cr

2026-07-20 00:54:09 -04:00 · 2024-11-12 23:24:49 -08:00 · 2024-11-12 23:19:15 -08:00 · 2024-11-10 19:03:15 -08:00 · 2024-11-10 17:47:58 -08:00 · 2024-11-10 16:25:37 -08:00
27 changed files with 6498 additions and 1056 deletions
@@ -7,8 +7,6 @@ assignees: ''

 ---

-_Note: we're aware of some missing content in the output and layout issues on tables. Please refrain from opening new issues on this topic unless if you think it's different from what has already been reported._
-
 **Describe the bug**
 Write a concise description of what the bug is.

@@ -19,19 +17,15 @@ If possible, please provide the PDF file causing the issue.
 If you have it, please provide the ID of the job you ran.
 You can find it here: https://cloud.llamaindex.ai/parse in the "History" tab.

-**Screenshots**
-Feel free to also provide screenshots if relevant.
-
 **Client:**
 Please remove untested options:
- - Frontend (cloud.llamaindex.ai)
 - Python Library
+ - API
+ - Frontend (cloud.llamaindex.ai)
 - Typescript Library
 - Notebook
- - API
-
-**Options**
-What options did you use? Multimodal, fast mode, parsing instructions, etc.

 **Additional context**
 Add any additional context about the problem here.
+What options did you use? Premium mode, multimodal, fast mode, parsing instructions, etc.
+Screenshots, code snippets, etc.
@@ -2,3 +2,4 @@
 __pycache__/
 *.pyc
 .DS_Store
+.idea
@@ -1,15 +1,26 @@
 # LlamaParse

-LlamaParse is an API created by LlamaIndex to efficiently parse and represent files for efficient retrieval and context augmentation using LlamaIndex frameworks.
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-parse)](https://pypi.org/project/llama-parse/)
+[![GitHub contributors](https://img.shields.io/github/contributors/run-llama/llama_parse)](https://github.com/run-llama/llama_parse/graphs/contributors)
+[![Discord](https://img.shields.io/discord/1059199217496772688)](https://discord.gg/dGcwcsnxhU)
+
+LlamaParse is a **GenAI-native document parser** that can parse complex document data for any downstream LLM use case (RAG, agents).
+
+It is really good at the following:
+
+- ✅ **Broad file type support**: Parsing a variety of unstructured file types (.pdf, .pptx, .docx, .xlsx, .html) with text, tables, visual elements, weird layouts, and more.
+- ✅ **Table recognition**: Parsing embedded tables accurately into text and semi-structured representations.
+- ✅ **Multimodal parsing and chunking**: Extracting visual elements (images/diagrams) into structured formats and return image chunks using the latest multimodal models.
+- ✅ **Custom parsing**: Input custom prompt instructions to customize the output the way you want it.

 LlamaParse directly integrates with [LlamaIndex](https://github.com/run-llama/llama_index).

-Free plan is up to 1000 pages a day. Paid plan is free 7k pages per week + 0.3c per additional page.
-
-There is a sandbox available to test the API [**https://cloud.llamaindex.ai/parse ↗**](https://cloud.llamaindex.ai/parse).
+The free plan is up to 1000 pages a day. Paid plan is free 7k pages per week + 0.3c per additional page by default. There is a sandbox available to test the API [**https://cloud.llamaindex.ai/parse ↗**](https://cloud.llamaindex.ai/parse).

 Read below for some quickstart information, or see the [full documentation](https://docs.cloud.llamaindex.ai/).

