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API Documentation

Try for Free

API Documentation

Try for Free

Bring knowledge to your AI apps

Batteries-included,
GraphRAG and RAG-as-a-Service Platform

Try Graphlit for free

SDKs available for Python, Node.js and .NET

Batteries-included,
GraphRAG and RAG-as-a-Service Platform

Try Graphlit for free

SDKs available for Python, Node.js and .NET

from graphlit import Graphlit
from graphlit_api import *

await graphlit.client.ingest_uri(
  uri="https://www.graphlit.com"
)

response = await graphlit.client.prompt_conversation(
  prompt="How can Graphlit accelerate my Generative AI app development?"
)

message = response.prompt_conversation.message.message

print(message)

from graphlit import Graphlit
from graphlit_api import *

await graphlit.client.ingest_uri(
  uri="https://www.graphlit.com"
)

response = await graphlit.client.prompt_conversation(
  prompt="How can Graphlit accelerate my 
Generative AI app development?"
)

message = response.prompt_conversation.message.message

print(message)

Integrate your unstructured data
with LLMs in minutes, not weeks

Chat with Documents

Summarize Website

Extract JSON from PDF

Summarize Podcast

Publish GitHub Report

What can I ingest?

Ingest from any data source such as:
Web sites, cloud storage, SharePoint, podcasts,
Jira, Notion, YouTube, email or Slack

Ingest any unstructured data format such as:
documents, audio, video, or images

See Pricing

Use Cases

Read Blog

Signup for Graphlit

Have Questions?

Read Graphlit Blog

Have Questions?

See Pricing

Read Graphlit Blog

Have Questions?

AUTOMATED ETL FOR LLMs

Unstructured data ingestion

Data feeds for automated ingestion
Extract text and tables from documents and images with OCR and LLMs
Configure automated content workflows
Build GraphRAG-ready knowledge graph from your content
Automated web scraping
Enrich data with external APIs, such as Wikipedia and Crunchbase

from graphlit import Graphlit
from graphlit_api import *		

input = FeedInput(
  name=f"{account-name}: {container-name}",
  type=FeedTypes.SITE,
  site=SiteFeedPropertiesInput(
    type=FeedServiceTypes.AZURE_BLOB,
    isRecursive=True,
    azureBlob=AzureBlobFeedPropertiesInput(
      accountName="{account-name}",
      containerName="{container-name}",
      storageAccessKey="{storage-key}",
      prefix="{prefix}"
    )
  ),
  workflow=EntityReferenceInput(
    id="{workflow-id}"
  )
)
  
response = await graphlit.client.create_feed(input)

from graphlit import Graphlit
from graphlit_api import *		

input = FeedInput(
  name=f"{account-name}: {container-name}",
  type=FeedTypes.SITE,
  site=SiteFeedPropertiesInput(
    type=FeedServiceTypes.AZURE_BLOB,
    isRecursive=True,
    azureBlob=AzureBlobFeedPropertiesInput(
      accountName="{account-name}",
      containerName="{container-name}",
      storageAccessKey="{storage-key}",
      prefix="{prefix}"
    )
  ),
  workflow=EntityReferenceInput(
    id="{workflow-id}"
  )
)
  
response = await graphlit.client.create_feed(input)

from graphlit import Graphlit
from graphlit_api import *		

input = WorkflowInput(
  name="Azure AI Document Intelligence",
  preparation=PreparationWorkflowStageInput(
    jobs=[
      PreparationWorkflowJobInput(
        connector=FilePreparationConnectorInput(
          type=FilePreparationServiceTypes.AZURE_DOCUMENT_INTELLIGENCE,
          azureDocument=AzureDocumentPreparationPropertiesInput(
            model=AzureDocumentIntelligenceModels.LAYOUT
          )
        )
      )
    ]
  )
)

response = await graphlit.client.create_workflow(input)

from graphlit import Graphlit
from graphlit_api import *		

input = WorkflowInput(
  name="Azure AI Document Intelligence",
  preparation=PreparationWorkflowStageInput(
    jobs=[
      PreparationWorkflowJobInput(
        connector=FilePreparationConnectorInput(
          type=FilePreparationServiceTypes.AZURE_DOCUMENT_INTELLIGENCE,
          azureDocument=AzureDocumentPreparationPropertiesInput(
            model=AzureDocumentIntelligenceModels.LAYOUT
          )
        )
      )
    ]
  )
)

response = await graphlit.client.create_workflow(input)

RAG-AS-A-Service

What else?

