> ## Documentation Index > Fetch the complete documentation index at: https://docs.trieve.ai/llms.txt > Use this file to discover all available pages before exploring further. # Searching with Trieve > Learn how to search over your data with Trieve ## Overview We provide the ability for you to search your data in a fast and performant manner. We have multiple search paradigms, which are exposed through the [search over chunks route](/api-reference/chunk/search), the [search within groups route](/api-reference/chunk-group/search-within-group), and the [search over groups route](/api-reference/chunk-group/search-over-groups). * `query`: The user query that is embedded and searched against the dataset. * `search_type`: Can be semantic, fulltext, or hybrid. Semantic: Uses cosine distance to determine the most relevant results. Fulltext: Uses a SPLADE model to find the most relevant results. Hybrid: Uses a reranker model that pulls one page of results from both fulltext and semantic searches to find the most relevant results. * `page`: The page of chunks to fetch. Pages are 1-indexed. * `page_size`: This lets you tune the number of results that are returned. * `highlight_results`: Enables subsentence highlighting of relevant portions of the text. * `slim_chunks`: Excludes chunk\_html from the returned results to reduce network bandwidth. Useful for large chunks. * `recency_bias`: A value from 0-1 that tunes how much the recency of chunks (based on the timestamp field) affects the ranking. * `sort_options`: Options on how to sort. * `filters`: Apply filters to get exactly the results you want. ## Search Modes Trieve offers 4 different types of search. ### Semantic Search Semantic search uses an embeddnig model to generate a query vector. Defaults to using cosine similarity and `jina-base-en` Trieve uses only the embedding model to select and rerank the results. This search\_type is `semantic`. ### Full Text search FullText search uses a SPLADE model to find the most relevant results to your given `query`. This search\_type is `fulltext`. ### BM25 BM25 is the classical type of search index, it uses the BM25 ranking function to determine the results that are most similar to your given `query`. This search\_type is `bm25`. ### Hybrid Hybrid search, does both a full text search, and semantic search. From those results it then uses a *reranker model* ( defaults to bge-reranker-large). This search\_type is `hybrid`. ## Search Paradigms We offer three different search strategies for you to choose from: 1. [**Search over chunks**](/api-reference/chunk/search): This strategy allows you to search all of your chunks independently. This is useful when your chunks are independent and do not need to be grouped together. 2. [**Search within groups**](/api-reference/chunk-group/search-within-group): This strategy lets you constrain your results to within a selected group. This is useful for searching distinct groups within your dataset independently. 3. [**Search over groups**](/api-reference/chunk-group/search-over-groups): This strategy allows you to search over the groups of chunks within your dataset. This returns the groups and the top chunks within each group that matched your query, providing better search quality for datasets with highly related chunks within groups. ## Search over chunks ```json theme={null} POST /api/chunk/search Headers: { "TR-Dataset": "", "Authorization": "tr-*******************" } Body: { "query": "How to search with Trieve", "search_type": "fulltext", "page": 1, "page_size": 10, "score_threshold": 0.5 } ``` ## Search within group ```json theme={null} POST /api/chunk_group/search Headers: { "TR-Dataset": "", "Authorization": "tr-*******************" } Body: { "group_tracking_id": "my-group-tracking-id", "query": "How to search with Trieve", "search_type": "fulltext", "page": 1, "page_size": 10, "score_threshold": 0.5 } ``` ## Search over groups ```json theme={null} POST api/chunk_group/group_oriented_search Headers: { "TR-Dataset": "", "Authorization": "tr-*******************" } Body: { "group_tracking_id": "my-group-tracking-id", "group_size": 5, "query": "How to search with Trieve", "search_type": "fulltext", "page": 1, "page_size": 10, "score_threshold": 0.5 } ``` You can use the search UI at [search.trieve.ai](https://search.trieve.ai) to A/B test which search method works best for you. ## Filters Trieve filters are structured around three clauses: * `must`: All filters within this clause must be matched to return the chunks. * `must_not`: All filters in this clause must not be matched to return the chunks. * `should`: Any of these conditions can be matched to return a chunk. Each clause contains a `field_condition`. * `range`: Match a number between a range of `lt`, `gt`, `lte` or `gte` * `match_all`: A list, every field must have have a match. * `match_any`: A list, at least 1 field must be present. * `date_range`: Match a date between a range of `lt`, `gt`, `lte` or `gte` * `geo_radius`: Match a radius based on a `center` and a `radius` * `boolean`: Matches if the field is true or false > Get chunks with both "CO" and "321" in their `tag_set` ```json theme={null} "filters": { "must": [ { "field": "tag_set", "match_all": ["CO", "321"] } ] } ``` > Get chunks with either "CO" OR "321" in their tag\_set: ```json theme={null} "filters": { "must": [ { "field": "tag_set", "match_any": ["CO", "321"] } ] } ``` > Get chunks that are tagged within a GEO radius ```json theme={null} "filters": { "must": [ { "field": "geo_radius", "geo_radius": { "center": { "lat": 20, "long": -30 }, "radius": 20, } } ] } ``` > Get chunks with neither "CO" nor "321" in their tag\_set: ```json theme={null} "filters": { "must_not": [ { "field": "tag_set", "match_all": ["CO", "321"] } ] } ``` > Get chunks that either don't have "CO" in their tag\_set or don't have "321" in their tag\_set: ```json theme={null} "filters": { "must_not": [ { "field": "tag_set", "match_any": ["CO", "321"] }, ] } ``` > Get chunks that either have "CO" in their tag\_set or "[http://example.com](http://example.com)" in their link: ```json theme={null} "filters": { "should": [ { "field": "tag_set", "match": ["CO"] }, { "field": "link", "match": ["http://example.com"] } ] } ``` > Get Chunks that have num\_value between 20 and 30 ```json theme={null} "filters": { "must": [ { "field": "num_value", "range": { "gte": 20.0, "lte": 30.0, "gt": 30.0, "lt": 20.0 } } ] } ``` ## Rerank By `rerank_type` can be either * `fulltext`: This will use the `fulltext` index to rerank the results, if `search_type` is `fulltext` then nothing different will happen. * `cross_encoder`: This will use the Reranker model that you predefined. By default `hybrid` search will use the `cross_encoder`. * `bm25`: This will use the `bm25` matching algorithm rerank the results, if `search_type` is `bm25` then nothing different will happen. * `semantic`: This will use the `semantic` vectors to rerank the results, if `search_type` is `semantic` then nothing different will happen. ```json theme={null} { "sort_options": { "sort_by" { "rerank_type": "fulltext" } } } ``` ## Multi Query MultiQuery provides a way to give multiple `query` objects with a given weight bias. To use the `multiquery`, instead of a single string, the `query` parameter receives a list of tuples, value 1 being the query and value 2 being a value on how important it is. As an example, search > Searching, but the search term of "iphone" and a color. ```json theme={null} "query": [ [ "Flagship", 2 ], [ "Red", 2 ], [ "Iphone mini", 10 ] ] ``` ## Customizing your search models Trieve offers many ways to customize your embedding models and reranker models. Different embedding models and different reranker models are better suited for different tasks. ### Embedding Models Trieve supports multiple embedding models that can be used to search over your data. You can specify the embedding model to use in the `server_configuration` field when creating a dataset. After creating a dataset, you cannot change the embedding model. If you need to change the embedding model, you must create a new dataset. ### Reranker models Trieve supports multiple reranker models that can be used to rerank the search results. Currently, Trieve supports the BAAI `bge-reranker-large` model, AIMon's `aimon-rerank` model, and Cohere's `rerank-v3.5` model. ![](https://cdn.trieve.ai/docs/reranker-model-edit.png) #### BAAI/bge-reranker-large `bge-reranker-large` is a model by the Beijing Academy of Artificial Intelligence (BAAI) and is hosted by Trieve. This model does not require any additional configuration and will be used by default on all `hybrid` searches. To manually select the `bge-reranker-large` as your reranker model, make a request to the [update dataset route](/api-reference/dataset/update-dataset-by-id-or-tracking-id) with the following parameters: ```json theme={null} curl --request POST \ --url https://api.trieve.ai/api/dataset \ --header 'Authorization: ' \ --header 'Content-Type: application/json' \ --header 'TR-Organization: ' \ --data '{ "dataset_name": "New Dataset", // Replace with a name for your dataset "organization_id": "********-****-****-****-************", // Update with the desired configurations for your dataset "server_configuration": { "RERANKER_MODEL_NAME":"bge-reranker-large" } }' ``` #### AIMon's aimon-rerank `aimon-rerank` is a model hosted by [AIMon](https://docs.aimon.ai/). To use this model, you must provide the `aimon_api_key` in the `server_configuration` field when creating a dataset. To switch your reranker model to AIMon's `aimon-rerank`, make a request to the [update dataset route](/api-reference/dataset/update-dataset-by-id-or-tracking-id) with the following parameters: ```json theme={null} curl --request POST \ --url https://api.trieve.ai/api/dataset \ --header 'Authorization: ' \ --header 'Content-Type: application/json' \ --header 'TR-Organization: ' \ --data '{ "dataset_name": "New Dataset", // Replace with a name for your dataset "organization_id": "********-****-****-****-************", // Update with the desired configurations for your dataset "server_configuration": { "RERANKER_BASE_URL":"https://pbe-api.aimon.ai/v1/rerank-icl", "RERANKER_MODEL_NAME":"aimon-rerank", "RERANKER_API_KEY":"", "AIMON_RERANKER_TASK_DEFINITION": "" // A task definition can be used to specify the domain of the context documents for AIMon reranker. // example of a task definition: "Your task is to grade the relevance of context document(s) in the domain of music and arts." } }' ``` #### Cohere's rerank-v3.5 `rerank-v3.5` is a model hosted by [Cohere](https://docs.cohere.com/cohere-documentation). To use this model, you must provide the `cohere_api_key` in the `server_configuration` field when creating a dataset. To switch your reranker model to Cohere's `rerank-v3.5`, make a request to the [update dataset route](/api-reference/dataset/update-dataset-by-id-or-tracking-id) with the following parameters: ```json theme={null} curl --request POST \ --url https://api.trieve.ai/api/dataset \ --header 'Authorization: ' \ --header 'Content-Type: application/json' \ --header 'TR-Organization: ' \ --data '{ "dataset_name": "New Dataset", // Replace with a name for your dataset "organization_id": "********-****-****-****-************", // Update with the desired configurations for your dataset "server_configuration": { "RERANKER_BASE_URL":"https://api.cohere.com/v2", "RERANKER_MODEL_NAME":"rerank-v3.5" "RERANKER_API_KEY":"" } }' ```