Methods

Embeddings is the engine that delivers semantic search. Data is transformed into embeddings vectors where similar concepts will produce similar vectors. Indexes both large and small are built with these vectors. The indexes are used to find results that have the same meaning, not necessarily the same keywords.

Source code in txtai/embeddings/base.py

class Embeddings:
    """
    Embeddings is the engine that delivers semantic search. Data is transformed into embeddings vectors where similar concepts
    will produce similar vectors. Indexes both large and small are built with these vectors. The indexes are used to find results
    that have the same meaning, not necessarily the same keywords.
    """
    # pylint: disable = W0231
    def __init__(self, config=None, models=None, **kwargs):
        """
        Creates a new embeddings index. Embeddings indexes are thread-safe for read operations but writes must be synchronized.
        Args:
            config: embeddings configuration
            models: models cache, used for model sharing between embeddings
            kwargs: additional configuration as keyword args
        """
        # Index configuration
        self.config = None
        # Dimensionality reduction - word vectors only
        self.reducer = None
        # Dense vector model - transforms data into similarity vectors
        self.model = None
        # Approximate nearest neighbor index
        self.ann = None
        # Document database
        self.database = None
        # Resolvable functions
        self.functions = None
        # Graph network
        self.graph = None
        # Sparse vectors
        self.scoring = None
        # Query model
        self.query = None
        # Index archive
        self.archive = None
        # Subindexes for this embeddings instance
        self.indexes = None
        # Models cache
        self.models = models
        # Merge configuration into single dictionary
        config = {**config, **kwargs} if config and kwargs else kwargs if kwargs else config
        # Set initial configuration
        self.configure(config)
    def score(self, documents):
        """
        Builds a term weighting scoring index. Only used by word vectors models.
        Args:
            documents: iterable of (id, data, tags), (id, data) or data
        """
        # Build scoring index for word vectors term weighting
        if self.isweighted():
            self.scoring.index(Stream(self)(documents))
    def index(self, documents, reindex=False):
        """
        Builds an embeddings index. This method overwrites an existing index.
        Args:
            documents: iterable of (id, data, tags), (id, data) or data
            reindex: if this is a reindex operation in which case database creation is skipped, defaults to False
        """
        # Initialize index
        self.initindex(reindex)
        # Create transform and stream
        transform = Transform(self, Action.REINDEX if reindex else Action.INDEX)
        stream = Stream(self, Action.REINDEX if reindex else Action.INDEX)
        with tempfile.NamedTemporaryFile(mode="wb", suffix=".npy") as buffer:
            # Load documents into database and transform to vectors
            ids, dimensions, embeddings = transform(stream(documents), buffer)
            if embeddings is not None:
                # Build LSA model (if enabled). Remove principal components from embeddings.
                if self.config.get("pca"):
                    self.reducer = Reducer(embeddings, self.config["pca"])
                    self.reducer(embeddings)
                # Normalize embeddings
                self.normalize(embeddings)
                # Save index dimensions
                self.config["dimensions"] = dimensions
                # Create approximate nearest neighbor index
                self.ann = self.createann()
                # Add embeddings to the index
                self.ann.index(embeddings)
            # Save indexids-ids mapping for indexes with no database, except when this is a reindex
            if ids and not reindex and not self.database:
                self.config["ids"] = ids
        # Index scoring, if necessary
        # This must occur before graph index in order to be available to the graph
        if self.issparse():
            self.scoring.index()
        # Index subindexes, if necessary
        if self.indexes:
            self.indexes.index()
        # Index graph, if necessary
        if self.graph:
            self.graph.index(Search(self, True), self.batchsimilarity)
    def upsert(self, documents):
        """
        Runs an embeddings upsert operation. If the index exists, new data is
        appended to the index, existing data is updated. If the index doesn't exist,
        this method runs a standard index operation.
        Args:
            documents: iterable of (id, data, tags), (id, data) or data
        """
        # Run standard insert if index doesn't exist or it has no records
        if not self.count():
            self.index(documents)
            return
        # Create transform and stream
        transform = Transform(self, Action.UPSERT)
        stream = Stream(self, Action.UPSERT)
        with tempfile.NamedTemporaryFile(mode="wb", suffix=".npy") as buffer:
            # Load documents into database and transform to vectors
