pinecone¶
A module for interacting with Pinecone vectorstores.
PineconeParams
¶
Bases: BaseModel
The parameters for Pinecone create_index
Source code in mirascope/pinecone/types.py
kwargs()
¶
Returns all parameters for the index as a keyword arguments dictionary.
PineconePodParams
¶
Bases: PineconeParams
, PodSpec
, BaseVectorStoreParams
The parameters for Pinecone create_index with pod spec and weave
Source code in mirascope/pinecone/types.py
kwargs()
¶
Returns all parameters for the index as a keyword arguments dictionary.
Source code in mirascope/pinecone/types.py
PineconeQueryResult
¶
Bases: BaseModel
The result of a Pinecone index query
Example:
from mirascope.pinecone import (
PineconeServerlessParams,
PineconeSettings,
PineconeVectorStore,
)
from mirascope.openai import OpenAIEmbedder
from mirascope.rag import TextChunker
class MyStore(ChromaVectorStore):
embedder = OpenAIEmbedder(dimensions=1536)
chunker = TextChunker(chunk_size=1000, chunk_overlap=200)
index_name = "my-store-0001"
api_key = settings.pinecone_api_key
client_settings = PineconeSettings()
vectorstore_params = PineconeServerlessParams(
cloud="aws",
region="us-west-2",
)
my_store = MyStore()
with open(f"{PATH_TO_FILE}") as file:
data = file.read()
my_store.add(data)
query_results = my_store.retrieve("my question")
#> QueryResult(ids=['0'], documents=['my answer'],
# scores=[0.9999999999999999], embeddings=[[0.0, 0.0, 0.0, ...]])
Source code in mirascope/pinecone/types.py
PineconeServerlessParams
¶
Bases: PineconeParams
, ServerlessSpec
, BaseVectorStoreParams
The parameters for Pinecone create_index with serverless spec and weave
Source code in mirascope/pinecone/types.py
kwargs()
¶
Returns all parameters for the index as a keyword arguments dictionary.
Source code in mirascope/pinecone/types.py
PineconeSettings
¶
Bases: BaseModel
Settings for Pinecone instance
Source code in mirascope/pinecone/types.py
kwargs()
¶
Returns all parameters for the index as a keyword arguments dictionary.
PineconeVectorStore
¶
Bases: BaseVectorStore
A vectorstore for Pinecone.
Example:
from mirascope.pinecone import (
PineconeServerlessParams,
PineconeSettings,
PineconeVectorStore,
)
from mirascope.openai import OpenAIEmbedder
from mirascope.rag import TextChunker
class MyStore(ChromaVectorStore):
embedder = OpenAIEmbedder(dimensions=1536)
chunker = TextChunker(chunk_size=1000, chunk_overlap=200)
index_name = "my-store-0001"
api_key = settings.pinecone_api_key
client_settings = PineconeSettings()
vectorstore_params = PineconeServerlessParams(
cloud="aws",
region="us-west-2",
)
my_store = MyStore()
with open(f"{PATH_TO_FILE}") as file:
data = file.read()
my_store.add(data)
documents = my_store.retrieve("my question").documents
print(documents)
Source code in mirascope/pinecone/vectorstores.py
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add(text, **kwargs)
¶
Takes unstructured data and upserts into vectorstore
Source code in mirascope/pinecone/vectorstores.py
retrieve(text, **kwargs)
¶
Queries the vectorstore for closest match