Vector Store Module

Vector storage adapters for semantic search and document retrieval

Vector Store Module

The Vector Store module provides concrete implementations of vector storage backends for semantic search and document retrieval.

Import

python

from praisonai.adapters import ChromaVectorStore
from praisonai.adapters.vector_stores import PineconeVectorStore

Quick Example

python

from praisonai.adapters import ChromaVectorStore

# Create ChromaDB vector store
store = ChromaVectorStore(
    namespace="my_documents",
    persist_directory=".praison/chroma"
)

# Add documents with embeddings
ids = store.add(
    texts=["Document 1", "Document 2"],
    embeddings=[[0.1, 0.2, ...], [0.3, 0.4, ...]],
    metadatas=[{"source": "file1.txt"}, {"source": "file2.txt"}]
)

# Query by similarity
results = store.query(embedding=[0.1, 0.2, ...], top_k=5)

Features

Multiple backend support (ChromaDB, Pinecone)
Namespace-based document organization
Persistent local storage with ChromaDB
Cloud vector database integration with Pinecone
Lazy loading of optional dependencies
Per-instance telemetry disabled via Settings(anonymized_telemetry=False)

Classes

`ChromaVectorStore`

ChromaDB vector store adapter with local persistence.

python

from praisonai.adapters import ChromaVectorStore

store = ChromaVectorStore(
    namespace="default",
    persist_directory=".praison/chroma"
)

Parameters:

Parameter	Type	Default	Description
`config`	`dict`	`None`	Configuration options
`namespace`	`str`	`"default"`	Collection namespace
`persist_directory`	`str`	`".praison/chroma"`	Storage directory

`PineconeVectorStore`

Pinecone cloud vector store adapter.

python

from praisonai.adapters.vector_stores import PineconeVectorStore

store = PineconeVectorStore(
    api_key="your-api-key",
    index_name="praisonai",
    namespace="default"
)

Parameters:

Parameter	Type	Default	Description
`api_key`	`str`	`PINECONE_API_KEY` env	Pinecone API key
`index_name`	`str`	`"praisonai"`	Pinecone index name
`namespace`	`str`	`"default"`	Vector namespace

Methods

`add(texts, embeddings, metadatas=None, ids=None, namespace=None)`

Add vectors to the store.

Parameters:

texts (List[str]): Document texts
embeddings (List[List[float]]): Vector embeddings
metadatas (List[dict], optional): Metadata for each document
ids (List[str], optional): Custom IDs (auto-generated if not provided)
namespace (str, optional): Override default namespace

Returns: List[str] – IDs of added documents

`query(embedding, top_k=10, namespace=None, filter=None)`

Query vectors by similarity.

Parameters:

embedding (List[float]): Query vector
top_k (int): Number of results to return
namespace (str, optional): Override default namespace
filter (dict, optional): Metadata filter

Returns: List[VectorRecord] – Matching records with scores. For ChromaVectorStore (v4.6.162+), VectorRecord.embedding holds each record’s stored embedding, not the query vector.

`delete(ids=None, namespace=None, filter=None, delete_all=False)`

Delete vectors from the store.

Parameters:

ids (List[str], optional): Specific IDs to delete
namespace (str, optional): Override default namespace
filter (dict, optional): Delete by metadata filter
delete_all (bool): Delete all vectors in the namespace. For ChromaVectorStore (v4.6.162+) this is scoped to the caller’s namespace — pass an explicit namespace= to avoid touching default.

Returns: int – Number of deleted vectors

`count(namespace=None)`

Get count of vectors in the store.

Returns: int – Vector count

`get(ids, namespace=None)`

Get vectors by ID.

Parameters:

ids (List[str]): IDs to retrieve

Returns: List[VectorRecord] – Retrieved records

Example: Full Workflow

python

from praisonai.adapters import ChromaVectorStore

# Initialize store
store = ChromaVectorStore(namespace="knowledge_base")

# Add documents
texts = ["Python is a programming language", "Machine learning uses data"]
embeddings = get_embeddings(texts)  # Your embedding function

ids = store.add(
    texts=texts,
    embeddings=embeddings,
    metadatas=[{"topic": "programming"}, {"topic": "ml"}]
)

# Query
query_embedding = get_embeddings(["What is Python?"])[0]
results = store.query(embedding=query_embedding, top_k=3)

for r in results:
    print(f"Score: {r.score:.3f} - {r.text[:50]}...")

# Count
print(f"Total documents: {store.count()}")

# Delete
store.delete(ids=ids[:1])

Environment Variables

bash

# Pinecone
export PINECONE_API_KEY=your-api-key

CLI Usage

bash

# Use ChromaDB (default)
praisonai knowledge query "What is Python?" --vector-store chroma

# Use Pinecone
praisonai knowledge query "What is Python?" --vector-store pinecone

Readers Module – Load documents
Retrieval Module – Retrieve documents
Reranker Module – Rerank results

Vector Store Module