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New Feature

Vector databases for enhanced retrieval

SeekrFlow introduces vector database capabilities that give you complete control over document ingestion, chunking, and semantic search for retrieval-augmented generation (RAG) workflows.

• Create and manage vector databases with configurable embedding models, dimensions, and distance metrics.
• Ingest documents using flexible chunking strategies—choose automatic chunking with smart defaults or define custom parameters for chunk size and overlap.
• Search across ingested documents using hybrid retrieval that combines semantic and keyword-based search.
• Fine-tune search results with ranking options and relevance thresholds to control response quality.
• Ingest multiple documents at once by organizing files into collections.

This release gives developers the infrastructure needed to build production RAG applications with precise control over how documents are processed and retrieved.

For more information, see the launch blog and Vector Stores.

AI Ready Data Engine

We have launched the Seekr AI-Ready Data Engine, a powerful new system within the SeekrFlow™ platform that transforms diverse enterprise data into a structured, AI-ready format—faster, more accurately, and at a lower cost. This intelligent, end-to-end pipeline eliminates manual bottlenecks, ensuring seamless integration and deployment of high-quality, trustworthy AI applications.

To learn more, read the launch blog.

API Key Management

Users can generate their own API keys directly from their profile. This enhancement streamlines access management and improves user autonomy and security, ensuring a seamless developer experience.

How to create your API Key

• Users can generate an API key by clicking the “+ Generate Key” button in their profile.
• The system will securely generate and return a unique API key.
• Once generated, users will be prompted to save their key before exiting.

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Improvements & Bug Fixes

Updated User Profile Page with Navigation

The SeekrFlow user profile page has been redesigned with a new sub-navigation component, improving accessibility and ease of use. The page is now labeled "My Account" and includes two sections:

• Profile: which serves as the default landing page
• API Key: where users can generate and manage their API access.

This update provides a more intuitive way to navigate account settings.

Bug Fixes and Stability Improvements

We have implemented several small bug fixes and performance enhancements to further optimize SeekrFlow. These updates improve the platform’s overall stability and ensure a smoother, more reliable experience for users.

🟣

UI/UX Enhancements

Improved User Profile Experience

Enhanced the User Profile experience with a new User Details section, including profile editing, password updates, sign-out functionality, and cookie settings management.

🟢

New Feature

Training Loss Chart in Run Details

We have added a dynamic training loss chart to the Run Details page, providing users with critical insights into model performance during fine-tuning:

• Y-Axis: Displays loss values to indicate model performance trends.
• Upper X-Axis: Shows the number of epochs selected during run creation.
• Lower X-Axis: Represents "steps," calculated as:
  Total Steps = (Total Number of Samples ÷ number of instances * Batch Size) × Number of Epochs

This new feature enables users to track loss convergence and make informed adjustments, reducing the number of repeated fine-tuning cycles needed to optimize results. By minimizing fine-tuning runs, it saves time, compute resources, and associated costs.

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Improvements & Bug Fixes

Enhanced Data Ingestion

Users can now upload Markdown, PDF, and Word files as valid file types for data ingestion during data creation. This update, available on API and SDK, makes working with diverse data formats easier, streamlining the ingestion process and reducing the need for file conversions.

Expanded Base Model Library

The base model library now includes the Mistral-7B-Instruct-v0.2 model from Mistral AI. This model is available for fine-tuning, inference, and testing in the Sandbox, offering more options to tailor your AI solutions.

Bug Fixes and Stability Improvements

Small bug fixes and performance updates to make SeekrFlow™ smoother and more reliable for users.

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Improvements & Bug Fixes

Tooltip for Icon Buttons in Model Library

We’ve improved the user experience in the Model Library. Icon buttons in base model cards now display a hover tooltip with descriptive text, ensuring users clearly understand the available actions.

Faster Response Rendering in Sandbox

The simulated typing animation for sandbox responses now matches the speed of token count updates. This enhancement delivers quicker rendering, offering a more seamless and accurate representation of response generation.

Run Description Added to Run Summary

The run summary now displays the description entered during the run creation wizard, enhancing clarity and alignment with the design specifications.

Minor Bug Fixes and Stability Improvements

This release includes several minor bug fixes and performance enhancements across SeekrFlow™ to ensure a smoother and more reliable user experience.

November’s release introduces new features designed to give users greater control and efficiency over their AI workflows—from optimizing model outputs to managing large-scale deployments. For more details, read our full release blog.

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New Features

Sandbox Input Parameters

We have added new input parameters to the Sandbox environment: Temperature, Top P, and Max Tokens. These options give more control over inference outputs, allowing users tailor responses for specific tasks and use cases.

Enhanced Inference Engine for Faster Inference

With the integration of vLLM, inference speeds have dramatically improved, making your AI workflows faster and more efficient. This integration ensures that even complex models deliver faster results, helping users achieve more in less time.

We conducted performance testing to compare TGI and vLLM on Intel Gaudi2 accelerators.

• TGI: tgi-gaudi v2.0.4
• vLLM: vllm-fork v0.5.3.post1-Gaudi-1.17.0

On average, the enhanced inference engine performed 32% faster when compared to TGI for the RAG experiments (full interaction traces, 10 concurrent requests) using Meta-Llama-3.1-8B-Instruct

Significant latency improvements in load testing with 100 concurrent users:

• Meta-Llama-3-8B-Instruct: ~45% faster
• Meta-Llama-3.1-8B-Instruct: ~39% faster

Enhanced OpenAI compatibility features

Our inference engine now seamlessly integrates with OpenAI’s ecosystem, expanding workflow capabilities and enhancing usability.

• Log Probabilities: New support for log_probs and top_logprobs, providing insights into model decision-making, aiding debugging, and improving output accuracy.
• Dynamic Tool Calling: Custom functions can now be automatically invoked by the model based on context, streamlining business logic integration.

