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haystack/haystack/components/extractors/llm_metadata_extractor.py
Seth Peters f025501792
fix: LLMMetadataExtractor bug in handling Document objects with no content 
* test(extractors): Add unit test for LLMMetadataExtractor with no content

Adds a new unit test `test_run_with_document_content_none` to `TestLLMMetadataExtractor`.

This test verifies that `LLMMetadataExtractor` correctly handles documents where `document.content` is None or an empty string.

It ensures that:

- Such documents are added to the `failed_documents` list.

- The correct error message ("Document has no content, skipping LLM call.") is present in their metadata.

- No actual LLM call is attempted for these documents.

This test provides coverage for the fix that prevents an AttributeError when processing documents with no content.

* chore: update comment to reflect new behavior in _run_on_thread method

* docs: Add release note for LLMMetadataExtractor no content fix

* Update releasenotes/notes/fix-llm-metadata-extractor-no-content-910067ea72094f18.yaml

* Update fix-llm-metadata-extractor-no-content-910067ea72094f18.yaml

---------

Co-authored-by: David S. Batista <dsbatista@gmail.com>
2025-05-23 18:57:39 +02:00

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# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0
import copy
import json
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Dict, List, Optional, Union
from jinja2 import meta
from jinja2.sandbox import SandboxedEnvironment
from haystack import Document, component, default_from_dict, default_to_dict, logging
from haystack.components.builders import PromptBuilder
from haystack.components.generators.chat.types import ChatGenerator
from haystack.components.preprocessors import DocumentSplitter
from haystack.core.serialization import component_to_dict
from haystack.dataclasses import ChatMessage
from haystack.utils import deserialize_chatgenerator_inplace, expand_page_range
logger = logging.getLogger(__name__)
@component
class LLMMetadataExtractor:
"""
Extracts metadata from documents using a Large Language Model (LLM).
The metadata is extracted by providing a prompt to an LLM that generates the metadata.
This component expects as input a list of documents and a prompt. The prompt should have a variable called
`document` that will point to a single document in the list of documents. So to access the content of the document,
you can use `{{ document.content }}` in the prompt.
The component will run the LLM on each document in the list and extract metadata from the document. The metadata
will be added to the document's metadata field. If the LLM fails to extract metadata from a document, the document
will be added to the `failed_documents` list. The failed documents will have the keys `metadata_extraction_error` and
`metadata_extraction_response` in their metadata. These documents can be re-run with another extractor to
extract metadata by using the `metadata_extraction_response` and `metadata_extraction_error` in the prompt.
```python
from haystack import Document
from haystack.components.extractors.llm_metadata_extractor import LLMMetadataExtractor
from haystack.components.generators.chat import OpenAIChatGenerator
NER_PROMPT = '''
-Goal-
Given text and a list of entity types, identify all entities of those types from the text.
-Steps-
1. Identify all entities. For each identified entity, extract the following information:
- entity_name: Name of the entity, capitalized
- entity_type: One of the following types: [organization, product, service, industry]
Format each entity as a JSON like: {"entity": <entity_name>, "entity_type": <entity_type>}
2. Return output in a single list with all the entities identified in steps 1.
-Examples-
######################
Example 1:
entity_types: [organization, person, partnership, financial metric, product, service, industry, investment strategy, market trend]
text: Another area of strength is our co-brand issuance. Visa is the primary network partner for eight of the top
10 co-brand partnerships in the US today and we are pleased that Visa has finalized a multi-year extension of
our successful credit co-branded partnership with Alaska Airlines, a portfolio that benefits from a loyal customer
base and high cross-border usage.
We have also had significant co-brand momentum in CEMEA. First, we launched a new co-brand card in partnership
with Qatar Airways, British Airways and the National Bank of Kuwait. Second, we expanded our strong global
Marriott relationship to launch Qatar's first hospitality co-branded card with Qatar Islamic Bank. Across the
United Arab Emirates, we now have exclusive agreements with all the leading airlines marked by a recent
agreement with Emirates Skywards.
And we also signed an inaugural Airline co-brand agreement in Morocco with Royal Air Maroc. Now newer digital
issuers are equally
------------------------
output:
