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814 lines
30 KiB
Python
814 lines
30 KiB
Python
from __future__ import annotations
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import base64
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import logging
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import uuid
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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Dict,
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Iterable,
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List,
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Optional,
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Tuple,
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Type,
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)
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import numpy as np
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from langchain_core._api import deprecated
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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.utils import xor_args
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from langchain_core.vectorstores import VectorStore
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from langchain_community.vectorstores.utils import maximal_marginal_relevance
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if TYPE_CHECKING:
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import chromadb
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import chromadb.config
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from chromadb.api.types import ID, OneOrMany, Where, WhereDocument
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logger = logging.getLogger()
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DEFAULT_K = 4 # Number of Documents to return.
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def _results_to_docs(results: Any) -> List[Document]:
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return [doc for doc, _ in _results_to_docs_and_scores(results)]
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def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
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return [
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# TODO: Chroma can do batch querying,
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# we shouldn't hard code to the 1st result
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(Document(page_content=result[0], metadata=result[1] or {}), result[2])
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for result in zip(
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results["documents"][0],
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results["metadatas"][0],
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results["distances"][0],
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)
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]
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class Chroma(VectorStore):
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"""`ChromaDB` vector store.
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To use, you should have the ``chromadb`` python package installed.
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Example:
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.. code-block:: python
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from langchain_community.vectorstores import Chroma
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from langchain_community.embeddings.openai import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings()
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vectorstore = Chroma("langchain_store", embeddings)
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"""
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_LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
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def __init__(
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self,
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collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
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embedding_function: Optional[Embeddings] = None,
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persist_directory: Optional[str] = None,
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client_settings: Optional[chromadb.config.Settings] = None,
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collection_metadata: Optional[Dict] = None,
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client: Optional[chromadb.Client] = None,
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relevance_score_fn: Optional[Callable[[float], float]] = None,
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) -> None:
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"""Initialize with a Chroma client."""
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try:
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import chromadb
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import chromadb.config
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except ImportError:
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raise ImportError(
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"Could not import chromadb python package. "
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"Please install it with `pip install chromadb`."
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)
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if client is not None:
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self._client_settings = client_settings
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self._client = client
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self._persist_directory = persist_directory
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else:
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if client_settings:
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# If client_settings is provided with persist_directory specified,
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# then it is "in-memory and persisting to disk" mode.
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client_settings.persist_directory = (
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persist_directory or client_settings.persist_directory
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)
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if client_settings.persist_directory is not None:
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# Maintain backwards compatibility with chromadb < 0.4.0
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major, minor, _ = chromadb.__version__.split(".")
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if int(major) == 0 and int(minor) < 4:
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client_settings.chroma_db_impl = "duckdb+parquet"
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_client_settings = client_settings
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elif persist_directory:
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# Maintain backwards compatibility with chromadb < 0.4.0
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major, minor, _ = chromadb.__version__.split(".")
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if int(major) == 0 and int(minor) < 4:
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_client_settings = chromadb.config.Settings(
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chroma_db_impl="duckdb+parquet",
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)
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else:
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_client_settings = chromadb.config.Settings(is_persistent=True)
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_client_settings.persist_directory = persist_directory
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else:
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_client_settings = chromadb.config.Settings()
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self._client_settings = _client_settings
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self._client = chromadb.Client(_client_settings)
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self._persist_directory = (
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_client_settings.persist_directory or persist_directory
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)
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self._embedding_function = embedding_function
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self._collection = self._client.get_or_create_collection(
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name=collection_name,
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embedding_function=None,
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metadata=collection_metadata,
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)
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self.override_relevance_score_fn = relevance_score_fn
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@property
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def embeddings(self) -> Optional[Embeddings]:
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return self._embedding_function
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@xor_args(("query_texts", "query_embeddings"))
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def __query_collection(
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self,
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query_texts: Optional[List[str]] = None,
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query_embeddings: Optional[List[List[float]]] = None,
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n_results: int = 4,
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where: Optional[Dict[str, str]] = None,
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where_document: Optional[Dict[str, str]] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Query the chroma collection."""
