🔧 chore(chains.mdx): add import statement for Admonition component to improve code organization and readability
🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 📝 chore(docs): update import statements for Admonition component in examples 📝 chore(docs): update link in Prompts component to use Admonition component 📝 chore(docs): update import statements for Admonition component in examples 📝 chore(docs): update link in Conversation Chain component to use Admonition component 📝 chore(docs): update import statements for Admonition component in examples 📝 chore(docs): update link in CSV Loader component to use Admonition component 📝 chore(docs): update import statements for Admonition component in examples 📝 chore(docs): update link in MidJourney Prompt Chain component to use Admonition component 📝 chore(docs): update import statements for Admonition component in examples 📝 chore(docs): update link in Multiple Vector Stores component to use Admonition component 📝 docs(examples/python-function.mdx): add import statement for Admonition component 📝 docs(examples/python-function.mdx): improve readability of tip admonition by breaking lines 📝 docs(examples/python-function.mdx): improve readability of info admonition by breaking lines 📝 docs(examples/serp-api-tool.mdx): add import statement for Admonition component 📝 docs(examples/serp-api-tool.mdx): improve readability of info admonition by breaking lines 📝 docs(guidelines/features.mdx): add import statement for Admonition component 📝 docs(guidelines/features.mdx): improve readability of caution admonition by breaking lines
This commit is contained in:
Gabriel Luiz Freitas Almeida
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import Admonition from "@theme/Admonition";
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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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@ -17,9 +19,10 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
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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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<Admonition type="note" title="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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</Admonition>
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import Admonition from "@theme/Admonition";
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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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<Admonition type="info">
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Make sure to always get the API key from the provider.
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:::
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</Admonition>
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## ⛓️ Langflow Example
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@ -21,8 +25,9 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
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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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<Admonition type="note" title="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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</Admonition>
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import Admonition from "@theme/Admonition";
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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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@ -7,13 +9,18 @@ The `VectoStoreAgent` component retrieves information from one or more vector st
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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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<Admonition type="info">
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The vector store is used for efficient semantic search, while
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`VectorStoreInfo` carries information about it, such as its name and
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description. Embeddings are a way to represent words, phrases, or any entities
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in a vector space. Learn more about them
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[here](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings).
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</Admonition>
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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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<Admonition type="tip">
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Once you build this flow, ask questions about the data in the chat interface
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(e.g., number of rows or columns).
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</Admonition>
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## ⛓️ Langflow Example
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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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<Admonition type="note" title="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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- [`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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</Admonition>
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import Admonition from "@theme/Admonition";
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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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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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<Admonition type="tip">
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Notice that the `ConversationSummaryMemory` stores a summary of the
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conversation over time. Try using it to create better prompts as the
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conversation goes on.
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</Admonition>
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## ⛓️ Langflow Example
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@ -33,8 +37,9 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
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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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<Admonition type="note" title="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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</Admonition>
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import Admonition from "@theme/Admonition";
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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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<Admonition type="info">
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Get the TXT file used
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[here](https://github.com/hwchase17/chat-your-data/blob/master/state_of_the_union.txt).
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</Admonition>
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URL used by the `WebBaseLoader`:
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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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<Admonition type="tip">
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When you build the flow, request information about one of the sources. The
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agent should be able to use the correct source to generate a response.
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</Admonition>
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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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<Admonition type="info">
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Learn more about Multiple Vector Stores
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[here](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore?highlight=Multiple%20Vector%20Stores#multiple-vectorstores).
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</Admonition>
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## ⛓️ Langflow Example
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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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<Admonition type="note" title="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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- [`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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</Admonition>
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import Admonition from "@theme/Admonition";
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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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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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<Admonition type="tip">
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When a tool is called, it is often desirable to have its output returned
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directly to the user. You can do this by setting the **return_direct** flag
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for a tool to be True.
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</Admonition>
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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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<Admonition type="info">
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The `PythonFunction` is a custom component that uses the LangChain 🦜🔗 tool
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decorator. Learn more about it
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[here](https://python.langchain.com/docs/modules/agents/tools/how_to/custom_tools).
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</Admonition>
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## ⛓️ Langflow Example
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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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<Admonition type="note" title="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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</Admonition>
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import Admonition from "@theme/Admonition";
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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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<Admonition type="info">
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To use the Serp API, you first need to sign up [Serp
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API](https://serpapi.com/) for an API key on the provider's website.
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</Admonition>
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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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<Admonition type="tip">
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In this example, we used [`ChatOpenAI`](https://platform.openai.com/) as the
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LLM, but feel free to experiment with other Language Models!
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</Admonition>
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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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<Admonition type="info">
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Learn more about the Serp API
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[here](https://python.langchain.com/docs/modules/agents/tools/integrations/serpapi).
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</Admonition>
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## ⛓️ Langflow Example
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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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<Admonition type="note" title="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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</Admonition>
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