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-# 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
-```