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try:
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import chromadb # noqa: F401
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except ImportError:
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raise ImportError(
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"Could not import chromadb python package. "
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"Please install it with `pip install chromadb`."
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)
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return self._collection.query(
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query_texts=query_texts,
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query_embeddings=query_embeddings,
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n_results=n_results,
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where=where,
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where_document=where_document,
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**kwargs,
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)
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def encode_image(self, uri: str) -> str:
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"""Get base64 string from image URI."""
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with open(uri, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode("utf-8")
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def add_images(
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self,
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uris: List[str],
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metadatas: Optional[List[dict]] = None,
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ids: Optional[List[str]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Run more images through the embeddings and add to the vectorstore.
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Args:
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uris List[str]: File path to the image.
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metadatas (Optional[List[dict]], optional): Optional list of metadatas.
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ids (Optional[List[str]], optional): Optional list of IDs.
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Returns:
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List[str]: List of IDs of the added images.
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"""
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# Map from uris to b64 encoded strings
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b64_texts = [self.encode_image(uri=uri) for uri in uris]
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# Populate IDs
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if ids is None:
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ids = [str(uuid.uuid4()) for _ in uris]
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embeddings = None
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# Set embeddings
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if self._embedding_function is not None and hasattr(
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self._embedding_function, "embed_image"
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):
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embeddings = self._embedding_function.embed_image(uris=uris)
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if metadatas:
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# fill metadatas with empty dicts if somebody
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# did not specify metadata for all images
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length_diff = len(uris) - len(metadatas)
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if length_diff:
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metadatas = metadatas + [{}] * length_diff
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empty_ids = []
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non_empty_ids = []
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for idx, m in enumerate(metadatas):
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if m:
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non_empty_ids.append(idx)
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else:
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empty_ids.append(idx)
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if non_empty_ids:
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metadatas = [metadatas[idx] for idx in non_empty_ids]
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images_with_metadatas = [b64_texts[idx] for idx in non_empty_ids]
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embeddings_with_metadatas = (
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[embeddings[idx] for idx in non_empty_ids] if embeddings else None
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)
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ids_with_metadata = [ids[idx] for idx in non_empty_ids]
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try:
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self._collection.upsert(
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metadatas=metadatas,
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embeddings=embeddings_with_metadatas,
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documents=images_with_metadatas,
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ids=ids_with_metadata,
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)
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except ValueError as e:
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if "Expected metadata value to be" in str(e):
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msg = (
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"Try filtering complex metadata using "
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"langchain_community.vectorstores.utils.filter_complex_metadata."
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)
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raise ValueError(e.args[0] + "\n\n" + msg)
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else:
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raise e
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if empty_ids:
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images_without_metadatas = [b64_texts[j] for j in empty_ids]
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embeddings_without_metadatas = (
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[embeddings[j] for j in empty_ids] if embeddings else None
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)
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ids_without_metadatas = [ids[j] for j in empty_ids]
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self._collection.upsert(
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embeddings=embeddings_without_metadatas,
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documents=images_without_metadatas,
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ids=ids_without_metadatas,
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)
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else:
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self._collection.upsert(
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embeddings=embeddings,
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documents=b64_texts,
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ids=ids,
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)
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return ids
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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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**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]], optional): Optional list of metadatas.
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ids (Optional[List[str]], optional): Optional list of IDs.
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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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# TODO: Handle the case where the user doesn't provide ids on the Collection
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if ids is None:
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ids = [str(uuid.uuid4()) for _ in texts]
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embeddings = None
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texts = list(texts)
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if self._embedding_function is not None:
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embeddings = self._embedding_function.embed_documents(texts)
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if metadatas:
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# fill metadatas with empty dicts if somebody
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# did not specify metadata for all texts
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length_diff = len(texts) - len(metadatas)
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if length_diff:
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metadatas = metadatas + [{}] * length_diff
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empty_ids = []
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non_empty_ids = []
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for idx, m in enumerate(metadatas):
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if m:
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non_empty_ids.append(idx)
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else:
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empty_ids.append(idx)
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if non_empty_ids:
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metadatas = [metadatas[idx] for idx in non_empty_ids]
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texts_with_metadatas = [texts[idx] for idx in non_empty_ids]
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embeddings_with_metadatas = (
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[embeddings[idx] for idx in non_empty_ids] if embeddings else None
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)
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ids_with_metadata = [ids[idx] for idx in non_empty_ids]
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try:
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self._collection.upsert(
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metadatas=metadatas,
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embeddings=embeddings_with_metadatas,
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documents=texts_with_metadatas,
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ids=ids_with_metadata,
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)
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except ValueError as e:
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if "Expected metadata value to be" in str(e):
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msg = (
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"Try filtering complex metadata from the document using "
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"langchain_community.vectorstores.utils.filter_complex_metadata."
