organized folders, added components tab, adjusted mendable style.
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carlosrcoelho
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77 changed files with 2291 additions and 11030 deletions
25
docs/docs/examples/buffer-memory.mdx
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docs/docs/examples/buffer-memory.mdx
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# Buffer Memory
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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.
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## ⛓️ LangFlow Example
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import ThemedImage from '@theme/ThemedImage';
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import useBaseUrl from '@docusaurus/useBaseUrl';
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import ZoomableImage from '/src/theme/ZoomableImage.js';
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: 'img/buffer-memory.png',
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}}
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/>
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#### <a target="\_blank" href="json_files/Buffer_Memory.json" download>Download Flow</a>
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:::note LangChain Components 🦜🔗
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- [`ConversationBufferMemory`](https://python.langchain.com/docs/modules/memory/how_to/buffer)
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- [`ConversationChain`](https://python.langchain.com/docs/modules/chains/)
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- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
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:::
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29
docs/docs/examples/conversation-chain.mdx
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docs/docs/examples/conversation-chain.mdx
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# Conversation Chain
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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.
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:::info
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Make sure to always get the API key from the provider.
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:::
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## ⛓️ LangFlow Example
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import ThemedImage from '@theme/ThemedImage';
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import useBaseUrl from '@docusaurus/useBaseUrl';
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import ZoomableImage from '/src/theme/ZoomableImage.js';
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: 'img/basic-chat.png',
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}}
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/>
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#### <a target="\_blank" href="json_files/Basic_Chat.json" download>Download Flow</a>
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:::note LangChain Components 🦜🔗
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- [`ConversationChain`](https://python.langchain.com/docs/modules/chains/)
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- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
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:::
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43
docs/docs/examples/csv-loader.mdx
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docs/docs/examples/csv-loader.mdx
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# CSV Loader
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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:
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- The `CSVLoader` loads a CSV file into a list of documents.
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- The extracted data is then processed by the `CharacterTextSplitter`, which splits the text into small, meaningful chunks (usually sentences).
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- These chunks feed the `Chroma` vector store, which converts them into vectors and stores them for fast indexing.
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- Finally, the agent accesses the information of the vector store through the `VectorStoreInfo` tool.
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:::info
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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).
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:::
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:::tip
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Once you build this flow, ask questions about the data in the chat interface (e.g., number of rows or columns).
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:::
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## ⛓️ LangFlow Example
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import ThemedImage from '@theme/ThemedImage';
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import useBaseUrl from '@docusaurus/useBaseUrl';
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import ZoomableImage from '/src/theme/ZoomableImage.js';
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: 'img/csv-loader.png',
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}}
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/>
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#### <a target="\_blank" href="json_files/CSV_Loader.json" download>Download Flow</a>
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:::note LangChain Components 🦜🔗
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- [`CSVLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv)
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- [`CharacterTextSplitter`](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter)
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- [`OpenAIEmbedding`](https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai)
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- [`Chroma`](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma)
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- [`VectorStoreInfo`](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
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- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
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- [`VectorStoreAgent`](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore)
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:::
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27
docs/docs/examples/how-upload-examples.mdx
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docs/docs/examples/how-upload-examples.mdx
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import ThemedImage from '@theme/ThemedImage';
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import useBaseUrl from '@docusaurus/useBaseUrl';
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import ZoomableImage from '/src/theme/ZoomableImage.js';
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# 📚 How to Upload Examples?
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We welcome all examples that can help our community learn and explore LangFlow's capabilities.
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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.
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<div style={{ marginBottom: '20px', display: 'flex', justifyContent: 'center' }}>
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: 'img/community-examples.png',
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}}
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/>
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</div>
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To upload examples, please follow these steps:
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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.
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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.
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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.
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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).
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40
docs/docs/examples/midjourney-prompt-chain.mdx
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docs/docs/examples/midjourney-prompt-chain.mdx
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# MidJourney Prompt Chain
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The `MidJourneyPromptChain` can be used to generate imaginative and detailed MidJourney prompts.
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For example, type something like:
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```bash
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Dragon
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```
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And get a response such as:
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```text
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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.
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```
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:::tip
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Notice that the `ConversationSummaryMemory` stores a summary of the conversation over time. Try using it to create better prompts as the conversation goes on.
