16933015230

Committed 13 Aug 2025 09:18AM UTC coverage: 92.184% (+0.2%) from 91.969%

Build # 16933015230

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Pull #9699

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web-flow

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Merge cfbd602e7 into 8160ea8bf

Pull Request Pull Request #9699: feat: Update `source_id_meta_field` in `SentenceWindowRetriever` to also accept a list of values

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haystack/components/extractors/llm_metadata_extractor.py

# 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 dataclasses import replace
from typing import Any, 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 of the entity
    - 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]:
        parsed_metadata: 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):
            new_meta = {**document.meta}
            if "error" in result:
                new_meta["metadata_extraction_error"] = result["error"]
                new_meta["metadata_extraction_response"] = None
                failed_documents.append(replace(document, meta=new_meta))
                continue

            parsed_metadata = self._extract_metadata(result["replies"][0].text)
            if "error" in parsed_metadata:
                new_meta["metadata_extraction_error"] = parsed_metadata["error"]
                new_meta["metadata_extraction_response"] = result["replies"][0]
                failed_documents.append(replace(document, meta=new_meta))
                continue

            for key in parsed_metadata:
                new_meta[key] = parsed_metadata[key]
                # Remove metadata_extraction_error and metadata_extraction_response if present from previous runs
                new_meta.pop("metadata_extraction_error", None)
                new_meta.pop("metadata_extraction_response", None)
            successful_documents.append(replace(document, meta=new_meta))

