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Merge remote-tracking branch 'origin/main' into wfh/docs/memstoreconcept

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William Fu-Hinthorn
2024-12-04 06:49:29 -08:00
parent fb188c4c5c a8db511e24
commit c5904135cc
3 changed files with 92 additions and 49 deletions
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docs/docs/cloud/reference/cli.md
+80 -37
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@@ -26,10 +26,11 @@ The LangGraph command line interface includes commands to build and run a LangGr
The LangGraph CLI requires a JSON configuration file with the following keys:
| Key | Description |
|--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `dependencies` | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name. |
| `graphs` | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul> |
| `env` | Path to `.env` file or a mapping from environment variable to its value. |
| `store` | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, meaningto index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul> |
| `python_version` | `3.11` or `3.12`. Defaults to `3.11`. |
| `pip_config_file` | Path to `pip` config file. |
| `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image. |
@@ -41,33 +42,75 @@ The LangGraph CLI requires a JSON configuration file with the following keys:
</p>
</div>
Example:
### Examples
#### Basic Configuration
```json
{
"dependencies": ["langchain_openai", "./your_package"],
"dependencies": ["."],
"graphs": {
"my_graph_id": "./your_package/your_file.py:variable"
},
"env": "./.env"
"chat": "./chat/graph.py:graph"
}
}
```
Example with environment variables:
#### Adding semantic search to the store
All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.
The `fields` configuration determines which parts of your documents to embed:
- If omitted or set to `["$"]`, the entire document will be embedded
- To embed specific fields, use JSON path notation: `["metadata.title", "content.text"]`
- Documents missing specified fields will still be stored but won't have embeddings for those fields
- You can still override which fields to embed on a specific item at `put` time using the `index` parameter
```json
{
"python_version": "3.11",
"dependencies": ["langchain_openai", "."],
"dependencies": ["."],
"graphs": {
"my_graph_id": "./your_package/your_file.py:make_graph"
"memory_agent": "./agent/graph.py:graph"
},
"env": {
"OPENAI_API_KEY": "secret-key"
"store": {
"index": {
"embed": "openai:text-embedding-3-small",
"dims": 1536,
"fields": ["$"]
}
}
}
```
#### Semantic search with a custom embedding function
If you want to use semantic search with a custom embedding function, you can pass a path to a custom embedding function:
```json
{
"dependencies": ["."],
"graphs": {
"memory_agent": "./agent/graph.py:graph"
},
"store": {
"index": {
"embed": "./embeddings.py:embed_texts",
"dims": 768,
"fields": ["text", "summary"]
}
}
}
```
The `embed` field in store configuration can reference a custom function that takes a list of strings and returns a list of embeddings. Example implementation:
```python
# embeddings.py
def embed_texts(texts: list[str]) -> list[list[float]]:
"""Custom embedding function for semantic search."""
# Implementation using your preferred embedding model
return [[0.1, 0.2, ...] for _ in texts] # dims-dimensional vectors
```
## Commands
The base command for the LangGraph CLI is `langgraph`.
@@ -98,16 +141,16 @@ langgraph dev [OPTIONS]
**Options**
| Option | Default | Description |
|----------------------------|------------------|--------------------------------------------------------------------------------------------|
| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
| `--host TEXT` | `127.0.0.1` | Host to bind the server to |
| `--port INTEGER` | `2024` | Port to bind the server to |
| `--no-reload` | | Disable auto-reload |
| `--n-jobs-per-worker INTEGER` | | Number of jobs per worker. Default is 10 |
| `--no-browser` | | Disable automatic browser opening |
| `--debug-port INTEGER` | | Port for debugger to listen on |
| `--help` | | Display command documentation |
| Option | Default | Description |
| ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
| `--host TEXT` | `127.0.0.1` | Host to bind the server to |
| `--port INTEGER` | `2024` | Port to bind the server to |
| `--no-reload` | | Disable auto-reload |
| `--n-jobs-per-worker INTEGER` | | Number of jobs per worker. Default is 10 |
| `--no-browser` | | Disable automatic browser opening |
| `--debug-port INTEGER` | | Port for debugger to listen on |
| `--help` | | Display command documentation |
### `build`
@@ -122,7 +165,7 @@ langgraph build [OPTIONS]
**Options**
| Option | Default | Description |
|----------------------|------------------|------------------------------------------------------------------------------------------------------------------------------|
| -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
| `--platform TEXT` | | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64` |
| `-t, --tag TEXT` | | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image` |
| `--pull / --no-pull` | `--pull` | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images. |
@@ -141,20 +184,20 @@ langgraph up [OPTIONS]
**Options**
| Option | Default | Description |
|------------------------------|---------------------------|-----------------------------------------------------------------------------------------------------------------------|
| `--wait` | | Wait for services to start before returning. Implies --detach |
| `--postgres-uri TEXT` | Local database | Postgres URI to use for the database. |
| `--watch` | | Restart on file changes |
| `--debugger-base-url TEXT` | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API. |
| `--debugger-port INTEGER` | | Pull the debugger image locally and serve the UI on specified port |
| `--verbose` | | Show more output from the server logs. |
| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
| `-d, --docker-compose FILE` | | Path to docker-compose.yml file with additional services to launch. |
