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Harrison/document cleanup (#2062)

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Co-authored-by: Delip Rao <delip@users.noreply.github.com>
searx
Harrison Chase 1 year ago committed by GitHub
parent a0cd6672aa
commit 30e3b31b04
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GPG Key ID: 4AEE18F83AFDEB23
10 changed files with 545 additions and 259 deletions
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17
docs/modules/agents/agents/examples/react.ipynb
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@ -12,7 +12,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 1,
"id": "4e272b47",
"metadata": {},
"outputs": [],
@ -40,7 +40,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 2,
"id": "8078c8f1",
"metadata": {},
"outputs": [
@ -61,7 +61,8 @@
"Observation 2: \u001b[36;1m\u001b[1;3mWilliam James Crowe Jr. (January 2, 1925 – October 18, 2007) was a United States Navy admiral and diplomat who served as the 11th chairman of the Joint Chiefs of Staff under Presidents Ronald Reagan and George H. W. Bush, and as the ambassador to the United Kingdom and Chair of the Intelligence Oversight Board under President Bill Clinton.\u001b[0m\n",
"Thought 3:\u001b[32;1m\u001b[1;3m The President William J. Crowe served as the ambassador to the United Kingdom under is Bill Clinton.\n",
"Action 3: Finish[Bill Clinton]\u001b[0m\n",
"\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
@ -70,7 +71,7 @@
"'Bill Clinton'"
]
},
"execution_count": 5,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
@ -79,6 +80,14 @@
"question = \"Author David Chanoff has collaborated with a U.S. Navy admiral who served as the ambassador to the United Kingdom under which President?\"\n",
"react.run(question)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc4a6efe",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

