llm-context

仓库: brave/brave-search-skills
安装量: 70
排名: #10926

安装

npx skills add https://github.com/brave/brave-search-skills --skill llm-context
LLM Context
Requires API Key
Get one at
https://api.search.brave.com
Plan
Included in the
Search
plan. See
https://api-dashboard.search.brave.com/app/subscriptions/subscribe
Brave LLM Context API delivers pre-extracted, relevance-ranked web content optimized for grounding LLM responses in real-time search results. Unlike traditional web search APIs that return links and snippets, LLM Context extracts the actual page content—text chunks, tables, code blocks, and structured data—so your LLM or AI agent can reason over it directly.
LLM Context vs AI Grounding
Feature
LLM Context (this)
AI Grounding (
answers
)
Output
Raw extracted content for YOUR LLM
End-to-end AI answers with citations
Interface
REST API (GET/POST)
OpenAI-compatible
/chat/completions
Searches
Single search per request
Multi-search (iterative research)
Speed
Fast (<1s)
Slower
Plan
Search
Answers
Endpoint
/res/v1/llm/context
/res/v1/chat/completions
Best for
AI agents, RAG pipelines, tool calls
Chat interfaces, research mode
Endpoint
GET https://api.search.brave.com/res/v1/llm/context
POST https://api.search.brave.com/res/v1/llm/context
Authentication
:
X-Subscription-Token:
header
Optional Headers
:
Accept-Encoding: gzip
— Enable gzip compression
Quick Start
GET Request
curl
-s
"https://api.search.brave.com/res/v1/llm/context?q=tallest+mountains+in+the+world"
\
-H
"Accept: application/json"
\
-H
"X-Subscription-Token:
${BRAVE_SEARCH_API_KEY}
"
POST Request (JSON body)
curl
-s
--compressed
-X
POST
"https://api.search.brave.com/res/v1/llm/context"
\
-H
"Accept: application/json"
\
-H
"Accept-Encoding: gzip"
\
-H
"X-Subscription-Token:
${BRAVE_SEARCH_API_KEY}
"
\
-H
"Content-Type: application/json"
\
-d
'{"q": "tallest mountains in the world"}'
With Goggles (Inline)
curl
-s
"https://api.search.brave.com/res/v1/llm/context"
\
-H
"Accept: application/json"
\
-H
"X-Subscription-Token:
${BRAVE_SEARCH_API_KEY}
"
\
-G
\
--data-urlencode
"q=rust programming"
\
--data-urlencode
'goggles=$discard
$site=docs.rs
$site=rust-lang.org'
Parameters
Query Parameters
Parameter
Type
Required
Default
Description
q
string
Yes
-
Search query (1-400 chars, max 50 words)
country
string
No
US
Search country (2-letter country code or
ALL
)
search_lang
string
No
en
Language preference (2+ char language code)
count
int
No
20
Max search results to consider (1-50)
Context Size Parameters
Parameter
Type
Required
Default
Description
maximum_number_of_urls
int
No
20
Max URLs in response (1-50)
maximum_number_of_tokens
int
No
8192
Approximate max tokens in context (1024-32768)
maximum_number_of_snippets
int
No
50
Max snippets across all URLs (1-100)
maximum_number_of_tokens_per_url
int
No
4096
Max tokens per individual URL (512-8192)
maximum_number_of_snippets_per_url
int
No
50
Max snippets per individual URL (1-100)
Filtering & Local Parameters
Parameter
Type
Required
Default
Description
context_threshold_mode
string
No
balanced
Relevance threshold for including content (
strict
/
balanced
/
lenient
)
enable_local
bool
No
null
Local recall control (
true
/
false
/
null
, see below)
goggles
string/list
No
null
Goggle URL or inline definition for custom re-ranking
Context Size Guidelines
Task Type
count
max_tokens
Example
Simple factual
5
2048
"What year was Python created?"
Standard queries
20
8192
"Best practices for React hooks"
Complex research
50
16384
"Compare AI frameworks for production"
Larger context windows provide more information but increase latency and cost (of your inference). Start with defaults and adjust.
Threshold Modes
Mode
Behavior
strict
Higher threshold — fewer but more relevant results
balanced
Default — good balance between coverage and relevance
lenient
Lower threshold — more results, may include less relevant content
Local Recall
The
enable_local
parameter controls location-aware recall:
Value
Behavior
null
(not set)
Auto-detect
— local recall enabled when any location header is provided
true
Force local
— always use local recall, even without location headers
false
Force standard
— always use standard web ranking, even with location headers
For most use cases, omit
enable_local
and let the API auto-detect from location headers.
Location Headers
Header
Type
Description
X-Loc-Lat
float
Latitude (-90.0 to 90.0)
X-Loc-Long
float
Longitude (-180.0 to 180.0)
X-Loc-City
string
City name
X-Loc-State
string
State/region code (ISO 3166-2)
X-Loc-State-Name
string
State/region name
X-Loc-Country
string
2-letter country code
X-Loc-Postal-Code
string
Postal code
Priority
:
X-Loc-Lat
+
X-Loc-Long
take precedence. When provided, text-based headers (City, State, Country, Postal-Code) are not used for location resolution. Provide text-based headers
only
