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Documentation Index

Fetch the complete documentation index at: https://docs.aipower.org/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

Knowledge Base stores the content AI Puffer can search before it generates an answer or a piece of content. Use it for support answers, product details, documentation, policies, posts, pages, WooCommerce products, uploaded documents, and other source text you want AI Puffer to use as context.

Providers

Use OpenAI Vector Stores, Pinecone, Qdrant, or Chroma.

Manage Vector Stores

Create, select, or delete vector targets.

Add Data

Add data and manage source records.

Settings

Configure visibility, chunking, embedding batches, content rules, and search.

Semantic Search

Publish a frontend vector search form.

Troubleshooting

Fix missing targets, dimension errors, and empty results.

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Providers

ProviderTarget nameHow it works
OpenAIVector storeAI Puffer sends the source data to OpenAI Vector Stores. No separate embedding model is selected in AI Puffer for this target.
PineconeIndexAI Puffer creates embeddings with the model you choose, then stores the vectors in a Pinecone index.
QdrantCollectionAI Puffer creates embeddings with the model you choose, then stores the vectors in a Qdrant collection.
ChromaCollectionAI Puffer creates embeddings with the model you choose, then stores the vectors in a Chroma collection.
For Pinecone, Qdrant, and Chroma, the index or collection dimension must match the embedding model. For example, if your Pinecone index, Qdrant collection, or Chroma collection is 3072 dimensions, use a 3072-dimension embedding model when adding data and when searching that data later.
If the dimension does not match, the vector provider can reject the data or return unusable search results.

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OpenAI

OpenAI Vector Stores use your OpenAI account directly.
  1. Go to AI Puffer > Settings > AI.
  2. Select OpenAI as the AI provider.
  3. Enter your OpenAI API key.
  4. Sync models if needed.
  5. Go to AI Puffer > Knowledge Base > Stores to create or refresh OpenAI vector stores.
OpenAI Vector Stores do not require a separate embedding model selection in Knowledge Base. OpenAI handles file storage, chunking, embedding, and vector search on its side.
OpenAI API key settings

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Pinecone

Pinecone is configured from the Integrations settings.
  1. Go to AI Puffer > Settings > Integrations.
  2. Select Pinecone.
  3. Enter your Pinecone API Key.
  4. Click Sync Indexes to load indexes from Pinecone.
  5. Go to AI Puffer > Knowledge Base > Stores to create, refresh, or delete indexes.
When you create a Pinecone index in AI Puffer, enter the dimension that matches the embedding model you plan to use.
Pinecone API key

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Qdrant

Qdrant requires both an endpoint URL and an API key.
  1. Go to AI Puffer > Settings > Integrations.
  2. Select Qdrant.
  3. Enter your Qdrant URL.
  4. Enter your Qdrant API Key.
  5. Click Sync Collections to load collections from Qdrant.
  6. Go to AI Puffer > Knowledge Base > Stores to create, refresh, or delete collections.
When you create a Qdrant collection in AI Puffer, enter the dimension that matches the embedding model you plan to use.
Qdrant API key

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Chroma

Chroma uses endpoint, tenant, and database settings.
  1. Go to AI Puffer > Settings > Integrations.
  2. Select Chroma.
  3. Enter your Chroma URL. For Cloud, you can use https://api.trychroma.com
  4. Enter your Chroma API Key if you use Chroma Cloud or an authenticated server.
  5. Enter the Tenant.
  6. Enter the Database.
  7. Click Sync Collections to load collections from Chroma.
  8. Go to AI Puffer > Knowledge Base > Stores to create, refresh, or delete collections.
For local Chroma, the default tenant is default_tenant and the default database is default_database.
Chroma API key

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Embedding Providers

Pinecone, Qdrant, and Chroma store vectors that AI Puffer creates with a selected embedding model. Before adding data to these providers, configure the embedding provider you want to use in AI Puffer > Settings > AI. Supported embedding providers include OpenAI, Google, Azure, and OpenRouter. The selected embedding model must match the dimension of the Pinecone index, Qdrant collection, or Chroma collection. xAI is not an embedding provider or vector store provider in the current integration. xAI chatbots, forms, and text workflows can still use retrieved Knowledge Base context from OpenAI, Pinecone, Qdrant, or Chroma because AI Puffer sends that context as text.