RAG Ready: Intelligent text extraction and chunking, built-in vector embeddings and conversation history
Semantic Search: Vector-based search, including metadata filtering
Content Creation: Automated text and transcript summarization, social media post generation, long-form content creation

ANY CONTENT, ANY FORMAT

Multimodal ready

Integrated with Large Multimodal Models (LMMs) including OpenAI
GPT-4o and Anthropic Sonnet 3.5
Automatic audio transcription
Generate image descriptions with visual object detection
Similarity search via image embeddings

from graphlit import Graphlit
from graphlit_api import *

response = await graphlit.client.query_contents(
  filter=ContentFilter(
    search_type=VECTOR,
    search="Unstructured data",
    types=[ FILE, PAGE ],
    contents=[
      EntityReferenceFilter(
        id="{content-id}"
      )
    ]
  )
)

from graphlit import Graphlit
from graphlit_api import *

response = await graphlit.client.query_contents(
  filter=ContentFilter(
    search_type=VECTOR,
    search="Unstructured data",
    types=[ FILE, PAGE ],
    contents=[
      EntityReferenceFilter(
        id="{content-id}"
      )
    ]
  )
)

BUILT FOR DEVELOPERS, BY DEVELOPERS

Easy integration

Native SDKs for Python, Node.js, .NET
No infrastructure to be deployed
Serverless, cloud-native platform
Multitenant-ready with RBAC
Data is encrypted-at-rest
Usage-based pricing

Our platform.
Your applications.
Any unstructured data.

For all developers building AI applications

For developers building chatbots, copilots, or vertical AI applications
with domain-specific data

For all developers building AI applications

Pricing

How much does Graphlit cost?

Free to get started, no credit card required.

With our paid tiers, your costs are based on how much content you ingest -
plus the usage costs of features such as audio transcription,

LLM tokens or PDF OCR text extraction.

Usage-based pricing starts at $0.10/credit

Let's chat!

Since every application is different,
we are happy to model out costs for your use case.
Schedule some time to talk

Signup Today

Free

per/month

Ingest any content type
(i.e. PDFs, MP3s, web pages)

Create content feeds
(i.e. RSS, Web, Notion, blob storage)

Search content by
text or vector similarity

Filter content by metadata

Create chatbot conversations
over your content

Configure content workflows

Includes Deepgram audio transcription

Includes all vector embeddings
and prompt completions

Supports multi-tenant apps

Includes 1000 credits

Includes 1GB content storage

Includes 1000 content items

Includes 3 feeds

Includes 100 chatbot
conversations

Community Discord support

Signup Today

Hobby

$49

/month + usage

Everything in Free tier

$0.10/credit usage

Includes 10GB content storage

Includes 10K content items

Includes 10 feeds

Unlimited chatbot conversations

Email and community Discord support

Signup Today

Starter

$199

/month + usage

Everything in Hobby tier

$0.09/credit usage (10% off)

Includes 100GB content storage

Includes 100K content items

Unlimited feeds

Unlimited chatbot conversations

Priority email, private Slack support

Signup Today

Growth

$999

/month + usage

Everything in Starter tier

$0.08/credit usage (20% off)

Unlimited content storage

Unlimited content items

Unlimited feeds

Unlimited chatbot conversations

Priority email, private Slack support

Dedicated technical contact

SLA (coming soon)

SOC 2 (coming soon)

Pricing details

Definitions:
Content: Any ingested file, web page, Slack message, email, etc.

Feed: Any automated data ingestion from Web site, SharePoint, S3 bucket, etc.

Conversation: Any threaded conversation with LLM

Credit: Aggregated unit of serverless cloud compute, cloud storage,
LLM tokens and third-party API usage

Examples:

60min podcast, automatically transcribed,
and made searchable and conversational via LLMs uses 8 credits ($0.80)

5 page PDF, using OCR for text extraction, GPT-4o for entity extraction,
and made RAG-ready uses 4 credits ($0.40),

or without OCR, uses 2.5 credits ($0.25)

Definitions:
Content: Any ingested file, web page, Slack message, email, etc.

Feed: Any automated data ingestion from Web site, SharePoint, S3 bucket, etc.

Conversation: Any threaded conversation with LLM

Credit: Aggregated unit of serverless cloud compute, cloud storage,
LLM tokens and third-party API usage

Examples:

60min podcast, automatically transcribed,
and made searchable and conversational via LLMs uses 8 credits ($0.80)

5 page PDF, using OCR for text extraction, GPT-4o for entity extraction,
and made RAG-ready uses 4 credits ($0.40),

or without OCR, uses 2.5 credits ($0.25)

Get Graphlit updates to your inbox

Contact Us

Got questions?

