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Add docs on streaming

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Nuno Campos
2024-01-08 16:36:19 -08:00
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README.md
+210 -9
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@@ -4,8 +4,8 @@
## Overview
LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) [LangChain](https://github.com/langchain-ai/langchain).
It extends the [LangChain Expression Language](https://python.langchain.com/docs/expression_language/) with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner.
LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) [LangChain](https://github.com/langchain-ai/langchain).
It extends the [LangChain Expression Language](https://python.langchain.com/docs/expression_language/) with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner.
It is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/).
The current interface exposed is one inspired by [NetworkX](https://networkx.org/documentation/latest/).
@@ -49,7 +49,7 @@ export LANGCHAIN_ENDPOINT=https://api.langchain.plus
### Define the LangChain Agent
This is the LangChain agent.
This is the LangChain agent.
Crucially, this agent is just responsible for deciding what actions to take.
For more information on what is happening here, please see [this documentation](https://python.langchain.com/docs/modules/agents/quick_start).
@@ -72,6 +72,7 @@ agent_runnable = create_openai_functions_agent(llm, tools, prompt)
```
### Define the nodes
We now need to define a few different nodes in our graph.
In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/docs/expression_language/).
There are two main nodes we need for this:
@@ -174,8 +175,8 @@ workflow.add_conditional_edges(
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge('tools', 'agent')
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
chain = workflow.compile()
```
@@ -189,6 +190,207 @@ This now exposes the [same interface](https://python.langchain.com/docs/expressi
chain.invoke({"input": "what is the weather in sf", "intermediate_steps": []})
```
### Streaming
One of the benefits of using LangGraph is that it is easy to stream output as it's produced by each node.
```python
for output in chain.stream(
{"input": "what is the weather in sf", "intermediate_steps": []}
):
# stream() yields dictionaries with output keyed by node name
for key, value in output.items():
print(f"Output from node '{key}':")
print("---")
print(value)
print("\n---\n")
```
```
Output from node 'agent':
---
{'agent_outcome': AgentActionMessageLog(tool='tavily_search_results_json', tool_input={'query': 'weather in San Francisco'}, log="\nInvoking: `tavily_search_results_json` with `{'query': 'weather in San Francisco'}`\n\n\n", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{"query":"weather in San Francisco"}', 'name': 'tavily_search_results_json'}})]),
'input': 'what is the weather in sf',
'intermediate_steps': []}
---
Output from node 'tools':
---
{'input': 'what is the weather in sf',
'intermediate_steps': [(AgentActionMessageLog(tool='tavily_search_results_json', tool_input={'query': 'weather in San Francisco'}, log="\nInvoking: `tavily_search_results_json` with `{'query': 'weather in San Francisco'}`\n\n\n", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{"query":"weather in San Francisco"}', 'name': 'tavily_search_results_json'}})]),
[{'content': 'Best time to go to San Francisco? '
'Weather in San Francisco in january '
'2024 How was the weather last january? '
'Here is the day by day recorded weather '
'in San Francisco in january 2023: '
'Seasonal average climate and '
'temperature of San Francisco in '
'january 8% 46% 29% 12% 8% Evolution of '
'daily average temperature and '
'precipitation in San Francisco in '
'januaryWeather in San Francisco in '
'january 2024. The weather in San '
'Francisco in january comes from '
'statistical datas on the past years. '
'You can view the weather statistics the '
'entire month, but also by using the '
'tabs for the beginning, the middle and '
'the end of the month. ... 08-01-2023 '
'52°F to 58°F. 09-01-2023 54°F to 61°F. '
'10-01-2023 52°F to ...',
'url': 'https://www.whereandwhen.net/when/north-america/california/san-francisco-ca/january/'}])]}
---
Output from node 'agent':
---
{'agent_outcome': AgentFinish(return_values={'output': 'The weather in San Francisco in January ranges from 52°F to 61°F. For more detailed and current weather information, you may want to check a reliable weather website or app.'}, log='The weather in San Francisco in January ranges from 52°F to 61°F. For more detailed and current weather information, you may want to check a reliable weather website or app.'),
'input': 'what is the weather in sf',
'intermediate_steps': [(AgentActionMessageLog(tool='tavily_search_results_json', tool_input={'query': 'weather in San Francisco'}, log="\nInvoking: `tavily_search_results_json` with `{'query': 'weather in San Francisco'}`\n\n\n", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{"query":"weather in San Francisco"}', 'name': 'tavily_search_results_json'}})]),