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Manage Vector Stores

Use AI Puffer > Knowledge Base > Stores to create, refresh, inspect, or delete vector targets. The Stores tab is where you manage OpenAI vector stores, Pinecone indexes, Qdrant collections, and Chroma collections. The Data tab uses these targets when you add content.
Knowledge Base provider selector

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OpenAI Vector Stores

  1. Add your OpenAI API key in AI Puffer > Settings > AI.
  2. Go to AI Puffer > Knowledge Base > Stores.
  3. Select OpenAI as the provider.
  4. Click Create Store.
  5. Enter a store name.
  6. Click Create.
OpenAI handles the vector store search on its side. AI Puffer stores a local source record so you can see what was added.
OpenAI Create Vector

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Pinecone Indexes

  1. Add your Pinecone API key in AI Puffer > Settings > Integrations.
  2. Go to AI Puffer > Knowledge Base > Stores.
  3. Select Pinecone as the provider.
  4. Select the embedding model you plan to use.
  5. Click Create Store.
  6. Enter an index name.
  7. Enter the dimension for the selected embedding model.
  8. Click Create.
Use the same embedding model when you add data to the index and when a module searches that index.
Pinecone Create Index

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Qdrant Collections

  1. Add your Qdrant URL and API key in AI Puffer > Settings > Integrations.
  2. Go to AI Puffer > Knowledge Base > Stores.
  3. Select Qdrant as the provider.
  4. Select the embedding model you plan to use.
  5. Click Create Store.
  6. Enter a collection name.
  7. Enter the dimension for the selected embedding model.
  8. Click Create.
Use the same embedding model when you add data to the collection and when a module searches that collection.
Qdrant Create collection

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Chroma Collections

  1. Add your Chroma endpoint, tenant, database, and API key in AI Puffer > Settings > Integrations.
  2. Go to AI Puffer > Knowledge Base > Stores.
  3. Select Chroma as the provider.
  4. Click Create Store.
  5. Enter a collection name.
  6. Click Create.
Chroma collections do not require a dimension when they are created in AI Puffer. Use the same embedding model when you add data to the collection and when a module searches that collection.
Chroma Create collection
Use Refresh when you need AI Puffer to fetch the latest stores, indexes, or collections from the selected provider. To delete a target, use the available action in the Stores table.

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Add Data

Use AI Puffer > Knowledge Base > Data to add new source data and manage existing source records. Before adding data:
  1. Go to AI Puffer > Knowledge Base > Data.
  2. Select a provider.
  3. Select the target vector store, index, or collection.
  4. For Pinecone, Qdrant, or Chroma, select the embedding model.
  5. Click + Add Data.
Knowledge Base Add data panel

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Q&A

Use Q&A for short answers that should be easy to retrieve later.
  1. Select Q&A.
  2. Enter the question.
  3. Enter the answer.
  4. Click Add Q&A.
AI Puffer stores the pair as text: 
Q: question text
A: answer text
Knowledge Base Q&A tab

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Text

Use Text for policies, instructions, product notes, support snippets, or any source text that does not already exist as WordPress content.
  1. Select Text.
  2. Paste the source text.
  3. Click Add Text.
Knowledge Base Text tab

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Files

Use Files when the source is already in a document.
  1. Select Files.
  2. Click Choose files.
  3. Select one or more files.
Files start uploading and training after selection. Supported file extensions: 
.pdf, .docx, .txt, .md, .csv, .json
For Pinecone, Qdrant, and Chroma, AI Puffer extracts text, splits large files into chunks, creates embeddings, and stores each chunk in the selected index or collection. File chunks can be embedded in batches to reduce the number of embedding API requests. File size is limited by your WordPress/PHP upload settings. OpenAI Vector Store uploads also use OpenAI’s file limits.
Knowledge Base Files tab

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Website

Use Website when the source is WordPress content.
  1. Select Website.
  2. Choose All or Choose items.
  3. Select the content types.
  4. If using Choose items, select the individual published items.
  5. Click Add Items.
Website training uses published content. Posts and pages are selected by default. WooCommerce products appear when WooCommerce is active. Public custom post types can also appear. When WordPress content is indexed, AI Puffer builds the source text from the URL, title, excerpt, content, public custom fields, public taxonomies, and available WooCommerce product data.
Knowledge Base Website all mode

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Manage Data

The source table in the Data tab shows the local records created while adding data.
ColumnWhat it shows
StatusTrained, Processing, Failed, or another provider status.
ItemPost title, text preview, file name, or source identifier. Provider, target, and embedding details appear below the item name.
TypeSite Content, Text, Q&A, File Upload, or User Upload.
UpdatedLast update time and relative age.
ActionsAvailable actions for the source.
Knowledge Base source table
Available actions:
ActionUse it for
ViewReview the stored source preview.
EditEdit a text source and save it again.
RetrainRe-index a WordPress content source after the content changes.
DeleteRemove the source from the external provider and from the local source table.
Knowledge Base source table
Knowledge Base source preview

