from __future__ import annotations
import asyncio
import logging
import os
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Iterable,
List,
Optional,
Tuple,
TypeVar,
)
import numpy as np
from langchain_core._api.deprecation import deprecated
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.utils.iter import batch_iterate
from langchain_core.vectorstores import VectorStore
from pinecone import Pinecone as PineconeClient
from pinecone import PineconeAsyncio as PineconeAsyncioClient
# conditional imports based on pinecone version
try:
from pinecone.db_data.index import ApplyResult
from pinecone.db_data.index import Index as _Index
from pinecone.db_data.index_asyncio import _IndexAsyncio
except ImportError:
from pinecone.data import _Index, _IndexAsyncio
from pinecone.data.index import ApplyResult
from langchain_pinecone._utilities import DistanceStrategy, maximal_marginal_relevance
if TYPE_CHECKING:
pass
logger = logging.getLogger(__name__)
VST = TypeVar("VST", bound=VectorStore)
[docs]
class PineconeVectorStore(VectorStore):
"""Pinecone vector store integration.
Setup:
Install ``langchain-pinecone`` and set the environment variable ``PINECONE_API_KEY``.
.. code-block:: bash
pip install -qU langchain-pinecone
export PINECONE_API_KEY = "your-pinecone-api-key"
Key init args — indexing params:
embedding: Embeddings
Embedding function to use.
Key init args — client params:
index: Optional[Index]
Index to use.
# TODO: Replace with relevant init params.
Instantiate:
.. code-block:: python
import time
import os
from pinecone import Pinecone, ServerlessSpec
from langchain_pinecone import PineconeVectorStore
from langchain_openai import OpenAIEmbeddings
pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
index_name = "langchain-test-index" # change if desired
existing_indexes = [index_info["name"] for index_info in pc.list_indexes()]
if index_name not in existing_indexes:
pc.create_index(
name=index_name,
dimension=1536,
metric="cosine",
spec=ServerlessSpec(cloud="aws", region="us-east-1"),
deletion_protection="enabled", # Defaults to "disabled"
)
while not pc.describe_index(index_name).status["ready"]:
time.sleep(1)
index = pc.Index(index_name)
vector_store = PineconeVectorStore(index=index, embedding=OpenAIEmbeddings())
Add Documents:
.. code-block:: python
from langchain_core.documents import Document
document_1 = Document(page_content="foo", metadata={"baz": "bar"})
document_2 = Document(page_content="thud", metadata={"bar": "baz"})
document_3 = Document(page_content="i will be deleted :(")
documents = [document_1, document_2, document_3]
ids = ["1", "2", "3"]
vector_store.add_documents(documents=documents, ids=ids)
Delete Documents:
.. code-block:: python
vector_store.delete(ids=["3"])
Search:
.. code-block:: python
results = vector_store.similarity_search(query="thud",k=1)
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* thud [{'bar': 'baz'}]
Search with filter:
.. code-block:: python
results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* thud [{'bar': 'baz'}]
Search with score:
.. code-block:: python
results = vector_store.similarity_search_with_score(query="qux",k=1)
for doc, score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.832268] foo [{'baz': 'bar'}]
Async:
.. code-block:: python
# add documents
# await vector_store.aadd_documents(documents=documents, ids=ids)
# delete documents
# await vector_store.adelete(ids=["3"])
# search
# results = vector_store.asimilarity_search(query="thud",k=1)
# search with score
results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
for doc,score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.832268] foo [{'baz': 'bar'}]
Use as Retriever:
.. code-block:: python
retriever = vector_store.as_retriever(
search_type="mmr",
search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
)
retriever.invoke("thud")
.. code-block:: python
[Document(metadata={'bar': 'baz'}, page_content='thud')]
""" # noqa: E501
_index: Optional[_Index] = None
_async_index: Optional[_IndexAsyncio] = None
[docs]
def __init__(
self,
# setting default params to bypass having to pass in
# the index and embedding objects - manually throw
# exceptions if they are not passed in or set in environment
# (keeping param for backwards compatibility)
index: Optional[Any] = None,
embedding: Optional[Embeddings] = None,
