API allows each memory node to be executed in parallel sends.append( Send( target, @@ -137,11 +188,11 @@ def scatter_schemas(state: State, config: RunnableConfig) -> list[Send]: return sends +# Add conditional edges to the graph builder.add_conditional_edges( "__start__", scatter_schemas, ["handle_patch_memory", "handle_insertion_memory"] ) +# Compile the graph graph = builder.compile() - - __all__ = ["graph"] From b4b4dd3470694fd90fb140ae04317369a4b8285d Mon Sep 17 00:00:00 2001 From: Lance Martin Date: Fri, 4 Oct 2024 13:48:16 -0700 Subject: [PATCH 2/7] Update README --- README.md | 132 +++++++++++++++++++++++++++----- static/memory_template_flow.png | Bin 0 -> 248156 bytes static/memory_types.png | Bin 0 -> 107860 bytes static/scheduling.png | Bin 0 -> 186283 bytes static/studio.png | Bin 0 -> 322676 bytes 5 files changed, 114 insertions(+), 18 deletions(-) create mode 100644 static/memory_template_flow.png create mode 100644 static/memory_types.png create mode 100644 static/scheduling.png create mode 100644 static/studio.png diff --git a/README.md b/README.md index 8893af6..4208627 100644 --- a/README.md +++ b/README.md @@ -4,24 +4,21 @@ [![Integration Tests](https://github.com/langchain-ai/memory-template/actions/workflows/integration-tests.yml/badge.svg)](https://github.com/langchain-ai/memory-template/actions/workflows/integration-tests.yml) [![Open in - LangGraph Studio](https://img.shields.io/badge/Open_in-LangGraph_Studio-00324d.svg?logo=data:image/svg%2bxml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI4NS4zMzMiIGhlaWdodD0iODUuMzMzIiB2ZXJzaW9uPSIxLjAiIHZpZXdCb3g9IjAgMCA2NCA2NCI+PHBhdGggZD0iTTEzIDcuOGMtNi4zIDMuMS03LjEgNi4zLTYuOCAyNS43LjQgMjQuNi4zIDI0LjUgMjUuOSAyNC41QzU3LjUgNTggNTggNTcuNSA1OCAzMi4zIDU4IDcuMyA1Ni43IDYgMzIgNmMtMTIuOCAwLTE2LjEuMy0xOSAxLjhtMzcuNiAxNi42YzIuOCAyLjggMy40IDQuMiAzLjQgNy42cy0uNiA0LjgtMy40IDcuNkw0Ny4yIDQzSDE2LjhsLTMuNC0zLjRjLTQuOC00LjgtNC44LTEwLjQgMC0xNS4ybDMuNC0zLjRoMzAuNHoiLz48cGF0aCBkPSJNMTguOSAyNS42Yy0xLjEgMS4zLTEgMS43LjQgMi41LjkuNiAxLjcgMS44IDEuNyAyLjcgMCAxIC43IDIuOCAxLjYgNC4xIDEuNCAxLjkgMS40IDIuNS4zIDMuMi0xIC42LS42LjkgMS40LjkgMS41IDAgMi43LS41IDIuNy0xIDAtLjYgMS4xLS44IDIuNi0uNGwyLjYuNy0xLjgtMi45Yy01LjktOS4zLTkuNC0xMi4zLTExLjUtOS44TTM5IDI2YzAgMS4xLS45IDIuNS0yIDMuMi0yLjQgMS41LTIuNiAzLjQtLjUgNC4yLjguMyAyIDEuNyAyLjUgMy4xLjYgMS41IDEuNCAyLjMgMiAyIDEuNS0uOSAxLjItMy41LS40LTMuNS0yLjEgMC0yLjgtMi44LS44LTMuMyAxLjYtLjQgMS42LS41IDAtLjYtMS4xLS4xLTEuNS0uNi0xLjItMS42LjctMS43IDMuMy0yLjEgMy41LS41LjEuNS4yIDEuNi4zIDIuMiAwIC43LjkgMS40IDEuOSAxLjYgMi4xLjQgMi4zLTIuMy4yLTMuMi0uOC0uMy0yLTEuNy0yLjUtMy4xLTEuMS0zLTMtMy4zLTMtLjUiLz48L3N2Zz4=)](https://langgraph-studio.vercel.app/templates/open?githubUrl=https://github.com/langchain-ai/memory-template) -This repo provides a simple example of a long-term memory service you can build and deploy using LangGraph. +Memory is a powerful way to improve and personalize applications, allowing storage of information (e.g., a user-specific profile or memories) that can be used to inform responses or decisions across multiple interactions. This template provides a simple example of a long-term memory service you can build and deploy using LangGraph. -This graph extracts memories from chat interactions and persists them to its store. This information can later be read via the API to provide personalized context when your bot is responding to a particular user. +It has three main components: -The memory graph handles debouncing when processing individual conversations (to help deduplicate work) and supports continuous updates to a single "memory schema" as well as "event-based" memories that can be fetched by recency and filtered. +(1) `Chatbot Graph`: This is a simple chatbot that interacts with a user. -This repo also provides an example chat bot (in this case, also a simple graph) that connects to the memory graph via the SDK. -Any time you send a message to the chat bot, it will query the memory service to fetch the most up-to-date memories (if any) for the configured user. These memories are put in the system prompt. After responding, it will post the conversation to the memory service to schedule long-term memory formation. +(2) `Memory Graph`: This graph contains the logic for creating memories with customizable schemas from the chatbot's user interactions. -This separation of concerns provides minimal overhead, allows deduplication of memory processing, and ensures you can optimize for better recall. +(3) `Memory Storage`: This stores created memories, making them accessible across multiple user interactions with the chatbot. -![Memory Diagram](./static/memory_graph.png) +![Flow](./static/memory_template_flow.png) ## Getting Started -This quickstart will get your memory service deployed on [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/). Once created, you can interact with it from any API. - -Assuming you have already [installed LangGraph Studio](https://github.com/langchain-ai/langgraph-studio?tab=readme-ov-file#download), to set up: +### Create .env file 1. Create a `.env` file. @@ -65,27 +62,126 @@ To use OpenAI's chat models: OPENAI_API_KEY=your-api-key ``` + +### Test in LangGraph Studio +If you want to test locally, [install the LangGraph Studio desktop app](https://github.com/langchain-ai/langgraph-studio?tab=readme-ov-file#download). +If you want to test in the cloud, [follow these instructions to deploy this repository to LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/) and open Studio in your browser. - +Open this repository in LangGraph studio. + +In LangGraph Studio, you can set your `user_id`, `model`, and other configurations. + +![Flow](./static/studio.png) + +Navigate to the "`chatbot`" graph and have a conversation with it! -3. Open in LangGraph studio. Navigate to the "`chatbot`" graph and have a conversation with it! Try sending some messages saying your name and other things the bot should remember. +Try sending some messages saying your name and other things the bot should remember. -Wait ~10-20 seconds and then create a *new* thread using the `+` icon. Then chat with the bot again - if you've completed your setup correctly, the bot should now have access to the memories you've saved! +Wait ~10-20 seconds for many memories to be created and saved, then create a *new* thread using the `+` icon. + +Then chat with the bot again - if you've completed your setup correctly, the bot should now have access to the memories you've saved! ## How it works -This chat bot reads from your memory graph's `Store` to easily list extracted memories. +### Memory Storage + +The LangGraph API comes with a built-in memory storage layer that can be used to store and retrieve information across threads. + +Studio useds the LangGraph API at the backend, packaging the code in this repository with the storage layer. + +In Studio, you can see the `Memories` button above your graph with any memories created and saved to the storage layer. + +The storage layer is shown in the above diagram in red. + +The central points are that it: + +1. It is accessible to both the `chatbot` and the `memory_graph` in all nodes. +2. It