langchain_community.vectorstores.thirdai_neuraldb.NeuralDBVectorStore¶
- class langchain_community.vectorstores.thirdai_neuraldb.NeuralDBVectorStore(db: Any)[source]¶
Vectorstore that uses ThirdAI’s NeuralDB.
To use, you should have the
thirdai[neural_db]python package installed.Example
from langchain_community.vectorstores import NeuralDBVectorStore from thirdai import neural_db as ndb db = ndb.NeuralDB() vectorstore = NeuralDBVectorStore(db=db)
Attributes
dbNeuralDB instance
embeddingsAccess the query embedding object if available.
Methods
__init__(db)aadd_documents(documents, **kwargs)Async run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])Async run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)Add or update documents in the vectorstore.
add_texts(texts[, metadatas])Run more texts through the embeddings and add to the vectorstore.
adelete([ids])Async delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)Async return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])Async return VectorStore initialized from texts and embeddings.
aget_by_ids(ids, /)Async get documents by their IDs.
amax_marginal_relevance_search(query[, k, ...])Async return docs selected using the maximal marginal relevance.
Async return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)Async return docs most similar to query using a specified search type.
asimilarity_search(query[, k])Async return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])Async return docs most similar to embedding vector.
Async return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score(*args, **kwargs)Async run similarity search with distance.
associate(source, target)The vectorstore associates a source phrase with a target phrase.
associate_batch(text_pairs)Given a batch of (source, target) pairs, the vectorstore associates each source phrase with the corresponding target phrase.
astreaming_upsert(items, /, batch_size, **kwargs)aupsert(items, /, **kwargs)delete([ids])Delete by vector ID or other criteria.
from_checkpoint(checkpoint[, thirdai_key])Create a NeuralDBVectorStore with a base model from a saved checkpoint
from_documents(documents, embedding, **kwargs)Return VectorStore initialized from documents and embeddings.
from_scratch([thirdai_key])Create a NeuralDBVectorStore from scratch.
from_texts(texts, embedding[, metadatas])Return VectorStore initialized from texts and embeddings.
get_by_ids(ids, /)Get documents by their IDs.
insert(sources[, train, fast_mode])Inserts files / document sources into the vectorstore.
max_marginal_relevance_search(query[, k, ...])Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
save(path)Saves a NeuralDB instance to disk.
search(query, search_type, **kwargs)Return docs most similar to query using a specified search type.
similarity_search(query[, k])Retrieve {k} contexts with for a given query
similarity_search_by_vector(embedding[, k])Return docs most similar to embedding vector.
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(*args, **kwargs)Run similarity search with distance.
streaming_upsert(items, /, batch_size, **kwargs)upsert(items, /, **kwargs)upvote(query, document_id)The vectorstore upweights the score of a document for a specific query.
upvote_batch(query_id_pairs)Given a batch of (query, document id) pairs, the vectorstore upweights the scores of the document for the corresponding queries.
validate_environments(values)Validate ThirdAI environment variables.
- Parameters
db (Any) –
- async aadd_documents(documents: List[Document], **kwargs: Any) List[str]¶
Async run more documents through the embeddings and add to the vectorstore.
- Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.
- Returns
List of IDs of the added texts.
- Raises
ValueError – If the number of IDs does not match the number of documents.
- Return type
List[str]
- async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) List[str]¶
Async run more texts through the embeddings and add to the vectorstore.
- Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts. Default is None.
**kwargs (Any) – vectorstore specific parameters.
- Returns
List of ids from adding the texts into the vectorstore.
- Raises
ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.
- Return type
List[str]
- add_documents(documents: List[Document], **kwargs: Any) List[str]¶
Add or update documents in the vectorstore.
- Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.
- Returns
List of IDs of the added texts.
- Raises
ValueError – If the number of ids does not match the number of documents.
- Return type
List[str]
- add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
- Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
kwargs (Any) – vectorstore specific parameters
- Returns
List of ids from adding the texts into the vectorstore.
- Return type
List[str]
- async adelete(ids: Optional[List[str]] = None, **kwargs: Any) Optional[bool]¶
Async delete by vector ID or other criteria.
- Parameters
ids (Optional[List[str]]) – List of ids to delete. If None, delete all. Default is None.
**kwargs (Any) – Other keyword arguments that subclasses might use.
- Returns
True if deletion is successful, False otherwise, None if not implemented.
- Return type
Optional[bool]
- async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST¶
Async return VectorStore initialized from documents and embeddings.
- Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.
- Returns
VectorStore initialized from documents and embeddings.
- Return type
- async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) VST¶
Async return VectorStore initialized from texts and embeddings.
- Parameters
texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts. Default is None.
kwargs (Any) – Additional keyword arguments.
- Returns
VectorStore initialized from texts and embeddings.
- Return type
- async aget_by_ids(ids: Sequence[str], /) List[Document]¶
Async get documents by their IDs.
The returned documents are expected to have the ID field set to the ID of the document in the vector store.
Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.
Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.
