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POST
/
search
/
full-text-search
Full Text Search
curl --request POST \
  --url https://api.usecortex.ai/search/full-text-search \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "query": "John Smith Jake",
  "tenant_id": "tenant_1234",
  "sub_tenant_id": "sub_tenant_4567",
  "operator": "and",
  "max_chunks": 123
}'
[
  {
    "chunk_uuid": "<chunk_uuid>",
    "source_id": "CortexDoc1234",
    "chunk_content": "<chunk_content>",
    "source_type": "<source_type>",
    "source_upload_time": "<source_upload_time>",
    "source_title": "<source_title>",
    "source_last_updated_time": "<source_last_updated_time>",
    "layout": "{\"offsets\": {\"document_level_start_index\": 1024, \"page_level_start_index\": 50}, \"page\": 2}",
    "relevancy_score": 123,
    "document_metadata": {
      "author": "John Doe",
      "category": "Internal"
    },
    "tenant_metadata": {
      "department": "R&D"
    }
  }
]
Hit the Try it button to try this API now in our playground. It’s the best way to check the full request and response in one place, customize your parameters, and generate ready-to-use code snippets.

​
Sample Request

curl --request POST \
  --url https://api.usecortex.ai/search/full-text-search \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "query": "John Smith Jake",
  "tenant_id": "tenant_1234",
  "sub_tenant_id": "sub_tenant_4567",
  "max_chunks": 25,
  "operator": "and"
}'

​
Search Options

The Full Text Search endpoint provides various operators and search capabilities to help you find exactly what you’re looking for in your indexed content.

​
Boolean Operators

​
AND Operator

  • Usage: "operator": "and"
  • Behavior: All search terms must be present in the document for it to match
  • Best for: Precise searches where you need all keywords to be present
  • Example: Searching for “machine learning algorithms” with AND will only return documents containing both “machine” AND “learning” AND “algorithms”

​
OR Operator

  • Usage: "operator": "or"
  • Behavior: At least one search term must be present in the document for it to match
  • Best for: Broad searches to find documents containing any of the specified terms
  • Example: Searching for “python javascript react” with OR will return documents containing any of these programming languages

​
Search Optimization Tips

​
For Better Precision

  • Use the AND operator when you need all terms to be present
  • Use specific terminology rather than generic terms

​
For Broader Results

  • Use the OR operator to find documents with any of the search terms
  • Try synonyms and related terms to expand your search

​
Response

Returns an array of relevant content chunks from your indexed sources based on the full text search query. 
[
  {
    "chunk_uuid": "CortexDoc37e854b429784b148d4fc910812bdc581753761779_20_v1",
    "source_id": "CortexDoc37e854b429784b148d4fc910812bdc581753761779",
    "source_title": "IEEE Transactions LaTeX Templates.pdf",
    "chunk_content": "JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 12 Fig. 10: Comparison of models with 4bit precision on Medical QA dataset...",
    "source_url": "ToayuCogoBdxZVoZQ1ft8ZoRzCO2/Hello/Hello/local_source/CortexDoc37e854b429784b148d4fc910812bdc581753761779",
    "source_upload_time": "1753761802.079415",
    "source_collection": [],
    "source_type": "file",
    "layout": "{\"coordinates\": {\"x\": 48.96399688720703, \"y\": 26.49277114868164, \"width\": 514.0717697143555, \"height\": 721.1119651794434}, \"page\": 12}",
    "version": "v1",
    "source_last_updated_time": "",
    "relevancy_score": 0.8363813161849976,
    "rerank_score": null
  },
  {
    "chunk_uuid": "CortexDoc04d73ba38fd54325800a74ec17aed9041753761993_4_v1",
    "source_id": "CortexDoc04d73ba38fd54325800a74ec17aed9041753761993",
    "source_title": "IEEE Transactions LaTeX Templates.pdf",
    "chunk_content": "III. METHODOLOGY Our proposed framework integrates federated learning (FL) with blockchain technology to fine-tune Large Language Mod-",
    "source_url": "ToayuCogoBdxZVoZQ1ft8ZoRzCO2/Hello/Hello/local_source/CortexDoc04d73ba38fd54325800a74ec17aed9041753761993",
    "source_upload_time": "1753762013.1677928",
    "source_collection": [],
    "source_type": "file",
    "layout": "{\"coordinates\": {\"x\": 311.9779968261719, \"y\": 712.3123168945312, \"width\": 251.05746459960938, \"height\": 35.72357177734375}, \"page\": 2}",
    "version": "v1",
    "source_last_updated_time": "",
    "relevancy_score": 0.800000011920929,
    "rerank_score": null
  }
]
Note: This API performs full text search with configurable operators for precise text matching. For conversational Q&A with AI-generated responses, use the /search/qna API instead.

​
Operator Parameter

The operator parameter controls how the search terms are combined:
  • OR operator (default): At least one token must be present in the document
  • AND operator: All tokens must be present in the document

​
Max Chunks Parameter

The max_chunks parameter controls the maximum number of results returned:
  • Must be between 1 and 1001
  • Defaults to the system limit if not specified

​
Use Cases

  • Precise keyword matching: Use AND operator when you need all search terms to be present
  • Broad search: Use OR operator to find documents containing any of the search terms
  • Exact phrase matching: Ideal for finding specific terminology or phrases in your documents

​
Error Responses

All endpoints return consistent error responses following the standard format. For detailed error information, see our Error Responses documentation.

Authorizations

​
Authorization
string
header
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json
​
query
string
required

Search terms to find in your content

Example:

"John Smith Jake"

​
tenant_id
string
required

Unique identifier for the tenant/organization

Example:

"tenant_1234"

​
sub_tenant_id
string | null
default:""

Optional sub-tenant identifier used to organize data within a tenant. If omitted, the default sub-tenant created during tenant setup will be used.

Example:

"sub_tenant_4567"

​
operator
enum<string>

How to combine search terms (OR or AND)

Available options:
or,
and
​
max_chunks
integer | null
default:25

Maximum number of results to return

Response

Successful Response

​
chunk_uuid
string
required

Unique identifier for this content chunk

Example:

"<chunk_uuid>"

​
source_id
string
required

Unique identifier for the source document

Example:

"CortexDoc1234"

​
chunk_content
string
required

The actual text content of this chunk

Example:

"<chunk_content>"

​
source_type
string
default:""

Type of the source document (file, webpage, etc.)

Example:

"<source_type>"

​
source_upload_time
string
default:""

When the source document was originally uploaded

Example:

"<source_upload_time>"

​
source_title
string
default:""

Title or name of the source document

Example:

"<source_title>"

​
source_last_updated_time
string
default:""

When the source document was last modified

Example:

"<source_last_updated_time>"

​
layout
string | null

Layout of the chunk in original document. You will generally receive a stringified dict with 2 keys, offsets and page(optional). Offsets will have document_level_start_index and page_level_start_index(optional)

Example:

"{\"offsets\": {\"document_level_start_index\": 1024, \"page_level_start_index\": 50}, \"page\": 2}"

​
relevancy_score
number | null

Score indicating how relevant this chunk is to your search query, with higher values indicating better matches

​
document_metadata
object | null

Metadata extracted from the source document

Example:
{
  "author": "John Doe",
  "category": "Internal"
}
​
tenant_metadata
object | null

Custom metadata associated with your tenant

Example:
{ "department": "R&D" }
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