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15 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Jerry Liu
|
45832e3ef9 | ||
|
Jerry Liu
|
b60b2e1f59 | ||
|
Pierre-Loic Doulcet
|
58257d546b | ||
|
Adam Reichert
|
5e99d810fd | ||
|
Jerry Liu
|
a2dc717d85 | ||
|
Logan
|
87062e6ca8 | ||
|
Jerry Liu
|
ae3a21c5ff | ||
|
Ravi Theja
|
ccebb8a2fa | ||
|
Pierre-Loic Doulcet
|
2d21d6e688 | ||
|
Logan
|
270d96b7e3 | ||
|
Ajit Mistry
|
ea21daa96f | ||
|
dependabot[bot]
|
42845f8d07 | ||
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dependabot[bot]
|
8b63ae9c46 | ||
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Pierre-Loic Doulcet
|
173060dc50 | ||
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Pierre-Loic Doulcet
|
d19b35cd48 |
@@ -72,7 +72,7 @@ repos:
|
||||
args:
|
||||
[
|
||||
"--ignore-words-list",
|
||||
"astroid,gallary,momento,narl,ot,rouge,nin,gere,te,inh",
|
||||
"astroid,gallary,momento,narl,ot,rouge,nin,gere,te,inh,vor",
|
||||
]
|
||||
- repo: https://github.com/srstevenson/nb-clean
|
||||
rev: 3.1.0
|
||||
|
||||
@@ -6,6 +6,8 @@ LlamaParse directly integrates with [LlamaIndex](https://github.com/run-llama/ll
|
||||
|
||||
Free plan is up to 1000 pages a day. Paid plan is free 7k pages per week + 0.3c per additional page.
|
||||
|
||||
Read below for some quickstart information, or see the [full documentation](https://docs.cloud.llamaindex.ai/).
|
||||
|
||||
## Getting Started
|
||||
|
||||
First, login and get an api-key from [**https://cloud.llamaindex.ai ↗**](https://cloud.llamaindex.ai).
|
||||
@@ -87,6 +89,10 @@ Several end-to-end indexing examples can be found in the examples folder
|
||||
- [Advanced RAG Example](examples/demo_advanced.ipynb)
|
||||
- [Raw API Usage](examples/demo_api.ipynb)
|
||||
|
||||
## Documentation
|
||||
|
||||
[https://docs.cloud.llamaindex.ai/](https://docs.cloud.llamaindex.ai/)
|
||||
|
||||
## Terms of Service
|
||||
|
||||
See the [Terms of Service Here](./TOS.pdf).
|
||||
|
||||
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-186
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||||
{
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||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0db58db5-d4ee-4631-af5b-4fc53eb05170",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# RAG with Excel Spreadsheet using LlamaPrase\n",
|
||||
"\n",
|
||||
"<a href=\"https://colab.research.google.com/github/run-llama/llama_parse/blob/main/examples/demo_excel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
|
||||
"\n",
|
||||
"This notebook constructs a RAG pipeline over a simple DCF template [here](https://eqvista.com/app/uploads/2020/09/Eqvista_DCF-Excel-Template.xlsx).\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5f7d99ad-6ebd-47d0-92a7-566630b0c22a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"We first setup and load the data. If you haven't already, [download the template](https://eqvista.com/app/uploads/2020/09/Eqvista_DCF-Excel-Template.xlsx) and name it `dcf_template.xlxs` locally."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d867d1a6-cfcf-4f53-952a-f4a6ff2fa205",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install llama-index\n",
|
||||
"%pip install llama-parse"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "103c7983-56d3-45be-b763-d1828d07c43e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import nest_asyncio\n",
|
||||
"\n",
|
||||
"nest_asyncio.apply()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7b694b56-e04b-4d87-aa37-f0725d6b3adb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from llama_parse import LlamaParse\n",
|
||||
"\n",
|
||||
"# api_key = \"llx-\" # get from cloud.llamaindex.ai"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9c4693c7-c1c8-47b4-8a8c-25d7e9ef9d2c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Started parsing the file under job_id 9332a14a-78d2-4392-bbe8-a271d7572cd1\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"parser = LlamaParse(\n",
|
||||
" # api_key=api_key, # can also be set in your env as LLAMA_CLOUD_API_KEY\n",
|
||||
" result_type=\"markdown\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"documents = parser.load_data(\"./dcf_template.xlsx\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7302f1c8-e405-4cda-8ff7-1d55185816f7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"# DCF Model\n",
|
||||
"\n",
|
||||
"|Discounted Cash Flow Excel Template | | | | | | | | | | | |\n",
|
||||
