add docs with components
This commit is contained in:
Rodrigo Nader
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docs/README.md
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docs/README.md
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# Website
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This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator.
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### Installation
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```
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$ yarn
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```
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### Local Development
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```
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$ yarn start
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```
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This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
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### Build
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```
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$ yarn build
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```
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This command generates static content into the `build` directory and can be served using any static contents hosting service.
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### Deployment
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Using SSH:
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```
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$ USE_SSH=true yarn deploy
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```
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Not using SSH:
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```
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$ GIT_USER=<Your GitHub username> yarn deploy
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```
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If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.
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module.exports = {
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presets: [require.resolve('@docusaurus/core/lib/babel/preset')],
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};
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# 🐦 Stay tunned for **LangFlow** on Twitter
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Follow [@logspace_ai](https://twitter.com/logspace_ai){.internal-link target=_blank} on **Twitter** to get the latest news about **LangFlow**.
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<br>
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---
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# ⭐️ Star **LangFlow** on GitHub
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You can "star" **LangFlow** in [GitHub](https://github.com/logspace-ai/langflow){.internal-link target=_blank}.
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<br>
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By adding a star, other users will be able to find it more easily and see that it has been already useful for others.
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<br>
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[](https://star-history.com/#logspace-ai/langflow&Date){.internal-link target=_blank}
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<br>
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---
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# 👀 Watch the GitHub repository for releases
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You can "watch" **LangFlow** in [GitHub](https://github.com/logspace-ai/langflow){.internal-link target=_blank}.
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<br>
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If you select "Watching" instead of "Releases only" you will receive notifications when someone creates a new issue or question. You can also specify that you only want to be notified about new issues, discussions, PRs, etc.
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<br>
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Then you can try and help them solve those questions.
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---
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Thanks! 🚀
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# 👋 Contributing
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Hello there! We welcome contributions from developers of all levels to our open-source project on [GitHub](https://github.com/logspace-ai/langflow){.internal-link target=_blank}. If you'd like to contribute, please check our contributing guidelines and help make LangFlow more accessible.
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<br>
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As an open-source project in a rapidly developing field, we are extremely open
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to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
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<br>
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To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects){.internal-link target=_blank} workflow.
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Please do not try to push directly to this repo unless you are a maintainer.
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`ConversationBufferMemory` allows you to store messages and then extract the messages in a variable. The **memory key** input is typically generated by encoding the input text using an encoder network, which maps the input text into a fixed-dimensional vector representation.
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<br>
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{width=50%}
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{width=50%}
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<br>
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Learn more about the [ConversationBufferMemory](https://python.langchain.com/en/latest/modules/memory/types/buffer.html){.internal-link target=\_blank} in the LangChain documentation.
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---
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### ⛓️LangFlow example
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<!-- <figure markdown> -->
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{width=100%}
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{width=100%}
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<br>
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[Download Flow](data/Conversation_buffer_memory.json){: .md-button download="Conversation_buffer_memory"}
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<br>
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`ConversationChain` is a chain to have a conversation and load context from memory. **Output Key** and **Input Key** are simply unique identifiers used to represent the data being passed between different modules or steps in a Conversation Chain. These keys help to ensure that the data is properly routed and processed by the appropriate modules in the conversation flow.
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<br>
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Output Key used the default: `response` and Input Key used the default: `input`.
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<br>
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In the LangFlow example, we used `ChatOpenAI` as the LLM, but you can use any LLM that has an API. Make sure to get the API key from the LLM provider. For example, [ChatOpenAI](https://platform.openai.com/){.internal-link target=\_blank} requires you to create an account to get your API key.
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<br>
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Check out the [ChatOpenAI](https://platform.openai.com/docs/introduction/overview){.internal-link target=\_blank} documentation to learn more about the API and the options that contain in the node.
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`ConversationSummaryMemory` creates a summary of the conversation over time, which can be useful for condensing information. The **memory key** input is typically generated by encoding the input text using an encoder network, which maps the input text into a fixed-dimensional vector representation.
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<br>
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{width=50%}
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{width=50%}
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<br>
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To understand more, check out the LangChain [ConversationSummaryMemory](https://python.langchain.com/en/latest/modules/memory/types/summary.html){.internal-link target=\_blank} documentation.
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---
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### ⛓️LangFlow example
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{width=100%}
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{width=100%}
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<br>
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[Download Flow](data/Conversation_summary_memory.json){: .md-button download="Conversation_summary_memory"}
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<br>
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In the LangFlow example, we used `ChatOpenAI` as the LLM, but you can use any LLM that has an API. Make sure to get the API key from the LLM provider. For example, [ChatOpenAI](https://platform.openai.com/){.internal-link target=\_blank} requires you to create an account to get your API key.
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||||
|
||||
<br>
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||||
|
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Check out the [ChatOpenAI](https://platform.openai.com/docs/introduction/overview){.internal-link target=\_blank} documentation to learn more about the API and the options that contain in the node.
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<br>
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`ConversationChain` is a chain to have a conversation and load context from memory. **Output Key** and **Input Key** are simply unique identifiers used to represent the data being passed between different modules or steps in a Conversation Chain. These keys help to ensure that the data is properly routed and processed by the appropriate modules in the conversation flow.
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<br>
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Output Key used the default: `response` and Input Key used the default: `input`.
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# Creating Flows
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Creating flows with LangFlow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. LangFlow provides a range of [LangChain components](https://langchain.readthedocs.io/en/latest/reference.html){.internal-link target=\_blank} to choose from, including LLMs, prompt serializers, agents, and chains.
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<br>
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Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow.
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<br>
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Once you're done, you can export your flow as a JSON file to use with LangChain.
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To do so, click the "Export" button in the top right corner of the canvas, then
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in Python, you can load the flow with:
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```py
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from langflow import load_flow_from_json
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flow = load_flow_from_json("path/to/flow.json")
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# Now you can use it like any chain
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flow("Hey, have you heard of LangFlow?")
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```
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The `CSVLoader` loads a CSV file into a list of documents.
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<br>
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{width=50%}
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{width=50%}
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<br>
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Check out more about the `CSVLoader` in [LangChain](https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/csv.html?highlight=CSV%20loader){.internal-link target=\_blank} documentation.
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---
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### ⛓️LangFlow example
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{width=100%}
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{width=100%}
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<br>
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[Download Flow](data/Csv_loader.json){: .md-button download="Csv_loader"}
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<br>
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`File path:`
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<br>
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[Download CSV](data/organizations-100.csv){: .md-button download="organizations-100.csv"}
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<br>
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`CharacterTextSplitter` implements splitting text based on characters.
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Text splitters operate as follows:
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- Split the text into small, meaningful chunks (usually sentences).
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- Combine these small chunks into larger ones until they reach a certain size (measured by a function).
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- Once a chunk reaches the desired size, make it its piece of text and create a new chunk with some overlap to maintain context.
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**Separator used**:
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```txt
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.
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```
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**Chunk size used**:
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```txt
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2000
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```
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**Chunk overlap used**:
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```txt
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200
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```
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<br>
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For the example, we used `OpenAI` as the LLM, but you can use any LLM that has an API. Make sure to get the API key from the LLM provider. For example, [OpenAI](https://platform.openai.com/){.internal-link target=_blank} requires you to create an account to get your API key.
|
||||
|
||||
<br>
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||||
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Check out the [OpenAI](https://platform.openai.com/docs/introduction/overview){.internal-link target=\_blank} documentation to learn more about the API and the options that contain in the node.
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<br>
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The `OpenAIEmbeddings`, wrapper around [OpenAI Embeddings](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings){.internal-link target=\_blank} models. Make sure to get the API key from the LLM provider, in this case [OpenAI](https://platform.openai.com/){.internal-link target=\_blank}.
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<br>
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`Chroma` vector databases can be used as vector stores to conduct a semantic search or to select examples, thanks to a wrapper around them.
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<br>
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A `VectorStoreInfo` set information about the vector store, such as the name and description.
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<br>
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**Name used**:
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```txt
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organizations-100
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```
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**Description used**:
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```txt
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A table contains 100 companies.
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```
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The `VectoStoreAgent`is an agent designed to retrieve information from one or more vector stores, either with or without sources.
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Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.
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|
||||
Last year COVID-19 kept us apart. This year we are finally together again.
|
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|
||||
Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans.
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With a duty to one another to the American people to the Constitution.
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||||
And with an unwavering resolve that freedom will always triumph over tyranny.
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Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated.
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He thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined.
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He met the Ukrainian people.
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From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.
|
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|
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Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland.
|
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|
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In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight.
|
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|
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Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world.
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|
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Please rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people.
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Throughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos.
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They keep moving.
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And the costs and the threats to America and the world keep rising.
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That’s why the NATO Alliance was created to secure peace and stability in Europe after World War 2.
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The United States is a member along with 29 other nations.
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It matters. American diplomacy matters. American resolve matters.
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Putin’s latest attack on Ukraine was premeditated and unprovoked.
|
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|
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He rejected repeated efforts at diplomacy.
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He thought the West and NATO wouldn’t respond. And he thought he could divide us at home. Putin was wrong. We were ready. Here is what we did.
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We prepared extensively and carefully.
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|
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We spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin.
|
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|
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I spent countless hours unifying our European allies. We shared with the world in advance what we knew Putin was planning and precisely how he would try to falsely justify his aggression.
|
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We countered Russia’s lies with truth.
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And now that he has acted the free world is holding him accountable.
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Along with twenty-seven members of the European Union including France, Germany, Italy, as well as countries like the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.
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|
||||
We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever.
|
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|
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Together with our allies –we are right now enforcing powerful economic sanctions.
|
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|
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We are cutting off Russia’s largest banks from the international financial system.
|
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Preventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless.
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|
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We are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come.
|
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|
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Tonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more.
|
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|
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The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs.
|
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|
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We are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains.
|
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|
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And tonight I am announcing that we will join our allies in closing off American air space to all Russian flights – further isolating Russia – and adding an additional squeeze –on their economy. The Ruble has lost 30% of its value.
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|
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The Russian stock market has lost 40% of its value and trading remains suspended. Russia’s economy is reeling and Putin alone is to blame.
|
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|
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Together with our allies we are providing support to the Ukrainians in their fight for freedom. Military assistance. Economic assistance. Humanitarian assistance.
|
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|
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We are giving more than $1 Billion in direct assistance to Ukraine.
|
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|
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And we will continue to aid the Ukrainian people as they defend their country and to help ease their suffering.
|
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|
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Let me be clear, our forces are not engaged and will not engage in conflict with Russian forces in Ukraine.
|
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|
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Our forces are not going to Europe to fight in Ukraine, but to defend our NATO Allies – in the event that Putin decides to keep moving west.
|
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|
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For that purpose we’ve mobilized American ground forces, air squadrons, and ship deployments to protect NATO countries including Poland, Romania, Latvia, Lithuania, and Estonia.
|
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|
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As I have made crystal clear the United States and our Allies will defend every inch of territory of NATO countries with the full force of our collective power.
|
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|
||||
And we remain clear-eyed. The Ukrainians are fighting back with pure courage. But the next few days weeks, months, will be hard on them.
|
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|
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Putin has unleashed violence and chaos. But while he may make gains on the battlefield – he will pay a continuing high price over the long run.
|
||||
|
||||
And a proud Ukrainian people, who have known 30 years of independence, have repeatedly shown that they will not tolerate anyone who tries to take their country backwards.
|
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|
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To all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world.
|
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|
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And I’m taking robust action to make sure the pain of our sanctions is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers.
|
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|
||||
Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world.
|
||||
|
||||
America will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies.
|
||||
|
||||
These steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming.
|
||||
|
||||
But I want you to know that we are going to be okay.
|
||||
|
||||
When the history of this era is written Putin’s war on Ukraine will have left Russia weaker and the rest of the world stronger.
|
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|
||||
While it shouldn’t have taken something so terrible for people around the world to see what’s at stake now everyone sees it clearly.
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|
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We see the unity among leaders of nations and a more unified Europe a more unified West. And we see unity among the people who are gathering in cities in large crowds around the world even in Russia to demonstrate their support for Ukraine.
|
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||||
In the battle between democracy and autocracy, democracies are rising to the moment, and the world is clearly choosing the side of peace and security.
|
||||
|
||||
This is a real test. It’s going to take time. So let us continue to draw inspiration from the iron will of the Ukrainian people.
|
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To our fellow Ukrainian Americans who forge a deep bond that connects our two nations we stand with you.
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||||
Putin may circle Kyiv with tanks, but he will never gain the hearts and souls of the Ukrainian people.
|
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||||
He will never extinguish their love of freedom. He will never weaken the resolve of the free world.
|
||||
|
||||
We meet tonight in an America that has lived through two of the hardest years this nation has ever faced.
