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Add api md (#631)

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@ -12,7 +12,7 @@ class ElasticsearchDocumentStore(BaseDocumentStore)
#### \_\_init\_\_
```python
| __init__(host: str = "localhost", port: int = 9200, username: str = "", password: str = "", index: str = "document", label_index: str = "label", search_fields: Union[str, list] = "text", text_field: str = "text", name_field: str = "name", embedding_field: str = "embedding", embedding_dim: int = 768, custom_mapping: Optional[dict] = None, excluded_meta_data: Optional[list] = None, faq_question_field: Optional[str] = None, analyzer: str = "standard", scheme: str = "http", ca_certs: bool = False, verify_certs: bool = True, create_index: bool = True, update_existing_documents: bool = False, refresh_type: str = "wait_for", similarity="dot_product", timeout=30, return_embedding: Optional[bool] = True)
| __init__(host: str = "localhost", port: int = 9200, username: str = "", password: str = "", index: str = "document", label_index: str = "label", search_fields: Union[str, list] = "text", text_field: str = "text", name_field: str = "name", embedding_field: str = "embedding", embedding_dim: int = 768, custom_mapping: Optional[dict] = None, excluded_meta_data: Optional[list] = None, faq_question_field: Optional[str] = None, analyzer: str = "standard", scheme: str = "http", ca_certs: bool = False, verify_certs: bool = True, create_index: bool = True, update_existing_documents: bool = False, refresh_type: str = "wait_for", similarity="dot_product", timeout=30, return_embedding: bool = False)
```
A DocumentStore using Elasticsearch to store and query the documents for our search.
@ -49,20 +49,36 @@ documents. When set as True, any document with an existing ID gets updated.
If set to False, an error is raised if the document ID of the document being
added already exists.
- `refresh_type`: Type of ES refresh used to control when changes made by a request (e.g. bulk) are made visible to search.
Values:
- 'wait_for' => continue only after changes are visible (slow, but safe)
- 'false' => continue directly (fast, but sometimes unintuitive behaviour when docs are not immediately available after ingestion)
If set to 'wait_for', continue only after changes are visible (slow, but safe).
If set to 'false', continue directly (fast, but sometimes unintuitive behaviour when docs are not immediately available after ingestion).
More info at https://www.elastic.co/guide/en/elasticsearch/reference/6.8/docs-refresh.html
- `similarity`: The similarity function used to compare document vectors. 'dot_product' is the default sine it is
more performant with DPR embeddings. 'cosine' is recommended if you are using a Sentence BERT model.
- `timeout`: Number of seconds after which an ElasticSearch request times out.
- `return_embedding`: To return document embedding
<a name="elasticsearch.ElasticsearchDocumentStore.get_document_by_id"></a>
#### get\_document\_by\_id
```python
| get_document_by_id(id: str, index=None) -> Optional[Document]
```
Fetch a document by specifying its text id string
<a name="elasticsearch.ElasticsearchDocumentStore.get_documents_by_id"></a>
#### get\_documents\_by\_id
```python
| get_documents_by_id(ids: List[str], index=None) -> List[Document]
```
Fetch documents by specifying a list of text id strings
<a name="elasticsearch.ElasticsearchDocumentStore.write_documents"></a>
#### write\_documents
```python
| @abstractmethod
| write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
```
@ -90,6 +106,125 @@ should be changed to what you have set for self.text_field and self.name_field.
None
<a name="elasticsearch.ElasticsearchDocumentStore.write_labels"></a>
#### write\_labels
```python
| write_labels(labels: Union[List[Label], List[dict]], index: Optional[str] = None)
```
Write annotation labels into document store.
<a name="elasticsearch.ElasticsearchDocumentStore.update_document_meta"></a>
#### update\_document\_meta
```python
| update_document_meta(id: str, meta: Dict[str, str])
```
Update the metadata dictionary of a document by specifying its string id
<a name="elasticsearch.ElasticsearchDocumentStore.get_document_count"></a>
#### get\_document\_count
```python
| get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
```
Return the number of documents in the document store.
<a name="elasticsearch.ElasticsearchDocumentStore.get_label_count"></a>
#### get\_label\_count
```python
| get_label_count(index: Optional[str] = None) -> int
```
Return the number of labels in the document store
<a name="elasticsearch.ElasticsearchDocumentStore.get_all_documents"></a>
#### get\_all\_documents
```python
| get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
```
Get documents from the document store.
**Arguments**:
- `index`: Name of the index to get the documents from. If None, the
DocumentStore's default index (self.index) will be used.
- `filters`: Optional filters to narrow down the documents to return.
Example: {"name": ["some", "more"], "category": ["only_one"]}
- `return_embedding`: Whether to return the document embeddings.
<a name="elasticsearch.ElasticsearchDocumentStore.get_all_labels"></a>
#### get\_all\_labels
```python
| get_all_labels(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None) -> List[Label]
```
Return all labels in the document store
<a name="elasticsearch.ElasticsearchDocumentStore.get_all_documents_in_index"></a>
#### get\_all\_documents\_in\_index
```python
| get_all_documents_in_index(index: str, filters: Optional[Dict[str, List[str]]] = None) -> List[dict]
```
Return all documents in a specific index in the document store
<a name="elasticsearch.ElasticsearchDocumentStore.query"></a>
#### query
```python
| query(query: Optional[str], filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, custom_query: Optional[str] = None, index: Optional[str] = None) -> List[Document]
```
Scan through documents in DocumentStore and return a small number documents
that are most relevant to the query as defined by the BM25 algorithm.
**Arguments**:
- `query`: The query
- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
- `top_k`: How many documents to return per query.
- `index`: The name of the index in the DocumentStore from which to retrieve documents
<a name="elasticsearch.ElasticsearchDocumentStore.query_by_embedding"></a>
#### query\_by\_embedding
```python
| query_by_embedding(query_emb: np.array, filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
```
Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
**Arguments**:
- `query_emb`: Embedding of the query (e.g. gathered from DPR)
- `filters`: Optional filters to narrow down the search space.