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)
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raise ValueError(e.args[0] + "\n\n" + msg)
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else:
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raise e
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if empty_ids:
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texts_without_metadatas = [texts[j] for j in empty_ids]
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embeddings_without_metadatas = (
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[embeddings[j] for j in empty_ids] if embeddings else None
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)
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ids_without_metadatas = [ids[j] for j in empty_ids]
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self._collection.upsert(
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embeddings=embeddings_without_metadatas,
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documents=texts_without_metadatas,
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ids=ids_without_metadatas,
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)
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else:
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self._collection.upsert(
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embeddings=embeddings,
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documents=texts,
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ids=ids,
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)
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return ids
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def similarity_search(
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self,
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query: str,
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k: int = DEFAULT_K,
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filter: Optional[Dict[str, str]] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Run similarity search with Chroma.
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Args:
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query (str): Query text to search for.
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k (int): Number of results to return. Defaults to 4.
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filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
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Returns:
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List[Document]: List of documents most similar to the query text.
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"""
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docs_and_scores = self.similarity_search_with_score(
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query, k, filter=filter, **kwargs
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)
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return [doc for doc, _ in docs_and_scores]
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def similarity_search_by_vector(
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self,
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embedding: List[float],
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k: int = DEFAULT_K,
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filter: Optional[Dict[str, str]] = None,
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where_document: Optional[Dict[str, str]] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs most similar to embedding vector.
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Args:
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embedding (List[float]): Embedding to look up documents similar to.
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k (int): Number of Documents to return. Defaults to 4.
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filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
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Returns:
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List of Documents most similar to the query vector.
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"""
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results = self.__query_collection(
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query_embeddings=embedding,
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n_results=k,
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where=filter,
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where_document=where_document,
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**kwargs,
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)
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return _results_to_docs(results)
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def similarity_search_by_vector_with_relevance_scores(
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self,
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embedding: List[float],
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k: int = DEFAULT_K,
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filter: Optional[Dict[str, str]] = None,
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where_document: Optional[Dict[str, str]] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""
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Return docs most similar to embedding vector and similarity score.
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Args:
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embedding (List[float]): Embedding to look up documents similar to.
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k (int): Number of Documents to return. Defaults to 4.
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filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
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Returns:
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List[Tuple[Document, float]]: List of documents most similar to
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the query text and cosine distance in float for each.
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Lower score represents more similarity.
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"""
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results = self.__query_collection(
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query_embeddings=embedding,
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n_results=k,
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where=filter,
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where_document=where_document,
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**kwargs,
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)
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return _results_to_docs_and_scores(results)
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|
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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 = DEFAULT_K,
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filter: Optional[Dict[str, str]] = None,
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where_document: Optional[Dict[str, str]] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Run similarity search with Chroma with distance.
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|
|
Args:
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query (str): Query text to search for.
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k (int): Number of results to return. Defaults to 4.
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filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
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|
|
Returns:
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List[Tuple[Document, float]]: List of documents most similar to
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the query text and cosine distance in float for each.
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Lower score represents more similarity.
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"""
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if self._embedding_function is None:
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results = self.__query_collection(
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query_texts=[query],
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n_results=k,
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where=filter,
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where_document=where_document,
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**kwargs,
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)
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else:
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query_embedding = self._embedding_function.embed_query(query)
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results = self.__query_collection(
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query_embeddings=[query_embedding],
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n_results=k,
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where=filter,
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where_document=where_document,
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**kwargs,
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)
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return _results_to_docs_and_scores(results)
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|
|
def _select_relevance_score_fn(self) -> Callable[[float], float]:
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"""
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The 'correct' relevance function
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may differ depending on a few things, including:
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- the distance / similarity metric used by the VectorStore
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- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
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- embedding dimensionality
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- etc.