when you don't have coordinates.
Example: With Coordinates
curl
-s
"https://api.search.brave.com/res/v1/llm/context"
\
-H
"Accept: application/json"
\
-H
"X-Subscription-Token:
${BRAVE_SEARCH_API_KEY}
"
\
-H
"X-Loc-Lat: 37.7749"
\
-H
"X-Loc-Long: -122.4194"
\
-G
\
--data-urlencode
"q=best coffee shops near me"
Example: With Place Name
curl
-s
"https://api.search.brave.com/res/v1/llm/context"
\
-H
"Accept: application/json"
\
-H
"X-Subscription-Token:
${BRAVE_SEARCH_API_KEY}
"
\
-H
"X-Loc-City: San Francisco"
\
-H
"X-Loc-State: CA"
\
-H
"X-Loc-Country: US"
\
-G
\
--data-urlencode
"q=best coffee shops near me"
Goggles (Custom Ranking) — Unique to Brave
Goggles let you
control which sources ground your LLM
— essential for RAG quality.
Use Case
Goggle Rules
Official docs only
$discard\n$site=docs.python.org
Exclude user content
$discard,site=reddit.com\n$discard,site=stackoverflow.com
Academic sources
$discard\n$site=arxiv.org\n$site=.edu
No paywalls
$discard,site=medium.com
Method
Example
Hosted
--data-urlencode "goggles=https://raw.githubusercontent.com/brave/goggles-quickstart/main/goggles/1k_short.goggle"
Inline
--data-urlencode 'goggles=$discard\n$site=example.com'
Hosted
goggles must be on GitHub/GitLab, include
! name:
,
! description:
,
! author:
headers, and be registered at
https://search.brave.com/goggles/create
.
Inline
rules need no registration.
Syntax
:
$boost=N
/
$downrank=N
(1–10),
$discard
,
$site=example.com
. Combine with commas:
$site=example.com,boost=3
. Separate rules with
\n
(
%0A
).
Allow list
:
$discard\n$site=docs.python.org\n$site=developer.mozilla.org
—
Block list
:
$discard,site=pinterest.com\n$discard,site=quora.com
Resources
:
Discover
·
Syntax
·
Quickstart
Response Format
Standard Response
{
"grounding"
:
{
"generic"
:
[
{
"url"
:
"https://example.com/page"
,
"title"
:
"Page Title"
,
"snippets"
:
[
"Relevant text chunk extracted from the page..."
,
"Another relevant passage from the same page..."
]
}
]
,
"map"
:
[
]
}
,
"sources"
:
{
"https://example.com/page"
:
{
"title"
:
"Page Title"
,
"hostname"
:
"example.com"
,
"age"
:
[
"Wednesday, January 15, 2025"
,
"2025-01-15"
,
"392 days ago"
]
}
}
}
Local Response (with
enable_local
)
{
"grounding"
:
{
"generic"
:
[
...
]
,
"poi"
:
{
"name"
:
"Business Name"
,
"url"
:
"https://business.com"
,
"title"
:
"Title of business.com website"
,
"snippets"
:
[
"Business details and information..."
]
}
,
"map"
:
[
{
"name"
:
"Place Name"
,
"url"
:
"https://place.com"
,
"title"
:
"Title of place.com website"
,
"snippets"
:
[
"Place information and details..."
]
}
]
}
,
"sources"
:
{
"https://business.com"
:
{
"title"
:
"Business Name"
,
"hostname"
:
"business.com"
,
"age"
:
null
}
}
}
Response Fields
Field
Type
Description
grounding
object
Container for all grounding content by type
grounding.generic
array
Array of URL objects with extracted content (main grounding data)
grounding.generic[].url
string
Source URL
grounding.generic[].title
string
Page title
grounding.generic[].snippets
array
Extracted smart chunks relevant to the query
grounding.poi
object/null
Point of interest data (only with local recall)
grounding.poi.name
string/null
Point of interest name
grounding.poi.url
string/null
POI source URL
grounding.poi.title
string/null
POI page title
grounding.poi.snippets
array/null
POI text snippets
grounding.map
array
Map/place results (only with local recall)
grounding.map[].name
string/null
Place name
grounding.map[].url
string/null
Place source URL
grounding.map[].title
string/null
Place page title
grounding.map[].snippets
array/null
Place text snippets
sources
object
Metadata for all referenced URLs, keyed by URL
sources[url].title
string
Page title
sources[url].hostname
string
Source hostname
sources[url].age
array/null
Page modification dates (when available)
Note
Snippets may contain plain text OR JSON-serialized structured data (tables, schemas, code blocks). LLMs handle this mixed format well.
Use Cases
AI Agents
Give your agent a web search tool that returns ready-to-use content in a single call
RAG Pipelines
Ground LLM responses in fresh, relevant web content
AI Assistants & Chatbots
Provide factual answers backed by real sources
Question Answering
Retrieve focused context for specific queries
Fact Checking
Verify claims against current web content
Content Research
Gather source material on any topic with one API call
Best Practices
Token budget
Start with defaults (
maximum_number_of_tokens=8192
,
count=20
). Reduce for simple lookups, increase for complex research.
Source quality
Use Goggles to restrict to trusted sources. Set
context_threshold_mode=strict
when precision > recall.
Performance
Use smallest count and maximum_number_of_tokens that meet your needs. For local queries, provide location headers.
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