mirror of
https://github.com/hwchase17/langchain
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81 lines
3.2 KiB
Markdown
81 lines
3.2 KiB
Markdown
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# 🦜️🔗 LangChain
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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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## Quick Install
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`pip install langchain`
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## 🤔 What is this?
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Large language models (LLMs) are emerging as a transformative technology, enabling
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developers to build applications that they previously could not.
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But using these LLMs in isolation is often not enough to
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create a truly powerful app - the real power comes when you are able to
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combine them with other sources of computation or knowledge.
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This library is aimed at assisting in the development of those types of applications.
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It aims to create:
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1. a comprehensive collection of pieces you would ever want to combine
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2. a flexible interface for combining pieces into a single comprehensive "chain"
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3. a schema for easily saving and sharing those chains
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## 🚀 What can I do with this
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This project was largely inspired by a few projects seen on Twitter for which we thought it would make sense to have more explicit tooling. A lot of the initial functionality was done in an attempt to recreate those. Those are:
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**[Self-ask-with-search](https://ofir.io/self-ask.pdf)**
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To recreate this paper, use the following code snippet or checkout the [example notebook](https://github.com/hwchase17/langchain/blob/master/examples/self_ask_with_search.ipynb).
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```
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from langchain import SelfAskWithSearchChain, OpenAI, SerpAPIChain
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llm = OpenAI(temperature=0)
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search = SerpAPIChain()
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self_ask_with_search = SelfAskWithSearchChain(llm=llm, search_chain=search)
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self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")
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```
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**[LLM Math](https://twitter.com/amasad/status/1568824744367259648?s=20&t=-7wxpXBJinPgDuyHLouP1w)**
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To recreate this example, use the following code snippet or check out the [example notebook](https://github.com/hwchase17/langchain/blob/master/examples/llm_math.ipynb).
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```
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from langchain import OpenAI, LLMMathChain
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llm = OpenAI(temperature=0)
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llm_math = LLMMathChain(llm=llm)
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llm_math.run("How many of the integers between 0 and 99 inclusive are divisible by 8?")
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```
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**Generic Prompting**
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You can also use this for simple prompting pipelines, as in the below example and this [example notebook](https://github.com/hwchase17/langchain/blob/master/examples/simple_prompts.ipynb).
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```
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from langchain import Prompt, OpenAI, LLMChain
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template = """Question: {question}
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Answer: Let's think step by step."""
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prompt = Prompt(template=template, input_variables=["question"])
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llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0))
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question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
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llm_chain.predict(question=question)
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```
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## 📖 Documentation
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The above examples are probably the most user friendly documentation that exists,
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but full API docs can be found [here](https://langchain.readthedocs.io/en/latest/?).
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