text_key: Optional[str] = "text",
namespace: Optional[str] = None,
distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE,
*,
pinecone_api_key: Optional[str] = None,
index_name: Optional[str] = None,
):
if embedding is None:
raise ValueError("Embedding must be provided")
self._embedding = embedding
if text_key is None:
raise ValueError("Text key must be provided")
self._text_key = text_key
self._namespace = namespace
self.distance_strategy = distance_strategy
if index:
# supports old way of initializing externally
if isinstance(index, _IndexAsyncio):
self._async_index = index
else:
self._index = index
self._index_host = index.config.host
self._pinecone_api_key = index.config.api_key
else:
# all internal initialization
_pinecone_api_key = (
pinecone_api_key or os.environ.get("PINECONE_API_KEY") or ""
)
if not _pinecone_api_key:
raise ValueError(
"Pinecone API key must be provided in either `pinecone_api_key` "
"or `PINECONE_API_KEY` environment variable"
)
self._pinecone_api_key = _pinecone_api_key
_index_name = index_name or os.environ.get("PINECONE_INDEX_NAME") or ""
if not _index_name:
raise ValueError(
"Pinecone index name must be provided in either `index_name` "
"or `PINECONE_INDEX_NAME` environment variable"
)
self._pinecone_api_key = _pinecone_api_key
client = PineconeClient(api_key=_pinecone_api_key, source_tag="langchain")
self._index = client.Index(name=_index_name)
@property
def index(self) -> _Index:
"""Get synchronous index instance."""
if self._index is None:
if not hasattr(self, "_pinecone_api_key"):
raise ValueError("No Pinecone API key available")
client = PineconeClient(
api_key=self._pinecone_api_key, source_tag="langchain"
)
return client.Index(host=self._index_host)
return self._index
@property
def async_index(self) -> _IndexAsyncio:
"""Get asynchronous index instance."""
if self._async_index is None:
client = PineconeAsyncioClient(
api_key=self._pinecone_api_key, source_tag="langchain"
)
return client.IndexAsyncio(host=self.index.config.host)
return self._async_index
@property
def embeddings(self) -> Optional[Embeddings]:
"""Access the query embedding object if available."""
return self._embedding
[docs]
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
namespace: Optional[str] = None,
batch_size: int = 32,
embedding_chunk_size: int = 1000,
*,
id_prefix: Optional[str] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Upsert optimization is done by chunking the embeddings and upserting them.
This is done to avoid memory issues and optimize using HTTP based embeddings.
For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index,
embedding_chunk_size>1000 and batch_size~64 for best performance.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids to associate with the texts.
namespace: Optional pinecone namespace to add the texts to.
batch_size: Batch size to use when adding the texts to the vectorstore.
embedding_chunk_size: Chunk size to use when embedding the texts.
id_prefix: Optional string to use as an ID prefix when upserting vectors.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if namespace is None:
namespace = self._namespace
texts = list(texts)
ids = ids or [str(uuid.uuid4()) for _ in texts]
if id_prefix:
ids = [
id_prefix + "#" + id if id_prefix + "#" not in id else id for id in ids
]
metadatas = metadatas or [{} for _ in texts]
for metadata, text in zip(metadatas, texts):
metadata[self._text_key] = text
# For loops to avoid memory issues and optimize when using HTTP based embeddings
# The first loop runs the embeddings, it benefits when using OpenAI embeddings
for i in range(0, len(texts), embedding_chunk_size):
chunk_texts = texts[i : i + embedding_chunk_size]
chunk_ids = ids[i : i + embedding_chunk_size]
chunk_metadatas = metadatas[i : i + embedding_chunk_size]
embeddings = self._embedding.embed_documents(chunk_texts)
vector_tuples = list(zip(chunk_ids, embeddings, chunk_metadatas))
self.index.upsert(
vectors=vector_tuples,
namespace=namespace,
**kwargs,
)
return ids
[docs]
async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
namespace: Optional[str] = None,
batch_size: int = 32,
embedding_chunk_size: int = 1000,
*,
id_prefix: Optional[str] = None,
**kwargs: Any,
) -> list[str]:
"""Asynchronously run more texts through the embeddings and add to the vectorstore.