        return {"documents": successful_documents, "failed_documents": failed_documents}

1	# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
2	#
3	# SPDX-License-Identifier: Apache-2.0
4
5	import copy	1✔
6	import json	1✔
7	from concurrent.futures import ThreadPoolExecutor	1✔
8	from dataclasses import replace	1✔
9	from typing import Any, Optional, Union	1✔
10
11	from jinja2 import meta	1✔
12	from jinja2.sandbox import SandboxedEnvironment	1✔
13
14	from haystack import Document, component, default_from_dict, default_to_dict, logging	1✔
15	from haystack.components.builders import PromptBuilder	1✔
16	from haystack.components.generators.chat.types import ChatGenerator	1✔
17	from haystack.components.preprocessors import DocumentSplitter	1✔
18	from haystack.core.serialization import component_to_dict	1✔
19	from haystack.dataclasses import ChatMessage	1✔
20	from haystack.utils import deserialize_chatgenerator_inplace, expand_page_range	1✔
21
22	logger = logging.getLogger(__name__)	1✔
23
24
25	@component	1✔
26	class LLMMetadataExtractor:	1✔
27	"""
28	Extracts metadata from documents using a Large Language Model (LLM).
29
30	The metadata is extracted by providing a prompt to an LLM that generates the metadata.
31
32	This component expects as input a list of documents and a prompt. The prompt should have a variable called
33	`document` that will point to a single document in the list of documents. So to access the content of the document,
34	you can use `{{ document.content }}` in the prompt.
35
36	The component will run the LLM on each document in the list and extract metadata from the document. The metadata
37	will be added to the document's metadata field. If the LLM fails to extract metadata from a document, the document
38	will be added to the `failed_documents` list. The failed documents will have the keys `metadata_extraction_error` and
39	`metadata_extraction_response` in their metadata. These documents can be re-run with another extractor to
40	extract metadata by using the `metadata_extraction_response` and `metadata_extraction_error` in the prompt.
41
42	```python
43	from haystack import Document
44	from haystack.components.extractors.llm_metadata_extractor import LLMMetadataExtractor
45	from haystack.components.generators.chat import OpenAIChatGenerator
46
47	NER_PROMPT = '''
48	-Goal-
49	Given text and a list of entity types, identify all entities of those types from the text.
50
51	-Steps-
52	1. Identify all entities. For each identified entity, extract the following information:
53	- entity: Name of the entity
54	- entity_type: One of the following types: [organization, product, service, industry]
55	Format each entity as a JSON like: {"entity": <entity_name>, "entity_type": <entity_type>}
56
57	2. Return output in a single list with all the entities identified in steps 1.
58
59	-Examples-
60	######################
61	Example 1:
62	entity_types: [organization, person, partnership, financial metric, product, service, industry, investment strategy, market trend]
63	text: Another area of strength is our co-brand issuance. Visa is the primary network partner for eight of the top
64	10 co-brand partnerships in the US today and we are pleased that Visa has finalized a multi-year extension of
65	our successful credit co-branded partnership with Alaska Airlines, a portfolio that benefits from a loyal customer
66	base and high cross-border usage.
67	We have also had significant co-brand momentum in CEMEA. First, we launched a new co-brand card in partnership
68	with Qatar Airways, British Airways and the National Bank of Kuwait. Second, we expanded our strong global
69	Marriott relationship to launch Qatar's first hospitality co-branded card with Qatar Islamic Bank. Across the
70	United Arab Emirates, we now have exclusive agreements with all the leading airlines marked by a recent
71	agreement with Emirates Skywards.
72	And we also signed an inaugural Airline co-brand agreement in Morocco with Royal Air Maroc. Now newer digital
73	issuers are equally
74	------------------------
75	output:
76	{"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"}]}
77	#############################
78	-Real Data-
79	######################
80	entity_types: [company, organization, person, country, product, service]
81	text: {{ document.content }}
82	######################
83	output:
84	'''
85
86	docs = [
87	Document(content="deepset was founded in 2018 in Berlin, and is known for its Haystack framework"),
88	Document(content="Hugging Face is a company that was founded in New York, USA and is known for its Transformers library")
89	]
90
91	chat_generator = OpenAIChatGenerator(
92	generation_kwargs={
93	"max_tokens": 500,
94	"temperature": 0.0,
95	"seed": 0,
96	"response_format": {"type": "json_object"},
97	},
98	max_retries=1,
99	timeout=60.0,
100	)
101
102	extractor = LLMMetadataExtractor(
103	prompt=NER_PROMPT,
104	chat_generator=generator,
105	expected_keys=["entities"],
106	raise_on_failure=False,
107	)
108
109	extractor.warm_up()
110	extractor.run(documents=docs)
111	>> {'documents': [