[{'content': 'Best time to go to San Francisco? '
'Weather in San Francisco in january '
'2024 How was the weather last january? '
'Here is the day by day recorded weather '
'in San Francisco in january 2023: '
'Seasonal average climate and '
'temperature of San Francisco in '
'january 8% 46% 29% 12% 8% Evolution of '
'daily average temperature and '
'precipitation in San Francisco in '
'januaryWeather in San Francisco in '
'january 2024. The weather in San '
'Francisco in january comes from '
'statistical datas on the past years. '
'You can view the weather statistics the '
'entire month, but also by using the '
'tabs for the beginning, the middle and '
'the end of the month. ... 08-01-2023 '
'52°F to 58°F. 09-01-2023 54°F to 61°F. '
'10-01-2023 52°F to ...',
'url': 'https://www.whereandwhen.net/when/north-america/california/san-francisco-ca/january/'}])]}
---
Output from node '__end__':
---
{'agent_outcome': AgentFinish(return_values={'output': 'The weather in San Francisco in January ranges from 52°F to 61°F. For more detailed and current weather information, you may want to check a reliable weather website or app.'}, log='The weather in San Francisco in January ranges from 52°F to 61°F. For more detailed and current weather information, you may want to check a reliable weather website or app.'),
'input': 'what is the weather in sf',
'intermediate_steps': [(AgentActionMessageLog(tool='tavily_search_results_json', tool_input={'query': 'weather in San Francisco'}, log="\nInvoking: `tavily_search_results_json` with `{'query': 'weather in San Francisco'}`\n\n\n", message_log=[AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{"query":"weather in San Francisco"}', 'name': 'tavily_search_results_json'}})]),
[{'content': 'Best time to go to San Francisco? '
'Weather in San Francisco in january '
'2024 How was the weather last january? '
'Here is the day by day recorded weather '
'in San Francisco in january 2023: '
'Seasonal average climate and '
'temperature of San Francisco in '
'january 8% 46% 29% 12% 8% Evolution of '
'daily average temperature and '
'precipitation in San Francisco in '
'januaryWeather in San Francisco in '
'january 2024. The weather in San '
'Francisco in january comes from '
'statistical datas on the past years. '
'You can view the weather statistics the '
'entire month, but also by using the '
'tabs for the beginning, the middle and '
'the end of the month. ... 08-01-2023 '
'52°F to 58°F. 09-01-2023 54°F to 61°F. '
'10-01-2023 52°F to ...',
'url': 'https://www.whereandwhen.net/when/north-america/california/san-francisco-ca/january/'}])]}
---
```
### Streaming LLM Tokens
You can also access the LLM tokens as they are produced by each node. In this case only the "agent" node produces LLM tokens.
```python
async for output in chain.astream_log(
{"input": "what is the weather in sf", "intermediate_steps": []},
include_types=["llm"],
):
# astream_log() yields the requested logs (here LLMs) in JSONPatch format
for op in output.ops:
if op["path"] == "/streamed_output/-":
# this is the output from .stream()
...
elif op["path"].startswith("/logs/") and op["path"].endswith(
"/streamed_output/-"
):
# these are tokens from the LLM
print(op["value"])
```
```
content='' additional_kwargs={'function_call': {'arguments': '', 'name': 'tavily_search_results_json'}}
content='' additional_kwargs={'function_call': {'arguments': '{"', 'name': ''}}
content='' additional_kwargs={'function_call': {'arguments': 'query', 'name': ''}}
content='' additional_kwargs={'function_call': {'arguments': '":"', 'name': ''}}
content='' additional_kwargs={'function_call': {'arguments': 'current', 'name': ''}}
content='' additional_kwargs={'function_call': {'arguments': ' weather', 'name': ''}}
content='' additional_kwargs={'function_call': {'arguments': ' in', 'name': ''}}
content='' additional_kwargs={'function_call': {'arguments': ' San', 'name': ''}}
content='' additional_kwargs={'function_call': {'arguments': ' Francisco', 'name': ''}}
content='' additional_kwargs={'function_call': {'arguments': '"}', 'name': ''}}
content=''
content=''
content='I'
content=' found'
content=' a'
content=' website'
content=' that'
content=' provides'
content=' detailed'
content=' weather'
content=' information'
content=' for'
content=' San'
content=' Francisco'
content='.'
content=' You'
content=' can'
content=' visit'
content=' the'
content=' following'
content=' link'
content=' for'
content=' the'
content=' current'
content=' weather'
content=' report'
content=':'
content=' ['
content='San'
content=' Francisco'
content=' Weather'
content=' Report'
content=']('
content='https'
content='://'
content='www'
content='.weather'
content='25'
content='.com'
content='/n'
content='orth'
content='-'
content='amer'
content='ica'
content='/'
content='usa'
content='/cal'
content='ifornia'
content='/s'
content='an'
content='-fr'
content='anc'
content='isco'
content=')'
content=''
```
## Documentation
There are only a few new APIs to use.
