From b6b61c65856438d253b6134f0ad18c4b69d9fa1f Mon Sep 17 00:00:00 2001 From: Nuno Campos Date: Mon, 8 Jan 2024 16:36:19 -0800 Subject: [PATCH] Add docs on streaming --- README.md | 219 +++++++++++++++++++++++++++++++++++++++++++-- examples/readme.py | 139 +++++++++++++++++++++++++--- poetry.lock | 207 +++++++++++++++++++++++++++++++++++++++--- pyproject.toml | 2 +- 4 files changed, 534 insertions(+), 33 deletions(-) diff --git a/README.md b/README.md index 2599d6e82..3ab09b99f 100644 --- a/README.md +++ b/README.md @@ -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 diff --git a/examples/readme.py b/examples/readme.py index 543da09b9..b7077a321 100644 --- a/examples/readme.py +++ b/examples/readme.py @@ -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()) diff --git a/poetry.lock b/poetry.lock index aea6a8163..163943b30 100644 --- a/poetry.lock +++ b/poetry.lock @@ -686,6 +686,17 @@ files = [ {file = "defusedxml-0.7.1.tar.gz", hash = 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