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|
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The pandemic has been punishing.
|
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|
||||
And so many families are living paycheck to paycheck, struggling to keep up with the rising cost of food, gas, housing, and so much more.
|
||||
|
||||
I understand.
|
||||
|
||||
I remember when my Dad had to leave our home in Scranton, Pennsylvania to find work. I grew up in a family where if the price of food went up, you felt it.
|
||||
|
||||
That’s why one of the first things I did as President was fight to pass the American Rescue Plan.
|
||||
|
||||
Because people were hurting. We needed to act, and we did.
|
||||
|
||||
Few pieces of legislation have done more in a critical moment in our history to lift us out of crisis.
|
||||
|
||||
It fueled our efforts to vaccinate the nation and combat COVID-19. It delivered immediate economic relief for tens of millions of Americans.
|
||||
|
||||
Helped put food on their table, keep a roof over their heads, and cut the cost of health insurance.
|
||||
|
||||
And as my Dad used to say, it gave people a little breathing room.
|
||||
|
||||
And unlike the $2 Trillion tax cut passed in the previous administration that benefitted the top 1% of Americans, the American Rescue Plan helped working people—and left no one behind.
|
||||
|
||||
And it worked. It created jobs. Lots of jobs.
|
||||
|
||||
In fact—our economy created over 6.5 Million new jobs just last year, more jobs created in one year
|
||||
than ever before in the history of America.
|
||||
|
||||
Our economy grew at a rate of 5.7% last year, the strongest growth in nearly 40 years, the first step in bringing fundamental change to an economy that hasn’t worked for the working people of this nation for too long.
|
||||
|
||||
For the past 40 years we were told that if we gave tax breaks to those at the very top, the benefits would trickle down to everyone else.
|
||||
|
||||
But that trickle-down theory led to weaker economic growth, lower wages, bigger deficits, and the widest gap between those at the top and everyone else in nearly a century.
|
||||
|
||||
Vice President Harris and I ran for office with a new economic vision for America.
|
||||
|
||||
Invest in America. Educate Americans. Grow the workforce. Build the economy from the bottom up
|
||||
and the middle out, not from the top down.
|
||||
|
||||
Because we know that when the middle class grows, the poor have a ladder up and the wealthy do very well.
|
||||
|
||||
America used to have the best roads, bridges, and airports on Earth.
|
||||
|
||||
Now our infrastructure is ranked 13th in the world.
|
||||
|
||||
We won’t be able to compete for the jobs of the 21st Century if we don’t fix that.
|
||||
|
||||
That’s why it was so important to pass the Bipartisan Infrastructure Law—the most sweeping investment to rebuild America in history.
|
||||
|
||||
This was a bipartisan effort, and I want to thank the members of both parties who worked to make it happen.
|
||||
|
||||
We’re done talking about infrastructure weeks.
|
||||
|
||||
We’re going to have an infrastructure decade.
|
||||
|
||||
It is going to transform America and put us on a path to win the economic competition of the 21st Century that we face with the rest of the world—particularly with China.
|
||||
|
||||
As I’ve told Xi Jinping, it is never a good bet to bet against the American people.
|
||||
|
||||
We’ll create good jobs for millions of Americans, modernizing roads, airports, ports, and waterways all across America.
|
||||
|
||||
And we’ll do it all to withstand the devastating effects of the climate crisis and promote environmental justice.
|
||||
|
||||
We’ll build a national network of 500,000 electric vehicle charging stations, begin to replace poisonous lead pipes—so every child—and every American—has clean water to drink at home and at school, provide affordable high-speed internet for every American—urban, suburban, rural, and tribal communities.
|
||||
|
||||
4,000 projects have already been announced.
|
||||
|
||||
And tonight, I’m announcing that this year we will start fixing over 65,000 miles of highway and 1,500 bridges in disrepair.
|
||||
|
||||
When we use taxpayer dollars to rebuild America – we are going to Buy American: buy American products to support American jobs.
|
||||
|
||||
The federal government spends about $600 Billion a year to keep the country safe and secure.
|
||||
|
||||
There’s been a law on the books for almost a century
|
||||
to make sure taxpayers’ dollars support American jobs and businesses.
|
||||
|
||||
Every Administration says they’ll do it, but we are actually doing it.
|
||||
|
||||
We will buy American to make sure everything from the deck of an aircraft carrier to the steel on highway guardrails are made in America.
|
||||
|
||||
But to compete for the best jobs of the future, we also need to level the playing field with China and other competitors.
|
||||
|
||||
That’s why it is so important to pass the Bipartisan Innovation Act sitting in Congress that will make record investments in emerging technologies and American manufacturing.
|
||||
|
||||
Let me give you one example of why it’s so important to pass it.
|
||||
|
||||
If you travel 20 miles east of Columbus, Ohio, you’ll find 1,000 empty acres of land.
|
||||
|
||||
It won’t look like much, but if you stop and look closely, you’ll see a “Field of dreams,” the ground on which America’s future will be built.
|
||||
|
||||
This is where Intel, the American company that helped build Silicon Valley, is going to build its $20 billion semiconductor “mega site”.
|
||||
|
||||
Up to eight state-of-the-art factories in one place. 10,000 new good-paying jobs.
|
||||
|
||||
Some of the most sophisticated manufacturing in the world to make computer chips the size of a fingertip that power the world and our everyday lives.
|
||||
|
||||
Smartphones. The Internet. Technology we have yet to invent.
|
||||
|
||||
But that’s just the beginning.
|
||||
|
||||
Intel’s CEO, Pat Gelsinger, who is here tonight, told me they are ready to increase their investment from
|
||||
$20 billion to $100 billion.
|
||||
|
||||
That would be one of the biggest investments in manufacturing in American history.
|
||||
|
||||
And all they’re waiting for is for you to pass this bill.
|
||||
|
||||
So let’s not wait any longer. Send it to my desk. I’ll sign it.
|
||||
|
||||
And we will really take off.
|
||||
|
||||
And Intel is not alone.
|
||||
|
||||
There’s something happening in America.
|
||||
|
||||
Just look around and you’ll see an amazing story.
|
||||
|
||||
The rebirth of the pride that comes from stamping products “Made In America.” The revitalization of American manufacturing.
|
||||
|
||||
Companies are choosing to build new factories here, when just a few years ago, they would have built them overseas.
|
||||
|
||||
That’s what is happening. Ford is investing $11 billion to build electric vehicles, creating 11,000 jobs across the country.
|
||||
|
||||
GM is making the largest investment in its history—$7 billion to build electric vehicles, creating 4,000 jobs in Michigan.
|
||||
|
||||
All told, we created 369,000 new manufacturing jobs in America just last year.
|
||||
|
||||
Powered by people I’ve met like JoJo Burgess, from generations of union steelworkers from Pittsburgh, who’s here with us tonight.
|
||||
|
||||
As Ohio Senator Sherrod Brown says, “It’s time to bury the label “Rust Belt.”
|
||||
|
||||
It’s time.
|
||||
|
||||
But with all the bright spots in our economy, record job growth and higher wages, too many families are struggling to keep up with the bills.
|
||||
|
||||
Inflation is robbing them of the gains they might otherwise feel.
|
||||
|
||||
I get it. That’s why my top priority is getting prices under control.
|
||||
|
||||
Look, our economy roared back faster than most predicted, but the pandemic meant that businesses had a hard time hiring enough workers to keep up production in their factories.
|
||||
|
||||
The pandemic also disrupted global supply chains.
|
||||
|
||||
When factories close, it takes longer to make goods and get them from the warehouse to the store, and prices go up.
|
||||
|
||||
Look at cars.
|
||||
|
||||
Last year, there weren’t enough semiconductors to make all the cars that people wanted to buy.
|
||||
|
||||
And guess what, prices of automobiles went up.
|
||||
|
||||
So—we have a choice.
|
||||
|
||||
One way to fight inflation is to drive down wages and make Americans poorer.
|
||||
|
||||
I have a better plan to fight inflation.
|
||||
|
||||
Lower your costs, not your wages.
|
||||
|
||||
Make more cars and semiconductors in America.
|
||||
|
||||
More infrastructure and innovation in America.
|
||||
|
||||
More goods moving faster and cheaper in America.
|
||||
|
||||
More jobs where you can earn a good living in America.
|
||||
|
||||
And instead of relying on foreign supply chains, let’s make it in America.
|
||||
|
||||
Economists call it “increasing the productive capacity of our economy.”
|
||||
|
||||
I call it building a better America.
|
||||
|
||||
My plan to fight inflation will lower your costs and lower the deficit.
|
||||
|
||||
17 Nobel laureates in economics say my plan will ease long-term inflationary pressures. Top business leaders and most Americans support my plan. And here’s the plan:
|
||||
|
||||
First – cut the cost of prescription drugs. Just look at insulin. One in ten Americans has diabetes. In Virginia, I met a 13-year-old boy named Joshua Davis.
|
||||
|
||||
He and his Dad both have Type 1 diabetes, which means they need insulin every day. Insulin costs about $10 a vial to make.
|
||||
|
||||
But drug companies charge families like Joshua and his Dad up to 30 times more. I spoke with Joshua’s mom.
|
||||
|
||||
Imagine what it’s like to look at your child who needs insulin and have no idea how you’re going to pay for it.
|
||||
|
||||
What it does to your dignity, your ability to look your child in the eye, to be the parent you expect to be.
|
||||
|
||||
Joshua is here with us tonight. Yesterday was his birthday. Happy birthday, buddy.
|
||||
|
||||
For Joshua, and for the 200,000 other young people with Type 1 diabetes, let’s cap the cost of insulin at $35 a month so everyone can afford it.
|
||||
|
||||
Drug companies will still do very well. And while we’re at it let Medicare negotiate lower prices for prescription drugs, like the VA already does.
|
||||
|
||||
Look, the American Rescue Plan is helping millions of families on Affordable Care Act plans save $2,400 a year on their health care premiums. Let’s close the coverage gap and make those savings permanent.
|
||||
|
||||
Second – cut energy costs for families an average of $500 a year by combatting climate change.
|
||||
|
||||
Let’s provide investments and tax credits to weatherize your homes and businesses to be energy efficient and you get a tax credit; double America’s clean energy production in solar, wind, and so much more; lower the price of electric vehicles, saving you another $80 a month because you’ll never have to pay at the gas pump again.
|
||||
|
||||
Third – cut the cost of child care. Many families pay up to $14,000 a year for child care per child.
|
||||
|
||||
Middle-class and working families shouldn’t have to pay more than 7% of their income for care of young children.
|
||||
|
||||
My plan will cut the cost in half for most families and help parents, including millions of women, who left the workforce during the pandemic because they couldn’t afford child care, to be able to get back to work.
|
||||
|
||||
My plan doesn’t stop there. It also includes home and long-term care. More affordable housing. And Pre-K for every 3- and 4-year-old.
|
||||
|
||||
All of these will lower costs.
|
||||
|
||||
And under my plan, nobody earning less than $400,000 a year will pay an additional penny in new taxes. Nobody.
|
||||
|
||||
The one thing all Americans agree on is that the tax system is not fair. We have to fix it.
|
||||
|
||||
I’m not looking to punish anyone. But let’s make sure corporations and the wealthiest Americans start paying their fair share.
|
||||
|
||||
Just last year, 55 Fortune 500 corporations earned $40 billion in profits and paid zero dollars in federal income tax.
|
||||
|
||||
That’s simply not fair. That’s why I’ve proposed a 15% minimum tax rate for corporations.
|
||||
|
||||
We got more than 130 countries to agree on a global minimum tax rate so companies can’t get out of paying their taxes at home by shipping jobs and factories overseas.
|
||||
|
||||
That’s why I’ve proposed closing loopholes so the very wealthy don’t pay a lower tax rate than a teacher or a firefighter.
|
||||
|
||||
So that’s my plan. It will grow the economy and lower costs for families.
|
||||
|
||||
So what are we waiting for? Let’s get this done. And while you’re at it, confirm my nominees to the Federal Reserve, which plays a critical role in fighting inflation.
|
||||
|
||||
My plan will not only lower costs to give families a fair shot, it will lower the deficit.
|
||||
|
||||
The previous Administration not only ballooned the deficit with tax cuts for the very wealthy and corporations, it undermined the watchdogs whose job was to keep pandemic relief funds from being wasted.
|
||||
|
||||
But in my administration, the watchdogs have been welcomed back.
|
||||
|
||||
We’re going after the criminals who stole billions in relief money meant for small businesses and millions of Americans.
|
||||
|
||||
And tonight, I’m announcing that the Justice Department will name a chief prosecutor for pandemic fraud.
|
||||
|
||||
By the end of this year, the deficit will be down to less than half what it was before I took office.
|
||||
|
||||
The only president ever to cut the deficit by more than one trillion dollars in a single year.
|
||||
|
||||
Lowering your costs also means demanding more competition.
|
||||
|
||||
I’m a capitalist, but capitalism without competition isn’t capitalism.
|
||||
|
||||
It’s exploitation—and it drives up prices.
|
||||
|
||||
When corporations don’t have to compete, their profits go up, your prices go up, and small businesses and family farmers and ranchers go under.
|
||||
|
||||
We see it happening with ocean carriers moving goods in and out of America.
|
||||
|
||||
During the pandemic, these foreign-owned companies raised prices by as much as 1,000% and made record profits.
|
||||
|
||||
Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers.
|
||||
|
||||
And as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.
|
||||
|
||||
That ends on my watch.
|
||||
|
||||
Medicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect.
|
||||
|
||||
We’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees.
|
||||
|
||||
Let’s pass the Paycheck Fairness Act and paid leave.
|
||||
|
||||
Raise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty.
|
||||
|
||||
Let’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.
|
||||
|
||||
And let’s pass the PRO Act when a majority of workers want to form a union—they shouldn’t be stopped.
|
||||
|
||||
When we invest in our workers, when we build the economy from the bottom up and the middle out together, we can do something we haven’t done in a long time: build a better America.
|
||||
|
||||
For more than two years, COVID-19 has impacted every decision in our lives and the life of the nation.
|
||||
|
||||
And I know you’re tired, frustrated, and exhausted.
|
||||
|
||||
But I also know this.
|
||||
|
||||
Because of the progress we’ve made, because of your resilience and the tools we have, tonight I can say
|
||||
we are moving forward safely, back to more normal routines.
|
||||
|
||||
We’ve reached a new moment in the fight against COVID-19, with severe cases down to a level not seen since last July.
|
||||
|
||||
Just a few days ago, the Centers for Disease Control and Prevention—the CDC—issued new mask guidelines.
|
||||
|
||||
Under these new guidelines, most Americans in most of the country can now be mask free.
|
||||
|
||||
And based on the projections, more of the country will reach that point across the next couple of weeks.
|
||||
|
||||
Thanks to the progress we have made this past year, COVID-19 need no longer control our lives.
|
||||
|
||||
I know some are talking about “living with COVID-19”. Tonight – I say that we will never just accept living with COVID-19.
|
||||
|
||||
We will continue to combat the virus as we do other diseases. And because this is a virus that mutates and spreads, we will stay on guard.
|
||||
|
||||
Here are four common sense steps as we move forward safely.
|
||||
|
||||
First, stay protected with vaccines and treatments. We know how incredibly effective vaccines are. If you’re vaccinated and boosted you have the highest degree of protection.
|
||||
|
||||
We will never give up on vaccinating more Americans. Now, I know parents with kids under 5 are eager to see a vaccine authorized for their children.
|
||||
|
||||
The scientists are working hard to get that done and we’ll be ready with plenty of vaccines when they do.
|
||||
|
||||
We’re also ready with anti-viral treatments. If you get COVID-19, the Pfizer pill reduces your chances of ending up in the hospital by 90%.
|
||||
|
||||
We’ve ordered more of these pills than anyone in the world. And Pfizer is working overtime to get us 1 Million pills this month and more than double that next month.