provides an interface for storing (`put` method) and retrieving (`search` method) memories in a namespaced manner. + +Learn more about the Memory Storage layer [here](https://langchain-ai.github.io/langgraph/how-tos/memory/shared-state/). + +### Chatbot Graph + +The `chatbot` graph, defined in [graph.py](./src/chatbot/graph.py), has two nodes, `bot` and `schedule_memories`. + +The `chatbot` is invoked with a `user_id` supplied by configuration. + +The `bot` node uses the `user_id` to fetch any existing memories for that user from the `Memory Storage` layer. + +These fetched memories are added to the system prompt of the chatbot to personalize the responses. + +The `schedule_memories` node is run after the `bot` node. + +It is responsible for scheduling long-term memory creation based upon the chatbot's updated interaction with the user. + +It uses the LangGraph SDK to call the `memory_graph`, suppling the chatbot's interaction with the user. + +### Memory Graph -Connecting to this type of memory service typically follows an interaction pattern similar to the one outlined below: +The `memory_graph` graph, defined in [graph.py](./src/memory_graph/graph.py) incoperates two different concepts: + +(1) We can define a schema for each of the types of memories we want to store. + +(2) We can update these memories in different ways using the [`trustcall` library](https://github.com/hinthornw/trustcall). + +The schema for each memory type is defined in [configuration.py](./src/memory_graph/configuration.py). + +The default schemas are `User` and `Note`. + +The `User` schema is used to store a single profile for a user, such as their name, age, and interests. + +We'll store a single JSON schema that can be updated with new information as the conversation progresses. + +The `Note` schema is used to store specific events or notes about the user's interactions with the chatbot. + +We'll store a list of JSON schemas that can be updated or added to as the conversation processes. + +The [`trustcall` library](https://github.com/hinthornw/trustcall) handles writing and updating either of these schemas. + +![Memory types](./static/memory_types.png) + +The `memory_graph` save both types of memories to the `Memory Storage` layer, namespaced by the `user_id` and the schema name. + +We can see the graph here in the LangGraph Studio, with a branch for each memory schemas. + +![Memory Diagram](./static/memory_graph.png) + +## Philosophy + +### Separation of Concerns + +This separation of concerns between the application logic (chatbot) and the memory processing (the memory graph) provides advantages: + +(1) minimal overhead by removing memory creation logic from the hotpath of the application (e.g., no latency cost for memory creation) + +(2) memory creation logic is handled in a background job, separate from the chatbot, with scheduling to avoid duplicate processing + +(3) memory graph can be updated and / or hosted (as a service) independently of the application (chatbot) ![Interaction Pattern](./static/memory_interactions.png) -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 cancelled to avoid duplicate processing of a thread. +### Scheduling + +Scheduling is handled by the LangGraph API's `after_seconds` parameter. + +This is the intuition: we want to wait until a thread is "complete" before we write memories to the storage layer. + +We don't know when a chat will end. + +So, we wait a pre-determined interval before invoking the memory graph to memories to the storage layer. + +If the chatbot makes a call second time within that interval, the memory run is cancelled to avoid duplicate processing of a thread. + +This cartoon highlights the concept: requests to call the `memory_graph` are delayed for some period of time. + +If any newer requests come in, the initial request is cancelled to avoid duplicate processing the same interaction. + +![DeBounce](./static/scheduling.png) ## How to evaluate diff --git a/static/memory_template_flow.png b/static/memory_template_flow.png new file 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zQjZQFXhKJ~vE;l*jK)!!T+=`p?&cuf`=9lo@eq?z+6fz!kSJ>v7Dd{|UTjC7mry`) zpnzgVHFSw6ZfPk^M_Q3bAUu;MRZ*m7j4OAFnwa!S{G(f9H#rBxq^UvNTSKgqK`AKVVzN7_T(N8BqTM4S({$o4d4xaB2n( zq&4{0T#nmCq#=iZ7%F(uf}F@M_M6}ZsZ%vF99Ck?xD;j!ZaQDh^o*ojr!X5@O{(6? z8_Up~OZ@>5bOUgrrr}2*e1IxNI+W#`cpC-Q=mxrmlgUzIO3`!24;P(70PX@y33|Ko z38(3ov_+AeU)~Ru@S{Uly_$1iwNXgCeX7BuZU6u9zk3e&{sV}9>wVMV=8f!~df?%) MYtPPyJA%*s6Z!Nk761SM literal 0 HcmV?d00001 From d0f1e1ee99c0bffb1f21dc5c65a6e3ca584eaa86 Mon Sep 17 00:00:00 2001 From: Lance Martin Date: Fri, 4 Oct 2024 14:13:36 -0700 Subject: [PATCH 3/7] Lint --- src/memory_graph/graph.py | 6 +----- tests/integration_tests/test_graph.py | 3 +-- 2 files changed, 2 insertions(+), 7 deletions(-) diff --git a/src/memory_graph/graph.py b/src/memory_graph/graph.py index 9f5b960..a0b686a 100644 --- a/src/memory_graph/graph.py +++ b/src/memory_graph/graph.py @@ -23,7 +23,6 @@ async def handle_patch_memory( state: ProcessorState, config: RunnableConfig, *, store: BaseStore ) -> dict: """Extract the user's state from the conversation and update the memory.""" - # Get the overall configuration configurable = configuration.Configuration.from_runnable_config(config) @@ -74,9 +73,7 @@ async def handle_patch_memory( async def handle_insertion_memory( state: ProcessorState, config: RunnableConfig, *, store: BaseStore ) -> dict[str, list]: - """Handle insertion memory events.""" - # Get the overall configuration configurable = configuration.Configuration.from_runnable_config(config) @@ -150,13 +147,12 @@ async def handle_insertion_memory( builder.add_node(handle_insertion_memory, input=ProcessorState) def scatter_schemas(state: State, config: RunnableConfig) -> list[Send]: - """The entry point to the graph will will iterate over all memory types in the configuration. + """Iterate over all memory types in the configuration. It will route each memory type from configuration to the corresponding memory update node. The memory update nodes will be executed in parallel. """ - # Get the configuration configurable = configuration.Configuration.from_runnable_config(config) sends = [] diff --git a/tests/integration_tests/test_graph.py b/tests/integration_tests/test_graph.py index 759b16b..f0ee4a5 100644 --- a/tests/integration_tests/test_graph.py +++ b/tests/integration_tests/test_graph.py @@ -5,9 +5,8 @@ import langsmith as ls import pytest from langgraph.store.memory import InMemoryStore -from pydantic import BaseModel, Field - from memory_graph.graph import builder +from pydantic import BaseModel, Field class User(BaseModel): From b42e273f0635c5a06948d645c0037b107b3c979e Mon Sep 17 00:00:00 2001 From: Lance Martin Date: Fri, 4 Oct 2024 14:15:13 -0700 Subject: [PATCH 4/7] Update README --- README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 4208627..23676cf 100644 --- a/README.md +++ b/README.md @@ -66,7 +66,7 @@ OPENAI_API_KEY=your-api-key End setup instructions --> -### Test in LangGraph Studio +### LangGraph Studio If you want to test locally, [install the LangGraph Studio desktop app](https://github.com/langchain-ai/langgraph-studio?tab=readme-ov-file#download). @@ -74,7 +74,7 @@ If you want to test in the cloud, [follow these instructions to deploy this repo Open this repository in LangGraph studio. -In LangGraph Studio, you can set your `user_id`, `model`, and other configurations. +In LangGraph Studio, you can set your `user_id`, `model`, or other configurations. ![Flow](./static/studio.png) @@ -82,7 +82,9 @@ Navigate to the "`chatbot`" graph and have a conversation with it! Try sending some messages saying your name and other things the bot should remember. -Wait ~10-20 seconds for many memories to be created and saved, then create a *new* thread using the `+` icon. +Wait ~10-20 seconds for many memories to be created and saved. + +Then, create a *new* thread using the `+` icon. Then chat with the bot again - if you've completed your setup correctly, the bot should now have access to the memories you've saved! 