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* refactor: remove azure-core, pydoc and hf-hub pins * fix: remove extra-comma * fix: force minimum version of azure forms recognizer * refactor: allow newer ocr libs * refactor: update more dependencies and container versions * refactor: remove extra comment * docs: pre-commit manual run * refactor: remove unnecessary dependency * tests: update weaviate container image version
140 lines
4.8 KiB
Markdown
140 lines
4.8 KiB
Markdown
<a id="docs2answers"></a>
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# Module docs2answers
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<a id="docs2answers.Docs2Answers"></a>
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## Docs2Answers
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```python
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class Docs2Answers(BaseComponent)
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```
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This Node is used to convert retrieved documents into predicted answers format.
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It is useful for situations where you are calling a Retriever only pipeline via REST API.
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This ensures that your output is in a compatible format.
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**Arguments**:
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- `progress_bar`: Whether to show a progress bar
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<a id="join_docs"></a>
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# Module join\_docs
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<a id="join_docs.JoinDocuments"></a>
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## JoinDocuments
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```python
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class JoinDocuments(JoinNode)
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```
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A node to join documents outputted by multiple retriever nodes.
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The node allows multiple join modes:
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* concatenate: combine the documents from multiple nodes. Any duplicate documents are discarded.
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The score is only determined by the last node that outputs the document.
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* merge: merge scores of documents from multiple nodes. Optionally, each input score can be given a different
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`weight` & a `top_k` limit can be set. This mode can also be used for "reranking" retrieved documents.
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* reciprocal_rank_fusion: combines the documents based on their rank in multiple nodes.
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<a id="join_docs.JoinDocuments.__init__"></a>
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#### JoinDocuments.\_\_init\_\_
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```python
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def __init__(join_mode: str = "concatenate",
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weights: Optional[List[float]] = None,
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top_k_join: Optional[int] = None,
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sort_by_score: bool = True)
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```
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**Arguments**:
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- `join_mode`: `concatenate` to combine documents from multiple retrievers `merge` to aggregate scores of
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individual documents, `reciprocal_rank_fusion` to apply rank based scoring.
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- `weights`: A node-wise list(length of list must be equal to the number of input nodes) of weights for
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adjusting document scores when using the `merge` join_mode. By default, equal weight is given
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to each retriever score. This param is not compatible with the `concatenate` join_mode.
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- `top_k_join`: Limit documents to top_k based on the resulting scores of the join.
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- `sort_by_score`: Whether to sort the incoming documents by their score. Set this to True if all your
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Documents are coming with `score` values. Set to False if any of the Documents come
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from sources where the `score` is set to `None`, like `TfidfRetriever` on Elasticsearch.
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<a id="join_answers"></a>
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# Module join\_answers
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<a id="join_answers.JoinAnswers"></a>
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## JoinAnswers
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```python
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class JoinAnswers(JoinNode)
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```
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A node to join `Answer`s produced by multiple `Reader` nodes.
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<a id="join_answers.JoinAnswers.__init__"></a>
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#### JoinAnswers.\_\_init\_\_
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```python
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def __init__(join_mode: str = "concatenate",
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weights: Optional[List[float]] = None,
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top_k_join: Optional[int] = None,
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sort_by_score: bool = True)
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```
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**Arguments**:
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- `join_mode`: `"concatenate"` to combine documents from multiple `Reader`s. `"merge"` to aggregate scores
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of individual `Answer`s.
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- `weights`: A node-wise list (length of list must be equal to the number of input nodes) of weights for
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adjusting `Answer` scores when using the `"merge"` join_mode. By default, equal weight is assigned to each
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`Reader` score. This parameter is not compatible with the `"concatenate"` join_mode.
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- `top_k_join`: Limit `Answer`s to top_k based on the resulting scored of the join.
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- `sort_by_score`: Whether to sort the incoming answers by their score. Set this to True if your Answers
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are coming from a Reader or TableReader. Set to False if any Answers come from a Generator since this assigns
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None as a score to each.
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<a id="route_documents"></a>
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# Module route\_documents
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<a id="route_documents.RouteDocuments"></a>
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## RouteDocuments
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```python
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class RouteDocuments(BaseComponent)
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```
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A node to split a list of `Document`s by `content_type` or by the values of a metadata field and route them to
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different nodes.
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<a id="route_documents.RouteDocuments.__init__"></a>
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#### RouteDocuments.\_\_init\_\_
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```python
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def __init__(split_by: str = "content_type",
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metadata_values: Optional[List[str]] = None)
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```
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**Arguments**:
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- `split_by`: Field to split the documents by, either `"content_type"` or a metadata field name.
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If this parameter is set to `"content_type"`, the list of `Document`s will be split into a list containing
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only `Document`s of type `"text"` (will be routed to `"output_1"`) and a list containing only `Document`s of
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type `"table"` (will be routed to `"output_2"`).
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If this parameter is set to a metadata field name, you need to specify the parameter `metadata_values` as
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well.
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- `metadata_values`: If the parameter `split_by` is set to a metadata field name, you need to provide a list
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of values to group the `Document`s to. `Document`s whose metadata field is equal to the first value of the
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provided list will be routed to `"output_1"`, `Document`s whose metadata field is equal to the second
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value of the provided list will be routed to `"output_2"`, etc.
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