|
||||
|
||||
And we’re launching the “Test to Treat” initiative so people can get tested at a pharmacy, and if they’re positive, receive antiviral pills on the spot at no cost.
|
||||
|
||||
If you’re immunocompromised or have some other vulnerability, we have treatments and free high-quality masks.
|
||||
|
||||
We’re leaving no one behind or ignoring anyone’s needs as we move forward.
|
||||
|
||||
And on testing, we have made hundreds of millions of tests available for you to order for free.
|
||||
|
||||
Even if you already ordered free tests tonight, I am announcing that you can order more from covidtests.gov starting next week.
|
||||
|
||||
Second – we must prepare for new variants. Over the past year, we’ve gotten much better at detecting new variants.
|
||||
|
||||
If necessary, we’ll be able to deploy new vaccines within 100 days instead of many more months or years.
|
||||
|
||||
And, if Congress provides the funds we need, we’ll have new stockpiles of tests, masks, and pills ready if needed.
|
||||
|
||||
I cannot promise a new variant won’t come. But I can promise you we’ll do everything within our power to be ready if it does.
|
||||
|
||||
Third – we can end the shutdown of schools and businesses. We have the tools we need.
|
||||
|
||||
It’s time for Americans to get back to work and fill our great downtowns again. People working from home can feel safe to begin to return to the office.
|
||||
|
||||
We’re doing that here in the federal government. The vast majority of federal workers will once again work in person.
|
||||
|
||||
Our schools are open. Let’s keep it that way. Our kids need to be in school.
|
||||
|
||||
And with 75% of adult Americans fully vaccinated and hospitalizations down by 77%, most Americans can remove their masks, return to work, stay in the classroom, and move forward safely.
|
||||
|
||||
We achieved this because we provided free vaccines, treatments, tests, and masks.
|
||||
|
||||
Of course, continuing this costs money.
|
||||
|
||||
I will soon send Congress a request.
|
||||
|
||||
The vast majority of Americans have used these tools and may want to again, so I expect Congress to pass it quickly.
|
||||
|
||||
Fourth, we will continue vaccinating the world.
|
||||
|
||||
We’ve sent 475 Million vaccine doses to 112 countries, more than any other nation.
|
||||
|
||||
And we won’t stop.
|
||||
|
||||
We have lost so much to COVID-19. Time with one another. And worst of all, so much loss of life.
|
||||
|
||||
Let’s use this moment to reset. Let’s stop looking at COVID-19 as a partisan dividing line and see it for what it is: A God-awful disease.
|
||||
|
||||
Let’s stop seeing each other as enemies, and start seeing each other for who we really are: Fellow Americans.
|
||||
|
||||
We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together.
|
||||
|
||||
I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera.
|
||||
|
||||
They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun.
|
||||
|
||||
Officer Mora was 27 years old.
|
||||
|
||||
Officer Rivera was 22.
|
||||
|
||||
Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers.
|
||||
|
||||
I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves.
|
||||
|
||||
I’ve worked on these issues a long time.
|
||||
|
||||
I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.
|
||||
|
||||
So let’s not abandon our streets. Or choose between safety and equal justice.
|
||||
|
||||
Let’s come together to protect our communities, restore trust, and hold law enforcement accountable.
|
||||
|
||||
That’s why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers.
|
||||
|
||||
That’s why the American Rescue Plan provided $350 Billion that cities, states, and counties can use to hire more police and invest in proven strategies like community violence interruption—trusted messengers breaking the cycle of violence and trauma and giving young people hope.
|
||||
|
||||
We should all agree: The answer is not to Defund the police. The answer is to FUND the police with the resources and training they need to protect our communities.
|
||||
|
||||
I ask Democrats and Republicans alike: Pass my budget and keep our neighborhoods safe.
|
||||
|
||||
And I will keep doing everything in my power to crack down on gun trafficking and ghost guns you can buy online and make at home—they have no serial numbers and can’t be traced.
|
||||
|
||||
And I ask Congress to pass proven measures to reduce gun violence. Pass universal background checks. Why should anyone on a terrorist list be able to purchase a weapon?
|
||||
|
||||
Ban assault weapons and high-capacity magazines.
|
||||
|
||||
Repeal the liability shield that makes gun manufacturers the only industry in America that can’t be sued.
|
||||
|
||||
These laws don’t infringe on the Second Amendment. They save lives.
|
||||
|
||||
The most fundamental right in America is the right to vote – and to have it counted. And it’s under assault.
|
||||
|
||||
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
|
||||
|
||||
We cannot let this happen.
|
||||
|
||||
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
|
||||
|
||||
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
|
||||
|
||||
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
|
||||
|
||||
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
|
||||
|
||||
A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.
|
||||
|
||||
And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.
|
||||
|
||||
We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.
|
||||
|
||||
We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.
|
||||
|
||||
We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster.
|
||||
|
||||
We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
|
||||
|
||||
We can do all this while keeping lit the torch of liberty that has led generations of immigrants to this land—my forefathers and so many of yours.
|
||||
|
||||
Provide a pathway to citizenship for Dreamers, those on temporary status, farm workers, and essential workers.
|
||||
|
||||
Revise our laws so businesses have the workers they need and families don’t wait decades to reunite.
|
||||
|
||||
It’s not only the right thing to do—it’s the economically smart thing to do.
|
||||
|
||||
That’s why immigration reform is supported by everyone from labor unions to religious leaders to the U.S. Chamber of Commerce.
|
||||
|
||||
Let’s get it done once and for all.
|
||||
|
||||
Advancing liberty and justice also requires protecting the rights of women.
|
||||
|
||||
The constitutional right affirmed in Roe v. Wade—standing precedent for half a century—is under attack as never before.
|
||||
|
||||
If we want to go forward—not backward—we must protect access to health care. Preserve a woman’s right to choose. And let’s continue to advance maternal health care in America.
|
||||
|
||||
And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong.
|
||||
|
||||
As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential.
|
||||
|
||||
While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.
|
||||
|
||||
And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things.
|
||||
|
||||
So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.
|
||||
|
||||
First, beat the opioid epidemic.
|
||||
|
||||
There is so much we can do. Increase funding for prevention, treatment, harm reduction, and recovery.
|
||||
|
||||
Get rid of outdated rules that stop doctors from prescribing treatments. And stop the flow of illicit drugs by working with state and local law enforcement to go after traffickers.
|
||||
|
||||
If you’re suffering from addiction, know you are not alone. I believe in recovery, and I celebrate the 23 million Americans in recovery.
|
||||
|
||||
Second, let’s take on mental health. Especially among our children, whose lives and education have been turned upside down.
|
||||
|
||||
The American Rescue Plan gave schools money to hire teachers and help students make up for lost learning.
|
||||
|
||||
I urge every parent to make sure your school does just that. And we can all play a part—sign up to be a tutor or a mentor.
|
||||
|
||||
Children were also struggling before the pandemic. Bullying, violence, trauma, and the harms of social media.
|
||||
|
||||
As Frances Haugen, who is here with us tonight, has shown, we must hold social media platforms accountable for the national experiment they’re conducting on our children for profit.
|
||||
|
||||
It’s time to strengthen privacy protections, ban targeted advertising to children, demand tech companies stop collecting personal data on our children.
|
||||
|
||||
And let’s get all Americans the mental health services they need. More people they can turn to for help, and full parity between physical and mental health care.
|
||||
|
||||
Third, support our veterans.
|
||||
|
||||
Veterans are the best of us.
|
||||
|
||||
I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home.
|
||||
|
||||
My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free.
|
||||
|
||||
Our troops in Iraq and Afghanistan faced many dangers.
|
||||
|
||||
One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more.
|
||||
|
||||
When they came home, many of the world’s fittest and best trained warriors were never the same.
|
||||
|
||||
Headaches. Numbness. Dizziness.
|
||||
|
||||
A cancer that would put them in a flag-draped coffin.
|
||||
|
||||
I know.
|
||||
|
||||
One of those soldiers was my son Major Beau Biden.
|
||||
|
||||
We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops.
|
||||
|
||||
But I’m committed to finding out everything we can.
|
||||
|
||||
Committed to military families like Danielle Robinson from Ohio.
|
||||
|
||||
The widow of Sergeant First Class Heath Robinson.
|
||||
|
||||
He was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq.
|
||||
|
||||
Stationed near Baghdad, just yards from burn pits the size of football fields.
|
||||
|
||||
Heath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter.
|
||||
|
||||
But cancer from prolonged exposure to burn pits ravaged Heath’s lungs and body.
|
||||
|
||||
Danielle says Heath was a fighter to the very end.
|
||||
|
||||
He didn’t know how to stop fighting, and neither did she.
|
||||
|
||||
Through her pain she found purpose to demand we do better.
|
||||
|
||||
Tonight, Danielle—we are.
|
||||
|
||||
The VA is pioneering new ways of linking toxic exposures to diseases, already helping more veterans get benefits.
|
||||
|
||||
And tonight, I’m announcing we’re expanding eligibility to veterans suffering from nine respiratory cancers.
|
||||
|
||||
I’m also calling on Congress: pass a law to make sure veterans devastated by toxic exposures in Iraq and Afghanistan finally get the benefits and comprehensive health care they deserve.
|
||||
|
||||
And fourth, let’s end cancer as we know it.
|
||||
|
||||
This is personal to me and Jill, to Kamala, and to so many of you.
|
||||
|
||||
Cancer is the #2 cause of death in America–second only to heart disease.
|
||||
|
||||
Last month, I announced our plan to supercharge
|
||||
the Cancer Moonshot that President Obama asked me to lead six years ago.
|
||||
|
||||
Our goal is to cut the cancer death rate by at least 50% over the next 25 years, turn more cancers from death sentences into treatable diseases.
|
||||
|
||||
More support for patients and families.
|
||||
|
||||
To get there, I call on Congress to fund ARPA-H, the Advanced Research Projects Agency for Health.
|
||||
|
||||
It’s based on DARPA—the Defense Department project that led to the Internet, GPS, and so much more.
|
||||
|
||||
ARPA-H will have a singular purpose—to drive breakthroughs in cancer, Alzheimer’s, diabetes, and more.
|
||||
|
||||
A unity agenda for the nation.
|
||||
|
||||
We can do this.
|
||||
|
||||
My fellow Americans—tonight , we have gathered in a sacred space—the citadel of our democracy.
|
||||
|
||||
In this Capitol, generation after generation, Americans have debated great questions amid great strife, and have done great things.
|
||||
|
||||
We have fought for freedom, expanded liberty, defeated totalitarianism and terror.
|
||||
|
||||
And built the strongest, freest, and most prosperous nation the world has ever known.
|
||||
|
||||
Now is the hour.
|
||||
|
||||
Our moment of responsibility.
|
||||
|
||||
Our test of resolve and conscience, of history itself.
|
||||
|
||||
It is in this moment that our character is formed. Our purpose is found. Our future is forged.
|
||||
|
||||
Well I know this nation.
|
||||
|
||||
We will meet the test.
|
||||
|
||||
To protect freedom and liberty, to expand fairness and opportunity.
|
||||
|
||||
We will save democracy.
|
||||
|
||||
As hard as these times have been, I am more optimistic about America today than I have been my whole life.
|
||||
|
||||
Because I see the future that is within our grasp.
|
||||
|
||||
Because I know there is simply nothing beyond our capacity.
|
||||
|
||||
We are the only nation on Earth that has always turned every crisis we have faced into an opportunity.
|
||||
|
||||
The only nation that can be defined by a single word: possibilities.
|
||||
|
||||
So on this night, in our 245th year as a nation, I have come to report on the State of the Union.
|
||||
|
||||
And my report is this: the State of the Union is strong—because you, the American people, are strong.
|
||||
|
||||
We are stronger today than we were a year ago.
|
||||
|
||||
And we will be stronger a year from now than we are today.
|
||||
|
||||
Now is our moment to meet and overcome the challenges of our time.
|
||||
|
||||
And we will, as one people.
|
||||
|
||||
One America.
|
||||
|
||||
The United States of America.
|
||||
|
||||
May God bless you all. May God protect our troops.
|
||||
82
docs/docs/components/agents.mdx
Normal file
82
docs/docs/components/agents.mdx
Normal file
|
|
@ -0,0 +1,82 @@
|
|||
# Agents
|
||||
|
||||
Agents are components that use reasoning to make decisions and take actions, designed to autonomously perform tasks or provide services with some degree of “freedom” (or agency). They combine the power of LLM chaining processes with access to external tools such as APIs to interact with applications and accomplish tasks.
|
||||
|
||||
---
|
||||
|
||||
### AgentInitializer
|
||||
|
||||
The `AgentInitializer` component is a quick way to construct a zero-shot agent from a language model (LLM) and tools.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the `AgentInitializer`.
|
||||
- **Memory:** Used to add memory functionality to an agent. It allows the agent to store and retrieve information from previous conversations.
|
||||
- **Tools:** Tools that the agent will have access to.
|
||||
- **Agent:** The type of agent to be instantiated. Current supported: `zero-shot-react-description`, `react-docstore`, `self-ask-with-search,conversational-react-description` and `openai-functions`.
|
||||
|
||||
---
|
||||
|
||||
### CSVAgent
|
||||
|
||||
A `CSVAgent` is an agent that is designed to interact with CSV (Comma-Separated Values) files. CSV files are a common format for storing tabular data, where each row represents a record and each column represents a field. The CSV agent can perform various tasks, such as reading and writing CSV files, processing the data, and generating tables. It can extract information from the CSV file, manipulate the data, and perform operations like filtering, sorting, and aggregating.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the `CSVAgent`.
|
||||
- **path:** The file path to the CSV data.
|
||||
|
||||
---
|
||||
|
||||
### JSONAgent
|
||||
|
||||
The `JSONAgent` deals with JSON (JavaScript Object Notation) data. Similar to the CSVAgent, it works with a language model (LLM) and a toolkit designed for JSON manipulation. This agent can iteratively explore a JSON blob to find the information needed to answer the user's question. It can list keys, get values, and navigate through the structure of the JSON object.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the `JSONAgent`.
|
||||
- **Toolkit:** Toolkit that the agent will have access to.
