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Author SHA1 Message Date
Logan Markewich b706363f11 vbump 2024-09-10 11:45:07 -06:00
Logan Markewich f603f507b6 linting 2024-09-10 11:32:50 -06:00
Pierre-Loic Doulcet 8aa08c590c Update llama_parse/base.py
Co-authored-by: Logan <logan.markewich@live.com>
2024-09-10 10:30:53 -07:00
Pierre-Loic Doulcet 42a98f921a Update llama_parse/base.py
Co-authored-by: Logan <logan.markewich@live.com>
2024-09-10 10:30:48 -07:00
Pierre-Loic Doulcet 1c453397de trailing whitespaces 2024-09-10 10:30:28 -07:00
Pierre-Loic Doulcet 3207daa7bd do not attach a filepath when a stram of bytes is passed 2024-09-10 10:20:57 -07:00
22 changed files with 1022 additions and 5193 deletions
+5 -25
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@@ -38,22 +38,7 @@ Lastly, install the package:
`pip install llama-parse`
Now you can parse your first PDF file using the command line interface. Use the command `llama-parse [file_paths]`. See the help text with `llama-parse --help`.
```bash
export LLAMA_CLOUD_API_KEY='llx-...'
# output as text
llama-parse my_file.pdf --result-type text --output-file output.txt
# output as markdown
llama-parse my_file.pdf --result-type markdown --output-file output.md
# output as raw json
llama-parse my_file.pdf --output-raw-json --output-file output.json
```
You can also create simple scripts:
Now you can run the following to parse your first PDF file:
```python
import nest_asyncio
@@ -102,18 +87,13 @@ parser = LlamaParse(
language="en", # Optionally you can define a language, default=en
)
file_name = "my_file1.pdf"
extra_info = {"file_name": file_name}
with open(f"./{file_name}", "rb") as f:
# must provide extra_info with file_name key with passing file object
documents = parser.load_data(f, extra_info=extra_info)
with open("./my_file1.pdf", "rb") as f:
documents = parser.load_data(f)
# you can also pass file bytes directly
with open(f"./{file_name}", "rb") as f:
with open("./my_file1.pdf", "rb") as f:
file_bytes = f.read()
# must provide extra_info with file_name key with passing file bytes
documents = parser.load_data(file_bytes, extra_info=extra_info)
documents = parser.load_data(file_bytes)
```
## Using with `SimpleDirectoryReader`
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@@ -342,7 +342,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "llama-parse-aNC435Vv-py3.10",
"language": "python",
"name": "python3"
},
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@@ -165,18 +165,7 @@
"execution_count": null,
"id": "ef82a985-4088-4bb7-9a21-0318e1b9207d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parsing text...\n",
"Started parsing the file under job_id 62f157a9-9ef9-4e5b-95ac-67093fa25800\n",
"..........Parsing PDF file...\n",
"Started parsing the file under job_id 1ddd5654-062b-4e19-b488-d66efc9c509d\n"
]
}
],
"outputs": [],
"source": [
"print(f\"Parsing text...\")\n",
"docs_text = parser_text.load_data(\"data/conocophillips.pdf\")\n",
@@ -185,36 +174,42 @@
"md_json_list = md_json_objs[0][\"pages\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7506b603-c01f-45de-b354-4a0728dde03c",
"metadata": {},
"outputs": [],
"source": [
"print(docs_text[0].get_content())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5318fb7b-fe6a-4a8a-b82e-4ed7b4512c37",
"metadata": {},
"outputs": [],
"source": [
"print(md_json_list[10][\"md\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7a46a73e-c6e2-4b0b-bd10-31b0d3e4b70f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Commitment to Disciplined Reinvestment Rate\n",
"\n",
"| Period | Description | Reinvestment Rate | WTI Average |\n",
"|--------------|--------------------------------------|-------------------|-------------|\n",
"| 2012-2016 | Industry Growth Focus | >100% | ~$75/BBL |\n",
"| 2017-2022 | ConocoPhillips Strategy Reset | <60% | ~$63/BBL |\n",
"| 2023E | | | at $80/BBL |\n",
"| 2024-2028 | Disciplined Reinvestment Rate | ~50% | at $60/BBL |\n",
"| 2029-2032 | | ~6% CFO CAGR | at $60/BBL |\n",
"\n",
"- **Historic Reinvestment Rate**: Gray bars\n",
"- **Reinvestment Rate at $60/BBL WTI**: Blue bars\n",