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"""
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if self.override_relevance_score_fn:
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return self.override_relevance_score_fn
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|
|
distance = "l2"
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distance_key = "hnsw:space"
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metadata = self._collection.metadata
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|
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if metadata and distance_key in metadata:
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distance = metadata[distance_key]
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|
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if distance == "cosine":
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return self._cosine_relevance_score_fn
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elif distance == "l2":
|
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return self._euclidean_relevance_score_fn
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|
elif distance == "ip":
|
|
return self._max_inner_product_relevance_score_fn
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|
else:
|
|
raise ValueError(
|
|
"No supported normalization function"
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|
f" for distance metric of type: {distance}."
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|
"Consider providing relevance_score_fn to Chroma constructor."
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)
|
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|
|
def max_marginal_relevance_search_by_vector(
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self,
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embedding: List[float],
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k: int = DEFAULT_K,
|
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fetch_k: int = 20,
|
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lambda_mult: float = 0.5,
|
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filter: Optional[Dict[str, str]] = None,
|
|
where_document: Optional[Dict[str, str]] = None,
|
|
**kwargs: Any,
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) -> List[Document]:
|
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"""Return docs selected using the maximal marginal relevance.
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|
Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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|
|
Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding
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to maximum diversity and 1 to minimum diversity.
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Defaults to 0.5.
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filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
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|
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Returns:
|
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List of Documents selected by maximal marginal relevance.
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"""
|
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|
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results = self.__query_collection(
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query_embeddings=embedding,
|
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n_results=fetch_k,
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where=filter,
|
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where_document=where_document,
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include=["metadatas", "documents", "distances", "embeddings"],
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**kwargs,
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)
|
|
mmr_selected = maximal_marginal_relevance(
|
|
np.array(embedding, dtype=np.float32),
|
|
results["embeddings"][0],
|
|
k=k,
|
|
lambda_mult=lambda_mult,
|
|
)
|
|
|
|
candidates = _results_to_docs(results)
|
|
|
|
selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected]
|
|
return selected_results
|
|
|
|
def max_marginal_relevance_search(
|
|
self,
|
|
query: str,
|
|
k: int = DEFAULT_K,
|
|
fetch_k: int = 20,
|
|
lambda_mult: float = 0.5,
|
|
filter: Optional[Dict[str, str]] = None,
|
|
where_document: Optional[Dict[str, 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 (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
|
|
|
|
Returns:
|
|
List of Documents selected by maximal marginal relevance.
|
|
"""
|
|
if self._embedding_function is None:
|
|
raise ValueError(
|
|
"For MMR search, you must specify an embedding function on" "creation."
|
|
)
|
|
|
|
embedding = self._embedding_function.embed_query(query)
|
|
docs = self.max_marginal_relevance_search_by_vector(
|
|
embedding,
|
|
k,
|
|
fetch_k,
|
|
lambda_mult=lambda_mult,
|
|
filter=filter,
|
|
where_document=where_document,
|
|
)
|
|
return docs
|
|
|
|
def delete_collection(self) -> None:
|
|
"""Delete the collection."""
|
|
self._client.delete_collection(self._collection.name)
|
|
|
|
def get(
|
|
self,
|
|
ids: Optional[OneOrMany[ID]] = None,
|
|
where: Optional[Where] = None,
|
|
limit: Optional[int] = None,
|
|
offset: Optional[int] = None,
|
|
where_document: Optional[WhereDocument] = None,
|
|
include: Optional[List[str]] = None,
|
|
) -> Dict[str, Any]:
|
|
"""Gets the collection.
|
|
|
|
Args:
|
|
ids: The ids of the embeddings to get. Optional.
|
|
where: A Where type dict used to filter results by.
|
|
E.g. `{"color" : "red", "price": 4.20}`. Optional.
|
|
limit: The number of documents to return. Optional.
