langchain（一）

### 基础案例
```
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI

prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
	input_variables=["adjective"],
	template=prompt_template,
)
llm = OpenAI()
chain = prompt | llm | StrOutputParser()

chain.invoke("your adjective here")
```

### 管道运算
之所以能使用管道运算符，是因为这些对象都继承自`Runnable`类，而这个类实现了`__or__`方法。
```
class Runnable(Generic[Input, Output], ABC):
    """A unit of work that can be invoked, batched, streamed, transformed and composed.

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.

Built-in optimizations:

- **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
      the sync counterpart using asyncio's thread pool.
      Override for native async.

All methods accept an optional config argument, which can be used to configure
    execution, add tags and metadata for tracing and debugging etc.

Runnables expose schematic information about their input, output and config via
    the input_schema property, the output_schema property and config_schema method.

LCEL and Composition
    ====================

The LangChain Expression Language (LCEL) is a declarative way to compose Runnables
    into chains. Any chain constructed this way will automatically have sync, async,
    batch, and streaming support.

The main composition primitives are RunnableSequence and RunnableParallel.

**RunnableSequence** invokes a series of runnables sequentially, with
    one Runnable's output serving as the next's input. Construct using
    the `|` operator or by passing a list of runnables to RunnableSequence.

**RunnableParallel** invokes runnables concurrently, providing the same input
    to each. Construct it using a dict literal within a sequence or by passing a
    dict to RunnableParallel.

For example,

.. code-block:: python

from langchain_core.runnables import RunnableLambda

# A RunnableSequence constructed using the `|` operator
        sequence = RunnableLambda(lambda x: x + 1) | RunnableLambda(lambda x: x * 2)
        sequence.invoke(1) # 4
        sequence.batch([1, 2, 3]) # [4, 6, 8]

# A sequence that contains a RunnableParallel constructed using a dict literal
        sequence = RunnableLambda(lambda x: x + 1) | {
            'mul_2': RunnableLambda(lambda x: x * 2),
            'mul_5': RunnableLambda(lambda x: x * 5)
        }
        sequence.invoke(1) # {'mul_2': 4, 'mul_5': 10}

Standard Methods
    ================

All Runnables expose additional methods that can be used to modify their behavior
    (e.g., add a retry policy, add lifecycle listeners, make them configurable, etc.).

These methods will work on any Runnable, including Runnable chains constructed
    by composing other Runnables. See the individual methods for details.

For example,

.. code-block:: python

from langchain_core.runnables import RunnableLambda

import random

def add_one(x: int) -> int:
            return x + 1

def buggy_double(y: int) -> int:
            '''Buggy code that will fail 70% of the time'''
            if random.random() > 0.3:
                print('This code failed, and will probably be retried!')  # noqa: T201
                raise ValueError('Triggered buggy code')
            return y * 2

sequence = (
            RunnableLambda(add_one) |
            RunnableLambda(buggy_double).with_retry( # Retry on failure
                stop_after_attempt=10,
                wait_exponential_jitter=False
            )
        )

print(sequence.input_schema.schema()) # Show inferred input schema
        print(sequence.output_schema.schema()) # Show inferred output schema
        print(sequence.invoke(2)) # invoke the sequence (note the retry above!!)

Debugging and tracing
    =====================

As the chains get longer, it can be useful to be able to see intermediate results
    to debug and trace the chain.

You can set the global debug flag to True to enable debug output for all chains:

.. code-block:: python

from langchain_core.globals import set_debug
            set_debug(True)

Alternatively, you can pass existing or custom callbacks to any given chain:

.. code-block:: python

from langchain_core.tracers import ConsoleCallbackHandler

chain.invoke(
                ...,
                config={'callbacks': [ConsoleCallbackHandler()]}
            )

For a UI (and much more) checkout LangSmith: https://docs.smith.langchain.com/
    """  # noqa: E501
```
`管道运算` 返回的结果是 `RunnableSequence`，这个类也实现了`__or__`方法，用于接收更多的步骤（每个步骤是一个`Runnable`对象）。这个类还有一个`invoke`方法，调用后开始执行整个流程。
```

class RunnableSequence(RunnableSerializable[Input, Output]):
    """Sequence of Runnables, where the output of each is the input of the next.

**RunnableSequence** is the most important composition operator in LangChain
    as it is used in virtually every chain.

A RunnableSequence can be instantiated(实例化) directly or more commonly by using the `|`
    operator where either the left or right operands (or both) must be a Runnable.

Any RunnableSequence automatically supports sync, async, batch.

The default implementations of `batch` and `abatch` utilize threadpools and
    asyncio gather and will be faster than naive invocation of invoke or ainvoke
    for IO bound Runnables.

Batching is implemented by invoking the batch method on each component of the
    RunnableSequence in order.

A RunnableSequence preserves the streaming properties of its components, so if all
    components of the sequence implement a `transform` method -- which
    is the method that implements the logic to map a streaming input to a streaming
    output -- then the sequence will be able to stream input to output!

If any component of the sequence does not implement transform then the
    streaming will only begin after this component is run. If there are
    multiple blocking components, streaming begins after the last one.

Please note: RunnableLambdas do not support `transform` by default! So if
        you need to use a RunnableLambdas be careful about where you place them in a
        RunnableSequence (if you need to use the .stream()/.astream() methods).

If you need arbitrary logic and need streaming, you can subclass
        Runnable, and implement `transform` for whatever logic you need.

Here is a simple example that uses simple functions to illustrate the use of
    RunnableSequence:

.. code-block:: python

from langchain_core.runnables import RunnableLambda

def add_one(x: int) -> int:
                return x + 1

def mul_two(x: int) -> int:
                return x * 2

runnable_1 = RunnableLambda(add_one)
            runnable_2 = RunnableLambda(mul_two)
            sequence = runnable_1 | runnable_2
            # Or equivalently:
            # sequence = RunnableSequence(first=runnable_1, last=runnable_2)
            sequence.invoke(1)
            await sequence.ainvoke(1)

sequence.batch([1, 2, 3])
            await sequence.abatch([1, 2, 3])

Here's an example that uses streams JSON output generated by an LLM:

.. code-block:: python

from langchain_core.output_parsers.json import SimpleJsonOutputParser
            from langchain_openai import ChatOpenAI

prompt = PromptTemplate.from_template(
                'In JSON format, give me a list of {topic} and their '
                'corresponding names in French, Spanish and in a '
                'Cat Language.'
            )

model = ChatOpenAI()
            chain = prompt | model | SimpleJsonOutputParser()

async for chunk in chain.astream({'topic': 'colors'}):
                print('-')  # noqa: T201
                print(chunk, sep='', flush=True)  # noqa: T201
    """
```