"- **Reinvestment Rate at $80/BBL WTI**: Dashed blue lines\n",
"\n",
"Reinvestment rate and cash from operations (CFO) are non-GAAP measures. Definitions and reconciliations are included in the Appendix.\n"
"dict_keys(['page', 'text', 'md', 'images', 'items'])\n"
]
}
],
"source": [
"print(md_json_list[10][\"md\"])"
"print(md_json_list[1].keys())"
]
},
{
@@ -304,7 +299,7 @@
" image_files = _get_sorted_image_files(image_dir) if image_dir is not None else None\n",
" md_texts = [d[\"md\"] for d in json_dicts] if json_dicts is not None else None\n",
"\n",
" doc_chunks = [c for d in docs for c in d.text.split(\"---\")]\n",
" doc_chunks = docs[0].text.split(\"---\")\n",
" for idx, doc_chunk in enumerate(doc_chunks):\n",
" chunk_metadata = {\"page_num\": idx + 1}\n",
" if image_files is not None:\n",
@@ -344,23 +339,25 @@
"output_type": "stream",
"text": [
"page_num: 11\n",
"image_path: data_images/1ddd5654-062b-4e19-b488-d66efc9c509d-page_39.jpg\n",
"image_path: data_images/d9137e19-3974-4b5d-998f-dac0cf29dd9d-page-10.jpg\n",
"parsed_text_markdown: # Commitment to Disciplined Reinvestment Rate\n",
"\n",
"| Period | Description | Reinvestment Rate | WTI Average |\n",
"|--------------|--------------------------------------|-------------------|-------------|\n",
"| 2012-2016 | Industry Growth Focus | >100% | ~$75/BBL |\n",
"| 2017-2022 | ConocoPhillips Strategy Reset | <60% | ~$63/BBL |\n",
"| 2023E | | | at $80/BBL |\n",
"| 2024-2028 | Disciplined Reinvestment Rate | ~50% | at $60/BBL |\n",
"| 2029-2032 | | ~6% CFO CAGR | at $60/BBL |\n",
"| Year | Reinvestment Rate | WTI Average Price | Reinvestment Rate at $60/BBL WTI | Reinvestment Rate at $80/BBL WTI |\n",
"|------------|-------------------|-------------------|----------------------------------|----------------------------------|\n",
"| 2012-2016 | >100% | ~$75/BBL | | |\n",
"| 2017-2022 | <60% | ~$63/BBL | | |\n",
"| 2023E | | | | at $80/BBL WTI |\n",
"| 2024-2028 | | | at $60/BBL WTI | at $80/BBL WTI |\n",
"| 2029-2032 | | | at $60/BBL WTI | at $80/BBL WTI |\n",
"\n",
"- **Historic Reinvestment Rate**: Gray bars\n",
"- **Reinvestment Rate at $60/BBL WTI**: Blue bars\n",
"- **Reinvestment Rate at $80/BBL WTI**: Dashed blue lines\n",
"**Disciplined Reinvestment Rate is the Foundation for Superior Returns on and of Capital, while Driving Durable CFO Growth**\n",
"\n",
"Reinvestment rate and cash from operations (CFO) are non-GAAP measures. Definitions and reconciliations are included in the Appendix.\n",
"parsed_text: Commitment to Disciplined Reinvestment Rate\n",
"- ~50% 10-Year Reinvestment Rate\n",
"- ~6% CFO CAGR 2024-2032 at $60/BBL WTI Mid-Cycle Planning Price\n",
"\n",
"**Note:** Reinvestment rate and cash from operations (CFO) are non-GAAP measures. Definitions and reconciliations are included in the Appendix.\n",
"parsed_text: \n",
"Commitment to Disciplined Reinvestment Rate\n",
" Industry ConocoPhillips\n",
" Strategy Reset Disciplined Reinvestment Rate is the Foundation for Superior\n",
" Growth Focus Returns on and of Capital, while Driving Durable CFO Growth\n",
@@ -377,7 +374,7 @@
" 0%\n",
" 2012-2016 2017-2022 2023E 2024-2028 2029-2032\n",
" Historic Reinvestment Rate Reinvestment Rate at $60/BBL WTI Reinvestment Rate at $80/BBL WTI\n",
" Reinvestment rate and cash from operations (CFO) are non-GAAP measures: Definitions and reconciliations are included in the Appendix ConocoPhillips\n"
" Reinvestment rate andcashfrom operations (CFO) are non-GAAP measures: Definitions and reconciliations are included in the Appendix ConocoPhillips\n"
]
}
],
@@ -400,17 +397,7 @@
"execution_count": null,
"id": "6ea53c31-0e38-421c-8d9b-0e3adaa1677e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages/tiktoken/core.py:50: RuntimeWarning: coroutine 'LlamaParse.aload_data' was never awaited\n",
" self._core_bpe = _tiktoken.CoreBPE(mergeable_ranks, special_tokens, pat_str)\n",
"RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n"
]
}
],
"outputs": [],
"source": [
"import os\n",
"from llama_index.core import (\n",
@@ -601,7 +588,7 @@
" Under $40/BBL Cost of Supply 10-Year Plan Cumulative Production (BBOE)\n",
" S50 S32/BBL Lower 48 Alaska\n",