{"entities": [{"entity": "Visa", "entity_type": "company"}, {"entity": "Alaska Airlines", "entity_type": "company"}, {"entity": "Qatar Airways", "entity_type": "company"}, {"entity": "British Airways", "entity_type": "company"}, {"entity": "National Bank of Kuwait", "entity_type": "company"}, {"entity": "Marriott", "entity_type": "company"}, {"entity": "Qatar Islamic Bank", "entity_type": "company"}, {"entity": "Emirates Skywards", "entity_type": "company"}, {"entity": "Royal Air Maroc", "entity_type": "company"}]}
#############################
-Real Data-
######################
entity_types: [company, organization, person, country, product, service]
text: {{ document.content }}
######################
output:
'''
docs = [
Document(content="deepset was founded in 2018 in Berlin, and is known for its Haystack framework"),
Document(content="Hugging Face is a company that was founded in New York, USA and is known for its Transformers library")
]
chat_generator = OpenAIChatGenerator(
generation_kwargs={
"max_tokens": 500,
"temperature": 0.0,
"seed": 0,
"response_format": {"type": "json_object"},
},
max_retries=1,
timeout=60.0,
)
extractor = LLMMetadataExtractor(
prompt=NER_PROMPT,
chat_generator=generator,
expected_keys=["entities"],
raise_on_failure=False,
)
extractor.warm_up()
extractor.run(documents=docs)
>> {'documents': [
Document(id=.., content: 'deepset was founded in 2018 in Berlin, and is known for its Haystack framework',
meta: {'entities': [{'entity': 'deepset', 'entity_type': 'company'}, {'entity': 'Berlin', 'entity_type': 'city'},
{'entity': 'Haystack', 'entity_type': 'product'}]}),
Document(id=.., content: 'Hugging Face is a company that was founded in New York, USA and is known for its Transformers library',
meta: {'entities': [
{'entity': 'Hugging Face', 'entity_type': 'company'}, {'entity': 'New York', 'entity_type': 'city'},
{'entity': 'USA', 'entity_type': 'country'}, {'entity': 'Transformers', 'entity_type': 'product'}
]})
]
'failed_documents': []
}
>>
```
""" # noqa: E501
def __init__( # pylint: disable=R0917
self,
prompt: str,
chat_generator: ChatGenerator,
expected_keys: Optional[List[str]] = None,
page_range: Optional[List[Union[str, int]]] = None,
raise_on_failure: bool = False,
max_workers: int = 3,
):
"""
Initializes the LLMMetadataExtractor.
:param prompt: The prompt to be used for the LLM.
:param chat_generator: a ChatGenerator instance which represents the LLM. In order for the component to work,
the LLM should be configured to return a JSON object. For example, when using the OpenAIChatGenerator, you
should pass `{"response_format": {"type": "json_object"}}` in the `generation_kwargs`.
:param expected_keys: The keys expected in the JSON output from the LLM.
:param page_range: A range of pages to extract metadata from. For example, page_range=['1', '3'] will extract
metadata from the first and third pages of each document. It also accepts printable range strings, e.g.:
['1-3', '5', '8', '10-12'] will extract metadata from pages 1, 2, 3, 5, 8, 10,11, 12.
If None, metadata will be extracted from the entire document for each document in the documents list.
This parameter is optional and can be overridden in the `run` method.
:param raise_on_failure: Whether to raise an error on failure during the execution of the Generator or
validation of the JSON output.
:param max_workers: The maximum number of workers to use in the thread pool executor.
"""
self.prompt = prompt
ast = SandboxedEnvironment().parse(prompt)
template_variables = meta.find_undeclared_variables(ast)
variables = list(template_variables)
if len(variables) > 1 or variables[0] != "document":
raise ValueError(
f"Prompt must have exactly one variable called 'document'. Found {','.join(variables)} in the prompt."
)
self.builder = PromptBuilder(prompt, required_variables=variables)
self.raise_on_failure = raise_on_failure
self.expected_keys = expected_keys or []
self.splitter = DocumentSplitter(split_by="page", split_length=1)
self.expanded_range = expand_page_range(page_range) if page_range else None
self.max_workers = max_workers
self._chat_generator = chat_generator
def warm_up(self):
"""
Warm up the LLM provider component.
"""
if hasattr(self._chat_generator, "warm_up"):
self._chat_generator.warm_up()
def to_dict(self) -> Dict[str, Any]:
"""
Serializes the component to a dictionary.
:returns:
Dictionary with serialized data.
"""
return default_to_dict(
self,
prompt=self.prompt,
chat_generator=component_to_dict(obj=self._chat_generator, name="chat_generator"),
expected_keys=self.expected_keys,
page_range=self.expanded_range,
raise_on_failure=self.raise_on_failure,
max_workers=self.max_workers,
)
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "LLMMetadataExtractor":
"""
Deserializes the component from a dictionary.
:param data:
Dictionary with serialized data.
:returns:
An instance of the component.
"""
deserialize_chatgenerator_inplace(data["init_parameters"], key="chat_generator")
return default_from_dict(cls, data)
def _extract_metadata(self, llm_answer: str) -> Dict[str, Any]:
try:
parsed_metadata = json.loads(llm_answer)