|
|
offset: The offset to start returning results from.
|
|
Useful for paging results with limit. Optional.
|
|
where_document: A WhereDocument type dict used to filter by the documents.
|
|
E.g. `{$contains: "hello"}`. Optional.
|
|
include: A list of what to include in the results.
|
|
Can contain `"embeddings"`, `"metadatas"`, `"documents"`.
|
|
Ids are always included.
|
|
Defaults to `["metadatas", "documents"]`. Optional.
|
|
"""
|
|
kwargs = {
|
|
"ids": ids,
|
|
"where": where,
|
|
"limit": limit,
|
|
"offset": offset,
|
|
"where_document": where_document,
|
|
}
|
|
|
|
if include is not None:
|
|
kwargs["include"] = include
|
|
|
|
return self._collection.get(**kwargs)
|
|
|
|
@deprecated(
|
|
since="0.1.17",
|
|
message=(
|
|
"Since Chroma 0.4.x the manual persistence method is no longer "
|
|
"supported as docs are automatically persisted."
|
|
),
|
|
removal="0.3.0",
|
|
)
|
|
def persist(self) -> None:
|
|
"""Persist the collection.
|
|
|
|
This can be used to explicitly persist the data to disk.
|
|
It will also be called automatically when the object is destroyed.
|
|
|
|
Since Chroma 0.4.x the manual persistence method is no longer
|
|
supported as docs are automatically persisted.
|
|
"""
|
|
if self._persist_directory is None:
|
|
raise ValueError(
|
|
"You must specify a persist_directory on"
|
|
"creation to persist the collection."
|
|
)
|
|
import chromadb
|
|
|
|
# Maintain backwards compatibility with chromadb < 0.4.0
|
|
major, minor, _ = chromadb.__version__.split(".")
|
|
if int(major) == 0 and int(minor) < 4:
|
|
self._client.persist()
|
|
|
|
def update_document(self, document_id: str, document: Document) -> None:
|
|
"""Update a document in the collection.
|
|
|
|
Args:
|
|
document_id (str): ID of the document to update.
|
|
document (Document): Document to update.
|
|
"""
|
|
return self.update_documents([document_id], [document])
|
|
|
|
def update_documents(self, ids: List[str], documents: List[Document]) -> None:
|
|
"""Update a document in the collection.
|
|
|
|
Args:
|
|
ids (List[str]): List of ids of the document to update.
|
|
documents (List[Document]): List of documents to update.
|
|
"""
|
|
text = [document.page_content for document in documents]
|
|
metadata = [document.metadata for document in documents]
|
|
if self._embedding_function is None:
|
|
raise ValueError(
|
|
"For update, you must specify an embedding function on creation."
|
|
)
|
|
embeddings = self._embedding_function.embed_documents(text)
|
|
|
|
if hasattr(
|
|
self._collection._client, "max_batch_size"
|
|
): # for Chroma 0.4.10 and above
|
|
from chromadb.utils.batch_utils import create_batches
|
|
|
|
for batch in create_batches(
|
|
api=self._collection._client,
|
|
ids=ids,
|
|
metadatas=metadata,
|
|
documents=text,
|
|
embeddings=embeddings,
|
|
):
|
|
self._collection.update(
|
|
ids=batch[0],
|
|
embeddings=batch[1],
|
|
documents=batch[3],
|
|
metadatas=batch[2],
|
|
)
|
|
else:
|
|
self._collection.update(
|
|
ids=ids,
|
|
embeddings=embeddings,
|
|
documents=text,
|
|
metadatas=metadata,
|
|
)
|
|
|
|
@classmethod
|
|
def from_texts(
|
|
cls: Type[Chroma],
|
|
texts: List[str],
|
|
embedding: Optional[Embeddings] = None,
|
|
metadatas: Optional[List[dict]] = None,
|
|
ids: Optional[List[str]] = None,
|
|
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
|
|
persist_directory: Optional[str] = None,
|
|
client_settings: Optional[chromadb.config.Settings] = None,
|
|
client: Optional[chromadb.Client] = None,
|
|
collection_metadata: Optional[Dict] = None,
|
|
**kwargs: Any,
|
|
) -> Chroma:
|
|
"""Create a Chroma vectorstore from a raw documents.
