Lancedb
LanceDB RAG with HoneyHive Tracing
Learn how to integrate LanceDB with HoneyHive for vector database monitoring and tracing in RAG applications
LanceDB
LanceDB is an open-source vector database built for AI applications and similarity search. By integrating LanceDB with HoneyHive, you can:
- Track vector database operations
- Monitor embedding quality and relevance
- Analyze retrieval performance in your RAG pipelines
- Identify opportunities for optimization
Prerequisites
- A HoneyHive account and API key
- Python 3.8+
- Basic understanding of vector databases and RAG pipelines
Installation
Install the required packages:
Basic Integration Example
The following example demonstrates a complete RAG pipeline with HoneyHive tracing for LanceDB operations. We’ll break down each component step by step.
Step 1: Initialize Clients and Setup
First, set up the necessary clients and configuration for HoneyHive, OpenAI, and LanceDB:
Step 2: Define Document Class
Create a simple document class to hold text chunks:
Step 3: Load and Process Documents with Tracing
Create functions to load and chunk documents with HoneyHive tracing:
The @trace
decorator logs these operations to HoneyHive, capturing metadata about document loading and chunking processes.
Step 4: Create LanceDB Table with Tracing
Set up a LanceDB table with embeddings:
This function creates a LanceDB table and adds document chunks with embeddings. The @trace
decorator logs information about the embedding model used and table creation process.
Step 5: Retrieve Documents with Tracing
Create a function to retrieve relevant documents from LanceDB:
The @trace
decorator logs information about the retrieval process, including the query and number of results.
Step 6: Generate Response with Tracing
Create a function to generate a response using OpenAI with tracing:
This function generates a response using OpenAI based on the retrieved documents. The @trace
decorator logs information about the generation process.
Step 7: Complete RAG Pipeline with Tracing
Create a function that combines all the previous steps into a complete RAG pipeline:
The @trace
decorator logs the entire RAG pipeline process, creating a parent span that contains all the child spans from the individual functions.
Step 8: Run the Example
Finally, create a main function to run the example:
Additional Resources
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