Example: {"name": ["some", "more"], "category": ["only_one"]}
- `top_k`: How many documents to return
- `index`: Index name for storing the docs and metadata
- `return_embedding`: To return document embedding
**Returns**:
<a name="elasticsearch.ElasticsearchDocumentStore.describe_documents"></a>
#### describe\_documents
```python
| describe_documents(index=None)
```
Return a summary of the documents in the document store
<a name="elasticsearch.ElasticsearchDocumentStore.update_embeddings"></a>
#### update\_embeddings
@ -181,6 +316,55 @@ separate index than the documents for search.
None
<a name="memory.InMemoryDocumentStore.write_labels"></a>
#### write\_labels
```python
| write_labels(labels: Union[List[dict], List[Label]], index: Optional[str] = None)
```
Write annotation labels into document store.
<a name="memory.InMemoryDocumentStore.get_document_by_id"></a>
#### get\_document\_by\_id
```python
| get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
```
Fetch a document by specifying its text id string
<a name="memory.InMemoryDocumentStore.get_documents_by_id"></a>
#### get\_documents\_by\_id
```python
| get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
```
Fetch documents by specifying a list of text id strings
<a name="memory.InMemoryDocumentStore.query_by_embedding"></a>
#### query\_by\_embedding
```python
| query_by_embedding(query_emb: List[float], filters: Optional[Dict[str, List[str]]] = None, top_k: int = 10, index: Optional[str] = None, return_embedding: Optional[bool] = None) -> List[Document]
```
Find the document that is most similar to the provided `query_emb` by using a vector similarity metric.
**Arguments**:
- `query_emb`: Embedding of the query (e.g. gathered from DPR)
- `filters`: Optional filters to narrow down the search space.
Example: {"name": ["some", "more"], "category": ["only_one"]}
- `top_k`: How many documents to return
- `index`: Index name for storing the docs and metadata
- `return_embedding`: To return document embedding
**Returns**:
<a name="memory.InMemoryDocumentStore.update_embeddings"></a>
#### update\_embeddings
@ -200,6 +384,50 @@ This can be useful if want to add or change the embeddings for your documents (e
None
<a name="memory.InMemoryDocumentStore.get_document_count"></a>
#### get\_document\_count
```python
| get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
```
Return the number of documents in the document store.
<a name="memory.InMemoryDocumentStore.get_label_count"></a>
#### get\_label\_count
```python
| get_label_count(index: Optional[str] = None) -> int
```
Return the number of labels in the document store
<a name="memory.InMemoryDocumentStore.get_all_documents"></a>
#### get\_all\_documents
```python
| get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
```
Get documents from the document store.
**Arguments**:
- `index`: Name of the index to get the documents from. If None, the
DocumentStore's default index (self.index) will be used.
- `filters`: Optional filters to narrow down the documents to return.
Example: {"name": ["some", "more"], "category": ["only_one"]}
- `return_embedding`: Whether to return the document embeddings.
<a name="memory.InMemoryDocumentStore.get_all_labels"></a>
#### get\_all\_labels
```python
| get_all_labels(index: str = None, filters: Optional[Dict[str, List[str]]] = None) -> List[Label]
```
Return all labels in the document store
<a name="memory.InMemoryDocumentStore.add_eval_data"></a>
#### add\_eval\_data
@ -236,143 +464,6 @@ Delete documents in an index. All documents are deleted if no filters are passed
None
<a name="sql"></a>
# Module sql
<a name="sql.SQLDocumentStore"></a>
## SQLDocumentStore Objects
```python
class SQLDocumentStore(BaseDocumentStore)
```
<a name="sql.SQLDocumentStore.__init__"></a>
#### \_\_init\_\_
```python
| __init__(url: str = "sqlite://", index: str = "document", label_index: str = "label", update_existing_documents: bool = False)
```
**Arguments**:
- `url`: URL for SQL database as expected by SQLAlchemy. More info here: https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls
- `index`: The documents are scoped to an index attribute that can be used when writing, querying, or deleting documents.
This parameter sets the default value for document index.
- `label_index`: The default value of index attribute for the labels.
- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
documents. When set as True, any document with an existing ID gets updated.
If set to False, an error is raised if the document ID of the document being
added already exists. Using this parameter coud cause performance degradation for document insertion.
<a name="sql.SQLDocumentStore.write_documents"></a>
#### write\_documents
```python
| write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
```
Indexes documents for later queries.
**Arguments**:
- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
For documents as dictionaries, the format is {"text": "<the-actual-text>"}.
Optionally: Include meta data via {"text": "<the-actual-text>",
"meta": {"name": "<some-document-name>, "author": "somebody", ...}}
It can be used for filtering and is accessible in the responses of the Finder.
- `index`: add an optional index attribute to documents. It can be later used for filtering. For instance,
documents for evaluation can be indexed in a separate index than the documents for search.
**Returns**:
None
<a name="sql.SQLDocumentStore.update_vector_ids"></a>
#### update\_vector\_ids
```python
| update_vector_ids(vector_id_map: Dict[str, str], index: Optional[str] = None)
```
Update vector_ids for given document_ids.
**Arguments**:
- `vector_id_map`: dict containing mapping of document_id -> vector_id.
- `index`: filter documents by the optional index attribute for documents in database.
<a name="sql.SQLDocumentStore.add_eval_data"></a>
#### add\_eval\_data
```python
| add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label")
```
Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
**Arguments**:
- `filename`: Name of the file containing evaluation data
:type filename: str
- `doc_index`: Elasticsearch index where evaluation documents should be stored
:type doc_index: str
- `label_index`: Elasticsearch index where labeled questions should be stored
:type label_index: str
<a name="sql.SQLDocumentStore.delete_all_documents"></a>
#### delete\_all\_documents
```python
| delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
```
Delete documents in an index. All documents are deleted if no filters are passed.
**Arguments**:
- `index`: Index name to delete the document from.
