langchain-decorators

Name: langchain-decorators
Author: ju-bezdek

234stars12forks

How to install langchain-decorators?

ju-bezdek/langchain-decorators

$git clone https://github.com/ju-bezdek/langchain-decorators

Installs into the current project.

Summary

LangChain Decorators is a lightweight layer built on top of LangChain that provides syntactic sugar 🍭 for writing custom prompts and chains.

Files · 1

View on GitHub

README.md

1	# LangChain Decorators ✨
2
3	LangChain Decorators is a lightweight layer built on top of LangChain that provides syntactic sugar 🍭 for writing custom prompts and chains.
4
5	> Note: This is an unofficial add-on to the LangChain library. It is not trying to compete—just to make using it easier. Many ideas here are opinionated.
6
7	Here is a simple example using LangChain Decorators ✨:
8
9	```python
10	@llm_prompt
11	def write_me_short_post(topic: str, platform: str = "twitter", audience: str = "developers") -> str:
12	"""
13	Write a short header for my post about {topic} for the {platform} platform.
14	It should be for a {audience} audience.
15	(Max 15 words)
16	"""
17	return
18
19	# Run it naturally
20	write_me_short_post(topic="starwars")
21	# or
22	write_me_short_post(topic="starwars", platform="reddit")
23	```
24
25	Main principles and benefits:
26	- A more Pythonic way to write prompts
27	- Write multi-line prompts without breaking your code’s indentation
28	- Leverage IDE support for hints, type checking, and doc popups to quickly see prompts and parameters
29	- Keep the power of the 🦜🔗 LangChain ecosystem
30	- Add support for optional parameters
31	- Easily share parameters between prompts by binding them to a class
32
33	[Quick start](#quick-start)
34
35	- [Installation](#installation)
36	- [Examples](#examples)
37
38	[Prompt declarations](#prompt-declarations)
39
40	- [Documenting your prompt](#documenting-your-prompt)
41	- [Chat messages in prompt](#chat-messages-in-prompt)
42	- [Optional sections](#optional-sections)
43	- [Output parsers](#output-parsers)
44
45	[Chat sessions / threads](#chat-sessions--threads)
46
47	[Tool calling](#tool-calling)
48
49	- [Enum arguments](#enum-arguments)
50
51	[Simplified streaming](#simplified-streaming)
52
53	[Structured output](#structured-output)
54
55	[Binding the prompt to an object](#prompts-as-object-methods)
56
57	[Defining custom settings](#defining-custom-settings)
58
59	[Debugging](#debugging)
60
61	## Quick start
62
63	### Installation
64
65	```bash
66	pip install langchain_decorators
67	```
68
69	### Examples
70
71	A good way to start is to review the examples here:
72
73	- [Jupyter notebook](example_notebook.ipynb)
74
75	## Prompt declarations
76
77	Define a prompt by creating a function with arguments as inputs and the function docstring as the prompt template:
78
79	```python
80	@llm_prompt
81	def write_me_short_post(topic: str, platform: str = "twitter", audience: str = "developers"):
82	"""
83	Write a short header for my post about {topic} for the {platform} platform.
84	It should be for a {audience} audience.
85	(Max 15 words)
86	"""
87	```
88
89	This default declaration is translated into a chat with a single user message.
90
91	If you want to define a prompt with multiple messages (common for chat models), add special code blocks inside the function docstring:
92
93	```python
94	@llm_prompt
95	def write_me_short_post(topic: str, platform: str = "twitter", audience: str = "developers"):
96	"""
97	```<prompt:system>
98	You are a social media manager.
99	```
100	```<prompt:user>
101	Write a short header for my post about {topic} for the {platform} platform.
102	It should be for a {audience} audience.
103	(Max 15 words)
104	```
105	```<prompt:assistant>
106	I need to think about it.
107	```
108	"""
109	```
110
111	The pattern is a series of consecutive code blocks with a “language” tag in this format: `<prompt:[message-role]>`.
112
113	### Documenting your prompt
114
115	You can specify which part of your docstring is the prompt by using a code block with the `<prompt>` tag:
116
117	```python
118	@llm_prompt
119	def write_me_short_post(topic: str, platform: str = "twitter", audience: str = "developers"):
120	"""
121	Here is a good way to write a prompt as part of a function docstring, with additional documentation for developers.
122
123	It needs to be a code block marked with `<prompt>`.
124	```<prompt:user>
125	Write a short header for my post about {topic} for the {platform} platform.
126	It should be for a {audience} audience.
127	(Max 15 words)
128	```
129
130	Only the code block above will be used as a prompt; the rest of the docstring is documentation for developers.
131	It also has a nice ben

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langchain-decorators

234stars12forks

View on GitHub

How to install langchain-decorators?

ju-bezdek/langchain-decorators

$git clone https://github.com/ju-bezdek/langchain-decorators

Installs into the current project.