|
||||
|
||||
---
|
||||
|
||||
### SQLAgent
|
||||
|
||||
A `SQLAgent` is an agent that is designed to interact with SQL databases. It is capable of performing various tasks, such as querying the database, retrieving data, and executing SQL statements. The agent can provide information about the structure of the database, including the tables and their schemas. It can also perform operations like inserting, updating, and deleting data in the database. The SQL agent is a helpful tool for managing and working with SQL databases efficiently.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the `SQLAgent`.
|
||||
- **database_uri:** A string representing the connection URI for the SQL database.
|
||||
|
||||
---
|
||||
|
||||
### VectorStoreAgent
|
||||
|
||||
The `VectorStoreAgent` is designed to work with a vector store – a data structure used for storing and querying vector-based representations of data. The `VectorStoreAgent` can query the vector store to find relevant information based on user inputs.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the `VectorStoreAgent`.
|
||||
- **Vector Store Info:** `VectorStoreInfo` to use in the `VectorStoreAgent`.
|
||||
|
||||
---
|
||||
|
||||
### VectorStoreRouterAgent
|
||||
|
||||
The `VectorStoreRouterAgent` is a custom agent that takes a vector store router as input. It is typically used when there’s a need to retrieve information from multiple vector stores. These can be connected through a `VectorStoreRouterToolkit` and sent over to the `VectorStoreRouterAgent`. An agent configured with multiple vector stores can route queries to the appropriate store based on the context.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the `VectorStoreRouterAgent`.
|
||||
- **Vector Store Router Toolkit:** `VectorStoreRouterToolkit` to use in the `VectorStoreRouterAgent`.
|
||||
|
||||
---
|
||||
|
||||
### ZeroShotAgent
|
||||
|
||||
The `ZeroShotAgent` is an agent that uses the ReAct framework to determine which tool to use based solely on the tool's description. It can be configured with any number of tools and requires a description for each tool. The agent is designed to be the most general-purpose action agent. It uses an `LLMChain` to determine which actions to take and in what order.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Allowed Tools:** Tools that the agent will have access to.
|
||||
- **LLM Chain:** LLM Chain to be used by the agent.
|
||||
137
docs/docs/components/chains.mdx
Normal file
137
docs/docs/components/chains.mdx
Normal file
|
|
@ -0,0 +1,137 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
# Chains
|
||||
|
||||
Chains, in the context of language models, refer to a series of calls made to a language model. It allows for the output of one call to be used as the input for another call. Different types of chains allow for different levels of complexity. Chains are useful for creating pipelines and executing specific scenarios.
|
||||
|
||||
---
|
||||
|
||||
### CombineDocsChain
|
||||
|
||||
The `CombineDocsChain` incorporates methods to combine or aggregate loaded documents for question-answering functionality.
|
||||
|
||||
:::info
|
||||
|
||||
Works as a proxy of LangChain’s [documents](https://python.langchain.com/docs/modules/chains/document/) chains generated by the `load_qa_chain` function.
|
||||
|
||||
:::
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the chain.
|
||||
- **chain_type:** The chain type to be used. Each one of them applies a different “combination strategy”.
|
||||
- **stuff**: The stuff [documents](https://python.langchain.com/docs/modules/chains/document/stuff) chain (“stuff" as in "to stuff" or "to fill") is the most straightforward of *the* document chains. It takes a list of documents, inserts them all into a prompt, and passes that prompt to an LLM. This chain is well-suited for applications where documents are small and only a few are passed in for most calls.
|
||||
- **map_reduce**: The map-reduce [documents](https://python.langchain.com/docs/modules/chains/document/map_reduce) chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. It then passes all the new documents to a separate combined documents chain to get a single output (the Reduce step). It can optionally first compress or collapse the mapped documents to make sure that they fit in the combined documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.
|
||||
- **map_rerank**: The map re-rank [documents](https://python.langchain.com/docs/modules/chains/document/map_rerank) chain runs an initial prompt on each document that not only tries to complete a task but also gives a score for how certain it is in its answer. The highest-scoring response is returned.
|
||||
- **refine**: The refine [documents](https://python.langchain.com/docs/modules/chains/document/refine) chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.
|
||||
|
||||
Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context. The obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain. There are also certain tasks that are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents.
|
||||
|
||||
---
|
||||
|
||||
### ConversationChain
|
||||
|
||||
The `ConversationChain` is a straightforward chain for interactive conversations with a language model, making it ideal for chatbots or virtual assistants. It allows for dynamic conversations, question-answering, and complex dialogues.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the chain.
|
||||
- **Memory:** Default memory store.
|
||||
- **input_key:** Used to specify the key under which the user input will be stored in the conversation memory. It allows you to provide the user's input to the chain for processing and generating a response.
|
||||
- **output_key:** Used to specify the key under which the generated response will be stored in the conversation memory. It allows you to retrieve the response using the specified key.
|
||||
- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can be helpful for debugging and understanding the chain's behavior. If set to False, it will suppress the verbose output — defaults to `False`.
|
||||
|
||||
---
|
||||
|
||||
### ConversationalRetrievalChain
|
||||
|
||||
The `ConversationalRetrievalChain` extracts information and provides answers by combining document search and question-answering abilities.
|
||||
|
||||
:::info
|
||||
|
||||
A retriever is a component that finds documents based on a query. It doesn't store the documents themselves, but it returns the ones that match the query.
|
||||
|
||||
:::
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the chain.
|
||||
- **Memory:** Default memory store.
|
||||
- **Retriever:** The retriever used to fetch relevant documents.
|
||||
- **chain_type:** The chain type to be used. Each one of them applies a different “combination strategy”.
|
||||
- **stuff**: The stuff [documents](https://python.langchain.com/docs/modules/chains/document/stuff) chain (“stuff" as in "to stuff" or "to fill") is the most straightforward of *the* document chains. It takes a list of documents, inserts them all into a prompt, and passes that prompt to an LLM. This chain is well-suited for applications where documents are small and only a few are passed in for most calls.
|
||||
- **map_reduce**: The map-reduce [documents](https://python.langchain.com/docs/modules/chains/document/map_reduce) chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. It then passes all the new documents to a separate combined documents chain to get a single output (the Reduce step). It can optionally first compress or collapse the mapped documents to make sure that they fit in the combined documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.
|
||||
- **map_rerank**: The map re-rank [documents](https://python.langchain.com/docs/modules/chains/document/map_rerank) chain runs an initial prompt on each document that not only tries to complete a task but also gives a score for how certain it is in its answer. The highest-scoring response is returned.
|
||||
- **refine**: The refine [documents](https://python.langchain.com/docs/modules/chains/document/refine) chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.
|
||||
|
||||
Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context. The obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain. There are also certain tasks that are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents.
|
||||
|
||||
- **return_source_documents:** Used to specify whether or not to include the source documents that were used to answer the question in the output. When set to `True`, source documents will be included in the output along with the generated answer. This can be useful for providing additional context or references to the user — defaults to `True`.
|
||||
- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
|
||||
|
||||
---
|
||||
|
||||
### LLMChain
|
||||
|
||||
The `LLMChain` is a straightforward chain that adds functionality around language models. It combines a prompt template with a language model. To use it, create input variables to format the prompt template. The formatted prompt is then sent to the language model, and the generated output is returned as the result of the `LLMChain`.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the chain.
|
||||
- **Memory:** Default memory store.
|
||||
- **Prompt**: Prompt template object to use in the chain.
|
||||
- **output_key:** This parameter is used to specify which key in the LLM output dictionary should be returned as the final output. By default, the `LLMChain` returns both the input and output key values — defaults to `text`.
|
||||
- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
|
||||
|
||||
---
|
||||
|
||||
### LLMMathChain
|
||||
|
||||
The `LLMMathChain` combines a language model (LLM) and a math calculation component. It allows the user to input math problems and get the corresponding solutions.
|
||||
|
||||
The `LLMMathChain` works by using the language model with an `LLMChain` to understand the input math problem and generate a math expression. It then passes this expression to the math component, which evaluates it and returns the result.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the chain.
|
||||
- **LLMChain:** LLM Chain to use in the chain.
|
||||
- **Memory:** Default memory store.
|
||||
- **input_key:** Used to specify the input value for the mathematical calculation. It allows you to provide the specific values or variables that you want to use in the calculation — defaults to `question`.
|
||||
- **output_key:** Used to specify the key under which the output of the mathematical calculation will be stored. It allows you to retrieve the result of the calculation using the specified key — defaults to `answer`.
|
||||
- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
|
||||
|
||||
---
|
||||
|
||||
### RetrievalQA
|
||||
|
||||
`RetrievalQA` is a chain used to find relevant documents or information to answer a given query. The retriever is responsible for returning the relevant documents based on the query, and the QA component then extracts the answer from those documents. The retrieval QA system combines the capabilities of both the retriever and the QA component to provide accurate and relevant answers to user queries.
|
||||
|
||||
:::info
|
||||
|
||||
A retriever is a component that finds documents based on a query. It doesn't store the documents themselves, but it returns the ones that match the query.
|
||||
|
||||
:::
|
||||
|
||||
**Params**
|
||||
|
||||
- **Combine Documents Chain:** Chain to use to combine the documents.
|
||||
- **Memory:** Default memory store.
|
||||
- **Retriever:** The retriever used to fetch relevant documents.
|
||||
- **input_key:** This parameter is used to specify the key in the input data that contains the question. It is used to retrieve the question from the input data and pass it to the question-answering model for generating the answer — defaults to `query`.
|
||||
- **output_key:** This parameter is used to specify the key in the output data where the generated answer will be stored. It is used to retrieve the answer from the output data after the question-answering model has generated it — defaults to `result`.
|
||||
- **return_source_documents:** Used to specify whether or not to include the source documents that were used to answer the question in the output. When set to `True`, source documents will be included in the output along with the generated answer. This can be useful for providing additional context or references to the user — defaults to `True`.
|
||||
- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
|
||||
|
||||
---
|
||||
|
||||
### SQLDatabaseChain
|
||||
|
||||
The `SQLDatabaseChain` finds answers to questions using a SQL database. It works by using the language model to understand the SQL query and generate the corresponding SQL code. It then passes the SQL code to the SQL database component, which executes the query on the database and returns the result.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Db:** SQL Database to connect to.
|
||||
- **LLM:** Language Model to use in the chain.
|
||||
- **Prompt:** Prompt template to translate natural language to SQL.
|
||||
67
docs/docs/components/embeddings.mdx
Normal file
67
docs/docs/components/embeddings.mdx
Normal file
|
|
@ -0,0 +1,67 @@
|
|||
# Embeddings
|
||||
|
||||
Embeddings are vector representations of text that capture the semantic meaning of the text. They are created using text embedding models and allow us to think about the text in a vector space, enabling us to perform tasks like semantic search, where we look for pieces of text that are most similar in the vector space.
|
||||
|
||||
---
|
||||
|
||||
### CohereEmbeddings
|
||||
|
||||
Used to load [Cohere’s](https://cohere.com/) embedding models.
|
||||
|
||||
**Params**
|
||||
|
||||
- **cohere_api_key:** Holds the API key required to authenticate with the Cohere service.
|
||||
|
||||
- **model:** The language model used for embedding text documents and performing queries —defaults to `embed-english-v2.0`.
|
||||
|
||||
- **truncate:** Used to specify whether or not to truncate the input text. Truncation is useful when dealing with long texts that exceed the model's maximum input length. By truncating the text, the user can ensure that it fits within the model's constraints.
|
||||
|
||||
---
|
||||
|
||||
### HuggingFaceEmbeddings
|
||||
|
||||
Used to load [HuggingFace’s](https://huggingface.co) embedding models.
|
||||
|
||||
**Params**
|
||||
|
||||
- **cache_folder:** Used to specify the folder where the embeddings will be cached. When embeddings are computed for a text, they can be stored in the cache folder so that they can be reused later without the need to recompute them. This can improve the performance of the application by avoiding redundant computations.
|
||||
|
||||
- **encode_kwargs:** Used to pass additional keyword arguments to the encoding method of the underlying HuggingFace model. These keyword arguments can be used to customize the encoding process, such as specifying the maximum length of the input sequence or enabling truncation or padding.
|
||||
|
||||
- **model_kwargs:** Used to customize the behavior of the model, such as specifying the model architecture, the tokenizer, or any other model-specific configuration options. By using `model_kwargs`, the user can configure the HuggingFace model according to specific needs and preferences.
|
||||
|
||||
- **model_name:** Used to specify the name or identifier of the HuggingFace model that will be used for generating embeddings. It allows users to choose a specific pre-trained model from the Hugging Face model hub — defaults to `sentence-transformers/all-mpnet-base-v2`.
|
||||
|
||||
---
|
||||
|
||||
### OpenAIEmbeddings
|
||||
|
||||
Used to load [OpenAI’s](https://openai.com/) embedding models.
|
||||
|
||||
**Params**
|
||||
|
||||
- **chunk_size:** Determines the maximum size of each chunk of text that is processed for embedding. If any of the incoming text chunks exceeds `chunk_size` characters, it will be split into multiple chunks of size `chunk_size` or less before being embedded — defaults to `1000`.
|
||||
|
||||
- **deployment:** Used to specify the deployment name or identifier of the text embedding model. It allows the user to choose a specific deployment of the model to use for embedding. When the deployment is provided, this can be useful when the user has multiple deployments of the same model with different configurations or versions — defaults to `text-embedding-ada-002`.
|
||||
|
||||
- **embedding_ctx_length:** This parameter determines the maximum context length for the text embedding model. It specifies the number of tokens that the model considers when generating embeddings for a piece of text — defaults to `8191` (this means that the model will consider up to 8191 tokens when generating embeddings).
|
||||
|
||||
- **max_retries:** Determines the maximum number of times to retry a request if the model provider returns an error from their API — defaults to `6`.
|
||||
|
||||
- **model:** Defines which pre-trained text embedding model to use — defaults to `text-embedding-ada-002`.
|
||||
|
||||
- **openai_api_base:** Refers to the base URL for the Azure OpenAI resource. It is used to configure the API to connect to the Azure OpenAI service. The base URL can be found in the Azure portal under the user Azure OpenAI resource.
|
||||
|
||||
- **openai_api_key:** Is used to authenticate and authorize access to the OpenAI service.
|
||||
|
||||
- **openai_api_type:** Is used to specify the type of OpenAI API being used, either the regular OpenAI API or the Azure OpenAI API. This parameter allows the `OpenAIEmbeddings` class to connect to the appropriate API service.