Upsert optimization is done by chunking the embeddings and upserting them.
This is done to avoid memory issues and optimize using HTTP based embeddings.
For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index,
embedding_chunk_size>1000 and batch_size~64 for best performance.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids to associate with the texts.
namespace: Optional pinecone namespace to add the texts to.
batch_size: Batch size to use when adding the texts to the vectorstore.
embedding_chunk_size: Chunk size to use when embedding the texts.
id_prefix: Optional string to use as an ID prefix when upserting vectors.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if namespace is None:
namespace = self._namespace
texts = list(texts)
ids = ids or [str(uuid.uuid4()) for _ in texts]
if id_prefix:
ids = [
id_prefix + "#" + id if id_prefix + "#" not in id else id for id in ids
]
metadatas = metadatas or [{} for _ in texts]
for metadata, text in zip(metadatas, texts):
metadata[self._text_key] = text
# For loops to avoid memory issues and optimize when using HTTP based embeddings
for i in range(0, len(texts), embedding_chunk_size):
chunk_texts = texts[i : i + embedding_chunk_size]
chunk_ids = ids[i : i + embedding_chunk_size]
chunk_metadatas = metadatas[i : i + embedding_chunk_size]
embeddings = await self._embedding.aembed_documents(chunk_texts)
vector_tuples = zip(chunk_ids, embeddings, chunk_metadatas)
async with self.async_index as idx:
# Split into batches and upsert asynchronously
tasks = []
for batch_vector_tuples in batch_iterate(batch_size, vector_tuples):
task = idx.upsert(
vectors=batch_vector_tuples,
namespace=namespace,
**kwargs,
)
tasks.append(task)
# Wait for all upserts to complete
await asyncio.gather(*tasks)
return ids
[docs]
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents most similar to the query and score for each
"""
return self.similarity_search_by_vector_with_score(
self._embedding.embed_query(query), k=k, filter=filter, namespace=namespace
)
[docs]
async def asimilarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> list[tuple[Document, float]]:
"""Asynchronously return pinecone documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents most similar to the query and score for each
"""
return await self.asimilarity_search_by_vector_with_score(
(await self._embedding.aembed_query(query)),
k=k,
filter=filter,
namespace=namespace,
)
[docs]
def similarity_search_by_vector_with_score(
self,
embedding: List[float],
*,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to embedding, along with scores."""
if namespace is None:
namespace = self._namespace
docs = []
results = self.index.query(
vector=embedding,
top_k=k,
include_metadata=True,
namespace=namespace,
filter=filter,
)
if isinstance(results, ApplyResult):
raise ValueError("Received asynchronous result from synchronous call.")
for res in results["matches"]:
metadata = res["metadata"]
id = res.get("id")
if self._text_key in metadata:
text = metadata.pop(self._text_key)
score = res["score"]
docs.append(
(Document(id=id, page_content=text, metadata=metadata), score)
)
else:
logger.warning(
f"Found document with no `{self._text_key}` key. Skipping."
)
return docs
[docs]
async def asimilarity_search_by_vector_with_score(
self,
embedding: List[float],
*,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to embedding, along with scores asynchronously."""
if namespace is None:
namespace = self._namespace
docs = []
async with self.async_index as idx:
results = await idx.query(
vector=embedding,
top_k=k,
include_metadata=True,
namespace=namespace,
filter=filter,
)
for res in results["matches"]:
metadata = res["metadata"]
id = res.get("id")
if self._text_key in metadata:
text = metadata.pop(self._text_key)
score = res["score"]
docs.append(
(Document(id=id, page_content=text, metadata=metadata), score)
)
else:
logger.warning(
f"Found document with no `{self._text_key}` key. Skipping."