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Settings

Open AI Puffer > Knowledge Base > Settings to configure Knowledge Base behavior.
Knowledge Base settings

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General

General controls how Knowledge Base records and indexing buttons appear in the admin.
SettingWhat it does
Hide user uploadsHides chatbot upload records from the main Knowledge Base source table.
Show index buttonShows vector indexing controls on supported WordPress list screens.
Knowledge Base general settings

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Chunking

Document chunking controls how AI Puffer splits large uploaded files before embedding them for Pinecone, Qdrant, or Chroma.
SettingDefaultRangeUse it for
Avg chars per token42 to 4Estimates how many characters equal one token.
Max tokens per chunk3000256 to 6000Sets the maximum chunk size before embedding.
Overlap tokens1500 to 1000Repeats a small part of the previous chunk so context does not break sharply.
Use smaller chunks when an embedding provider rejects long input. Keep some overlap for long documents where meaning continues across sections. OpenAI Vector Store file uploads use OpenAI File Search chunking instead of the Pinecone, Qdrant, and Chroma chunking settings above.
OpenAI File Search settingDefaultRangeUse it for
Indexing strategyAutoAuto or CustomLets OpenAI choose chunking automatically, or lets AI Puffer send custom chunking values.
Max chunk size tokens800100 to 4096Sets the maximum OpenAI File Search chunk size when Custom is selected.
Chunk overlap tokens4000 to half of max chunk sizeRepeats part of the previous OpenAI File Search chunk when Custom is selected.

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Embedding Batches

Embedding Batches controls how many file chunks AI Puffer sends to the embedding provider in one request.
  1. Go to AI Puffer > Knowledge Base > Settings.
  2. Open Chunking & Batches.
  3. Click Embedding Batches to reveal the provider batch settings.
  4. Adjust the batch size for the embedding provider you use.
  5. Wait for the settings autosave to finish.
ProviderDefaultMaximum
Google100100
OpenAI50100
OpenRouter50100
Azure50100
Ollama10100
Knowledge Base Batch settings
For example, a batch size of 50 means AI Puffer sends up to 50 prepared file chunks to the embedding API at once. Larger batches can make file upload training much faster because they reduce repeated API calls.
Embedding batch settings apply only to chunked file uploads for Pinecone, Qdrant, and Chroma. They do not change Q&A, Text, Website training, semantic search queries, or OpenAI Vector Store file uploads.
If a provider returns rate limit errors such as HTTP 429, lower that provider’s batch size and try again. AI Puffer can pause and retry file upload processing when the provider sends a retry delay, but lowering the batch size is usually better for accounts with stricter quotas.
Knowledge Base chunking and batches settings

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Indexing Controls

Indexing controls define which WordPress fields are included when Website training or list-screen indexing sends WordPress content to a vector target.
  1. Go to AI Puffer > Knowledge Base > Settings.
  2. In Indexing Controls, click Configure.
  3. Select a post type.
  4. Adjust Basic Labels if you want different labels for source URL, title, excerpt, or content.
  5. Enable or disable custom fields.
  6. Enable or disable taxonomies.
  7. For WooCommerce products, enable or disable product data such as SKU, price, stock, dimensions, and attributes.
  8. Save.
If the Save button shows Upgrade, activate Pro before saving indexing rules.
Knowledge Base indexing controls
When Show index button is enabled, supported WordPress list screens can show vector indexing controls.
ControlWhat it does
Index Status columnShows whether a post has already been indexed.
Index Status filterFilters content by indexed or not indexed.
Add to Vector Store actionSends selected posts to a vector target.
Semantic Search publishes a search form that queries a Pinecone index, Qdrant collection, or Chroma collection from the frontend. Open AI Puffer > Knowledge Base > Settings. In Semantic Search, click Configure.
  1. Select Vector DB: Pinecone, Qdrant, or Chroma.
  2. Select the index or collection.
  3. Select the embedding model.
  4. Set Number of Results.
  5. Set No Results Text.
  6. Test a query in Try semantic search.
  7. Copy the shortcode.
[aipkit_semantic_search]
Semantic Search uses the global settings from this panel. It does not use OpenAI Vector Stores in the current UI. Use the same embedding model that was used when the Pinecone, Qdrant, or Chroma data was added.
Knowledge Base Semantic Search settings

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Troubleshooting

Configure the provider credentials, then sync or create the vector target again.
Confirm the embedding model dimension matches the index or collection dimension.
Check Knowledge Base > Settings > Content Rules for that post type.
Enable Knowledge Base > Settings > Basics > Show index button and confirm the user role can access the vector content indexer module.
Confirm the selected target contains trained data and the same embedding model is selected.