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Commitcbbf028

Browse files
jsaied99jas8dz
and
jas8dz
authored
Adding filtering based on metadata using kwargs (#682)
Co-authored-by: jas8dz <jsaied@mail.missouri.edu>
1 parentb872694 commitcbbf028

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2 files changed

+14
-2
lines changed

2 files changed

+14
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‎pgml-sdks/python/pgml/examples/question_answering.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -33,7 +33,7 @@
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start=time()
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query="Who won 20 grammy awards?"
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results=collection.vector_search(query,top_k=5)
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results=collection.vector_search(query,top_k=5,title="Beyoncé")
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_end=time()
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console.print("\nResults for '%s'"% (query),style="bold")
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console.print(results)

‎pgml-sdks/python/pgml/pgml/collection.py‎

Lines changed: 13 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -683,6 +683,7 @@ def vector_search(
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top_k:int=5,
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model_id:int=1,
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splitter_id:int=1,
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**kwargs:Any,
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)->List[Dict[str,Any]]:
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"""
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This function performs a vector search on a database using a query and returns the top matching
@@ -702,6 +703,9 @@ def vector_search(
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splitter used to split the documents into chunks. It is used to retrieve the embeddings table
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associated with the specified splitter, defaults to 1
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:type splitter_id: int (optional)
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:param kwargs: Additional filtering parameters to be used in the search query. These parameters
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are from the metadata of the documents and can be used to filter the search results based on
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metadata values.
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:return: a list of dictionaries containing search results for a given query. Each dictionary
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contains the following keys: "score", "text", and "metadata". The "score" key contains a float
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value representing the similarity score between the query and the search result. The "text" key
@@ -749,6 +753,13 @@ def vector_search(
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% (model_id,splitter_id,model_id,splitter_id)
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)
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return []
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ifkwargs:
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metadata_filter= [f"documents.metadata->>'{k}' = '{v}'"ifisinstance(v,str)elsef"documents.metadata->>'{k}' ={v}"fork,vinkwargs.items()]
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metadata_filter=" AND ".join(metadata_filter)
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metadata_filter=f"AND{metadata_filter}"
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else:
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metadata_filter=""
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cte_select_statement="""
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WITH query_cte AS (
@@ -764,7 +775,7 @@ def vector_search(
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SELECT cte.score, chunks.chunk, documents.metadata
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FROM cte
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INNER JOIN {chunks_table} chunks ON chunks.id = cte.chunk_id
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INNER JOIN {documents_table} documents ON documents.id = chunks.document_id;
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INNER JOIN {documents_table} documents ON documents.id = chunks.document_id {metadata_filter}
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""".format(
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model=sql.Literal(model).as_string(conn),
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query_text=query,
@@ -773,6 +784,7 @@ def vector_search(
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top_k=top_k,
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chunks_table=self.chunks_table,
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documents_table=self.documents_table,
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metadata_filter=metadata_filter,
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)
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search_results=run_select_statement(

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