2aa3754024
# Ensuring that users pass a single prompt when calling a LLM
- This PR adds a check to the `__call__` method of the `BaseLLM` class
to ensure that it is called with a single prompt
- Raises a `ValueError` if users try to call a LLM with a list of prompt
and instructs them to use the `generate` method instead
## Why this could be useful
I stumbled across this by accident. I accidentally called the OpenAI LLM
with a list of prompts instead of a single string and still got a
result:
```
>>> from langchain.llms import OpenAI
>>> llm = OpenAI()
>>> llm(["Tell a joke"]*2)
"\n\nQ: Why don't scientists trust atoms?\nA: Because they make up everything!"
```
It might be better to catch such a scenario preventing unnecessary costs
and irritation for the user.
## Proposed behaviour
```
>>> from langchain.llms import OpenAI
>>> llm = OpenAI()
>>> llm(["Tell a joke"]*2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/marcus/Projects/langchain/langchain/llms/base.py", line 291, in __call__
raise ValueError(
ValueError: Argument `prompt` is expected to be a single string, not a list. If you want to run the LLM on multiple prompts, use `generate` instead.
```
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| .github | ||
| docs | ||
| langchain | ||
| tests | ||
| .dockerignore | ||
| .flake8 | ||
| .gitignore | ||
| .readthedocs.yaml | ||
| CITATION.cff | ||
| Dockerfile | ||
| LICENSE | ||
| Makefile | ||
| poetry.lock | ||
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| README.md | ||
🦜️🔗 LangChain
⚡ Building applications with LLMs through composability ⚡
Looking for the JS/TS version? Check out LangChain.js.
Production Support: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.
Quick Install
pip install langchain
or
conda install langchain -c conda-forge
🤔 What is this?
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
This library aims to assist in the development of those types of applications. Common examples of these applications include:
❓ Question Answering over specific documents
- Documentation
- End-to-end Example: Question Answering over Notion Database
💬 Chatbots
- Documentation
- End-to-end Example: Chat-LangChain
🤖 Agents
- Documentation
- End-to-end Example: GPT+WolframAlpha
📖 Documentation
Please see here for full documentation on:
- Getting started (installation, setting up the environment, simple examples)
- How-To examples (demos, integrations, helper functions)
- Reference (full API docs)
- Resources (high-level explanation of core concepts)
🚀 What can this help with?
There are six main areas that LangChain is designed to help with. These are, in increasing order of complexity:
📃 LLMs and Prompts:
This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
🔗 Chains:
Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
📚 Data Augmented Generation:
Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
🤖 Agents:
Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
🧠 Memory:
Memory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
🧐 Evaluation:
[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
For more information on these concepts, please see our full documentation.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see here.