+If you're a company interested in enterprise RAG solutions, and/or high volume/on-prem usage of LlamaParse, come [talk to us](https://www.llamaindex.ai/contact).
+
 ## Getting Started

 First, login and get an api-key from [**https://cloud.llamaindex.ai/api-key ↗**](https://cloud.llamaindex.ai/api-key).
@@ -27,7 +38,22 @@ Lastly, install the package:

 `pip install llama-parse`

-Now you can run the following to parse your first PDF file:
+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:

 ```python
 import nest_asyncio
@@ -76,13 +102,18 @@ parser = LlamaParse(
    language="en",  # Optionally you can define a language, default=en
 )

-with open("./my_file1.pdf", "rb") as f:
-    documents = parser.load_data(f)
+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)

 # you can also pass file bytes directly
-with open("./my_file1.pdf", "rb") as f:
+with open(f"./{file_name}", "rb") as f:
    file_bytes = f.read()
-    documents = parser.load_data(file_bytes)
+    # must provide extra_info with file_name key with passing file bytes
+    documents = parser.load_data(file_bytes, extra_info=extra_info)
 ```

 ## Using with `SimpleDirectoryReader`
@@ -126,3 +157,9 @@ Several end-to-end indexing examples can be found in the examples folder
 ## Terms of Service