112	Document(id=.., content: 'deepset was founded in 2018 in Berlin, and is known for its Haystack framework',
113	meta: {'entities': [{'entity': 'deepset', 'entity_type': 'company'}, {'entity': 'Berlin', 'entity_type': 'city'},
114	{'entity': 'Haystack', 'entity_type': 'product'}]}),
115	Document(id=.., content: 'Hugging Face is a company that was founded in New York, USA and is known for its Transformers library',
116	meta: {'entities': [
117	{'entity': 'Hugging Face', 'entity_type': 'company'}, {'entity': 'New York', 'entity_type': 'city'},
118	{'entity': 'USA', 'entity_type': 'country'}, {'entity': 'Transformers', 'entity_type': 'product'}
119	]})
120	]
121	'failed_documents': []
122	}
123	>>
124	```
125	""" # noqa: E501
126
127	def __init__( # pylint: disable=R0917	1✔
128	self,
129	prompt: str,
130	chat_generator: ChatGenerator,
131	expected_keys: Optional[list[str]] = None,
132	page_range: Optional[list[Union[str, int]]] = None,
133	raise_on_failure: bool = False,
134	max_workers: int = 3,
135	):
136	"""
137	Initializes the LLMMetadataExtractor.
138
139	:param prompt: The prompt to be used for the LLM.
140	:param chat_generator: a ChatGenerator instance which represents the LLM. In order for the component to work,
141	the LLM should be configured to return a JSON object. For example, when using the OpenAIChatGenerator, you
142	should pass `{"response_format": {"type": "json_object"}}` in the `generation_kwargs`.
143	:param expected_keys: The keys expected in the JSON output from the LLM.
144	:param page_range: A range of pages to extract metadata from. For example, page_range=['1', '3'] will extract
145	metadata from the first and third pages of each document. It also accepts printable range strings, e.g.:
146	['1-3', '5', '8', '10-12'] will extract metadata from pages 1, 2, 3, 5, 8, 10,11, 12.
147	If None, metadata will be extracted from the entire document for each document in the documents list.
148	This parameter is optional and can be overridden in the `run` method.
149	:param raise_on_failure: Whether to raise an error on failure during the execution of the Generator or
150	validation of the JSON output.
151	:param max_workers: The maximum number of workers to use in the thread pool executor.
152	"""
153	self.prompt = prompt	1✔
154	ast = SandboxedEnvironment().parse(prompt)	1✔
155	template_variables = meta.find_undeclared_variables(ast)	1✔
156	variables = list(template_variables)	1✔
157	if len(variables) > 1 or variables[0] != "document":	1✔
158	raise ValueError(	1✔
159	f"Prompt must have exactly one variable called 'document'. Found {','.join(variables)} in the prompt."
160	)
161	self.builder = PromptBuilder(prompt, required_variables=variables)	1✔
162	self.raise_on_failure = raise_on_failure	1✔
163	self.expected_keys = expected_keys or []	1✔
164	self.splitter = DocumentSplitter(split_by="page", split_length=1)	1✔
165	self.expanded_range = expand_page_range(page_range) if page_range else None	1✔
166	self.max_workers = max_workers	1✔
167	self._chat_generator = chat_generator	1✔
168
169	def warm_up(self):	1✔
170	"""
171	Warm up the LLM provider component.
172	"""
173	if hasattr(self._chat_generator, "warm_up"):	1✔
174	self._chat_generator.warm_up()	1✔
175
176	def to_dict(self) -> dict[str, Any]:	1✔
177	"""
178	Serializes the component to a dictionary.
179
180	:returns:
181	Dictionary with serialized data.
182	"""
183
184	return default_to_dict(	1✔
185	self,
186	prompt=self.prompt,
187	chat_generator=component_to_dict(obj=self._chat_generator, name="chat_generator"),
188	expected_keys=self.expected_keys,
189	page_range=self.expanded_range,
190	raise_on_failure=self.raise_on_failure,
191	max_workers=self.max_workers,
192	)
193
194	@classmethod	1✔
195	def from_dict(cls, data: dict[str, Any]) -> "LLMMetadataExtractor":	1✔
196	"""
197	Deserializes the component from a dictionary.
198
199	:param data:
200	Dictionary with serialized data.
201	:returns:
202	An instance of the component.
203	"""
204
205	deserialize_chatgenerator_inplace(data["init_parameters"], key="chat_generator")	1✔
206	return default_from_dict(cls, data)	1✔
207
208	def _extract_metadata(self, llm_answer: str) -> dict[str, Any]:	1✔
209	parsed_metadata: dict[str, Any] = {}	1✔
210
211	try:	1✔
212	parsed_metadata = json.loads(llm_answer)	1✔
213	except json.JSONDecodeError as e:	1✔
214	logger.warning(	1✔
215	"Response from the LLM is not valid JSON. Skipping metadata extraction. Received output: {response}",
216	response=llm_answer,
217	)
218	if self.raise_on_failure:	1✔
219	raise e	1✔
220	return {"error": "Response is not valid JSON. Received JSONDecodeError: " + str(e)}	1✔
221
222	if not all(key in parsed_metadata for key in self.expected_keys):	1✔
223	logger.warning(	1✔
224	"Expected response from LLM to be a JSON with keys {expected_keys}, got {parsed_json}. "
225	"Continuing extraction with received output.",
226	expected_keys=self.expected_keys,
227	parsed_json=parsed_metadata,
228	)
229
230	return parsed_metadata	1✔
231
232	def _prepare_prompts(	1✔
233	self, documents: list[Document], expanded_range: Optional[list[int]] = None
