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
db
NeuralDB instance
embeddings
Access the query embedding object if available.
Methods
__init__
(db)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.
add_documents
(documents, **kwargs)Run more documents through the embeddings and add to the vectorstore.
add_texts
(texts[, metadatas])Run more texts through the embeddings and add to the vectorstore.
adelete
([ids])Delete by vector ID or other criteria.
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.
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.
Return docs and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score
(*args, **kwargs)Run similarity search with distance asynchronously.
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.
delete
([ids])Delete by vector ID or other criteria.
from_bazaar
(base[, bazaar_cache, thirdai_key])Create a NeuralDBVectorStore with a base model from the ThirdAI model bazaar.
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.
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 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.
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] ¶
Run more documents through the embeddings and add to the vectorstore.
- Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
documents (List[Document]) –
kwargs (Any) –
- Returns
List of IDs of the added texts.
- Return type
List[str]
- async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) List[str] ¶
Run more texts through the embeddings and add to the vectorstore.
- Parameters
texts (Iterable[str]) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –
- Return type
List[str]
- 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.
documents (List[Document]) –
kwargs (Any) –
- Returns
List of IDs of the added texts.
- 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] ¶
Delete by vector ID or other criteria.
- Parameters
ids (Optional[List[str]]) – List of ids to delete.
**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 ¶
Return VectorStore initialized from documents and embeddings.
- Parameters
documents (List[Document]) –
embedding (Embeddings) –
kwargs (Any) –
- Return type
VST
- async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) VST ¶
Return VectorStore initialized from texts and embeddings.
- Parameters
texts (List[str]) –
embedding (Embeddings) –
metadatas (Optional[List[dict]]) –
kwargs (Any) –
- Return type
VST
- 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.
- Parameters
query (str) –
k (int) –
fetch_k (int) –
lambda_mult (float) –
kwargs (Any) –
- 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] ¶
Return docs selected using the maximal marginal relevance.
- Parameters
embedding (List[float]) –
k (int) –
fetch_k (int) –
lambda_mult (float) –
kwargs (Any) –
- Return type
List[Document]
- 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
kwargs (Any) –
- 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] ¶
Return docs most similar to query using specified search type.
- Parameters
query (str) –
search_type (str) –
kwargs (Any) –
- Return type
List[Document]
- async asimilarity_search(query: str, k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to query.
- Parameters
query (str) –
k (int) –
kwargs (Any) –
- Return type
List[Document]
- async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) List[Document] ¶
Return docs most similar to embedding vector.
- Parameters
embedding (List[float]) –
k (int) –
kwargs (Any) –
- Return type
List[Document]
- async asimilarity_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], asynchronously.
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]] ¶
Run similarity search with distance asynchronously.
- Parameters
args (Any) –
kwargs (Any) –
- 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) –
- 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.
**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_bazaar(base: str, bazaar_cache: Optional[str] = None, thirdai_key: Optional[str] = None)[source]¶
Create a NeuralDBVectorStore with a base model from the ThirdAI model bazaar.
To use, set the
THIRDAI_KEY
environment variable with your ThirdAI API key, or passthirdai_key
as a named parameter.Example
from langchain_community.vectorstores import NeuralDBVectorStore vectorstore = NeuralDBVectorStore.from_bazaar( base="General QnA", 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
base (str) –
bazaar_cache (Optional[str]) –
thirdai_key (Optional[str]) –
- 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_KEY
environment variable with your ThirdAI API key, or passthirdai_key
as 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]) –
embedding (Embeddings) –
kwargs (Any) –
- Return type
VST
- classmethod from_scratch(thirdai_key: Optional[str] = None, **model_kwargs)[source]¶
Create a NeuralDBVectorStore from scratch.
To use, set the
THIRDAI_KEY
environment variable with your ThirdAI API key, or passthirdai_key
as 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
- 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.
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]
- 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.
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]
- 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 specified search type.
- Parameters
query (str) –
search_type (str) –
kwargs (Any) –
- 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) –
- 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) –
kwargs (Any) –
- Return type
List[Tuple[Document, float]]
- 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) –