instrukt.indexes.chroma.ChromaWrapper

class instrukt.indexes.chroma.ChromaWrapper(client: chromadb.Client, collection_name: str, loading: bool = True, embedding_function: Embeddings | HuggingFaceEmbeddings | HuggingFaceInstructEmbeddings | HuggingFaceBgeEmbeddings | None = None, collection_metadata: Dict[str, Any] | None = None, **kwargs)

Bases: Chroma

Wrapper around Chroma DB.

Initialize with Chroma client.

Methods

__init__(client, collection_name[, loading, ...])	Initialize with Chroma client.
aadd_documents(documents, **kwargs)	Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])	Run more texts through the embeddings and add to the vectorstore.
acount()
add_documents(documents, **kwargs)	Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids])	Run more texts through the embeddings and add to the vectorstore.
adelete([ids, where])
adelete_collection()
adelete_named_collection(collection_name)
afrom_documents(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])	Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])	Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)	Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)	Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)	Return docs most similar to query using specified search type.
asimilarity_search(query[, k])	Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])	Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)	Return docs most similar to query.
delete([ids])	Delete by vector IDs.
delete_collection()	Delete the collection.
from_documents(documents[, embedding, ids, ...])	Create a Chroma vectorstore from a list of documents.
from_texts(texts[, embedding, metadatas, ...])	Create a Chroma vectorstore from a raw documents.
get([ids, where, limit, offset, ...])	Gets the collection.
get_retrieval_tool([description, ...])	Get a retrieval tool for this collection.
list_collections()	Bypass default chroma listing method that does not rely on embeddings function.
max_marginal_relevance_search(query[, k, ...])	Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)	Return docs selected using the maximal marginal relevance.
persist()	Persist the collection.
search(query, search_type, **kwargs)	Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])	Run similarity search with Chroma.
similarity_search_by_vector(embedding[, k, ...])	Return docs most similar to embedding vector.
similarity_search_by_vector_with_relevance_scores(...)	Return docs most similar to embedding vector and similarity score.
similarity_search_with_relevance_scores(query)	Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, filter])	Run similarity search with Chroma with distance.
update_document(document_id, document)	Update a document in the collection.

Attributes

count
description	Return the collection's description if it exists.
embeddings	Access the query embedding object if available.
metadata	Returns the collection metadata.
name

async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]

Run more documents through the embeddings and add to the vectorstore.

Parameters:

(List[Document] (documents) – Documents to add to the vectorstore.

Returns:

List of IDs of the added texts.

Return type:

List[str]

async aadd_texts(texts: Iterable[str], metadatas: List[dict] | None = None, **kwargs: Any) → List[str]

Run more texts through the embeddings and add to the vectorstore.

async acount() → int

add_documents(documents: List[Document], **kwargs: Any) → List[str]

Run more documents through the embeddings and add to the vectorstore.

Parameters:

(List[Document] (documents) – Documents to add to the vectorstore.

Returns:

List of IDs of the added texts.

Return type:

List[str]

add_texts(texts: Iterable[str], metadatas: List[dict] | None = None, ids: List[str] | None = None, **kwargs: Any) → List[str]

Run more texts through the embeddings and add to the vectorstore.

Parameters:

• texts (Iterable[str]) – Texts to add to the vectorstore.

• metadatas (Optional[List[dict]], optional) – Optional list of metadatas.

• ids (Optional[List[str]], optional) – Optional list of IDs.

Returns:

List of IDs of the added texts.

Return type:

List[str]

async adelete(ids: list[str] | None = None, where: dict[Any, Any] | None = None)

async adelete_collection()

async adelete_named_collection(collection_name: str)

async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Return VectorStore initialized from documents and embeddings.

async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: List[dict] | None = None, **kwargs: Any) → VST

Return VectorStore initialized from texts and embeddings.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

as_retriever(**kwargs: Any) → VectorStoreRetriever

Return VectorStoreRetriever initialized from this VectorStore.

Parameters:

• search_type (Optional[str]) – Defines the type of search that the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.

