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Documentation Index

Fetch the complete documentation index at: https://docs.praison.ai/llms.txt

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Vector Store Module

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

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Import

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

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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)

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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
  • Automatic telemetry disabling

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Classes

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ChromaVectorStore

ChromaDB vector store adapter with local persistence. 
from praisonai.adapters import ChromaVectorStore

store = ChromaVectorStore(
    namespace="default",
    persist_directory=".praison/chroma"
)
Parameters:
ParameterTypeDefaultDescription
configdictNoneConfiguration options
namespacestr"default"Collection namespace
persist_directorystr".praison/chroma"Storage directory
Requires chromadb package: pip install chromadb

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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:
ParameterTypeDefaultDescription
api_keystrPINECONE_API_KEY envPinecone API key
index_namestr"praisonai"Pinecone index name
namespacestr"default"Vector namespace
Requires pinecone package: pip install pinecone

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Methods

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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

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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

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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 namespace
Returns: int - Number of deleted vectors

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count(namespace=None)

Get count of vectors in the store. Returns: int - Vector count

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get(ids, namespace=None)

Get vectors by ID. Parameters:
  • ids (List[str]): IDs to retrieve
Returns: List[VectorRecord] - Retrieved records

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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])

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Environment Variables

# Pinecone
export PINECONE_API_KEY=your-api-key

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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