Datasets:

mole-code

/

lancedb

like 0

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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