This method should NOT raise exceptions if no documents are found for some IDs.
- Parameters
ids (Sequence[str]) – List of ids to retrieve.
- Returns
List of Documents.
- Return type
List[Document]
New in version 0.2.11.
- async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) List[Document]¶
Async return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.
lambda_mult (float) – 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.
kwargs (Any) –
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
- 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]¶
Async return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.
lambda_mult (float) – 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.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
- as_retriever(**kwargs: Any) VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
- Parameters
**kwargs (Any) –
Keyword arguments to pass to the search function. Can include: 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
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]¶
Async return docs most similar to query using a specified search type.
- Parameters
query (str) – Input text.
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query.
- Raises
ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.
- Return type
List[Document]
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) List[Document]¶
Async return docs most similar to query.
- Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query.
- Return type
List[Document]
- async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document]¶
Async return docs most similar to embedding vector.
- Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query vector.
- Return type
List[Document]
- async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) List[Tuple[Document, float]]¶
Async return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
- Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) –
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)
- Return type
List[Tuple[Document, float]]
- async asimilarity_search_with_score(*args: Any, **kwargs: Any) List[Tuple[Document, float]]¶
Async run similarity search with distance.
- Parameters
*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Tuples of (doc, similarity_score).
- Return type
List[Tuple[Document, float]]
- associate(source: str, target: str)[source]¶
The vectorstore associates a source phrase with a target phrase. When the vectorstore sees the source phrase, it will also consider results that are relevant to the target phrase.
- Parameters
source (str) – text to associate to target.
target (str) – text to associate source to.
- associate_batch(text_pairs: List[Tuple[str, str]])[source]¶
Given a batch of (source, target) pairs, the vectorstore associates each source phrase with the corresponding target phrase.
- Parameters
text_pairs (List[Tuple[str, str]]) – list of (source, target) text pairs. For each pair in
list (this) –
target. (the source will be associated with the) –
- astreaming_upsert(items: AsyncIterable[Document], /, batch_size: int, **kwargs: Any) AsyncIterator[UpsertResponse]¶
Beta
Added in 0.2.11. The API is subject to change.
Upsert documents in a streaming fashion. Async version of streaming_upsert.
- Parameters
items (AsyncIterable[Document]) – Iterable of Documents to add to the vectorstore.
batch_size (int) – The size of each batch to upsert.
kwargs (Any) – Additional keyword arguments. kwargs should only include parameters that are common to all documents. (e.g., timeout for indexing, retry policy, etc.) kwargs should not include ids to avoid ambiguous semantics. Instead the ID should be provided as part of the Document object.
- Yields
UpsertResponse – A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.
- Return type
AsyncIterator[UpsertResponse]
New in version 0.2.11.
- async aupsert(items: Sequence[Document], /, **kwargs: Any) UpsertResponse¶
Beta
Added in 0.2.11. The API is subject to change.
Add or update documents in the vectorstore. Async version of upsert.
The upsert functionality should utilize the ID field of the Document object if it is provided. If the ID is not provided, the upsert method is free to generate an ID for the document.
When an ID is specified and the document already exists in the vectorstore, the upsert method should update the document with the new data. If the document does not exist, the upsert method should add the document to the vectorstore.
- Parameters
items (Sequence[Document]) – Sequence of Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.
- Returns
A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.
- Return type
New in version 0.2.11.
- delete(ids: Optional[List[str]] = None, **kwargs: Any) Optional[bool]¶
Delete by vector ID or other criteria.
- Parameters
ids (Optional[List[str]]) – List of ids to delete. If None, delete all. Default is None.
**kwargs (Any) – Other keyword arguments that subclasses might use.
- Returns
True if deletion is successful, False otherwise, None if not implemented.
- Return type
Optional[bool]
- classmethod from_checkpoint(checkpoint: Union[str, Path], thirdai_key: Optional[str] = None)[source]¶
Create a NeuralDBVectorStore with a base model from a saved checkpoint
To use, set the
THIRDAI_KEYenvironment variable with your ThirdAI API key, or passthirdai_keyas a named parameter.Example
from langchain_community.vectorstores import NeuralDBVectorStore vectorstore = NeuralDBVectorStore.from_checkpoint( checkpoint="/path/to/checkpoint.ndb", thirdai_key="your-thirdai-key", ) vectorstore.insert([ "/path/to/doc.pdf", "/path/to/doc.docx", "/path/to/doc.csv", ]) documents = vectorstore.similarity_search("AI-driven music therapy")
- Parameters
checkpoint (Union[str, Path]) –
thirdai_key (Optional[str]) –
- classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) VST¶
Return VectorStore initialized from documents and embeddings.
- Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – Additional keyword arguments.
- Returns
VectorStore initialized from documents and embeddings.
- Return type
- classmethod from_scratch(thirdai_key: Optional[str] = None, **model_kwargs)[source]¶
Create a NeuralDBVectorStore from scratch.