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zTXl0Wt7o!8`yybl~HJI&gZa@aixAZP?#m900O=;-?xD=>PmO z(9Qwu)|d*a{NKIE32+0HRLdM6`d?V(PdkBBu)a@<#P~mkeCEJ#Fb9T`lm3;-|9&lJ n3Z?)GBba~l`}>O;(qm6CKPzs>ABB4mz#nm88KE*kJ)i#%1>UfY From e2ec497f5517233fe259c3d99c83ab6ec8dd45e7 Mon Sep 17 00:00:00 2001 From: Lance Martin Date: Fri, 4 Oct 2024 15:32:10 -0700 Subject: [PATCH 6/7] Address comments --- README.md | 167 ++++++++++++++++++++--------------- static/memory_motivation.png | Bin 0 -> 172853 bytes 2 files changed, 94 insertions(+), 73 deletions(-) create mode 100644 static/memory_motivation.png diff --git a/README.md b/README.md index 23676cf..5e3c628 100644 --- a/README.md +++ b/README.md @@ -4,29 +4,21 @@ [![Integration Tests](https://github.com/langchain-ai/memory-template/actions/workflows/integration-tests.yml/badge.svg)](https://github.com/langchain-ai/memory-template/actions/workflows/integration-tests.yml) [![Open in - LangGraph Studio](https://img.shields.io/badge/Open_in-LangGraph_Studio-00324d.svg?logo=data:image/svg%2bxml;base64,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)](https://langgraph-studio.vercel.app/templates/open?githubUrl=https://github.com/langchain-ai/memory-template) -Memory is a powerful way to improve and personalize applications, allowing storage of information (e.g., a user-specific profile or memories) that can be used to inform responses or decisions across multiple interactions. This template provides a simple example of a long-term memory service you can build and deploy using LangGraph. +## Motivation -It has three main components: +Memory is a powerful way to improve and personalize AI applications. As an example, memory can be used to store user-specific information across multiple interactions with that user. But, it can also extend to any information that you may want to preserve across multiple interactions with an application. This template show how you can build and deploy a long-term memory service using LangGraph by combining a memory service with a simple chatbot application. -(1) `Chatbot Graph`: This is a simple chatbot that interacts with a user. +![Motivation](./static/memory_motivation.png) -(2) `Memory Graph`: This graph contains the logic for creating memories with customizable schemas from the chatbot's user interactions. +## Quickstart -(3) `Memory Storage`: This stores created memories, making them accessible across multiple user interactions with the chatbot. - -![Flow](./static/memory_template_flow.png) - -## Getting Started - -### Create .env file - -1. Create a `.env` file. +Create a `.env` file. ```bash cp .env.example .env ``` -2. Define required API keys in your `.env` file. +Set the required API keys in your `.env` file. -### LangGraph Studio - -If you want to test locally, [install the LangGraph Studio desktop app](https://github.com/langchain-ai/langgraph-studio?tab=readme-ov-file#download). +If you want to test the memory service locally, [install the LangGraph Studio desktop app](https://github.com/langchain-ai/langgraph-studio?tab=readme-ov-file#download). -If you want to test in the cloud, [follow these instructions to deploy this repository to LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/) and open Studio in your browser. +If you want to test in the cloud, [follow these instructions to deploy this repository to LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/) and use Studio in your browser. -Open this repository in LangGraph studio. +Open this repository in LangGraph studio and navigate to the `chatbot` graph. -In LangGraph Studio, you can set your `user_id`, `model`, or other configurations. +Optionally, you can set your `user_id`, `model`, or other configurations directly in the Studio UI. ![Flow](./static/studio.png) -Navigate to the "`chatbot`" graph and have a conversation with it! - Try sending some messages saying your name and other things the bot should remember. -Wait ~10-20 seconds for many memories to be created and saved. +Wait ~10-20 seconds for memories to be created and saved. + +Create a *new* thread using the `+` icon. -Then, create a *new* thread using the `+` icon. +Then chat with the bot again. -Then chat with the bot again - if you've completed your setup correctly, the bot should now have access to the memories you've saved! +The bot should have access to the memories you've saved, and will use them to personalize its responses. ## How it works -### Memory Storage +There are several problems to solve when building a memory service: -The LangGraph API comes with a built-in memory storage layer that can be used to store and retrieve information across threads. +1. What should each memory contain? +2. How should memories be updated? +3. How frequently should memories be updated or created? +4. Where should memories be stored? +5. How to call the memory service from our application? -Studio useds the LangGraph API at the backend, packaging the code in this repository with the storage layer. +We'll address these challenges below, and explain how this LangGraph template approaches them. -In Studio, you can see the `Memories` button above your graph with any memories created and saved to the storage layer. +### Memory Schema -The storage layer is shown in the above diagram in red. +The memory schema defines what each memory will contain. -The central points are that it: +By default, this template uses two memory schemas: `User` and `Note`. Both are JSON schemas. -1. It is accessible to both the `chatbot` and the `memory_graph` in all nodes. -2. It provides an interface for storing (`put` method) and retrieving (`search` method) memories in a namespaced manner. +The schemas are defined in [memory_graph/configuration.py](./src/memory_graph/configuration.py). -Learn more about the Memory Storage layer [here](https://langchain-ai.github.io/langgraph/how-tos/memory/shared-state/). +The `User` schema is used to store a single profile for a user with a set of predefined properties (e.g., name, age, interests). -### Chatbot Graph +The `Note` schema is more flexible, containing a `context` and `content` field to capture any type of information as