Conversational artificial intelligence for understanding source code repositories

A system comprising:

• one or more processors;
• a memory coupled to the one or more processors, the memory storing a plurality of instructions executable by the one or more processors, the plurality of instructions comprising instructions that when executed by the one or more processors cause the one or more processors to perform operations including:
• receiving an utterance concerning code in a codebase;
• generating an utterance vector embedding based on the utterance;
• executing a similarity search on a vector index by comparing the utterance vector embedding to vector embeddings stored in the vector index using a similarity metric and identifying relevant nodes in a graph-based representation of the codebase based on the comparing, wherein each of the vector embeddings stored in the vector index represent parameters or attributes of the code in a continuous vector space, and wherein the parameters or attributes for each of the vector embeddings are associated with a node in the graph-based representation of the codebase;
• executing, based on types of constructs associated with the relevant nodes, queries on a graph database to retrieve additional context associated with the relevant nodes, wherein the graph database stores the graph-based representation of the codebase;
• generating a prompt comprising the utterance, the relevant nodes, and the additional context associated with the relevant nodes;
• and
• generating, by a generative model, a response to the utterance based on the prompt,
• wherein the comparing the utterance vector embedding to the vector embeddings further includes:
• calculating the similarity metric between the utterance vector embedding and each of the vector embeddings,
• comparing the similarity metric between the utterance vector embedding and each of the vector embeddings to a predetermined similarity threshold,
• when the similarity metric is equal to or greater than the predetermined similarity threshold, the node associated with the utterance vector embedding is identified as the relevant node, and
• when the similarity metric is less than the predetermined similarity threshold, the node associated with the utterance vector embedding is identified as a non-relevant node.

A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform operations including:

• receiving an utterance concerning code in a codebase;
• generating an utterance vector embedding based on the utterance;
• executing a similarity search on a vector index by comparing the utterance vector embedding to vector embeddings stored in the vector index using a similarity metric and identifying relevant nodes in a graph-based representation of the codebase based on the comparing, wherein each of the vector embeddings stored in the vector index represent parameters or attributes of the code in a continuous vector space, and wherein the parameters or attributes for each of the vector embeddings are associated with a node in the graph-based representation of the codebase;
• executing, based on types of constructs associated with the relevant nodes, queries on a graph database to retrieve additional context associated with the relevant nodes, wherein the graph database stores the graph-based representation of the codebase;
• generating a prompt comprising the utterance, the relevant nodes, and the additional context associated with the relevant nodes;
• and
• generating, by a generative model, a response to the utterance based on the prompt,
• wherein the comparing the utterance vector embedding to the vector embeddings further includes:
• calculating the similarity metric between the utterance vector embedding and each of the vector embeddings,
• comparing the similarity metric between the utterance vector embedding and each of the vector embeddings to a predetermined similarity threshold,
• when the similarity metric is equal to or greater than the predetermined similarity threshold, the node associated with the utterance vector embedding is identified as the relevant node, and
• when the similarity metric is less than the predetermined similarity threshold, the node associated with the utterance vector embedding is identified as a non-relevant node.