forked from Archives/langchain
59 lines
1.5 KiB
ReStructuredText
59 lines
1.5 KiB
ReStructuredText
Prompts
|
|
==========================
|
|
|
|
.. note::
|
|
`Conceptual Guide <https://docs.langchain.com/docs/components/prompts>`_
|
|
|
|
|
|
The new way of programming models is through prompts.
|
|
A "prompt" refers to the input to the model.
|
|
This input is rarely hard coded, but rather is often constructed from multiple components.
|
|
A PromptTemplate is responsible for the construction of this input.
|
|
LangChain provides several classes and functions to make constructing and working with prompts easy.
|
|
|
|
This section of documentation is split into four sections:
|
|
|
|
**LLM Prompt Templates**
|
|
|
|
How to use PromptTemplates to prompt Language Models.
|
|
|
|
**Chat Prompt Templates**
|
|
|
|
How to use PromptTemplates to prompt Chat Models.
|
|
|
|
**Example Selectors**
|
|
|
|
Often times it is useful to include examples in prompts.
|
|
These examples can be hardcoded, but it is often more powerful if they are dynamically selected.
|
|
This section goes over example selection.
|
|
|
|
|
|
**Output Parsers**
|
|
|
|
Language models (and Chat Models) output text.
|
|
But many times you may want to get more structured information than just text back.
|
|
This is where output parsers come in.
|
|
Output Parsers are responsible for (1) instructing the model how output should be formatted,
|
|
(2) parsing output into the desired formatting (including retrying if necessary).
|
|
|
|
Getting Started
|
|
---------------
|
|
|
|
.. toctree::
|
|
:maxdepth: 1
|
|
|
|
./prompts/getting_started.ipynb
|
|
|
|
|
|
|
|
Go Deeper
|
|
---------
|
|
|
|
.. toctree::
|
|
:maxdepth: 1
|
|
|
|
./prompts/prompt_templates.rst
|
|
./prompts/chat_prompt_template.ipynb
|
|
./prompts/example_selectors.rst
|
|
./prompts/output_parsers.rst
|