|
||||
|
||||
- **openai_api_version:** Is used to specify the version of the OpenAI API being used. This parameter allows the `OpenAIEmbeddings` class to connect to the appropriate version of the OpenAI API service.
|
||||
|
||||
- **openai_organization:** Is used to specify the organization associated with the OpenAI API key. If not provided, the default organization associated with the API key will be used.
|
||||
|
||||
- **openai_proxy:** Proxy enables better budgeting and cost management for making OpenAI API calls, including more transparency into pricing.
|
||||
|
||||
- **request_timeout:** Used to specify the maximum amount of time, in milliseconds, to wait for a response from the OpenAI API when generating embeddings for a given text.
|
||||
|
||||
- **tiktoken_model_name:** Used to count the number of tokens in documents to constrain them to be under a certain limit. By default, when set to None, this will be the same as the embedding model name.
|
||||
2
docs/docs/components/llms.mdx
Normal file
2
docs/docs/components/llms.mdx
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
# LLMs
|
||||
(coming soon)
|
||||
2
docs/docs/components/loaders.mdx
Normal file
2
docs/docs/components/loaders.mdx
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
# Loaders
|
||||
(coming soon)
|
||||
2
docs/docs/components/memories.mdx
Normal file
2
docs/docs/components/memories.mdx
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
# Memories
|
||||
(coming soon)
|
||||
15
docs/docs/components/prompts.mdx
Normal file
15
docs/docs/components/prompts.mdx
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
# Prompts
|
||||
|
||||
A prompt refers to the input given to a language model. It is constructed from multiple components and can be parametrized using prompt templates. A prompt template is a reproducible way to generate prompts and allow for easy customization through input variables.
|
||||
|
||||
---
|
||||
|
||||
### PromptTemplate
|
||||
|
||||
The `PromptTemplate` component allows users to create prompts and define variables that provide control over instructing the model. The template can take in a set of variables from the end user and generates the prompt once the conversation is initiated.
|
||||
|
||||
:::info
|
||||
Once a variable is defined in the prompt template, it becomes a component input of its own. Check out [Prompt Customization](../guidelines/prompt-customization.mdx) to learn more.
|
||||
:::
|
||||
|
||||
- **template:** Template used to format an individual request.
|
||||
2
docs/docs/components/text-splitters.mdx
Normal file
2
docs/docs/components/text-splitters.mdx
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
# Text Splitters
|
||||
(coming soon)
|
||||
2
docs/docs/components/toolkits.mdx
Normal file
2
docs/docs/components/toolkits.mdx
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
# Toolkits
|
||||
(coming soon)
|
||||
2
docs/docs/components/tools.mdx
Normal file
2
docs/docs/components/tools.mdx
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
# Tools
|
||||
(coming soon)
|
||||
2
docs/docs/components/utilities.mdx
Normal file
2
docs/docs/components/utilities.mdx
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
# Utilities
|
||||
(coming soon)
|
||||
2
docs/docs/components/vector-stores.mdx
Normal file
2
docs/docs/components/vector-stores.mdx
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
# Vector Stores
|
||||
(coming soon)
|
||||
2
docs/docs/components/wrappers.mdx
Normal file
2
docs/docs/components/wrappers.mdx
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
# Wrappers
|
||||
(coming soon)
|
||||
38
docs/docs/contributing/community.md
Normal file
38
docs/docs/contributing/community.md
Normal file
|
|
@ -0,0 +1,38 @@
|
|||
# Community
|
||||
|
||||
## 🤖 Join **Langflow** Discord server
|
||||
|
||||
Join us to ask questions and showcase your projects.
|
||||
|
||||
Let's bring together the building blocks of AI integration!
|
||||
|
||||
Langflow [Discord](https://discord.gg/EqksyE2EX9) server.
|
||||
|
||||
---
|
||||
|
||||
## 🐦 Stay tunned for **Langflow** on Twitter
|
||||
|
||||
Follow [@logspace_ai](https://twitter.com/logspace_ai) on **Twitter** to get the latest news about **Langflow**.
|
||||
|
||||
---
|
||||
## ⭐️ Star **Langflow** on GitHub
|
||||
|
||||
You can "star" **Langflow** in [GitHub](https://github.com/logspace-ai/langflow).
|
||||
|
||||
By adding a star, other users will be able to find it more easily and see that it has been already useful for others.
|
||||
|
||||
---
|
||||
|
||||
## 👀 Watch the GitHub repository for releases
|
||||
|
||||
You can "watch" **Langflow** in [GitHub](https://github.com/logspace-ai/langflow).
|
||||
|
||||
|
||||
If you select "Watching" instead of "Releases only" you will receive notifications when someone creates a new issue or question. You can also specify that you only want to be notified about new issues, discussions, PRs, etc.
|
||||
|
||||
|
||||
Then you can try and help them solve those questions.
|
||||
|
||||
---
|
||||
|
||||
Thanks! 🚀
|
||||
|
|
@ -1,13 +1,11 @@
|
|||
# 🚩GitHub Issues
|
||||
# GitHub Issues
|
||||
|
||||
Our [issues](https://github.com/logspace-ai/langflow/issues){.internal-link target=_blank} page is kept up to date
|
||||
Our [issues](https://github.com/logspace-ai/langflow/issues) page is kept up to date
|
||||
with bugs, improvements, and feature requests. There is a taxonomy of labels to help
|
||||
with sorting and discovery of issues of interest.
|
||||
|
||||
<br>
|
||||
|
||||
If you're looking for help with your code, consider posting a question on the
|
||||
[GitHub Discussions board](https://github.com/logspace-ai/langflow/discussions){.internal-link target=_blank}. Please
|
||||
[GitHub Discussions board](https://github.com/logspace-ai/langflow/discussions). Please
|
||||
understand that we won't be able to provide individual support via email. We
|
||||
also believe that help is much more valuable if it's **shared publicly**,
|
||||
so that more people can benefit from it.
|
||||
|
|
@ -21,10 +19,9 @@ so that more people can benefit from it.
|
|||
|
||||
- **Sharing long blocks of code or logs:** If you need to include long code,
|
||||
logs or tracebacks, you can wrap them in `<details>` and `</details>`. This
|
||||
[collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details){.internal-link target=_blank}
|
||||
so it only becomes visible on click, making the issue easier to read and follow.
|
||||
[collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details) so it only becomes visible on click, making the issue easier to read and follow.
|
||||
|
||||
### Issue labels
|
||||
|
||||
[See this page](https://github.com/logspace-ai/langflow/labels){.internal-link target=_blank} for an overview of
|
||||
the system we use to tag our issues and pull requests.
|
||||
## Issue labels
|
||||
|
||||
[See this page](https://github.com/logspace-ai/langflow/labels) for an overview of the system we use to tag our issues and pull requests.
|
||||
62
docs/docs/contributing/how-contribute.md
Normal file
62
docs/docs/contributing/how-contribute.md
Normal file
|
|
@ -0,0 +1,62 @@
|
|||
# How to contribute?
|
||||
|
||||
👋 Hello there! We welcome contributions from developers of all levels to our open-source project on [GitHub](https://github.com/logspace-ai/langflow). If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.
|
||||
|
||||
As an open-source project in a rapidly developing field, we are extremely open
|
||||
to contributions, whether in the form of a new feature, improved infra, or better documentation.
|
||||
|
||||
To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
|
||||
|
||||
Please do not try to push directly to this repo unless you are a maintainer.
|
||||
|
||||
---
|
||||
## Local development
|
||||
|
||||
You can develop Langflow using docker compose, or locally.
|
||||
|
||||
We provide a .vscode/launch.json file for debugging the backend in VSCode, which is a lot faster than using docker compose.
|
||||
|
||||
Setting up hooks:
|
||||
```bash
|
||||
make init
|
||||
```
|
||||
|
||||
This will install the pre-commit hooks, which will run `make format` on every commit.
|
||||
|
||||
It is advised to run `make lint` before pushing to the repository.
|
||||
|
||||
---
|
||||
|
||||
## Run locally
|
||||
|
||||
Langflow can run locally by cloning the repository and installing the dependencies. We recommend using a virtual environment to isolate the dependencies from your system.
|
||||
|
||||
Before you start, make sure you have the following installed:
|
||||
|
||||
- Poetry (>=1.4)
|
||||
- Node.js
|
||||
|
||||
Then install the dependencies and start the development server for the backend:
|
||||
|
||||
```bash
|
||||
make install_backend
|
||||
make backend
|
||||
```
|
||||
|
||||
And the frontend:
|
||||
|
||||
```bash
|
||||
make frontend
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Docker compose
|
||||
|
||||
The following snippet will run the backend and frontend in separate containers. The frontend will be available at `localhost:3000` and the backend at `localhost:7860`.
|
||||
|
||||
```bash
|
||||
docker compose up --build
|
||||
# or
|
||||
make dev build=1
|
||||
```
|
||||
|
|
@ -1,36 +1,32 @@
|
|||
# Deploy Langflow on Google Cloud Platform
|
||||
# Deploy on Google Cloud Platform
|
||||
|
||||
Follow our step-by-step guide to deploy Langflow on Google Cloud Platform (GCP) using Google Cloud Shell. The guide is available in the [**Langflow in Google Cloud Platform**](GCP_DEPLOYMENT.md) document.
|
||||
|
||||
<br>
|
||||
|
||||
Alternatively, click the **"Open in Cloud Shell"** button below to launch Google Cloud Shell, clone the Langflow repository, and start an **interactive tutorial** that will guide you through the process of setting up the necessary resources and deploying Langflow on your GCP project.
|
||||
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md){.internal-link target=_blank}
|
||||
|
||||
---
|
||||
## Run Langflow from a New Google Cloud Project
|
||||
|
||||
This guide will help you set up a Langflow development VM in a Google Cloud Platform project using Google Cloud Shell.
|
||||
|
||||
> **Note**: When Cloud Shell opens, be sure to select **Trust repo**. Some `gcloud` commands might not run in an ephemeral Cloud Shell environment.
|
||||
:::note
|
||||
When Cloud Shell opens, be sure to select **Trust repo**. Some `gcloud` commands might not run in an ephemeral Cloud Shell environment.
|
||||
:::
|
||||
|
||||
|
||||
## Standard VM
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md){.internal-link target=_blank}
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md)
|
||||
|
||||
This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment.
|
||||
<hr>
|
||||
|
||||
---
|
||||
|
||||
## Spot/Preemptible Instance
|
||||
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md){.internal-link target=_blank}
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
|
||||
When running as a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible){.internal-link target=_blank}, the code and VM will behave the same way as in a regular instance, executing the startup script to configure the environment, install necessary dependencies, and run the Langflow application. However, **due to the nature of spot instances, the VM may be terminated at any time if Google Cloud needs to reclaim the resources**. This makes spot instances suitable for fault-tolerant, stateless, or interruptible workloads that can handle unexpected terminations and restarts.
|
||||
When running as a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible), the code and VM will behave the same way as in a regular instance, executing the startup script to configure the environment, install necessary dependencies, and run the Langflow application. However, **due to the nature of spot instances, the VM may be terminated at any time if Google Cloud needs to reclaim the resources**. This makes spot instances suitable for fault-tolerant, stateless, or interruptible workloads that can handle unexpected terminations and restarts.
|
||||
|
||||
---
|
||||
|
||||
## Pricing (approximate)
|
||||
> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator){.internal-link target=_blank}
|
||||
<br>
|
||||
> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator)
|
||||
|
||||
|
||||
| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes |
|
||||
| -------------- | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | ----- |
|
||||
101
docs/docs/deployment/jina-deployment.md
Normal file
101
docs/docs/deployment/jina-deployment.md
Normal file
|
|
@ -0,0 +1,101 @@
|
|||
# Deploy on Jina AI Cloud
|
||||
|
||||
Langflow integrates with langchain-serve to provide a one-command deployment to [Jina AI Cloud](https://github.com/jina-ai/langchain-serve).
|
||||
|
||||
Start by installing `langchain-serve` with
|
||||
|
||||
```bash
|
||||
pip install -U langchain-serve
|
||||
```
|
||||
|
||||
Then, run:
|
||||
|
||||
```bash
|
||||
langflow --jcloud
|
||||
```
|
||||
|
||||
```text
|
||||
🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
|
||||
🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://<your-app>.wolf.jina.ai/
|
||||
📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
|
||||
```
|
||||
|
||||
**Complete (example) output:**
|
||||
|
||||
```text
|
||||
🚀 Deploying Langflow server on Jina AI Cloud
|
||||
╭───────────────────────── 🎉 Flow is available! ──────────────────────────╮
|
||||
│ │
|
||||
│ ID langflow-e3dd8820ec │
|
||||
│ Gateway (Websocket) wss://langflow-e3dd8820ec.wolf.jina.ai │
|
||||
│ Dashboard https://dashboard.wolf.jina.ai/flow/e3dd8820ec │
|
||||
│ │
|
||||
╰──────────────────────────────────────────────────────────────────────────╯
|
||||
╭──────────────┬──────────────────────────────────────────────────────────────────────────────╮
|
||||
│ App ID │ langflow-e3dd8820ec │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ Phase │ Serving │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ Endpoint │ wss://langflow-e3dd8820ec.wolf.jina.ai │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ App logs │ dashboards.wolf.jina.ai │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ Swagger UI │ https://langflow-e3dd8820ec.wolf.jina.ai/docs │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ OpenAPI JSON │ https://langflow-e3dd8820ec.wolf.jina.ai/openapi.json │
|
||||
╰──────────────┴──────────────────────────────────────────────────────────────────────────────╯
|
||||
|
||||
🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
|
||||
🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://langflow-e3dd8820ec.wolf.jina.ai/
|
||||
📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
|
||||
```
|
||||
## API Usage (with python)
|
||||
|
||||
You can use Langflow directly on your browser or the API endpoints on Jina AI Cloud to interact with the server.