 See the [Terms of Service Here](./TOS.pdf).
+
+## Get in Touch (LlamaCloud)
+
+LlamaParse is part of LlamaCloud, our e2e enterprise RAG platform that provides out-of-the-box, production-ready connectors, indexing, and retrieval over your complex data sources. We offer SaaS and VPC options.
+
+LlamaCloud is currently available via waitlist (join by [creating an account](https://cloud.llamaindex.ai/)). If you're interested in state-of-the-art quality and in centralizing your RAG efforts, come [get in touch with us](https://www.llamaindex.ai/contact).
@@ -342,7 +342,7 @@
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "llama-parse-aNC435Vv-py3.10",
+   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
@@ -165,7 +165,18 @@
   "execution_count": null,
   "id": "ef82a985-4088-4bb7-9a21-0318e1b9207d",
   "metadata": {},
-   "outputs": [],
+   "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"
+     ]
+    }
+   ],
   "source": [
    "print(f\"Parsing text...\")\n",
    "docs_text = parser_text.load_data(\"data/conocophillips.pdf\")\n",
@@ -174,42 +185,36 @@
    "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": [
-      "dict_keys(['page', 'text', 'md', 'images', 'items'])\n"
+      "# 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"
     ]
    }
   ],
   "source": [
-    "print(md_json_list[1].keys())"
+    "print(md_json_list[10][\"md\"])"
   ]
  },
  {
@@ -299,7 +304,7 @@
    "    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 = docs[0].text.split(\"---\")\n",
+    "    doc_chunks = [c for d in docs for c in d.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",
@@ -339,25 +344,23 @@
     "output_type": "stream",
     "text": [
      "page_num: 11\n",
-      "image_path: data_images/d9137e19-3974-4b5d-998f-dac0cf29dd9d-page-10.jpg\n",
+      "image_path: data_images/1ddd5654-062b-4e19-b488-d66efc9c509d-page_39.jpg\n",
      "parsed_text_markdown: # Commitment to Disciplined Reinvestment Rate\n",
      "\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",
+      "| 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",
-      "**Disciplined Reinvestment Rate is the Foundation for Superior Returns on and of Capital, while Driving Durable CFO Growth**\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",
-      "- ~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",
+      "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",
      "                         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",
@@ -374,7 +377,7 @@
      "          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 andcashfrom operations (CFO) are non-GAAP measures: Definitions and reconciliations are included in the Appendix                                    ConocoPhillips\n"
+      " Reinvestment rate and cash from operations (CFO) are non-GAAP measures: Definitions and reconciliations are included in the Appendix                                  ConocoPhillips\n"
     ]
    }
   ],
@@ -397,7 +400,17 @@
   "execution_count": null,
   "id": "6ea53c31-0e38-421c-8d9b-0e3adaa1677e",
   "metadata": {},
-   "outputs": [],
+   "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"
+     ]
+    }
+   ],
   "source": [
    "import os\n",
    "from llama_index.core import (\n",
@@ -588,7 +601,7 @@
      "                            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",
@@ -599,7 +612,7 @@
      "                                                                                                                      APLNG        Montney\n",
      "       S0\n",
      "                                               10              15              20                         Bakken\n",
-      "                                         Resource (BBOE)                           Eagle Ford             Other       MalaysiaChina Surmont\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"
@@ -687,70 +700,126 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "id": "1cdce5d8-6bb3-4cd3-929d-1cec249d9052",
+   "id": "d78e53cf-35cb-4ef8-b03e-1b47ba15ae64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Added user message to memory: How does the Conoco Phillips capex/EUR in the delaware basin compare against other competitors?\n",
+      "Added user message to memory: Tell me about the diverse geographies where Conoco Phillips has a production base\n",
      "=== Calling Function ===\n",
-      "Calling function: vector_tool with args: {\"input\": \"Conoco Phillips capex/EUR in the Delaware Basin\"}\n",
+      "Calling function: vector_tool with args: {\"input\": \"Conoco Phillips production base geographies\"}\n",
      "=== Function Output ===\n",
-      "The ConocoPhillips capex/EUR in the Delaware Basin is $10/BOE.\n",
+      "ConocoPhillips' production base geographies include:\n",
      "\n",
-      "I obtained this information from the image provided. The image clearly shows a bar chart under the section \"Delaware Basin Well Capex/EUR ($/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 $10/BOE in the Delaware Basin. There are no discrepancies between the image and the parsed markdown text in this case.\n",
-      "=== Calling Function ===\n",
-      "Calling function: vector_tool with args: {\"input\": \"competitors capex/EUR in the Delaware Basin\"}\n",
-      "=== Function Output ===\n",
-      "The competitors' Capex/EUR in the Delaware Basin can be found in the image on the slide titled \"Delaware: Vast Inventory with Proven Track Record of Performance.\" The relevant information is presented in a bar chart under the section \"Delaware Basin Well Capex/EUR ($/BOE)\".\n",
+      "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",
-      "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",
+      "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",
-      "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",
+      "ConocoPhillips has a diverse production base spread across various geographies, including:\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",
+      "1. **Lower 48**:\n",
+      "   - Permian Basin\n",
+      "   - Eagle Ford\n",
+      "   - Bakken\n",
+      "   - Other regions within the continental United States\n",
      "\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",
+      "2. **Alaska**:\n",
+      "   - Greater Kuparuk Area (GKA)\n",
+      "   - Greater Prudhoe Area (GPA)\n",
+      "   - Greater Willow Area (GWA)\n",
+      "   - Western North Slope (WNS)\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",
+      "3. **EMENA (Europe, Middle East, and North Africa)**:\n",
+      "   - Norway\n",
+      "   - Libya\n",
+      "   - Qatar\n",
      "\n",
-      "This data indicates that ConocoPhillips has a more cost-efficient operation in the Delaware Basin compared to its competitors.\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",
+      "=== Calling Function ===\n",
+      "Calling function: vector_tool with args: {\"input\": \"diverse geographies where Conoco Phillips has a production base\"}\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",
+      "=== LLM Response ===\n",
+      "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"
     ]
    }
   ],
   "source": [
-    "# 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",
+    "query = (\n",
+    "    \"Tell me about the diverse geographies where Conoco Phillips has a production base\"\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))"
   ]
  },
@@ -764,85 +833,82 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "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",
+      "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",
      "\n",
-      "## Prolific Acreage Spanning Over ~659,000 Net Acres¹\n",
+      "## ~20 BBOE of Resource\n",
+      "Under $40/BBL Cost of Supply\n",
      "\n",
-      "![Map of Delaware Basin](image)\n",
+      "### ~ $32/BBL\n",
+      "Average Cost of Supply\n",
      "\n",
-      "### Total 10-Year Operated Permian Inventory\n",
+      "### WTI Cost of Supply ($/BBL)\n",
      "\n",
-      "- Delaware Basin: 65%\n",
-      "- Midland Basin: 35%\n",
+      "| Cost ($/BBL) | Resource (BBOE) |\n",
+      "|--------------|-----------------|\n",
+      "| $0           | 0               |\n",
+      "| $10          |                 |\n",
+      "| $20          |                 |\n",
+      "| $30          |                 |\n",
+      "| $40          |                 |\n",
+      "| $50          |                 |\n",
      "\n",
-      "### High Single-Digit Production Growth\n",
+      "- **Legend:**\n",
+      "  - Lower 48\n",
+      "  - Canada\n",
+      "  - Alaska\n",
+      "  - EMENA\n",
+      "  - Asia Pacific\n",
      "\n",
-      "## 12-Month Cumulative Production³ (BOE/FT)\n",
+      "*Costs assume a mid-cycle price environment of $60/BBL WTI.*\n",
      "\n",
-      "| Months | 2019 | 2020 | 2021 | 2022 |\n",
-      "|--------|------|------|------|------|\n",
-      "| 1      | 0    | 0    | 0    | 0    |\n",
-      "| 2      | 5    | 6    | 7    | 8    |\n",
-      "| 3      | 10   | 12   | 14   | 16   |\n",
-      "| 4      | 15   | 18   | 21   | 24   |\n",
-      "| 5      | 20   | 24   | 28   | 32   |\n",
-      "| 6      | 25   | 30   | 35   | 40   |\n",
-      "| 7      | 30   | 36   | 42   | 48   |\n",
-      "| 8      | 35   | 42   | 49   | 56   |\n",
-      "| 9      | 40   | 48   | 56   | 64   |\n",
-      "| 10     | 45   | 54   | 63   | 72   |\n",
-      "| 11     | 50   | 60   | 70   | 80   |\n",
-      "| 12     | 55   | 66   | 77   | 88   |\n",
+      "## Diverse Production Base\n",
+      "10-Year Plan Cumulative Production (BBOE)\n",
      "\n",
-      "~30% Improved Performance from 2019 to 2022\n",
-      "\n",
-      "## Delaware Basin Well Capex/EUR⁴ ($/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"
+      "| 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"
     ]
    }
   ],
   "source": [
-    "print(response.source_nodes[0].get_content(metadata_mode=\"all\"))"
+    "print(response.source_nodes[7].get_content(metadata_mode=\"all\"))"
   ]
  },
  {
@@ -855,26 +921,20 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "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"
+      "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"
     ]
    }
   ],
   "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))"
   ]
  },
@@ -888,30 +948,31 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "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"
+      "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"
     ]
    }
   ],
   "source": [
-    "print(base_response.source_nodes[0].get_content(metadata_mode=\"llm\"))"
+    "print(base_response.source_nodes[1].get_content(metadata_mode=\"all\"))"
   ]
  }
 ],
@@ -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>\""
   ]
  },
  {
@@ -1,17 +1,22 @@
 import os
 import asyncio
+from io import TextIOWrapper
+
 import httpx
 import mimetypes
 import time
-from pathlib import Path
-from typing import AsyncGenerator, List, Optional, Union
+from pathlib import Path, PurePath, PurePosixPath
+from typing import AsyncGenerator, Any, Dict, List, Optional, Union
 from contextlib import asynccontextmanager
 from io import BufferedIOBase