)
return docs
[docs]
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return pinecone documents most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents most similar to the query and score for each
"""
docs_and_scores = self.similarity_search_with_score(
query, k=k, filter=filter, namespace=namespace, **kwargs
)
return [doc for doc, _ in docs_and_scores]
[docs]
async def asimilarity_search(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> list[Document]:
docs_and_scores = await self.asimilarity_search_with_score(
query, k=k, filter=filter, namespace=namespace, **kwargs
)
return [doc for doc, _ in docs_and_scores]
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
"""
if self.distance_strategy == DistanceStrategy.COSINE:
return self._cosine_relevance_score_fn
elif self.distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
return self._max_inner_product_relevance_score_fn
elif self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
return self._euclidean_relevance_score_fn
else:
raise ValueError(
"Unknown distance strategy, must be cosine, max_inner_product "
"(dot product), or euclidean"
)
@staticmethod
def _cosine_relevance_score_fn(score: float) -> float:
"""Pinecone returns cosine similarity scores between [-1,1]"""
return (score + 1) / 2
[docs]
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents selected by maximal marginal relevance.
"""
if namespace is None:
namespace = self._namespace
results = self.index.query(
vector=embedding,
top_k=fetch_k,
include_values=True,
include_metadata=True,
namespace=namespace,
filter=filter,
)
if isinstance(results, ApplyResult):
raise ValueError("Received asynchronous result from synchronous call.")
mmr_selected = maximal_marginal_relevance(
np.array([embedding], dtype=np.float32),
[item["values"] for item in results["matches"]],
k=k,
lambda_mult=lambda_mult,
)
selected = [results["matches"][i]["metadata"] for i in mmr_selected]
return [
Document(page_content=metadata.pop((self._text_key)), metadata=metadata)
for metadata in selected
]
[docs]
async def amax_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance asynchronously.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents selected by maximal marginal relevance.
"""
if namespace is None:
namespace = self._namespace
async with self.async_index as idx:
results = await idx.query(
vector=embedding,
top_k=fetch_k,
include_values=True,
include_metadata=True,
namespace=namespace,
filter=filter,
)
mmr_selected = maximal_marginal_relevance(
np.array([embedding], dtype=np.float32),
[item["values"] for item in results["matches"]],
k=k,
lambda_mult=lambda_mult,
)
selected = [results["matches"][i]["metadata"] for i in mmr_selected]
return [
Document(page_content=metadata.pop(self._text_key), metadata=metadata)
for metadata in selected
]
[docs]
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding = self._embedding.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult, filter, namespace
)
[docs]
async def amax_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> list[Document]:
embedding = await self._embedding.aembed_query(query)
return await self.amax_marginal_relevance_search_by_vector(
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
namespace=namespace,
)
[docs]
@classmethod
def get_pinecone_index(
cls,
index_name: Optional[str],
pool_threads: int = 4,
*,
pinecone_api_key: Optional[str] = None,
) -> _Index:
"""Return a Pinecone Index instance.
Args:
index_name: Name of the index to use.
pool_threads: Number of threads to use for index upsert.
pinecone_api_key: The api_key of Pinecone.
Returns:
Pinecone Index instance."""
_pinecone_api_key = pinecone_api_key or os.environ.get("PINECONE_API_KEY") or ""
client = PineconeClient(
api_key=_pinecone_api_key, pool_threads=pool_threads, source_tag="langchain"
)
indexes = client.list_indexes()
index_names = [i.name for i in indexes.index_list["indexes"]]
if index_name and index_name in index_names:
index = client.Index(index_name)
elif len(index_names) == 0:
raise ValueError(
"No active indexes found in your Pinecone project, "
"are you sure you're using the right Pinecone API key and Environment? "
"Please double check your Pinecone dashboard."
)
else:
raise ValueError(
f"Index '{index_name}' not found in your Pinecone project. "
f"Did you mean one of the following indexes: {', '.join(index_names)}"
)
return index
[docs]
@classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
batch_size: int = 32,
text_key: str = "text",
namespace: Optional[str] = None,
index_name: Optional[str] = None,
upsert_kwargs: Optional[dict] = None,
pool_threads: int = 4,
embeddings_chunk_size: int = 1000,
*,
id_prefix: Optional[str] = None,
**kwargs: Any,
) -> PineconeVectorStore:
"""Construct Pinecone wrapper from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Adds the documents to a provided Pinecone index
This is intended to be a quick way to get started.
The `pool_threads` affects the speed of the upsert operations.
Setup: set the `PINECONE_API_KEY` environment variable to your Pinecone API key.