@@ -202,7 +404,6 @@ from langgraph.graph import Graph
This class is responsible for constructing the graph.
It exposes an interface inspired by [NetworkX](https://networkx.org/documentation/latest/).
### `.add_node`
```python
@@ -320,10 +521,10 @@ from langchain_core.agents import AgentActionMessageLog
def first_agent(inputs):
action = AgentActionMessageLog(
# We force call this tool
tool="tavily_search_results_json",
tool="tavily_search_results_json",
# We just pass in the `input` key to this tool
tool_input=inputs["input"],
log="",
tool_input=inputs["input"],
log="",
message_log=[]
)
inputs["agent_outcome"] = action
examples/readme.py
+128 -11
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@@ -1,15 +1,132 @@
from langgraph.pregel import Channel, Pregel
import asyncio
from pprint import pprint
grow_value = (
Channel.subscribe_to("value")
| (lambda x: x + x)
| Channel.write_to(value=lambda x: x if len(x) < 10 else None)
from langchain import hub
from langchain.agents import create_openai_functions_agent
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.agents import AgentFinish
from langchain_core.runnables import RunnablePassthrough
from langchain_openai.chat_models import ChatOpenAI
from langgraph.graph import END, Graph
tools = [TavilySearchResults(max_results=1)]
# Get the prompt to use - you can modify this!
prompt = hub.pull("hwchase17/openai-functions-agent")
# Choose the LLM that will drive the agent
llm = ChatOpenAI(model="gpt-3.5-turbo-1106")
# Construct the OpenAI Functions agent
agent_runnable = create_openai_functions_agent(llm, tools, prompt)
# Define the agent
# Note that here, we are using `.assign` to add the output of the agent to the dictionary
# This dictionary will be returned from the node
# The reason we don't want to return just the result of `agent_runnable` from this node is
# that we want to continue passing around all the other inputs
agent = RunnablePassthrough.assign(agent_outcome=agent_runnable)
# Define the function to execute tools
def execute_tools(data):
# Get the most recent agent_outcome - this is the key added in the `agent` above
agent_action = data.pop("agent_outcome")
# Get the tool to use
tool_to_use = {t.name: t for t in tools}[agent_action.tool]
# Call that tool on the input
observation = tool_to_use.invoke(agent_action.tool_input)
# We now add in the action and the observation to the `intermediate_steps` list
# This is the list of all previous actions taken and their output
data["intermediate_steps"].append((agent_action, observation))
return data
# Define logic that will be used to determine which conditional edge to go down
def should_continue(data):
# If the agent outcome is an AgentFinish, then we return `exit` string
# This will be used when setting up the graph to define the flow
if isinstance(data["agent_outcome"], AgentFinish):
return "exit"
# Otherwise, an AgentAction is returned
# Here we return `continue` string
# This will be used when setting up the graph to define the flow
else:
return "continue"
# Define the graph
workflow = Graph()
# Add the agent node, we give it name `agent` which we will use later
workflow.add_node("agent", agent)
# Add the tools node, we give it name `tools` which we will use later
workflow.add_node("tools", execute_tools)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.set_entry_point("agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
# Finally we pass in a mapping.
# The keys are strings, and the values are other nodes.
# END is a special node marking that the graph should finish.
# What will happen is we will call `should_continue`, and then the output of that
# will be matched against the keys in this mapping.
# Based on which one it matches, that node will then be called.
{
# If `tools`, then we call the tool node.
"continue": "tools",
# Otherwise we finish.
"exit": END,
},
)
app = Pregel(
chains={"grow_value": grow_value},
input="value",
output="value",
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("tools", "agent")
assert app.invoke("a") == "aaaaaaaa"
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable
chain = workflow.compile()
def main():
for output in chain.stream(
{"input": "what is the weather in sf", "intermediate_steps": []}
):
for key, value in output.items():
print(f"Output from node '{key}':")
print("---")
pprint(value)
print("\n---\n")
async def amain():
async for output in chain.astream_log(
{"input": "what is the weather in sf", "intermediate_steps": []},
include_types=["llm"],
):
for op in output.ops:
if op["path"] == "/streamed_output/-":
# this is the output from .stream()
...