-from llama_index.core.async_utils import run_jobs
+from fsspec import AbstractFileSystem
+from fsspec.spec import AbstractBufferedFile
+from llama_index.core.async_utils import asyncio_run, run_jobs
 from llama_index.core.bridge.pydantic import Field, field_validator
 from llama_index.core.constants import DEFAULT_BASE_URL
 from llama_index.core.readers.base import BasePydanticReader
+from llama_index.core.readers.file.base import get_default_fs
 from llama_index.core.schema import Document
 from llama_parse.utils import (
    nest_asyncio_err,
@@ -86,6 +91,14 @@ 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.",
+    )
+    continuous_mode: bool = Field(
+        default=False,
+        description="Parse documents continuously, leading to better results on documents where tables span across two pages.",
+    )
    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.",
@@ -110,6 +123,10 @@ class LlamaParse(BasePydanticReader):
        default=None,
        description="The API key for the GPT-4o API. Lowers the cost of parsing.",
    )
+    guess_xlsx_sheet_names: Optional[bool] = Field(
+        default=False,
+        description="Whether to guess the sheet names of the xlsx file.",
+    )
    bounding_box: Optional[str] = Field(
        default=None,
        description="The bounding box to use to extract text from documents describe as a string containing the bounding box margins",
@@ -145,6 +162,34 @@ 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
@@ -178,7 +223,10 @@ class LlamaParse(BasePydanticReader):