" Average Cost of Supply\n",
" 3 $40 GKA GWA\n",
" 3$40 GKA GWA\n",
" GPA WNS\n",
" $30 EMENA\n",
" 3 Norway\n",
@@ -612,7 +599,7 @@
" APLNG Montney\n",
" S0\n",
" 10 15 20 Bakken\n",
" Resource (BBOE) Eagle Ford Other Malaysia ChinaSurmont\n",
" Resource (BBOE) Eagle Ford Other MalaysiaChina Surmont\n",
" Lower 48 Canada Alaska EMENA Asia Pacific\n",
"Costs assumemid-cycle price environment of S60/BBL WTI:\n",
" ConocoPhillips\n"
@@ -700,126 +687,70 @@
{
"cell_type": "code",
"execution_count": null,
"id": "d78e53cf-35cb-4ef8-b03e-1b47ba15ae64",
"id": "1cdce5d8-6bb3-4cd3-929d-1cec249d9052",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Added user message to memory: Tell me about the diverse geographies where Conoco Phillips has a production base\n",
"Added user message to memory: How does the Conoco Phillips capex/EUR in the delaware basin compare against other competitors?\n",
"=== Calling Function ===\n",
"Calling function: vector_tool with args: {\"input\": \"Conoco Phillips production base geographies\"}\n",
"Calling function: vector_tool with args: {\"input\": \"Conoco Phillips capex/EUR in the Delaware Basin\"}\n",
"=== Function Output ===\n",
"ConocoPhillips' production base geographies include:\n",
"The ConocoPhillips capex/EUR in the Delaware Basin is $10/BOE.\n",
"\n",
"1. **Lower 48** (Permian, Eagle Ford, Bakken, Other)\n",
"2. **Alaska** (GKA, GWA, GPA, WNS)\n",
"3. **EMENA** (Norway, Libya, Qatar)\n",
"4. **Asia Pacific** (APLNG, Malaysia, China)\n",
"5. **Canada** (Montney, Surmont)\n",
"\n",
"This information was derived from the image on page 14, which provides a detailed breakdown of the diverse production base and the regions involved. The parsed markdown and raw text also support this information, but the image provides the clearest and most comprehensive view. There are no discrepancies between the image and the parsed text in this case.\n",
"=== LLM Response ===\n",
"ConocoPhillips has a diverse production base spread across various geographies, including:\n",
"\n",
"1. **Lower 48**:\n",
" - Permian Basin\n",
" - Eagle Ford\n",
" - Bakken\n",
" - Other regions within the continental United States\n",
"\n",
"2. **Alaska**:\n",
" - Greater Kuparuk Area (GKA)\n",
" - Greater Prudhoe Area (GPA)\n",
" - Greater Willow Area (GWA)\n",
" - Western North Slope (WNS)\n",
"\n",
"3. **EMENA (Europe, Middle East, and North Africa)**:\n",
" - Norway\n",
" - Libya\n",
" - Qatar\n",
"\n",
"4. **Asia Pacific**:\n",
" - Australia Pacific LNG (APLNG)\n",
" - Malaysia\n",
" - China\n",
"\n",
"5. **Canada**:\n",
" - Montney\n",
" - Surmont\n",
"\n",
"These regions highlight the global reach and diverse geographical footprint of ConocoPhillips' production operations.\n",
"Added user message to memory: Tell me about the diverse geographies where Conoco Phillips has a production base\n",
"I obtained this information from the image provided. The image clearly shows a bar chart under the section \"Delaware Basin Well Capex/EUR ($/BOE)\" where ConocoPhillips is listed with a capex/EUR of $10/BOE. This information is consistent with the parsed markdown text, which also lists ConocoPhillips' capex/EUR as $10/BOE in the Delaware Basin. There are no discrepancies between the image and the parsed markdown text in this case.\n",
"=== Calling Function ===\n",
"Calling function: vector_tool with args: {\"input\": \"diverse geographies where Conoco Phillips has a production base\"}\n",
"Calling function: vector_tool with args: {\"input\": \"competitors capex/EUR in the Delaware Basin\"}\n",
"=== Function Output ===\n",
"ConocoPhillips has a diverse production base that includes the Lower 48 (Permian, Bakken, Eagle Ford), Alaska, Canada (Montney, Surmont), EMENA (Norway, Libya), Asia Pacific (Malaysia, China, APLNG), and Qatar.\n",
"The competitors' Capex/EUR in the Delaware Basin can be found in the image on the slide titled \"Delaware: Vast Inventory with Proven Track Record of Performance.\" The relevant information is presented in a bar chart under the section \"Delaware Basin Well Capex/EUR ($/BOE)\".\n",
"\n",
"Here are the details:\n",
"\n",
