mirror of
https://github.com/hwchase17/langchain
synced 2024-11-11 19:11:02 +00:00
270 lines
8.8 KiB
Python
270 lines
8.8 KiB
Python
from __future__ import annotations
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import logging
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import uuid
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from typing import Any, Iterable, List, Optional, Tuple
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.vectorstores import VectorStore
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from langchain_community.vectorstores.utils import DistanceStrategy
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logger = logging.getLogger(__name__)
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class KDBAI(VectorStore):
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"""`KDB.AI` vector store.
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See [https://kdb.ai](https://kdb.ai)
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To use, you should have the `kdbai_client` python package installed.
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Args:
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table: kdbai_client.Table object to use as storage,
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embedding: Any embedding function implementing
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`langchain.embeddings.base.Embeddings` interface,
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distance_strategy: One option from DistanceStrategy.EUCLIDEAN_DISTANCE,
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DistanceStrategy.DOT_PRODUCT or DistanceStrategy.COSINE.
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See the example [notebook](https://github.com/KxSystems/langchain/blob/KDB.AI/docs/docs/integrations/vectorstores/kdbai.ipynb).
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"""
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def __init__(
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self,
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table: Any,
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embedding: Embeddings,
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distance_strategy: Optional[
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DistanceStrategy
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] = DistanceStrategy.EUCLIDEAN_DISTANCE,
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):
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try:
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import kdbai_client # noqa
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except ImportError:
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raise ImportError(
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"Could not import kdbai_client python package. "
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"Please install it with `pip install kdbai_client`."
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)
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self._table = table
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self._embedding = embedding
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self.distance_strategy = distance_strategy
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@property
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def embeddings(self) -> Optional[Embeddings]:
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if isinstance(self._embedding, Embeddings):
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return self._embedding
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return None
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def _embed_documents(self, texts: Iterable[str]) -> List[List[float]]:
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if isinstance(self._embedding, Embeddings):
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return self._embedding.embed_documents(list(texts))
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return [self._embedding(t) for t in texts]
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def _embed_query(self, text: str) -> List[float]:
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if isinstance(self._embedding, Embeddings):
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return self._embedding.embed_query(text)
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return self._embedding(text)
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def _insert(
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self,
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texts: List[str],
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ids: Optional[List[str]],
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metadata: Optional[Any] = None,
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) -> None:
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try:
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import numpy as np
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except ImportError:
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raise ImportError(
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"Could not import numpy python package. "
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"Please install it with `pip install numpy`."
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)
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try:
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import pandas as pd
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except ImportError:
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raise ImportError(
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"Could not import pandas python package. "
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"Please install it with `pip install pandas`."
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)
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embeds = self._embedding.embed_documents(texts)
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df = pd.DataFrame()
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df["id"] = ids
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df["text"] = [t.encode("utf-8") for t in texts]
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df["embeddings"] = [np.array(e, dtype="float32") for e in embeds]
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if metadata is not None:
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df = pd.concat([df, metadata], axis=1)
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self._table.insert(df, warn=False)
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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ids: Optional[List[str]] = None,
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batch_size: int = 32,
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**kwargs: Any,
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) -> List[str]:
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"""Run more texts through the embeddings and add to the vectorstore.
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Args:
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texts (Iterable[str]): Texts to add to the vectorstore.
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metadatas (Optional[List[dict]]): List of metadata corresponding to each
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chunk of text.
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ids (Optional[List[str]]): List of IDs corresponding to each chunk of text.
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batch_size (Optional[int]): Size of batch of chunks of text to insert at
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once.
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Returns:
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List[str]: List of IDs of the added texts.
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"""
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try:
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import pandas as pd
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except ImportError:
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raise ImportError(
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"Could not import pandas python package. "
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"Please install it with `pip install pandas`."
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)
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texts = list(texts)
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metadf: pd.DataFrame = None
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if metadatas is not None:
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if isinstance(metadatas, pd.DataFrame):
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metadf = metadatas
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else:
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metadf = pd.DataFrame(metadatas)
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out_ids: List[str] = []
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nbatches = (len(texts) - 1) // batch_size + 1
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for i in range(nbatches):
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istart = i * batch_size
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iend = (i + 1) * batch_size
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batch = texts[istart:iend]
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if ids:
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batch_ids = ids[istart:iend]
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else:
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batch_ids = [str(uuid.uuid4()) for _ in range(len(batch))]
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if metadf is not None:
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batch_meta = metadf.iloc[istart:iend].reset_index(drop=True)
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else:
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batch_meta = None
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self._insert(batch, batch_ids, batch_meta)
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out_ids = out_ids + batch_ids
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return out_ids
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def add_documents(
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self, documents: List[Document], batch_size: int = 32, **kwargs: Any
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) -> List[str]:
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"""Run more documents through the embeddings and add to the vectorstore.
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Args:
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documents (List[Document]: Documents to add to the vectorstore.
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batch_size (Optional[int]): Size of batch of documents to insert at once.
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Returns:
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List[str]: List of IDs of the added texts.
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"""
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try:
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import pandas as pd
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except ImportError:
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raise ImportError(
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"Could not import pandas python package. "
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"Please install it with `pip install pandas`."
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)
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texts = [x.page_content for x in documents]
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metadata = pd.DataFrame([x.metadata for x in documents])
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return self.add_texts(texts, metadata=metadata, batch_size=batch_size)
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def similarity_search_with_score(
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self,
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query: str,
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k: int = 1,
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filter: Optional[List] = [],
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Run similarity search with distance from a query string.
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Args:
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query (str): Query string.
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k (Optional[int]): number of neighbors to retrieve.
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filter (Optional[List]): KDB.AI metadata filter clause: https://code.kx.com/kdbai/use/filter.html
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Returns:
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List[Document]: List of similar documents.
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"""
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return self.similarity_search_by_vector_with_score(
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self._embed_query(query), k=k, filter=filter, **kwargs
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)
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def similarity_search_by_vector_with_score(
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self,
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embedding: List[float],
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*,
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k: int = 1,
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filter: Optional[List] = [],
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Return pinecone documents most similar to embedding, along with scores.
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Args:
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embedding (List[float]): query vector.
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k (Optional[int]): number of neighbors to retrieve.
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filter (Optional[List]): KDB.AI metadata filter clause: https://code.kx.com/kdbai/use/filter.html
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Returns:
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List[Document]: List of similar documents.
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"""
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if "n" in kwargs:
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k = kwargs.pop("n")
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matches = self._table.search(vectors=[embedding], n=k, filter=filter, **kwargs)[
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0
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]
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docs = []
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for row in matches.to_dict(orient="records"):
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text = row.pop("text")
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score = row.pop("__nn_distance")
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docs.append(
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(
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Document(
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page_content=text,
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metadata={k: v for k, v in row.items() if k != "text"},
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),
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score,
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)
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)
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return docs
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def similarity_search(
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self,
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query: str,
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k: int = 1,
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filter: Optional[List] = [],
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**kwargs: Any,
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) -> List[Document]:
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"""Run similarity search from a query string.
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Args:
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query (str): Query string.
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k (Optional[int]): number of neighbors to retrieve.
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filter (Optional[List]): KDB.AI metadata filter clause: https://code.kx.com/kdbai/use/filter.html
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Returns:
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List[Document]: List of similar documents.
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"""
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docs_and_scores = self.similarity_search_with_score(
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query, k=k, filter=filter, **kwargs
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)
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return [doc for doc, _ in docs_and_scores]
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@classmethod
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def from_texts(
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cls: Any,
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> Any:
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"""Not implemented."""
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raise Exception("Not implemented.")
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