    # upload a document and get back a job_id
    async def _create_job(
-        self, file_input: FileInput, extra_info: Optional[dict] = None
+        self,
+        file_input: FileInput,
+        extra_info: Optional[dict] = None,
+        fs: Optional[AbstractFileSystem] = None,
    ) -> str:
        headers = {"Authorization": f"Bearer {self.api_key}"}
        url = f"{self.base_url}/api/parsing/upload"
@@ -193,7 +241,7 @@ class LlamaParse(BasePydanticReader):
            file_name = extra_info["file_name"]
            mime_type = mimetypes.guess_type(file_name)[0]
            files = {"file": (file_name, file_input, mime_type)}
-        elif isinstance(file_input, (str, Path)):
+        elif isinstance(file_input, (str, Path, PurePosixPath, PurePath)):
            file_path = str(file_input)
            file_ext = os.path.splitext(file_path)[1].lower()
            if file_ext not in SUPPORTED_FILE_TYPES:
@@ -203,7 +251,9 @@ class LlamaParse(BasePydanticReader):
                )
            mime_type = mimetypes.guess_type(file_path)[0]
            # Open the file here for the duration of the async context
-            file_handle = open(file_path, "rb")
+            # load data, set the mime type
+            fs = fs or get_default_fs()
+            file_handle = fs.open(file_input, "rb")
            files = {"file": (os.path.basename(file_path), file_handle, mime_type)}
        else:
            raise ValueError(
@@ -217,6 +267,8 @@ 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,
+            "continuous_mode": self.continuous_mode,
            "do_not_unroll_columns": self.do_not_unroll_columns,
            "gpt4o_mode": self.gpt4o_mode,
            "gpt4o_api_key": self.gpt4o_api_key,
@@ -224,6 +276,11 @@ 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,
+            "guess_xlsx_sheet_names": self.guess_xlsx_sheet_names,
+            "is_formatting_instruction": self.is_formatting_instruction,
+            "annotate_links": self.annotate_links,
+            "from_python_package": True,
        }

        # only send page separator to server if it is not None
@@ -243,6 +300,22 @@ 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(
@@ -259,9 +332,15 @@ class LlamaParse(BasePydanticReader):
            if file_handle is not None:
                file_handle.close()

+    @staticmethod
+    def __get_filename(f: Union[TextIOWrapper, AbstractBufferedFile]) -> str:
+        if isinstance(f, TextIOWrapper):
+            return f.name
+        return f.full_name
+
    async def _get_job_result(
        self, job_id: str, result_type: str, verbose: bool = False
-    ) -> dict:
+    ) -> Dict[str, Any]:
        result_url = f"{self.base_url}/api/parsing/job/{job_id}/result/{result_type}"
        status_url = f"{self.base_url}/api/parsing/job/{job_id}"
        headers = {"Authorization": f"Bearer {self.api_key}"}
@@ -287,7 +366,8 @@ class LlamaParse(BasePydanticReader):
                    continue

                # Allowed values "PENDING", "SUCCESS", "ERROR", "CANCELED"
-                status = result.json()["status"]
+                result_json = result.json()
+                status = result_json["status"]
                if status == "SUCCESS":
                    parsed_result = await client.get(result_url, headers=headers)
                    return parsed_result.json()
@@ -299,22 +379,25 @@ class LlamaParse(BasePydanticReader):
                        print(".", end="", flush=True)

                    await asyncio.sleep(self.check_interval)
-
-                    continue
                else:
-                    raise Exception(
-                        f"Failed to parse the file: {job_id}, status: {status}"
+                    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,
        file_path: FileInput,
        extra_info: Optional[dict] = None,
+        fs: Optional[AbstractFileSystem] = None,
        verbose: bool = False,
    ) -> List[Document]:
        """Load data from the input path."""
        try:
-            job_id = await self._create_job(file_path, extra_info=extra_info)
+            job_id = await self._create_job(file_path, extra_info=extra_info, fs=fs)
            if verbose:
                print("Started parsing the file under job_id %s" % job_id)

@@ -345,17 +428,19 @@ class LlamaParse(BasePydanticReader):
        self,
        file_path: Union[List[FileInput], FileInput],
        extra_info: Optional[dict] = None,
+        fs: Optional[AbstractFileSystem] = None,
    ) -> List[Document]:
        """Load data from the input path."""
-        if isinstance(file_path, (str, Path, bytes, BufferedIOBase)):
+        if isinstance(file_path, (str, PurePosixPath, Path, bytes, BufferedIOBase)):
            return await self._aload_data(
-                file_path, extra_info=extra_info, verbose=self.verbose
+                file_path, extra_info=extra_info, fs=fs, verbose=self.verbose
            )
        elif isinstance(file_path, list):
            jobs = [
                self._aload_data(
                    f,
                    extra_info=extra_info,
+                    fs=fs,
                    verbose=self.verbose and not self.show_progress,
                )
                for f in file_path
@@ -384,10 +469,11 @@ class LlamaParse(BasePydanticReader):
        self,
        file_path: Union[List[FileInput], FileInput],
        extra_info: Optional[dict] = None,
+        fs: Optional[AbstractFileSystem] = None,
    ) -> List[Document]:
        """Load data from the input path."""
        try:
-            return asyncio.run(self.aload_data(file_path, extra_info))
+            return asyncio_run(self.aload_data(file_path, extra_info, fs=fs))
        except RuntimeError as e:
            if nest_asyncio_err in str(e):
                raise RuntimeError(nest_asyncio_msg)
@@ -402,12 +488,13 @@ class LlamaParse(BasePydanticReader):
            job_id = await self._create_job(file_path, extra_info=extra_info)
            if self.verbose:
                print("Started parsing the file under job_id %s" % job_id)
-
            result = await self._get_job_result(job_id, "json")
            result["job_id"] = job_id
-            result["file_path"] = file_path
-            return [result]

+            if not isinstance(file_path, (bytes, BufferedIOBase)):
+                result["file_path"] = str(file_path)
+
+            return [result]
        except Exception as e:
            file_repr = file_path if isinstance(file_path, str) else "<bytes/buffer>"
            print(f"Error while parsing the file '{file_repr}':", e)
@@ -453,7 +540,7 @@ class LlamaParse(BasePydanticReader):
    ) -> List[dict]:
        """Parse the input path."""
        try:
-            return asyncio.run(self.aget_json(file_path, extra_info))
+            return asyncio_run(self.aget_json(file_path, extra_info))
        except RuntimeError as e:
            if nest_asyncio_err in str(e):
                raise RuntimeError(nest_asyncio_msg)
@@ -492,7 +579,9 @@ class LlamaParse(BasePydanticReader):

