Performance Needs<\/strong><\/td>| Some overhead due to LLM coordination<\/td> | High-speed queries, scalable<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n Using both together is often the most effective solution. LlamaIndex structures the data and provides context for the LLM, while the vector database ensures fast retrieval of relevant embeddings.<\/p>\n Example in Practice:<\/strong><\/p>\nImagine building a company\u2019s knowledge base. LlamaIndex structures your documents so the LLM can interpret them meaningfully, while the vector database quickly retrieves the most relevant sections for any user query.<\/p>\n How to Integrate LlamaIndex with Vector Databases?<\/h3>\nFor projects that need both intelligent LLM reasoning and fast retrieval, integrating LlamaIndex with a vector database is straightforward:
\n1. Store embeddings in the vector database<\/strong>: Convert documents or data into embeddings and store them in the database.
\n2. Connect LlamaIndex to the database<\/strong>: Configure LlamaIndex to query the vector database whenever the LLM needs relevant context.
\n3. Retrieve and process data<\/strong>: When a user asks a question, LlamaIndex fetches the most relevant embeddings from the vector database and passes them to the LLM for reasoning and generating an answer.
\n4. Optional: Add caching or batching<\/strong>: For high-volume applications, caching frequent queries or batching requests can reduce latency.<\/p>\nLlamaIndex supports popular vector databases like Pinecone, FAISS, Weaviate, and Milvus, giving developers flexibility in choosing the most suitable option for their scale and performance needs.<\/p>\n This integration ensures fast retrieval and intelligent LLM reasoning, making your application both efficient and capable of understanding complex data.<\/p>\n <\/span>Frequently Asked Questions (FAQ)<\/span><\/h2>\n |