| `-p, --port INTEGER` | `8123` | Port to expose. Example: `langgraph up --port 8000` |
| Option | Default | Description |
| ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
| `--wait` | | Wait for services to start before returning. Implies --detach |
| `--postgres-uri TEXT` | Local database | Postgres URI to use for the database. |
| `--watch` | | Restart on file changes |
| `--debugger-base-url TEXT` | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API. |
| `--debugger-port INTEGER` | | Pull the debugger image locally and serve the UI on specified port |
| `--verbose` | | Show more output from the server logs. |
| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
| `-d, --docker-compose FILE` | | Path to docker-compose.yml file with additional services to launch. |
| `-p, --port INTEGER` | `8123` | Port to expose. Example: `langgraph up --port 8000` |
| `--pull / --no-pull` | `pull` | Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull` |
| `--recreate / --no-recreate` | `no-recreate` | Recreate containers even if their configuration and image haven't changed |
| `--help` | | Display command documentation. |
| `--recreate / --no-recreate` | `no-recreate` | Recreate containers even if their configuration and image haven't changed |
| `--help` | | Display command documentation. |
### `dockerfile`
@@ -169,7 +212,7 @@ langgraph dockerfile [OPTIONS] SAVE_PATH
**Options**
| Option | Default | Description |
|---------------------|------------------|-----------------------------------------------------------------------------------------------------------------|
| ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
| `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
| `--help` | | Show this message and exit. |
docs/docs/concepts/human_in_the_loop.md
+11 -11
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@@ -27,8 +27,8 @@ Adding a [breakpoint](./low_level.md#breakpoints) a specific location in the gra
Here, we compile our graph with a checkpointer and a breakpoint at the node we want to interrupt before, `step_for_human_in_the_loop`. We then perform one of the above interaction patterns, which will create a new checkpoint if a human edits the graph state. The new checkpoint is saved to the `thread` and we can resume the graph execution from there by passing in `None` as the input.
```python
# Compile our graph with a checkpoitner and a breakpoint before "step_for_human_in_the_loop"
graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["step_for_human_in_the_loop"])
# Compile our graph with a checkpointer and a breakpoint before "step_for_human_in_the_loop"
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["step_for_human_in_the_loop"])
# Run the graph up to the breakpoint
thread_config = {"configurable": {"thread_id": "1"}}
@@ -98,8 +98,8 @@ With persistence, we can surface the current agent state as well as the next ste
If approved, the graph resumes execution from the last saved checkpoint, which is saved to the `thread`:
```python
# Compile our graph with a checkpoitner and a breakpoint before the step to approve
graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["node_2"])
# Compile our graph with a checkpointer and a breakpoint before the step to approve
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
# Run the graph up to the breakpoint
for event in graph.stream(inputs, thread, stream_mode="values"):
@@ -131,8 +131,8 @@ We can edit the graph state by forking the current checkpoint, which is saved to
We can then proceed with the graph from our forked checkpoint as done before.
```python
# Compile our graph with a checkpoitner and a breakpoint before the step to review
graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["node_2"])
# Compile our graph with a checkpointer and a breakpoint before the step to review
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["node_2"])
# Run the graph up to the breakpoint
for event in graph.stream(inputs, thread, stream_mode="values"):
@@ -173,8 +173,8 @@ With input, we explicitly define a node in our graph for collecting human input!
The state update with the human input then runs *as this node*.
```python
# Compile our graph with a checkpoitner and a breakpoint before the step to to collect human input
graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["human_input"])
# Compile our graph with a checkpointer and a breakpoint before the step to to collect human input
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_input"])
# Run the graph up to the breakpoint
for event in graph.stream(inputs, thread, stream_mode="values"):
@@ -211,8 +211,8 @@ Even if the tool call is correct, we may also want to apply discretion:
With these points in mind, we can combine the above ideas to create a human-in-the-loop review of a tool call.
```python
# Compile our graph with a checkpoitner and a breakpoint before the step to to review the tool call from the LLM
graph = builder.compile(checkpointer=checkpoitner, interrupt_before=["human_review"])
# Compile our graph with a checkpointer and a breakpoint before the step to to review the tool call from the LLM
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["human_review"])
# Run the graph up to the breakpoint
for event in graph.stream(inputs, thread, stream_mode="values"):
@@ -319,4 +319,4 @@ for event in graph.stream(None, config, stream_mode="values"):
See [this additional conceptual guide](https://langchain-ai.github.io/langgraph/concepts/persistence/#update-state) for related context on forking.
See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
See see [this guide](../how-tos/human_in_the_loop/time-travel.ipynb) for a detailed how-to on doing time-travel!
libs/sdk-py/langgraph_sdk/client.py
+1 -1
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@@ -190,7 +190,7 @@ class LangGraphClient:
class HttpClient:
"""Hancle async requests to the LangGraph API.
"""Handle async requests to the LangGraph API.
Adds additional error messaging & content handling above the
provided httpx client.