deepset-ai/haystack-core-integrations
Nov 2024 – Nov 2024
1 Month active
Languages Used
Python
Technical Skills
API IntegrationBackend DevelopmentFull Stack DevelopmentPython
latifboubyan
PROFILE
Latifboubyan
Developed batch embedding functionality for the OllamaDocumentEmbedder within the deepset-ai/haystack-core-integrations repository, focusing on scalable document processing. Introduced a batch_size parameter and implemented batch processing logic in Python to efficiently handle large sets of documents, improving throughput for embedding pipelines. Enhanced the API integration and backend workflow while maintaining minimal changes to existing interfaces. Expanded and updated unit tests to ensure robust coverage of the new batching feature, including edge cases, and maintained clear traceability through detailed commits. This work addressed the need for higher performance and scalability in document embedding workflows, leveraging full stack development and backend expertise.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
54
Activity Months1
Your Network
64 peopleSame Organization
@bankboubyan.com1
Shared Repositories
63
ByteWiseMember
Br1anMember
abhay23-AIMember
HaystackBotMember
Adnan AlkattanMember
AftabMember
alex-stoicaMember
aelhajjMember
Amna MubasharMember
Work History
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 focused on delivering scalable embeddings within haystack-core-integrations. Implemented Batch Embedding for OllamaDocumentEmbedder by adding a batch_size parameter and batch processing of documents to improve throughput on large document sets. Updated unit tests to reflect batching functionality and ensure robustness. This work enhances performance and scalability for large-scale document pipelines with minimal API changes, and provides clear traceability through the related commit.
1 Commits • 1 Features
Nov 1, 2024November 2024 focused on delivering scalable embeddings within haystack-core-integrations. Implemented Batch Embedding for OllamaDocumentEmbedder by adding a batch_size parameter and batch processing of documents to improve throughput on large document sets. Updated unit tests to reflect batching functionality and ensure robustness. This work enhances performance and scalability for large-scale document pipelines with minimal API changes, and provides clear traceability through the related commit.
November 2024
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API IntegrationBackend DevelopmentFull Stack DevelopmentPython
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline