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# Copyright 2023 LanceDB Developers # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import random from unittest import mock import lancedb as ldb import numpy as np import pandas as pd import pytest pytest.importorskip("lancedb.fts") tantivy = pytest.importorskip("tantivy") @pytest.fixture def table(tmp_path) -> ldb.table.LanceTable: db = ldb.connect(tmp_path) vectors = [np.random.randn(128) for _ in range(100)] nouns = ("puppy", "car", "rabbit", "girl", "monkey") verbs = ("runs", "hits", "jumps", "drives", "barfs") adv = ("crazily.", "dutifully.", "foolishly.", "merrily.", "occasionally.") adj = ("adorable", "clueless", "dirty", "odd", "stupid") text = [ " ".join( [ nouns[random.randrange(0, 5)], verbs[random.randrange(0, 5)], adv[random.randrange(0, 5)], adj[random.randrange(0, 5)], ] ) for _ in range(100) ] table = db.create_table( "test", data=pd.DataFrame( { "vector": vectors, "id": [i % 2 for i in range(100)], "text": text, "text2": text, "nested": [{"text": t} for t in text], } ), ) return table def test_create_index(tmp_path): index = ldb.fts.create_index(str(tmp_path / "index"), ["text"]) assert isinstance(index, tantivy.Index) assert os.path.exists(str(tmp_path / "index")) def test_populate_index(tmp_path, table): index = ldb.fts.create_index(str(tmp_path / "index"), ["text"]) assert ldb.fts.populate_index(index, table, ["text"]) == len(table) def test_search_index(tmp_path, table): index = ldb.fts.create_index(str(tmp_path / "index"), ["text"]) ldb.fts.populate_index(index, table, ["text"]) index.reload() results = ldb.fts.search_index(index, query="puppy", limit=10) assert len(results) == 2 assert len(results[0]) == 10 # row_ids assert len(results[1]) == 10 # _distance def test_create_index_from_table(tmp_path, table): table.create_fts_index("text") df = table.search("puppy").limit(10).select(["text"]).to_pandas() assert len(df) <= 10 assert "text" in df.columns # Check whether it can be updated table.add( [ { "vector": np.random.randn(128), "id": 101, "text": "gorilla", "text2": "gorilla", "nested": {"text": "gorilla"}, } ] ) with pytest.raises(ValueError, match="already exists"): table.create_fts_index("text") table.create_fts_index("text", replace=True) assert len(table.search("gorilla").limit(1).to_pandas()) == 1 def test_create_index_multiple_columns(tmp_path, table): table.create_fts_index(["text", "text2"]) df = table.search("puppy").limit(10).to_pandas() assert len(df) == 10 assert "text" in df.columns assert "text2" in df.columns def test_empty_rs(tmp_path, table, mocker): table.create_fts_index(["text", "text2"]) mocker.patch("lancedb.fts.search_index", return_value=([], [])) df = table.search("puppy").limit(10).to_pandas() assert len(df) == 0 def test_nested_schema(tmp_path, table): table.create_fts_index("nested.text") rs = table.search("puppy").limit(10).to_list() assert len(rs) == 10 def test_search_index_with_filter(table): table.create_fts_index("text") orig_import = __import__ def import_mock(name, *args): if name == "duckdb": raise ImportError return orig_import(name, *args) # no duckdb with mock.patch("builtins.__import__", side_effect=import_mock): rs = table.search("puppy").where("id=1").limit(10) # test schema assert rs.to_arrow().drop("score").schema.equals(table.schema) rs = rs.to_list() for r in rs: assert r["id"] == 1 # yes duckdb rs2 = table.search("puppy").where("id=1").limit(10).to_list() for r in rs2: assert r["id"] == 1 assert rs == rs2 rs = table.search("puppy").where("id=1").with_row_id(True).limit(10).to_list() for r in rs: assert r["id"] == 1 assert r["_rowid"] is not None def test_null_input(table): table.add( [ { "vector": np.random.randn(128), "id": 101, "text": None, "text2": None, "nested": {"text": None}, } ] ) table.create_fts_index("text") def test_syntax(table): # https://github.com/lancedb/lancedb/issues/769 table.create_fts_index("text") with pytest.raises(ValueError, match="Syntax Error"): table.search("they could have been dogs OR cats").limit(10).to_list() # these should work # terms queries table.search('"they could have been dogs" OR cats').limit(10).to_list() table.search("(they AND could) OR (have AND been AND dogs) OR cats").limit( 10 ).to_list() # phrase queries table.search("they could have been dogs OR cats").phrase_query().limit(10).to_list() table.search('"they could have been dogs OR cats"').limit(10).to_list() table.search('''"the cats OR dogs were not really 'pets' at all"''').limit( 10 ).to_list() table.search('the cats OR dogs were not really "pets" at all').phrase_query().limit( 10 ).to_list() table.search('the cats OR dogs were not really "pets" at all').phrase_query().limit( 10 ).to_list()

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import pytest import os import openai import argparse import lancedb import re import pickle import requests import zipfile from pathlib import Path from main import get_document_title from langchain.document_loaders import BSHTMLLoader from langchain.embeddings import OpenAIEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores import LanceDB from langchain.llms import OpenAI from langchain.chains import RetrievalQA # TESTING =============================================================== @pytest.fixture def mock_embed(monkeypatch): def mock_embed_query(query, x): return [0.5, 0.5] monkeypatch.setattr(OpenAIEmbeddings, "embed_query", mock_embed_query) def test_main(mock_embed): os.mkdir("./tmp") args = argparse.Namespace(query="test", openai_key="test") os.environ["OPENAI_API_KEY"] = "test" docs_path = Path("docs.pkl") docs = [] pandas_docs = requests.get( "https://eto-public.s3.us-west-2.amazonaws.com/datasets/pandas_docs/pandas.documentation.zip" ) with open("./tmp/pandas.documentation.zip", "wb") as f: f.write(pandas_docs.content) file = zipfile.ZipFile("./tmp/pandas.documentation.zip") file.extractall(path="./tmp/pandas_docs") if not docs_path.exists(): for p in Path("./tmp/pandas_docs/pandas.documentation").rglob("*.html"): print(p) if p.is_dir(): continue loader = BSHTMLLoader(p, open_encoding="utf8") raw_document = loader.load() m = {} m["title"] = get_document_title(raw_document[0]) m["version"] = "2.0rc0" raw_document[0].metadata = raw_document[0].metadata | m raw_document[0].metadata["source"] = str(raw_document[0].metadata["source"]) docs = docs + raw_document with docs_path.open("wb") as fh: pickle.dump(docs, fh) else: with docs_path.open("rb") as fh: docs = pickle.load(fh) text_splitter = RecursiveCharacterTextSplitter( chunk_size=1000, chunk_overlap=200, ) documents = text_splitter.split_documents(docs) db = lancedb.connect("./tmp/lancedb") table = db.create_table( "pandas_docs", data=[ { "vector": OpenAIEmbeddings().embed_query("Hello World"), "text": "Hello World", "id": "1", } ], mode="overwrite", ) # docsearch = LanceDB.from_documents(documents, OpenAIEmbeddings, connection=table) # qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever()) # result = qa.run(args.query) # print(result)

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