• search_kwargs (Optional[Dict]) –

  Keyword arguments to pass to the search function. Can include things like:

  k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

  for similarity_score_threshold

  fetch_k: Amount of documents to pass to MMR algorithm (Default: 20) lambda_mult: Diversity of results returned by MMR;

  1 for minimum diversity and 0 for maximum. (Default: 0.5)

  filter: Filter by document metadata

Returns:

Retriever class for VectorStore.

Return type:

VectorStoreRetriever

Examples:

# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)

# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)

# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)

# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})

# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)

async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to query.

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector.

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query.

delete(ids: List[str] | None = None, **kwargs: Any) → None

Delete by vector IDs.

Parameters:

ids – List of ids to delete.

delete_collection() → None

Delete the collection.

classmethod from_documents(documents: List[Document], embedding: Embeddings | None = None, ids: List[str] | None = None, collection_name: str = 'langchain', persist_directory: str | None = None, client_settings: chromadb.config.Settings | None = None, client: chromadb.Client | None = None, collection_metadata: Dict | None = 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.

Parameters:

• 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 vectorstore.

Return type:

Chroma

classmethod from_texts(texts: List[str], embedding: Embeddings | None = None, metadatas: List[dict] | None = None, ids: List[str] | None = None, collection_name: str = 'langchain', persist_directory: str | None = None, client_settings: chromadb.config.Settings | None = None, client: chromadb.Client | None = None, collection_metadata: Dict | None = 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.

Parameters:

• 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 vectorstore.

Return type:

Chroma

get(ids: OneOrMany[ID] | None = None, where: Where | None = None, limit: int | None = None, offset: int | None = None, where_document: WhereDocument | None = None, include: List[str] | None = None) → Dict[str, Any]

Gets the collection.

Parameters:

• 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: {“text”: “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.

get_retrieval_tool(description: str | None = None, return_direct: bool = False, with_sources: bool = False, with_citation: bool = False, **kwargs) → SomeTool

Get a retrieval tool for this collection.

list_collections() → Sequence[Collection]

Bypass default chroma listing method that does not rely on embeddings function.

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, str] | None = 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.

Parameters:

• 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.

max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, str] | None = 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.

Parameters:

• 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 (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

Returns:

List of Documents selected by maximal marginal relevance.

persist() → 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.

search(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

similarity_search(query: str, k: int = 4, filter: Dict[str, str] | None = None, **kwargs: Any) → List[Document]

Run similarity search with Chroma.

Parameters:

• query (str) – Query text to search for.

• k (int) – Number of results to return. Defaults to 4.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

Returns:

List of documents most similar to the query text.

Return type:

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Dict[str, str] | None = None, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector. :param embedding: Embedding to look up documents similar to. :type embedding: List[float] :param k: Number of Documents to return. Defaults to 4. :type k: int :param filter: Filter by metadata. Defaults to None. :type filter: Optional[Dict[str, str]]

Returns:

List of Documents most similar to the query vector.

similarity_search_by_vector_with_relevance_scores(embedding: List[float], k: int = 4, filter: Dict[str, str] | None = None, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to embedding vector and similarity score.

Parameters:

• embedding (List[float]) – Embedding to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

Returns:

List of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity.

Return type:

List[Tuple[Document, float]]

similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

Return docs and relevance scores in the range [0, 1].

0 is dissimilar, 1 is most similar.

Parameters:

• query – input text

• k – Number of Documents to return. Defaults to 4.

• **kwargs –

  kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

  filter the resulting set of retrieved docs

Returns:

List of Tuples of (doc, similarity_score)

similarity_search_with_score(query: str, k: int = 4, filter: Dict[str, str] | None = None, **kwargs: Any) → List[Tuple[Document, float]]

Run similarity search with Chroma with distance.

Parameters:

• query (str) – Query text to search for.

• k (int) – Number of results to return. Defaults to 4.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

Returns:

List of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity.

Return type:

List[Tuple[Document, float]]

update_document(document_id: str, document: Document) → None

Update a document in the collection.

Parameters:

• document_id (str) – ID of the document to update.

• document (Document) – Document to update.

property count: int

property description: str | None

Return the collection’s description if it exists.

property embeddings: Embeddings | None

Access the query embedding object if available.

property metadata: dict[Any, Any] | None

Returns the collection metadata.

property name: str