- `filters`: Optional filters to narrow down the documents to be deleted.
**Returns**:
None
<a name="base"></a>
# Module base
<a name="base.BaseDocumentStore"></a>
## BaseDocumentStore Objects
```python
class BaseDocumentStore(ABC)
```
Base class for implementing Document Stores.
<a name="base.BaseDocumentStore.write_documents"></a>
#### write\_documents
```python
| @abstractmethod
| write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
```
Indexes documents for later queries.
**Arguments**:
- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
For documents as dictionaries, the format is {"text": "<the-actual-text>"}.
Optionally: Include meta data via {"text": "<the-actual-text>",
"meta":{"name": "<some-document-name>, "author": "somebody", ...}}
It can be used for filtering and is accessible in the responses of the Finder.
- `index`: Optional name of index where the documents shall be written to.
If None, the DocumentStore's default index (self.index) will be used.
**Returns**:
None
<a name="faiss"></a>
# Module faiss
@ -395,7 +486,7 @@ the vector embeddings are indexed in a FAISS Index.
#### \_\_init\_\_
```python
| __init__(sql_url: str = "sqlite:///", index_buffer_size: int = 10_000, vector_dim: int = 768, faiss_index_factory_str: str = "Flat", faiss_index: Optional[faiss.swigfaiss.Index] = None, return_embedding: Optional[bool] = True, update_existing_documents: bool = False, index: str = "document", **kwargs, ,)
| __init__(sql_url: str = "sqlite:///", index_buffer_size: int = 10_000, vector_dim: int = 768, faiss_index_factory_str: str = "Flat", faiss_index: Optional[faiss.swigfaiss.Index] = None, return_embedding: bool = False, update_existing_documents: bool = False, index: str = "document", **kwargs, ,)
```
**Arguments**:
@ -467,6 +558,23 @@ This can be useful if want to add or change the embeddings for your documents (e
None
<a name="faiss.FAISSDocumentStore.get_all_documents"></a>
#### get\_all\_documents
```python
| get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
```
Get documents from the document store.
**Arguments**:
- `index`: Name of the index to get the documents from. If None, the
DocumentStore's default index (self.index) will be used.
- `filters`: Optional filters to narrow down the documents to return.
Example: {"name": ["some", "more"], "category": ["only_one"]}
- `return_embedding`: Whether to return the document embeddings.
<a name="faiss.FAISSDocumentStore.train_index"></a>
#### train\_index
@ -487,6 +595,15 @@ You can pass either documents (incl. embeddings) or just the plain embeddings th
None
<a name="faiss.FAISSDocumentStore.delete_all_documents"></a>
#### delete\_all\_documents
```python
| delete_all_documents(index=None)
```
Delete all documents from the document store.
<a name="faiss.FAISSDocumentStore.query_by_embedding"></a>
#### query\_by\_embedding
@ -549,3 +666,247 @@ smaller chunks to reduce memory footprint.
<a name="sql"></a>
# Module sql
<a name="sql.SQLDocumentStore"></a>
## SQLDocumentStore Objects
```python
class SQLDocumentStore(BaseDocumentStore)
```
<a name="sql.SQLDocumentStore.__init__"></a>
#### \_\_init\_\_
```python
| __init__(url: str = "sqlite://", index: str = "document", label_index: str = "label", update_existing_documents: bool = False)
```
**Arguments**:
- `url`: URL for SQL database as expected by SQLAlchemy. More info here: https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls
- `index`: The documents are scoped to an index attribute that can be used when writing, querying, or deleting documents.
This parameter sets the default value for document index.
- `label_index`: The default value of index attribute for the labels.
- `update_existing_documents`: Whether to update any existing documents with the same ID when adding
documents. When set as True, any document with an existing ID gets updated.
If set to False, an error is raised if the document ID of the document being
added already exists. Using this parameter coud cause performance degradation for document insertion.
<a name="sql.SQLDocumentStore.get_document_by_id"></a>
#### get\_document\_by\_id
```python
| get_document_by_id(id: str, index: Optional[str] = None) -> Optional[Document]
```
Fetch a document by specifying its text id string
<a name="sql.SQLDocumentStore.get_documents_by_id"></a>
#### get\_documents\_by\_id
```python
| get_documents_by_id(ids: List[str], index: Optional[str] = None) -> List[Document]
```
Fetch documents by specifying a list of text id strings
<a name="sql.SQLDocumentStore.get_documents_by_vector_ids"></a>
#### get\_documents\_by\_vector\_ids
```python
| get_documents_by_vector_ids(vector_ids: List[str], index: Optional[str] = None)
```
Fetch documents by specifying a list of text vector id strings
<a name="sql.SQLDocumentStore.get_all_documents"></a>
#### get\_all\_documents
```python
| get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
```
Get documents from the document store.
**Arguments**:
- `index`: Name of the index to get the documents from. If None, the
DocumentStore's default index (self.index) will be used.
- `filters`: Optional filters to narrow down the documents to return.
Example: {"name": ["some", "more"], "category": ["only_one"]}
- `return_embedding`: Whether to return the document embeddings.
<a name="sql.SQLDocumentStore.get_all_labels"></a>
#### get\_all\_labels
```python
| get_all_labels(index=None, filters: Optional[dict] = None)
```
Return all labels in the document store
<a name="sql.SQLDocumentStore.write_documents"></a>
#### write\_documents
```python
| write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
```
Indexes documents for later queries.
**Arguments**:
- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
For documents as dictionaries, the format is {"text": "<the-actual-text>"}.
Optionally: Include meta data via {"text": "<the-actual-text>",
"meta":{"name": "<some-document-name>, "author": "somebody", ...}}
It can be used for filtering and is accessible in the responses of the Finder.
- `index`: add an optional index attribute to documents. It can be later used for filtering. For instance,
documents for evaluation can be indexed in a separate index than the documents for search.
**Returns**:
None
<a name="sql.SQLDocumentStore.write_labels"></a>
#### write\_labels
```python
| write_labels(labels, index=None)
```
Write annotation labels into document store.