|
|
|
|
If a persist_directory is specified, the collection will be persisted there.
|
|
Otherwise, the data will be ephemeral in-memory.
|
|
|
|
Args:
|
|
texts (List[str]): List of texts to add to the collection.
|
|
collection_name (str): Name of the collection to create.
|
|
persist_directory (Optional[str]): Directory to persist the collection.
|
|
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
|
|
metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
|
|
ids (Optional[List[str]]): List of document IDs. Defaults to None.
|
|
client_settings (Optional[chromadb.config.Settings]): Chroma client settings
|
|
collection_metadata (Optional[Dict]): Collection configurations.
|
|
Defaults to None.
|
|
|
|
Returns:
|
|
Chroma: Chroma vectorstore.
|
|
"""
|
|
chroma_collection = cls(
|
|
collection_name=collection_name,
|
|
embedding_function=embedding,
|
|
persist_directory=persist_directory,
|
|
client_settings=client_settings,
|
|
client=client,
|
|
collection_metadata=collection_metadata,
|
|
**kwargs,
|
|
)
|
|
if ids is None:
|
|
ids = [str(uuid.uuid4()) for _ in texts]
|
|
if hasattr(
|
|
chroma_collection._client, "max_batch_size"
|
|
): # for Chroma 0.4.10 and above
|
|
from chromadb.utils.batch_utils import create_batches
|
|
|
|
for batch in create_batches(
|
|
api=chroma_collection._client,
|
|
ids=ids,
|
|
metadatas=metadatas,
|
|
documents=texts,
|
|
):
|
|
chroma_collection.add_texts(
|
|
texts=batch[3] if batch[3] else [],
|
|
metadatas=batch[2] if batch[2] else None,
|
|
ids=batch[0],
|
|
)
|
|
else:
|
|
chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
|
|
return chroma_collection
|
|
|
|
@classmethod
|
|
def from_documents(
|
|
cls: Type[Chroma],
|
|
documents: List[Document],
|
|
embedding: Optional[Embeddings] = None,
|
|
ids: Optional[List[str]] = None,
|
|
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
|
|
persist_directory: Optional[str] = None,
|
|
client_settings: Optional[chromadb.config.Settings] = None,
|
|
client: Optional[chromadb.Client] = None, # Add this line
|
|
collection_metadata: Optional[Dict] = None,
|
|
**kwargs: Any,
|
|
) -> Chroma:
|
|
"""Create a Chroma vectorstore from a list of documents.
|
|
|
|
If a persist_directory is specified, the collection will be persisted there.
|
|
Otherwise, the data will be ephemeral in-memory.
|
|
|
|
Args:
|
|
collection_name (str): Name of the collection to create.
|
|
persist_directory (Optional[str]): Directory to persist the collection.
|
|
ids (Optional[List[str]]): List of document IDs. Defaults to None.
|
|
documents (List[Document]): List of documents to add to the vectorstore.
|
|
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
|
|
client_settings (Optional[chromadb.config.Settings]): Chroma client settings
|
|
collection_metadata (Optional[Dict]): Collection configurations.
|
|
Defaults to None.
|
|
|
|
Returns:
|
|
Chroma: Chroma vectorstore.
|
|
"""
|
|
texts = [doc.page_content for doc in documents]
|
|
metadatas = [doc.metadata for doc in documents]
|
|
return cls.from_texts(
|
|
texts=texts,
|
|
embedding=embedding,
|
|
metadatas=metadatas,
|
|
ids=ids,
|
|
collection_name=collection_name,
|
|
persist_directory=persist_directory,
|
|
client_settings=client_settings,
|
|
client=client,
|
|
collection_metadata=collection_metadata,
|
|
**kwargs,
|
|
)
|
|
|
|
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
|
|
"""Delete by vector IDs.
|
|
|
|
Args:
|
|
ids: List of ids to delete.
|
|
"""
|
|
self._collection.delete(ids=ids)
|
|
|
|
def __len__(self) -> int:
|
|
"""Count the number of documents in the collection."""
|
|
return self._collection.count()
|