            ids, _, embeddings = transform(stream(documents), buffer)
            if embeddings is not None:
                # Remove principal components from embeddings, if necessary
                if self.reducer:
                    self.reducer(embeddings)
                # Normalize embeddings
                self.normalize(embeddings)
                # Append embeddings to the index
                self.ann.append(embeddings)
            # Save indexids-ids mapping for indexes with no database
            if ids and not self.database:
                self.config["ids"] = self.config["ids"] + ids
        # Scoring upsert, if necessary
        # This must occur before graph upsert in order to be available to the graph
        if self.issparse():
            self.scoring.upsert()
        # Subindexes upsert, if necessary
        if self.indexes:
            self.indexes.upsert()
        # Graph upsert, if necessary
        if self.graph:
            self.graph.upsert(Search(self, True), self.batchsimilarity)
    def delete(self, ids):
        """
        Deletes from an embeddings index. Returns list of ids deleted.
        Args:
            ids: list of ids to delete
        Returns:
            list of ids deleted
        """
        # List of internal indices for each candidate id to delete
        indices = []
        # List of deleted ids
        deletes = []
        if self.database:
            # Retrieve indexid-id mappings from database
            ids = self.database.ids(ids)
            # Parse out indices and ids to delete
            indices = [i for i, _ in ids]
            deletes = sorted(set(uid for _, uid in ids))
            # Delete ids from database
            self.database.delete(deletes)
        elif self.ann or self.scoring:
            # Lookup indexids from config for indexes with no database
            indexids = self.config["ids"]
            # Find existing ids
            for uid in ids:
                indices.extend([index for index, value in enumerate(indexids) if uid == value])
            # Clear config ids
            for index in indices:
                deletes.append(indexids[index])
                indexids[index] = None
        # Delete indices for all indexes and data stores
        if indices:
            # Delete ids from ann
            if self.isdense():
                self.ann.delete(indices)
            # Delete ids from scoring
            if self.issparse():
                self.scoring.delete(indices)
            # Delete ids from subindexes
            if self.indexes:
                self.indexes.delete(indices)
            # Delete ids from graph
            if self.graph:
                self.graph.delete(indices)
        return deletes
    def reindex(self, config=None, function=None, **kwargs):
        """
        Recreates embeddings index using config. This method only works if document content storage is enabled.
        Args:
            config: new config
            function: optional function to prepare content for indexing
            kwargs: additional configuration as keyword args
        """
        if self.database:
            # Merge configuration into single dictionary
            config = {**config, **kwargs} if config and kwargs else config if config else kwargs
            # Keep content and objects parameters to ensure database is preserved
            config["content"] = self.config["content"]
            if "objects" in self.config:
                config["objects"] = self.config["objects"]
            # Reset configuration
            self.configure(config)
            # Reset function references
            if self.functions:
                self.functions.reset()
            # Reindex
            if function:
                self.index(function(self.database.reindex(self.config)), True)
            else:
                self.index(self.database.reindex(self.config), True)
    def transform(self, document):
        """
        Transforms document into an embeddings vector.
        Args:
            documents: iterable of (id, data, tags), (id, data) or data
        Returns:
            embeddings vector
        """
        return self.batchtransform([document])[0]
    def batchtransform(self, documents, category=None):
        """
        Transforms documents into embeddings vectors.
        Args:
            documents: iterable of (id, data, tags), (id, data) or data
            category: category for instruction-based embeddings
        Returns:
            embeddings vectors
        """
        # Initialize default parameters, if necessary
        self.defaults()
        # Convert documents into sentence embeddings
        embeddings = self.model.batchtransform(Stream(self)(documents), category)
        # Reduce the dimensionality of the embeddings. Scale the embeddings using this
        # model to reduce the noise of common but less relevant terms.
        if self.reducer:
            self.reducer(embeddings)
        # Normalize embeddings
        self.normalize(embeddings)
        return embeddings
    def count(self):
        """
        Total number of elements in this embeddings index.
        Returns:
            number of elements in this embeddings index
        """
        if self.ann:
            return self.ann.count()
        if self.scoring:
