VectrixDB Easy API - The Simplest Vector Database

Zero config. Text in, results out. One line for everything.

Public fields

name

Collection name

path

Storage path

dimension

Vector dimension

model_name

Model identifier

model_type

Model type

language

Language setting

tier

Storage tier

Methods

Public methods

• Vectrix$new()

• Vectrix$add()

• Vectrix$set_language()

• Vectrix$search()

• Vectrix$delete()

• Vectrix$clear()

• Vectrix$count()

• Vectrix$get()

• Vectrix$similar()

• Vectrix$close()

• Vectrix$print()

• Vectrix$clone()

---

Method new()

Create or open a VectrixDB collection

Usage

Vectrix$new(
  name = "default",
  path = NULL,
  model = NULL,
  dimension = NULL,
  embed_fn = NULL,
  model_path = NULL,
  language = NULL,
  tier = "dense",
  auto_download = TRUE
)

Arguments

name

Collection name

path

Storage path. Defaults to a session temp directory.

model

Embedding model: "tfidf" (default), "glove-50", "glove-100", "glove-200", "glove-300", or "word2vec"

dimension

Vector dimension (auto-detected for GloVe)

embed_fn

Custom embedding function: fn(texts) -> matrix

model_path

Path to pre-trained word vectors (GloVe .txt or word2vec .bin)

language

Language behavior: "en" (English-focused) or "ml" (multilingual/Unicode)

tier

Storage tier: "dense", "hybrid", "ultimate", or "graph"

auto_download

Automatically download GloVe vectors if needed (default: TRUE)

Examples

\dontrun{
# Default TF-IDF embeddings (no external files needed)
db <- Vectrix$new("docs")

# With GloVe 100d word vectors (auto-downloads ~130MB)
db <- Vectrix$new("docs", model = "glove-100")

# With pre-downloaded GloVe
db <- Vectrix$new("docs", model_path = "path/to/glove.6B.100d.txt")

# Custom embedding function
db <- Vectrix$new("docs", embed_fn = my_embed_function, dimension = 768)
}

---

Method add()

Add texts to the collection

Usage

Vectrix$add(texts, metadata = NULL, ids = NULL)

Arguments

texts

Single text or character vector of texts

metadata

Optional metadata list or list of lists

ids

Optional custom IDs

Returns

Self for chaining

Examples

\dontrun{
db$add(c("text 1", "text 2"))
db$add("another text", metadata = list(source = "web"))
}

---

Method set_language()

Update collection language behavior

Usage

Vectrix$set_language(language = "en")

Arguments

language

Language behavior: "en" or "ml"

Returns

Self for chaining

---

Method search()

Search the collection

Usage

Vectrix$search(
  query,
  limit = 10,
  mode = "hybrid",
  rerank = NULL,
  filter = NULL,
  diversity = 0.7
)

Arguments

query

Search query text

limit

Number of results (default: 10)

mode

Search mode: "dense", "sparse", "hybrid", "ultimate"

rerank

Reranking method: NULL, "mmr", "exact", "cross-encoder"

filter

Metadata filter

diversity

Diversity parameter for MMR (0-1)

Returns

Results object with search results

Examples

\dontrun{
results <- db$search("python programming")
results <- db$search("AI", mode = "ultimate", rerank = "mmr")
print(results$top()$text)
}

---

Method delete()

Delete documents by ID

Usage

Vectrix$delete(ids)

Arguments

ids

Document ID(s) to delete

Returns

Self for chaining

---

Method clear()

Clear all documents from collection

Usage

Vectrix$clear()

Returns

Self for chaining

---

Method count()

Get number of documents

Usage

Vectrix$count()

Returns

Integer count

---

Method get()

Get documents by ID

Usage

Vectrix$get(ids)

Arguments

ids

Document ID(s)

Returns

List of Result objects

---

Method similar()

Find similar documents to a given document

Usage

Vectrix$similar(id, limit = 10)

Arguments

id

Document ID

limit

Number of results

Returns

Results object

---

Method close()

Close the database connection

Usage

Vectrix$close()

---

Method print()

Print Vectrix summary

Usage

Vectrix$print()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Vectrix$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # \dontrun{
# Create and add - ONE LINE
db <- Vectrix$new("my_docs")$add(c("Python is great", "Machine learning is fun"))

# Search - ONE LINE
results <- db$search("programming")

# Full power - STILL ONE LINE
results <- db$search("AI", mode = "ultimate")  # dense + sparse + rerank
} # }


## ------------------------------------------------
## Method `Vectrix$new`
## ------------------------------------------------

if (FALSE) { # \dontrun{
# Default TF-IDF embeddings (no external files needed)
db <- Vectrix$new("docs")

# With GloVe 100d word vectors (auto-downloads ~130MB)
db <- Vectrix$new("docs", model = "glove-100")

# With pre-downloaded GloVe
db <- Vectrix$new("docs", model_path = "path/to/glove.6B.100d.txt")

# Custom embedding function
db <- Vectrix$new("docs", embed_fn = my_embed_function, dimension = 768)
} # }

## ------------------------------------------------
## Method `Vectrix$add`
## ------------------------------------------------

if (FALSE) { # \dontrun{
db$add(c("text 1", "text 2"))
db$add("another text", metadata = list(source = "web"))
} # }

## ------------------------------------------------
## Method `Vectrix$search`
## ------------------------------------------------

if (FALSE) { # \dontrun{
results <- db$search("python programming")
results <- db$search("AI", mode = "ultimate", rerank = "mmr")
print(results$top()$text)
} # }