Integrated private cloud AI platform
View Patent ↗Systems and methods are provided for an integrated private cloud AI platform that is deployable at a customer site. The integrated private cloud AI platform can include components deployed at the customer site that utilize an embedding model that accesses/integrates with various knowledge bases and vector data stores locally at the customer site, and permit chatbot-accessible queries to an LLM that utilizes the previously-uploaded embeddings.
1 . A computer-implemented method comprising:
receiving customer data associated with a customer site and converting the customer data into embeddings using an embedding model;
receiving a search query from a client device;
authenticating the client device;
in response to authenticating, providing the search query to a large language model (LLM);
initiating an authorization process confirming that the client device is allowed to access the customer data accessible by the search query;
in response to the authorization process, accessing the customer data identified by the LLM that is stored as the embeddings; and
generating and providing a response to the search query based on the customer data.
2 . The computer-implemented method of claim 1 , wherein the LLM is reused for different search queries and for different use cases.
3 . The computer-implemented method of claim 1 , wherein the embedding model, the LLM, and the authorization process are implemented in a private cloud stored at the customer site, and the client device is configured to access the private cloud.
4 . The computer-implemented method of claim 1 , further comprising:
storing the embeddings in an embedding data store; and
storing vector representations of the embeddings in a vector data store.
5 . The computer-implemented method of claim 1 , wherein the search query is received from a chatbot module that is accessed by the client device.
6 . The computer-implemented method of claim 1 , further comprising:
receiving the embedding model at the customer site; and
once the embedding model is activated, automatically accessing the customer data at the customer site.
7 . The computer-implemented method of claim 1 , further comprising:
receiving a knowledge base from an administrative user before the search query is received;
converting the knowledge base into second embeddings using the embedding model; and
storing the second embeddings in a vector data store at the customer site.
8 . The computer-implemented method of claim 7 , wherein an automated processing job uploads the second embeddings to a namespace associated with the client device.
9 . The computer-implemented method of claim 7 , wherein the embeddings accessed by the LLM are stored in a vector data store.
10 . A private cloud platform comprising:
a memory storing instructions; and
a processor communicatively coupled to the memory and configured to execute the instructions to:
receive customer data associated with a customer site and converting the customer data into embeddings using an embedding model;
receive a search query from a client device;
authenticate the client device;
in response to authenticating, provide the search query to a large language model (LLM);
initiate an authorization process confirming that the client device is allowed to access the customer data accessible by the search query;
in response to the authorization process, access the customer data identified by the LLM that is stored as the embeddings; and
generate and provide a response to the search query based on the customer data.
11 . The private cloud platform of claim 10 , wherein the LLM is reused for different search queries and for different use cases.
12 . The private cloud platform of claim 10 , wherein the embedding model, the LLM, and the authorization process are implemented in a private cloud stored at the customer site, and the client device is configured to access the private cloud.
13 . The private cloud platform of claim 10 , wherein the processor is further configured to:
store the embeddings in an embedding data store; and
store vector representations of the embeddings in a vector data store.
14 . The private cloud platform of claim 10 , wherein the search query is received from a chatbot module that is accessed by the client device.
15 . The private cloud platform of claim 10 , wherein the processor is further configured to:
receive the embedding model at the customer site; and
once the embedding model is activated, automatically access the customer data at the customer site.
16 . The private cloud platform of claim 10 , wherein the processor is further configured to:
receive a knowledge base from an administrative user before the search query is received;
convert the knowledge base into second embeddings using the embedding model; and
store the second embeddings in a vector data store at the customer site.
17 . The private cloud platform of claim 16 , wherein an automated processing job uploads the second embeddings to a namespace associated with the client device.
18 . A non-transitory computer-readable storage medium storing a plurality of instructions executable by a processor, the plurality of instructions when executed by the processor cause the processor to:
receive customer data associated with a customer site and converting the customer data into embeddings using an embedding model;
receive a search query from a client device;
authenticate the client device;
in response to authenticating, provide the search query to a large language model (LLM);
initiate an authorization process confirming that the client device is allowed to access the customer data accessible by the search query;
in response to the authorization process, access the customer data identified by the LLM that is stored as the embeddings; and
generate and provide a response to the search query based on the customer data.
19 . The non-transitory computer-readable storage medium of claim 18 , wherein the LLM is reused for different search queries and for different use cases.
20 . The non-transitory computer-readable storage medium of claim 18 , wherein the embedding model, the LLM, and the authorization process are implemented in a private cloud stored at the customer site, and the client device is configured to access the private cloud.