in models with between 2 and 7 billion parameters, new capabilities emerge such as the ability to generate different creative text in formats like poems, code, scripts, musical pieces, emails, and letters, and to answer even open-ended and challenging questions in an informative way.

ConversationSummaryMemory is a type of memory in LangChain that generates a summary of the conversation as it progresses. Instead of storing all messages verbatim, it condenses the information, providing a summarized version of the conversation.

Prompting supplies context, chaining enables inference steps, and retrieval incorporates facts. Together, these transform stochastic parrots into reasoning engines.

milvus is immensely popular; however, other libraries such as qdrant, weviate, and chroma have been catching up.

LLMs struggle with challenges like the compositionality gap (Measuring and Narrowing the Compositionality Gap in Language Models by Ofir Press and colleagues; 2023). This means LLMs cannot connect inferences or adapt responses to new situations. Overcoming these obstacles requires augmenting LLMs with techniques that add true comprehension.

There are numerous integrations for vector storage. These include Alibaba Cloud OpenSearch, AnalyticDB for PostgreSQL, Meta AI’s Annoy library for Approximate Nearest Neighbor (ANN) search, Cassandra, Chroma, Elasticsearch, Facebook AI Similarity Search (Faiss), MongoDB Atlas Vector Search, PGVector as a vector similarity search for Postgres, Pinec

Hugging Face offer various other libraries within their ecosystem, including Datasets for dataset processing, Evaluate for model evaluation, Simulate for simulation, and Gradio for machine learning demos.

Innovations like prompting, chain-of-thought reasoning, retrieval grounding, and others are needed to educate models.

Vector libraries, like Facebook (Meta) Faiss or Spotify Annoy, provide functionality for working with vector data. In the context of vector search, a vector library is specifically designed to store and perform similarity search on vector embeddings.