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docs: deprecation of OpenAI functions agent, astream_events docstring (#18164)

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Co-authored-by: Hershenson, Isaac (Extern) <isaac.hershenson.extern@bayer04.de>
Co-authored-by: Bagatur <baskaryan@gmail.com>
pull/18189/head^2
Isaac Francisco 7 months ago committed by GitHub
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23
docs/docs/modules/agents/agent_types/openai_functions_agent.ipynb
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@ -17,27 +17,28 @@
"source": [
"# OpenAI functions\n",
"\n",
"Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to detect when a function should be called and respond with the inputs that should be passed to the function. In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call those functions. The goal of the OpenAI Function APIs is to more reliably return valid and useful function calls than a generic text completion or chat API.\n",
"\n",
"A number of open source models have adopted the same format for function calls and have also fine-tuned the model to detect when a function should be called.\n",
"\n",
"The OpenAI Functions Agent is designed to work with these models.\n",
"\n",
"Install `openai`, `tavily-python` packages which are required as the LangChain packages call them internally.\n",
"\n",
"\n",
":::info\n",
":::{.callout-caution}\n",
"\n",
"OpenAI API has deprecated `functions` in favor of `tools`. The difference between the two is that the `tools` API allows the model to request that multiple functions be invoked at once, which can reduce response times in some architectures. It's recommended to use the tools agent for OpenAI models.\n",
"\n",
"See the following links for more information:\n",
"\n",
"[OpenAI Tools](./openai_tools)\n",
"\n",
"[OpenAI chat create](https://platform.openai.com/docs/api-reference/chat/create)\n",
"\n",
"[OpenAI function calling](https://platform.openai.com/docs/guides/function-calling)\n",
":::\n",
"\n",
":::tip\n",
"Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to detect when a function should be called and respond with the inputs that should be passed to the function. In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call those functions. The goal of the OpenAI Function APIs is to more reliably return valid and useful function calls than a generic text completion or chat API.\n",
"\n",
"A number of open source models have adopted the same format for function calls and have also fine-tuned the model to detect when a function should be called.\n",
"\n",
"The OpenAI Functions Agent is designed to work with these models.\n",
"\n",
"Install `openai`, `tavily-python` packages which are required as the LangChain packages call them internally.\n",
"\n",
":::{.callout-tip}\n",
"The `functions` format remains relevant for open source models and providers that have adopted it, and this agent is expected to work for such models.\n",
":::\n"
]

85
libs/core/langchain_core/runnables/base.py
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@ -106,17 +106,17 @@ class Runnable(Generic[Input, Output], ABC):
Key Methods
===========
* invoke/ainvoke: Transforms a single input into an output.
* batch/abatch: Efficiently transforms multiple inputs into outputs.
* stream/astream: Streams output from a single input as it's produced.
* astream_log: Streams output and selected intermediate results from an input.
- **invoke/ainvoke**: Transforms a single input into an output.
- **batch/abatch**: Efficiently transforms multiple inputs into outputs.
- **stream/astream**: Streams output from a single input as it's produced.
- **astream_log**: Streams output and selected intermediate results from an input.
Built-in optimizations:
* Batch: By default, batch runs invoke() in parallel using a thread pool executor.
- **Batch**: By default, batch runs invoke() in parallel using a thread pool executor.
Override to optimize batching.
* Async: Methods with "a" suffix are asynchronous. By default, they execute
- **Async**: Methods with "a" suffix are asynchronous. By default, they execute
the sync counterpart using asyncio's thread pool.
Override for native async.
@ -228,7 +228,7 @@ class Runnable(Generic[Input, Output], ABC):
)
For a UI (and much more) checkout LangSmith: https://docs.smith.langchain.com/
"""
""" # noqa: E501
name: Optional[str] = None
"""The name of the runnable. Used for debugging and tracing."""
@ -707,78 +707,97 @@ class Runnable(Generic[Input, Output], ABC):
) -> AsyncIterator[StreamEvent]:
"""Generate a stream of events.
Use to create an iterator ove StreamEvents that provide real-time information
Use to create an iterator over StreamEvents that provide real-time information
about the progress of the runnable, including StreamEvents from intermediate
results.
A StreamEvent is a dictionary with the following schema:
* ``event``: str - Event names are of the
- ``event``: **str** - Event names are of the
format: on_[runnable_type]_(start|stream|end).
* ``name``: str - The name of the runnable that generated the event.
* ``run_id``: str - randomly generated ID associated with the given execution of
- ``name``: **str** - The name of the runnable that generated the event.
- ``run_id``: **str** - randomly generated ID associated with the given execution of
the runnable that emitted the event.
A child runnable that gets invoked as part of the execution of a
parent runnable is assigned its own unique ID.
* ``tags``: Optional[List[str]] - The tags of the runnable that generated
- ``tags``: **Optional[List[str]]** - The tags of the runnable that generated
the event.
* ``metadata``: Optional[Dict[str, Any]] - The metadata of the runnable
- ``metadata``: **Optional[Dict[str, Any]]** - The metadata of the runnable
that generated the event.
* ``data``: Dict[str, Any]
- ``data``: **Dict[str, Any]**
Below is a table that illustrates some evens that might be emitted by various
chains. Metadata fields have been omitted from the table for brevity.
Chain definitions have been included after the table.
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| event | name | chunk | input | output |
|----------------------|------------------|---------------------------------|-----------------------------------------------|-------------------------------------------------|
+======================+==================+=================================+===============================================+=================================================+
| on_chat_model_start | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chat_model_stream | [model name] | AIMessageChunk(content="hello") | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chat_model_end | [model name] | | {"messages": [[SystemMessage, HumanMessage]]} | {"generations": [...], "llm_output": None, ...} |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_start | [model name] | | {'input': 'hello'} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_stream | [model name] | 'Hello' | | |
| on_llm_end | [model name] | | 'Hello human!' |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_llm_end | [model name] | | 'Hello human!' | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_start | format_docs | | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_stream | format_docs | "hello world!, goodbye world!" | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_chain_end | format_docs | | [Document(...)] | "hello world!, goodbye world!" |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_tool_start | some_tool | | {"x": 1, "y": "2"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_tool_stream | some_tool | {"x": 1, "y": "2"} | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_tool_end | some_tool | | | {"x": 1, "y": "2"} |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_retriever_start | [retriever name] | | {"query": "hello"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_retriever_chunk | [retriever name] | {documents: [...]} | | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_retriever_end | [retriever name] | | {"query": "hello"} | {documents: [...]} |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_prompt_start | [template_name] | | {"question": "hello"} | |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
| on_prompt_end | [template_name] | | {"question": "hello"} | ChatPromptValue(messages: [SystemMessage, ...]) |
+----------------------+------------------+---------------------------------+-----------------------------------------------+-------------------------------------------------+
Here are declarations associated with the events shown above:
`format_docs`:
```python
def format_docs(docs: List[Document]) -> str:
'''Format the docs.'''
return ", ".join([doc.page_content for doc in docs])
.. code-block:: python
def format_docs(docs: List[Document]) -> str:
'''Format the docs.'''
return ", ".join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs)
```
format_docs = RunnableLambda(format_docs)
`some_tool`:
```python
@tool
def some_tool(x: int, y: str) -> dict:
'''Some_tool.'''
return {"x": x, "y": y}
```
.. code-block:: python
@tool
def some_tool(x: int, y: str) -> dict:
'''Some_tool.'''
return {"x": x, "y": y}
`prompt`:
```python
template = ChatPromptTemplate.from_messages(
[("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
```
.. code-block:: python
template = ChatPromptTemplate.from_messages(
[("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
Example:

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