            return self.scoring.count()
        if self.database:
            return self.database.count()
        if self.config.get("ids"):
            return len([uid for uid in self.config["ids"] if uid is not None])
        # Default to 0 when no suitable method found
        return 0
    def search(self, query, limit=None, weights=None, index=None):
        """
        Finds documents most similar to the input query. This method will run either an index search
        or an index + database search depending on if a database is available.
        Args:
            query: input query
            limit: maximum results
            weights: hybrid score weights, if applicable
            index: index name, if applicable
        Returns:
            list of (id, score) for index search, list of dict for an index + database search
        """
        results = self.batchsearch([query], limit, weights, index)
        return results[0] if results else results
    def batchsearch(self, queries, limit=None, weights=None, index=None):
        """
        Finds documents most similar to the input queries. This method will run either an index search
        or an index + database search depending on if a database is available.
        Args:
            queries: input queries
            limit: maximum results
            weights: hybrid score weights, if applicable
            index: index name, if applicable
        Returns:
            list of (id, score) per query for index search, list of dict per query for an index + database search
        """
        return Search(self)(queries, limit, weights, index)
    def similarity(self, query, data):
        """
        Computes the similarity between query and list of data. Returns a list of
        (id, score) sorted by highest score, where id is the index in data.
        Args:
            query: input query
            data: list of data
        Returns:
            list of (id, score)
        """
        return self.batchsimilarity([query], data)[0]
    def batchsimilarity(self, queries, data):
        """
        Computes the similarity between list of queries and list of data. Returns a list
        of (id, score) sorted by highest score per query, where id is the index in data.
        Args:
            queries: input queries
            data: list of data
        Returns:
            list of (id, score) per query
        """
        # Convert queries to embedding vectors
        queries = self.batchtransform(((None, query, None) for query in queries), "query")
        data = self.batchtransform(((None, row, None) for row in data), "data")
        # Dot product on normalized vectors is equal to cosine similarity
        scores = np.dot(queries, data.T).tolist()
        # Add index and sort desc based on score
        return [sorted(enumerate(score), key=lambda x: x[1], reverse=True) for score in scores]
    def explain(self, query, texts=None, limit=None):
        """
        Explains the importance of each input token in text for a query. This method requires either content to be enabled
        or texts to be provided.
        Args:
            query: input query
            texts: optional list of (text|list of tokens), otherwise runs search query
            limit: optional limit if texts is None
        Returns:
            list of dict per input text where a higher token scores represents higher importance relative to the query
        """
        results = self.batchexplain([query], texts, limit)
        return results[0] if results else results
    def batchexplain(self, queries, texts=None, limit=None):
        """
        Explains the importance of each input token in text for a list of queries. This method requires either content to be enabled
        or texts to be provided.
        Args:
            queries: input queries
            texts: optional list of (text|list of tokens), otherwise runs search queries
            limit: optional limit if texts is None
        Returns:
            list of dict per input text per query where a higher token scores represents higher importance relative to the query
        """
        return Explain(self)(queries, texts, limit)
    def terms(self, query):
        """
        Extracts keyword terms from a query.
        Args:
            query: input query
        Returns:
            query reduced down to keyword terms
        """
        return self.batchterms([query])[0]
    def batchterms(self, queries):
        """
        Extracts keyword terms from a list of queries.
        Args:
            queries: list of queries
        Returns:
            list of queries reduced down to keyword term strings
        """
        return Terms(self)(queries)
    def exists(self, path=None, cloud=None, **kwargs):
        """
        Checks if an index exists at path.
        Args:
            path: input path
            cloud: cloud storage configuration
            kwargs: additional configuration as keyword args
        Returns:
            True if index exists, False otherwise
        """
        # Check if this exists in a cloud instance
        cloud = self.createcloud(cloud=cloud, **kwargs)
        if cloud:
            return cloud.exists(path)
        # Check if this is an archive file and exists
        path, apath = self.checkarchive(path)
        if apath:
            return os.path.exists(apath)
