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Methods

API

Bases: Application

Base API template. The API is an extended txtai application, adding the ability to cluster API instances together.

Downstream applications can extend this base template to add/modify functionality.

Source code in txtai/api/base.py 
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class API(Application):
    """
    Base API template. The API is an extended txtai application, adding the ability to cluster API instances together.

    Downstream applications can extend this base template to add/modify functionality.
    """

    def __init__(self, config, loaddata=True):
        super().__init__(config, loaddata)

        # Embeddings cluster
        self.cluster = None
        if self.config.get("cluster"):
            self.cluster = Cluster(self.config["cluster"])

    # pylint: disable=W0221
    def search(self, query, limit=None, weights=None, index=None, parameters=None, graph=False, request=None):
        # When search is invoked via the API, limit is set from the request
        # When search is invoked directly, limit is set using the method parameter
        limit = self.limit(request.query_params.get("limit") if request and hasattr(request, "query_params") else limit)
        weights = self.weights(request.query_params.get("weights") if request and hasattr(request, "query_params") else weights)
        index = request.query_params.get("index") if request and hasattr(request, "query_params") else index
        parameters = request.query_params.get("parameters") if request and hasattr(request, "query_params") else parameters
        graph = request.query_params.get("graph") if request and hasattr(request, "query_params") else graph

        # Decode parameters
        parameters = json.loads(parameters) if parameters and isinstance(parameters, str) else parameters

        if self.cluster:
            return self.cluster.search(query, limit, weights, index, parameters, graph)

        return super().search(query, limit, weights, index, parameters, graph)

    def batchsearch(self, queries, limit=None, weights=None, index=None, parameters=None, graph=False):
        if self.cluster:
            return self.cluster.batchsearch(queries, self.limit(limit), weights, index, parameters, graph)

        return super().batchsearch(queries, limit, weights, index, parameters, graph)

    def add(self, documents):
        """
        Adds a batch of documents for indexing.

        Downstream applications can override this method to also store full documents in an external system.

        Args:
            documents: list of {id: value, text: value}

        Returns:
            unmodified input documents
        """

        if self.cluster:
            self.cluster.add(documents)
        else:
            super().add(documents)

        return documents

    def index(self):
        """
        Builds an embeddings index for previously batched documents.
        """

        if self.cluster:
            self.cluster.index()
        else:
            super().index()

    def upsert(self):
        """
        Runs an embeddings upsert operation for previously batched documents.
        """

        if self.cluster:
            self.cluster.upsert()
        else:
            super().upsert()

    def delete(self, ids):
        """
        Deletes from an embeddings index. Returns list of ids deleted.

        Args:
            ids: list of ids to delete

        Returns:
            ids deleted
        """

        if self.cluster:
            return self.cluster.delete(ids)

        return super().delete(ids)

    def reindex(self, config, function=None):
        """
        Recreates this 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
        """

        if self.cluster:
            self.cluster.reindex(config, function)
        else:
            super().reindex(config, function)

    def count(self):
        """
        Total number of elements in this embeddings index.

        Returns:
            number of elements in embeddings index
        """

        if self.cluster:
            return self.cluster.count()

        return super().count()

    def limit(self, limit):
        """
        Parses the number of results to return from the request. Allows range of 1-250, with a default of 10.

        Args:
            limit: limit parameter

        Returns:
            bounded limit
        """

        # Return between 1 and 250 results, defaults to 10
        return max(1, min(250, int(limit) if limit else 10))

    def weights(self, weights):
        """
        Parses the weights parameter from the request.

        Args:
            weights: weights parameter

        Returns:
            weights
        """

        return float(weights) if weights else weights

add(documents)

Adds a batch of documents for indexing.

Downstream applications can override this method to also store full documents in an external system.

Parameters:

Name Type Description Default
documents

list of {id: value, text: value}

 required

Returns:

Type Description

unmodified input documents
Source code in txtai/api/base.py 
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def add(self, documents):
    """
    Adds a batch of documents for indexing.

    Downstream applications can override this method to also store full documents in an external system.

    Args:
        documents: list of {id: value, text: value}

    Returns:
        unmodified input documents
    """

    if self.cluster:
        self.cluster.add(documents)
    else:
        super().add(documents)

    return documents

addobject(data, uid, field)

Helper method that builds a batch of object documents.

Parameters:

Name Type Description Default
data

object content

 required
uid

optional list of corresponding uids

 required
field

optional field to set

 required

Returns:

Type Description

documents
Source code in txtai/app/base.py 
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def addobject(self, data, uid, field):
    """
    Helper method that builds a batch of object documents.

    Args:
        data: object content
        uid: optional list of corresponding uids
        field: optional field to set

    Returns:
        documents
    """

    # Raise error if index is not writable
    if not self.config.get("writable"):
        raise ReadOnlyError("Attempting to add documents to a read-only index (writable != True)")

    documents = []
    for x, content in enumerate(data):
        if field:
            row = {"id": uid[x], field: content} if uid else {field: content}
        elif uid:
            row = (uid[x], content)
        else:
            row = content

        documents.append(row)

    return self.add(documents)

agent(name, *args, **kwargs)

Executes an agent.

Parameters:

Name Type Description Default
name

agent name

 required
args

agent positional arguments

 ()
kwargs

agent keyword arguments

 {}
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def agent(self, name, *args, **kwargs):
    """
    Executes an agent.

    Args:
        name: agent name
        args: agent positional arguments
        kwargs: agent keyword arguments
    """

    if name in self.agents:
        return self.agents[name](*args, **kwargs)

    return None

batchexplain(queries, texts=None, limit=10)

Explains the importance of each input token in text for a list of queries.

Parameters:

Name Type Description Default
query

queries text

 required
texts

optional list of text, otherwise runs search queries

 None
limit

optional limit if texts is None

 10

Returns:

Type Description

list of dict per input text per query where a higher token scores represents higher importance relative to the query
Source code in txtai/app/base.py 
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def batchexplain(self, queries, texts=None, limit=10):
    """
    Explains the importance of each input token in text for a list of queries.

    Args:
        query: queries text
        texts: optional list of text, 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
    """

    if self.embeddings:
        with self.lock:
            return self.embeddings.batchexplain(queries, texts, limit)

    return None

batchsimilarity(queries, texts)

Computes the similarity between list of queries and list of text. Returns a list of {id: value, score: value} sorted by highest score per query, where id is the index in texts.

Parameters:

Name Type Description Default
queries

queries text

 required
texts

list of text

 required

Returns:

Type Description

list of {id: value, score: value} per query
Source code in txtai/app/base.py 
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def batchsimilarity(self, queries, texts):
    """
    Computes the similarity between list of queries and list of text. Returns a list
    of {id: value, score: value} sorted by highest score per query, where id is the
    index in texts.

    Args:
        queries: queries text
        texts: list of text

    Returns:
        list of {id: value, score: value} per query
    """

    # Use similarity instance if available otherwise fall back to embeddings model
    if "similarity" in self.pipelines:
        return [[{"id": uid, "score": float(score)} for uid, score in r] for r in self.pipelines["similarity"](queries, texts)]
    if self.embeddings:
        return [[{"id": uid, "score": float(score)} for uid, score in r] for r in self.embeddings.batchsimilarity(queries, texts)]

    return None

batchtransform(texts, category=None, index=None)

Transforms list of text into embeddings arrays.

Parameters:

Name Type Description Default
texts

list of text

 required
category

category for instruction-based embeddings

 None
index

index name, if applicable

 None

Returns:

Type Description

embeddings arrays
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def batchtransform(self, texts, category=None, index=None):
    """
    Transforms list of text into embeddings arrays.

    Args:
        texts: list of text
        category: category for instruction-based embeddings
        index: index name, if applicable

    Returns:
        embeddings arrays
    """

    if self.embeddings:
        return [[float(x) for x in result] for result in self.embeddings.batchtransform(texts, category, index)]

    return None

count()

Total number of elements in this embeddings index.

Returns:

Type Description

number of elements in embeddings index
Source code in txtai/api/base.py 
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def count(self):
    """
    Total number of elements in this embeddings index.

    Returns:
        number of elements in embeddings index
    """

    if self.cluster:
        return self.cluster.count()

    return super().count()

createagents()

Create agents.

Source code in txtai/app/base.py 
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def createagents(self):
    """
    Create agents.
    """

    # Agent definitions
    self.agents = {}

    # Create agents
    if "agent" in self.config:
        for agent, config in self.config["agent"].items():
            # Create copy of config
            config = config.copy()

            # Resolve LLM
            config["llm"] = self.function("llm")

            # Resolve tools
            for tool in config.get("tools", []):
                if isinstance(tool, dict) and "target" in tool:
                    tool["target"] = self.function(tool["target"])

            # Create agent
            self.agents[agent] = Agent(**config)

createpipelines()

Create pipelines.

Source code in txtai/app/base.py 
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def createpipelines(self):
    """
    Create pipelines.
    """

    # Pipeline definitions
    self.pipelines = {}

    # Default pipelines
    pipelines = list(PipelineFactory.list().keys())

    # Add custom pipelines
    for key in self.config:
        if "." in key:
            pipelines.append(key)

    # Move dependent pipelines to end of list
    dependent = ["similarity", "extractor", "rag"]
    pipelines = sorted(pipelines, key=lambda x: dependent.index(x) + 1 if x in dependent else 0)

    # Create pipelines
    for pipeline in pipelines:
        if pipeline in self.config:
            config = self.config[pipeline] if self.config[pipeline] else {}

            # Add application reference, if requested
            if "application" in config:
                config["application"] = self

            # Custom pipeline parameters
            if pipeline in ["extractor", "rag"]:
                if "similarity" not in config:
                    # Add placeholder, will be set to embeddings index once initialized
                    config["similarity"] = None

                # Resolve reference pipelines
                if config.get("similarity") in self.pipelines:
                    config["similarity"] = self.pipelines[config["similarity"]]

                if config.get("path") in self.pipelines:
                    config["path"] = self.pipelines[config["path"]]

            elif pipeline == "similarity" and "path" not in config and "labels" in self.pipelines:
                config["model"] = self.pipelines["labels"]

            self.pipelines[pipeline] = PipelineFactory.create(config, pipeline)

delete(ids)

Deletes from an embeddings index. Returns list of ids deleted.

Parameters:

Name Type Description Default
ids

list of ids to delete

 required

Returns:

Type Description

ids deleted
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def delete(self, ids):
    """
    Deletes from an embeddings index. Returns list of ids deleted.

    Args:
        ids: list of ids to delete

    Returns:
        ids deleted
    """

    if self.cluster:
        return self.cluster.delete(ids)

    return super().delete(ids)

explain(query, texts=None, limit=10)

Explains the importance of each input token in text for a query.

Parameters:

Name Type Description Default
query

query text

 required
texts

optional list of text, otherwise runs search query

 None
limit

optional limit if texts is None

 10

Returns:

Type Description

list of dict per input text where a higher token scores represents higher importance relative to the query
Source code in txtai/app/base.py 
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def explain(self, query, texts=None, limit=10):
    """
    Explains the importance of each input token in text for a query.

    Args:
        query: query text
        texts: optional list of text, 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
    """

    if self.embeddings:
        with self.lock:
            return self.embeddings.explain(query, texts, limit)

    return None

extract(queue, texts=None)

Extracts answers to input questions.

Parameters:

Name Type Description Default
queue

list of {name: value, query: value, question: value, snippet: value}

 required
texts

optional list of text

 None

Returns:

Type Description

list of {name: value, answer: value}
Source code in txtai/app/base.py 
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def extract(self, queue, texts=None):
    """
    Extracts answers to input questions.

    Args:
        queue: list of {name: value, query: value, question: value, snippet: value}
        texts: optional list of text

    Returns:
        list of {name: value, answer: value}
    """

    if self.embeddings and "extractor" in self.pipelines:
        # Get extractor instance
        extractor = self.pipelines["extractor"]

        # Run extractor and return results as dicts
        return extractor(queue, texts)

    return None

index()

Builds an embeddings index for previously batched documents.

Source code in txtai/api/base.py 
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def index(self):
    """
    Builds an embeddings index for previously batched documents.
    """

    if self.cluster:
        self.cluster.index()
    else:
        super().index()

label(text, labels)

Applies a zero shot classifier to text using a list of labels. Returns a list of {id: value, score: value} sorted by highest score, where id is the index in labels.

Parameters:

Name Type Description Default
text

text|list

 required
labels

list of labels

 required

Returns:

Type Description

list of {id: value, score: value} per text element
Source code in txtai/app/base.py 
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def label(self, text, labels):
    """
    Applies a zero shot classifier to text using a list of labels. Returns a list of
    {id: value, score: value} sorted by highest score, where id is the index in labels.

    Args:
        text: text|list
        labels: list of labels

    Returns:
        list of {id: value, score: value} per text element
    """

    if "labels" in self.pipelines:
        # Text is a string
        if isinstance(text, str):
            return [{"id": uid, "score": float(score)} for uid, score in self.pipelines["labels"](text, labels)]

        # Text is a list
        return [[{"id": uid, "score": float(score)} for uid, score in result] for result in self.pipelines["labels"](text, labels)]

    return None

pipeline(name, *args, **kwargs)

Generic pipeline execution method.

Parameters:

Name Type Description Default
name

pipeline name

 required
args

pipeline positional arguments

 ()
kwargs

pipeline keyword arguments

 {}

Returns:

Type Description

pipeline results
Source code in txtai/app/base.py 
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def pipeline(self, name, *args, **kwargs):
    """
    Generic pipeline execution method.

    Args:
        name: pipeline name
        args: pipeline positional arguments
        kwargs: pipeline keyword arguments

    Returns:
        pipeline results
    """

    # Backwards compatible with previous pipeline function arguments
    args = args[0] if args and len(args) == 1 and isinstance(args[0], tuple) else args

    if name in self.pipelines:
        return self.pipelines[name](*args, **kwargs)

    return None

reindex(config, function=None)

Recreates this embeddings index using config. This method only works if document content storage is enabled.

Parameters:

Name Type Description Default
config

new config

 required
function

optional function to prepare content for indexing

 None
Source code in txtai/api/base.py 
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def reindex(self, config, function=None):
    """
    Recreates this 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
    """

    if self.cluster:
        self.cluster.reindex(config, function)
    else:
        super().reindex(config, function)

similarity(query, texts)

Computes the similarity between query and list of text. Returns a list of {id: value, score: value} sorted by highest score, where id is the index in texts.

Parameters:

Name Type Description Default
query

query text

 required
texts

list of text

 required

Returns:

Type Description

list of {id: value, score: value}
Source code in txtai/app/base.py 
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def similarity(self, query, texts):
    """
    Computes the similarity between query and list of text. Returns a list of
    {id: value, score: value} sorted by highest score, where id is the index
    in texts.

    Args:
        query: query text
        texts: list of text

    Returns:
        list of {id: value, score: value}
    """

    # Use similarity instance if available otherwise fall back to embeddings model
    if "similarity" in self.pipelines:
        return [{"id": uid, "score": float(score)} for uid, score in self.pipelines["similarity"](query, texts)]
    if self.embeddings:
        return [{"id": uid, "score": float(score)} for uid, score in self.embeddings.similarity(query, texts)]

    return None

transform(text, category=None, index=None)

Transforms text into embeddings arrays.

Parameters:

Name Type Description Default
text

input text

 required
category

category for instruction-based embeddings

 None
index

index name, if applicable

 None

Returns:

Type Description

embeddings array
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def transform(self, text, category=None, index=None):
    """
    Transforms text into embeddings arrays.

    Args:
        text: input text
        category: category for instruction-based embeddings
        index: index name, if applicable

    Returns:
        embeddings array
    """

    if self.embeddings:
        return [float(x) for x in self.embeddings.transform(text, category, index)]

    return None

upsert()

Runs an embeddings upsert operation for previously batched documents.

Source code in txtai/api/base.py 
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def upsert(self):
    """
    Runs an embeddings upsert operation for previously batched documents.
    """

    if self.cluster:
        self.cluster.upsert()
    else:
        super().upsert()

wait()

Closes threadpool and waits for completion.

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def wait(self):
    """
    Closes threadpool and waits for completion.
    """

    if self.pool:
        self.pool.close()
        self.pool.join()
        self.pool = None

workflow(name, elements)

Executes a workflow.

Parameters:

Name Type Description Default
name

workflow name

 required
elements

elements to process

 required

Returns:

Type Description

processed elements
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def workflow(self, name, elements):
    """
    Executes a workflow.

    Args:
        name: workflow name
        elements: elements to process

    Returns:
        processed elements
    """

    if hasattr(elements, "__len__") and hasattr(elements, "__getitem__"):
        # Convert to tuples and return as a list since input is sized
        elements = [tuple(element) if isinstance(element, list) else element for element in elements]
    else:
        # Convert to tuples and return as a generator since input is not sized
        elements = (tuple(element) if isinstance(element, list) else element for element in elements)

    # Execute workflow
    return self.workflows[name](elements)