elif op["path"].startswith("/logs/") and op["path"].endswith(
"/streamed_output/-"
):
# these are tokens from the LLM
print(op["value"])
asyncio.run(amain())
poetry.lock
Generated
+195 -12
View File
@@ -686,6 +686,17 @@ files = [
{file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
]
[[package]]
name = "distro"
version = "1.9.0"
description = "Distro - an OS platform information API"
optional = false
python-versions = ">=3.6"
files = [
{file = "distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2"},
{file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
]
[[package]]
name = "exceptiongroup"
version = "1.2.0"
@@ -1570,6 +1581,23 @@ tenacity = ">=8.1.0,<9.0.0"
[package.extras]
extended-testing = ["jinja2 (>=3,<4)"]
[[package]]
name = "langchain-openai"
version = "0.0.2"
description = "An integration package connecting OpenAI and LangChain"
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
{file = "langchain_openai-0.0.2-py3-none-any.whl", hash = "sha256:0a46067be13ce95a029fdca339cd1034a61be1a727786178fbad702668a060f9"},
{file = "langchain_openai-0.0.2.tar.gz", hash = "sha256:713af4a638f65b3af2f741a9d61991011c31939b070d81ede5b2e3cba625e01a"},
]
[package.dependencies]
langchain-core = ">=0.1.7,<0.2"
numpy = ">=1,<2"
openai = ">=1.6.1,<2.0.0"
tiktoken = ">=0.5.2,<0.6.0"
[[package]]
name = "langchainhub"
version = "0.1.14"
@@ -2026,25 +2054,26 @@ files = [
[[package]]
name = "openai"
version = "0.27.10"
description = "Python client library for the OpenAI API"
version = "1.6.1"
description = "The official Python library for the openai API"
optional = false
python-versions = ">=3.7.1"
files = [
{file = "openai-0.27.10-py3-none-any.whl", hash = "sha256:beabd1757e3286fa166dde3b70ebb5ad8081af046876b47c14c41e203ed22a14"},
{file = "openai-0.27.10.tar.gz", hash = "sha256:60e09edf7100080283688748c6803b7b3b52d5a55d21890f3815292a0552d83b"},
{file = "openai-1.6.1-py3-none-any.whl", hash = "sha256:bc9f774838d67ac29fb24cdeb2d58faf57de8b311085dcd1348f7aa02a96c7ee"},
{file = "openai-1.6.1.tar.gz", hash = "sha256:d553ca9dbf9486b08e75b09e8671e4f638462aaadccfced632bf490fc3d75fa2"},
]
[package.dependencies]
aiohttp = "*"
requests = ">=2.20"
tqdm = "*"
anyio = ">=3.5.0,<5"
distro = ">=1.7.0,<2"
httpx = ">=0.23.0,<1"
pydantic = ">=1.9.0,<3"
sniffio = "*"
tqdm = ">4"
typing-extensions = ">=4.7,<5"
[package.extras]
datalib = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
dev = ["black (>=21.6b0,<22.0)", "pytest (==6.*)", "pytest-asyncio", "pytest-mock"]
embeddings = ["matplotlib", "numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "plotly", "scikit-learn (>=1.0.2)", "scipy", "tenacity (>=8.0.1)"]
wandb = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "wandb"]
datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
[[package]]
name = "overrides"
@@ -2795,6 +2824,108 @@ files = [
attrs = ">=22.2.0"
rpds-py = ">=0.7.0"
[[package]]
name = "regex"
version = "2023.12.25"
description = "Alternative regular expression module, to replace re."
optional = false
python-versions = ">=3.7"
files = [
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test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
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]
[package.dependencies]
regex = ">=2022.1.18"
requests = ">=2.26.0"
[package.extras]
blobfile = ["blobfile (>=2)"]
[[package]]
name = "tinycss2"
version = "1.2.1"
@@ -3568,4 +3751,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = ">=3.9.0,<4.0"
content-hash = "859cb37bda0ed0d3e8f94df41f0f6c5785aaf144e4e1dbb3835b945aa958ef1e"
content-hash = "3329b683659a6d0f15ce64a0e7135cfc26affe4c8d19c32dd481e1398cc41b3f"
pyproject.toml
+1 -1
View File
@@ -36,9 +36,9 @@ optional = true
[tool.poetry.group.dev.dependencies]
jupyter = "^1.0.0"
openai = "^0.27.8"
langchain = "^0.1.0"
langchainhub = "^0.1.14"
langchain-openai = "^0.0.2"
[tool.ruff]
select = [ "E", "F", "I" ]