"- ConocoPhillips: $10/BOE\n",
"- Competitor 1: $15/BOE\n",
"- Competitor 2: $20/BOE\n",
"- Competitor 3: $25/BOE\n",
"- Competitor 4: $30/BOE\n",
"- Competitor 5: $35/BOE\n",
"- Competitor 6: $40/BOE\n",
"- Competitor 7: $45/BOE\n",
"\n",
"This information was obtained directly from the image, which provides a clear visual representation of the Capex/EUR values for ConocoPhillips and its competitors in the Delaware Basin. The parsed markdown text also confirms these values, ensuring consistency between the image and the text.\n",
"=== LLM Response ===\n",
"ConocoPhillips has a diverse production base spanning several key geographies:\n",
"The capital expenditure per estimated ultimate recovery (capex/EUR) for ConocoPhillips in the Delaware Basin is $10 per barrel of oil equivalent (BOE). When compared to its competitors, ConocoPhillips has a significantly lower capex/EUR. Here are the capex/EUR values for ConocoPhillips and its competitors:\n",
"\n",
"1. **Lower 48 (United States)**: This includes major production areas such as the Permian Basin, Bakken Formation, and Eagle Ford Shale.\n",
"2. **Alaska**: Significant operations in the North Slope region.\n",
"3. **Canada**: Operations in the Montney Formation and the Surmont oil sands project.\n",
"4. **EMENA (Europe, Middle East, and North Africa)**: Notable operations in Norway and Libya.\n",
"5. **Asia Pacific**: Includes operations in Malaysia, China, and the Australia Pacific LNG (APLNG) project.\n",
"6. **Qatar**: Involvement in the country's energy sector.\n",
"- **ConocoPhillips**: $10/BOE\n",
"- **Competitor 1**: $15/BOE\n",
"- **Competitor 2**: $20/BOE\n",
"- **Competitor 3**: $25/BOE\n",
"- **Competitor 4**: $30/BOE\n",
"- **Competitor 5**: $35/BOE\n",
"- **Competitor 6**: $40/BOE\n",
"- **Competitor 7**: $45/BOE\n",
"\n",
"These regions highlight the company's extensive and varied geographical footprint in the energy production industry.\n"
"This data indicates that ConocoPhillips has a more cost-efficient operation in the Delaware Basin compared to its competitors.\n",
"The capital expenditure per estimated ultimate recovery (capex/EUR) for ConocoPhillips in the Delaware Basin is $10 per barrel of oil equivalent (BOE). When compared to its competitors, ConocoPhillips has a significantly lower capex/EUR. Here are the capex/EUR values for ConocoPhillips and its competitors:\n",
"\n",
"- **ConocoPhillips**: $10/BOE\n",
"- **Competitor 1**: $15/BOE\n",
"- **Competitor 2**: $20/BOE\n",
"- **Competitor 3**: $25/BOE\n",
"- **Competitor 4**: $30/BOE\n",
"- **Competitor 5**: $35/BOE\n",
"- **Competitor 6**: $40/BOE\n",
"- **Competitor 7**: $45/BOE\n",
"\n",
"This data indicates that ConocoPhillips has a more cost-efficient operation in the Delaware Basin compared to its competitors.\n"
]
}
],
"source": [
"query = (\n",
" \"Tell me about the diverse geographies where Conoco Phillips has a production base\"\n",
"# response = agent.query(\"Tell me about the different regions and subregions where Conoco Phillips has a production base.\")\n",
"response = agent.query(\n",
" \"How does the Conoco Phillips capex/EUR in the delaware basin compare against other competitors?\"\n",
")\n",
"response = agent.query(query)\n",
"base_response = base_agent.query(query)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "355d2aa4-c26f-480e-b512-4446acbd9227",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ConocoPhillips has a diverse production base spread across various geographies, including:\n",
"\n",
"1. **Lower 48**:\n",
" - Permian Basin\n",
" - Eagle Ford\n",
" - Bakken\n",
" - Other regions within the continental United States\n",
"\n",
"2. **Alaska**:\n",
" - Greater Kuparuk Area (GKA)\n",
" - Greater Prudhoe Area (GPA)\n",
" - Greater Willow Area (GWA)\n",
" - Western North Slope (WNS)\n",
"\n",
"3. **EMENA (Europe, Middle East, and North Africa)**:\n",
" - Norway\n",
" - Libya\n",
" - Qatar\n",
"\n",
"4. **Asia Pacific**:\n",
" - Australia Pacific LNG (APLNG)\n",
" - Malaysia\n",
" - China\n",
"\n",
"5. **Canada**:\n",
" - Montney\n",
" - Surmont\n",
"\n",
