Metadata-Version: 2.1
Name: pyvespa
Version: 0.1.3
Summary: Vespa python API
Home-page: https://github.com/vespa-engine/pyvespa/tree/master/
Author: Thiago G. Martins
Author-email: tmartins@verizonmedia.com
License: Apache Software License 2.0
Description: # Vespa library for data analysis
        > Provide data analysis support for Vespa applications
        
        
        ## Install
        
        `pip install pyvespa`
        
        ## Connect to a Vespa app
        
        > Connect to a running Vespa application
        
        ```
        from vespa.application import Vespa
        
        app = Vespa(url = "https://api.cord19.vespa.ai")
        ```
        
        ## Define a Query model
        
        > Easily define matching and ranking criteria
        
        ```
        from vespa.query import Query, Union, WeakAnd, ANN, RankProfile
        from random import random
        
        match_phase = Union(
            WeakAnd(hits = 10), 
            ANN(
                doc_vector="title_embedding", 
                query_vector="title_vector", 
                embedding_model=lambda x: [random() for x in range(768)],
                hits = 10,
                label="title"
            )
        )
        
        rank_profile = RankProfile(name="bm25", list_features=True)
        
        query_model = Query(match_phase=match_phase, rank_profile=rank_profile)
        ```
        
        ## Query the vespa app
        
        > Send queries via the query API. See the [query page](/vespa/query) for more examples.
        
        ```
        query_result = app.query(
            query="Is remdesivir an effective treatment for COVID-19?", 
            query_model=query_model
        )
        ```
        
        ```
        query_result.number_documents_retrieved
        ```
        
        ## Labelled data
        
        > How to structure labelled data
        
        ```
        labelled_data = [
            {
                "query_id": 0, 
                "query": "Intrauterine virus infections and congenital heart disease",
                "relevant_docs": [{"id": 0, "score": 1}, {"id": 3, "score": 1}]
            },
            {
                "query_id": 1, 
                "query": "Clinical and immunologic studies in identical twins discordant for systemic lupus erythematosus",
                "relevant_docs": [{"id": 1, "score": 1}, {"id": 5, "score": 1}]
            }
        ]
        ```
        
        Non-relevant documents are assigned `"score": 0` by default. Relevant documents will be assigned `"score": 1` by default if the field is missing from the labelled data. The defaults for both relevant and non-relevant documents can be modified on the appropriate methods.
        
        ## Collect training data
        
        > Collect training data to analyse and/or improve ranking functions. See the [collect training data page](/vespa/collect_training_data) for more examples.
        
        ```
        training_data_batch = app.collect_training_data(
            labelled_data = labelled_data,
            id_field = "id",
            query_model = query_model,
            number_additional_docs = 2
        )
        training_data_batch
        ```
        
        ## Evaluating a query model
        
        > Define metrics and evaluate query models. See the [evaluation page](/vespa/evaluation) for more examples.
        
        We will define the following evaluation metrics:
        * % of documents retrieved per query
        * recall @ 10 per query
        * MRR @ 10 per query
        
        ```
        from vespa.evaluation import MatchRatio, Recall, ReciprocalRank
        
        eval_metrics = [MatchRatio(), Recall(at=10), ReciprocalRank(at=10)]
        ```
        
        Evaluate:
        
        ```
        evaluation = app.evaluate(
            labelled_data = labelled_data,
            eval_metrics = eval_metrics, 
            query_model = query_model, 
            id_field = "id",
        )
        evaluation
        ```
        
Keywords: vespa,search engine,data science
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