We would be happy to talk about what you're building.

Email questions@graphlit.com, or schedule a call below.

We would be happy to talk about what you're building.

Email questions@graphlit.com, or

join us on Discord and let's chat.

We would be happy to talk about what you're building.

Email questions@graphlit.com, or

join us on Discord and let's chat.

Can I use this to build a chatbot or copilot?

Definitely! Graphlit provides everything you need to build a RAG application, such as a chatbot or copilot, with our easy-to-use API. Compared to the OpenAI Assistants API, Graphlit handles a wider range of content formats, and offers higher limits on storage capacity. Also, we provide vector-based semantic search as well as RAG conversations. In addition to a small monthly platform fee, you are charged for credit usage based on the volume of content ingested into the platform, LLM tokens used, and other cloud API usage. But you can also bring your own LLM keys, and pay for those tokens yourself.

Can I extract text from PDFs or Word documents?

Yes! You can ingest any type of document, including PDF, Word document, Powerpoint presentation, Markdown, etc. and Graphlit will extract structured text. You can optionally use a content workflow with Azure AI Document Intelligence models, for high-quality OCR text and table extraction. In our testing, we have seen comparable or better results using Azure AI Document Intelligence for OCR, compared to Unstructured.IO, LlamaParse, and other open source PDF extractors.

How is this different than LangChain?

LangChain is a leading frameworks for building LLM applications, but it is not a managed platform. When building RAG applications, you would need to integrate LangChain with LLMs, vector databases, and cloud infrastructure. There are many pieces to assemble to have a production-grade application with LangChain, and you are left to solve these DevOps problems yourself. WIth Graphlit, you can start building your RAG application immediately - no assembly required. We provide a scalable, managed API, where you just have to point us at your unstructured data, and we do the rest. We handle text extraction, text chunking, vector embeddings, as well as RAG conversations (with history). Also, Graphlit handles multi-tenant semantic search out of the box.

How is this different than Unstructured.IO?

Unstructured.IO is focused on the extraction of text from PDFs and other documents, and the partitioning of those documents. They are not a RAG-ready platform, in that they don't directly connect your data to LLMs. They are also not multimodal-ready. They are currently focused on textual formats, not any other media formats. You still need to build an end-to-end unstructured data pipeline around their technology. By using Graphlit, you can start building multimodal RAG applications immediately - no assembly required. We offer best-in-class OCR and text extraction, using Azure AI Document Intelligence. Graphlit also supports a wider range of file formats and metadata than Unstructured.IO, and has built-in audio transcription.

Do I need my own vector database, like Pinecone or Qdrant?

Nope! With Graphlit, you get everything you need to create RAG applications out of the box. We already have integrated best-in-class vector search and vector embeddings into the platform.

Do you support a JSON mode for text extraction?

Yes! Graphlit stores extracted text and tables in our own JSON format, which you can access through our API. You have the option of using Graphlit for ingestion and text extraction only, and then post-processing the JSON format with your own application. But in most cases, you would have Graphlit generate vector embeddings from the extracted text, and handle the RAG conversations for you.

Do you support audio and video transcription?

For sure, we handle a variety of audio and video formats, such as MP4, MP3, WAV and AAC/M4A. Any audio or video file ingested into Graphlit will have audio automatically transcribed using the latest Deepgram speech-to-text model.

Can I build a knowledge graph from my data?

Certainly! Graphlit automatically builds a knowledge graph from the content you ingest into the platform. We maintain the relationships between your content, and the content sources (i.e. feeds). Optionally, you can enable entity extraction to identify people, places, organizations, products, etc. from your text or transcriptis, and add those to your knowledge graph. You can also enable entity enrichment to connect Wikipedia, Crunchbase or other APIs, and import additional metadata into the knowledge graph.

Do you offer a free trial?

Good question! When you signup to Graphlit, you are on the Free Tier. This gives you full access to all the features of the Graphlit API, with no trial expiration. You are only limited by the amount of content you can ingest into your account. By adding a payment method, you can upgrade to a paid tier, which gives higher quota limits - and you will pay a flat monthly platform fee, and any credit usage.

Can I use this to build a chatbot or copilot?

Can I extract text from PDFs or Word documents?

How is this different than LangChain?

How is this different than Unstructured.IO?

Do I need my own vector database, like Pinecone or Qdrant?

Nope! With Graphlit, you get everything you need to create RAG applications out of the box. We already have integrated best-in-class vector search and vector embeddings into the platform.

Bring knowledge to your AI apps