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Harrison/initial glossary (#61)
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⚡ Building applications with LLMs through composability ⚡
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⚡ Building applications with LLMs through composability ⚡
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[](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [](https://opensource.org/licenses/MIT)
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[](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [](https://opensource.org/licenses/MIT) [](https://discord.gg/6adMQxSpJS)
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@ -38,6 +38,7 @@ extensions = [
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"sphinx.ext.autosummary",
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"sphinx.ext.autosummary",
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"sphinx.ext.napoleon",
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"sphinx.ext.napoleon",
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"sphinxcontrib.autodoc_pydantic",
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"sphinxcontrib.autodoc_pydantic",
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"myst_parser",
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]
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]
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autodoc_pydantic_model_show_json = False
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autodoc_pydantic_model_show_json = False
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@ -68,6 +69,14 @@ exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
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html_theme = "sphinx_rtd_theme"
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html_theme = "sphinx_rtd_theme"
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# html_theme = "sphinx_typlog_theme"
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# html_theme = "sphinx_typlog_theme"
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html_context = {
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"display_github": True, # Integrate GitHub
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"github_user": "hwchase17", # Username
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"github_repo": "langchain", # Repo name
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"github_version": "master", # Version
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"conf_py_path": "/docs/", # Path in the checkout to the docs root
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}
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# Add any paths that contain custom static files (such as style sheets) here,
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# Add any paths that contain custom static files (such as style sheets) here,
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# relative to this directory. They are copied after the builtin static files,
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# relative to this directory. They are copied after the builtin static files,
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# so a file named "default.css" will overwrite the builtin "default.css".
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# so a file named "default.css" will overwrite the builtin "default.css".
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71
docs/glossary.md
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docs/glossary.md
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# Glossary
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This is a collection of terminology commonly used when developing LLM applications.
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It contains reference to external papers or sources where the concept was first introduced,
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as well as to places in LangChain where the concept is used.
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### Chain of Thought Prompting
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A prompting technique used to encourage the model to generate a series of intermediate reasoning steps.
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A less formal way to induce this behavior is to include “Let’s think step-by-step” in the prompt.
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Resources:
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- [Chain-of-Thought Paper](https://arxiv.org/pdf/2201.11903.pdf)
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- [Step-by-Step Paper](https://arxiv.org/abs/2112.00114)
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### Action Plan Generation
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A prompt usage that uses a language model to generate actions to take.
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The results of these actions can then be fed back into the language model to generate a subsequent action.
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Resources:
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- [WebGPT Paper](https://arxiv.org/pdf/2112.09332.pdf)
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- [SayCan Paper](https://say-can.github.io/assets/palm_saycan.pdf)
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### ReAct Prompting
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A prompting technique that combines Chain-of-Thought prompting with action plan generation.
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This induces the to model to think about what action to take, then take it.
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Resources:
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- [Paper](https://arxiv.org/pdf/2210.03629.pdf)
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- [LangChain Example](https://github.com/hwchase17/langchain/blob/master/examples/react.ipynb)
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### Self-ask
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A prompting method that builds on top of chain-of-thought prompting.
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In this method, the model explicitly asks itself follow-up questions, which are then answered by an external search engine.
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Resources:
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- [Paper](https://ofir.io/self-ask.pdf)
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- [LangChain Example](https://github.com/hwchase17/langchain/blob/master/examples/self_ask_with_search.ipynb)
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### Prompt Chaining
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Combining multiple LLM calls together, with the output of one step being the input to the next.
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Resources:
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- [Paper](https://arxiv.org/pdf/2203.06566.pdf)
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### Mimetic Proxy
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Encouraging the LLM to respond in a certain way framing the discussion in a context that the model knows of and that will result in that type of response. For example, as a conversation between a student and a teacher.
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Resources:
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- [Paper](https://arxiv.org/pdf/2102.07350.pdf)
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### Self Consistency
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A decoding strategy that samples a diverse set of reasoning paths and then selects the most consistent answer.
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Is most effective when combined with Chain-of-thought prompting.
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Resources:
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- [Paper](https://arxiv.org/pdf/2203.11171.pdf)
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### Inception
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Also called “First Person Instruction”.
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Encouraging the model to think a certain way by including the start of the model’s response in the prompt.
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Resources:
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- [Example](https://twitter.com/goodside/status/1583262455207460865?s=20&t=8Hz7XBnK1OF8siQrxxCIGQ)
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@ -9,3 +9,10 @@ Welcome to LangChain
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modules/llms
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modules/llms
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modules/embeddings
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modules/embeddings
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modules/chains
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modules/chains
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.. toctree::
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:maxdepth: 1
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:caption: Resources
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glossary.md
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Discord <https://discord.gg/6adMQxSpJS>
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@ -3,3 +3,4 @@ sphinx-autobuild==2021.3.14
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sphinx_rtd_theme==1.0.0
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sphinx_rtd_theme==1.0.0
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sphinx-typlog-theme==0.8.0
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sphinx-typlog-theme==0.8.0
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autodoc_pydantic==1.8.0
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autodoc_pydantic==1.8.0
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myst_parser
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