This was our first time using vector databases (pgvector) and learning how embeddings store semantic meaning. Understanding similarity search, cosine distance, and how to structure news embeddings for fast retrieval was a major learning milestone.
+
This was our first time using vector databases (pgvector) and learning how embeddings store semantic meaning. Understanding similarity search and how to structure news embeddings for fast retrieval was a major learning milestone.
#### 3. Python Ecosystem & NLP Processing
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We learned Python more deeply — from building ingestion pipelines to integrating NLP models, handling async tasks, and optimizing data processing. This included working with libraries like Pandas, Sentence-Transformers, LangChain, and the Gemini API.
+
We learned Python more deeply — from building ingestion pipelines to integrating NLP models, handling async tasks, and optimizing data processing. This included working with libraries like Sentence-Transformers, LangChain, and the Gemini API.
#### 4. Backend & Infrastructure Challenges
We faced challenges in:
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- Setting up servers for both Python (FastAPI) and Node.js
+
- Setting up servers for both Python and Node.js
- Managing API scaling and efficient database querying
@@ 227,8 227,6 @@
#### Long-Term Goals (6+ months)
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- Open-source the core sentiment analysis engine
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- Build an Admin Web Application for controlling ingestion pipeline, users, source management, and system monitoring
- Introduce automated trading signal suggestions based on long-term sentiment patterns