def test_search_ids():
kiri = Kiri(local=True)
docs = get_docs()
docs[0].id = "123"
kiri.upload(docs)
results = kiri.search("another", ids=["123"])
assert len(results.results) == 1, "Invalid number of search results"
def test_doc_summarise():
kiri = Kiri(local=True)
c = "I am a document."
doc = Document(c)
kiri.upload([doc])
out = doc.emotion()
assert type(out) == str
def test_doc_summarise():
kiri = Kiri(local=True)
c = "I am a document."
doc = Document(c)
kiri.upload([doc])
out = doc.classify(["random"])
assert isinstance(out, dict)
def test_doc_qa():
kiri = Kiri(local=True)
c = "I am a document."
doc = Document(c)
kiri.upload([doc])
out = doc.qa("What are you?")
assert type(out) == str
def test_upload_dup_id():
kiri = Kiri(local=True)
docs = get_docs()
for doc in docs:
doc.id = "123"
with pytest.raises(ValueError):
kiri.upload(docs)
def test_upload():
kiri = Kiri(local=True)
docs = get_docs()
kiri.upload(docs)
assert docs[0].vector is not None, "Document not vectorised"
assert docs[1].vector is not None, "Document not vectorised"
assert len(
kiri._store.documents) == 2, "Incorrect number of documents in mem"
def test_upload_chunked():
kiri = Kiri(local=True)
docs = get_chunked_docs(chunking_level=1)
kiri.upload(docs)
assert len(
kiri._store.documents) == 2, "Incorrect number of documents in mem"
for doc in docs:
assert doc.vector is not None, "Document not vectorised"
assert len(doc.chunk_vectors) == 2, "Invalid number of chunk vectors"
def test_qa_chunk():
kiri = Kiri(local=True)
docs = get_chunked_docs()
kiri.upload(docs)
results = kiri.qa("another?")
assert isinstance(results, list)
for result in results:
assert type(result[0]) == str
assert isinstance(result[1], SearchResult)
def init_elastic_kiri(doc_class=ElasticDocument):
index_name = "temp_test"
elastic_url = "http://localhost:9200"
requests.delete(f"{elastic_url}/{index_name}")
store = ElasticDocStore(elastic_url, index=index_name, doc_class=doc_class)
kiri = Kiri(store=store, local=True)
return kiri
def test_search():
kiri = Kiri(local=True)
docs = get_docs()
kiri.upload(docs)
results = kiri.search("another")
assert len(results.results) == 2, "Invalid number of search results"
def test_upload_mixed_type():
kiri = Kiri(local=True)
docs = [Document("a"), ChunkedDocument("b")]
with pytest.raises(ValueError):
kiri.upload(docs)
def test_summarise():
kiri = Kiri(local=True)
out = kiri.summarise(text)
assert type(out) == str
Here's example functionality for a customer-service email system.
This shows two capabilities of Kiri: zero-shot classification, and sentiment detection.
Zero-shot classification is categorizing into a group of labels that were never seen during training.
Sentiment detection... detects text sentiment.
A full list of sentiments is availble in the README -- only a few are noted in this example.
"""
# Common labels for e.g. an e-commerce store's emails
labels = ["Returns", "Promotional", "Technical Issues", "Product Inquiries", "Shipping Questions", "Other"]
# Negative sentiment, give special attention to these.
negative_sentiments = ["annoyance", "disapproval", "disappointment", "anger", "disgust"]
kiri = Kiri()
kiri.classify("This is just to get rid of the example message before printing", ["test"])
# Print example, just to display local results
print("Inbox")
print("==================")
for email in emails:
classification_results = kiri.classify(email, labels)
label = max(classification_results, key=classification_results.get)
emote = kiri.emotion(email)
high_priority = any([e in emote for e in negative_sentiments])
print(f"Category: {label}")
if high_priority:
print("\033[91mPRIORITY\033[0m")
def test_classify():
kiri = Kiri(local=True)
out = kiri.classify(text, ["interests", "alcoholism"])
assert isinstance(out, dict)
def test_emotion():
kiri = Kiri(local=True)
out = kiri.emotion(text)
assert type(out) == str
def test_search_max_results_chunk():
kiri = Kiri(local=True)
docs = get_chunked_docs()
kiri.upload(docs)
results = kiri.search("another", max_results=1)
assert len(results.results) == 1, "Invalid number of search results"
For this example, just the answer in the most relevant document is returned to the user,
and used for additional context.
"""
elastic = False
if elastic:
doc_store = ElasticDocStore("http://localhost:9000",
doc_class=ElasticChunkedDocument,
index="kiri_default")
documents = big_n_docs["elastic"]
else:
doc_store = InMemoryDocStore(doc_class=ChunkedDocument)
documents = big_n_docs["memory"]
kiri = Kiri(doc_store, local=True)
kiri.upload(documents)
print("Hello! I'm a Kiri chatbot.")
# Hold previous Q/A pairs for additional context
session_qa = []
while True:
try:
question = input()
answers = kiri.qa(question, prev_qa=session_qa)
# Only showing the top-rated answer
print(answers[0][0])
prev_qa = (question, answers[0][0])
session_qa += prev_qa
print()
def test_init():
kiri = Kiri(local=True)
- Deployment strategy
- Node dependencies
"""
elastic = False
if elastic:
doc_store = ElasticDocStore("http://localhost:9000",
doc_class=ElasticChunkedDocument,
index="kiri_default")
docs = [d["elastic"] for d in tech_docs]
else:
doc_store = InMemoryDocStore(doc_class=ChunkedDocument)
docs = [d["memory"] for d in tech_docs]
kiri = Kiri(doc_store, local=True)
kiri.upload(docs)
query = ""
if len(argv) == 1:
print("Supply a query when running this script")
print("Usage: python doc_search.py \"<your query here>\"")
exit(0)
elif len(argv) == 2:
query = argv[1]
else:
query = " ".join(argv[1:])
print(f"Query: {query}")
results = kiri.search(query, max_results=3, min_score=0.01)
def test_qa():
kiri = Kiri(local=True)
out = kiri.qa("What does Jon like?", text)
assert type(out) == str