"These regions highlight the global reach and diverse geographical footprint of ConocoPhillips' production operations.\n"
]
}
],
"source": [
"print(str(response))"
]
},
@@ -833,82 +764,85 @@
"name": "stdout",
"output_type": "stream",
"text": [
"page_num: 14\n",
"image_path: data_images/1ddd5654-062b-4e19-b488-d66efc9c509d-page_12.jpg\n",
"parsed_text_markdown: # Our Differentiated Portfolio: Deep, Durable and Diverse\n",
"page_num: 38\n",
"image_path: data_images/d9137e19-3974-4b5d-998f-dac0cf29dd9d-page-37.jpg\n",
"parsed_text_markdown: # Delaware: Vast Inventory with Proven Track Record of Performance\n",
"\n",
"## ~20 BBOE of Resource\n",
"Under $40/BBL Cost of Supply\n",
"## Prolific Acreage Spanning Over ~659,000 Net Acres¹\n",
"\n",
"### ~ $32/BBL\n",
"Average Cost of Supply\n",
"![Map of Delaware Basin](image)\n",
"\n",
"### WTI Cost of Supply ($/BBL)\n",
"### Total 10-Year Operated Permian Inventory\n",
"\n",
"| Cost ($/BBL) | Resource (BBOE) |\n",
"|--------------|-----------------|\n",
"| $0 | 0 |\n",
"| $10 | |\n",
"| $20 | |\n",
"| $30 | |\n",
"| $40 | |\n",
"| $50 | |\n",
"- Delaware Basin: 65%\n",
"- Midland Basin: 35%\n",
"\n",
"- **Legend:**\n",
" - Lower 48\n",
" - Canada\n",
" - Alaska\n",
" - EMENA\n",
" - Asia Pacific\n",
"### High Single-Digit Production Growth\n",
"\n",
"*Costs assume a mid-cycle price environment of $60/BBL WTI.*\n",
"## 12-Month Cumulative Production³ (BOE/FT)\n",
"\n",
"## Diverse Production Base\n",
"10-Year Plan Cumulative Production (BBOE)\n",
"| Months | 2019 | 2020 | 2021 | 2022 |\n",
"|--------|------|------|------|------|\n",
"| 1 | 0 | 0 | 0 | 0 |\n",
"| 2 | 5 | 6 | 7 | 8 |\n",
"| 3 | 10 | 12 | 14 | 16 |\n",
"| 4 | 15 | 18 | 21 | 24 |\n",
"| 5 | 20 | 24 | 28 | 32 |\n",
"| 6 | 25 | 30 | 35 | 40 |\n",
"| 7 | 30 | 36 | 42 | 48 |\n",
"| 8 | 35 | 42 | 49 | 56 |\n",
"| 9 | 40 | 48 | 56 | 64 |\n",
"| 10 | 45 | 54 | 63 | 72 |\n",
"| 11 | 50 | 60 | 70 | 80 |\n",
"| 12 | 55 | 66 | 77 | 88 |\n",
"\n",
"| Region | Sub-region |\n",
"|--------------|-----------------|\n",
"| Lower 48 | Permian |\n",
"| | Eagle Ford |\n",
"| | Bakken |\n",
"| | Other |\n",
"| Alaska | GKA |\n",
"| | GWA |\n",
"| | GPA |\n",
"| | WNS |\n",
"| EMENA | Norway |\n",
"| | Libya |\n",
"| | Qatar |\n",
"| Asia Pacific | APLNG |\n",
"| | Malaysia |\n",
"| | China |\n",
"| Canada | Montney |\n",
"| | Surmont |\n",
"parsed_text: Our Differentiated Portfolio: Deep; Durable and Diverse\n",
" 20 BBOE of Resource Diverse Production Base\n",
" Under $40/BBL Cost of Supply 10-Year Plan Cumulative Production (BBOE)\n",
" S50 S32/BBL Lower 48 Alaska\n",
" Average Cost of Supply\n",
" 3 $40 GKA GWA\n",
" GPA WNS\n",
" $30 EMENA\n",
" 3 Norway\n",
" 8 $20\n",
" E Qatar Libya\n",
" Asia Pacific Canada\n",
" $10 Permian\n",
" APLNG Montney\n",
" S0\n",
" 10 15 20 Bakken\n",
" Resource (BBOE) Eagle Ford Other Malaysia ChinaSurmont\n",
" Lower 48 Canada Alaska EMENA Asia Pacific\n",
"Costs assumemid-cycle price environment of S60/BBL WTI:\n",
" ConocoPhillips\n"
"~30% Improved Performance from 2019 to 2022\n",
"\n",
"## Delaware Basin Well Capex/EUR⁴ ($/BOE)\n",
"\n",
"| Company | Capex/EUR |\n",
"|------------------|-----------|\n",
"| ConocoPhillips | 10 |\n",
"| Competitor 1 | 15 |\n",
"| Competitor 2 | 20 |\n",
"| Competitor 3 | 25 |\n",
"| Competitor 4 | 30 |\n",
"| Competitor 5 | 35 |\n",
"| Competitor 6 | 40 |\n",
"| Competitor 7 | 45 |\n",
"\n",
"---\n",
"\n",
"¹ Unconventional acres. \n",
"² Source: Enverus and ConocoPhillips (March 2023). \n",
"³ Source: Enverus (March 2023) based on wells online year. \n",
"⁴ Source: Enverus (March 2023). Average single well capex/EUR. Top eight public operators based on wells online in years 2021-2022, greater than 50% oil weight. COP based on COP well design. Competitors include: CVX, DVN, EOG, MTDR, OXY, PR and XOM.\n",
"parsed_text: \n",
"Delaware: Vast Inventory with Proven Track Record of Performance\n",
" New Prolific Acreage Spanning Over 12-Month Cumulative Production? (BOE/FT)\n",
" Mexico 659,000 Net Acres' 40\n",
" Texas 3828\n",
" 30 2019\n",
" 20 30%\n",
" 10 Improved Performancefrom 2019 to 2022\n",
" Total\n",
" Permian Inventory\n",
" 10-Year Operated\n",
" 2 10 11 12\n",
" Months\n",
" Delaware Basin Well Capex/EUR4 (S/BOE)\n",
" 65% 25\n",
" Delaware Basin 20\n",
" Midland Basin 15\n",
" Low HighCost of Supplyz 10 ConocoPhillips\n",
" High Single-Digit Production Growth\n",
" \"Unconventional acres. 2Source: Enverus and ConocoPhillips (March 2023). 3SourceEnverus (March 2023) based on wells online year: \"Source; Enverus (March 2023). Average single well capex/EUR Top eight public operators based on\n",
"wells online in years 2021-2022, greater than 50% oil weight; COP based on COP well design: Competitors include; CVX DVN, EOG; MTDR, OXY, PR and XOM: ConocoPhillips\n"
]
}
],
"source": [
"print(response.source_nodes[7].get_content(metadata_mode=\"all\"))"
"print(response.source_nodes[0].get_content(metadata_mode=\"all\"))"
]
},
{
@@ -921,20 +855,26 @@
"name": "stdout",
"output_type": "stream",
"text": [
"ConocoPhillips has a diverse production base spanning several key geographies:\n",
"\n",
"1. **Lower 48 (United States)**: This includes major production areas such as the Permian Basin, Bakken Formation, and Eagle Ford Shale.\n",
"2. **Alaska**: Significant operations in the North Slope region.\n",
"3. **Canada**: Operations in the Montney Formation and the Surmont oil sands project.\n",
"4. **EMENA (Europe, Middle East, and North Africa)**: Notable operations in Norway and Libya.\n",
"5. **Asia Pacific**: Includes operations in Malaysia, China, and the Australia Pacific LNG (APLNG) project.\n",
"6. **Qatar**: Involvement in the country's energy sector.\n",
"\n",
"These regions highlight the company's extensive and varied geographical footprint in the energy production industry.\n"
"Added user message to memory: How does the Conoco Phillips capex/EUR in the delaware basin compare against other competitors?\n",
"=== Calling Function ===\n",
"Calling function: vector_tool with args: {\"input\": \"Conoco Phillips capex/EUR in the Delaware Basin\"}\n",
"=== Function Output ===\n",
"ConocoPhillips' capex/EUR in the Delaware Basin is approximately $20/BOE.\n",
"=== Calling Function ===\n",
"Calling function: vector_tool with args: {\"input\": \"competitors capex/EUR in the Delaware Basin\"}\n",
"=== Function Output ===\n",
"The average single well capex/EUR for competitors in the Delaware Basin is between $10 and $25 per BOE.\n",
"=== LLM Response ===\n",
"ConocoPhillips' capex/EUR in the Delaware Basin is approximately $20 per BOE. In comparison, the average capex/EUR for competitors in the Delaware Basin ranges between $10 and $25 per BOE. This places ConocoPhillips' capex/EUR towards the higher end of the competitive range.\n",
"ConocoPhillips' capex/EUR in the Delaware Basin is approximately $20 per BOE. In comparison, the average capex/EUR for competitors in the Delaware Basin ranges between $10 and $25 per BOE. This places ConocoPhillips' capex/EUR towards the higher end of the competitive range.\n"
]
}
],
"source": [
"# base_response = base_agent.query(\"Tell me about the different regions and subregions where Conoco Phillips has a production base.\")\n",
"base_response = base_agent.query(\n",
" \"How does the Conoco Phillips capex/EUR in the delaware basin compare against other competitors?\"\n",
")\n",
"print(str(base_response))"
]
},
@@ -948,31 +888,30 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Our Differentiated Portfolio: Deep; Durable and Diverse\n",
" 20 BBOE of Resource Diverse Production Base\n",
" Under $40/BBL Cost of Supply 10-Year Plan Cumulative Production (BBOE)\n",
" S50 S32/BBL Lower 48 Alaska\n",
" Average Cost of Supply\n",
" 3 $40 GKA GWA\n",
" GPA WNS\n",
" $30 EMENA\n",
" 3 Norway\n",
" 8 $20\n",
" E Qatar Libya\n",
" Asia Pacific Canada\n",
" $10 Permian\n",
" APLNG Montney\n",
" S0\n",
" 10 15 20 Bakken\n",
" Resource (BBOE) Eagle Ford Other Malaysia ChinaSurmont\n",
" Lower 48 Canada Alaska EMENA Asia Pacific\n",
"Costs assumemid-cycle price environment of S60/BBL WTI:\n",
" ConocoPhillips\n"
"Deep, Durable and Diverse Portfolio with Significant Growth Runway\n",
" 1,2002022 Lower 48 Unconventional Production' (MBOED S50 ~S32/BBL\n",
" 000 ConocoPhillips Cost of SupplyAverage\n",
" 00 S40\n",
" 500 3\n",
" 400 1 S30\n",
" 200\n",
" 5\n",
" 15,000ConocoPhillipsNet Remaining Well Inventory? 1 S20\n",
" 12,000 S10\n",
" 000\n",
" 0o0 SO\n",
" 3,000 10\n",
" Resource (BBOE)\n",
" Delaware Basin Midland Basin Eagle Ford Bakken Other\n",
" Largest Lower 48 Unconventional Producer; Growing into the Next Decade\n",
" onshore operated inventory that achieves 15% IRR at $SO/BBL WTI, Competitors include CVX, DVN, EOG, FANG, MRO, OXY, PXD,and XOM:\n",
" Source: Wood Mackenzie Lower 48 Unconventional Plays 2022 ProductionCompetitors include CVX, DVN; EOG, FANG, MRO, OXY, PXD and XOM; greaterthan50% liquids weight: ?Source: Wood Mackenzie (March 2023), Lower 48\n",
" ConocoPhillips\n"
]
}
],
"source": [
"print(base_response.source_nodes[1].get_content(metadata_mode=\"all\"))"
"print(base_response.source_nodes[0].get_content(metadata_mode=\"llm\"))"
]
}
],
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@@ -46,7 +46,7 @@
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"<LLAMA_CLOUD_API_KEY>\""
"os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"<LLAMA_CLOUD_API_KEY>"
]
},
{
+1 -62
View File
@@ -91,10 +91,6 @@ class LlamaParse(BasePydanticReader):
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.",
)
premium_mode: bool = Field(
default=False,
description="Use our best parser mode if set to True.",
)
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.",
@@ -154,34 +150,6 @@ class LlamaParse(BasePydanticReader):
custom_client: Optional[httpx.AsyncClient] = Field(
default=None, description="A custom HTTPX client to use for sending requests."
)
disable_ocr: bool = Field(
default=False,
description="Disable the OCR on the document. LlamaParse will only extract the copyable text from the document.",
)
is_formatting_instruction: bool = Field(
default=True,
description="Allow the parsing instruction to also format the output. Disable to have a cleaner markdown output.",
)
annotate_links: bool = Field(
default=False,
description="Annotate links found in the document to extract their URL.",
)
webhook_url: Optional[str] = Field(
default=None,
description="A URL that needs to be called at the end of the parsing job.",
)
azure_openai_deployment_name: Optional[str] = Field(
default=None, description="Azure Openai Deployment Name"
)
azure_openai_endpoint: Optional[str] = Field(
default=None, description="Azure Openai Endpoint"
)
azure_openai_api_version: Optional[str] = Field(
default=None, description="Azure Openai API Version"
)
azure_openai_key: Optional[str] = Field(
default=None, description="Azure Openai Key"
)
@field_validator("api_key", mode="before", check_fields=True)
@classmethod
@@ -259,7 +227,6 @@ class LlamaParse(BasePydanticReader):
"skip_diagonal_text": self.skip_diagonal_text,
"do_not_cache": self.do_not_cache,
"fast_mode": self.fast_mode,
"premium_mode": self.premium_mode,
"do_not_unroll_columns": self.do_not_unroll_columns,
"gpt4o_mode": self.gpt4o_mode,
"gpt4o_api_key": self.gpt4o_api_key,
@@ -267,9 +234,6 @@ class LlamaParse(BasePydanticReader):
"use_vendor_multimodal_model": self.use_vendor_multimodal_model,
"vendor_multimodal_model_name": self.vendor_multimodal_model_name,
"take_screenshot": self.take_screenshot,
"disable_ocr": self.disable_ocr,
"is_formatting_instruction": self.is_formatting_instruction,
"annotate_links": self.annotate_links,
}
# only send page separator to server if it is not None
@@ -289,22 +253,6 @@ class LlamaParse(BasePydanticReader):
if self.target_pages is not None:
data["target_pages"] = self.target_pages
if self.webhook_url is not None:
data["webhook_url"] = self.webhook_url
# Azure OpenAI
if self.azure_openai_deployment_name is not None:
data["azure_openai_deployment_name"] = self.azure_openai_deployment_name
if self.azure_openai_endpoint is not None:
data["azure_openai_endpoint"] = self.azure_openai_endpoint
if self.azure_openai_api_version is not None:
data["azure_openai_api_version"] = self.azure_openai_api_version
if self.azure_openai_key is not None:
data["azure_openai_key"] = self.azure_openai_key
try:
async with self.client_context() as client:
response = await client.post(