a memory. -The `chatbot` graph, defined in [graph.py](./src/chatbot/graph.py), has two nodes, `bot` and `schedule_memories`. +These schemas are customizable! You can create new schemas, or add / remove properties as needed for your application. -The `chatbot` is invoked with a `user_id` supplied by configuration. +### Memory Updates -The `bot` node uses the `user_id` to fetch any existing memories for that user from the `Memory Storage` layer. +These memory schemas need to be updated with new information over time. -These fetched memories are added to the system prompt of the chatbot to personalize the responses. +The `User` schema is a single JSON object. -The `schedule_memories` node is run after the `bot` node. +We want to update it with new information about the user as the conversation progresses. -It is responsible for scheduling long-term memory creation based upon the chatbot's updated interaction with the user. +Each `Note`, in contrast, is captured in a list. -It uses the LangGraph SDK to call the `memory_graph`, suppling the chatbot's interaction with the user. +We want the flexibility to update existing `Note` schemas or update the list with new ones. -### Memory Graph +We use the [`trustcall` library](https://github.com/hinthornw/trustcall) to do both of these types of updates. -The `memory_graph` graph, defined in [graph.py](./src/memory_graph/graph.py) incoperates two different concepts: +This is a library that we created for updating JSON schemas via a LLM. -(1) We can define a schema for each of the types of memories we want to store. +These updates are performed in [memory_graph/graph.py](./src/memory_graph/graph.py). -(2) We can update these memories in different ways using the [`trustcall` library](https://github.com/hinthornw/trustcall). +The `memory_graph` saves both types of memories. -The schema for each memory type is defined in [configuration.py](./src/memory_graph/configuration.py). +We can see the graph here in the LangGraph Studio, with a branch for each of the defined memory schemas: -The default schemas are `User` and `Note`. +* `handle_patch_memory` is for `User` schema memories. +* `handle_insert_memory` is for `Note` schema memories. -The `User` schema is used to store a single profile for a user, such as their name, age, and interests. +![Memory Diagram](./static/memory_graph.png) -We'll store a single JSON schema that can be updated with new information as the conversation progresses. +### Memory Scheduling -The `Note` schema is used to store specific events or notes about the user's interactions with the chatbot. +Memory updates need to be scheduled to avoid duplicate processing. -We'll store a list of JSON schemas that can be updated or added to as the conversation processes. +Ideally, we want to wait until a chat is complete before we create memories. -The [`trustcall` library](https://github.com/hinthornw/trustcall) handles writing and updating either of these schemas. +But, we don't know when a chat session will end. -![Memory types](./static/memory_types.png) +So, we wait a pre-determined interval before invoking the memory graph to memories to the storage layer. -The `memory_graph` save both types of memories to the `Memory Storage` layer, namespaced by the `user_id` and the schema name. +If the chatbot makes a call second time within that interval, the initial memory is cancelled. -We can see the graph here in the LangGraph Studio, with a branch for each memory schemas. +Scheduling is handled by the LangGraph SDK's `after_seconds` parameter. -![Memory Diagram](./static/memory_graph.png) +We call the `memory_graph` from our application (e.g., `chatbot`) using the LangGraph SDK in [chatbot/graph.py](./src/chatbot/graph.py). -## Philosophy +![DeBounce](./static/scheduling.png) -### Separation of Concerns +### Memory Storage -This separation of concerns between the application logic (chatbot) and the memory processing (the memory graph) provides advantages: +The LangGraph API comes with a built-in memory storage layer that can be used to store and retrieve information across threads. -(1) minimal overhead by removing memory creation logic from the hotpath of the application (e.g., no latency cost for memory creation) +Learn more about the Memory Storage layer [here](https://langchain-ai.github.io/langgraph/how-tos/memory/shared-state/). -(2) memory creation logic is handled in a background job, separate from the chatbot, with scheduling to avoid duplicate processing +Importantly, the memory storage layer is namespaced by a tuple; in this case, we use the `user_id` as well as the schema name. -(3) memory graph can be updated and / or hosted (as a service) independently of the application (chatbot) +In addition, the memory storage layer is accessible to both the `chatbot` and the `memory_graph` in all graph nodes. -![Interaction Pattern](./static/memory_interactions.png) +This diagram shows how these pieces fit together: -### Scheduling +![Memory types](./static/memory_types.png) -Scheduling is handled by the LangGraph API's `after_seconds` parameter. +### Calling the Memory Service -This is the intuition: we want to wait until a thread is "complete" before we write memories to the storage layer. +Studio uses the LangGraph API as its backend, packaging the specified code repository with the storage layer. -We don't know when a chat will end. +The `langgraph.json` file is used to configure the LangGraph API specifies the graphs to be run in Studio: -So, we wait a pre-determined interval before invoking the memory graph to memories to the storage layer. +```json + "graphs": { + "chatbot": "./src/chatbot/graph.py:graph", + "memory_graph": "./src/memory_graph/graph.py:graph" + }, +``` -If the chatbot makes a call second time within that interval, the memory run is cancelled to avoid duplicate processing of a thread. +The chatbot can directly access all stored memories when it's preparing responses for the user. -This cartoon highlights the concept: requests to call the `memory_graph` are delayed for some period of time. +You can see this in the in the `bot` node in [chatbot/graph.py](./src/chatbot/graph.py): -If any newer requests come in, the initial request is cancelled to avoid duplicate processing the same interaction. +```python +items = await store.asearch(namespace) +``` -![DeBounce](./static/scheduling.png) +To schedule creation of new memories, the chatbot can use the LangGraph SDK to access the memory graph. + +This is done in the `schedule_memories` node in [chatbot/graph.py](./src/chatbot/graph.py) + +This passes the chatbot's interaction with the user along with the scheduling parameter, `after_seconds`, to the `memory_graph`. + +![Flow](./static/memory_template_flow.png) + +## Benefits + +The separation of concerns between the application logic (chatbot) and the memory (the memory graph) a few advantages: + +(1) minimal overhead by removing memory creation logic from the hotpath of the application (e.g., no latency cost for memory creation) + +(2) memory creation logic is handled in a background job, separate from the chatbot, with scheduling to avoid duplicate processing + +(3) memory graph can be updated and / or hosted (as a service) independently of the application (chatbot) + +Here is a schematic of the interaction pattern: + +![Interaction Pattern](./static/memory_interactions.png) ## How to evaluate diff --git a/static/memory_motivation.png b/static/memory_motivation.png new file mode 100644 index 0000000000000000000000000000000000000000..5d1ee54b5894d436124a7a87d824d9ff1dc4518a GIT binary patch literal 172853 zcmd?RcR1B={5WjSL=jn0k`*c|n=&GMZ<4(;j(N;fNE8w}$T+h1UZG)R9^=>}^Ee!t zIUIiPQ_<)1{q%c&&-MKAT-S3w{}}iCocn(5{k|hK)D$Vm7|3vNa43}S$!X!