Merge zustand/io/migration
This commit is contained in:
igorrCarvalho
commit
69c4575f90
95 changed files with 1368 additions and 868 deletions
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@ -98,9 +98,9 @@ Used to load [OpenAI’s](https://openai.com/) embedding models.
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Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings).
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:::info
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<Admonition type="info">
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Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP). It provides access, management, and development of applications and services through global data centers. To use Vertex AI PaLM, you need to have the [google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/) Python package installed and credentials configured for your environment.
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:::
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</Admonition>
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- **credentials:** The default custom credentials (google.auth.credentials.Credentials) to use.
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- **location:** The default location to use when making API calls – defaults to `us-central1`.
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@ -1,9 +1,8 @@
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import Admonition from '@theme/Admonition';
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import Admonition from "@theme/Admonition";
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# I/O
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## ChatInput
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### ChatInput
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This component is designed to get user input from the chat.
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@ -15,9 +14,7 @@ This component is designed to get user input from the chat.
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- **Message:** specifies the message text. It is a multiline text input.
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- **Session ID:** specifies the session ID of the chat history.
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- **As Record:** if true, the message will be returned as a _`Record`_. Defaults to _`false`_.
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- **Session ID:** specifies the session ID of the chat history. If provided, the message will be saved in the Message History.
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<Admonition type="note" title="Note">
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<p>
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@ -25,7 +22,7 @@ This component is designed to get user input from the chat.
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</p>
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</Admonition>
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## ChatOutput
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### ChatOutput
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This component is designed to send a message to the chat.
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@ -35,65 +32,21 @@ This component is designed to send a message to the chat.
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- **Sender Name:** specifies the name of the sender. Defaults to _`"AI"`_.
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- **Session ID:** specifies the session ID of the chat history.
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- **Session ID:** specifies the session ID of the chat history. If provided, the message will be saved in the Message History.
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- **Message:** specifies the message text.
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- **As Record:** if true, the message will be returned as a _`Record`_. Defaults to _`false`_.
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<Admonition type="note" title="Note">
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<p>
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If _`As Record`_ is _`true`_ and the _`Message`_ is a _`Record`_, the data of the _`Record`_ will be updated with the _`Sender`_, _`Sender Name`_, and _`Session ID`_.
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</p>
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</Admonition>
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## StoreMessages
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This component is designed to store messages in a structured format. It can store messages provided directly as records or construct records from provided texts and metadata.
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### TextInput
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**Params**
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This component is designed for simple text input, allowing users to pass textual data to subsequent components in the workflow. It's particularly useful for scenarios where a brief user input is required to initiate or influence the flow.
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- **Records:** (Optional) Specifies the list of records to store. Each `Record` should contain the keys `'sender'`, `'sender_name'`, and `'session_id'`. If not provided, `texts` must be used along with `session_id`, `sender`, and `sender_name` to construct the records.
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- **Texts:** (Optional) Specifies the list of text messages to store. If `records` is not provided, `texts` must be provided along with `session_id`, `sender`, and `sender_name`.
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- **Session ID:** (Optional) Specifies the session ID associated with the messages. Required if `records` is not provided and `texts` are used.
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- **Sender:** (Optional) Specifies the identifier of the sender. Required if `records` is not provided and `texts` are used.
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- **Sender Name:** (Optional) Specifies the name of the sender. Required if `records` is not provided and `texts` are used.
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<Admonition type="note" title="Note">
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<p>
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The component prioritizes the use of `Records` for storing messages. If `Records` are not provided, it requires `Texts`, `Session ID`, `Sender`, and `Sender Name` to construct and store records. Ensure that either `Records` or the combination of `Texts`, `Session ID`, `Sender`, and `Sender Name` is provided.
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</p>
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</Admonition>
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## MessageHistory
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This component is designed to retrieve stored messages based on various filters such as sender type, sender name, and session ID. It allows for a flexible retrieval of chat history, providing insights into past interactions.