<a name="sql.SQLDocumentStore.update_vector_ids"></a>
#### update\_vector\_ids
```python
| update_vector_ids(vector_id_map: Dict[str, str], index: Optional[str] = None)
```
Update vector_ids for given document_ids.
**Arguments**:
- `vector_id_map`: dict containing mapping of document_id -> vector_id.
- `index`: filter documents by the optional index attribute for documents in database.
<a name="sql.SQLDocumentStore.update_document_meta"></a>
#### update\_document\_meta
```python
| update_document_meta(id: str, meta: Dict[str, str])
```
Update the metadata dictionary of a document by specifying its string id
<a name="sql.SQLDocumentStore.add_eval_data"></a>
#### add\_eval\_data
```python
| add_eval_data(filename: str, doc_index: str = "eval_document", label_index: str = "label")
```
Adds a SQuAD-formatted file to the DocumentStore in order to be able to perform evaluation on it.
**Arguments**:
- `filename`: Name of the file containing evaluation data
:type filename: str
- `doc_index`: Elasticsearch index where evaluation documents should be stored
:type doc_index: str
- `label_index`: Elasticsearch index where labeled questions should be stored
:type label_index: str
<a name="sql.SQLDocumentStore.get_document_count"></a>
#### get\_document\_count
```python
| get_document_count(filters: Optional[Dict[str, List[str]]] = None, index: Optional[str] = None) -> int
```
Return the number of documents in the document store.
<a name="sql.SQLDocumentStore.get_label_count"></a>
#### get\_label\_count
```python
| get_label_count(index: Optional[str] = None) -> int
```
Return the number of labels in the document store
<a name="sql.SQLDocumentStore.delete_all_documents"></a>
#### delete\_all\_documents
```python
| delete_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None)
```
Delete documents in an index. All documents are deleted if no filters are passed.
**Arguments**:
- `index`: Index name to delete the document from.
- `filters`: Optional filters to narrow down the documents to be deleted.
**Returns**:
None
<a name="base"></a>
# Module base
<a name="base.BaseDocumentStore"></a>
## BaseDocumentStore Objects
```python
class BaseDocumentStore(ABC)
```
Base class for implementing Document Stores.
<a name="base.BaseDocumentStore.write_documents"></a>
#### write\_documents
```python
| @abstractmethod
| write_documents(documents: Union[List[dict], List[Document]], index: Optional[str] = None)
```
Indexes documents for later queries.
**Arguments**:
- `documents`: a list of Python dictionaries or a list of Haystack Document objects.
For documents as dictionaries, the format is {"text": "<the-actual-text>"}.
Optionally: Include meta data via {"text": "<the-actual-text>",
"meta":{"name": "<some-document-name>, "author": "somebody", ...}}
It can be used for filtering and is accessible in the responses of the Finder.
- `index`: Optional name of index where the documents shall be written to.
If None, the DocumentStore's default index (self.index) will be used.
**Returns**:
None
<a name="base.BaseDocumentStore.get_all_documents"></a>
#### get\_all\_documents
```python
| @abstractmethod
| get_all_documents(index: Optional[str] = None, filters: Optional[Dict[str, List[str]]] = None, return_embedding: Optional[bool] = None) -> List[Document]
```
Get documents from the document store.
**Arguments**:
- `index`: Name of the index to get the documents from. If None, the
DocumentStore's default index (self.index) will be used.
- `filters`: Optional filters to narrow down the documents to return.
Example: {"name": ["some", "more"], "category": ["only_one"]}
- `return_embedding`: Whether to return the document embeddings.

164
docs/_src/api/api/file_converter.md
View File

@ -1,87 +1,3 @@
<a name="txt"></a>
# Module txt
<a name="txt.TextConverter"></a>
## TextConverter Objects
```python
class BaseConverter()
```
Base class for implementing file converts to transform input documents to text format for ingestion in DocumentStore.
<a name="base.BaseConverter.__init__"></a>
#### \_\_init\_\_
```python
| __init__(remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None)
```
**Arguments**:
- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
The tabular structures in documents might be noise for the reader model if it
does not have table parsing capability for finding answers. However, tables
may also have long strings that could possible candidate for searching answers.
The rows containing strings are thus retained in this option.
- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
(https://en.wikipedia.org/wiki/ISO_639-1) format.
This option can be used to add test for encoding errors. If the extracted text is
not one of the valid languages, then it might likely be encoding error resulting
in garbled text.
<a name="base.BaseConverter.convert"></a>
#### convert
```python
| @abstractmethod
| convert(file_path: Path, meta: Optional[Dict[str, str]]) -> Dict[str, Any]
```
Convert a file to a dictionary containing the text and any associated meta data.
File converters may extract file meta like name or size. In addition to it, user
supplied meta data like author, url, external IDs can be supplied as a dictionary.
**Arguments**:
- `file_path`: path of the file to convert
- `meta`: dictionary of meta data key-value pairs to append in the returned document.
<a name="base.BaseConverter.validate_language"></a>
#### validate\_language
```python
| validate_language(text: str) -> bool
```
Validate if the language of the text is one of valid languages.
<a name="docx"></a>
# docx
<a name="docx.DocxToTextConverter"></a>
## DocxToTextConverter
```python
class DocxToTextConverter(BaseConverter)
```
<a name="docx.DocxToTextConverter.convert"></a>
#### convert
```python
| convert(file_path: Path, meta: Optional[Dict[str, str]] = None) -> Dict[str, Any]
```
Extract text from a .docx file.
Note: As docx doesn't contain "page" information, we actually extract and return a list of paragraphs here.
For compliance with other converters we nevertheless opted for keeping the methods name.
**Arguments**:
- `file_path`: Path to the .docx file you want to convert
<a name="docx"></a>
# Module docx
@ -153,11 +69,11 @@ in garbled text.
a list of pages and the extracted meta data of the file.
