i 've been trying to read pdfs, docx,

At a glance

i 've been trying to read pdfs, docx, directories but am still getting this error:

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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("/Users/home/Library/Mobile Documents/com~apple~CloudDocs/Academia/Legal Research").load_data()
index = VectorStoreIndex.from_documents(documents)

---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
/Users/home/Downloads/OpenAI_Finetuning_Distill_GPT_4_to_GPT_3_5_(v2).ipynb Cell 13 line 1
----> 1 from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
      3 documents = SimpleDirectoryReader("/Users/home/Library/Mobile Documents/com~apple~CloudDocs/Academia/Legal Research").load_data()
      4 index = VectorStoreIndex.from_documents(documents)

File ~/.ooba/text-generation-ui/installer_files/env/lib/python3.10/site-packages/llama_index/__init__.py:21
     17 from llama_index.embeddings import OpenAIEmbedding
     19 # indices
     20 # loading
---> 21 from llama_index.indices import (
     22     ComposableGraph,
     23     DocumentSummaryIndex,
     24     GPTDocumentSummaryIndex,
     25     GPTKeywordTableIndex,
     26     GPTKnowledgeGraphIndex,
     27     GPTListIndex,
     28     GPTRAKEKeywordTableIndex,
     29     GPTSimpleKeywordTableIndex,
     30     GPTTreeIndex,
     31     GPTVectorStoreIndex,
     32     KeywordTableIndex,
     33     KnowledgeGraphIndex,
     34     ListIndex,
...
File ~/.ooba/text-generation-ui/installer_files/env/lib/python3.10/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()

ValidationError: 1 validation error for DataSource

i 've installed all required packages i think with

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pip install llama-index-core llama-index-readers-file llama-index-llms-ollama llama-index-embeddings-huggingface

19 comments

LLogan M

Works in colab
https://colab.research.google.com/drive/1X9lEwoZqoSeTcJGxBZLq3pBiw3taxvSi?usp=sharing

Maybe start with a fresh venv?

MMitchMcD

this is a scannable pdf, unstructured should handle it well, does "SimpleDirectory" include unstructured?

LLogan M

not by default, but you can use

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from llama_index.readers.file import UnstructuredReader

file_extractor = {".pdf": UnstructuredReader()}

documents = SimpleDirectoryReader("./data", file_extractor=file_extractor).load_data()

MMitchMcD

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from llama_index.readers.file import UnstructuredReader

file_extractor = {".pdf": UnstructuredReader()}

documents = SimpleDirectoryReader("./data", file_extractor=file_extractor).load_data()

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ImportError                               Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/llama_index/core/readers/file/base.py in load_file(input_file, file_metadata, file_extractor, filename_as_id, encoding, errors)
    324                 # ensure that ImportError is raised so user knows
    325                 # about missing dependencies
--> 326                 raise ImportError(str(e))
    327             except Exception as e:
    328                 # otherwise, just skip the file and report the error

ImportError: No module named 'unstructured'

@Logan M do i need to install it separately?

LLogan M

hmmm I think so (I thought it was in the package deps, I guess not)

MMitchMcD

apparently , the unstructured that is in the llama package cannot decypher this file, but it worked well when i used their pay as you go Saas api

MMitchMcD

the question now how to convert it to the format that llama uses to create an index

LLogan M

I'm guessing their paid offering is more capable than open-source 😅

LLogan M

the output of the reader should be document objects right?

LLogan M

VectorStoreIndex.from_documents(documents)

MMitchMcD

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{'type': 'ListItem',
  'element_id': '685c346992da8cb638277234e18455dc',
  'text': '3. Analysis and application of the rule of law to the facts of the case. This step is composed of three parts:',
  'metadata': {'filetype': 'application/pdf',
   'languages': ['eng'],
   'page_number': 48,
   'parent_id': '1f5eb66d4519bd762414d483640a7cd1',
   'filename': 'Legal Research_Part 1.pdf'}},
 {'type': 'NarrativeText',
  'element_id': '269a00dfa7fbab67e141c6c5600e8440',
  'text': 'a. A determination of the elements or requirements of the rule of law b. A matching of the facts of the client’s case to the elements and a determi- nation of how the rule of law applies to the facts ¢. A counteranalysis that addresses any counterarguments to the analysis 4. A conclusion that summarizes the previous steps. The conclusion may also include a weighing of the merits of the case and an identification of other information or avenues of research that should be pursued.',
  'metadata': {'filetype': 'application/pdf',
   'languages': ['eng'],
   'page_number': 48,
... 
 50,
   'parent_id': '8aebadf99302a64184ebba5341df7d89',
   'filename': 'Legal Research_Part 1.pdf'}}]

MMitchMcD

json?

MMitchMcD

@Logan M how do i turn it into the format that could be processed and indexed by llama ?

LLogan M

Oh you are using raw unstructured, and not the unstructured reader?

Turn it into a document object

LLogan M

from llama_index.core import Document

doc = Document(text=text, metadata=metadata)

MMitchMcD

thank you. will i miss then the chapters and subchapters partitions then?

LLogan M

Not if you do that for every partition?