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**Params**
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- **Sender Type:** (Optional) Specifies the type of the sender. Options are _`"Machine"`_ or _`"User"`_. Filters the messages by the type of sender.
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- **Sender Name:** (Optional) Specifies the name of the sender. Filters the messages by the name of the sender.
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- **Session ID:** (Optional) Specifies the session ID of the chat history. Filters the messages belonging to a specific session.
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- **Number of Messages:** Specifies the number of messages to retrieve. Defaults to _`5`_. Determines how many recent messages from the chat history to fetch.
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<Admonition type="note" title="Note">
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<p>
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The component retrieves messages based on the provided criteria. If no specific criteria are provided, it will return the most recent messages up to the specified limit. This component can be used to review past interactions and analyze the flow of conversations.
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</p>
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</Admonition>
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## TextInput
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This component is designed for simple text input, allowing users to pass textual data to subsequent components in the workflow. It's particularly useful for scenarios where a brief user input is required to initiate or influence the process flow.
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**Params**
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@ -101,6 +54,20 @@ This component is designed for simple text input, allowing users to pass textual
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<Admonition type="note" title="Note">
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<p>
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The `TextInput` component serves as a straightforward means for capturing user input. It ensures that textual data can be seamlessly integrated into the Langflow's component-based processing pipeline, fostering interactivity within automated workflows.
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The `TextInput` component serves as a straightforward means for setting Text input values in the chat window. It ensures that textual data can be seamlessly passed to subsequent components in the flow.
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</p>
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</Admonition>
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### TextOutput
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This component is designed to display text data to the user. It's particularly useful for scenarios where you don't want to send the text data to the chat, but still want to display it.
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**Params**
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- **Value:** Specifies the text data to be displayed. This is where the text data to be displayed is provided. If no value is provided, it defaults to an empty string.
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<Admonition type="note" title="Note">
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<p>
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The `TextOutput` component serves as a straightforward means for displaying text data. It ensures that textual data can be seamlessly observed in the chat window throughout your flow.
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</p>
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</Admonition>
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@ -40,9 +40,10 @@ Wrapper around Anthropic's large language model used for chat-based interactions
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The `CTransformers` component provides access to the Transformer models implemented in C/C++ using the [GGML](https://github.com/ggerganov/ggml) library.
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:::info
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<Admonition type="info">
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Make sure to have the `ctransformers` python package installed. Learn more about installation, supported models, and usage [here](https://github.com/marella/ctransformers).
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:::
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</Admonition>
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**config:** Configuration for the Transformer models. Check out [config](https://github.com/marella/ctransformers#config). Defaults to:
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@ -115,9 +116,9 @@ Wrapper around [Cohere's](https://cohere.com) large language models.
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Wrapper around [HuggingFace](https://www.huggingface.co/models) models.
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:::info
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<Admonition type="info">
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The HuggingFace Hub is an online platform that hosts over 120k models, 20k datasets, and 50k demo apps, all of which are open-source and publicly available. Discover more at [HuggingFace](http://www.huggingface.co).
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:::
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</Admonition>
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- **huggingfacehub_api_token:** Token needed to authenticate the API.
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- **model_kwargs:** Keyword arguments to pass to the model.
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@ -130,9 +131,9 @@ The HuggingFace Hub is an online platform that hosts over 120k models, 20k datas
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The `LlamaCpp` component provides access to the `llama.cpp` models.
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:::info
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||||
<Admonition type="info">
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||||
Make sure to have the `llama.cpp` python package installed. Learn more about installation, supported models, and usage [here](https://github.com/ggerganov/llama.cpp).
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:::
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</Admonition>
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- **echo:** Whether to echo the prompt – defaults to `False`.
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- **f16_kv:** Use half-precision for key/value cache – defaults to `True`.
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@ -181,9 +182,9 @@ Wrapper around [OpenAI's](https://openai.com) large language models.
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Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) large language models.
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||||
:::info
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||||
<Admonition type="info">
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||||
Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP). It provides access, management, and development of applications and services through global data centers. To use Vertex AI PaLM, you need to have the [google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/) Python package installed and credentials configured for your environment.