<a name="base"></a>
# Module base
<a name="txt"></a>
# Module txt
<a name="base.BaseConverter"></a>
## BaseConverter Objects
<a name="txt.TextConverter"></a>
## TextConverter Objects
```python
class TextConverter(BaseConverter)
@ -232,3 +148,75 @@ This option can be used to add test for encoding errors. If the extracted text i
not one of the valid languages, then it might likely be encoding error resulting
in garbled text.
<a name="pdf.PDFToTextConverter.convert"></a>
#### convert
```python
| convert(file_path: Path, meta: Optional[Dict[str, str]] = None) -> Dict[str, Any]
```
Extract text from a .pdf file.
**Arguments**:
- `file_path`: Path to the .pdf file you want to convert
<a name="base"></a>
# Module base
<a name="base.BaseConverter"></a>
## BaseConverter Objects
```python
class BaseConverter()
```
Base class for implementing file converts to transform input documents to text format for ingestion in DocumentStore.
<a name="base.BaseConverter.__init__"></a>
#### \_\_init\_\_
```python
| __init__(remove_numeric_tables: Optional[bool] = None, valid_languages: Optional[List[str]] = None)
```
**Arguments**:
- `remove_numeric_tables`: This option uses heuristics to remove numeric rows from the tables.
The tabular structures in documents might be noise for the reader model if it
does not have table parsing capability for finding answers. However, tables
may also have long strings that could possible candidate for searching answers.
The rows containing strings are thus retained in this option.
- `valid_languages`: validate languages from a list of languages specified in the ISO 639-1
(https://en.wikipedia.org/wiki/ISO_639-1) format.
This option can be used to add test for encoding errors. If the extracted text is
not one of the valid languages, then it might likely be encoding error resulting
in garbled text.
<a name="base.BaseConverter.convert"></a>
#### convert
```python
| @abstractmethod
| convert(file_path: Path, meta: Optional[Dict[str, str]]) -> Dict[str, Any]
```
Convert a file to a dictionary containing the text and any associated meta data.
File converters may extract file meta like name or size. In addition to it, user
supplied meta data like author, url, external IDs can be supplied as a dictionary.
**Arguments**:
- `file_path`: path of the file to convert
- `meta`: dictionary of meta data key-value pairs to append in the returned document.
<a name="base.BaseConverter.validate_language"></a>
#### validate\_language
```python
| validate_language(text: str) -> bool
```
Validate if the language of the text is one of valid languages.

65
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@ -22,27 +22,30 @@ i.e. the model can easily adjust to domain documents even after training has fin
**Example**
```python
> question = "who got the first nobel prize in physics?"
# Retrieve related documents from retriever
> retrieved_docs = retriever.retrieve(query=question)
> # Now generate answer from question and retrieved documents
> generator.predict(
> question=question,
> documents=retrieved_docs,
> top_k=1
> )
{'question': 'who got the first nobel prize in physics',
'answers':
[{'question': 'who got the first nobel prize in physics',
'answer': ' albert einstein',
'meta': { 'doc_ids': [...],
'doc_scores': [80.42758 ...],
'doc_probabilities': [40.71379089355469, ...
'texts': ['Albert Einstein was a ...]
'titles': ['"Albert Einstein"', ...]
}}]}
| question = "who got the first nobel prize in physics?"
|
| # Retrieve related documents from retriever
| retrieved_docs = retriever.retrieve(query=question)
|
| # Now generate answer from question and retrieved documents
| generator.predict(
| question=question,
| documents=retrieved_docs,
| top_k=1
| )
|
| # Answer
|
| {'question': 'who got the first nobel prize in physics',
| 'answers':
| [{'question': 'who got the first nobel prize in physics',
| 'answer': ' albert einstein',
| 'meta': { 'doc_ids': [...],
| 'doc_scores': [80.42758 ...],
| 'doc_probabilities': [40.71379089355469, ...
| 'texts': ['Albert Einstein was a ...]
| 'titles': ['"Albert Einstein"', ...]
| }}]}
```
<a name="transformers.RAGenerator.__init__"></a>
@ -91,16 +94,16 @@ These document can for example be retrieved via the Retriever.
Generated answers plus additional infos in a dict like this:
```python
> {'question': 'who got the first nobel prize in physics',
> 'answers':
> [{'question': 'who got the first nobel prize in physics',
> 'answer': ' albert einstein',
> 'meta': { 'doc_ids': [...],
> 'doc_scores': [80.42758 ...],
> 'doc_probabilities': [40.71379089355469, ...
> 'texts': ['Albert Einstein was a ...]
> 'titles': ['"Albert Einstein"', ...]
> }}]}
| {'question': 'who got the first nobel prize in physics',
| 'answers':
| [{'question': 'who got the first nobel prize in physics',
| 'answer': ' albert einstein',
| 'meta': { 'doc_ids': [...],
| 'doc_scores': [80.42758 ...],
| 'doc_probabilities': [40.71379089355469, ...
| 'texts': ['Albert Einstein was a ...]
| 'titles': ['"Albert Einstein"', ...]
| }}]}
```
<a name="base"></a>

51
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@ -34,8 +34,26 @@ Set the value to None to disable striding behaviour.
to True, the individual split will always have complete sentences &
the number of words will be <= split_length.
<a name="cleaning"></a>
# Module cleaning
<a name="preprocessor.PreProcessor.clean"></a>
#### clean
```python
| clean(document: dict) -> dict
```
Perform document cleaning on a single document and return a single document. This method will deal with whitespaces, headers, footers
and empty lines. Its exact functionality is defined by the parameters passed into PreProcessor.__init__().
<a name="preprocessor.PreProcessor.split"></a>
#### split
```python
| split(document: dict) -> List[dict]
```
Perform document splitting on a single document. This method can split on different units, at different lengths,
with different strides. It can also respect sectence boundaries. Its exact functionality is defined by
the parameters passed into PreProcessor.__init__(). Takes a single document as input and returns a list of documents.