        # Return true if path has a config or config.json file and an embeddings (dense) or scoring (sparse) file
        return (
            path
            and (os.path.exists(f"{path}/config") or os.path.exists(f"{path}/config.json"))
            and (os.path.exists(f"{path}/embeddings") or os.path.exists(f"{path}/scoring"))
        )
    def load(self, path=None, cloud=None, **kwargs):
        """
        Loads an existing index from path.
        Args:
            path: input path
            cloud: cloud storage configuration
            kwargs: additional configuration as keyword args
        """
        # Load from cloud, if configured
        cloud = self.createcloud(cloud=cloud, **kwargs)
        if cloud:
            path = cloud.load(path)
        # Check if this is an archive file and extract
        path, apath = self.checkarchive(path)
        if apath:
            self.archive.load(apath)
        # Load index configuration
        self.config = self.loadconfig(path)
        # Approximate nearest neighbor index - stores dense vectors
        self.ann = self.createann()
        if self.ann:
            self.ann.load(f"{path}/embeddings")
        # Dimensionality reduction model - word vectors only
        if self.config.get("pca"):
            self.reducer = Reducer()
            self.reducer.load(f"{path}/lsa")
        # Document database - stores document content
        self.database = self.createdatabase()
        if self.database:
            self.database.load(f"{path}/documents")
        # Sparse vectors - stores term sparse arrays
        self.scoring = self.createscoring()
        if self.scoring:
            self.scoring.load(f"{path}/scoring")
        # Subindexes
        self.indexes = self.createindexes()
        if self.indexes:
            self.indexes.load(f"{path}/indexes")
        # Graph network - stores relationships
        self.graph = self.creategraph()
        if self.graph:
            self.graph.load(f"{path}/graph")
        # Dense vectors - transforms data to embeddings vectors
        self.model = self.loadvectors()
        # Query model
        self.query = self.loadquery()
    def save(self, path, cloud=None, **kwargs):
        """
        Saves an index in a directory at path unless path ends with tar.gz, tar.bz2, tar.xz or zip.
        In those cases, the index is stored as a compressed file.
        Args:
            path: output path
            cloud: cloud storage configuration
            kwargs: additional configuration as keyword args
        """
        if self.config:
            # Check if this is an archive file
            path, apath = self.checkarchive(path)
            # Create output directory, if necessary
            os.makedirs(path, exist_ok=True)
            # Copy sentence vectors model
            if self.config.get("storevectors"):
                shutil.copyfile(self.config["path"], os.path.join(path, os.path.basename(self.config["path"])))
                self.config["path"] = os.path.basename(self.config["path"])
            # Save index configuration
            self.saveconfig(path)
            # Save approximate nearest neighbor index
            if self.ann:
                self.ann.save(f"{path}/embeddings")
            # Save dimensionality reduction model (word vectors only)
            if self.reducer:
                self.reducer.save(f"{path}/lsa")
            # Save document database
            if self.database:
                self.database.save(f"{path}/documents")
            # Save scoring index
            if self.scoring:
                self.scoring.save(f"{path}/scoring")
            # Save subindexes
            if self.indexes:
                self.indexes.save(f"{path}/indexes")
            # Save graph
            if self.graph:
                self.graph.save(f"{path}/graph")
            # If this is an archive, save it
            if apath:
                self.archive.save(apath)
            # Save to cloud, if configured
            cloud = self.createcloud(cloud=cloud, **kwargs)
            if cloud:
                cloud.save(apath if apath else path)
    def close(self):
        """
        Closes this embeddings index and frees all resources.
        """
        self.ann, self.config, self.graph, self.archive = None, None, None, None
        self.reducer, self.query, self.model, self.models = None, None, None, None
        # Close database connection if open
        if self.database:
            self.database.close()
            self.database, self.functions = None, None
        # Close scoring instance if open
        if self.scoring:
            self.scoring.close()
            self.scoring = None
        # Close indexes if open
        if self.indexes:
            self.indexes.close()
            self.indexes = None
    def info(self):
        """
        Prints the current embeddings index configuration.
        """
        if self.config:
            # Copy and edit config
            config = self.config.copy()
            # Remove ids array if present
            config.pop("ids", None)
            # Print configuration
            print(json.dumps(config, sort_keys=True, default=str, indent=2))
    def issparse(self):
        """
        Checks if this instance has an associated scoring instance with term indexing enabled.
        Returns:
            True if term index is enabled, False otherwise
        """
        return self.scoring and self.scoring.hasterms()
    def isdense(self):
        """
        Checks if this instance has an associated ANN instance.
        Returns:
            True if this instance has an associated ANN, False otherwise
        """
        return self.ann is not None
    def isweighted(self):
        """
        Checks if this instance has an associated scoring instance with term weighting enabled.
        Returns:
            True if term weighting is enabled, False otherwise
        """
        return self.scoring and not self.scoring.hasterms()
    def configure(self, config):
        """
        Sets the configuration for this embeddings index and loads config-driven models.
        Args:
            config: embeddings configuration
        """
        # Configuration
        self.config = config
        # Dimensionality reduction model
        self.reducer = None
        # Create scoring instance for word vectors term weighting
        scoring = self.config.get("scoring") if self.config else None
        self.scoring = self.createscoring() if scoring and (not isinstance(scoring, dict) or not scoring.get("terms")) else None
        # Dense vectors - transforms data to embeddings vectors
        self.model = self.loadvectors() if self.config else None
        # Query model
        self.query = self.loadquery() if self.config else None
    def initindex(self, reindex):
        """
        Initialize new index.
        Args:
            reindex: if this is a reindex operation in which case database creation is skipped, defaults to False
        """
        # Initialize default parameters, if necessary
        self.defaults()
        # Create document database, if necessary
        if not reindex:
            self.database = self.createdatabase()
            # Reset archive since this is a new index
            self.archive = None
        # Initialize ANN, will be created after index transformations complete
        self.ann = None
        # Create scoring only if term indexing is enabled
        scoring = self.config.get("scoring")
        if scoring and isinstance(scoring, dict) and self.config["scoring"].get("terms"):
            self.scoring = self.createscoring()
        # Create subindexes, if necessary
        self.indexes = self.createindexes()
        # Create graph, if necessary
        self.graph = self.creategraph()
    def defaults(self):
        """
        Apply default parameters to current configuration.
        Returns:
            configuration with default parameters set
        """
        self.config = self.config if self.config else {}
        # Expand sparse index shortcuts
        if not self.config.get("scoring") and any(self.config.get(key) for key in ["keyword", "hybrid"]):
            self.config["scoring"] = {"method": "bm25", "terms": True, "normalize": True}
        # Check if default model should be loaded
        if not self.model and self.defaultallowed():
            self.config["path"] = "sentence-transformers/all-MiniLM-L6-v2"
            # Load dense vectors model
            self.model = self.loadvectors()
    def defaultallowed(self):
        """
        Tests if this embeddings instance can use a default model if not otherwise provided.
        Returns:
            True if a default model is allowed, False otherwise
        """
        params = [("keyword", False), ("defaults", True)]
        return all(self.config.get(key, default) == default for key, default in params)
    def loadconfig(self, path):
        """
        Loads index configuration. This method supports both config pickle files and config.json files.
        Args:
            path: path to directory
        Returns:
            dict
        """
        # Configuration
        config = None
        # Determine if config is json or pickle
        jsonconfig = os.path.exists(f"{path}/config.json")
        # Set config file name
        name = "config.json" if jsonconfig else "config"
        # Load configuration
        with open(f"{path}/{name}", "r" if jsonconfig else "rb") as handle:
            config = json.load(handle) if jsonconfig else pickle.load(handle)
        # Build full path to embedding vectors file
        if config.get("storevectors"):
            config["path"] = os.path.join(path, config["path"])
        return config
    def saveconfig(self, path):
        """
        Saves index configuration. This method saves to JSON if possible, otherwise it falls back to pickle.
        Args:
            path: path to directory
        Returns:
            dict
        """
        # Default to pickle config
        jsonconfig = self.config.get("format", "pickle") == "json"
        # Set config file name
        name = "config.json" if jsonconfig else "config"
        # Write configuration
        with open(f"{path}/{name}", "w" if jsonconfig else "wb", encoding="utf-8" if jsonconfig else None) as handle:
            if jsonconfig:
                # Write config as JSON
                json.dump(self.config, handle, default=str, indent=2)
            else:
                # Write config as pickle format
                pickle.dump(self.config, handle, protocol=__pickle__)
    def loadvectors(self):
        """
        Loads a vector model set in config.
        Returns:
            vector model