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:::
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||||
</Admonition>
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- **credentials:** The default custom credentials (google.auth.credentials.Credentials) to use.
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- **location:** The default location to use when making API calls – defaults to `us-central1`.
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@ -203,9 +204,9 @@ Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP).
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Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) large language models.
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||||
|
||||
:::info
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||||
<Admonition type="info">
|
||||
Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP). It provides access, management, and development of applications and services through global data centers. To use Vertex AI PaLM, you need to have the [google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/) Python package installed and credentials configured for your environment.
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:::
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||||
</Admonition>
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- **credentials:** The default custom credentials (google.auth.credentials.Credentials) to use.
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- **location:** The default location to use when making API calls – defaults to `us-central1`.
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@ -12,6 +12,26 @@ Memory is a concept in chat-based applications that allows the system to remembe
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---
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### MessageHistory
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||||
This component is designed to retrieve stored messages based on various filters such as sender type, sender name, session ID, and a specific file path where messages are stored. It allows for a flexible retrieval of chat history, providing insights into past interactions.
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||||
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||||
**Params**
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||||
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||||
- **Sender Type:** (Optional) Specifies the type of the sender. Options are _`"Machine"`_, _`"User"`_, or _`"Machine and User"`_. Filters the messages by the type of the sender.
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- **Sender Name:** (Optional) Specifies the name of the sender. Filters the messages by the name of the sender.
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||||
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- **Session ID:** (Optional) Specifies the session ID of the chat history. Filters the messages belonging to a specific session.
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||||
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||||
- **Number of Messages:** Specifies the number of messages to retrieve. Defaults to _`5`_. Determines how many recent messages from the chat history to fetch.
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||||
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||||
<Admonition type="note" title="Note">
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||||
<p>
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||||
The component retrieves messages based on the provided criteria, including the specific file path for stored messages. If no specific criteria are provided, it will return the most recent messages up to the specified limit. This component can be used to review past interactions and analyze the flow of conversations.
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||||
</p>
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</Admonition>
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### ConversationBufferMemory
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The `ConversationBufferMemory` component is a type of memory system that plainly stores the last few inputs and outputs of a conversation.
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@ -27,7 +47,7 @@ The `ConversationBufferMemory` component is a type of memory system that plainly
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### ConversationBufferWindowMemory
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`ConversationBufferWindowMemory` is a variation of the `ConversationBufferMemory` that maintains a list of the recent interactions in a conversation. It only keeps the last K interactions in memory, which can be useful for maintaining a sliding window of the most recent interactions without letting the buffer get too large.
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`ConversationBufferWindowMemory` is a variation of the `ConversationBufferMemory` that maintains a list of the recent interactions in a conversation. It only keeps the last K interactions in memory, which can be useful for maintaining a sliding window of the most recent interactions without letting the buffer get too large.
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**Params**
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@ -72,7 +92,7 @@ The `ConversationEntityMemory` component incorporates intricate memory structure
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### ConversationSummaryMemory
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The `ConversationSummaryMemory` is a memory component that creates a summary of the conversation over time. It condenses information from the conversation and stores the current summary in memory. It is particularly useful for longer conversations where keeping the entire message history in the prompt would take up too many tokens.
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The `ConversationSummaryMemory` is a memory component that creates a summary of the conversation over time. It condenses information from the conversation and stores the current summary in memory. It is particularly useful for longer conversations where keeping the entire message history in the prompt would take up too many tokens.
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**Params**
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@ -9,6 +9,21 @@ import Admonition from '@theme/Admonition';
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</Admonition>
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### SearchApi
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Real-time search engine results API. Returns structured JSON data that includes answer box, knowledge graph, organic results, and more.
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**Parameters**
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- **Api Key:** A unique identifier for the SearchApi, necessary for authenticating requests to real-time search engines. This key can be retrieved from the [SearchApi dashboard](https://www.searchapi.io/).