<a name="utils"></a>
# Module utils
@ -126,3 +144,32 @@ bool if anything got fetched
<a name="base"></a>
# Module base
<a name="base.BasePreProcessor"></a>
## BasePreProcessor Objects
```python
class BasePreProcessor()
```
<a name="base.BasePreProcessor.process"></a>
#### process
```python
| process(document: dict) -> List[dict]
```
Perform document cleaning and splitting. Takes a single document as input and returns a list of documents.
<a name="cleaning"></a>
# Module cleaning
<a name="cleaning.clean_wiki_text"></a>
#### clean\_wiki\_text
```python
clean_wiki_text(text: str) -> str
```
Clean wikipedia text by removing multiple new lines, removing extremely short lines,
adding paragraph breaks and removing empty paragraphs

180
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@ -1,5 +1,90 @@
<a name="base"></a>
# base
<a name="transformers"></a>
# Module transformers
<a name="transformers.TransformersReader"></a>
## TransformersReader Objects
```python
class TransformersReader(BaseReader)
```
Transformer based model for extractive Question Answering using the HuggingFace's transformers framework
(https://github.com/huggingface/transformers).
While the underlying model can vary (BERT, Roberta, DistilBERT ...), the interface remains the same.
With this reader, you can directly get predictions via predict()
<a name="transformers.TransformersReader.__init__"></a>
#### \_\_init\_\_
```python
| __init__(model_name_or_path: str = "distilbert-base-uncased-distilled-squad", tokenizer: Optional[str] = None, context_window_size: int = 70, use_gpu: int = 0, top_k_per_candidate: int = 4, return_no_answers: bool = True, max_seq_len: int = 256, doc_stride: int = 128)
```
Load a QA model from Transformers.
Available models include:
- ``'distilbert-base-uncased-distilled-squad`'``
- ``'bert-large-cased-whole-word-masking-finetuned-squad``'
- ``'bert-large-uncased-whole-word-masking-finetuned-squad``'
See https://huggingface.co/models for full list of available QA models
**Arguments**:
- `model_name_or_path`: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
See https://huggingface.co/models for full list of available models.
- `tokenizer`: Name of the tokenizer (usually the same as model)
- `context_window_size`: Num of chars (before and after the answer) to return as "context" for each answer.
The context usually helps users to understand if the answer really makes sense.
- `use_gpu`: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
- `top_k_per_candidate`: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
Note that this is not the number of "final answers" you will receive
(see `top_k` in TransformersReader.predict() or Finder.get_answers() for that)
and that no_answer can be included in the sorted list of predictions.
- `return_no_answers`: If True, the HuggingFace Transformers model could return a "no_answer" (i.e. when there is an unanswerable question)
If False, it cannot return a "no_answer". Note that `no_answer_boost` is unfortunately not available with TransformersReader.
If you would like to set no_answer_boost, use a `FARMReader`.
- `max_seq_len`: max sequence length of one input text for the model
- `doc_stride`: length of striding window for splitting long texts (used if len(text) > max_seq_len)
<a name="transformers.TransformersReader.predict"></a>
#### predict
```python
| predict(question: str, documents: List[Document], top_k: Optional[int] = None)
```
Use loaded QA model to find answers for a question in the supplied list of Document.
Returns dictionaries containing answers sorted by (desc.) probability.
Example:
```python
|{
| 'question': 'Who is the father of Arya Stark?',
| 'answers':[
| {'answer': 'Eddard,',
| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
| 'offset_answer_start': 147,
| 'offset_answer_end': 154,
| 'probability': 0.9787139466668613,
| 'score': None,
| 'document_id': '1337'
| },...
| ]
|}
```
**Arguments**:
- `question`: Question string
- `documents`: List of Document in which to search for the answer
- `top_k`: The maximum number of answers to return
**Returns**:
Dict containing question and answers
<a name="farm"></a>
# Module farm
@ -283,97 +368,6 @@ float32 could still be be more performant.
- `task_type`: Type of task for the model. Available options: "question_answering" or "embeddings".
- `opset_version`: ONNX opset version
<a name="transformers"></a>
# Module transformers
<a name="transformers.TransformersReader"></a>
## TransformersReader Objects
```python
class TransformersReader(BaseReader)
```
Transformer based model for extractive Question Answering using the HuggingFace's transformers framework
(https://github.com/huggingface/transformers).
While the underlying model can vary (BERT, Roberta, DistilBERT ...), the interface remains the same.
| With the reader, you can:
- directly get predictions via predict()
<a name="transformers.TransformersReader.__init__"></a>
#### \_\_init\_\_
```python
| __init__(model_name_or_path: str = "distilbert-base-uncased-distilled-squad", tokenizer: Optional[str] = None, context_window_size: int = 70, use_gpu: int = 0, top_k_per_candidate: int = 4, return_no_answers: bool = True, max_seq_len: int = 256, doc_stride: int = 128)
```
Load a QA model from Transformers.
Available models include:
- ``'distilbert-base-uncased-distilled-squad`'``
- ``'bert-large-cased-whole-word-masking-finetuned-squad``'
- ``'bert-large-uncased-whole-word-masking-finetuned-squad``'
See https://huggingface.co/models for full list of available QA models
**Arguments**:
- `model_name_or_path`: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
See https://huggingface.co/models for full list of available models.
- `tokenizer`: Name of the tokenizer (usually the same as model)
- `context_window_size`: Num of chars (before and after the answer) to return as "context" for each answer.
The context usually helps users to understand if the answer really makes sense.
- `use_gpu`: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
- `top_k_per_candidate`: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
Note that this is not the number of "final answers" you will receive
(see `top_k` in TransformersReader.predict() or Finder.get_answers() for that)
and that no_answer can be included in the sorted list of predictions.
- `return_no_answers`: If True, the HuggingFace Transformers model could return a "no_answer" (i.e. when there is an unanswerable question)
If False, it cannot return a "no_answer". Note that `no_answer_boost` is unfortunately not available with TransformersReader.
