Together.ai Unveils Rerank API and Salesforce LlamaRank for Enhanced Enterprise Search

In
a
significant
development
for
enterprise
search
and
Retrieval
Augmented
Generation
(RAG)
systems,
Together.ai
has
announced
the
launch
of
its
new
serverless
Together
Rerank
API.
This
release
also
includes
exclusive
access
to
LlamaRank,
a
cutting-edge
reranker
model
developed
by
Salesforce
AI
Research,
according
to

Together
Ai
blog.

Revolutionizing
Enterprise
Search

The
newly
introduced
Together
Rerank
API
is
a
serverless
endpoint
designed
to
integrate
seamlessly
with
enterprise
applications.
This
API
simplifies
the
process
for
developers,
enabling
the
incorporation
of
supported
reranker
models
with
minimal
code.
Key
features
of
the
API
include:

• Flagship
  support
  for
  Salesforce’s
  LlamaRank
  model
• Support
  for
  JSON
  and
  tabular
  data
• Long
  8K
  context
  per
  document
• Low
  latency
  for
  fast
  search
  queries
• Compatibility
  with
  Cohere’s
  Rerank
  API

Exclusive
Access
to
LlamaRank

LlamaRank,
developed
by
Salesforce
AI
Research,
has
shown
superior
performance
compared
to
other
leading
rerank
models
like
Cohere
Rerank
v3
and
Mistral-7B.
This
model
enhances
document
ranking
capabilities,
thereby
improving
the
accuracy
and
efficiency
of
information
retrieval
in
both
RAG
and
traditional
search
systems.
LlamaRank
supports
documents
up
to
8,000
tokens
in
length
and
is
particularly
effective
for
semi-structured
data
such
as
JSON,
email,
tables,
and
code.

What
is
a
Reranker
Model?

A
reranker
is
a
specialized
model
that
improves
search
relevancy
by
reassessing
and
reordering
a
set
of
documents
based
on
their
relevance
to
a
given
query.
For
example,
in
a
technical
support
scenario,
a
user
query
about
resetting
a
password
would
result
in
the
reranker
prioritizing
the
most
relevant
documents,
thus
enhancing
the
search
results.

How
Reranking
Improves
Search
and
RAG

Reranking
is
a
critical
component
in
modern
search
and
RAG
systems,
acting
as
a
quality
filter
that
reassesses
initially
retrieved
documents.
This
step
enhances
the
quality
of
information
fed
into
language
models,
reducing
the
likelihood
of
inaccurate
or
irrelevant
results.
Rerankers
are
particularly
valuable
in
enterprise
settings,
where
large
volumes
of
data
in
various
formats
require
precise
and
accurate
retrieval
for
decision-making.

Salesforce
LlamaRank:
A
More
Accurate
Enterprise
Reranker
Model

Salesforce’s
LlamaRank
model
is
a
fine-tuned
version
of
Llama3-8B-Instruct,
trained
using
both
synthesized
data
and
human-labeled
data
from
Salesforce’s
in-house
data
analysts.
The
model
excels
in
ranking
both
general
documents
and
code,
making
it
highly
useful
for
various
enterprise
applications.
Salesforce
evaluated
LlamaRank
on
public
datasets
such
as
SQuAD,
TriviaQA,
Neural
Code
Search,
and
TrailheadQA,
where
it
demonstrated
superior
performance.

Together
Rerank
API

The
Together
Rerank
API
is
designed
to
provide
a
seamless
developer
experience
for
building
RAG
applications.
It
allows
developers
to
integrate
supported
reranker
models
into
their
enterprise
applications
easily.
The
API
takes
in
a
query
and
a
set
of
documents,
returning
a
relevancy
score
and
ordering
index
for
each
document.
It
can
also
filter
responses
to
show
only
the
most
relevant
documents.

How
to
Get
Started

To
get
started,
developers
can
create
an
API
key
with
Together
AI
and
follow
the
steps
in
the
quickstart
documentation
to
try
Salesforce’s
LlamaRank
model.
For
production-scale
deployment,
enterprises
are
encouraged
to
contact
Together.ai’s
sales
team.

Image
source:
Shutterstock