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import time
from typing import List, Dict
from loguru import logger
from pinecone import Pinecone, ServerlessSpec
from .embedding_client import EmbeddingAPIClient
class PineconeVectorDB:
"""A client for interacting with Pinecone for hybrid (dense-sparse) vector search."""
def __init__(
self,
api_key: str,
embedding_api_url: str,
cloud: str = "aws",
region: str = "us-east-1",
) -> None:
"""
Initializes the PineconeVectorDB client.
Args:
api_key (str): Your Pinecone API key.
embedding_api_url (str): The base URL for the embedding API service.
cloud (str): The cloud provider for the Pinecone index. Defaults to "aws".
region (str): The region for the Pinecone index. Defaults to "us-east-1".
"""
self.pc = Pinecone(api_key=api_key)
self.api_client = EmbeddingAPIClient(embedding_api_url)
self.cloud = cloud
self.region = region
def create_index_db(
self,
index_name: str,
dimension: int,
) -> None:
"""
Creates a new Pinecone index if it doesn't already exist.
Args:
index_name (str): The name of the index to create.
dimension (int): The dimension of the dense vectors.
"""
if index_name not in self.pc.list_indexes().names():
logger.info(f"📦 Creating index: {index_name}")
self.pc.create_index(
name=index_name,
dimension=dimension,
metric="dotproduct",
spec=ServerlessSpec(cloud=self.cloud, region=self.region),
)
while not self.pc.describe_index(index_name).status["ready"]:
logger.debug("⏳ Waiting for index to be ready...")
time.sleep(1)
logger.success(f"✅ Index {index_name} created successfully")
else:
logger.info(f"ℹ️ Index {index_name} already exists")
index = self.pc.Index(index_name)
stats = index.describe_index_stats()
logger.info(f"📊 Index stats: {stats}")
async def push_data_to_index(
self, documents: List[Dict[str, str]], index_name: str, batch_size: int = 8
) -> None:
"""
Uploads documents to a Pinecone index in batches.
Args:
documents (List[Dict[str, str]]): A list of documents, where each document is a dictionary
with 'id', 'question', and 'context' keys.
index_name (str): The name of the Pinecone index.
batch_size (int): The size of each batch for processing. Defaults to 8.
"""
index = self.pc.Index(index_name)
total_docs = len(documents)
logger.info(
f"📤 Uploading {total_docs} documents in batches of {batch_size}..."
)
for i in range(0, total_docs, batch_size):
batch = documents[i : i + batch_size]
batch_num = i // batch_size + 1
total_batches = (total_docs + batch_size - 1) // batch_size
logger.debug(
f"\n🔄 Processing batch {batch_num}/{total_batches} ({len(batch)} docs)..."
)
texts = [doc["context"] for doc in batch]
ids = [doc["id"] for doc in batch]
await self._push_hybrid_batch(index, batch, texts, ids)
logger.info(f"✅ Batch {batch_num}/{total_batches} uploaded")
logger.success(f"\n🎉 All {total_docs} documents uploaded successfully!")
async def _push_hybrid_batch(
self,
index,
batch: List[Dict],
texts: List[str],
ids: List[str],
) -> None:
"""
A helper method to generate and upload a batch of hybrid vectors.
Args:
index: The Pinecone index object.
batch (List[Dict]): The batch of original documents.
texts (List[str]): The list of texts ('context') to embed.
ids (List[str]): The list of document IDs.
"""
embeddings = await self.api_client.get_dense_embeddings(texts)
logger.info(
f" ✓ Generated {len(embeddings)} dense embeddings (dim: {len(embeddings[0])})"
)
sparse_embeddings = await self.api_client.get_sparse_embeddings(texts)
logger.info(f" ✓ Generated {len(sparse_embeddings)} sparse embeddings")
vectors = []
for doc_id, doc, embedding, sparse_emb in zip(
ids, batch, embeddings, sparse_embeddings
):
vectors.append(
{
"id": doc_id,
"values": embedding,
"sparse_values": {
"indices": sparse_emb["indices"],
"values": sparse_emb["values"],
},
"metadata": {
"question": doc["question"],
"context": doc["context"],
},
}
)
index.upsert(vectors=vectors)
logger.info(f" ✓ Uploaded {len(vectors)} hybrid vectors")
async def query(
self,
query: str,
index_name: str,
alpha: float = 0.5,
top_k: int = 5,
include_metadata: bool = True,
) -> Dict:
"""
Performs a hybrid search query on a Pinecone index.