234	) -> list[Union[ChatMessage, None]]:
235	all_prompts: list[Union[ChatMessage, None]] = []	1✔
236	for document in documents:	1✔
237	if not document.content:	1✔
238	logger.warning("Document {doc_id} has no content. Skipping metadata extraction.", doc_id=document.id)	1✔
239	all_prompts.append(None)	1✔
240	continue	1✔
241
242	if expanded_range:	1✔
243	doc_copy = copy.deepcopy(document)	1✔
244	pages = self.splitter.run(documents=[doc_copy])	1✔
245	content = ""	1✔
246	for idx, page in enumerate(pages["documents"]):	1✔
247	if idx + 1 in expanded_range:	1✔
248	content += page.content	1✔
249	doc_copy.content = content	1✔
250	else:
251	doc_copy = document	1✔
252
253	prompt_with_doc = self.builder.run(template=self.prompt, template_variables={"document": doc_copy})	1✔
254
255	# build a ChatMessage with the prompt
256	message = ChatMessage.from_user(prompt_with_doc["prompt"])	1✔
257	all_prompts.append(message)	1✔
258
259	return all_prompts	1✔
260
261	def _run_on_thread(self, prompt: Optional[ChatMessage]) -> dict[str, Any]:	1✔
262	# If prompt is None, return an error dictionary
263	if prompt is None:	1✔
264	return {"error": "Document has no content, skipping LLM call."}	1✔
265
266	try:	×
267	result = self._chat_generator.run(messages=[prompt])	×
268	except Exception as e:	×
269	if self.raise_on_failure:	×
270	raise e	×
271	logger.error(	×
272	"LLM {class_name} execution failed. Skipping metadata extraction. Failed with exception '{error}'.",
273	class_name=self._chat_generator.__class__.__name__,
274	error=e,
275	)
276	result = {"error": "LLM failed with exception: " + str(e)}	×
277	return result	×
278
279	@component.output_types(documents=list[Document], failed_documents=list[Document])	1✔
280	def run(self, documents: list[Document], page_range: Optional[list[Union[str, int]]] = None):	1✔
281	"""
282	Extract metadata from documents using a Large Language Model.
283
284	If `page_range` is provided, the metadata will be extracted from the specified range of pages. This component
285	will split the documents into pages and extract metadata from the specified range of pages. The metadata will be
286	extracted from the entire document if `page_range` is not provided.
287
288	The original documents will be returned updated with the extracted metadata.
289
290	:param documents: List of documents to extract metadata from.
291	:param page_range: A range of pages to extract metadata from. For example, page_range=['1', '3'] will extract
292	metadata from the first and third pages of each document. It also accepts printable range
293	strings, e.g.: ['1-3', '5', '8', '10-12'] will extract metadata from pages 1, 2, 3, 5, 8, 10,
294	11, 12.
295	If None, metadata will be extracted from the entire document for each document in the
296	documents list.
297	:returns:
298	A dictionary with the keys:
299	- "documents": A list of documents that were successfully updated with the extracted metadata.
300	- "failed_documents": A list of documents that failed to extract metadata. These documents will have
301	"metadata_extraction_error" and "metadata_extraction_response" in their metadata. These documents can be
302	re-run with the extractor to extract metadata.
303	"""
304	if len(documents) == 0:	1✔
305	logger.warning("No documents provided. Skipping metadata extraction.")	1✔
306	return {"documents": [], "failed_documents": []}	1✔
307
308	expanded_range = self.expanded_range	1✔
309	if page_range:	1✔
310	expanded_range = expand_page_range(page_range)	×
311
312	# Create ChatMessage prompts for each document
313	all_prompts = self._prepare_prompts(documents=documents, expanded_range=expanded_range)	1✔
314
315	# Run the LLM on each prompt
316	with ThreadPoolExecutor(max_workers=self.max_workers) as executor:	1✔
317	results = executor.map(self._run_on_thread, all_prompts)	1✔
318
319	successful_documents = []	1✔
320	failed_documents = []	1✔
321	for document, result in zip(documents, results):	1✔
322	new_meta = {**document.meta}	1✔
323	if "error" in result:	1✔
324	new_meta["metadata_extraction_error"] = result["error"]	1✔
325	new_meta["metadata_extraction_response"] = None	1✔
326	failed_documents.append(replace(document, meta=new_meta))	1✔
327	continue	1✔
328
329	parsed_metadata = self._extract_metadata(result["replies"][0].text)	×
330	if "error" in parsed_metadata:	×
331	new_meta["metadata_extraction_error"] = parsed_metadata["error"]	×
332	new_meta["metadata_extraction_response"] = result["replies"][0]	×
333	failed_documents.append(replace(document, meta=new_meta))	×
334	continue	×
335
336	for key in parsed_metadata:	×
337	new_meta[key] = parsed_metadata[key]	×
338	# Remove metadata_extraction_error and metadata_extraction_response if present from previous runs
339	new_meta.pop("metadata_extraction_error", None)	×
340	new_meta.pop("metadata_extraction_response", None)	×
341	successful_documents.append(replace(document, meta=new_meta))	×
342
343	return {"documents": successful_documents, "failed_documents": failed_documents}	1✔

deepset-ai / haystack / 16933015230

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