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Feature: linkcheck-action (langchain-ai#534) (langchain-ai#542)
- Add support for local build and linkchecking of docs - Add GitHub Action to automatically check links before prior to publication - Minor reformat of Contributing readme - Fix existing broken links Co-authored-by: Hunter Gerlach <[email protected]> Co-authored-by: Hunter Gerlach <[email protected]> Co-authored-by: Hunter Gerlach <[email protected]>
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name: linkcheck | ||
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on: | ||
push: | ||
branches: [master] | ||
pull_request: | ||
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env: | ||
POETRY_VERSION: "1.3.1" | ||
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jobs: | ||
build: | ||
runs-on: ubuntu-latest | ||
strategy: | ||
matrix: | ||
python-version: | ||
- "3.11" | ||
steps: | ||
- uses: actions/checkout@v3 | ||
- name: Install poetry | ||
run: | | ||
pipx install poetry==$POETRY_VERSION | ||
- name: Set up Python ${{ matrix.python-version }} | ||
uses: actions/setup-python@v4 | ||
with: | ||
python-version: ${{ matrix.python-version }} | ||
cache: poetry | ||
- name: Install dependencies | ||
run: | | ||
poetry install --with docs | ||
- name: Build the docs | ||
run: | | ||
make docs_build | ||
- name: Analyzing the docs with linkcheck | ||
run: | | ||
make docs_linkcheck |
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# Environments | ||
.env | ||
.venv | ||
.venvs | ||
env/ | ||
venv/ | ||
ENV/ | ||
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# Glossary | ||
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This is a collection of terminology commonly used when developing LLM applications. | ||
It contains reference to external papers or sources where the concept was first introduced, | ||
It contains reference to external papers or sources where the concept was first introduced, | ||
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. | ||
A prompting technique used to encourage the model to generate a series of intermediate reasoning steps. | ||
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) | ||
- [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. | ||
A prompt usage that uses a language model to generate actions to take. | ||
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) | ||
- [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. | ||
This induces the to model to think about what action to take, then take it. | ||
A prompting technique that combines Chain-of-Thought prompting with action plan generation. | ||
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) | ||
- [LangChain Example](https://github.com/hwchase17/langchain/blob/master/docs/examples/agents/react.ipynb) | ||
- [LangChain Example](./modules/agents/implementations/react.ipynb) | ||
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## Self-ask | ||
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A prompting method that builds on top of chain-of-thought prompting. | ||
In this method, the model explicitly asks itself follow-up questions, which are then answered by an external search engine. | ||
A prompting method that builds on top of chain-of-thought prompting. | ||
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) | ||
- [LangChain Example](https://github.com/hwchase17/langchain/blob/master/docs/examples/agents/self_ask_with_search.ipynb) | ||
- [LangChain Example](./modules/agents/implementations/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. | ||
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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Resources: | ||
- [PromptChainer Paper](https://arxiv.org/pdf/2203.06566.pdf) | ||
- [Language Model Cascades](https://arxiv.org/abs/2207.10342) | ||
- [ICE Primer Book](https://primer.ought.org/) | ||
- [Socratic Models](https://socraticmodels.github.io/) | ||
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## Memetic 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. | ||
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. | ||
Is most effective when combined with Chain-of-thought prompting. | ||
A decoding strategy that samples a diverse set of reasoning paths and then selects the most consistent answer. | ||
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”. | ||
Encouraging the model to think a certain way by including the start of the model’s response in the prompt. | ||
Also called “First Person Instruction”. | ||
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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## MemPrompt | ||
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MemPrompt maintains a memory of errors and user feedback, and uses them to prevent repetition of mistakes. | ||
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Resources: | ||
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- [Paper](https://memprompt.com/) |
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