"|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------|-----------|-----------|-----------------------|-----------|-----------------------|--------------|-----------|-----------|-----------|--------------|\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"|Here is a simple discounted cash flow excel template for estimating your company value based on this income valuation approach | | | | | | | | | | | |\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"|Instructions: | | | | | | | | | | | |\n",
|
||||
"|1) Fill out the two assumptions in yellow highlight | | | | | | | | | | | |\n",
|
||||
"|2) Fill in either the 5 year or 3 year weighted average figures in yellow highlight | | | | | | | | | | | |\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"|Assumptions | | | | | | | | | | | |\n",
|
||||
"|Tax Rate |20% | | | | | | | | | | |\n",
|
||||
"|Discount Rate |15% | | | | | | | | | | |\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"|5 Year Weighted Moving Average | | | | | | | | | | | |\n",
|
||||
"|Indication of Company Value |$242,995.43 | | | | | | | | | | |\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"|3 Year Weighted Moving Average | | | | | | | | | | | |\n",
|
||||
"|Indication of Company Value |$158,651.07 | | | | | | | | | | |\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"| |5 Year Weighted Moving Average| | | | | | | | | | |\n",
|
||||
"| |Past Years | | | | |Forecasted Future Years| | | | | |\n",
|
||||
"| |Year 1 |Year 2 |Year 3 |Year 4 |Year 5 |Year 6 |Year 7 |Year 8 |Year 9 |Year 10 |Terminal Value|\n",
|
||||
"|Pre-tax income |50,000.00 |55,000.00 |45,000.00 |52,000.00 |60,000.00 | | | | | | |\n",
|
||||
"|Income Taxes |10,000.00 |11,000.00 |9,000.00 |10,400.00 |12,000.00 | | | | | | |\n",
|
||||
"|Net Income |40,000.00 |44,000.00 |36,000.00 |41,600.00 |48,000.00 | | | | | | |\n",
|
||||
"|Depreciation Expense |5,000.00 |4,000.00 |3,000.00 |2,000.00 |1,000.00 | | | | | | |\n",
|
||||
"|Capital Expenditures |10,000.00 |8,000.00 |5,000.00 |5,000.00 |7,000.00 | | | | | | |\n",
|
||||
"|Debt Repayments |5,000.00 |5,000.00 |5,000.00 |5,000.00 |5,000.00 | | | | | | |\n",
|
||||
"|Net Cash Flow |20,000.00 |27,000.00 |23,000.00 |29,600.00 |35,000.00 |29,093.33 |29,817.78 |30,177.48 |30,469.23 |30,379.74 |287,188.00 |\n",
|
||||
"|Discounting Factor | | | | | |0.8696 |0.7561 |0.6575 |0.5718 |0.4972 |0.4972 |\n",
|
||||
"|Present Value of Future Cash Flow | | | | | |25,298.55 |22,546.52 |19,842.18 |17,420.88 |15,104.10 |142,783.19 |\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"| |3 Year Weighted Moving Average| | | | | | | | | | |\n",
|
||||
"| |Past Years | | |Forecasted Future Years| | | | | | | |\n",
|
||||
"| |Year 1 |Year 2 |Year 3 |Year 4 |Year 5 |Year 6 |Terminal Value| | | | |\n",
|
||||
"|Pre-tax income |50,000.00 |55,000.00 |45,000.00 | | | | | | | | |\n",
|
||||
"|Income Taxes |10,000.00 |11,000.00 |9,000.00 | | | | | | | | |\n",
|
||||
"|Net Income |40,000.00 |44,000.00 |36,000.00 | | | | | | | | |\n",
|
||||
"|Depreciation Expense |5,000.00 |4,000.00 |3,000.00 | | | | | | | | |\n",
|
||||
"|Capital Expenditures |10,000.00 |8,000.00 |5,000.00 | | | | | | | | |\n",
|
||||
"|Debt Repayments |5,000.00 |5,000.00 |5,000.00 | | | | | | | | |\n",
|
||||
"|Net Cash Flow |20,000.00 |27,000.00 |23,000.00 |23,833.33 |24,083.33 |23,819.44 |158,253.59 | | | | |\n",
|
||||
"|Discounting Factor | | | |0.8696 |0.7561 |0.6575 |0.6575 | | | | |\n",
|
||||
"|Present Value of Future Cash Flow | | | |20,724.64 |18,210.46 |15,661.67 |104,054.30 | | | | |\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"|Notes: | | | | | | | | | | | |\n",
|
||||
"|-We based this simple discounted cash flow excel model based on the weighted moving averages (5 year or 3 year) for simplicity, in case a constant growth rate cannot be easily determined.| | | | | | | | | | | |\n",
|
||||
"|-The factors such as Depreciation Expense, Capital Expense and Debt Repayments remain constant, so consider this when looking at the forecasted figures. | | | | | | | | | | | |\n",
|
||||
"|-For the terminal value constant growth rate, we make the assumption of the growth from the last forecasted year compared to the first forecasted year. Adjust in the formula as needed. | | | | | | | | | | | |\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(documents[1].get_content())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1aedd4bb-7939-4fbc-8f07-d362e24d9772",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Ask Questions over this Data\n",
|
||||
"\n",
|
||||
"Now let's try asking some questions over this data!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f7c056a8-d098-4ebe-9341-d9f07081067c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from llama_index.llms.openai import OpenAI\n",
