add docs with components
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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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docs/babel.config.js
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docs/babel.config.js
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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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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.
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<br>
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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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