        """
        # Create model cache if subindexes are enabled
        if "indexes" in self.config and self.models is None:
            self.models = {}
        # Model path
        path = self.config.get("path")
        # Check if model is cached
        if self.models and path in self.models:
            return self.models[path]
        # Load and store uncached model
        model = VectorsFactory.create(self.config, self.scoring)
        if self.models is not None and path:
            self.models[path] = model
        return model
    def loadquery(self):
        """
        Loads a query model set in config.
        Returns:
            query model
        """
        if "query" in self.config:
            return Query(**self.config["query"])
        return None
    def checkarchive(self, path):
        """
        Checks if path is an archive file.
        Args:
            path: path to check
        Returns:
            (working directory, current path) if this is an archive, original path otherwise
        """
        # Create archive instance, if necessary
        self.archive = ArchiveFactory.create()
        # Check if path is an archive file
        if self.archive.isarchive(path):
            # Return temporary archive working directory and original path
            return self.archive.path(), path
        return path, None
    def createcloud(self, **cloud):
        """
        Creates a cloud instance from config.
        Args:
            cloud: cloud configuration
        """
        # Merge keyword args and keys under the cloud parameter
        config = cloud
        if "cloud" in config and config["cloud"]:
            config.update(config.pop("cloud"))
        # Create cloud instance from config and return
        return CloudFactory.create(config) if config else None
    def createann(self):
        """
        Creates an ANN from config.
        Returns:
            new ANN, if enabled in config
        """
        return ANNFactory.create(self.config) if self.config.get("path") or self.defaultallowed() else None
    def createdatabase(self):
        """
        Creates a database from config. This method will also close any existing database connection.
        Returns:
            new database, if enabled in config
        """
        # Free existing database resources
        if self.database:
            self.database.close()
        config = self.config.copy()
        # Create references to callable functions
        self.functions = Functions(self) if "functions" in config else None
        if self.functions:
            config["functions"] = self.functions(config)
        # Create database from config and return
        return DatabaseFactory.create(config)
    def creategraph(self):
        """
        Creates a graph from config.
        Returns:
            new graph, if enabled in config
        """
        if "graph" in self.config:
            # Get or create graph configuration
            config = self.config["graph"] if self.config["graph"] else {}
            # Create configuration with custom columns, if necessary
            config = self.columns(config)
            return GraphFactory.create(config)
        return None
    def createindexes(self):
        """
        Creates subindexes from config.
        Returns:
            list of subindexes
        """
        # Load subindexes
        if "indexes" in self.config:
            indexes = {}
            for index, config in self.config["indexes"].items():
                # Create index with shared model cache
                indexes[index] = Embeddings(config, models=self.models)
            # Wrap as Indexes object
            return Indexes(self, indexes)
        return None
    def createscoring(self):
        """
        Creates a scoring from config.
        Returns:
            new scoring, if enabled in config
        """
        # Free existing resources
        if self.scoring:
            self.scoring.close()
        if "scoring" in self.config:
            # Expand scoring to a dictionary, if necessary
            config = self.config["scoring"]
            config = config if isinstance(config, dict) else {"method": config}
            # Create configuration with custom columns, if necessary
            config = self.columns(config)
            return ScoringFactory.create(config)
        return None
    def columns(self, config):
        """
        Adds custom text/object column information if it's provided.
        Args:
            config: input configuration
        Returns:
            config with column information added
        """
        # Add text/object columns if custom
        if "columns" in self.config:
            # Work on copy of configuration
            config = config.copy()
            # Copy columns to config
            config["columns"] = self.config["columns"]
        return config
    def normalize(self, embeddings):
        """
        Normalizes embeddings using L2 normalization. Operation applied directly on array.
        Args:
            embeddings: input embeddings matrix
        """
        # Calculation is different for matrices vs vectors
        if len(embeddings.shape) > 1:
            embeddings /= np.linalg.norm(embeddings, axis=1)[:, np.newaxis]
        else:
            embeddings /= np.linalg.norm(embeddings)