|
||||
"from llama_index.core import Settings\n",
|
||||
"\n",
|
||||
"llm = OpenAI(model=\"gpt-4-turbo-preview\")\n",
|
||||
"Settings.llm = llm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c0fa2630-ee1b-4ce7-91e9-f9ffff8347f9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from llama_index.core import SummaryIndex\n",
|
||||
"\n",
|
||||
"index = SummaryIndex.from_documents([documents[1]])\n",
|
||||
"\n",
|
||||
"query_engine = index.as_query_engine()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a875a20e-a6b6-46b7-80d4-614546215ffc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"response = query_engine.query(\n",
|
||||
" \"Tell me about the income taxes in the past years (year 3-5) for the 5 year WMA table\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "06b0b072-f159-47c4-9cad-9f0cc0d56b28",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"The income taxes in the past years (year 3 to year 5) for the 5-year Weighted Moving Average table are as follows: Year 3 - $9,000.00, Year 4 - $10,400.00, and Year 5 - $12,000.00.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(str(response))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8bd0998f-4f7f-46f9-9b51-cfb510f384ee",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"# DCF Model\n",
|
||||
"\n",
|
||||
"|Discounted Cash Flow Excel Template | | | | | | | | | | | |\n",
|
||||
"|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------|-----------|-----------|-----------------------|-----------|-----------------------|--------------|-----------|-----------|-----------|--------------|\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"|Here is a simple discounted cash flow excel template for estimating your company value based on this income valuation approach | | | | | | | | | | | |\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"|Instructions: | | | | | | | | | | | |\n",
|
||||
"|1) Fill out the two assumptions in yellow highlight | | | | | | | | | | | |\n",
|
||||
"|2) Fill in either the 5 year or 3 year weighted average figures in yellow highlight | | | | | | | | | | | |\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"|Assumptions | | | | | | | | | | | |\n",
|
||||
"|Tax Rate |20% | | | | | | | | | | |\n",
|
||||
"|Discount Rate |15% | | | | | | | | | | |\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"|5 Year Weighted Moving Average | | | | | | | | | | | |\n",
|
||||
"|Indication of Company Value |$242,995.43 | | | | | | | | | | |\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"|3 Year Weighted Moving Average | | | | | | | | | | | |\n",
|
||||
"|Indication of Company Value |$158,651.07 | | | | | | | | | | |\n",
|
||||
"| | | | | | | | | | | | |\n",
|
||||
"| |5 Year Weighted Moving Average| | | | | | | | | | |\n",
|
||||
"| |Past Years | | | | |Forecasted Future Years| | | | | |\n",
|
||||
"| |Year 1 |Year 2 |Year 3 |Year 4 |Year 5 |Year 6 |Year 7 |Year 8 |Year 9 |Year 10 |Terminal Value|\n",
|
||||
"|Pre-tax income |50,000.00 |55,000.00 |45,000.00 |52,000.00 |60,000.00 | | | | | | |\n",
|
||||
"|Income Taxes |10,000.00 |11,000.00 |9,000.00 |10,400.00 |12,000.00 | | | | | | |\n",
|
||||
"|Net Income |40,000.00 |44,000.00 |36,000.00 |41,600.00 |48,000.00 | | | | | | |\n",
|
||||
"|Depreciation Expense |5,000.00 |4,000.00 |3,000.00 |2,000.00 |1,000.00 | | | | | | |\n",
|
||||
"|Capital Expenditures |10,000.00 |8,000.00 |5,000.00 |5,000.00 |7,000.00 | | | | | | |\n",
|
||||
"|Debt Repayments |5,000.00 |5,000.00 |5,000.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(response.source_nodes[0].get_content())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "fa699117-9867-4bb5-a290-30d35c59624f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"The discounting factors for years 4-6 in the 3-year weighted moving average scenario are 0.8696, 0.7561, and 0.6575, respectively.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"response = query_engine.query(\n",
|
||||
" \"Tell me about the discounting factors in years 4-6 for the 3 year WMA\"\n",
|
||||
")\n",
|
||||
"print(str(response))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "llama_parse",
|
||||
"language": "python",
|
||||
"name": "llama_parse"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
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+32
-1
@@ -57,6 +57,30 @@ class LlamaParse(BasePydanticReader):
|
||||
parsing_instruction: Optional[str] = Field(
|
||||
default="", description="The parsing instruction for the parser."
