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Add HybridRAG as a new application in the GenAIExamples #1968
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Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Signed-off-by: siddhivelankar23 <siddhi.velankar@intel.com>
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Dependency ReviewThe following issues were found:
License IssuesHybridRAG/ui/svelte/package.json
Scanned Files
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for more information, see https://pre-commit.ci
http://${host_ip}:6007/v1/dataprep/ingest | ||
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By default, the application is pre-seeded with structured data and schema. To create knowledge graph with custom data and schema, |
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this default pre-seeding refers to https://github.com/opea-project/GenAIComps/blob/d5db8826a2fea34bc6440bed53f45c9a5ac29e23/comps/text2cypher/src/integrations/cypher_utils.py#L20 ? or specific to this demo somewhere else?
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the test about pre-seeding is the part of the text2cypher, so yes it's here: https://github.com/opea-project/GenAIComps/blob/d5db8826a2fea34bc6440bed53f45c9a5ac29e23/comps/text2cypher/src/integrations/cypher_utils.py#L20
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
for more information, see https://pre-commit.ci
Not sure why |
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
Signed-off-by: jeanyu-habana <jean1.yu@intel.com>
The license is available, https://www.npmjs.com/package/lodash, the tool should be wrong. So please ignore this issue. |
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
Description
Enterprise AI systems require solutions that handle both structured data (databases, transactions, CSVs, JSON) and unstructured data (documents, images, audio). While traditional VectorRAG excels at semantic search across documents, it struggles with complex queries requiring global context or relationship-aware reasoning. HybridRAG application addresses these gaps by combining GraphRAG (knowledge graph-based retrieval) and VectorRAG (vector database retrieval) for enhanced accuracy and contextual relevance.
Issues
n/a
Type of change
List the type of change like below. Please delete options that are not relevant.
Dependencies
n/a
Tests
tested each components as well as the overall application.