>5Q^jA z;FFLL0V~9wH(h~WxE@-HcW{b&m{xFbSaFo(WOaPZkdwr3O?3Bqnlorz$*Z zHGaG!DPZfqv|a&$OhPtm`5(5gH+rx8riptjO6(x^&#hSD9g=@L6j+nLEK^d> znfLO}U!e-dX;EAMFDySd=g$h_vyZ6@ii(PA$S!hI0DBji zkv;wQ-c+=9uCCPyJo;n7xcCz+Rfp$%4%VvIp0T}nX4+T!<&vYwq^I_gi3O4VX18k~ z%%(S$d3=jMXg_ML>0q6nse_MX-b^N>r|qox*Voq#&x`MWi;;Hu_9dnl6SdGmPYPO8 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z1YnemY2tGNX`@%cB0Njzk3BLItX6eZGeUbZS8m|Wo;QCwfirj(*Q$9RaQeSB4deo|Rw}+jd&rf)K9EVz|65li(LkGc z+7)j6ixz?{L-FyyH1=??|CIN^F0He(Q&>t$zm+rR6@lK=%Rsap#Q-gPFhUToQ>Us# z-OrycDgZM2w*55p2m>9V>j^$~TuW@H@gd*Q_O0}iqq0pV09GAzT3(LJRtLYmK@Q3FOfm8OO&iPmMUjdKw6wGYe$C#F#_G@%&|KjKddhG}+zGnVY-|fHFkxZZFon1IZZ8Q& zK#m>1>d*LUk`k;=G*C_D($+MBP|_J_5OJkqjD#)PO(QUIzX+|H1Th0MRFv|4ieMFJ z%7GoTzIMp62_V!KjZWP|?Y$55wBm=$CBn+9IRn=2y+h-idI=C@vdTj+q+FoAN@4DE zLjRiAfHcEo^tcHG^8tcCxHd@<2qp) Date: Fri, 4 Oct 2024 15:45:30 -0700 Subject: [PATCH 7/7] Lint --- README.md | 251 +++++++++++++++----------- src/memory_graph/graph.py | 67 +++---- tests/integration_tests/test_graph.py | 3 +- 3 files changed, 178 insertions(+), 143 deletions(-) diff --git a/README.md b/README.md index 5e3c628..11d3bde 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ ## Motivation -Memory is a powerful way to improve and personalize AI applications. As an example, memory can be used to store user-specific information across multiple interactions with that user. But, it can also extend to any information that you may want to preserve across multiple interactions with an application. This template show how you can build and deploy a long-term memory service using LangGraph by combining a memory service with a simple chatbot application. +Memory lets your AI applications learn from each user interaction. It makes them more effective as they learn from mistakes and engaging as they adapt to personal tastes. This template shows you how to build and deploy a long-term memory service that you can connect to from any LangGraph agent so they can manage user-scoped memories. ![Motivation](./static/memory_motivation.png) @@ -44,12 +44,14 @@ To use Anthropic's chat models: ``` ANTHROPIC_API_KEY=your-api-key ``` + #### OpenAI To use OpenAI's chat models: 1. Sign up for an [OpenAI API key](https://platform.openai.com/signup). 2. Once you have your API key, add it to your `.env` file: + ``` OPENAI_API_KEY=your-api-key ``` @@ -58,116 +60,157 @@ OPENAI_API_KEY=your-api-key End setup instructions --> -If you want to test the memory service locally, [install the LangGraph Studio desktop app](https://github.com/langchain-ai/langgraph-studio?tab=readme-ov-file#download). - -If you want to test in the cloud, [follow these instructions to deploy this repository to LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/) and use Studio in your browser. - -Open this repository in LangGraph studio and navigate to the `chatbot` graph. +[Open this template](https://langgraph-studio.vercel.app/templates/open?githubUrl=https://github.com/langchain-ai/memory-template) in LangGraph studio to get started and navigate to the `chatbot` graph. -Optionally, you can set your `user_id`, `model`, or other configurations directly in the Studio UI. +_If you want to deploy to the cloud, [follow these instructions to deploy this repository to LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/) and use Studio in your browser._ ![Flow](./static/studio.png) -Try sending some messages saying your name and other things the bot should remember. +Try chatting with the bot. Saying your name and other the bot may want to remember. Wait ~10-20 seconds for memories to be created and saved. -Create a *new* thread using the `+` icon. - -Then chat with the bot again. +Create a _new_ thread using the `+` icon and chat with the bot again. The bot should have access to the memories you've saved, and will use them to personalize its responses. ## How it works -There are several problems to solve when building a memory service: +An effective memory service should address some key questions: 1. What should each memory contain? -2. How should memories be updated? -3. How frequently should memories be updated or created? -4. Where should memories be stored? -5. How to call the memory service from our application? - -We'll address these challenges below, and explain how this LangGraph template approaches them. +2. How should memories be updated? (and on what schedule?) +3. How should your bot recall memories? -### Memory Schema +The "correct" answer to these questions can be application-specific. We'll address these challenges below, and explain how this template lets you flexibly +configure what and how memories are managed to keep your bot's memory on-topic and up-to-date. First, we'll talk about how you configure "what each memory should contain" using memory schemas. -The memory schema defines what each memory will contain. +### Memory Schemas -By default, this template uses two memory schemas: `User` and `Note`. Both are JSON schemas. +Memory schemas tell the service the "shape" of individual memories and how to update them. You can define any custom memory schema by providing `memory_types` as configuration. Let's review the [two default schemas](./src/memory_graph/configuration.py) we've provided along the template to get a better sense of what they are doing. -The schemas are defined in [memory_graph/configuration.py](./src/memory_graph/configuration.py). +The first schema is the `User` profile schema, copied below: -The `User` schema is used to store a single profile for a user with a set of predefined properties (e.g., name, age, interests). +```json +{ + "name": "User", + "description": "Update this document to maintain up-to-date information about the user in the conversation.", + "update_mode": "patch", + "parameters": { + "type": "object", + "properties": { + "user_name": { + "type": "string", + "description": "The user's preferred name" + }, + "age": { + "type": "integer", + "description": "The user's age" + }, + "interests": { + "type": "array", + "items": { "type": "string" }, + "description": "A list of the user's interests" + }, + "home": { + "type": "string", + "description": "Description of the user's home town/neighborhood, etc." + }, + "occupation": { + "type": "string", + "description": "The user's current occupation or profession" + }, + "conversation_preferences": { + "type": "array", + "items": { "type": "string" }, + "description": "A list of the user's preferred conversation styles, pronouns, topics they want