__init__(self, config=None, models=None, **kwargs) special

Creates a new embeddings index. Embeddings indexes are thread-safe for read operations but writes must be synchronized.

Parameters:

Source code in txtai/embeddings/base.py

def __init__(self, config=None, models=None, **kwargs):
    """
    Creates a new embeddings index. Embeddings indexes are thread-safe for read operations but writes must be synchronized.
    Args:
        config: embeddings configuration
        models: models cache, used for model sharing between embeddings
        kwargs: additional configuration as keyword args
    """
    # Index configuration
    self.config = None
    # Dimensionality reduction - word vectors only
    self.reducer = None
    # Dense vector model - transforms data into similarity vectors
    self.model = None
    # Approximate nearest neighbor index
    self.ann = None
    # Document database
    self.database = None
    # Resolvable functions
    self.functions = None
    # Graph network
    self.graph = None
    # Sparse vectors
    self.scoring = None
    # Query model
    self.query = None
    # Index archive
    self.archive = None
    # Subindexes for this embeddings instance
    self.indexes = None
    # Models cache
    self.models = models
    # Merge configuration into single dictionary
    config = {**config, **kwargs} if config and kwargs else kwargs if kwargs else config
    # Set initial configuration
    self.configure(config)

batchexplain(self, queries, texts=None, limit=None)

Explains the importance of each input token in text for a list of queries. This method requires either content to be enabled or texts to be provided.

Parameters:

Returns:

Source code in txtai/embeddings/base.py

def batchexplain(self, queries, texts=None, limit=None):
    """
    Explains the importance of each input token in text for a list of queries. This method requires either content to be enabled
    or texts to be provided.
    Args:
        queries: input queries
        texts: optional list of (text|list of tokens), otherwise runs search queries
        limit: optional limit if texts is None
    Returns:
        list of dict per input text per query where a higher token scores represents higher importance relative to the query
    """
    return Explain(self)(queries, texts, limit)

batchsearch(self, queries, limit=None, weights=None, index=None)

Finds documents most similar to the input queries. This method will run either an index search or an index + database search depending on if a database is available.

Parameters:

Returns:

Source code in txtai/embeddings/base.py

def batchsearch(self, queries, limit=None, weights=None, index=None):
    """
    Finds documents most similar to the input queries. This method will run either an index search
    or an index + database search depending on if a database is available.
    Args:
        queries: input queries
        limit: maximum results
        weights: hybrid score weights, if applicable
        index: index name, if applicable
    Returns:
        list of (id, score) per query for index search, list of dict per query for an index + database search
    """
    return Search(self)(queries, limit, weights, index)

batchsimilarity(self, queries, data)

Computes the similarity between list of queries and list of data. Returns a list of (id, score) sorted by highest score per query, where id is the index in data.

