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Haystack and Llamaindex fst and gsi examples #62
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Oct 30, 2025
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haystack and llamaindex example for gsi and fts
shyam-cb f48c9e5
refactor
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added not for default linear vector search
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haystack cookbook update
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566 changes: 566 additions & 0 deletions
566
haystack/fts/RAG_with_Couchbase_Capella_and_OpenAI.ipynb
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|---|---|---|
| @@ -0,0 +1,22 @@ | ||
| --- | ||
| # frontmatter | ||
| path: "/tutorial-openai-haystack-rag" | ||
| title: "Retrieval-Augmented Generation (RAG) with OpenAI and Haystack" | ||
| short_title: "RAG with Openai and Haystack" | ||
| description: | ||
| - Learn how to build a semantic search engine using Couchbase's Search vector index. | ||
| - This tutorial demonstrates how to integrate Couchbase's vector search capabilities with the embeddings generated by OpenAI Services. | ||
| - You will understand how to perform Retrieval-Augmented Generation (RAG) using Haystack, Couchbase and OpenAI services. | ||
| content_type: tutorial | ||
| filter: sdk | ||
| technology: | ||
| - vector search | ||
| tags: | ||
| - OpenAI | ||
| - Artificial Intelligence | ||
| - Haystack | ||
| - FTS | ||
| sdk_language: | ||
| - python | ||
| length: 60 Mins | ||
| --- |
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| @@ -0,0 +1,71 @@ | ||
| { | ||
| "name": "vector_search", | ||
| "type": "fulltext-index", | ||
| "sourceType": "gocbcore", | ||
| "sourceName": "test_bucket", | ||
| "planParams": { | ||
| "indexPartitions": 1, | ||
| "numReplicas": 0 | ||
| }, | ||
| "params": { | ||
| "doc_config": { | ||
| "docid_prefix_delim": "", | ||
| "docid_regexp": "", | ||
| "mode": "scope.collection.type_field", | ||
| "type_field": "type" | ||
| }, | ||
| "mapping": { | ||
| "default_analyzer": "standard", | ||
| "default_datetime_parser": "dateTimeOptional", | ||
| "index_dynamic": true, | ||
| "store_dynamic": true, | ||
| "default_mapping": { | ||
| "dynamic": true, | ||
| "enabled": false | ||
| }, | ||
| "types": { | ||
| "test_scope.test_collection": { | ||
| "dynamic": true, | ||
| "enabled": true, | ||
| "properties": { | ||
| "text": { | ||
| "dynamic": false, | ||
| "enabled": true, | ||
| "fields": [ | ||
| { | ||
| "analyzer": "en", | ||
| "index": true, | ||
| "name": "text", | ||
| "store": true, | ||
| "type": "text" | ||
| } | ||
| ] | ||
| }, | ||
| "embedding": { | ||
| "enabled": true, | ||
| "dynamic": false, | ||
| "fields":[ | ||
| { | ||
| "vector_index_optimized_for": "recall", | ||
| "docvalues": true, | ||
| "dims": 3072, | ||
| "include_in_all": false, | ||
| "include_term_vectors": false, | ||
| "index": true, | ||
| "name": "embedding", | ||
| "similarity": "dot_product", | ||
| "store": true, | ||
| "type": "vector" | ||
| } | ||
| ] | ||
| }, | ||
| "meta": { | ||
| "dynamic": true, | ||
| "enabled": true | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } |
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| pandas>=2.1.4 | ||
| datasets>=2.14.5 | ||
| setuptools>=75.8.0 | ||
| couchbase-haystack==2.* | ||
| transformers[torch]>=4.49.0 | ||
| tensorflow>=2.18.0 | ||
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The dependencies in this
requirements.txtfile are inconsistent with the packages used in the corresponding notebook. The notebook useshaystack-aiandopenai, which are missing here. Also,transformersandtensorfloware included here but not used in the notebook. Please update the requirements to match the notebook's dependencies.