Chroma

RAG（检索增强生成）技术架构将文档切片、向量化、存入向量数据库，查询时先检索相关片段再交给模型生成回答

The RAG workflow is divided into offline and online phases: offline, documents are chunked and converted into vectors stored in a vector database; online, questions are converted into vectors and the most similar text segments are retrieved via ANN algorithms and concatenated into the context.

Vector databases such as FAISS, Chroma, and Weaviate build indexes by encoding text as high-dimensional vectors, enabling semantic similarity retrieval in milliseconds.

RAG addresses the limitation of large models' knowledge cutoff dates by splitting private documents into text chunks, converting them into vector representations stored in a vector database, and injecting retrieved relevant content into the Prompt.

Chroma is suitable for lightweight prototype development and local experiments, while Milvus and Pinecone target production-grade large-scale scenarios, supporting distributed deployment and efficient retrieval of hundreds of millions of vectors.

Vector databases (such as ChromaDB, Milvus, and Pinecone) use indexing algorithms such as HNSW and IVF to achieve millisecond-level retrieval among millions or even hundreds of millions of vectors.

常见的向量数据库包括ChromaDB、Milvus、Pinecone、Weaviate，它们针对高维向量的近似最近邻（ANN）搜索进行了专门优化

Milvus, Pinecone, and Chroma are vector databases used for storing and retrieving text vectors and are core components of the RAG architecture.

Long-term memory is typically implemented with the help of vector databases (such as Chroma, Pinecone, Weaviate, Milvus, etc.) to enable semantic retrieval.

In RAG implementations, text is converted into high-dimensional vectors by embedding models and stored in vector databases such as Pinecone, Weaviate, and Milvus; at query time, the closest segments are found via cosine similarity.

Mainstream solutions to the long-term memory problem in AI narratives include storing key plot nodes in vector databases, dynamically recalling memory fragments via RAG technology, and dedicated memory management modules.

典型的RAG流程包括文档切片、向量化嵌入、存入向量数据库、语义检索、上下文拼接和最终生成六个步骤

Mainstream vector databases include Faiss, Milvus, Qdrant, and Weaviate, which can complete Approximate Nearest Neighbor (ANN) searches over millions of vectors in milliseconds.

ChromaDB is an open-source vector database that supports multiple similarity calculation methods such as cosine similarity and Euclidean distance, and provides persistent storage and metadata filtering capabilities.

Chroma and Pinecone are vector databases used to store vectorized data.

During the RAG indexing phase, documents are chunked and converted into vectors via an embedding model, then stored in vector databases such as Faiss, Pinecone, and Weaviate.

The industry typically employs a RAG architecture to address the long-term memory problem in AI companions, storing historical conversations as vectors in a local database and dynamically retrieving them to inject into prompts.

Vector databases (such as Milvus, Chroma, and Weaviate) use approximate nearest neighbor algorithms to find content that is similar in meaning rather than identical in wording.

向量存储常用专用向量数据库如Pinecone、Chroma、Weaviate，支持近似最近邻检索

Semantic retrieval is based on mathematical metrics such as cosine similarity rather than simple keyword matching.