|
||||
)
|
||||
skip_diagonal_text: Optional[bool] = Field(
|
||||
default=False,
|
||||
description="If set to true, the parser will ignore diagonal text (when the text rotation in degrees modulo 90 is not 0).",
|
||||
)
|
||||
invalidate_cache: Optional[bool] = Field(
|
||||
default=False,
|
||||
description="If set to true, the cache will be ignored and the document re-processes. All document are kept in cache for 48hours after the job was completed to avoid processing the same document twice.",
|
||||
)
|
||||
do_not_cache: Optional[bool] = Field(
|
||||
default=False,
|
||||
description="If set to true, the document will not be cached. This mean that you will be re-charged it you reprocess them as they will not be cached.",
|
||||
)
|
||||
fast_mode: Optional[bool] = Field(
|
||||
default=False,
|
||||
description="Note: Non compatible with gpt-4o. If set to true, the parser will use a faster mode to extract text from documents. This mode will skip OCR of images, and table/heading reconstruction.",
|
||||
)
|
||||
do_not_unroll_columns: Optional[bool] = Field(
|
||||
default=False,
|
||||
description="If set to true, the parser will keep column in the text according to document layout. Reduce reconstruction accuracy, and LLM's/embedings performances in most case.",
|
||||
)
|
||||
page_separator: Optional[str] = Field(
|
||||
default=None,
|
||||
description="The page separator to use to split the text. Default is None, which means the parser will use the default separator '\\n---\\n'.",
|
||||
)
|
||||
gpt4o_mode: bool = Field(
|
||||
default=False,
|
||||
description="Whether to use gpt-4o extract text from documents.",
|
||||
@@ -121,6 +145,12 @@ class LlamaParse(BasePydanticReader):
|
||||
data={
|
||||
"language": self.language.value,
|
||||
"parsing_instruction": self.parsing_instruction,
|
||||
"invalidate_cache": self.invalidate_cache,
|
||||
"skip_diagonal_text": self.skip_diagonal_text,
|
||||
"do_not_cache": self.do_not_cache,
|
||||
"fast_mode": self.fast_mode,
|
||||
"do_not_unroll_columns": self.do_not_unroll_columns,
|
||||
"page_separator": self.page_separator,
|
||||
"gpt4o_mode": self.gpt4o_mode,
|
||||
"gpt4o_api_key": self.gpt4o_api_key,
|
||||
},
|
||||
@@ -341,7 +371,8 @@ class LlamaParse(BasePydanticReader):
|
||||
|
||||
# get a valid image path
|
||||
if not image_path.endswith(".png"):
|
||||
image_path += ".png"
|
||||
if not image_path.endswith(".jpg"):
|
||||
image_path += ".png"
|
||||
|
||||
image["path"] = image_path
|
||||
image["job_id"] = job_id
|
||||
|
||||
+76
-35
@@ -101,52 +101,93 @@ class Language(str, Enum):
|
||||
|
||||
SUPPORTED_FILE_TYPES = [
|
||||
".pdf",
|
||||
# Microsoft word - all versions
|
||||
# document and presentations
|
||||
".602",
|
||||
".abw",
|
||||
".cgm",
|
||||
".cwk",
|
||||
".doc",
|
||||
".docx",
|
||||
".docm",
|
||||
".dot",
|
||||
".dotx",
|
||||
".dotm",
|
||||
# Rich text format
|
||||
".hwp",
|
||||
".key",
|
||||
".lwp",
|
||||
".mw",
|
||||
".mcw",
|
||||
".pages",
|
||||
".pbd",
|
||||
".ppt",
|
||||
".pptm",
|
||||
".pptx",
|
||||
".pot",
|
||||
".potm",
|
||||
".potx",
|
||||
".rtf",
|
||||
# Microsoft Works
|
||||
".wps",
|
||||
# Word Perfect
|
||||
".wpd",
|
||||
# Open Office
|
||||
".sda",
|
||||
".sdd",
|
||||
".sdp",
|
||||
".sdw",
|
||||
".sgl",
|
||||
".sti",
|
||||
".sxi",
|
||||
".sxw",
|
||||
".stw",
|
||||
".sxg",
|
||||
# Apple
|
||||
".pages",
|
||||
# Mac Write
|
||||
".mw",
|
||||
".mcw",
|
||||
# Unified Office Format text
|
||||
".uot",
|
||||
".txt",
|
||||