218
docs/modules/indexes/document_loaders/examples/dataframe.ipynb
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@ -0,0 +1,218 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "213a38a2",
"metadata": {},
"source": [
"# DataFrame Loader\n",
"\n",
"This notebook goes over how to load data from a pandas dataframe"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "79331964",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e487044c",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('example_data/mlb_teams_2012.csv')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ac273ca1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Team</th>\n",
" <th>\"Payroll (millions)\"</th>\n",
" <th>\"Wins\"</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Nationals</td>\n",
" <td>81.34</td>\n",
" <td>98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Reds</td>\n",
" <td>82.20</td>\n",
" <td>97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Yankees</td>\n",
" <td>197.96</td>\n",
" <td>95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Giants</td>\n",
" <td>117.62</td>\n",
" <td>94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Braves</td>\n",
" <td>83.31</td>\n",
" <td>94</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Team \"Payroll (millions)\" \"Wins\"\n",
"0 Nationals 81.34 98\n",
"1 Reds 82.20 97\n",
"2 Yankees 197.96 95\n",
"3 Giants 117.62 94\n",
"4 Braves 83.31 94"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "66e47a13",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import DataFrameLoader"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2334caca",
"metadata": {},
"outputs": [],
"source": [
"loader = DataFrameLoader(df, page_content_column=\"Team\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d616c2b0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Nationals', metadata={' \"Payroll (millions)\"': 81.34, ' \"Wins\"': 98}),\n",
" Document(page_content='Reds', metadata={' \"Payroll (millions)\"': 82.2, ' \"Wins\"': 97}),\n",
" Document(page_content='Yankees', metadata={' \"Payroll (millions)\"': 197.96, ' \"Wins\"': 95}),\n",
" Document(page_content='Giants', metadata={' \"Payroll (millions)\"': 117.62, ' \"Wins\"': 94}),\n",
" Document(page_content='Braves', metadata={' \"Payroll (millions)\"': 83.31, ' \"Wins\"': 94}),\n",
" Document(page_content='Athletics', metadata={' \"Payroll (millions)\"': 55.37, ' \"Wins\"': 94}),\n",
" Document(page_content='Rangers', metadata={' \"Payroll (millions)\"': 120.51, ' \"Wins\"': 93}),\n",
" Document(page_content='Orioles', metadata={' \"Payroll (millions)\"': 81.43, ' \"Wins\"': 93}),\n",
" Document(page_content='Rays', metadata={' \"Payroll (millions)\"': 64.17, ' \"Wins\"': 90}),\n",
" Document(page_content='Angels', metadata={' \"Payroll (millions)\"': 154.49, ' \"Wins\"': 89}),\n",
" Document(page_content='Tigers', metadata={' \"Payroll (millions)\"': 132.3, ' \"Wins\"': 88}),\n",
" Document(page_content='Cardinals', metadata={' \"Payroll (millions)\"': 110.3, ' \"Wins\"': 88}),\n",
" Document(page_content='Dodgers', metadata={' \"Payroll (millions)\"': 95.14, ' \"Wins\"': 86}),\n",
" Document(page_content='White Sox', metadata={' \"Payroll (millions)\"': 96.92, ' \"Wins\"': 85}),\n",
" Document(page_content='Brewers', metadata={' \"Payroll (millions)\"': 97.65, ' \"Wins\"': 83}),\n",
" Document(page_content='Phillies', metadata={' \"Payroll (millions)\"': 174.54, ' \"Wins\"': 81}),\n",
" Document(page_content='Diamondbacks', metadata={' \"Payroll (millions)\"': 74.28, ' \"Wins\"': 81}),\n",
" Document(page_content='Pirates', metadata={' \"Payroll (millions)\"': 63.43, ' \"Wins\"': 79}),\n",
" Document(page_content='Padres', metadata={' \"Payroll (millions)\"': 55.24, ' \"Wins\"': 76}),\n",
" Document(page_content='Mariners', metadata={' \"Payroll (millions)\"': 81.97, ' \"Wins\"': 75}),\n",
" Document(page_content='Mets', metadata={' \"Payroll (millions)\"': 93.35, ' \"Wins\"': 74}),\n",
" Document(page_content='Blue Jays', metadata={' \"Payroll (millions)\"': 75.48, ' \"Wins\"': 73}),\n",
" Document(page_content='Royals', metadata={' \"Payroll (millions)\"': 60.91, ' \"Wins\"': 72}),\n",
" Document(page_content='Marlins', metadata={' \"Payroll (millions)\"': 118.07, ' \"Wins\"': 69}),\n",
" Document(page_content='Red Sox', metadata={' \"Payroll (millions)\"': 173.18, ' \"Wins\"': 69}),\n",
" Document(page_content='Indians', metadata={' \"Payroll (millions)\"': 78.43, ' \"Wins\"': 68}),\n",
" Document(page_content='Twins', metadata={' \"Payroll (millions)\"': 94.08, ' \"Wins\"': 66}),\n",
" Document(page_content='Rockies', metadata={' \"Payroll (millions)\"': 78.06, ' \"Wins\"': 64}),\n",
" Document(page_content='Cubs', metadata={' \"Payroll (millions)\"': 88.19, ' \"Wins\"': 61}),\n",
" Document(page_content='Astros', metadata={' \"Payroll (millions)\"': 60.65, ' \"Wins\"': 55})]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loader.load()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "beb55c2f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

28
langchain/agents/react/base.py
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@ -87,13 +87,15 @@ class DocstoreExplorer:
"""Initialize with a docstore, and set initial document to None."""
self.docstore = docstore
self.document: Optional[Document] = None
self.lookup_str = ""
self.lookup_index = 0
def search(self, term: str) -> str:
"""Search for a term in the docstore, and if found save."""
result = self.docstore.search(term)
if isinstance(result, Document):
self.document = result
return self.document.summary
return self._summary
else:
self.document = None
return result
@ -102,7 +104,29 @@ class DocstoreExplorer:
"""Lookup a term in document (if saved)."""
if self.document is None:
raise ValueError("Cannot lookup without a successful search first")
return self.document.lookup(term)
if term.lower() != self.lookup_str:
self.lookup_str = term.lower()
self.lookup_index = 0
else:
self.lookup_index += 1
lookups = [p for p in self._paragraphs if self.lookup_str in p.lower()]
if len(lookups) == 0:
return "No Results"
elif self.lookup_index >= len(lookups):
return "No More Results"
else:
result_prefix = f"(Result {self.lookup_index + 1}/{len(lookups)})"
return f"{result_prefix} {lookups[self.lookup_index]}"
@property
def _summary(self) -> str:
return self._paragraphs[0]
@property
def _paragraphs(self) -> List[str]:
if self.document is None:
raise ValueError("Cannot get paragraphs without a document")
return self.document.page_content.split("\n\n")
class ReActTextWorldAgent(ReActDocstoreAgent, BaseModel):