Summary

LangChain Decorators is a lightweight layer built on top of LangChain that provides syntactic sugar 🍭 for writing custom prompts and chains.

Files · 1

View on GitHub

README.md

1	# LangChain Decorators ✨
2
3	LangChain Decorators is a lightweight layer built on top of LangChain that provides syntactic sugar 🍭 for writing custom prompts and chains.
4
5	> Note: This is an unofficial add-on to the LangChain library. It is not trying to compete—just to make using it easier. Many ideas here are opinionated.
6
7	Here is a simple example using LangChain Decorators ✨:
8
9	```python
10	@llm_prompt
11	def write_me_short_post(topic: str, platform: str = "twitter", audience: str = "developers") -> str:
12	"""
13	Write a short header for my post about {topic} for the {platform} platform.
14	It should be for a {audience} audience.
15	(Max 15 words)
16	"""
17	return
18
19	# Run it naturally
20	write_me_short_post(topic="starwars")
21	# or
22	write_me_short_post(topic="starwars", platform="reddit")
23	```
24
25	Main principles and benefits:
26	- A more Pythonic way to write prompts
27	- Write multi-line prompts without breaking your code’s indentation
28	- Leverage IDE support for hints, type checking, and doc popups to quickly see prompts and parameters
29	- Keep the power of the 🦜🔗 LangChain ecosystem
30	- Add support for optional parameters
31	- Easily share parameters between prompts by binding them to a class
32
33	[Quick start](#quick-start)
34
35	- [Installation](#installation)
36	- [Examples](#examples)
37
38	[Prompt declarations](#prompt-declarations)
39
40	- [Documenting your prompt](#documenting-your-prompt)
41	- [Chat messages in prompt](#chat-messages-in-prompt)
42	- [Optional sections](#optional-sections)
43	- [Output parsers](#output-parsers)
44
45	[Chat sessions / threads](#chat-sessions--threads)
46
47	[Tool calling](#tool-calling)
48
49	- [Enum arguments](#enum-arguments)
50
51	[Simplified streaming](#simplified-streaming)
52
53	[Structured output](#structured-output)
54
55	[Binding the prompt to an object](#prompts-as-object-methods)
56
57	[Defining custom settings](#defining-custom-settings)
58
59	[Debugging](#debugging)
60
61	## Quick start
62
63	### Installation
64
65	```bash
66	pip install langchain_decorators
67	```
68
69	### Examples
70
71	A good way to start is to review the examples here:
72
73	- [Jupyter notebook](example_notebook.ipynb)
74
75	## Prompt declarations
76
77	Define a prompt by creating a function with arguments as inputs and the function docstring as the prompt template:
78
79	```python
80	@llm_prompt
81	def write_me_short_post(topic: str, platform: str = "twitter", audience: str = "developers"):
82	"""
83	Write a short header for my post about {topic} for the {platform} platform.
84	It should be for a {audience} audience.
85	(Max 15 words)
86	"""
87	```
88
89	This default declaration is translated into a chat with a single user message.
90
91	If you want to define a prompt with multiple messages (common for chat models), add special code blocks inside the function docstring:
92
93	```python
94	@llm_prompt
95	def write_me_short_post(topic: str, platform: str = "twitter", audience: str = "developers"):
96	"""
97	```<prompt:system>
98	You are a social media manager.
99	```
100	```<prompt:user>
101	Write a short header for my post about {topic} for the {platform} platform.
102	It should be for a {audience} audience.
103	(Max 15 words)
104	```
105	```<prompt:assistant>
106	I need to think about it.
107	```
108	"""
109	```
110
111	The pattern is a series of consecutive code blocks with a “language” tag in this format: `<prompt:[message-role]>`.
112
113	### Documenting your prompt
114
115	You can specify which part of your docstring is the prompt by using a code block with the `<prompt>` tag:
116
117	```python
118	@llm_prompt
119	def write_me_short_post(topic: str, platform: str = "twitter", audience: str = "developers"):
120	"""
121	Here is a good way to write a prompt as part of a function docstring, with additional documentation for developers.
122
123	It needs to be a code block marked with `<prompt>`.
124	```<prompt:user>
125	Write a short header for my post about {topic} for the {platform} platform.
126	It should be for a {audience} audience.
127	(Max 15 words)
128	```
129
130	Only the code block above will be used as a prompt; the rest of the docstring is documentation for developers.
131	It also has a nice ben

People who liked this also viewed

6 picks

Type