to avoid, etc." + } + } + } +} +``` -The `Note` schema is more flexible, containing a `context` and `content` field to capture any type of information as a memory. +The schema has a name and description, as well as JSON schema parameters that are all passed to an LLM. The LLM infers the values for the schema based on the conversations you send to the memory service. -These schemas are customizable! You can create new schemas, or add / remove properties as needed for your application. +The schema also has an `update_mode` parameter that defines **how** the service should update its memory when new information is provided. The **patch** update_mode instructs the graph that we should always have a single JSON object to represent this user. When new information is provided, the model can generate "patches", or small updates to extend, delete, or replace content in the current memory document. This type of `update_mode` is useful if you want strict visibility into a user's representation at any given point or if you want to let the end user directly view and update their own representation for the bot. By defining these specific parameters, we are decideing that this (and only this) information is relevant to track and excluding other information (like "relationships" or "religion", etc.) from being tracked. It's an easy way for us to bias the service into focusing on what we think is important for our specific bot. -### Memory Updates +The second memory schema we provide is the **Note** schema, shown below: -These memory schemas need to be updated with new information over time. +```json +{ + "name": "Note", + "description": "Save notable memories the user has shared with you for later recall.", + "update_mode": "insert", + "parameters": { + "type": "object", + "properties": { + "context": { + "type": "string", + "description": "The situation or circumstance where this memory may be relevant. Include any caveats or conditions that contextualize the memory. For example, if a user shares a preference, note if it only applies in certain situations (e.g., 'only at work'). Add any other relevant 'meta' details that help fully understand when and how to use this memory." + }, + "content": { + "type": "string", + "description": "The specific information, preference, or event being remembered." + } + }, + "required": ["context", "content"] + } +} +``` -The `User` schema is a single JSON object. +Just like the previous example, this schema has a name, description, and parameters. Notic that the `update_mode` this time is "insert". This instructs the LLM in the memory service to **insert new memories to the list or update existing ones**. The number of memories for this `update_mode` is **unbound** since the model can continue to store new notes any time something interesting shows up in the conversation. Each time the service runs, the model can generate multiple schemas, some to update or re-contextualize existing memories, some to document new information. Note taht these memory schemas tend to have fewer parameters and are usually most effective if you have a field to let the service provide contextual information (so that if your bot fetches this memory, it isn't taken out-of-context). -We want to update it with new information about the user as the conversation progresses. +To wrap up this section: `memory_schemas` provide a name, description, and parameters that the LLM populates to store in the database. The `update_mode` controls whether new information should always overwrite an existing memory or whether it should insert new memories (while optionally updating existing ones). -Each `Note`, in contrast, is captured in a list. +These schemas are fully customizable! Try extending the above and seeing how it updates memory formation in the studio by passing in via configuration (or defining in an assistant). -We want the flexibility to update existing `Note` schemas or update the list with new ones. +### Handling Memory Updates -We use the [`trustcall` library](https://github.com/hinthornw/trustcall) to do both of these types of updates. +In the previous section we showed how the memory schemas define how memories should be updated with new information over time. Let's now turn our attention to _how_ new information is handled. Each update type using tool calling in slightly different ways. We will use the [`trustcall` library](https://github.com/hinthornw/trustcall), which we created as a simple interface for generating and continuously updating json documents, to handle all of the cases below: -This is a library that we created for updating JSON schemas via a LLM. +#### patch -These updates are performed in [memory_graph/graph.py](./src/memory_graph/graph.py). +If no memory has been saved yet, `trust_call` prompts the model to populate the document. It additionally does schema validation to ensure the output is correct. -The `memory_graph` saves both types of memories. +If a memory already exists, you _could_ simply prompt the model to re-geerate the schema anew on each round. Doing so, however, leads to frequent information loss, especially on complicated schemas, since LLMs are wont to forget or omit previously stored details when regenerating information from scratch if it doesn't happen to be immediately relevant. -We can see the graph here in the LangGraph Studio, with a branch for each of the defined memory schemas: +To avoid memory loss, your memory schema is placed in the system prompt but **not** made available as a tool for the model to call. Instead, the LLM is provided a `PatchDoc` tool. This forces the model to generate a chain-of-thought of 0 or more planned edits, along with patches to individual JSON paths to be modified. -* `handle_patch_memory` is for `User` schema memories. -* `handle_insert_memory` is for `Note` schema memories. +Applying updates as JSON patches helps minimize information loss, save token costs, and simplifies the memory management task. -![Memory Diagram](./static/memory_graph.png) +#### insert -### Memory Scheduling +If no memories have been saved yet, the model is given a single tool (the schema from your memory config). It is prompted to use multi-tool callint to generate 0 or more instances of your schema depending on the conversation context. -Memory updates need to be scheduled to avoid duplicate processing. +If memories exist for this user, the memory graph searches for existing ones to provide additional context. These are put in the system promt along with two two tools: your memory schema as well as a "PatchDoc" tool. The LLM is prompted to invoke whichever tools are appropriate given the conversational context. The LLM can call the PatchDoc tool to update existing memories in case they are no longer correct or require additional context. It can also call your memory schema tool any number of times to save new memories or notes. Either way, it calls these tools in a single generation step, and the graph upserts the results to the memory store. -Ideally, we want to wait until a chat is complete before we create memories. +![Memory