2
langchain/document_loaders/__init__.py
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@ -12,6 +12,7 @@ from langchain.document_loaders.blackboard import BlackboardLoader
from langchain.document_loaders.college_confidential import CollegeConfidentialLoader
from langchain.document_loaders.conllu import CoNLLULoader
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.document_loaders.dataframe import DataFrameLoader
from langchain.document_loaders.directory import DirectoryLoader
from langchain.document_loaders.email import UnstructuredEmailLoader
from langchain.document_loaders.evernote import EverNoteLoader
@ -111,6 +112,7 @@ __all__ = [
"GoogleApiClient",
"CSVLoader",
"BlackboardLoader",
"DataFrameLoader",
"AzureBlobStorageFileLoader",
"AzureBlobStorageContainerLoader",
"SitemapLoader",

30
langchain/document_loaders/dataframe.py
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@ -0,0 +1,30 @@
"""Load from Dataframe object"""
from typing import List
import pandas as pd
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
class DataFrameLoader(BaseLoader):
"""Load Pandas DataFrames."""
def __init__(self, data_frame: pd.DataFrame, page_content_column: str = "text"):
"""Initialize with dataframe object."""
self.data_frame = data_frame
self.page_content_column = page_content_column
def load(self) -> List[Document]:
"""Load from the dataframe."""
result = []
# For very large dataframes, this needs to yeild instead of building a list
# but that would require chaging return type to a generator for BaseLoader
# and all its subclasses, which is a bigger refactor. Marking as future TODO.
# This change will allow us to extend this to Spark and Dask dataframes.
for _, row in self.data_frame.iterrows():
text = row[self.page_content_column]
metadata = row.to_dict()
metadata.pop(self.page_content_column)
result.append(Document(page_content=text, metadata=metadata))
return result

28
langchain/schema.py
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@ -244,36 +244,8 @@ class Document(BaseModel):
"""Interface for interacting with a document."""
page_content: str
lookup_str: str = ""
lookup_index = 0
metadata: dict = Field(default_factory=dict)
@property
def paragraphs(self) -> List[str]:
"""Paragraphs of the page."""
return self.page_content.split("\n\n")
@property
def summary(self) -> str:
"""Summary of the page (the first paragraph)."""
return self.paragraphs[0]
def lookup(self, string: str) -> str:
"""Lookup a term in the page, imitating cmd-F functionality."""
if string.lower() != self.lookup_str:
self.lookup_str = string.lower()
self.lookup_index = 0
else:
self.lookup_index += 1
lookups = [p for p in self.paragraphs if self.lookup_str in p.lower()]
if len(lookups) == 0:
return "No Results"
elif self.lookup_index >= len(lookups):
return "No More Results"
else:
result_prefix = f"(Result {self.lookup_index + 1}/{len(lookups)})"
return f"{result_prefix} {lookups[self.lookup_index]}"
class BaseRetriever(ABC):
@abstractmethod