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
BASE_API_URL = "https://langflow-e3dd8820ec.wolf.jina.ai/api/v1/predict"
|
||||
FLOW_ID = "864c4f98-2e59-468b-8e13-79cd8da07468"
|
||||
# You can tweak the flow by adding a tweaks dictionary
|
||||
# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
|
||||
TWEAKS = {
|
||||
"ChatOpenAI-g4jEr": {},
|
||||
"ConversationChain-UidfJ": {}
|
||||
}
|
||||
|
||||
def run_flow(message: str, flow_id: str, tweaks: dict = None) -> dict:
|
||||
"""
|
||||
Run a flow with a given message and optional tweaks.
|
||||
|
||||
:param message: The message to send to the flow
|
||||
:param flow_id: The ID of the flow to run
|
||||
:param tweaks: Optional tweaks to customize the flow
|
||||
:return: The JSON response from the flow
|
||||
"""
|
||||
api_url = f"{BASE_API_URL}/{flow_id}"
|
||||
|
||||
payload = {"message": message}
|
||||
|
||||
if tweaks:
|
||||
payload["tweaks"] = tweaks
|
||||
|
||||
response = requests.post(api_url, json=payload)
|
||||
return response.json()
|
||||
|
||||
# Setup any tweaks you want to apply to the flow
|
||||
print(run_flow("Your message", flow_id=FLOW_ID, tweaks=TWEAKS))
|
||||
```
|
||||
|
||||
```json
|
||||
{
|
||||
"result": "Great choice! Bangalore in the 1920s was a vibrant city with a rich cultural and political scene. Here are some suggestions for things to see and do:\n\n1. Visit the Bangalore Palace - built in 1887, this stunning palace is a perfect example of Tudor-style architecture. It was home to the Maharaja of Mysore and is now open to the public.\n\n2. Attend a performance at the Ravindra Kalakshetra - this cultural center was built in the 1920s and is still a popular venue for music and dance performances.\n\n3. Explore the neighborhoods of Basavanagudi and Malleswaram - both of these areas have retained much of their old-world charm and are great places to walk around and soak up the atmosphere.\n\n4. Check out the Bangalore Club - founded in 1868, this exclusive social club was a favorite haunt of the British expat community in the 1920s.\n\n5. Attend a meeting of the Indian National Congress - founded in 1885, the INC was a major force in the Indian independence movement and held many meetings and rallies in Bangalore in the 1920s.\n\nHope you enjoy your trip to 1920s Bangalore!"
|
||||
}
|
||||
```
|
||||
|
||||
:::info
|
||||
|
||||
Read more about resource customization, cost, and management of Langflow apps on Jina AI Cloud in the **[langchain-serve](https://github.com/jina-ai/langchain-serve)** repository.
|
||||
|
||||
:::
|
||||
25
docs/docs/examples/buffer-memory.mdx
Normal file
25
docs/docs/examples/buffer-memory.mdx
Normal file
|
|
@ -0,0 +1,25 @@
|
|||
# Buffer Memory
|
||||
|
||||
For certain applications, retaining past interactions is crucial. For that, chains and agents may accept a memory component as one of their input parameters. The `ConversationBufferMemory` component is one of them. It stores messages and extracts them into variables.
|
||||
|
||||
## ⛓️ Langflow Example
|
||||
|
||||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/buffer-memory.png",
|
||||
}}
|
||||
/>
|
||||
|
||||
#### <a target="\_blank" href="json_files/Buffer_Memory.json" download>Download Flow</a>
|
||||
|
||||
:::note LangChain Components 🦜🔗
|
||||
|
||||
- [`ConversationBufferMemory`](https://python.langchain.com/docs/modules/memory/how_to/buffer)
|
||||
- [`ConversationChain`](https://python.langchain.com/docs/modules/chains/)
|
||||
- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
|
||||
:::
|
||||
28
docs/docs/examples/conversation-chain.mdx
Normal file
28
docs/docs/examples/conversation-chain.mdx
Normal file
|
|
@ -0,0 +1,28 @@
|
|||
# Conversation Chain
|
||||
|
||||
This example shows how to instantiate a simple `ConversationChain` component using a Language Model (LLM). Once the Node Status turns green 🟢, the chat will be ready to take in user messages. Here, we used `ChatOpenAI` to act as the required LLM input, but you can use any LLM for this purpose.
|
||||
|
||||
:::info
|
||||
Make sure to always get the API key from the provider.
|
||||
:::
|
||||
|
||||
## ⛓️ Langflow Example
|
||||
|
||||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/basic-chat.png",
|
||||
}}
|
||||
/>
|
||||
|
||||
#### <a target="\_blank" href="json_files/Basic_Chat.json" download>Download Flow</a>
|
||||
|
||||
:::note LangChain Components 🦜🔗
|
||||
|
||||
- [`ConversationChain`](https://python.langchain.com/docs/modules/chains/)
|
||||
- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
|
||||
:::
|
||||
42
docs/docs/examples/csv-loader.mdx
Normal file
42
docs/docs/examples/csv-loader.mdx
Normal file
|
|
@ -0,0 +1,42 @@
|
|||
# CSV Loader
|
||||
|
||||
The `VectoStoreAgent` component retrieves information from one or more vector stores. This example shows a `VectoStoreAgent` connected to a CSV file through the `Chroma` vector store. Process description:
|
||||
|
||||
- The `CSVLoader` loads a CSV file into a list of documents.
|
||||
- The extracted data is then processed by the `CharacterTextSplitter`, which splits the text into small, meaningful chunks (usually sentences).
|
||||
- These chunks feed the `Chroma` vector store, which converts them into vectors and stores them for fast indexing.
|
||||
- Finally, the agent accesses the information of the vector store through the `VectorStoreInfo` tool.
|
||||
|
||||
:::info
|
||||
The vector store is used for efficient semantic search, while `VectorStoreInfo` carries information about it, such as its name and description. Embeddings are a way to represent words, phrases, or any entities in a vector space. Learn more about them [here](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings).
|
||||
:::
|
||||
|
||||
:::tip
|
||||
Once you build this flow, ask questions about the data in the chat interface (e.g., number of rows or columns).
|
||||
:::
|
||||
|
||||
## ⛓️ Langflow Example
|
||||
|
||||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/csv-loader.png",
|
||||
}}
|
||||
/>
|
||||
|
||||
#### <a target="\_blank" href="json_files/CSV_Loader.json" download>Download Flow</a>
|
||||
|
||||
:::note LangChain Components 🦜🔗
|
||||
|
||||
- [`CSVLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv)
|
||||
- [`CharacterTextSplitter`](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter)
|
||||
- [`OpenAIEmbedding`](https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai)
|
||||
- [`Chroma`](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma)
|
||||
- [`VectorStoreInfo`](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
|
||||
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
|
||||
- [`VectorStoreAgent`](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore)
|
||||
:::
|
||||
29
docs/docs/examples/how-upload-examples.mdx
Normal file
29
docs/docs/examples/how-upload-examples.mdx
Normal file
|
|
@ -0,0 +1,29 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
# 📚 How to Upload Examples?
|
||||
|
||||
We welcome all examples that can help our community learn and explore Langflow's capabilities.
|
||||
Langflow Examples is a repository on [GitHub](https://github.com/logspace-ai/langflow_examples) that contains examples of flows that people can use for inspiration and learning.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/community-examples.png",
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
|
||||
To upload examples, please follow these steps:
|
||||
|
||||
1. **Create a Flow:** First, create a flow using Langflow. You can use any of the available templates or create a new flow from scratch.
|
||||
|
||||
2. **Export the Flow:** Once you have created a flow, export it as a JSON file. Make sure to give your file a descriptive name and include a brief description of what it does.
|
||||
|
||||
3. **Submit a Pull Request:** Finally, submit a pull request (PR) to the examples repo. Make sure to include your JSON file in the PR.
|
||||
|
||||
If your example uses any third-party libraries or packages, please include them in your PR and make sure that your example follows the [**⛓️ Langflow Code Of Conduct**](https://github.com/logspace-ai/langflow/blob/dev/CODE_OF_CONDUCT.md).
|
||||
40
docs/docs/examples/midjourney-prompt-chain.mdx
Normal file
40
docs/docs/examples/midjourney-prompt-chain.mdx
Normal file
|
|
@ -0,0 +1,40 @@
|
|||
# MidJourney Prompt Chain
|
||||
|
||||
The `MidJourneyPromptChain` can be used to generate imaginative and detailed MidJourney prompts.
|
||||
|
||||
For example, type something like:
|
||||
|
||||
```bash
|
||||
Dragon
|
||||
```
|
||||
|
||||
And get a response such as:
|
||||
|
||||
```text
|
||||
Imagine a mysterious forest, the trees are tall and ancient, their branches reaching up to the sky. Through the darkness, a dragon emerges from the shadows, its scales shimmering in the moonlight. Its wingspan is immense, and its eyes glow with a fierce intensity. It is a majestic and powerful creature, one that commands both respect and fear.
|
||||
```
|
||||
|
||||
:::tip
|
||||
Notice that the `ConversationSummaryMemory` stores a summary of the conversation over time. Try using it to create better prompts as the conversation goes on.
|
||||
:::
|
||||
|
||||
## ⛓️ Langflow Example
|
||||
|
||||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/midjourney-prompt-chain.png",
|
||||
}}
|
||||
/>
|
||||
|
||||
#### <a target="\_blank" href="json_files/MidJourney_Prompt_Chain.json" download>Download Flow</a>
|
||||
|
||||
:::note LangChain Components 🦜🔗
|
||||
|
||||
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
|
||||
- [`ConversationSummaryMemory`](https://python.langchain.com/docs/modules/memory/how_to/summary)
|
||||
:::
|
||||
52
docs/docs/examples/multiple-vectorstores.mdx
Normal file
52
docs/docs/examples/multiple-vectorstores.mdx
Normal file
|
|
@ -0,0 +1,52 @@
|
|||
# Multiple Vector Stores
|
||||
|
||||
The example below shows an agent operating with two vector stores built upon different data sources.
|
||||
|
||||
The `TextLoader` loads a TXT file, while the `WebBaseLoader` pulls text from webpages into a document format to accessed downstream. The `Chroma` vector stores are created analogous to what we have demonstrated in our [CSV Loader](/examples/csv-loader.mdx) example. Finally, the `VectorStoreRouterAgent` constructs an agent that routes between the vector stores.
|
||||
|
||||
:::info
|
||||
Get the TXT file used [here](https://github.com/hwchase17/chat-your-data/blob/master/state_of_the_union.txt).
|
||||
:::
|
||||
|
||||
URL used by the `WebBaseLoader`:
|
||||
|
||||
```txt
|
||||
https://pt.wikipedia.org/wiki/Harry_Potter
|
||||
```
|
||||
|
||||
:::tip
|
||||
When you build the flow, request information about one of the sources. The agent should be able to use the correct source to generate a response.
|
||||
:::
|
||||
|
||||
:::info
|
||||
Learn more about Multiple Vector Stores [here](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore?highlight=Multiple%20Vector%20Stores#multiple-vectorstores).
|
||||
:::
|
||||
|
||||
## ⛓️ Langflow Example
|
||||
|
||||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/multiple-vectorstores.png",
|
||||
}}
|
||||
/>
|
||||
|
||||
#### <a target="\_blank" href="json_files/Multiple_Vector_Stores.json" download>Download Flow</a>
|
||||
|
||||
:::note LangChain Components 🦜🔗
|
||||
|
||||
- [`WebBaseLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_base)
|
||||
- [`TextLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/unstructured_file)
|
||||
- [`CharacterTextSplitter`](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter)
|
||||
- [`OpenAIEmbedding`](https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai)
|
||||
- [`Chroma`](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma)
|
||||
- [`VectorStoreInfo`](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
|
||||
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
|
||||
- [`VectorStoreRouterToolkit`](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore)
|
||||
- [`VectorStoreRouterAgent`](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore)
|
||||
|
||||
:::
|
||||
48
docs/docs/examples/python-function.mdx
Normal file
48
docs/docs/examples/python-function.mdx
Normal file
|
|
@ -0,0 +1,48 @@
|
|||
# Python Function
|
||||
|
||||
Langflow allows you to create a customized tool using the `PythonFunction` connected to a `Tool` component. In this example, Regex is used in Python to validate a pattern.
|
||||
|
||||
```python
|
||||
import re
|
||||
|
||||
def is_brazilian_zipcode(zipcode: str) -> bool:
|
||||
pattern = r"\d{5}-?\d{3}"
|
||||
|
||||
# Check if the zip code matches the pattern
|
||||
if re.match(pattern, zipcode):
|
||||
return True
|
||||
|
||||
return False
|
||||
```
|
||||
|
||||
:::tip
|
||||
When a tool is called, it is often desirable to have its output returned directly to the user. You can do this by setting the **return_direct** flag for a tool to be True.
|
||||
:::
|
||||
|
||||
The `AgentInitializer` component is a quick way to construct an agent from the model and tools.
|
||||
|
||||
:::info
|
||||
The `PythonFunction` is a custom component that uses the LangChain 🦜🔗 tool decorator. Learn more about it [here](https://python.langchain.com/docs/modules/agents/tools/how_to/custom_tools).
|
||||
:::
|
||||
|
||||
## ⛓️ Langflow Example
|
||||
|
||||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/python-function.png",
|
||||
}}
|
||||
/>
|
||||
|
||||
#### <a target="\_blank" href="json_files/Python_Function.json" download>Download Flow</a>
|
||||
|
||||
:::note LangChain Components 🦜🔗
|
||||
|
||||
- [`PythonFunctionTool`](https://python.langchain.com/docs/modules/agents/tools/how_to/custom_tools)
|
||||
- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
|
||||
- [`AgentInitializer`](https://python.langchain.com/docs/modules/agents/)
|
||||
:::
|
||||
45
docs/docs/examples/serp-api-tool.mdx
Normal file
45
docs/docs/examples/serp-api-tool.mdx
Normal file
|
|
@ -0,0 +1,45 @@
|
|||
# Serp API Tool
|
||||
|
||||
The [Serp API](https://serpapi.com/) (Search Engine Results Page) allows developers to scrape results from search engines such as Google, Bing and Yahoo, and can be used as in Langflow through the `Search` component.
|
||||
|
||||
:::info
|
||||
To use the Serp API, you first need to sign up [Serp API](https://serpapi.com/) for an API key on the provider's website.
|
||||
:::
|
||||
|
||||
Here, the `ZeroShotPrompt` component specifies a prompt template for the `ZeroShotAgent`. Set a _Prefix_ and _Suffix_ with rules for the agent to obey. In the example, we used default templates.
|
||||
|
||||
The `LLMChain` is a simple chain that takes in a prompt template, formats it with the user input, and returns the response from an LLM.