Example:
.. code-block:: python
from langchain_pinecone import PineconeVectorStore, PineconeEmbeddings
embeddings = PineconeEmbeddings(model="multilingual-e5-large")
index_name = "my-index"
vectorstore = PineconeVectorStore.from_texts(
texts,
index_name=index_name,
embedding=embedding,
namespace=namespace,
)
"""
pinecone_index = cls.get_pinecone_index(index_name, pool_threads)
pinecone = cls(pinecone_index, embedding, text_key, namespace, **kwargs)
pinecone.add_texts(
texts,
metadatas=metadatas,
ids=ids,
namespace=namespace,
batch_size=batch_size,
embedding_chunk_size=embeddings_chunk_size,
id_prefix=id_prefix,
**(upsert_kwargs or {}),
)
return pinecone
[docs]
@classmethod
async def afrom_texts(
cls: type[PineconeVectorStore],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
batch_size: int = 32,
text_key: str = "text",
namespace: Optional[str] = None,
index_name: Optional[str] = None,
upsert_kwargs: Optional[dict] = None,
embeddings_chunk_size: int = 1000,
*,
id_prefix: Optional[str] = None,
**kwargs: Any,
) -> PineconeVectorStore:
pinecone = cls(
index_name=index_name,
embedding=embedding,
text_key=text_key,
namespace=namespace,
**kwargs,
)
await pinecone.aadd_texts(
texts,
metadatas=metadatas,
ids=ids,
namespace=namespace,
batch_size=batch_size,
embedding_chunk_size=embeddings_chunk_size,
id_prefix=id_prefix,
**(upsert_kwargs or {}),
)
return pinecone
[docs]
@classmethod
def from_existing_index(
cls,
index_name: str,
embedding: Embeddings,
text_key: str = "text",
namespace: Optional[str] = None,
pool_threads: int = 4,
) -> PineconeVectorStore:
"""Load pinecone vectorstore from index name."""
pinecone_index = cls.get_pinecone_index(index_name, pool_threads)
return cls(pinecone_index, embedding, text_key, namespace)
[docs]
def delete(
self,
ids: Optional[List[str]] = None,
delete_all: Optional[bool] = None,
namespace: Optional[str] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> None:
"""Delete by vector IDs or filter.
Args:
ids: List of ids to delete.
delete_all: Whether delete all vectors in the index.
filter: Dictionary of conditions to filter vectors to delete.
namespace: Namespace to search in. Default will search in '' namespace.
"""
if namespace is None:
namespace = self._namespace
if delete_all:
self.index.delete(delete_all=True, namespace=namespace, **kwargs)
elif ids is not None:
chunk_size = 1000
for i in range(0, len(ids), chunk_size):
chunk = ids[i : i + chunk_size]
self.index.delete(ids=chunk, namespace=namespace, **kwargs)
elif filter is not None:
self.index.delete(filter=filter, namespace=namespace, **kwargs)
else:
raise ValueError("Either ids, delete_all, or filter must be provided.")
return None
[docs]
async def adelete(
self,
ids: Optional[List[str]] = None,
delete_all: Optional[bool] = None,
namespace: Optional[str] = None,
filter: Optional[dict] = None,
**kwargs: Any,
) -> None:
if namespace is None:
namespace = self._namespace
if delete_all:
async with self.async_index as idx:
await idx.delete(delete_all=True, namespace=namespace, **kwargs)
elif ids is not None:
chunk_size = 1000
async with self.async_index as idx:
tasks = []
for i in range(0, len(ids), chunk_size):
chunk = ids[i : i + chunk_size]
tasks.append(idx.delete(ids=chunk, namespace=namespace, **kwargs))
await asyncio.gather(*tasks)
elif filter is not None:
async with self.async_index as idx:
await idx.delete(filter=filter, namespace=namespace, **kwargs)
else:
raise ValueError("Either ids, delete_all, or filter must be provided.")
return None
[docs]
@deprecated(since="0.0.3", removal="1.0.0", alternative="PineconeVectorStore")
class Pinecone(PineconeVectorStore):
"""Deprecated. Use PineconeVectorStore instead."""
pass