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- **Engine:** Specifies the search engine. For instance: google, google_scholar, bing, youtube, and youtube_transcripts. A full list of supported engines is available in the [documentation](https://www.searchapi.io/docs/google).
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- **Parameters:** Allows the selection of any parameters recognized by SearchApi, with some being required and others optional.
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**Output**
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- **Document:** The JSON response from the request as a Document.
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### BingSearchRun
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Bing Search is a web search engine owned and operated by Microsoft. It provides search results for various types of content, including web pages, images, videos, and news articles. It uses a combination of algorithms and human editors to deliver search results to users.
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@ -60,4 +75,4 @@ Tool for getting metadata about a SQL database. The input to this tool is a comm
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**Params**
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- **Db:** SQLDatabase to query.
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- **Db:** SQLDatabase to query.
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@ -4,9 +4,8 @@
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This guide will help you set up a Langflow development VM in a Google Cloud Platform project using Google Cloud Shell.
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:::note
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When Cloud Shell opens, be sure to select **Trust repo**. Some `gcloud` commands might not run in an ephemeral Cloud Shell environment.
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:::
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> Note: When Cloud Shell opens, be sure to select **Trust repo**. Some `gcloud` commands might not run in an ephemeral Cloud Shell environment.
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## Standard VM
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@ -14,6 +14,7 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
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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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dark: "img/buffer-memory.png",
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}}
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/>
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@ -20,6 +20,7 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
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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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||||
dark: "img/basic-chat.png",
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}}
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/>
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@ -32,6 +32,7 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
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alt="Docusaurus themed image"
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||||
sources={{
|
||||
light: "img/csv-loader.png",
|
||||
dark: "img/csv-loader.png",
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||||
}}
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||||
/>
|
||||
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||||
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@ -39,12 +40,12 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
|
||||
<Admonition type="note" title="LangChain Components 🦜🔗">
|
||||
|
||||
- [`CSVLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv)
|
||||
- [`CSVLoader`](https://python.langchain.com/docs/integrations/document_loaders/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)
|
||||
- [`OpenAIEmbedding`](https://python.langchain.com/docs/integrations/text_embedding/openai)
|
||||
- [`Chroma`](https://python.langchain.com/docs/integrations/vectorstores/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)
|
||||
- [`VectorStoreAgent`](https://js.langchain.com/docs/modules/agents/tools/how_to/agents_with_vectorstores)
|
||||
|
||||
</Admonition>
|
||||
|
|
|
|||
|
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@ -14,6 +14,7 @@ The CustomComponent class allows us to create components that interact with Lang
|
|||
alt="Document Processor Component"
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||||
sources={{
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||||
light: "img/flow_runner.png",
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||||
dark: "img/flow_runner.png",
|
||||
}}
|
||||
style={{
|
||||
width: "30%",
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||||
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@ -339,6 +340,7 @@ Done! This is what our script and custom component looks like:
|
|||
alt="Document Processor Code"
|
||||
sources={{
|
||||
light: "img/flow_runner_code.png",
|
||||
dark: "img/flow_runner_code.png",
|
||||
}}
|
||||
style={{
|
||||
maxWidth: "100%",
|
||||
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@ -353,6 +355,7 @@ Done! This is what our script and custom component looks like:
|
|||
alt="Document Processor Component"
|
||||
sources={{
|
||||
light: "img/flow_runner.png",
|
||||
dark: "img/flow_runner.png",
|
||||
}}
|
||||
style={{
|
||||
width: "40%",
|
||||
|
|
|
|||
|
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@ -12,6 +12,7 @@ Langflow Examples is a repository on [GitHub](https://github.com/logspace-ai/lan
|
|||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/community-examples.png",
|
||||
dark: "img/community-examples.png",
|
||||
}}
|
||||
style={{ width: "100%" }}
|
||||
/>
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||||
|
|
|
|||
|
|
@ -32,6 +32,7 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/midjourney-prompt-chain.png",
|
||||
dark: "img/midjourney-prompt-chain.png",
|
||||
}}
|
||||
/>
|
||||
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||||
|
|
@ -40,6 +41,6 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
<Admonition type="note" title="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)
|
||||
- [`ConversationSummaryMemory`](https://python.langchain.com/docs/modules/memory/types/summary)
|
||||
|
||||
</Admonition>
|
||||
|
|
|
|||
|
|
@ -24,7 +24,7 @@ https://pt.wikipedia.org/wiki/Harry_Potter
|
|||
|
||||
<Admonition type="info">
|
||||
Learn more about Multiple Vector Stores
|
||||
[here](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore?highlight=Multiple%20Vector%20Stores#multiple-vectorstores).