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:::
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## ⛓️ LangFlow Example
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import ThemedImage from '@theme/ThemedImage';
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import useBaseUrl from '@docusaurus/useBaseUrl';
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import ZoomableImage from '/src/theme/ZoomableImage.js';
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: 'img/midjourney-prompt-chain.png',
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}}
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/>
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#### <a target="\_blank" href="json_files/MidJourney_Prompt_Chain.json" download>Download Flow</a>
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:::note LangChain Components 🦜🔗
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- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
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- [`ConversationSummaryMemory`](https://python.langchain.com/docs/modules/memory/how_to/summary)
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:::
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54
docs/docs/examples/multiple-vectorstores.mdx
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docs/docs/examples/multiple-vectorstores.mdx
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# Multiple Vector Stores
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The example below shows an agent operating with two vector stores built upon different data sources.
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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.
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:::info
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Get the TXT file used [here](https://github.com/hwchase17/chat-your-data/blob/master/state_of_the_union.txt).
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:::
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URL used by the `WebBaseLoader`:
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```txt
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https://pt.wikipedia.org/wiki/Harry_Potter
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```
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:::tip
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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.
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:::
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:::info
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Learn more about Multiple Vector Stores [here](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore?highlight=Multiple%20Vector%20Stores#multiple-vectorstores).
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:::
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## ⛓️ LangFlow Example
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import ThemedImage from '@theme/ThemedImage';
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import useBaseUrl from '@docusaurus/useBaseUrl';
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import ZoomableImage from '/src/theme/ZoomableImage.js';
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: 'img/multiple-vectorstores.png',
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}}
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/>
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#### <a target="\_blank" href="json_files/Multiple_Vector_Stores.json" download>Download Flow</a>
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:::note LangChain Components 🦜🔗
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- [`WebBaseLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_base)
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- [`TextLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/unstructured_file)
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- [`CharacterTextSplitter`](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter)
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- [`OpenAIEmbedding`](https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai)
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- [`Chroma`](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma)
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- [`VectorStoreInfo`](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
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- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
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- [`VectorStoreRouterToolkit`](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore)
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- [`VectorStoreRouterAgent`](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore)
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:::
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50
docs/docs/examples/python-function.mdx
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docs/docs/examples/python-function.mdx
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# Python Function
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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.
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```python
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import re
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def is_brazilian_zipcode(zipcode: str) -> bool:
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pattern = r"\d{5}-?\d{3}"
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# Check if the zip code matches the pattern
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if re.match(pattern, zipcode):
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return True
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return False
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```
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:::tip
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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.
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:::
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The `AgentInitializer` component is a quick way to construct an agent from the model and tools.
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:::info
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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).
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:::
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## ⛓️ LangFlow Example
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import ThemedImage from '@theme/ThemedImage';
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import useBaseUrl from '@docusaurus/useBaseUrl';
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import ZoomableImage from '/src/theme/ZoomableImage.js';
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: 'img/python-function.png',
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}}
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/>
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#### <a target="\_blank" href="json_files/Python_Function.json" download>Download Flow</a>
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:::note LangChain Components 🦜🔗
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- [`PythonFunctionTool`](https://python.langchain.com/docs/modules/agents/tools/how_to/custom_tools)
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- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
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- [`AgentInitializer`](https://python.langchain.com/docs/modules/agents/)
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:::
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46
docs/docs/examples/serp-api-tool.mdx
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docs/docs/examples/serp-api-tool.mdx
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# Serp API Tool
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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.
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:::info
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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.
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:::
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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.
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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.
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:::tip
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In this example, we used [`ChatOpenAI`](https://platform.openai.com/) as the LLM, but feel free to experiment with other Language Models!
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:::
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The `ZeroShotAgent` takes the `LLMChain` and the `Search` tool as inputs, using the tool to find information when necessary.
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:::info
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Learn more about the Serp API [here](https://python.langchain.com/docs/modules/agents/tools/integrations/serpapi).
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:::
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## ⛓️ LangFlow Example
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import ThemedImage from '@theme/ThemedImage';
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import useBaseUrl from '@docusaurus/useBaseUrl';
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import ZoomableImage from '/src/theme/ZoomableImage.js';
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<ZoomableImage
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alt="Docusaurus themed image"
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sources={{
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light: 'img/serp-api-tool.png',
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}}
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/>
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#### <a target="\_blank" href="json_files/SerpAPI_Tool.json" download>Download Flow</a>
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:::note LangChain Components 🦜🔗
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- [`ZeroShotPrompt`](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/)
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- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
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- [`LLMChain`](https://python.langchain.com/docs/modules/chains/foundational/llm_chain)
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- [`Search`](https://python.langchain.com/docs/modules/agents/tools/integrations/serpapi)
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- [`ZeroShotAgent`](https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent)
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:::
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