|
||||
|
||||
:::tip
|
||||
In this example, we used [`ChatOpenAI`](https://platform.openai.com/) as the LLM, but feel free to experiment with other Language Models!
|
||||
:::
|
||||
|
||||
The `ZeroShotAgent` takes the `LLMChain` and the `Search` tool as inputs, using the tool to find information when necessary.
|
||||
|
||||
:::info
|
||||
Learn more about the Serp API [here](https://python.langchain.com/docs/modules/agents/tools/integrations/serpapi).
|
||||
:::
|
||||
|
||||
## ⛓️ Langflow Example
|
||||
|
||||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/serp-api-tool.png",
|
||||
}}
|
||||
/>
|
||||
|
||||
#### <a target="\_blank" href="json_files/SerpAPI_Tool.json" download>Download Flow</a>
|
||||
|
||||
:::note LangChain Components 🦜🔗
|
||||
|
||||
- [`ZeroShotPrompt`](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/)
|
||||
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
|
||||
- [`LLMChain`](https://python.langchain.com/docs/modules/chains/foundational/llm_chain)
|
||||
- [`Search`](https://python.langchain.com/docs/modules/agents/tools/integrations/serpapi)
|
||||
- [`ZeroShotAgent`](https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent)
|
||||
:::
|
||||
37
docs/docs/getting-started/creating-flows.mdx
Normal file
37
docs/docs/getting-started/creating-flows.mdx
Normal file
|
|
@ -0,0 +1,37 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
|
||||
# 🎨 Creating Flows
|
||||
|
||||
## Compose
|
||||
|
||||
Creating flows with Langflow is easy. Drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://python.langchain.com/docs/modules/) to choose from, including LLMs, prompt serializers, agents, and chains.
|
||||
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/langflow_canvas.png",
|
||||
}}
|
||||
/>
|
||||
|
||||
## Fork
|
||||
|
||||
The easiest way to start with Langflow is by forking a **community example**. Forking an example stores a copy in your project collection, allowing you to edit and save the modified version as a new flow.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ReactPlayer playing controls url="/videos/langflow_fork.mp4" />
|
||||
</div>
|
||||
|
||||
## Build
|
||||
|
||||
Building a flow means validating if the components have prerequisites fulfilled and are properly instantiated. When a chat message is sent, the flow will run for the first time, executing the pipeline.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ReactPlayer playing controls url="/videos/langflow_build.mp4" />
|
||||
</div>
|
||||
20
docs/docs/getting-started/hugging-face-spaces.mdx
Normal file
20
docs/docs/getting-started/hugging-face-spaces.mdx
Normal file
|
|
@ -0,0 +1,20 @@
|
|||
# 🤗 HuggingFace Spaces
|
||||
|
||||
A fully featured version of Langflow can be accessed via HuggingFace spaces with no installation required.
|
||||
|
||||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/hugging-face.png",
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
|
||||
Check out Langflow on [HuggingFace Spaces](https://huggingface.co/spaces/Logspace/Langflow).
|
||||
15
docs/docs/getting-started/installation.md
Normal file
15
docs/docs/getting-started/installation.md
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
# 📦 How to install?
|
||||
|
||||
## Installation
|
||||
|
||||
You can install Langflow from pip:
|
||||
|
||||
```bash
|
||||
pip install langflow
|
||||
```
|
||||
|
||||
Next, run:
|
||||
|
||||
```bash
|
||||
langflow
|
||||
```
|
||||
64
docs/docs/guidelines/chat-interface.mdx
Normal file
64
docs/docs/guidelines/chat-interface.mdx
Normal file
|
|
@ -0,0 +1,64 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
|
||||
# Chat Interface
|
||||
|
||||
Langflow’s chat interface provides a user-friendly experience and functionality to interact with the model and customize the prompt. The sidebar brings options that allow users to view and edit pre-defined prompt variables. This feature facilitates quick experimentation by enabling the modification of variable values right in the chat.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/chat_interface.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
||||
Notice that editing variables in the chat interface take place temporarily and won’t change their original value in the components once the chat is closed.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/chat_interface2.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
To view the complete prompt in its original, structured format, click the "Display Prompt" option. This feature lets you see the prompt exactly as it entered the model.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/chat_interface3.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
||||
In the chat interface, you can redefine which variable should be interpreted as the chat input. This gives you control over these inputs and allows dynamic and creative interactions.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/chat_interface4.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
</div>
|
||||
13
docs/docs/guidelines/collection.mdx
Normal file
13
docs/docs/guidelines/collection.mdx
Normal file
|
|
@ -0,0 +1,13 @@
|
|||
import ThemedImage from '@theme/ThemedImage';
|
||||
import useBaseUrl from '@docusaurus/useBaseUrl';
|
||||
import ZoomableImage from '/src/theme/ZoomableImage.js';
|
||||
import ReactPlayer from 'react-player';
|
||||
|
||||
# Collection
|
||||
|
||||
A collection is a snapshot of the flows available in the database. You can download your entire collection for local storage and upload it anytime for future use.
|
||||
|
||||
<div style={{ marginBottom: '20px', display: 'flex', justifyContent: 'center' }}>
|
||||
<ReactPlayer playing controls url='/videos/langflow_collection.mp4'
|
||||
/>
|
||||
</div>
|
||||
59
docs/docs/guidelines/components.mdx
Normal file
59
docs/docs/guidelines/components.mdx
Normal file
|
|
@ -0,0 +1,59 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
|
||||
# Component
|
||||
|
||||
Components are the building blocks of the flows. They are made of inputs, outputs, and parameters that define their functionality, providing a convenient and straightforward way to compose LLM-based applications. Learn more about components and how they work in the LangChain [documentation](https://docs.langchain.com/docs/category/components) section.
|
||||
|
||||
### Component's Features
|
||||
|
||||
<div style={{ marginBottom: "20px" }}>
|
||||
During the flow creation process, you will notice handles (colored circles)
|
||||
attached to one or both sides of a component. These handles represent the
|
||||
availability to connect to other components, while their colors are type hints
|
||||
(hover over a handle to see connection details).
|
||||
</div>
|
||||
|
||||
<div style={{ marginBottom: "20px" }}>
|
||||
For example, if you select a <code>ConversationChain</code> component, you
|
||||
will see orange <span style={{ color: "orange" }}>o</span> and purple{" "}
|
||||
<span style={{ color: "purple" }}>o</span> input handles. They indicate that
|
||||
this component accepts an LLM and a Memory component as inputs. The red
|
||||
asterisk <span style={{ color: "red" }}>*</span> means that at least one input
|
||||
of that type is required.
|
||||
</div>
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/single-compenent.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div style={{ marginBottom: "20px" }}>
|
||||
On the top right corner, you will find the component status icon 🔴. Make the
|
||||
necessary connections, build the flow (⚡ zap icon on the bottom right of the
|
||||
canvas) and once the validation is completed, the status of each validated
|
||||
component should light green 🟢. Hover over the component status to reveal the
|
||||
outputs going through it in case of success, or the detected error in case of
|
||||
failure.
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
### Component's Parameters
|
||||
|
||||
Langflow components can be edited in the component settings button. Hide parameters to reduce complexity and keep the canvas clean and intuitive for experimentation.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ReactPlayer playing controls url="/videos/langflow_parameters.mp4" />
|
||||
</div>
|
||||
69
docs/docs/guidelines/features.mdx
Normal file
69
docs/docs/guidelines/features.mdx
Normal file
|
|
@ -0,0 +1,69 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
|
||||
# Features
|
||||
|
||||
<div style={{ marginBottom: "20px" }}>
|
||||
When you click for New Project, you will see on the top left corner of the
|
||||
screen, some options such as <strong>Import</strong>, <strong>Export</strong>,{" "}
|
||||
<strong>Code</strong> and <strong>Save</strong>, as displayed in the image
|
||||
below:
|
||||
</div>
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/features.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div style={{ marginBottom: "20px" }}>
|
||||
Further down, we will explain each of these options.
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
### Import and Export
|
||||
|
||||
Flows can be exported and imported as JSON files.
|
||||
|
||||
:::caution
|
||||
Watch out for API keys being stored in local files.
|
||||
:::
|
||||
|
||||
---
|
||||
|
||||
### Code
|
||||
|
||||
The Code button shows snippets to use your flow as a Python object or an API.
|
||||
|
||||
**Python Code**
|
||||
|
||||
Through the Langflow package, you can load a flow from a JSON file and use it as a LangChain object.
|
||||
|
||||
```py
|
||||
from langflow import load_flow_from_json
|
||||
|
||||
flow = load_flow_from_json("path/to/flow.json")
|
||||
# Now you can use it like any chain
|
||||
flow("Hey, have you heard of Langflow?")
|
||||
```
|
||||
|
||||
**API**
|
||||
|
||||
Once you save a flow, the API endpoint is created with your latest changes. Click the "code" button to use that flow as an API. You can post-adjust component parameters using the global variable TWEAKS.
|
||||
|
||||
The example below shows a Python script making a POST request to a local API endpoint, which gets a prediction based on the message input.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ReactPlayer playing controls url="/videos/langflow_api.mp4" />
|
||||
</div>
|
||||
86
docs/docs/guidelines/prompt-customization.mdx
Normal file
86
docs/docs/guidelines/prompt-customization.mdx
Normal file
|
|
@ -0,0 +1,86 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
|
||||
# Prompt Customization
|
||||
|
||||
The prompt template allows users to create prompts and define variables that provide control over instructing the model.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/prompt_customization.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
||||
Variables can be used to define instructions, questions, context, inputs, or examples for the model and can be created with any chosen name in curly brackets, e.g., `{variable_name}`. They act as placeholders for parts of the text that can be easily modified.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/prompt_customization2.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
||||
Once inserted, these variables are immediately recognized as new fields in the prompt component. Here, you can define their values within the component itself or leave a field empty to be adjusted over the chat interface.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/prompt_customization3.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
You can also use documents or output parsers as prompt variables. By plugging them into prompt handles, they’ll disable and feed that input field.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/prompt_customization4.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
With this, users can interact with documents, webpages, or any other type of content directly from the prompt, which allows for seamless integration of external resources with the language model.
|
||||
|
||||
|
||||
|
||||
If working with an interactive (chat-like) flow, remember to keep one of the input variables empty to behave as the chat input.
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/prompt_customization5.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
||||
78
docs/docs/guides/chatprompttemplate_guide.mdx
Normal file
78
docs/docs/guides/chatprompttemplate_guide.mdx
Normal file
|
|
@ -0,0 +1,78 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
|
||||
# Building chatbots with System Message
|
||||
|
||||
## Overview
|
||||
|
||||
In this guide, we will modify the "Basic Chat with Prompt and History" example, integrating the ChatPromptTemplate with the SystemMessagePromptTemplate and HumanMessagePromptTemplate components. By following these steps, you'll be able to build a personalized chatbot that can interpret and respond based on user-defined System messages.
|
||||
|
||||
## Interactive Guide
|
||||
|
||||
<iframe
|
||||
src="https://app.tango.us/app/embed/f05bb3a7-4ceb-4b61-921f-628563b562f6?iframe=true"
|
||||
sandbox="allow-scripts allow-top-navigation-by-user-activation allow-popups allow-same-origin"
|
||||
security="restricted"
|
||||
title="Step-by-Step Instructions to Customize and Build a Chatbot with custom System Message"
|
||||
width="100%"
|
||||
height="800px"
|
||||
referrerpolicy="strict-origin-when-cross-origin"
|
||||
frameborder="0"
|
||||
webkitallowfullscreen="webkitallowfullscreen"
|
||||
mozallowfullscreen="mozallowfullscreen"
|
||||
allowfullscreen="allowfullscreen"
|
||||
></iframe>
|
||||
|
||||
## Step-by-Step Instructions
|
||||
|
||||
1. Navigate to the "Community Examples" section.
|
||||
|
||||
2. Locate the "Basic Chat with Prompt and History" example, and click on "Fork Example".
|
||||
|
||||
3. Once in the editor, find the "PromptTemplate" component and remove it.
|
||||
|
||||
4. Now, add these three components: ChatPromptTemplate, SystemMessagePromptTemplate, and HumanMessagePromptTemplate.
|
||||
|
||||
> **Note:** Remember to set the model to gpt-3.5-turbo-0613 or the most up-to-date version. The latest models have improved capabilities to comprehend System messages.
|
||||
|
||||
5. Open the "Prompt" field on the SystemMessagePromptTemplate component.
|
||||
|
||||
6. Enter the text: `You are a {role} that {behavior}.`
|
||||
|
||||
7. Save your changes by clicking on "Check & Save".
|
||||
|
||||
8. Define the 'role' variable by typing "obedient assistant".
|
||||
|
||||
9. Next, navigate to the HumanMessagePromptTemplate and open the "Prompt" field.
|
||||
|
||||
10. Here, simply enter `{input}`.
|
||||
|
||||
11. Save these changes by clicking on "Check & Save".
|
||||
|
||||
12. Now, you should see your flow populated with the variables you defined.
|
||||
|
||||
13. In the Memory component, set the 'Input Key' to "input".
|
||||
|
||||
> **Tip:** When using a Memory component with multiple variables, it's crucial to specify which variable should be used to generate the conversation history.
|
||||
|
||||
14. Click on the "Build" button to implement your changes.
|
||||
|
||||
15. Open the chat interface to test your modifications.
|
||||
|
||||
16. You should now be able to see and use the defined variables in the chat interface.
|
||||
|
||||
17. Click on 'role' to examine the variable you established in the canvas.
|
||||
|
||||
18. Now, let's define the 'behavior' variable.
|
||||
|
||||
19. Enter the text: "writes the word 'Langflow' at the end of every sentence."
|
||||
|
||||
20. Test your chatbot by typing "How can you help me?"
|
||||
|
||||
21. If everything was set up correctly, your chatbot should respond appropriately, following the defined behavior.
|
||||
|
||||
22. Congratulations! You have successfully customized and built your chatbot.
|
||||
|
||||
By following these instructions, you have created a dynamic chatbot capable of understanding and responding based on custom system messages, enhancing the user experience and interaction. Enjoy your personalized assistant!