378
poetry.lock generated
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@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.4.1 and should not be changed by hand.
[[package]]
name = "absl-py"
@ -1029,35 +1029,31 @@ toml = ["tomli"]
[[package]]
name = "cryptography"
version = "39.0.2"
version = "40.0.1"
description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
category = "main"
optional = false
python-versions = ">=3.6"
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[[package]]
@ -6870,14 +6916,14 @@ test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"]
[[package]]
name = "transformers"
version = "4.27.2"
version = "4.27.3"
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
category = "main"
optional = true
python-versions = ">=3.7.0"
files = [
{file = "transformers-4.27.2-py3-none-any.whl", hash = "sha256:499a5ac4c1160f27b71bbd3c080e36ee3fd8cc2922aeaa52a8b163adf5ff4f19"},
{file = "transformers-4.27.2.tar.gz", hash = "sha256:940a6c0e04d89d14d012ee7214f8fd4e2aa2924a70a41106e0beb6ff25227752"},
{file = "transformers-4.27.3-py3-none-any.whl", hash = "sha256:d764c351b6d590952ac6104573721abe323b3eabd55e53bae0215c14750d5f13"},
{file = "transformers-4.27.3.tar.gz", hash = "sha256:26bff783ad6ed463fe3f0954406e7c30a8313758b6488677827886e48e6bd5a9"},
]
[package.dependencies]
@ -6959,14 +7005,14 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.
[[package]]
name = "types-pyopenssl"
version = "23.0.0.4"
version = "23.1.0.0"
description = "Typing stubs for pyOpenSSL"
category = "dev"
optional = false
python-versions = "*"
files = [
{file = "types-pyOpenSSL-23.0.0.4.tar.gz", hash = "sha256:8b3550b6e19d51ce78aabd724b0d8ebd962081a5fce95e7f85a592dfcdbc16bf"},
{file = "types_pyOpenSSL-23.0.0.4-py3-none-any.whl", hash = "sha256:ad49e15bb8bb2f251b8fc24776f414d877629e44b1b049240063ab013b5a6a7d"},
{file = "types-pyOpenSSL-23.1.0.0.tar.gz", hash = "sha256:acc153718bff497e8f6ca3beecb5ea7a3087c796e40d569fded8bafbfca73605"},
{file = "types_pyOpenSSL-23.1.0.0-py3-none-any.whl", hash = "sha256:9dacec020a3484ef5e4ea4bd9d403a981765b80821d5a40b790b2ba2f09d58db"},
]
[package.dependencies]
@ -6986,14 +7032,14 @@ files = [
[[package]]
name = "types-redis"
version = "4.5.2.0"
version = "4.5.3.0"
description = "Typing stubs for redis"
category = "dev"
optional = false
python-versions = "*"
files = [
{file = "types-redis-4.5.2.0.tar.gz", hash = "sha256:f3243339702cde2966e21f7fc34f8e9be1b55b407e8242ba204a42360f248678"},
{file = "types_redis-4.5.2.0-py3-none-any.whl", hash = "sha256:54964b1b18b7c868c543011eafab7aafd3d57e5285075ae1b574288535d93b4e"},
{file = "types-redis-4.5.3.0.tar.gz", hash = "sha256:f23415e448ca25ec5028c24fdf3717a13f0c905eb1933733e8a8a7d4952f6908"},
{file = "types_redis-4.5.3.0-py3-none-any.whl", hash = "sha256:7c1d5fdb0a2d5fd92eac37ce382fdb47d99a69889e7d6c2bc4479148ac646c73"},
]
[package.dependencies]
@ -7002,14 +7048,14 @@ types-pyOpenSSL = "*"
[[package]]
name = "types-requests"
version = "2.28.11.15"
version = "2.28.11.16"
description = "Typing stubs for requests"
category = "dev"
optional = false
python-versions = "*"
files = [
{file = "types-requests-2.28.11.15.tar.gz", hash = "sha256:fc8eaa09cc014699c6b63c60c2e3add0c8b09a410c818b5ac6e65f92a26dde09"},
{file = "types_requests-2.28.11.15-py3-none-any.whl", hash = "sha256:a05e4c7bc967518fba5789c341ea8b0c942776ee474c7873129a61161978e586"},
{file = "types-requests-2.28.11.16.tar.gz", hash = "sha256:9d4002056df7ebc4ec1f28fd701fba82c5c22549c4477116cb2656aa30ace6db"},
{file = "types_requests-2.28.11.16-py3-none-any.whl", hash = "sha256:a86921028335fdcc3aaf676c9d3463f867db6af2303fc65aa309b13ae1e6dd53"},
]
[package.dependencies]
@ -7197,14 +7243,14 @@ files = [
[[package]]
name = "weaviate-client"
version = "3.15.2"
version = "3.15.3"
description = "A python native weaviate client"
category = "main"
optional = true
python-versions = ">=3.7"
files = [
{file = "weaviate-client-3.15.2.tar.gz", hash = "sha256:fe3e48d87411e6dc8ba9814e4c5e6341b1d418d5a289098e386a6d1d6edd2ed6"},
{file = "weaviate_client-3.15.2-py3-none-any.whl", hash = "sha256:ed8c5a72fa20b3793449a56312976de42c43633f2121b1224e6c044e86b7263f"},
{file = "weaviate-client-3.15.3.tar.gz", hash = "sha256:7ad0ef2d23c3c082e1beeb14719a93f6a2b090a534d4aa45d9d720fff6d58edc"},
{file = "weaviate_client-3.15.3-py3-none-any.whl", hash = "sha256:f269caec18b45454913a575f3e8f136dc914bbe8b9b10b55919cc6ccc142a67c"},
]
[package.dependencies]
@ -7573,4 +7619,4 @@ llms = ["anthropic", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "op
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "988b849cb64171e69e85d3b0bb76894154c20ddf7cb36155ca73b507a12de7aa"
content-hash = "aa39daaadadca5d5536cbeb854e0011663d101865e1ef1124e1f037f96d93fa4"

