
Most common position in a pipeline	After PreProcessors in an indexing pipeline
Mandatory init variables	"prompt": The prompt to instruct the LLM on how to extract metadata from the document "generator_api": The API provider for the LLM. Currently supported providers are: `openai`, `openai_azure`, `aws_bedrock`, `google_vertex`.
Mandatory run variables	“documents”: A list of documents
Output variables	“documents”: A list of documents
API reference	Extractors
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/extractors/llm_metadata_extractor.py

Overview

The LLMMetadataExtractor extraction relies on an LLM and a prompt to perform the metadata extraction. At initialization time, it expects an LLM, a Haystack Generator, and a prompt describing the metadata extraction process.

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.

At runtime, it expects a list of documents and will run the LLM on each document in the list, extracting 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, it will be added to the failed_documents list. The failed documents' metadata will contain the keys metadata_extraction_error and metadata_extraction_response.

These documents can be re-run with another extractor to extract metadata using the metadata_extraction_response and metadata_extraction_error in the prompt.

The current implementation supports the following Haystack Generators:

Usage

Here's an example of using the LLMMetadataExtractor to extract named entities and add them to the document's metadata.

Let's define some documents:

docs = [
    Document(content="deepset was founded in 2018 in Berlin, and is known for its Haystack framework"),
    Document(content="Hugging Face is a company founded in New York, USA and is known for its Transformers library"
    )
]

And now, a prompt that extracts named entities from the documents:

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 {"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:
"""

Now, let's define a simple indexing pipeline that uses the LLMMetadataExtractor to extract named entities from the documents:


doc_store = InMemoryDocumentStore()
extractor = LLMMetadataExtractor(prompt=ner_prompt, expected_keys=["entities"]
generator_api=LLMProvider.OPENAI)
writer = DocumentWriter(document_store=doc_store)

pipeline = Pipeline()
pipeline.add_component("extractor", extractor)
pipeline.add_component("doc_writer", writer)
pipeline.connect("extractor.documents", "doc_writer.documents")
pipeline.run(data={"documents": docs})

doc_store_docs = doc_store.filter_documents()