If you would like to set no_answer_boost, use a `FARMReader`.
- `max_seq_len`: max sequence length of one input text for the model
- `doc_stride`: length of striding window for splitting long texts (used if len(text) > max_seq_len)
<a name="transformers.TransformersReader.predict"></a>
#### predict
```python
| predict(question: str, documents: List[Document], top_k: Optional[int] = None)
```
Use loaded QA model to find answers for a question in the supplied list of Document.
Returns dictionaries containing answers sorted by (desc.) probability.
Example:
```python
|{
| 'question': 'Who is the father of Arya Stark?',
| 'answers':[
| {'answer': 'Eddard,',
| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
| 'offset_answer_start': 147,
| 'offset_answer_end': 154,
| 'probability': 0.9787139466668613,
| 'score': None,
| 'document_id': '1337'
| },...
| ]
|}
```
**Arguments**:
- `question`: Question string
- `documents`: List of Document in which to search for the answer
- `top_k`: The maximum number of answers to return
**Returns**:
Dict containing question and answers
<a name="base"></a>
# Module base

346
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View File

@ -1,142 +1,3 @@
<a name="base"></a>
# base
<a name="base.BaseRetriever"></a>
## BaseRetriever
```python
class BaseRetriever(ABC)
```
<a name="base.BaseRetriever.retrieve"></a>
#### retrieve
```python
| @abstractmethod
| retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
```
Scan through documents in DocumentStore and return a small number documents
that are most relevant to the query.
**Arguments**:
- `query`: The query
- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
- `top_k`: How many documents to return per query.
- `index`: The name of the index in the DocumentStore from which to retrieve documents
<a name="base.BaseRetriever.eval"></a>
#### eval
```python
| eval(label_index: str = "label", doc_index: str = "eval_document", label_origin: str = "gold_label", top_k: int = 10, open_domain: bool = False, return_preds: bool = False) -> dict
```
Performs evaluation on the Retriever.
Retriever is evaluated based on whether it finds the correct document given the question string and at which
position in the ranking of documents the correct document is.
| Returns a dict containing the following metrics:
- "recall": Proportion of questions for which correct document is among retrieved documents
- "mrr": Mean of reciprocal rank. Rewards retrievers that give relevant documents a higher rank.
Only considers the highest ranked relevant document.
- "map": Mean of average precision for each question. Rewards retrievers that give relevant
documents a higher rank. Considers all retrieved relevant documents. If ``open_domain=True``,
average precision is normalized by the number of retrieved relevant documents per query.
If ``open_domain=False``, average precision is normalized by the number of all relevant documents
per query.
**Arguments**:
- `label_index`: Index/Table in DocumentStore where labeled questions are stored
- `doc_index`: Index/Table in DocumentStore where documents that are used for evaluation are stored
- `top_k`: How many documents to return per question
- `open_domain`: If ``True``, retrieval will be evaluated by checking if the answer string to a question is
contained in the retrieved docs (common approach in open-domain QA).
If ``False``, retrieval uses a stricter evaluation that checks if the retrieved document ids
are within ids explicitly stated in the labels.
- `return_preds`: Whether to add predictions in the returned dictionary. If True, the returned dictionary
contains the keys "predictions" and "metrics".
<a name="sparse"></a>
# Module sparse
<a name="sparse.ElasticsearchRetriever"></a>
## ElasticsearchRetriever Objects
```python
class ElasticsearchRetriever(BaseRetriever)
```
<a name="sparse.ElasticsearchRetriever.__init__"></a>
#### \_\_init\_\_
```python
| __init__(document_store: ElasticsearchDocumentStore, custom_query: str = None)
```
**Arguments**:
- `document_store`: an instance of a DocumentStore to retrieve documents from.
- `custom_query`: query string as per Elasticsearch DSL with a mandatory question placeholder($question).
Optionally, ES `filter` clause can be added where the values of `terms` are placeholders
that get substituted during runtime. The placeholder(${filter_name_1}, ${filter_name_2}..)
names must match with the filters dict supplied in self.retrieve().
::
**An example custom_query:**
```python
| {
| "size": 10,
| "query": {
| "bool": {
| "should": [{"multi_match": {
| "query": "${question}", // mandatory $question placeholder
| "type": "most_fields",
| "fields": ["text", "title"]}}],
| "filter": [ // optional custom filters
| {"terms": {"year": "${years}"}},
| {"terms": {"quarter": "${quarters}"}},
| {"range": {"date": {"gte": "${date}"}}}
| ],
| }
| },
| }
```
**For this custom_query, a sample retrieve() could be:**
```python
| self.retrieve(query="Why did the revenue increase?",
| filters={"years": ["2019"], "quarters": ["Q1", "Q2"]})
```
<a name="sparse.ElasticsearchFilterOnlyRetriever"></a>
## ElasticsearchFilterOnlyRetriever Objects
```python
class ElasticsearchFilterOnlyRetriever(ElasticsearchRetriever)
```
Naive "Retriever" that returns all documents that match the given filters. No impact of query at all.
Helpful for benchmarking, testing and if you want to do QA on small documents without an "active" retriever.
<a name="sparse.TfidfRetriever"></a>
## TfidfRetriever Objects
```python
class TfidfRetriever(BaseRetriever)
```
Read all documents from a SQL backend.
Split documents into smaller units (eg, paragraphs or pages) to reduce the
computations when text is passed on to a Reader for QA.
It uses sklearn's TfidfVectorizer to compute a tf-idf matrix.
<a name="dense"></a>
# Module dense
@ -195,6 +56,23 @@ The title is expected to be present in doc.meta["name"] and can be supplied in t
before writing them to the DocumentStore like this:
{"text": "my text", "meta": {"name": "my title"}}.
<a name="dense.DensePassageRetriever.retrieve"></a>
#### retrieve
```python
| retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
```
Scan through documents in DocumentStore and return a small number documents
that are most relevant to the query.
**Arguments**:
- `query`: The query
- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
- `top_k`: How many documents to return per query.