To use, set the
THIRDAI_KEYenvironment variable with your ThirdAI API key, or passthirdai_keyas a named parameter.Example
from langchain_community.vectorstores import NeuralDBVectorStore vectorstore = NeuralDBVectorStore.from_scratch( thirdai_key="your-thirdai-key", ) vectorstore.insert([ "/path/to/doc.pdf", "/path/to/doc.docx", "/path/to/doc.csv", ]) documents = vectorstore.similarity_search("AI-driven music therapy")
- Parameters
thirdai_key (Optional[str]) –
- classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) NeuralDBVectorStore[source]¶
Return VectorStore initialized from texts and embeddings.
- Parameters
texts (List[str]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –
- Return type
- get_by_ids(ids: Sequence[str], /) List[Document]¶
Get documents by their IDs.
The returned documents are expected to have the ID field set to the ID of the document in the vector store.
Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.
Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.
This method should NOT raise exceptions if no documents are found for some IDs.
- Parameters
ids (Sequence[str]) – List of ids to retrieve.
- Returns
List of Documents.
- Return type
List[Document]
New in version 0.2.11.
- insert(sources: List[Any], train: bool = True, fast_mode: bool = True, **kwargs)[source]¶
Inserts files / document sources into the vectorstore.
- Parameters
train (bool) – When True this means that the underlying model in the
files. (NeuralDB will undergo unsupervised pretraining on the inserted) –
True. (Defaults to) –
fast_mode (bool) – Much faster insertion with a slight drop in performance.
True. –
sources (List[Any]) –
- max_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.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.
lambda_mult (float) – 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.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
- max_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.
Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.
- Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 20.
lambda_mult (float) – 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.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents selected by maximal marginal relevance.
- Return type
List[Document]
- save(path: str)[source]¶
Saves a NeuralDB instance to disk. Can be loaded into memory by calling NeuralDB.from_checkpoint(path)
- Parameters
path (str) – path on disk to save the NeuralDB instance to.
- search(query: str, search_type: str, **kwargs: Any) List[Document]¶
Return docs most similar to query using a specified search type.
- Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query.
- Raises
ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.
- Return type
List[Document]
- similarity_search(query: str, k: int = 10, **kwargs: Any) List[Document][source]¶
Retrieve {k} contexts with for a given query
- Parameters
query (str) – Query to submit to the model
k (int) – The max number of context results to retrieve. Defaults to 10.
kwargs (Any) –
- Return type
List[Document]
- similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document]¶
Return docs most similar to embedding vector.
- Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Documents most similar to the query vector.
- Return type
List[Document]
- 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 (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) –
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).
- Return type
List[Tuple[Document, float]]
- similarity_search_with_score(*args: Any, **kwargs: Any) List[Tuple[Document, float]]¶
Run similarity search with distance.
- Parameters
*args (Any) – Arguments to pass to the search method.
**kwargs (Any) – Arguments to pass to the search method.
- Returns
List of Tuples of (doc, similarity_score).
- Return type
List[Tuple[Document, float]]
- streaming_upsert(items: Iterable[Document], /, batch_size: int, **kwargs: Any) Iterator[UpsertResponse]¶
Beta
Added in 0.2.11. The API is subject to change.
Upsert documents in a streaming fashion.
- Parameters
items (Iterable[Document]) – Iterable of Documents to add to the vectorstore.
batch_size (int) – The size of each batch to upsert.
kwargs (Any) – Additional keyword arguments. kwargs should only include parameters that are common to all documents. (e.g., timeout for indexing, retry policy, etc.) kwargs should not include ids to avoid ambiguous semantics. Instead, the ID should be provided as part of the Document object.
- Yields
UpsertResponse – A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.
- Return type
Iterator[UpsertResponse]
New in version 0.2.11.
- upsert(items: Sequence[Document], /, **kwargs: Any) UpsertResponse¶
Beta
Added in 0.2.11. The API is subject to change.
Add or update documents in the vectorstore.
The upsert functionality should utilize the ID field of the Document object if it is provided. If the ID is not provided, the upsert method is free to generate an ID for the document.
When an ID is specified and the document already exists in the vectorstore, the upsert method should update the document with the new data. If the document does not exist, the upsert method should add the document to the vectorstore.
- Parameters
items (Sequence[Document]) – Sequence of Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.
- Returns
A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.
- Return type
New in version 0.2.11.
- upvote(query: str, document_id: Union[int, str])[source]¶
The vectorstore upweights the score of a document for a specific query. This is useful for fine-tuning the vectorstore to user behavior.
- Parameters
query (str) – text to associate with document_id
document_id (Union[int, str]) – id of the document to associate query with.
- upvote_batch(query_id_pairs: List[Tuple[str, int]])[source]¶
Given a batch of (query, document id) pairs, the vectorstore upweights the scores of the document for the corresponding queries. This is useful for fine-tuning the vectorstore to user behavior.
- Parameters
query_id_pairs (List[Tuple[str, int]]) – list of (query, document id) pairs. For each pair in
list (this) –
query. (the model will upweight the document id for the) –