1
pyproject.toml
Unescape Escape View File

@ -17,6 +17,7 @@ SQLAlchemy = "^1"
requests = "^2"
PyYAML = ">=5.4.1"
numpy = "^1"
pandas = ">=1.5.3"
faiss-cpu = {version = "^1", optional = true}
wikipedia = {version = "^1", optional = true}
elasticsearch = {version = "^8", optional = true}

59
tests/unit_tests/docstore/test_document.py
Unescape Escape View File

@ -1,59 +0,0 @@
"""Test document functionality."""
from langchain.docstore.document import Document
_PAGE_CONTENT = """This is a page about LangChain.
It is a really cool framework.
What isn't there to love about langchain?
Made in 2022."""
def test_document_summary() -> None:
"""Test that we extract the summary okay."""
page = Document(page_content=_PAGE_CONTENT)
assert page.summary == "This is a page about LangChain."
def test_document_lookup() -> None:
"""Test that can lookup things okay."""
page = Document(page_content=_PAGE_CONTENT)
# Start with lookup on "LangChain".
output = page.lookup("LangChain")
assert output == "(Result 1/2) This is a page about LangChain."
# Now switch to looking up "framework".
output = page.lookup("framework")
assert output == "(Result 1/1) It is a really cool framework."
# Now switch back to looking up "LangChain", should reset.
output = page.lookup("LangChain")
assert output == "(Result 1/2) This is a page about LangChain."
# Lookup "LangChain" again, should go to the next mention.
output = page.lookup("LangChain")
assert output == "(Result 2/2) What isn't there to love about langchain?"
def test_document_lookups_dont_exist() -> None:
"""Test lookup on term that doesn't exist in the document."""
page = Document(page_content=_PAGE_CONTENT)
# Start with lookup on "harrison".
output = page.lookup("harrison")
assert output == "No Results"
def test_document_lookups_too_many() -> None:
"""Test lookup on term too many times."""
page = Document(page_content=_PAGE_CONTENT)
# Start with lookup on "framework".
output = page.lookup("framework")
assert output == "(Result 1/1) It is a really cool framework."
# Now try again, should be exhausted.
output = page.lookup("framework")
assert output == "No More Results"

43
tests/unit_tests/document_loader/test_dataframe.py
Unescape Escape View File

@ -0,0 +1,43 @@
import pandas as pd
import pytest
from langchain.document_loaders import DataFrameLoader
from langchain.schema import Document
@pytest.fixture
def sample_data_frame() -> pd.DataFrame:
data = {
"text": ["Hello", "World"],
"author": ["Alice", "Bob"],
"date": ["2022-01-01", "2022-01-02"],
}
return pd.DataFrame(data)
def test_load_returns_list_of_documents(sample_data_frame: pd.DataFrame) -> None:
loader = DataFrameLoader(sample_data_frame)
docs = loader.load()
assert isinstance(docs, list)
assert all(isinstance(doc, Document) for doc in docs)
assert len(docs) == 2
def test_load_converts_dataframe_columns_to_document_metadata(
sample_data_frame: pd.DataFrame,
) -> None:
loader = DataFrameLoader(sample_data_frame)
docs = loader.load()
for i, doc in enumerate(docs):
assert doc.metadata["author"] == sample_data_frame.loc[i, "author"]
assert doc.metadata["date"] == sample_data_frame.loc[i, "date"]
def test_load_uses_page_content_column_to_create_document_text(
sample_data_frame: pd.DataFrame,
) -> None:
sample_data_frame = sample_data_frame.rename(columns={"text": "dummy_test_column"})
loader = DataFrameLoader(sample_data_frame, page_content_column="dummy_test_column")
docs = loader.load()
assert docs[0].page_content == "Hello"
assert docs[1].page_content == "World"
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