CRM Expert Using OpenAI, TensorFlow Hub, Google Sentence Encoder, Numpy.
- Past chat answers to questions which were persisted to disk are assumed to be groomed into the best answers
- Load past chat history from disk into memory for speed
- Return 90% semantically similar matches as the correct answer without querying OpenAI
- Use lesser matches as 'few shot' examples to increase OpenAI relevance to missing or newer information from when OpenAI stopped crawling the web
- Use low temperature in OpenAI to reduce the chance of OpenAI just making up false information
- Save AI responses to disk and memory
- A human is checking the chat answers JSON files to make sure they are the best answers. Or even better preloading based on a good source such as consultant answers to Email request/response, Slack request/response threads, Salesforce Support Case request/response, etc...
- Collect search engine urls
- Filter out garbage urls, not relevant, have no parseable content, etc...
- Collect text content from urls
- Summarize with OpenAI
- Add to 'few shot' previous knowledge