Try it yourself!

The example code below shows you how to leverage the OpenAI client and SeekrFlow's inference engine to create a custom unit conversion tool that can be configured dynamically.

Create the client and make an API request

import os 
import openai

# Set the API key
os.environ["OPENAI_API_KEY"] = "Paste your API key here"

# Create the OpenAI client and retrieve the API key.
client = openai.OpenAI( 
  base_url="https://flow.seekr.com/v1/inference", 
  api_key=os.environ.get("OPENAI_API_KEY"

) 
  
# Send a request to the OpenAI API to leverage the specified Llama model as a unit conversion tool.  
response = client.chat.completions.create( 
  model="meta-llama/Llama-3.1-8B-Instruct", 
  stream=False, 
  messages=[{ 
    "role": "user", 
    "content": "Convert from 5 kilometers to miles" 
}], 
max_tokens=100, 
tools=[{ 
  "type": "function", 
  "function": { 
      "name": "convert_units", 
      "description": "Convert between different units of measurement", 
      "parameters": { 
          "type": "object", 
          "properties": { 
              "value": {"type": "number"}, 
              "from_unit": {"type": "string"}, 
              "to_unit": {"type": "string"} 

}, 

  "required": ["value", "from_unit", "to_unit"] 

} 
} 
}] 
) 

Register a function from JSON

Next, define and register a Python function from JSON data.

# Parse json and register
def register_from_json(json_obj):
   code = f"def {json_obj['name']}({', '.join(json_obj['args'])}):\n{json_obj['docstring']}\n{json_obj['code']}"
   print(code)
   namespace = {}
   exec(code, namespace)
   return namespace[json_obj["name"]]

Run the unit conversion tool

This function executes the tool call, given an LLM response object.

# Execute our tool
def execute_tool_call(resp):
    tool_call = resp.choices[0].message.tool_calls[0]
    
    func_name = tool_call.function.name  
    args = tool_call.function.arguments
    
    func = globals().get(func_name)
    if not func:
        raise ValueError(f"Function {func_name} not found")
    
    if isinstance(args, str):
        import json
        args = json.loads(args)
    
    return func(**args)

execute_tool_call(response)

Sample output

This is the output expected in response to the request made earlier to convert 5 kilometers to miles.

3.106855

Federated Login with Intel

Intel® Tiber™ AI Cloud users can now access SeekrFlow™ with a new federated login feature

First-time users: Start by using your Intel® Tiber™ AI Cloud credentials, which will auto-populate the sign-up form for quick and easy access to SeekrFlow.

Returning users: Simply log in with your Intel® Tiber™ AI Cloud credentials for direct access to SeekrFlow.

This integration simplifies user management and access for those connected to Intel® Tiber™ AI Cloud.

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Improvements & Bug Fixes

Streaming in Sandbox

We have enabled streaming for chat responses in the Sandbox, delivering results incrementally so users can utilize results without a delay.

Clear and Restart Button Fix

We have resolved an issue where the “Clear and Restart” button in Sandbox didn’t function. A dialog box now appears, confirming that chat history will be cleared, allowing you to start fresh.

Dataset Directory Update

Uploaded file improvements in the Create Run Modal

• Successfully uploaded files immediately appear in the dataset directory.
• Switching to the directory view auto-selects the newly uploaded file.
• Radio buttons now remain active, ensuring smooth file selection.

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UI/UX Enhancements

We have made several updates to improve the user experience and provide clearer guidance across the platform

• Sandbox: Updated language to better support the new model parameter settings for improved clarity.
• Deployment Dashboard: Enhanced explanations of cost transparency features for better understanding of resource usage.
• Projects: Cancellation dialogs now show detailed cost information related to token usage, offering users more visibility into their resource consumption.

These updates aim to make SeekrFlow’s interface more intuitive and user-friendly, enhancing navigation and overall clarity.

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UI/UX Enhancements

Quickstart Card Reorganization

We have reorganized the Quickstart cards to improve navigation and help users find essential features and tools faster, making it easier to get started and accelerate the time to deployment.

New Icons and Tooltips

New icons and tooltips have been introduced across the platform. These provide quick access to relevant documentation without interrupting your workflow, ensuring that users can reference helpful resources whenever needed.

Enhanced User Guidance

We have updated the user guidance system to provide clearer instructions throughout the process, with a focus on alignment and data creation. Users will now find an embedded documentation tab within the UI, offering step-by-step instructions and best practices for principle alignment and data creation. These updates are designed to support users of all technical backgrounds, helping them navigate the platform with confidence and accelerate their AI model deployment.

The New SeekrFlow™ Self-Service Enterprise AI Platform

SeekrFlow™ is now available as a complete, self-service platform that empowers enterprises to train, validate, deploy, and scale trusted AI applications with ease.

This includes:

• Lifecycle Management: Manage the entire AI lifecycle through a single API call, SDK, or no-code interface.
• Principle Alignment: An intelligent agent that simplifies the process of aligning AI models to domain-specific knowledge, such as company policies, industry regulation,s or brand guidelines. This feature enhances the accuracy and relevance of base model responses by 3x and 6x respectively, at 90% reduced cost compared to traditional data preparation methods.
• Five-step Deployment: Simplify and accelerate the deployment process with an intuitive, guided workflow.
• Model Validation Tools: Ensure model accuracy with advanced validation features, including side-by-side comparisons and token-level confidence scoring.
• Real-time Monitoring: Monitor model performance and production health to maintain reliability and optimize outcomes.
• Flexible Cloud and Hardware Deployment: Deploy models seamlessly on all leading cloud providers and hardware platforms, offering the flexibility to scale based on your needs.

For more details, read the full blog here.