yujunjun/haystack
1
0
Fork 0
mirror of https://github.com/deepset-ai/haystack.git synced 2025-12-25 05:58:57 +00:00
Code Issues Packages Projects Releases Wiki Activity

Add index arg to Finder.get_answers() and _via_similar_questions() (#362)

Browse Source 
Co-authored-by: Max Uppenkamp <max.uppenkamp@inform-software.com>
This commit is contained in:
maxupp 2020-09-09 12:39:13 +02:00 committed by GitHub
parent b1cdc68d6c
commit 06e8be30ea
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 6 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
haystack/finder.py
View File

@ -29,7 +29,7 @@ class Finder:
if self.reader is None and self.retriever is None:
raise AttributeError("Finder: self.reader and self.retriever can not be both None")
def get_answers(self, question: str, top_k_reader: int = 1, top_k_retriever: int = 10, filters: Optional[dict] = None):
def get_answers(self, question: str, top_k_reader: int = 1, top_k_retriever: int = 10, filters: Optional[dict] = None, index: str = None):
"""
Get top k answers for a given question.
@ -38,6 +38,7 @@ class Finder:
:param top_k_retriever: number of text units to be retrieved
:param filters: limit scope to documents having the given meta data values.
The format for the dict is {"key-1": ["value-1", "value-2"], "key-2": ["value-3]" ...}
:param index: index to retrieve documents from
:return:
"""
@ -45,7 +46,7 @@ class Finder:
raise AttributeError("Finder.get_answers requires self.retriever AND self.reader")
# 1) Apply retriever(with optional filters) to get fast candidate documents
documents = self.retriever.retrieve(question, filters=filters, top_k=top_k_retriever)
documents = self.retriever.retrieve(question, filters=filters, top_k=top_k_retriever, index=index)
if len(documents) == 0:
logger.info("Retriever did not return any documents. Skipping reader ...")
@ -69,7 +70,7 @@ class Finder:
return results
def get_answers_via_similar_questions(self, question: str, top_k_retriever: int = 10, filters: Optional[dict] = None):
def get_answers_via_similar_questions(self, question: str, top_k_retriever: int = 10, filters: Optional[dict] = None, index: str = None):
"""
Get top k answers for a given question using only a retriever.
@ -77,6 +78,7 @@ class Finder:
:param top_k_retriever: number of text units to be retrieved
:param filters: limit scope to documents having the given meta data values.
The format for the dict is {"key-1": ["value-1", "value-2"], "key-2": ["value-3]" ...}
:param index: index to retrieve documents from
:return:
"""
@ -87,7 +89,7 @@ class Finder:
# 1) Apply retriever to match similar questions via cosine similarity of embeddings
documents = self.retriever.retrieve(question, top_k=top_k_retriever, filters=filters)
documents = self.retriever.retrieve(question, top_k=top_k_retriever, filters=filters, index=index)
# 2) Format response
for doc in documents: