embedding component

The embedding component runs one embedding operation and returns a structured result: a SQLite keyword store for index, search, upsert, and delete, an embedding backend for vectorize and embed_query, and Cohere for rerank. It is a kdeps component from the components asset set: call it with a component: resource, no install step. Its call is the kdeps API's embedding tool, which runs the native embedding executor. A workflow that uses embedding: directly - primary or in a before:/after: step - is rejected.

It works in both modes: as a DAG step in workflow mode, and inside any workflow the LLM calls as a tool in agent mode.

Configuration

# resources/store.yaml
actionId: store
name: Store Document
component:
  name: embedding                   # from the components asset set; a components/embedding/ of your own wins
  with:
    operation: index                # required: index | search | upsert | delete | vectorize | embed_query | rerank
    text: "{{ get('text') }}"       # text to index, search for, upsert or delete
    collection: docs                # optional namespace (default: "default")
    dbPath: /data/store.db          # optional SQLite path; default from the kdeps embedding defaults
    limit: 5                        # optional max search results

Input	Type	Required	Default	Description
operation	string	yes	-	index, search, upsert, delete, vectorize, embed_query, or rerank
text	string	see below	-	Text to index, search for, upsert, or delete. Required for index, search, and upsert; empty deletes the whole collection on delete
collection	string	no	"default"	Namespace inside the SQLite database
dbPath	string	no	kdeps embedding default	SQLite database file path
limit	number	no	kdeps embedding default	Max results for search
inputs	array	yes for vectorize, embed_query	-	Texts to embed
model	string	no	backend default	Embedding model, e.g. text-embedding-3-small
backend	string	no	kdeps LLM backend	openai, ollama, or google
baseURL	string	no	-	Custom base URL for an OpenAI-compatible backend
rerankQuery	string	yes for rerank	-	Query the candidate documents are ranked against
rerankDocuments	array	yes for rerank	-	Candidate documents to rerank
rerankTopN	number	no	0 (all)	Number of top reranked results

Operations

Operation	Backing	Description
index	SQLite	Insert text (ignored if duplicate)
upsert	SQLite	Insert or replace text
search	SQLite	Case-insensitive keyword search via LIKE
delete	SQLite	Delete by text, or the whole collection when text is empty
vectorize	embedding backend	Convert inputs into vectors
embed_query	embedding backend	Convert text into a query vector
rerank	Cohere	Rank rerankDocuments against rerankQuery

Output

{
  "operation": "search",
  "collection": "docs",
  "query": "compiled language",
  "results": ["Go is a statically typed, compiled programming language."],
  "count": 1,
  "json": "{\"collection\":\"docs\",...}"
}

Read fields with output('store').results, output('store').count, and so on.

Key	Type	Description
operation	string	The operation that was performed
collection	string	The collection used
success	bool	true on success
results	[]string	Matching texts (search only)
count	integer	Number of results (search only)
affected	integer	Rows deleted (delete only)
json	string	Full result as a JSON string

A failed operation does not return this map: the call fails and onError: on the resource decides what happens next.

Examples

Build a RAG pipeline

# Step 1: Scrape content
actionId: fetch
component:
  name: web_scraper
  with:
    url: "{{ get('url') }}"

# Step 2: Index it
actionId: storeDoc
requires: [fetch]
component:
  name: embedding
  with:
    operation: index
    text: "{{ output('fetch').content }}"
    collection: knowledge
    dbPath: /data/store.db

# Step 3: Search on user query
actionId: findDocs
component:
  name: embedding
  with:
    operation: search
    text: "{{ get('query') }}"
    collection: knowledge
    dbPath: /data/store.db
    limit: 5

# Step 4: Answer with context
actionId: answer
requires: [findDocs]
chat:
  model: llama3.2:1b
  prompt: |
    Context: {{ output('findDocs').results }}
    Question: {{ get('query') }}
apiResponse:
  response: "{{ output('answer') }}"

Vector embeddings

# resources/embed.yaml
actionId: embed
component:
  name: embedding
  with:
    operation: vectorize
    inputs: ["{{ get('text') }}"]    # texts to embed
    model: text-embedding-3-small
    backend: openai                  # KDEPS_OPENAI_API_KEY or ~/.kdeps/config.yaml

Collections

Use collection to namespace documents - useful for multi-tenant or multi-topic stores:

# Index into separate collections
component:
  name: embedding
  with:
    operation: index
    text: "..."
    collection: contracts

# Search only within one collection
component:
  name: embedding
  with:
    operation: search
    text: termination clause
    collection: contracts

---

Note: index, search, upsert, and delete use keyword (LIKE) matching, not vector similarity. Use vectorize and embed_query with a backend for vector embeddings.

See also

• Search resource - search local files and the web
• LLM resource - use search results as context