Diagram](./static/memory_graph.png) -But, we don't know when a chat session will end. +### Memory Scheduling -So, we wait a pre-determined interval before invoking the memory graph to memories to the storage layer. +All of this sounds like a lot of tokens! If we were to process memories on every new message to your chat bot, the costs could indeed mount up. We only really need to process memories after a conversation ends, but in reality we typically don't know when the thread is finished. -If the chatbot makes a call second time within that interval, the initial memory is cancelled. +As a compromise, our memory service supports **debouncing** by deferring when it will process memories. Memory updates are scheduled for some point in the future (using the LangGraph SDK's `after_seconds` parameter). -Scheduling is handled by the LangGraph SDK's `after_seconds` parameter. +If the chatbot makes a call second time within that interval, the initial request is cancelled and a **new** request for memory processing is scheduled. -We call the `memory_graph` from our application (e.g., `chatbot`) using the LangGraph SDK in [chatbot/graph.py](./src/chatbot/graph.py). +See this in the code here: [chatbot/graph.py](./src/chatbot/graph.py). ![DeBounce](./static/scheduling.png) ### Memory Storage -The LangGraph API comes with a built-in memory storage layer that can be used to store and retrieve information across threads. - -Learn more about the Memory Storage layer [here](https://langchain-ai.github.io/langgraph/how-tos/memory/shared-state/). +All these memories need to go somewhere reliable. All LangGraph deployments come with a built-in memory storage layer that you can use to persist information across conversations. -Importantly, the memory storage layer is namespaced by a tuple; in this case, we use the `user_id` as well as the schema name. +You can learn more about Storage in LangGraph [here](https://langchain-ai.github.io/langgraph/how-tos/memory/shared-state/). -In addition, the memory storage layer is accessible to both the `chatbot` and the `memory_graph` in all graph nodes. - -This diagram shows how these pieces fit together: +In our case, we are saving all memories namespaced by `user_id` and by the memory scheam you provide. That way you can easily search for memories for a given user and of a particualr type. This diagram shows how these pieces fit together: ![Memory types](./static/memory_types.png) ### Calling the Memory Service -Studio uses the LangGraph API as its backend, packaging the specified code repository with the storage layer. - -The `langgraph.json` file is used to configure the LangGraph API specifies the graphs to be run in Studio: +The studio uses the LangGraph API as its backend and exposes graph endpoints for all the graphs defied in your `langgraph.json` file. ```json "graphs": { @@ -176,20 +219,14 @@ The `langgraph.json` file is used to configure the LangGraph API specifies the g }, ``` -The chatbot can directly access all stored memories when it's preparing responses for the user. - -You can see this in the in the `bot` node in [chatbot/graph.py](./src/chatbot/graph.py): +You can interact with your server and storage using the studio UI or the LangGraph SDK. ```python -items = await store.asearch(namespace) +from langgraph_sdk import get_client +client = get_client(url="http:...") # your server +items = await client.store.search_items(namespace) ``` -To schedule creation of new memories, the chatbot can use the LangGraph SDK to access the memory graph. - -This is done in the `schedule_memories` node in [chatbot/graph.py](./src/chatbot/graph.py) - -This passes the chatbot's interaction with the user along with the scheduling parameter, `after_seconds`, to the `memory_graph`. - ![Flow](./static/memory_template_flow.png) ## Benefits @@ -222,64 +259,60 @@ Customize memory memory_types: This memory graph supports two different `update_ ```json { - "name": "User", - "description": "Update this document to maintain up-to-date information about the user in the conversation.", - "update_mode": "patch", - "parameters": { - "type": "object", - "properties": { - "user_name": { - "type": "string", - "description": "The user's preferred name" - }, - "age": { - "type": "integer", - "description": "The user's age" + "name": "User", + "description": "Update this document to maintain up-to-date information about the user in the conversation.", + "update_mode": "patch", + "parameters": { + "type": "object", + "properties": { + "user_name": { + "type": "string", + "description": "The user's preferred name" + }, + "age": { + "type": "integer", + "description": "The user's age" + }, + "interests": { + "type": "array", + "items": { + "type": "string" }, - "interests": { - "type": "array", - "items": { - "type": "string" - }, - "description": "A list of the user's interests" - } + "description": "A list of the user's interests" } } } +} ``` 2. Insertion Schema: This allows inserting individual "event" memories, such as key pieces of information or summaries from the conversation. You can define custom memory_types for these event memories by providing a JSON schema when initializing the InsertionMemorySchema. For example: ```json { - "name": "Note", - "description": "Save notable memories the user has shared with you for later recall.", - "update_mode": "insert", - "parameters": { - "type": "object", - "properties": { - "context": { - "type": "string", - "description": "The situation or circumstance in which the memory occurred that inform when it would be useful to recall this." - }, - "content": { - "type": "string", - "description": "The specific information, preference, or event being remembered." - } + "name": "Note", + "description": "Save notable memories the user has shared with you for later recall.", + "update_mode": "insert", + "parameters": { + "type": "object", + "properties": { + "context": { + "type": "string", + "description": "The situation or circumstance in which the memory occurred that inform when it would be useful to recall this." }, - "required": ["context", "content"] - } + "content": { + "type": "string", + "description": "The specific information, preference, or event being remembered." + } + }, + "required": ["context", "content"] } +} ``` 3. Select a different model: We default to anthropic/claude-3-5-sonnet-20240620. You can select a compatible chat model using provider/model-name via configuration. Example: openai/gpt-4. 