except json.JSONDecodeError as e:
logger.warning(
"Response from the LLM is not valid JSON. Skipping metadata extraction. Received output: {response}",
response=llm_answer,
)
if self.raise_on_failure:
raise e
return {"error": "Response is not valid JSON. Received JSONDecodeError: " + str(e)}
if not all(key in parsed_metadata for key in self.expected_keys):
logger.warning(
"Expected response from LLM to be a JSON with keys {expected_keys}, got {parsed_json}. "
"Continuing extraction with received output.",
expected_keys=self.expected_keys,
parsed_json=parsed_metadata,
)
return parsed_metadata
def _prepare_prompts(
self, documents: List[Document], expanded_range: Optional[List[int]] = None
) -> List[Union[ChatMessage, None]]:
all_prompts: List[Union[ChatMessage, None]] = []
for document in documents:
if not document.content:
logger.warning("Document {doc_id} has no content. Skipping metadata extraction.", doc_id=document.id)
all_prompts.append(None)
continue
if expanded_range:
doc_copy = copy.deepcopy(document)
pages = self.splitter.run(documents=[doc_copy])
content = ""
for idx, page in enumerate(pages["documents"]):
if idx + 1 in expanded_range:
content += page.content
doc_copy.content = content
else:
doc_copy = document
prompt_with_doc = self.builder.run(template=self.prompt, template_variables={"document": doc_copy})
# build a ChatMessage with the prompt
message = ChatMessage.from_user(prompt_with_doc["prompt"])
all_prompts.append(message)
return all_prompts
def _run_on_thread(self, prompt: Optional[ChatMessage]) -> Dict[str, Any]:
# If prompt is None, return an error dictionary
if prompt is None:
return {"error": "Document has no content, skipping LLM call."}
try:
result = self._chat_generator.run(messages=[prompt])
except Exception as e:
if self.raise_on_failure:
raise e
logger.error(
"LLM {class_name} execution failed. Skipping metadata extraction. Failed with exception '{error}'.",
class_name=self._chat_generator.__class__.__name__,
error=e,
)
result = {"error": "LLM failed with exception: " + str(e)}
return result
@component.output_types(documents=List[Document], failed_documents=List[Document])
def run(self, documents: List[Document], page_range: Optional[List[Union[str, int]]] = None):
"""
Extract metadata from documents using a Large Language Model.
If `page_range` is provided, the metadata will be extracted from the specified range of pages. This component
will split the documents into pages and extract metadata from the specified range of pages. The metadata will be
extracted from the entire document if `page_range` is not provided.
The original documents will be returned updated with the extracted metadata.
:param documents: List of documents to extract metadata from.
:param page_range: A range of pages to extract metadata from. For example, page_range=['1', '3'] will extract
metadata from the first and third pages of each document. It also accepts printable range
strings, e.g.: ['1-3', '5', '8', '10-12'] will extract metadata from pages 1, 2, 3, 5, 8, 10,
11, 12.
If None, metadata will be extracted from the entire document for each document in the
documents list.
:returns:
A dictionary with the keys:
- "documents": A list of documents that were successfully updated with the extracted metadata.
- "failed_documents": A list of documents that failed to extract metadata. These documents will have
"metadata_extraction_error" and "metadata_extraction_response" in their metadata. These documents can be
re-run with the extractor to extract metadata.
"""
if len(documents) == 0:
logger.warning("No documents provided. Skipping metadata extraction.")
return {"documents": [], "failed_documents": []}
expanded_range = self.expanded_range
if page_range:
expanded_range = expand_page_range(page_range)
# Create ChatMessage prompts for each document
all_prompts = self._prepare_prompts(documents=documents, expanded_range=expanded_range)
# Run the LLM on each prompt
with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
results = executor.map(self._run_on_thread, all_prompts)
successful_documents = []
failed_documents = []
for document, result in zip(documents, results):
if "error" in result:
document.meta["metadata_extraction_error"] = result["error"]
document.meta["metadata_extraction_response"] = None
failed_documents.append(document)
continue
parsed_metadata = self._extract_metadata(result["replies"][0].text)
if "error" in parsed_metadata:
document.meta["metadata_extraction_error"] = parsed_metadata["error"]
document.meta["metadata_extraction_response"] = result["replies"][0]
failed_documents.append(document)
continue
for key in parsed_metadata:
document.meta[key] = parsed_metadata[key]
# Remove metadata_extraction_error and metadata_extraction_response if present from previous runs
document.meta.pop("metadata_extraction_error", None)
document.meta.pop("metadata_extraction_response", None)
successful_documents.append(document)
return {"documents": successful_documents, "failed_documents": failed_documents}
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