RAG from scratch: Part 10 (Routing)

Summary

In this 10th installment of the 'Rack from Scratch' series by Lance from Lang Chain, the focus is on query translation and routing. The video explains how to transform a question into a retrievable form and then route it to the appropriate data source, such as a vector store, relational database, or graph database. Two routing methods are discussed: logical routing, where an LLM reasons which data source to use, and semantic routing, which uses question embeddings to find the most similar prompt. The tutorial includes code examples for both methods, demonstrating how to set up a data model for logical routing and compute similarities for semantic routing. The goal is to direct the question to the right chain or retriever based on its content, showcasing practical applications of routing in handling queries efficiently.

Takeaways

• 📚 The video is part of a series focused on routing in building a language model from scratch.
• 🗣️ Query translation is discussed as the process of transforming a question into a form that is better suited for retrieval.
• 🔄 Routing is the subsequent step after query translation, which directs the decomposed question to the appropriate source, potentially different databases.
• 📚 The example provided includes routing a question to a vector store, relational database, or graph database based on the question's content.
• 🤖 Two methods of routing are introduced: logical routing, where the language model (LLM) decides the data source, and semantic routing, which uses similarity between the question and prompts.
• 🧠 Logical routing involves giving the LLM knowledge of various data sources and letting it reason which to use for the question.
• 🔍 Semantic routing involves embedding questions and prompts, computing their similarity, and choosing a prompt based on this similarity.
• 🛠️ The video includes a practical example of routing using a data model bound to an LLM to output one of several structured options.
• 🔧 The structured output is created by converting an object definition into a function schema and binding it to the LLM, which then adheres to a specified schema.
• 🔗 The routing mechanism is demonstrated with a toy example involving routing questions to different document sources based on the programming language mentioned.
• 🚀 The video concludes by encouraging viewers to experiment with logical and semantic routing as useful tools in developing language models.

Q & A

What is the main focus of the 10th video in the 'Rack from Scratch' series by Lance from Lang chain?

The main focus of the 10th video is on routing, which involves taking a question and translating it into a form that is better suited for retrieval, and then routing that potentially decomposed question to the right source, which could be a different database.

What are the two types of routing discussed in the video?

The two types of routing discussed are logical routing and semantic routing. Logical routing involves giving an LLM knowledge of various data sources and letting it reason about which one to apply the question to. Semantic routing involves embedding a question and computing the similarity between the question and prompts to choose the most relevant one.

How does logical routing work in the context of the video?

In logical routing, the LLM is given knowledge of the various data sources available and is allowed to reason which data store to use for a given question. It's like applying logic to determine which data source to use, such as a vector store, a relational database, or a graph database.

Can you explain how semantic routing is different from logical routing?

Semantic routing is different from logical routing in that it involves embedding the question and prompts, computing the similarity between them, and choosing a prompt based on that similarity. This method focuses on the semantic similarity between the question and the available prompts rather than logical reasoning.

What is an example of how routing can be applied in the video?

An example given in the video is routing a question about Python code to a Python documentation source. This is done by setting up a data model that is bound to the LLM, allowing it to output one of the predefined data source options as a structured object.

What is the purpose of the structured object in logical routing?

The structured object in logical routing serves as a classification plus function calling mechanism to produce a structured output that is constrained to a few specific possibilities. This helps in directing the LLM's output to one of the predefined data sources.

How does the video script illustrate the process of routing a question to a specific data source?

The script illustrates this process by defining a structured object for the LLM to output, converting it into an open function schema, and binding it to the LLM. When a question is asked, the LLM invokes a function on the output to produce an output that adheres to the specified schema.

What is the role of embedding in semantic routing as described in the video?

In semantic routing, embedding plays a crucial role as it helps in transforming the question and prompts into a form that can be compared for similarity. The most similar prompt is then chosen based on the computed similarity to the question.

How does the video script demonstrate semantic routing with an example?

The script demonstrates semantic routing by embedding prompts for physics and math, and then embedding a user's question about black holes. It computes the similarity between the question and the prompts and chooses the most similar prompt (in this case, the physics prompt) to route the question.

What are the potential applications of the routing mechanisms discussed in the video?

The routing mechanisms can be applied to various use cases such as routing questions to different databases like vector stores, relational databases, or graph databases. They can also be used to route questions to different prompts or chains tailored for specific domains like programming languages or scientific concepts.