                        image["path"] = image_path
                        image["job_id"] = job_id
-                        image["original_pdf_path"] = result["file_path"]
+
+                        image["original_file_path"] = result.get("file_path", None)
+
                        image["page_number"] = page["page"]
                        with open(image_path, "wb") as f:
                            image_url = f"{self.base_url}/api/parsing/job/{job_id}/result/image/{image_name}"
@@ -514,7 +603,61 @@ class LlamaParse(BasePydanticReader):
    def get_images(self, json_result: List[dict], download_path: str) -> List[dict]:
        """Download images from the parsed result."""
        try:
-            return asyncio.run(self.aget_images(json_result, download_path))
+            return asyncio_run(self.aget_images(json_result, download_path))
+        except RuntimeError as e:
+            if nest_asyncio_err in str(e):
+                raise RuntimeError(nest_asyncio_msg)
+            else:
+                raise e
+
+    async def aget_xlsx(
+        self, json_result: List[dict], download_path: str
+    ) -> List[dict]:
+        """Download images from the parsed result."""
+        headers = {"Authorization": f"Bearer {self.api_key}"}
+
+        # make the download path
+        if not os.path.exists(download_path):
+            os.makedirs(download_path)
+        try:
+            xlsx_list = []
+            for result in json_result:
+                job_id = result["job_id"]
+                if self.verbose:
+                    print("> XLSX")
+
+                xlsx_path = os.path.join(download_path, f"{job_id}.xlsx")
+
+                xlsx = {}
+
+                xlsx["path"] = xlsx_path
+                xlsx["job_id"] = job_id
+                xlsx["original_file_path"] = result.get("file_path", None)
+
+                with open(xlsx_path, "wb") as f:
+                    xlsx_url = (
+                        f"{self.base_url}/api/parsing/job/{job_id}/result/raw/xlsx"
+                    )
+                    async with self.client_context() as client:
+                        res = await client.get(
+                            xlsx_url, headers=headers, timeout=self.max_timeout
+                        )
+                        res.raise_for_status()
+                        f.write(res.content)
+                xlsx_list.append(xlsx)
+            return xlsx_list
+
+        except Exception as e:
+            print("Error while downloading xlsx:", e)
+            if self.ignore_errors:
+                return []
+            else:
+                raise e
+
+    def get_xlsx(self, json_result: List[dict], download_path: str) -> List[dict]:
+        """Download xlsx from the parsed result."""
+        try:
+            return asyncio_run(self.aget_xlsx(json_result, download_path))
        except RuntimeError as e:
            if nest_asyncio_err in str(e):
                raise RuntimeError(nest_asyncio_msg)
@@ -0,0 +1,92 @@
+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()
@@ -10,7 +10,6 @@ class ResultType(str, Enum):

    TXT = "text"
    MD = "markdown"
-    JSON = "json"


 class Language(str, Enum):
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"

 [tool.poetry]
 name = "llama-parse"
-version = "0.5.2"
+version = "0.5.13"
 description = "Parse files into RAG-Optimized formats."
 authors = ["Logan Markewich <logan@llamaindex.ai>"]
 license = "MIT"
@@ -14,7 +14,11 @@ 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"
@@ -1,6 +1,8 @@
 import os
 import pytest
+from fsspec.implementations.local import LocalFileSystem
 from httpx import AsyncClient
+
 from llama_parse import LlamaParse


@@ -70,6 +72,20 @@ def test_simple_page_markdown_buffer(markdown_parser: LlamaParse) -> None:
        assert len(result[0].text) > 0