Args:
query (str): The query string.
index_name (str): The name of the Pinecone index.
alpha (float): The weight for hybrid search, between 0 and 1.
1 for pure dense search, 0 for pure sparse search. Defaults to 0.5.
top_k (int): The number of results to return. Defaults to 5.
include_metadata (bool): Whether to include metadata in the response. Defaults to True.
Returns:
Dict: The query response from Pinecone.
"""
index = self.pc.Index(index_name)
logger.info("🎯 Performing hybrid search...")
logger.info("Generate sparse & dense embeddings...")
query_embedding = await self.api_client.get_dense_embeddings([query])
query_sparse_embedding = await self.api_client.get_sparse_embeddings([query])
sparse_vec, dense_vec = self.hybrid_scale(
query_embedding[0], query_sparse_embedding[0], alpha
)
query_response = index.query(
vector=dense_vec,
sparse_vector=sparse_vec,
top_k=top_k,
include_metadata=include_metadata,
)
return query_response
async def query_with_rerank(
self,
query: str,
index_name: str,
alpha: float = 0.5,
initial_top_k: int = 20,
final_top_k: int = 5,
) -> List[Dict]:
"""
Performs a query and then reranks the results for improved accuracy.
Args:
query (str): The query string.
index_name (str): The name of the Pinecone index.
alpha (float): The weight for the initial hybrid search. Defaults to 0.5.
initial_top_k (int): The number of documents to retrieve from the initial vector search.
Defaults to 20.
final_top_k (int): The number of documents to return after reranking. Defaults to 5.
Returns:
List[Dict]: A list of reranked documents with their scores and metadata.
"""
search_results = await self.query(
query=query, index_name=index_name, alpha=alpha, top_k=initial_top_k
)
contexts = []
metadata_map = {}
for match in search_results["matches"]:
context = match["metadata"].get("context", "")
contexts.append(context)
metadata_map[context] = {
"id": match["id"],
"score": match["score"],
"question": match["metadata"].get("question", ""),
"metadata": match["metadata"],
}
if not contexts:
return []
logger.info(f"🎯 Reranking top {initial_top_k} results to {final_top_k}...")
reranked = await self.api_client.rerank_documents(
query=query, documents=contexts, top_k=final_top_k
)
final_results = []
for item in reranked:
context = item["text"]
original_data = metadata_map.get(context, {})
final_results.append(
{
"id": original_data.get("id"),
"rerank_score": item["score"],
"original_score": original_data.get("score"),
"question": original_data.get("question"),
"context": context,
"metadata": original_data.get("metadata", {}),
}
)
logger.success("✅ Reranking complete!")
return final_results
def delete_index(self, index_name: str) -> None:
"""
Deletes a Pinecone index.
Args:
index_name (str): The name of the index to delete.
"""
if index_name in self.pc.list_indexes().names():
self.pc.delete_index(index_name)
logger.success(f"🗑️ Deleted index: {index_name}")
else:
logger.warning(f"⚠️ Index {index_name} not found")
def hybrid_scale(
self, dense: List[float], sparse: Dict[str, List], alpha: float
) -> tuple:
"""
Scales dense and sparse vectors according to the alpha weight.
Args:
dense (List[float]): The dense vector.
sparse (Dict[str, List]): The sparse vector, containing 'indices' and 'values'.
alpha (float): The weighting factor, between 0 and 1.
alpha=1 gives full weight to dense, alpha=0 gives full weight to sparse.
Returns:
tuple: A tuple containing the scaled sparse vector and the scaled dense vector.
"""
if alpha < 0 or alpha > 1:
raise ValueError("Alpha must be between 0 and 1")
# Scale sparse values
hsparse = {
"indices": sparse["indices"],
"values": [v * (1 - alpha) for v in sparse["values"]],
}
# Scale dense values
hdense = [v * alpha for v in dense]
return hsparse, hdense
async def close(self):
"""
Closes the underlying EmbeddingAPIClient.
This should be called to ensure that the HTTP client session is properly
terminated and resources are released.
"""
await self.api_client.close()
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