@@ -355,8 +303,7 @@ class LlamaParse(BasePydanticReader):
continue
# Allowed values "PENDING", "SUCCESS", "ERROR", "CANCELED"
result_json = result.json()
status = result_json["status"]
status = result.json()["status"]
if status == "SUCCESS":
parsed_result = await client.get(result_url, headers=headers)
return parsed_result.json()
@@ -368,14 +315,6 @@ class LlamaParse(BasePydanticReader):
print(".", end="", flush=True)
await asyncio.sleep(self.check_interval)
else:
error_code = result_json.get("error_code", "No error code found")
error_message = result_json.get(
"error_message", "No error message found"
)
exception_str = f"Job ID: {job_id} failed with status: {status}, Error code: {error_code}, Error message: {error_message}"
raise Exception(exception_str)
async def _aload_data(
self,
View File
-92
View File
@@ -1,92 +0,0 @@
import click
import json
from enum import Enum
from pathlib import Path
from pydantic.fields import FieldInfo
from typing import Any, Callable, List
from llama_parse.base import LlamaParse
def pydantic_field_to_click_option(name: str, field: FieldInfo) -> click.Option:
"""Convert a Pydantic field to a Click option."""
kwargs = {
"default": field.default if field.default else None,
"help": field.description,
}
if isinstance(kwargs["default"], Enum):
kwargs["default"] = kwargs["default"].value
if field.annotation is bool:
kwargs["is_flag"] = True
if field.default and field.default is True:
name = f"no-{name}"
return click.option(f'--{name.replace("_", "-")}', **kwargs)
def add_options(options: List[click.Option]) -> Callable:
def _add_options(func: Callable) -> Callable:
for option in reversed(options):
func = option(func)
return func
return _add_options
@click.command()
@click.argument("file_paths", nargs=-1, type=click.Path(exists=True, path_type=Path))
@click.option(
"--output-file", type=click.Path(path_type=Path), help="Path to save the output"
)
@click.option("--output-raw-json", is_flag=True, help="Output the raw JSON result")
@add_options(
[
pydantic_field_to_click_option(name, field)
for name, field in LlamaParse.model_fields.items()
if name not in ["custom_client"]
]
)
def parse(**kwargs: Any) -> None:
"""Parse files using LlamaParse and output the results."""
file_paths = kwargs.pop("file_paths")
output_file = kwargs.pop("output_file")
output_raw_json = kwargs.pop("output_raw_json")
# Remove None values to use LlamaParse defaults
kwargs = {k: v for k, v in kwargs.items() if v is not None}
# Remove no- prefix for boolean flags
kwargs = {k.replace("no_", ""): v for k, v in kwargs.items()}
parser = LlamaParse(**kwargs)
if output_raw_json:
results = parser.get_json_result(list(file_paths))
if output_file:
with output_file.open("w") as f:
json.dump(results, f)
click.echo(f"Results saved to {output_file}")
else:
click.echo(results)
else:
results = parser.load_data(list(file_paths))
if output_file:
with output_file.open("w") as f:
for i, doc in enumerate(results):
f.write(f"File: {doc.metadata.get('file_path', 'Unknown')}\n") # type: ignore
f.write(doc.text) # type: ignore
if i < len(results) - 1:
f.write("\n\n---\n\n")
click.echo(f"Results saved to {output_file}")
else:
for i, doc in enumerate(results):
click.echo(f"File: {doc.metadata.get('file_path', 'Unknown')}") # type: ignore
click.echo(doc.text) # type: ignore
if i < len(results) - 1:
click.echo("\n---\n")
if __name__ == "__main__":
parse()
+1
View File
@@ -10,6 +10,7 @@ class ResultType(str, Enum):
TXT = "text"
MD = "markdown"
JSON = "json"
class Language(str, Enum):
Generated
+821 -1020
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+1 -5
View File
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "llama-parse"
version = "0.5.10"
version = "0.5.5"
description = "Parse files into RAG-Optimized formats."
authors = ["Logan Markewich <logan@llamaindex.ai>"]
license = "MIT"
@@ -14,11 +14,7 @@ packages = [{include = "llama_parse"}]
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
llama-index-core = ">=0.11.0"
click = "^8.1.7"
[tool.poetry.group.dev.dependencies]
pytest = "^8.0.0"
ipykernel = "^6.29.0"
[tool.poetry.scripts]
llama-parse = "llama_parse.cli.main:parse"