Parameters:

Returns:

Source code in txtai/embeddings/base.py

def batchsimilarity(self, queries, data):
    """
    Computes the similarity between list of queries and list of data. Returns a list
    of (id, score) sorted by highest score per query, where id is the index in data.
    Args:
        queries: input queries
        data: list of data
    Returns:
        list of (id, score) per query
    """
    # Convert queries to embedding vectors
    queries = self.batchtransform(((None, query, None) for query in queries), "query")
    data = self.batchtransform(((None, row, None) for row in data), "data")
    # Dot product on normalized vectors is equal to cosine similarity
    scores = np.dot(queries, data.T).tolist()
    # Add index and sort desc based on score
    return [sorted(enumerate(score), key=lambda x: x[1], reverse=True) for score in scores]

batchterms(self, queries)

Extracts keyword terms from a list of queries.

Parameters:

Returns:

Source code in txtai/embeddings/base.py

def batchterms(self, queries):
    """
    Extracts keyword terms from a list of queries.
    Args:
        queries: list of queries
    Returns:
        list of queries reduced down to keyword term strings
    """
    return Terms(self)(queries)

batchtransform(self, documents, category=None)

Transforms documents into embeddings vectors.

Parameters:

Returns:

Source code in txtai/embeddings/base.py

def batchtransform(self, documents, category=None):
    """
    Transforms documents into embeddings vectors.
    Args:
        documents: iterable of (id, data, tags), (id, data) or data
        category: category for instruction-based embeddings
    Returns:
        embeddings vectors
    """
    # Initialize default parameters, if necessary
    self.defaults()
    # Convert documents into sentence embeddings
    embeddings = self.model.batchtransform(Stream(self)(documents), category)
    # Reduce the dimensionality of the embeddings. Scale the embeddings using this
    # model to reduce the noise of common but less relevant terms.
    if self.reducer:
        self.reducer(embeddings)
    # Normalize embeddings
    self.normalize(embeddings)
    return embeddings

checkarchive(self, path)

Checks if path is an archive file.

Parameters:

Returns:

Source code in txtai/embeddings/base.py

def checkarchive(self, path):
    """
    Checks if path is an archive file.
    Args:
        path: path to check
    Returns:
        (working directory, current path) if this is an archive, original path otherwise
    """
    # Create archive instance, if necessary
    self.archive = ArchiveFactory.create()
    # Check if path is an archive file
    if self.archive.isarchive(path):
        # Return temporary archive working directory and original path
        return self.archive.path(), path
    return path, None

close(self)

Closes this embeddings index and frees all resources.

Source code in txtai/embeddings/base.py

def close(self):
    """
    Closes this embeddings index and frees all resources.
    """
    self.ann, self.config, self.graph, self.archive = None, None, None, None
    self.reducer, self.query, self.model, self.models = None, None, None, None
    # Close database connection if open
    if self.database:
        self.database.close()
        self.database, self.functions = None, None
    # Close scoring instance if open
    if self.scoring:
        self.scoring.close()
        self.scoring = None
    # Close indexes if open
    if self.indexes:
        self.indexes.close()
        self.indexes = None

columns(self, config)

Adds custom text/object column information if it’s provided.

Parameters:

Returns:

Source code in txtai/embeddings/base.py

def columns(self, config):
    """
    Adds custom text/object column information if it's provided.
    Args:
        config: input configuration
    Returns:
        config with column information added
    """
    # Add text/object columns if custom
    if "columns" in self.config:
        # Work on copy of configuration
        config = config.copy()
        # Copy columns to config
        config["columns"] = self.config["columns"]
    return config

configure(self, config)

Sets the configuration for this embeddings index and loads config-driven models.

Parameters:

Source code in txtai/embeddings/base.py

def configure(self, config):
    """
    Sets the configuration for this embeddings index and loads config-driven models.
    Args:
        config: embeddings configuration
    """
    # Configuration
    self.config = config
    # Dimensionality reduction model
    self.reducer = None
    # Create scoring instance for word vectors term weighting
    scoring = self.config.get("scoring") if self.config else None
    self.scoring = self.createscoring() if scoring and (not isinstance(scoring, dict) or not scoring.get("terms")) else None
    # Dense vectors - transforms data to embeddings vectors
    self.model = self.loadvectors() if self.config else None
    # Query model
    self.query = self.loadquery() if self.config else None