- Add PLATFORM query type that bypasses RAG and uses a lighter model
- Introduce OLLAMA_MODEL_NAME_CONVERSATIONAL env var to route CONVERSATIONAL
and PLATFORM queries to a separate (smaller) Ollama model
- Replace raw message history in classifier with compact intent history
(classify_history) to eliminate anchoring bias in small models
- Add <history_rule> and <platform_priority_rule> to classifier prompt so
the model evaluates each message independently while still resolving
ambiguous references from prior turns
- Add fast-path detection for known platform-injected prompt prefixes
- Add PLATFORM_PROMPT for account/metrics/usage responses
- Persist classify_history in classify_history_store alongside session_store
- Document decisions in ADR-0008
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Created `n00 Beir Analysis_cosqa.ipynb` for analyzing CoSQA dataset with BEIR.
- Created `n00 first Analysis.ipynb` for initial analysis using Ragas and Ollama embeddings.
- Implemented `evaluate_embeddings_pipeline.py` to evaluate embedding models across CodexGlue, CoSQA, and SciFact benchmarks.
- Added adapters for Ollama and HuggingFace embeddings to ensure compatibility with BEIR.
- Included functions to load datasets and evaluate models with detailed metrics.
- Created `n00 Beir Analysis.ipynb` for analyzing BEIR dataset with Ollama embeddings.
- Added `n00 Beir Analysis_cosqa.ipynb` for evaluating the CosQA dataset using similar methods.
- Introduced `n00 first Analysis.ipynb` for initial analysis with Ragas embeddings and semantic similarity evaluation.
- Implemented data loading, processing, and evaluation metrics for each notebook.
- Included functionality to save results to JSON files for further analysis.
- Added `export_documents` function to save processed documents to JSON.
- Extended `ElasticHandshake` to include chunk metadata during ingestion.
- Updated `process_documents` to include extra metadata for each chunk.
- Modified `ingest_documents` to return Elasticsearch responses for further processing.
- Adjusted `elasticsearch_ingestion` command to accept output path for exported JSON.
- Introduced sections on Persistence, Connectors, and Native ORM, detailing the avapConnector, ORM commands, and data access abstraction.
- Documented System Utilities and Transformation, covering time management, string manipulation, and security operations.
- Explained Function Architecture and Scopes, including function definition, invocation, and middleware usage.
- Provided a Master Example that integrates various sections to demonstrate practical application.
- Detailed the dynamic nature of AVAP™ as a programming language, including dynamic typing and memory management.
- Established notation conventions and lexical analysis processes for code clarity and structure.
- Outlined data types and structures available in AVAP™, emphasizing their usage in program development.
- Discussed variable management, including local and global variables, and best practices for comments.
- Explained expressions in AVAP™, including types, operators, and practical examples with lists.
rafa-ruiz
izapata
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