|
||||
[here](https://python.langchain.com/docs/modules/data_connection/vectorstores/).
|
||||
</Admonition>
|
||||
|
||||
## ⛓️ Langflow Example
|
||||
|
|
@ -37,6 +37,7 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/multiple-vectorstores.png",
|
||||
dark: "img/multiple-vectorstores.png",
|
||||
}}
|
||||
/>
|
||||
|
||||
|
|
@ -44,14 +45,14 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
|
||||
<Admonition type="note" title="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)
|
||||
- [`WebBaseLoader`](https://python.langchain.com/docs/integrations/document_loaders/web_base)
|
||||
- [`TextLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/)
|
||||
- [`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)
|
||||
- [`OpenAIEmbedding`](https://python.langchain.com/docs/integrations/text_embedding/openai)
|
||||
- [`Chroma`](https://python.langchain.com/docs/integrations/vectorstores/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)
|
||||
- [`VectorStoreRouterToolkit`](https://js.langchain.com/docs/modules/agents/tools/how_to/agents_with_vectorstores)
|
||||
- [`VectorStoreRouterAgent`](https://js.langchain.com/docs/modules/agents/tools/how_to/agents_with_vectorstores)
|
||||
|
||||
</Admonition>
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ The `AgentInitializer` component is a quick way to construct an agent from the m
|
|||
<Admonition type="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).
|
||||
[here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
|
||||
</Admonition>
|
||||
|
||||
## ⛓️ Langflow Example
|
||||
|
|
@ -41,6 +41,7 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/python-function.png",
|
||||
dark: "img/python-function.png",
|
||||
}}
|
||||
/>
|
||||
|
||||
|
|
@ -48,7 +49,7 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
|
||||
<Admonition type="note" title="LangChain Components 🦜🔗">
|
||||
|
||||
- [`PythonFunctionTool`](https://python.langchain.com/docs/modules/agents/tools/how_to/custom_tools)
|
||||
- [`PythonFunctionTool`](https://python.langchain.com/docs/modules/agents/tools/custom_tools)
|
||||
- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
|
||||
- [`AgentInitializer`](https://python.langchain.com/docs/modules/agents/)
|
||||
|
||||
|
|
|
|||
52
docs/docs/examples/searchapi-tool.mdx
Normal file
52
docs/docs/examples/searchapi-tool.mdx
Normal file
|
|
@ -0,0 +1,52 @@
|
|||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# SearchApi Tool
|
||||
|
||||
The [SearchApi](https://www.searchapi.io/) allows developers to retrieve results from search engines such as Google, Google Scholar, YouTube, YouTube transcripts, and more, and can be used as in Langflow through the `SearchApi` tool.
|
||||
|
||||
<Admonition type="info">
|
||||
To use the SearchApi, you must first obtain an API key by registering at [SearchApi's website](https://www.searchapi.io/).
|
||||
</Admonition>
|
||||
|
||||
In the given example, we specify `engine` as `youtube_transcripts` and provide a `video_id`.
|
||||
|
||||
<Admonition type="info">
|
||||
All engines and parameters can be found in [SearchApi documentation](https://www.searchapi.io/docs/google).
|
||||
</Admonition>
|
||||
|
||||
The `RetrievalQA` chain processes a `Document` along with a user's question to return an answer.
|
||||
|
||||
<Admonition type="tip">
|
||||
In this example, we used [`ChatOpenAI`](https://platform.openai.com/) as the
|
||||
LLM, but feel free to experiment with other Language Models!
|
||||
</Admonition>
|
||||
|
||||
The `RetrievalQA` takes `CombineDocsChain` and `SearchApi` tool as inputs, using the tool as a `Document` to answer questions.
|
||||
|
||||
<Admonition type="info">
|
||||
Learn more about the SearchApi
|
||||
[here](https://python.langchain.com/docs/integrations/tools/searchapi).
|
||||
</Admonition>
|
||||
|
||||
## ⛓️ 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/searchapi-tool.png",
|
||||
}}
|
||||
/>
|
||||
|
||||
#### <a target="\_blank" href="json_files/SearchApi_Tool.json" download>Download Flow</a>
|
||||
|
||||
<Admonition type="note" title="LangChain Components 🦜🔗">
|
||||
|
||||
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
|
||||
- [`SearchApiAPIWrapper`](https://python.langchain.com/docs/integrations/providers/searchapi#wrappers)
|
||||
- [`ZeroShotAgent`](https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent)
|
||||
|
||||
</Admonition>
|
||||
|
|
@ -22,7 +22,7 @@ The `ZeroShotAgent` takes the `LLMChain` and the `Search` tool as inputs, using
|
|||
|
||||
<Admonition type="info">
|
||||
Learn more about the Serp API
|
||||
[here](https://python.langchain.com/docs/modules/agents/tools/integrations/serpapi).
|
||||
[here](https://python.langchain.com/docs/integrations/providers/serpapi ).
|
||||
</Admonition>
|
||||
|
||||
## ⛓️ Langflow Example
|
||||
|
|
@ -35,6 +35,7 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/serp-api-tool.png",
|
||||
dark: "img/serp-api-tool.png",
|
||||
}}
|
||||
/>
|
||||
|
||||
|
|
@ -45,7 +46,7 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
- [`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)
|
||||
- [`Search`](https://python.langchain.com/docs/integrations/providers/serpapi)
|
||||
- [`ZeroShotAgent`](https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent)
|
||||
|
||||
</Admonition>
|
||||
|
|
|
|||
|
|
@ -13,6 +13,7 @@ Creating flows with Langflow is easy. Drag sidebar components onto the canvas an
|
|||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/langflow_canvas.png",
|
||||
dark: "img/langflow_canvas.png"
|
||||
}}
|
||||
/>
|
||||
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/hugging-face.png",
|
||||
dark: "img/hugging-face.png",
|
||||
}}
|
||||
style={{ width: "100%" }}
|
||||
/>
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ Langflow offers an API Key functionality that allows users to access their indiv
|
|||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/api-key.png"),
|
||||
dark: useBaseUrl("img/api-key.png"),
|
||||
}}
|
||||
style={{ width: "50%", maxWidth: "600px", margin: "0 auto" }}
|
||||
/>
|
||||
|
|
|
|||
|
|
@ -13,6 +13,7 @@ Langflow’s chat interface provides a user-friendly experience and functionalit
|
|||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/chat_interface.png"),
|
||||
dark: useBaseUrl("img/chat_interface.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
|
|
@ -25,6 +26,7 @@ Notice that editing variables in the chat interface take place temporarily and w
|
|||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/chat_interface2.png"),
|
||||
dark: useBaseUrl("img/chat_interface2.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
|
|
@ -36,6 +38,7 @@ To view the complete prompt in its original, structured format, click the "Displ
|
|||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/chat_interface3.png"),
|
||||
dark: useBaseUrl("img/chat_interface3.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
|
|
@ -47,6 +50,7 @@ In the chat interface, you can redefine which variable should be interpreted as
|
|||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: useBaseUrl("img/chat_interface4.png"),
|
||||
dark: useBaseUrl("img/chat_interface4.png"),
|
||||
}}
|
||||
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
|
||||
/>
|
||||
|
|
|
|||
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Reference in a new issue