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Proposal for Use-Case-Description changes
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@volkerstampa
volkerstampa committed Oct 31, 2023
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Expand Up @@ -20,15 +20,15 @@ To give you a starting point for using the Intelligence Layer, we provide some p

| Type | Task | Description |
| --------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------- |
| Classify | [EmbeddingBasedClassify](./src/intelligence_layer/use_cases/classify/embedding_based_classify.py) | Classify a text using the cosine similarity of its embeddings to examples |
| Classify | [SingleLabelClassify](./src/intelligence_layer/use_cases/classify/single_label_classify.py) | Classify a text given labels using zero-shot prompting |
| QA | [LongContextQa](./src/intelligence_layer/use_cases/qa/long_context_qa.py) | Answer a question based on one document of any length |
| QA | [MultipleChunkQa](./src/intelligence_layer/use_cases/qa/multiple_chunk_qa.py) | Answer a question based a list of text chunks |
| QA | [RetrieverBasedQa](./src/intelligence_layer/use_cases/qa/retriever_based_qa.py) | Answer a question based on a document base |
| QA | [SingleChunkQa](./src/intelligence_layer/use_cases/qa/single_chunk_qa.py) | Answer a question based on a single text chunk |
| Search | [QdrantSearch](./src/intelligence_layer/use_cases/search/qdrant_search.py) | Search through texts given a query and some filters |
| Search | [Search](./src/intelligence_layer/use_cases/search/search.py) | Search through texts given a query |
| Summarize | [ShortBodySummarize](./src/intelligence_layer/use_cases/summarize/summarize.py) | Summarize a single text chunk into a short body text |
| Classify | [EmbeddingBasedClassify](./src/intelligence_layer/use_cases/classify/embedding_based_classify.py) | Classify a text of limited size that fits into the models context size with a single class where each class is defined by multiple examples. |
| Classify | [SingleLabelClassify](./src/intelligence_layer/use_cases/classify/single_label_classify.py) | Classify a text of limited size that fits into the models context size with a single class where each class is defined just by its name using zero-shot prompting. |
| QA | [LongContextQa](./src/intelligence_layer/use_cases/qa/long_context_qa.py) | Answer a question based on one document of any length. |
| QA | [MultipleChunkQa](./src/intelligence_layer/use_cases/qa/multiple_chunk_qa.py) | Answer a question based a list of text where each element is of limited size that fits into the models context. |
| QA | [RetrieverBasedQa](./src/intelligence_layer/use_cases/qa/retriever_based_qa.py) | Answer a question based on a document base that is accessed through a [BaseRetriever](...) implementation. |
| QA | [SingleChunkQa](./src/intelligence_layer/use_cases/qa/single_chunk_qa.py) | Answer a question based on a text of limited size that fits into the models context. |
| Search | [QdrantSearch](./src/intelligence_layer/use_cases/search/qdrant_search.py) | Search through texts given a query and some filters (Move to core?). |
| Search | [Search](./src/intelligence_layer/use_cases/search/search.py) | Search a document based for document chunks that fit to a given query by using a [BaseRetriever](...) implementation. |
| Summarize | [ShortBodySummarize](./src/intelligence_layer/use_cases/summarize/summarize.py) | Summarize a text of limited size that fits into the models context size into a short body text. |

### How to make your own

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