Vector Store Module

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

Import

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

Quick Example

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)

ChromaVectorStore semantics changed in v4.6.162:

Every namespace (default and ad-hoc) uses {"hnsw:space": "cosine"}; a legacy L2 collection logs a one-time warning instead of auto-migrating.
query() returns each record’s stored embedding, not the query vector.
delete_all=True deletes and recreates the caller’s namespace, not default.

See the Vector Store feature page for the long-form explanation.

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)

ChromaDB telemetry: PraisonAI’s ChromaDB client disables anonymous telemetry via per-instance Settings(anonymized_telemetry=False). It no longer sets the ANONYMIZED_TELEMETRY=False environment variable globally (changed in PR #2070). If your application uses additional ChromaDB clients that should also opt out, set os.environ['ANONYMIZED_TELEMETRY'] = 'False' yourself before constructing them.

Classes

`ChromaVectorStore`

ChromaDB vector store adapter with local persistence.

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

Requires chromadb package: pip install chromadb

`PineconeVectorStore`

Pinecone cloud vector store adapter.

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

Requires pinecone package: pip install pinecone

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

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

# Pinecone
export PINECONE_API_KEY=your-api-key

CLI Usage

# 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

Guide

Reference

Vector Store Module

Vector Store Module

Import

Quick Example

Features

Classes

`ChromaVectorStore`

`PineconeVectorStore`

Methods

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

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

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

`count(namespace=None)`

`get(ids, namespace=None)`

Example: Full Workflow

Environment Variables

CLI Usage

​Vector Store Module

​Import

​Quick Example

​Features

​Classes

​ChromaVectorStore

​PineconeVectorStore

​Methods

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

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

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

​count(namespace=None)

​get(ids, namespace=None)

​Example: Full Workflow

​Environment Variables

​CLI Usage

​Related

Vector Store Module

Import

Quick Example

Features

Classes

`ChromaVectorStore`

`PineconeVectorStore`

Methods

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

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

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

`count(namespace=None)`

`get(ids, namespace=None)`

Example: Full Workflow

Environment Variables

CLI Usage

Related