|
||||
64
docs/docs/guides/loading_document.mdx
Normal file
64
docs/docs/guides/loading_document.mdx
Normal file
|
|
@ -0,0 +1,64 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import ReactPlayer from "react-player";
|
||||
|
||||
# Integrating documents with prompt variables
|
||||
|
||||
## Overview
|
||||
|
||||
This guide takes you through the process of augmenting the "Basic Chat with Prompt and History" example. You'll learn how to embed documents as context into the PromptTemplate component utilizing a WebBaseLoader.
|
||||
|
||||
## Interactive Guide
|
||||
|
||||
<iframe
|
||||
src="https://app.tango.us/app/embed/76578e84-a700-4b3b-a6ef-8a0350472781?iframe=true"
|
||||
sandbox="allow-scripts allow-top-navigation-by-user-activation allow-popups allow-same-origin"
|
||||
security="restricted"
|
||||
title="Loading a document into a PromptTemplate variable"
|
||||
width="100%"
|
||||
height="800px"
|
||||
referrerpolicy="strict-origin-when-cross-origin"
|
||||
frameborder="0"
|
||||
webkitallowfullscreen="webkitallowfullscreen"
|
||||
mozallowfullscreen="mozallowfullscreen"
|
||||
allowfullscreen="allowfullscreen"
|
||||
></iframe>
|
||||
|
||||
## Step-by-Step Instructions
|
||||
|
||||
1. Start by navigating to the "Community Examples" section.
|
||||
|
||||
2. Find the "Basic Chat with Prompt and History" example and click on "Fork Example".
|
||||
|
||||
3. In the editor, open the "Template" field.
|
||||
|
||||
4. Here, introduce the `{context}` variable, placing it somewhere before the "Current conversation:" text.
|
||||
|
||||
5. Once done, save your changes by clicking on "Check & Save".
|
||||
|
||||
6. Next, open the search bar and type "web".
|
||||
|
||||
7. Drag and drop a WebBaseLoader (or any other loader of your choice) onto the canvas.
|
||||
|
||||
8. Connect this loader to the `{context}` variable that we just added.
|
||||
|
||||
9. In the "Web Page" field, enter "https://langflow.org/how-upload-examples".
|
||||
|
||||
10. Now, click on "ConversationBufferMemory".
|
||||
|
||||
11. In the "Input Key" field, enter "text" to define the Chat variable.
|
||||
|
||||
> **Tip:** When defining more than one variable and using a Memory component, it's crucial to specify which variable should be used to create the conversation history.
|
||||
|
||||
12. Click on the "Build" button to implement your changes.
|
||||
|
||||
13. Open the chat interface to test your modifications.
|
||||
|
||||
14. Try asking something like, "How do I upload examples?"
|
||||
|
||||
15. Click on "Display Prompt" to view your template.
|
||||
|
||||
16. Now, you can see what the model used as a basis to generate its response.
|
||||
|
||||
By following these instructions, you have successfully loaded a document into a PromptTemplate variable, allowing for more enriched and context-aware chat responses. This customization enhances user interaction by integrating relevant document content into the chat flow.
|
||||
18
docs/docs/index.mdx
Normal file
18
docs/docs/index.mdx
Normal file
|
|
@ -0,0 +1,18 @@
|
|||
# 👋 Welcome to Langflow
|
||||
|
||||
Langflow is an easy way to prototype [LangChain](https://github.com/hwchase17/langchain) flows. The drag-and-drop feature allows quick and effortless experimentation, while the built-in chat interface facilitates real-time interaction. It provides options to edit prompt parameters, create chains and agents, track thought processes, and export flows.
|
||||
|
||||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
|
||||
<div
|
||||
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
|
||||
>
|
||||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/new_langflow2.gif",
|
||||
}}
|
||||
/>
|
||||
</div>
|
||||
127
docs/docusaurus.config.js
Normal file
127
docs/docusaurus.config.js
Normal file
|
|
@ -0,0 +1,127 @@
|
|||
const lightCodeTheme = require("prism-react-renderer/themes/github");
|
||||
|
||||
// With JSDoc @type annotations, IDEs can provide config autocompletion
|
||||
/** @type {import('@docusaurus/types').DocusaurusConfig} */
|
||||
(
|
||||
module.exports = {
|
||||
title: "Langflow Documentation",
|
||||
tagline: "Langflow is a GUI for LangChain, designed with react-flow",
|
||||
favicon: "img/favicon.ico",
|
||||
url: "https://logspace-ai.github.io",
|
||||
baseUrl: "/",
|
||||
onBrokenLinks: "throw",
|
||||
onBrokenMarkdownLinks: "warn",
|
||||
organizationName: "logspace-ai",
|
||||
projectName: "langflow",
|
||||
trailingSlash: false,
|
||||
customFields: {
|
||||
mendableAnonKey: process.env.MENDABLE_ANON_KEY,
|
||||
},
|
||||
i18n: {
|
||||
defaultLocale: "en",
|
||||
locales: ["en"],
|
||||
},
|
||||
presets: [
|
||||
[
|
||||
"@docusaurus/preset-classic",
|
||||
/** @type {import('@docusaurus/preset-classic').Options} */
|
||||
({
|
||||
docs: {
|
||||
routeBasePath: "/",
|
||||
sidebarPath: require.resolve("./sidebars.js"),
|
||||
path: "docs",
|
||||
// sidebarPath: 'sidebars.js',
|
||||
},
|
||||
theme: {
|
||||
customCss: require.resolve("./src/css/custom.css"),
|
||||
},
|
||||
}),
|
||||
],
|
||||
],
|
||||
plugins: [
|
||||
["docusaurus-node-polyfills", { excludeAliases: ["console"] }],
|
||||
"docusaurus-plugin-image-zoom",
|
||||
// ....
|
||||
async function myPlugin(context, options) {
|
||||
return {
|
||||
name: "docusaurus-tailwindcss",
|
||||
configurePostCss(postcssOptions) {
|
||||
// Appends TailwindCSS and AutoPrefixer.
|
||||
postcssOptions.plugins.push(require("tailwindcss"));
|
||||
postcssOptions.plugins.push(require("autoprefixer"));
|
||||
return postcssOptions;
|
||||
},
|
||||
};
|
||||
},
|
||||
],
|
||||
themeConfig:
|
||||
/** @type {import('@docusaurus/preset-classic').ThemeConfig} */
|
||||
({
|
||||
navbar: {
|
||||
hideOnScroll: true,
|
||||
title: "Langflow",
|
||||
logo: {
|
||||
alt: "Langflow",
|
||||
src: "img/chain.png",
|
||||
},
|
||||
items: [
|
||||
// right
|
||||
{
|
||||
position: "right",
|
||||
href: "https://github.com/logspace-ai/langflow",
|
||||
position: "right",
|
||||
className: "header-github-link",
|
||||
target: "_blank",
|
||||
rel: null,
|
||||
},
|
||||
{
|
||||
position: "right",
|
||||
href: "https://twitter.com/logspace_ai",
|
||||
position: "right",
|
||||
className: "header-twitter-link",
|
||||
target: "_blank",
|
||||
rel: null,
|
||||
},
|
||||
{
|
||||
position: "right",
|
||||
href: "https://discord.gg/EqksyE2EX9",
|
||||
position: "right",
|
||||
className: "header-discord-link",
|
||||
target: "_blank",
|
||||
rel: null,
|
||||
},
|
||||
],
|
||||
},
|
||||
tableOfContents: {
|
||||
minHeadingLevel: 2,
|
||||
maxHeadingLevel: 5,
|
||||
},
|
||||
colorMode: {
|
||||
defaultMode: "light",
|
||||
disableSwitch: true,
|
||||
respectPrefersColorScheme: false,
|
||||
},
|
||||
announcementBar: {
|
||||
content:
|
||||
'⭐️ If you like ⛓️Langflow, star it on <a target="_blank" rel="noopener noreferrer" href="https://github.com/logspace-ai/langflow">GitHub</a>! ⭐️',
|
||||
backgroundColor: "#B53D38", //Mustard Yellow #D19900 #D4B20B - Salmon #E9967A
|
||||
textColor: "#fff",
|
||||
isCloseable: false,
|
||||
},
|
||||
footer: {
|
||||
links: [],
|
||||
copyright: `Copyright © ${new Date().getFullYear()} Logspace.`,
|
||||
},
|
||||
zoom: {
|
||||
selector: ".markdown :not(a) > img:not(.no-zoom)",
|
||||
background: {
|
||||
light: "rgba(240, 240, 240, 0.9)",
|
||||
},
|
||||
config: {},
|
||||
},
|
||||
prism: {
|
||||
theme: lightCodeTheme,
|
||||
},
|
||||
}),
|
||||
}
|
||||
);
|
||||
|
|
@ -1,115 +0,0 @@
|
|||
# Guidelines
|
||||
|
||||
## Component
|
||||
|
||||
A component is a self-contained and reusable building block in software development. It is a modular unit that performs a specific function or task within a larger system. They are created to provide a convenient and straightforward way to work with language models.
|
||||
|
||||
<br>
|
||||
Component **agents** can refer to an entity that is capable of performing actions or making decisions autonomously or on behalf of someone or something else. In the case of a language model like ChatGPT, the model itself can be considered an agent as it can generate responses and interact with users based on the input it receives.
|
||||
|
||||
<br>
|
||||
If want to learn more about the components and how they work, make sure to check out the LangChain [documentation](https://docs.langchain.com/docs/category/components){.internal-link target=\_blank} section.
|
||||
|
||||
---
|
||||
|
||||
## Component's Features
|
||||
|
||||
During the flow creation process, you will notice a colored circle <span style="color:green">o</span>. Components marked with a red asterisk <span style="color:red">\*</span> must be connected. If you don't connect it, a red line will appear around it. Make the necessary connections to make your flow work. Hovering over the small circle will reveal the component that needs to be connected.
|
||||
|
||||
<br>
|
||||
|
||||
In some components, at the top of it, you will see a small gear icon ⚙️, which you can click to edit the parameters. You also have the option to delete it by clicking the trash can icon 🗑️.
|
||||
|
||||
<br>
|
||||
|
||||
{width=50%}
|
||||
{width=50%}
|
||||
|
||||
---
|
||||
|
||||
## Features
|
||||
|
||||
Located in the right top corner of the screen there are some features that you can use, such as **Code**, **Import**, **Export**, **Dark Mode** and **Notification**, as you can see in the image below:
|
||||
|
||||
<br>
|
||||
|
||||
{width=60%}
|
||||
{width=60%}
|
||||
|
||||
<br>
|
||||
|
||||
Further down, we will explain each of these features.
|
||||
|
||||
---
|
||||
|
||||
### Code
|
||||
|
||||
{width=60%}
|
||||
{width=60%}
|
||||
|
||||
<br>
|
||||
|
||||
API Access: Export Your Flow for Code Usage. The API Access feature allows you to export your flow from the platform and utilize it with your own code. This feature provides two different tabs within the platform, the first being the "Python API" tab and the second being the "Python Code" tab. Each tab offers a unique set of functionalities to integrate the exported flow into your codebase seamlessly.
|
||||
|
||||
<br>
|
||||
|
||||
**Python API Tab:**
|
||||
|
||||
To access the Python API tab, you can utilize the code snippet in the first tab. You can import the required libraries and define a predict function. This function takes a message as input and performs the following steps:
|
||||
|
||||
- Opens the "`Conversation_buffer_memory.json`" file, which contains the exported flow information.
|
||||
- Constructs a payload consisting of the exported flow data and the input message.
|
||||
- Sends a POST request to the specified API URL with the payload as JSON.
|
||||
- Returns the response as a JSON object, which includes the predicted result.
|
||||
|
||||
<br>
|
||||
|
||||
**Python Code Tab:**
|
||||
|
||||
To access the Python Code tab, you can utilize the code snippet in the secon tab. You can import the `load_flow_from_json` function from the "`langflow`" library. This function loads the exported flow from the "`Conversation_buffer_memory.json`" file and assigns it to the variable `flow`. Once the flow is loaded, you can use it as a chain to process input messages. In the provided example, the flow variable is used to process the message "`Hey, have you heard of LangFlow?`".
|
||||
|
||||
<br>
|
||||
|
||||
By utilizing the Python Code tab, you can seamlessly integrate the exported flow into your code and leverage its capabilities for natural language processing tasks.
|
||||
|
||||
<br>
|
||||
|
||||
The API Access feature empowers you to leverage the full potential of your exported flow by seamlessly integrating it into your codebase. Whether you want to incorporate advanced conversational capabilities or automate specific tasks, this feature provides a flexible and efficient solution to enhance your conversational applications.
|
||||
|
||||
---
|
||||
|
||||
### Import and Export
|
||||
|
||||
Flows can be exported and imported as JSON files. We already have some examples on **Import** option, check them out.
|
||||
|
||||
<br>
|
||||
|
||||
{width=50%}
|
||||
{width=50%}
|
||||
|
||||
<br>
|
||||
|
||||
The **Export** option allows you to export your flow setting a name and description. You have the option to save the file with your API keys.
|
||||
|
||||
<br>
|
||||
|
||||
| Import | Export |
|
||||
| :------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: |
|
||||
| {width=100%}{width=100%} | {width=100%}{width=100%} |
|
||||
|
||||
---
|
||||
|
||||
### Dark Mode and Notifications
|
||||
|
||||
The background color can be set to dark 🌙 or light ☀️ mode. The bell icon 🔔 indicates that the component has a notification.
|
||||
|
||||
---
|
||||
|
||||
## Chat
|
||||
|
||||
A chat icon 💬 located in the bottom right corner of the screen allows you to chat. When you click over 💬 a new screen will pop up. You can start a conversation by typing in the text box and pressing enter. The chat will respond to your message. In the top right corner of the screen, you will see an eraser icon {width=2.5%} {width=2.5%}. Clicking on it will clear the chat history.
|
||||
|
||||
<br>
|
||||
|
||||
{width=50%}
|
||||
{width=50%}
|
||||
|
|
@ -1,10 +0,0 @@
|
|||
# Hugging Face Spaces
|
||||
|
||||
LangFlow can be used on Hugging Face Spaces. A fully featured version of LangFlow can be accessed in your browser with just one click.
|
||||
|
||||
<br>
|
||||
{width=100%}
|
||||
{width=100%}
|
||||
|
||||
<br>
|
||||
Check out LangFlow on [Hugging Face Spaces](https://huggingface.co/spaces/Logspace/LangFlow){.internal-link target=\_blank}.
|
||||
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