Spaces:
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title: Agronomy | |
sdk: streamlit | |
emoji: π | |
colorFrom: green | |
colorTo: blue | |
# ElasticDocs_GPT | |
Combining the search power of Elasticsearch with the Question Answering power of GPT | |
--- | |
title: {{title}} | |
emoji: {{emoji}} | |
colorFrom: {{colorFrom}} | |
colorTo: {{colorTo}} | |
sdk: {{sdk}} | |
sdk_version: {{sdkVersion}} | |
app_file: app.py | |
pinned: false | |
--- | |
[Blog - ChatGPT and Elasticsearch: OpenAI meets private data](https://www.elastic.co/blog/chatgpt-elasticsearch-openai-meets-private-data) | |
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1. Python interface accepts user questions | |
- Generate a hybrid search request for Elasticsearch | |
- BM25 match on the title field | |
- kNN search on the title-vector field | |
- Boost kNN search results to align scores | |
- Set size=1 to return only the top scored document | |
2. Search request is sent to Elasticsearch | |
3. Documentation body and original url are returned to python | |
4. API call is made to OpenAI ChatCompletion | |
- Prompt: "answer this question <question> using only this document <body_content from top search result>" | |
5. Generated response is returned to python | |
6. Python adds on original documentation source url to generated response and prints it to the screen for the user | |
# Examples | |
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