# Search the top K semantically similar records ### Search the top K semantically similar records If you have defined indices, you can query Epsilla with natural language questions. The question will be embedded using the same model as the index definition: {% tabs %} {% tab title="Python" %} ```python status_code, response = db.query( table_name="MyTable", # The name of the table to query against. query_index="Index", # The index name to query against. query_text="Where can I find a serene environment, ideal for relaxation and introspection?", # The query question. limit=2, # The top K result to return. response_fields=["Doc"], # (Optional) which fields to be included in # the response. If not provided, will include # all fields in the table. filter="ID < 6 AND Doc <> 'London'", # (Optional) a boolean expression for filtering # out the results. with_distance=True # (Optional) include the distance or not in # the response. Default is False. When given # as True, each matched record will have a # @distance field returned. ) print(response) ``` Response example: ``` { 'message': 'Query search successfully.', 'result': [ { '@distance': 0.4707561135292053, 'Doc': 'The library was a quiet haven, filled with the scent of old books and the soft rustling of pages.', 'ID': 3 }, { '@distance': 0.47871750593185425, 'Doc': 'High in the mountains, the air was crisp and clear, revealing breathtaking views of the valley below.', 'ID': 4 } ], 'statusCode': 200 } ``` {% endtab %} {% tab title="JavaScript" %} ```javascript const query = await db.query( 'MyTable', // The name of the table to query against. { queryIndex: 'Index', // The index name to query against. query: 'Where can I find a serene environment, ideal for relaxation and introspection?', // The query question. limit: 2, // The top K result to return. response: ["Doc"], // (Optional) which fields to be included in // the response. If not provided, will include // all fields in the table. filter: 'ID < 6 AND Doc <> \'London\'', // (Optional) filter: a boolean expression for filtering // out the results. withDistance: true // (Optional) include the distance or not in // the response. Default is False. When given // as true, each matched record will have a // @distance field returned. } ); console.log(JSON.stringify(query, undefined, 2)); ``` Response example: ``` { "statusCode": 200, "message": "Query search successfully.", "result": [ { "@distance": 0.4707561135292053, "Doc": "The library was a quiet haven, filled with the scent of old books and the soft rustling of pages.", "ID": 3 }, { "@distance": 0.47871750593185425, "Doc": "High in the mountains, the air was crisp and clear, revealing breathtaking views of the valley below.", "ID": 4 } ] } ``` {% endtab %} {% endtabs %} If there is only one embedding field, the query field parameter can be omitted. Below is an example with minimum required parameters: {% tabs %} {% tab title="Python" %} ```python status_code, response = db.query( table_name="MyTable", query_text="Where can I find a serene environment, ideal for relaxation and introspection?", limit=2 ) ``` {% endtab %} {% tab title="JavaScript" %} ```javascript const query = await db.query( 'MyTable', { query: "Where can I find a serene environment, ideal for relaxation and introspection?", limit: 2 } ); ``` {% endtab %} {% endtabs %} If you brought in your own embedding vectors via vector fields, you can query by providing the target vector: {% tabs %} {% tab title="Python" %} ```python status_code, response = db.query( table_name="MyTable", # The name of the table to query against. query_field="Embedding", # The embedding field name to query against. query_vector=[0.35, 0.55, 0.47, 0.94], # The embedded vector from the question. limit=2, # The top K result to return. response_fields=["Doc"], # (Optional) which fields to be included in # the response. If not provided, will include # all fields in the table. filter="ID < 6 AND Doc <> 'London'", # (Optional) a boolean expression for filtering # out the results. with_distance=True # (Optional) include the distance or not in # the response. Default is False. When given # as True, each matched record will have a # @distance field returned. ) print(response) ``` Response example: ``` { 'statusCode': 200, 'message': 'Query search successfully.', 'result': [ { '@distance': 0.0001000004049274139, 'Doc': 'The library was a quiet haven, filled with the scent of old books and the soft rustling of pages.' }, { '@distance': 0.2176999747753143, 'Doc': 'The garden was blooming with vibrant flowers, attracting butterflies and bees with their sweet nectar.' } ] } ``` {% endtab %} {% tab title="JavaScript" %} ```javascript const query = await db.query( 'MyTable', // The name of the table to query against. { queryField: 'Embedding', // The embedding field name to query against. queryVector: [0.35, 0.55, 0.47, 0.94], // The embedded vector from the question. limit: 2, // The top K result to return. response: ["Doc"], // (Optional) which fields to be included in // the response. If not provided, will include // all fields in the table. filter: 'ID < 6 AND Doc <> \'London\'', // (Optional) filter: a boolean expression for filtering // out the results. withDistance: true // (Optional) include the distance or not in // the response. Default is False. When given // as true, each matched record will have a // @distance field returned. } ); console.log(JSON.stringify(query, undefined, 2)); ``` Response example: ``` { "statusCode": 200, "message": "Query search successfully.", "result": [ { "@distance": 0.0001000004049274139, "Doc": "The library was a quiet haven, filled with the scent of old books and the soft rustling of pages." }, { "@distance": 0.2176999747753143, "Doc": "The garden was blooming with vibrant flowers, attracting butterflies and bees with their sweet nectar." } ] } ``` {% endtab %} {% endtabs %} {% hint style="info" %} If there is only one embedding field, the query field parameter can be omitted. {% endhint %} #### Filter Expression Epsilla supports filter expression in search that is compatible with SQL. The following operators are supported: | Operator | Description | | -------- | ----------------------------------------------------- | | + | Add | | - | Subtract | | \* | Multiply | | / | Divide | | % | Modulo | | = | Equal to | | > | Greater than | | < | Less than | | >= | Greater than or equal to | | <= | Less than or equal to | | <> | Not equal to | | AND | TRUE if all the conditions separated by AND is TRUE | | NOT | TRUE if the condition afterward is NOT TRUE | | OR | TRUE if any of the conditions separated by OR is TRUE | A string literal needs to be surrounded by single quotes. Refer to a field of the record directly by the field name. Providing an empty filter is same as not providing the filter at all (all records will pass the evaluation). #### Filter on @distance Epsilla supports filter the result by the distance. For example, use the following filter condition to only return the matching results that have a semantic similarity distance less than 0.5: ``` "filter": "@distance < 0.5" ``` @distance can be combined and nested with all the other filter condition operators. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://epsilla-inc.gitbook.io/epsilladb/epsilla-vector-database/user-manual/search-the-top-k-semantically-similar-records.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.