Imagine giving your business an intelligent bot to talk to customers. Chatbots are commonly used to talk to customers and provide them with help or information. But, the usual chatbots sometimes struggle to answer complicated questions.
Retrieval Augmented Generation (RAG) is a method that makes chatbots better at understanding and responding to tough questions. This Generative AI design pattern combines large language models (LLMs) with external knowledge retrieval. It allows real-time data to be integrated into your AI applications during the generation process (inference time). By providing the LLM with this contextual information, RAG significantly improves the accuracy and quality of the generated outputs.
Here are some of the benefits of using RAG:
Pinecone's vector database excels at managing complex data searches with pinpoint accuracy, while the Databricks Data Intelligence Platform streamlines the handling and analysis of vast datasets.
The integration with Pinecone is seamless, enabling Databricks to efficiently store and retrieve vector embeddings at scale. This integration simplifies the development of high-performance vector search applications that leverage Pinecone and Databricks.
Using Databricks and Pinecone together, you can create a more accurate and efficient chatbot than traditional chatbots.
In this blog, we walk you through building a chatbot that can answer any questions around Databricks, by leveraging Databricks documentation and whitepapers.
There are four key stages required in building a chatbot. The first stage is ingesting and data preparation. The next stage is storing the data in a vector database like Pinecone, for efficient information retrieval. The third stage is to set up a RAG retriever and chain that uses Pinecone for retrieval and an LLM like Llama 3.1 to generate responses. The final stage is registering the chatbot to Databricks Unity Catalog and deploying it via Databricks Mosaic AI Model Serving. Continue reading for a step-by-step walkthrough of this process.
We then can query the Pinecone vector index, using the query API. This API takes the question embedding as input.
Querying Pinecone directly via the API allows you to integrate Pinecone and Databricks into arbitrary code.
In the next section, we show how to simplify this workflow using the popular LangChain framework.
Langchain is a framework that simplifies building applications powered by LLMs (large language models). Its Databricks Embeddings help simplify interacting with embedding models, and its integration with Pinecone provides a simplified query interface.
Langchain wrappers make it easy, by handling all the underlying logic and API calls for you. The LangChain code below abstracts away the need to explicitly convert the query text to a vector.
Above, we showed how to do a similarity search on our Pinecone vector index. To create a RAG chatbot, we will use the LangChain Retriever interface to wrap the index.
We first initiate Pinecone to set the API key and environment. Then, we create a VectorStore instance from the existing Pinecone index we created earlier, with the correct namespace and keys.
Now, we can put the retriever into a chain defining our chatbot!
Let's see if our chatbot can correctly extract the question from the chat messages and retrieve relevant context from Pinecone.
As we iterate on our chatbot, we will want to track model objects, model versions, and metadata, as well as manage access controls. For that, we will use MLflow's Model Registry, integrated with Unity Catalog.
You can register the chatbot chain as a model using mlflow.langchain.log_model
, with the Unity Catalog. The signature of the model can be inferred using infer_signature
in mlflow. Remember to put pinecone-client
into the dependencies. Set "mlflow.models.set_model(model=full_chain)"
in the notebook where you defined the chain. In a new driver notebook, register the chatbot and deploy chatbot to Model Serving.
The model is registered with Databricks Unity Catalog, which centralizes access control, auditing, lineage, and discovery for all data and AI assets.
Now let's deploy the chatbot chain mode as a Model Serving endpoint. Below, we put PINECONE_API_KEY
and DATABRICKS_TOKEN
into the environment variables as the serving endpoint will use them to talk to Pinecone and Databricks Foundation Models. This allows us to grant access to the served model, without revealing these secrets in code or to users.
The Model Serving UI provides real-time information on the health of the model being served.
After deploying the chatbot, you can test it with a REST API or Databricks SDK.
You can also test it using the Query UI available as part of model serving.
Improve your customer service with Databricks and Pinecone by deploying cutting-edge RAG chatbots. Unlike traditional bots, these advanced chatbots leverage the Databricks Data Intelligence Platform and Pinecone's vector database to deliver precise, timely responses. They rapidly sift through vast data to find the exact information needed, providing customers with accurate answers in seconds. This not only elevates the customer experience but also sets a new benchmark for digital engagement.
For business leaders, embracing this technology is more than just an upgrade—it's a strategic move to lead in customer service innovation. By adopting data-driven, intelligent solutions, you can place your business at the forefront of customer engagement, showcasing a commitment to excellence that resonates with your audience.
Check out our free training to learn more about Generative AI with Databricks, and read additional Pinecone and Databricks documentation here. Access sample notebooks from this blog here.