4. Customize the prompts: We provide default prompts in the graph definition. You can easily update these via configuration. -For quick prototyping, these configurations can be set in the LangGraph Studio UI. - -You can also quickly extend this template by: - -- Adding additional nodes and edges in [graph.py](./src/memory_graph/graph.py) to modify the memory processing flow. +We'd also encourage you to extend this template by adding additional memory types! "Patch" and "insert" are incredibly powerful already, but you could also extend the logic to add more reflection over related memories to build stronger associations between the saved content. Make the code your own! \ No newline at end of file +--> diff --git a/src/memory_graph/graph.py b/src/memory_graph/graph.py index a0b686a..9ead059 100644 --- a/src/memory_graph/graph.py +++ b/src/memory_graph/graph.py @@ -23,49 +23,49 @@ async def handle_patch_memory( state: ProcessorState, config: RunnableConfig, *, store: BaseStore ) -> dict: """Extract the user's state from the conversation and update the memory.""" - # Get the overall configuration + # Get the overall configuration configurable = configuration.Configuration.from_runnable_config(config) # Namespace for memory events, where function_name is the name of the memory schema namespace = (configurable.user_id, "user_states", state.function_name) - - # Fetch existing memories from the store for this (patch) memory schema + + # Fetch existing memories from the store for this (patch) memory schema existing_item = await store.aget(namespace, "memory") existing = {existing_item.key: existing_item.value} if existing_item else None - + # Get the configuration for this memory schema (identified by function_name) memory_config = next( conf for conf in configurable.memory_types if conf.name == state.function_name ) - # This is what we use to generate new memories + # This is what we use to generate new memories extractor = create_extractor( utils.init_model(configurable.model), - # We pass the specified (patch) memory schema as a tool + # We pass the specified (patch) memory schema as a tool tools=[ { # Tool name "name": memory_config.name, # Tool description "description": memory_config.description, - # Schema for patch memory + # Schema for patch memory "parameters": memory_config.parameters, } ], tool_choice=memory_config.name, ) - # Prepare the messages + # Prepare the messages prepared_messages = utils.prepare_messages( state.messages, memory_config.system_prompt ) - # Pass messages and existing patch to the extractor + # Pass messages and existing patch to the extractor inputs = {"messages": prepared_messages, "existing": existing} - # Update the patch memory + # Update the patch memory result = await extractor.ainvoke(inputs, config) extracted = result["responses"][0].model_dump(mode="json") - # Save to storage + # Save to storage await store.aput(namespace, "memory", extracted) return {"messages": []} @@ -74,47 +74,47 @@ async def handle_insertion_memory( state: ProcessorState, config: RunnableConfig, *, store: BaseStore ) -> dict[str, list]: """Handle insertion memory events.""" - # Get the overall configuration + # Get the overall configuration configurable = configuration.Configuration.from_runnable_config(config) # Namespace for memory events, where function_name is the name of the memory schema namespace = (configurable.user_id, "events", state.function_name) - - # Fetch existing memories from the store (5 most recent ones) for the this (insert) memory schema + + # Fetch existing memories from the store (5 most recent ones) for the this (insert) memory schema existing_items = await store.asearch(namespace, limit=5) - + # Get the configuration for this memory schema (identified by function_name) memory_config = next( conf for conf in configurable.memory_types if conf.name == state.function_name ) - # This is what we use to generate new memories + # This is what we use to generate new memories extractor = create_extractor( utils.init_model(configurable.model), - # We pass the specified (insert) memory schema as a tool + # We pass the specified (insert) memory schema as a tool tools=[ { # Tool name "name": memory_config.name, # Tool description "description": memory_config.description, - # Schema for insert memory + # Schema for insert memory "parameters": memory_config.parameters, } ], tool_choice="any", - # This allows the extractor to insert new memories + # This allows the extractor to insert new memories enable_inserts=True, ) - # Generate new memories or update existing memories + # Generate new memories or update existing memories extracted = await extractor.ainvoke( { - # Prepare the messages + # Prepare the messages "messages": utils.prepare_messages( state.messages, memory_config.system_prompt ), - # Prepare the existing memories + # Prepare the existing memories "existing": ( [ (existing_item.key, state.function_name, existing_item.value) @@ -126,8 +126,8 @@ async def handle_insertion_memory( }, config, ) - - # Add the memories to storage + + # Add the memories to storage await asyncio.gather( *( store.aput( @@ -146,6 +146,7 @@ async def handle_insertion_memory( builder.add_node(handle_patch_memory, input=ProcessorState) builder.add_node(handle_insertion_memory, input=ProcessorState) + def scatter_schemas(state: State, config: RunnableConfig) -> list[Send]: """Iterate over all memory types in the configuration. @@ -153,28 +154,28 @@ def scatter_schemas(state: State, config: RunnableConfig) -> list[Send]: The memory update nodes will be executed in parallel. """ - # Get the configuration + # Get the configuration configurable = configuration.Configuration.from_runnable_config(config) sends = [] current_state = asdict(state) - - # Loop over all memory types specified in the configuration + + # Loop over all memory types specified in the configuration for v in configurable.memory_types: update_mode = v.update_mode - # This specifies the type of memory update to perform from the configuration + # This specifies the type of memory update to perform from the configuration match update_mode: case "patch": - # This is the corresponding node in the graph for the patch-based memory update + # This is the corresponding node in the graph for the patch-based memory update target = "handle_patch_memory" case "insert": - # This is the corresponding node in the graph for the insert-based memory update + # This is the corresponding node in the graph for the insert-based memory update target = "handle_insertion_memory" case _: raise ValueError(f"Unknown update mode: {update_mode}") # Use Send API to route to the target node and pass the name of the memory schema as function_name - # Send API allows each memory node to be executed in parallel + # Send API allows each memory node to be executed in parallel sends.append( Send( target, @@ -184,11 +185,11 @@ def scatter_schemas(state: State, config: RunnableConfig) -> list[Send]: return sends -# Add conditional edges to the graph +# Add conditional edges to the graph builder.add_conditional_edges( "__start__", scatter_schemas, ["handle_patch_memory", "handle_insertion_memory"] ) -# Compile the graph +# Compile the graph graph = builder.compile() __all__ = ["graph"] diff --git a/tests/integration_tests/test_graph.py b/tests/integration_tests/test_graph.py index f0ee4a5..759b16b 100644 --- a/tests/integration_tests/test_graph.py +++ b/tests/integration_tests/test_graph.py @@ -5,9 +5,10 @@ import langsmith as ls import pytest from langgraph.store.memory import InMemoryStore -from memory_graph.graph import builder from pydantic import BaseModel, Field +from memory_graph.graph import builder + class User(BaseModel): """Store all important information about a user here."""