- `index`: The name of the index in the DocumentStore from which to retrieve documents
<a name="dense.DensePassageRetriever.embed_queries"></a>
#### embed\_queries
@ -262,6 +140,33 @@ train a DensePassageRetrieval model
- `query_encoder_save_dir`: directory inside save_dir where query_encoder model files are saved
- `passage_encoder_save_dir`: directory inside save_dir where passage_encoder model files are saved
<a name="dense.DensePassageRetriever.save"></a>
#### save
```python
| save(save_dir: Union[Path, str])
```
Save DensePassageRetriever to the specified directory.
**Arguments**:
- `save_dir`: Directory to save to.
**Returns**:
None
<a name="dense.DensePassageRetriever.load"></a>
#### load
```python
| @classmethod
| load(cls, load_dir: Union[Path, str], document_store: BaseDocumentStore, max_seq_len_query: int = 64, max_seq_len_passage: int = 256, use_gpu: bool = True, batch_size: int = 16, embed_title: bool = True, use_fast_tokenizers: bool = True, similarity_function: str = "dot_product")
```
Load DensePassageRetriever from the specified directory.
<a name="dense.EmbeddingRetriever"></a>
## EmbeddingRetriever Objects
@ -296,6 +201,23 @@ Options:
- `emb_extraction_layer`: Number of layer from which the embeddings shall be extracted (for farm / transformers models only).
Default: -1 (very last layer).
<a name="dense.EmbeddingRetriever.retrieve"></a>
#### retrieve
```python
| retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
```
Scan through documents in DocumentStore and return a small number documents
that are most relevant to the query.
**Arguments**:
- `query`: The query
- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
- `top_k`: How many documents to return per query.
- `index`: The name of the index in the DocumentStore from which to retrieve documents
<a name="dense.EmbeddingRetriever.embed"></a>
#### embed
@ -347,6 +269,143 @@ Create embeddings for a list of passages. For this Retriever type: The same as c
Embeddings, one per input passage
<a name="sparse"></a>
# Module sparse
<a name="sparse.ElasticsearchRetriever"></a>
## ElasticsearchRetriever Objects
```python
class ElasticsearchRetriever(BaseRetriever)
```
<a name="sparse.ElasticsearchRetriever.__init__"></a>
#### \_\_init\_\_
```python
| __init__(document_store: ElasticsearchDocumentStore, custom_query: str = None)
```
**Arguments**:
- `document_store`: an instance of a DocumentStore to retrieve documents from.
- `custom_query`: query string as per Elasticsearch DSL with a mandatory question placeholder($question).
Optionally, ES `filter` clause can be added where the values of `terms` are placeholders
that get substituted during runtime. The placeholder(${filter_name_1}, ${filter_name_2}..)
names must match with the filters dict supplied in self.retrieve().
::
**An example custom_query:**
```python
| {
| "size": 10,
| "query": {
| "bool": {
| "should": [{"multi_match": {
| "query": "${question}", // mandatory $question placeholder
| "type": "most_fields",
| "fields": ["text", "title"]}}],
| "filter": [ // optional custom filters
| {"terms": {"year": "${years}"}},
| {"terms": {"quarter": "${quarters}"}},
| {"range": {"date": {"gte": "${date}"}}}
| ],
| }
| },
| }
```
**For this custom_query, a sample retrieve() could be:**
```python
| self.retrieve(query="Why did the revenue increase?",
| filters={"years": ["2019"], "quarters": ["Q1", "Q2"]})
```
<a name="sparse.ElasticsearchRetriever.retrieve"></a>
#### retrieve
```python
| retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
```
Scan through documents in DocumentStore and return a small number documents
that are most relevant to the query.
**Arguments**:
- `query`: The query
- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
- `top_k`: How many documents to return per query.
- `index`: The name of the index in the DocumentStore from which to retrieve documents
<a name="sparse.ElasticsearchFilterOnlyRetriever"></a>
## ElasticsearchFilterOnlyRetriever Objects
```python
class ElasticsearchFilterOnlyRetriever(ElasticsearchRetriever)
```
Naive "Retriever" that returns all documents that match the given filters. No impact of query at all.
Helpful for benchmarking, testing and if you want to do QA on small documents without an "active" retriever.
<a name="sparse.ElasticsearchFilterOnlyRetriever.retrieve"></a>
#### retrieve
```python
| retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
```
Scan through documents in DocumentStore and return a small number documents
that are most relevant to the query.
**Arguments**:
- `query`: The query
- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
- `top_k`: How many documents to return per query.
- `index`: The name of the index in the DocumentStore from which to retrieve documents
<a name="sparse.TfidfRetriever"></a>
## TfidfRetriever Objects
```python
class TfidfRetriever(BaseRetriever)
```
Read all documents from a SQL backend.
Split documents into smaller units (eg, paragraphs or pages) to reduce the
computations when text is passed on to a Reader for QA.
It uses sklearn's TfidfVectorizer to compute a tf-idf matrix.
<a name="sparse.TfidfRetriever.retrieve"></a>
#### retrieve
```python
| retrieve(query: str, filters: dict = None, top_k: int = 10, index: str = None) -> List[Document]
```
Scan through documents in DocumentStore and return a small number documents
that are most relevant to the query.
**Arguments**:
- `query`: The query
- `filters`: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
- `top_k`: How many documents to return per query.
- `index`: The name of the index in the DocumentStore from which to retrieve documents
<a name="sparse.TfidfRetriever.fit"></a>
#### fit
```python
| fit()
```
Performing training on this class according to the TF-IDF algorithm.
<a name="base"></a>
# Module base
@ -375,6 +434,15 @@ that are most relevant to the query.
- `top_k`: How many documents to return per query.
- `index`: The name of the index in the DocumentStore from which to retrieve documents
<a name="base.BaseRetriever.timing"></a>
#### timing
```python
| timing(fn)
```
Wrapper method used to time functions.
<a name="base.BaseRetriever.eval"></a>
#### eval