+@pytest.mark.skipif(
+    os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
+    reason="LLAMA_CLOUD_API_KEY not set",
+)
+def test_simple_page_with_custom_fs() -> None:
+    parser = LlamaParse(result_type="markdown")
+    fs = LocalFileSystem()
+    filepath = os.path.join(
+        os.path.dirname(__file__), "test_files/attention_is_all_you_need.pdf"
+    )
+    result = parser.load_data(filepath, fs=fs)
+    assert len(result) == 1
+
+
@pytest.mark.skipif(
    os.environ.get("LLAMA_CLOUD_API_KEY", "") == "",
    reason="LLAMA_CLOUD_API_KEY not set",
Author	SHA1	Message	Date
Jerry Liu	fd14b5a6e5	cr	2024-11-12 23:24:49 -08:00
Jerry Liu	15cc7c4070	cr	2024-11-12 23:19:15 -08:00
Jerry Liu	fc709438a7	cr	2024-11-10 19:03:15 -08:00
Jerry Liu	5801375f8c	cr	2024-11-10 17:47:58 -08:00
Jerry Liu	8a45ba81ba	cr	2024-11-10 16:25:37 -08:00
Jerry Liu	e16644f9c1	cr	2024-11-10 16:17:48 -08:00
Jerry Liu	2e85a0c0c0	cr	2024-11-09 22:37:08 -08:00
Pierre-Loic Doulcet	89348aa8e5	add xlsx support (#472 )	2024-11-01 10:09:17 -06:00
Thiago Salvatore	3ab2ce27b5	Add PurePosixPath to list of allowed file-paths (#464 )	2024-10-25 10:45:47 -06:00
Sacha Bron	265261862f	Add continuous_mode (#460 )	2024-10-22 19:45:46 +02:00
Sacha Bron	66cf052b8c	Update issue templates (#457 ) * Update issue templates * Update issue templates	2024-10-21 19:51:46 +02:00
Jerry Liu	2ca2d81e58	fix RFP example (#455 )	2024-10-21 09:13:24 -07:00
Sacha Bron	951ba4dfd8	Release is_formatting_instruction parameter (#446 ) * Release is_formatting_instruction parameter * Add annotate links	2024-10-17 12:29:05 +02:00
Adam Reichert	386d210e8b	CLI Testing Tool for Parsing Results to Standard Output (#363 )	2024-10-16 12:40:00 -06:00
Sacha Bron	9321602845	Add missing parameters (#441 )	2024-10-15 10:57:32 -06:00
Jerry Liu	26c06353f0	Add RFP Response generation workflow (#438 )	2024-10-14 08:45:04 -07:00
Jerry Liu	62cf12d6eb	add multimodal RAG pipeline with contextual retrieval (#429 )	2024-10-06 15:25:57 -07:00
Logan	253ee61463	improve error handling for jobs (#426 )	2024-10-02 18:57:46 -06:00
Sourabh Desai	2ccd2a9397	Update README.md to convey need to specify extra_info["file_name"] (#417 )	2024-09-29 17:07:12 -07:00
Jerry Liu	c139e8e3e6	fix excel notebook (#416 )	2024-09-24 17:11:33 -07:00
Ravi Theja	6e6e96c422	Update excel rag with o1 notebook (#415 )	2024-09-24 07:42:00 -07:00
Jerry Liu	b677e5226d	nit: move o1 excel notebook (#414 )	2024-09-23 10:51:51 -07:00
Ravi Theja	df723584b6	Compare Excel RAG with o1 models (#409 )	2024-09-23 10:42:47 -07:00
Sacha Bron	efe06ffff0	Bump to v0.5.6	2024-09-19 14:33:30 +02:00
Pierre-Loic Doulcet	6ba052d58f	add premium mode support (#406 )	2024-09-18 11:48:45 +02:00
Sacha Bron	8cf52058b5	Remove JSON from valid result types (#400 )	2024-09-18 11:48:22 +02:00
Jerry Liu	1bae09126c	fix multimodal RAG over slide deck (#402 )	2024-09-17 13:08:10 +08:00
Pierre-Loic Doulcet	bbbae9de9d	do not attach a filepath when a stram of bytes is passed (#394 )	2024-09-10 11:53:53 -06:00
Thiago Salvatore	7cb6d06316	Enable support for custom filesystem (#117 )	2024-09-10 10:39:46 -06:00
Jerry Liu	bca5492829	update README (#386 )	2024-09-09 17:31:58 -06:00
Sourabh Desai	f6a4d8681f	bump to 0.5.3 (#388 )	2024-09-09 11:06:58 -06:00