".uof",
|
||||
".uos",
|
||||
".uop",
|
||||
# Microsoft powerpoints
|
||||
".ppt",
|
||||
".pptx",
|
||||
".pot",
|
||||
".pptm",
|
||||
".potx",
|
||||
".potm",
|
||||
# Apple keynote
|
||||
".key",
|
||||
# Open Office Presentations
|
||||
".odp",
|
||||
".odg",
|
||||
".otp",
|
||||
".fopd",
|
||||
".sxi",
|
||||
".sti",
|
||||
# ebook
|
||||
".uot",
|
||||
".vor",
|
||||
".wpd",
|
||||
".wps",
|
||||
".xml",
|
||||
".zabw",
|
||||
".epub",
|
||||
# html
|
||||
".html",
|
||||
# images
|
||||
".jpg",
|
||||
".jpeg",
|
||||
".png",
|
||||
".gif",
|
||||
".bmp",
|
||||
".svg",
|
||||
".tiff",
|
||||
".webp",
|
||||
# web
|
||||
".htm",
|
||||
".html",
|
||||
# spreadsheets
|
||||
".xlsx",
|
||||
".xls",
|
||||
".xlsm",
|
||||
".xlsb",
|
||||
".xlw",
|
||||
".csv",
|
||||
".dif",
|
||||
".sylk",
|
||||
".slk",
|
||||
".prn",
|
||||
".numbers",
|
||||
".et",
|
||||
".ods",
|
||||
".fods",
|
||||
".uos1",
|
||||
".uos2",
|
||||
".dbf",
|
||||
".wk1",
|
||||
".wk2",
|
||||
".wk3",
|
||||
".wk4",
|
||||
".wks",
|
||||
".123",
|
||||
".wq1",
|
||||
".wq2",
|
||||
".wb1",
|
||||
".wb2",
|
||||
".wb3",
|
||||
".qpw",
|
||||
".xlr",
|
||||
".eth",
|
||||
".tsv",
|
||||
]
|
||||
|
||||
Generated
+10
-9
@@ -1,4 +1,4 @@
|
||||
# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "aiohttp"
|
||||
@@ -1354,8 +1354,8 @@ files = [
|
||||
[package.dependencies]
|
||||
numpy = [
|
||||
{version = ">=1.20.3", markers = "python_version < \"3.10\""},
|
||||
{version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
|
||||
{version = ">=1.23.2", markers = "python_version >= \"3.11\""},
|
||||
{version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
|
||||
]
|
||||
python-dateutil = ">=2.8.2"
|
||||
pytz = ">=2020.1"
|
||||
@@ -1838,6 +1838,7 @@ files = [
|
||||
{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
|
||||
@@ -2081,13 +2082,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "requests"
|
||||
version = "2.31.0"
|
||||
version = "2.32.2"
|
||||
description = "Python HTTP for Humans."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "requests-2.31.0-py3-none-any.whl", hash = "sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f"},
|
||||
{file = "requests-2.31.0.tar.gz", hash = "sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1"},
|
||||
{file = "requests-2.32.2-py3-none-any.whl", hash = "sha256:fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c"},
|
||||
{file = "requests-2.32.2.tar.gz", hash = "sha256:dd951ff5ecf3e3b3aa26b40703ba77495dab41da839ae72ef3c8e5d8e2433289"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -2327,13 +2328,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "tqdm"
|
||||
version = "4.66.2"
|
||||
version = "4.66.3"
|
||||
description = "Fast, Extensible Progress Meter"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
|
||||
{file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
|
||||
{file = "tqdm-4.66.3-py3-none-any.whl", hash = "sha256:4f41d54107ff9a223dca80b53efe4fb654c67efaba7f47bada3ee9d50e05bd53"},
|
||||
{file = "tqdm-4.66.3.tar.gz", hash = "sha256:23097a41eba115ba99ecae40d06444c15d1c0c698d527a01c6c8bd1c5d0647e5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
||||
+1
-1
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.poetry]
|
||||
name = "llama-parse"
|
||||
version = "0.4.3"
|
||||
version = "0.4.4"
|
||||
description = "Parse files into RAG-Optimized formats."
|
||||
authors = ["Logan Markewich <logan@llamaindex.ai>"]
|
||||
license = "MIT"
|
||||
|
||||
Reference in New Issue
Block a user