I have discovered a bug located within `.github/workflows/_release.yml` which is the primary cause of continuous integration (CI) errors. The problem can be solved; therefore, I have constructed a PR to address the issue. ## The Issue Access the following link to view the exact errors: [Langhain Release Workflow](https://github.com/langchain-ai/langchain/actions/workflows/langchain_release.yml) The instances of these errors take place for **each PR** that updates `pyproject.toml`, excluding those specifically associated with bumping PRs. See below for the specific error message: ``` Error: Error 422: Validation Failed: {"resource":"Release","code":"already_exists","field":"tag_name"} ``` An image of the error can be viewed here:  The `_release.yml` document contains the following if-condition: ```yaml if: | ${{ github.event.pull_request.merged == true }} && ${{ contains(github.event.pull_request.labels.*.name, 'release') }} ``` ## The Root Cause The above job constantly runs as the `if-condition` is always identified as `true`. ## The Logic The `if-condition` can be defined as `if: ${{ b1 }} && ${{ b2 }}`, where `b1` and `b2` are boolean values. However, in terms of condition evaluation with GitHub Actions, `${{ false }}` is identified as a string value, thereby rendering it as truthy as per the [official documentation](https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idif). I have run some tests regarding this behavior within my forked repository. You can consult my [debug PR](https://github.com/zawakin/langchain/pull/1) for reference. Here is the result of the tests: |If-Condition|Outcome| |:--:|:--:| |`if: true && ${{ false }}`|Execution| |`if: ${{ false }}` |Skipped| |`if: true && false` |Skipped| |`if: false`|Skipped| |`if: ${{ true && false }}` |Skipped| In view of the first and second results, we can infer that `${{ false }}` can only be interpreted as `true` for conditions composed of some expressions. It is consistent that the condition of `if: ${{ inputs.working-directory == 'libs/langchain' }}` works. It is surprised to be skipped for the second case but it seems the spec of GitHub Actions 😓 Anyway, the PR would fix these errors, I believe 👍 Could you review this? @hwchase17 or @shoelsch , who is the author of [PR](https://github.com/langchain-ai/langchain/pull/360). |
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LICENSE | ||
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MIGRATE.md | ||
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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 hands-on support. Fill out this form to share more about what you're building, and our team will get in touch.
🚨Breaking Changes for select chains (SQLDatabase) on 7/28/23
In an effort to make langchain
leaner and safer, we are moving select chains to langchain_experimental
.
This migration has already started, but we are remaining backwards compatible until 7/28.
On that date, we will remove functionality from langchain
.
Read more about the motivation and the progress here.
Read how to migrate your code here.
Quick Install
pip install langchain
or
pip install langsmith && 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.