Use a multi-layered approach for agent memory as described in MEM0 and MMOs to structure data retrieval and context management.

Leverage smaller, local LLMs to extract key metadata or summaries from documents for lightweight pipelines without hitting large external APIs.

Use vector embeddings to improve retrieval performance for moderately sized, domain-specific datasets instead of relying solely on prompt engineering.

Rather than vectorizing and storing your entire data corpus, vectorize only the subset relevant to each query to keep storage and compute costs manage...Show more

Use metadata embedding searches to find a relevant pointer, then invoke SQL or graph queries to retrieve full, detailed context in a two-step Retrieva...Show more

Combine vector embeddings, SQL-based retrieval, and graph databases to create layered RAG processes for more precise data access.

Use a graph database to represent concepts as nodes and relationships as edges for retrieval-augmented generation, offering semantic search alternativ...Show more

Implement a robust ETL pipeline for email data that handles cleaning tasks like date normalization, emoji removal, and format standardization before A...Show more

Instead of sending raw text to the LLM, pull out key attributes (sender, receiver, body, organization) into JSON to drastically reduce data volume and...Show more

Choose chunk sizes and extraction methods based on data type—plain text, structured documents with charts and relationships, or images—to preserve con...Show more

Define high-level outcomes, chunk and extract data, perform vectorization with appropriate overlaps, add metadata, and store for search to build an ef...Show more

Define and implement an ETL pipeline by extracting raw use-case data, transforming it into structured themes, deduplicating and merging, and loading i...Show more

Use a lightweight on-device LLM paired with a retrieval augmented generation pipeline over vectorized schema metadata to handle interactive user input...Show more

Transform JSON schema fields with metadata into vector embeddings and use a simple LLM to retrieve and fill the right fields, bypassing complex prompt...Show more

Use prompt engineering over streaming transcripts to map free-form conversational descriptions—like “windows look a bit shabby”—to specific, metadata-...Show more

Implement a chat-based LLM pipeline that processes each turn of streamed natural-language input, identifies relevant fields in a predefined JSON schem...Show more

Use lightweight vectorization of JSON schemas and data dictionaries to map natural language inputs to structured outputs via vector search instead of ...Show more

Use UMAP to reduce high-dimensional embeddings (e.g., 784-dim FashionMNIST) into 2D/3D to visualize and identify semantic clusters such as trousers, d...Show more

Augment an LLM with a specific curated dataset via retrieval augmented generation (RAG) to drive it toward desired outcomes using more relevant data.

Represent tokens, words, documents, or images as high-dimensional vectors and store them in a vector database so an LLM can efficiently traverse and s...Show more