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Bump to 0.3.0 and update docs (#189)
Version bump to 0.3.0 and updates documentation/README!
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‎README.md

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‎docs/_static/js/sidebar.js

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{ title: "Install", path: "/overview/installation.html" },
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{ title: "CLI", path: "/overview/cli.html" },
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]},
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{ header: "User Guides", toc: [
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{ title: "Getting Started", path: "/user_guide/getting_started_01.html" },
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{ title: "Query and Filter", path: "/user_guide/hybrid_queries_02.html" },
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{ title: "JSON vs Hash Storage", path: "/user_guide/hash_vs_json_05.html" },
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{ title: "Vectorizers", path: "/user_guide/vectorizers_04.html" },
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{ title: "Rerankers", path: "/user_guide/rerankers_06.html" },
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{ title: "Semantic Caching", path: "/user_guide/llmcache_03.html" },
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{ title: "Semantic Routing", path: "/user_guide/semantic_router_08.html" },
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]},
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{ header: "API", toc: [
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{ title: "Schema", path: "/api/schema.html"},
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{ title: "Search Index", path: "/api/searchindex.html" },
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{ title: "Query", path: "/api/query.html" },
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{ title: "Filter", path: "/api/filter.html" },
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]},
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{ header: "Utils", toc: [
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{ title: "Vectorizers", path: "/api/vectorizer.html" },
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{ title: "Rerankers", path: "/api/reranker.html" },
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]},
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{ header: "Extensions", toc: [
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{ title: "LLM Cache", path: "/api/cache.html" },
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{ title: "LLM Session Manager", path: "/api/session_manager.html" },
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{ title: "Semantic Router", path: "/api/router.html" },
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]}
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]},
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{ header: "User Guides", toc: [
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{ title: "Getting Started", path: "/user_guide/getting_started_01.html" },
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{ title: "Query and Filter", path: "/user_guide/hybrid_queries_02.html" },
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{ title: "JSON vs Hash Storage", path: "/user_guide/hash_vs_json_05.html" },
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{ title: "Using Vectorizers", path: "/user_guide/vectorizers_04.html" },
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{ title: "Using Rerankers", path: "/user_guide/rerankers_06.html" },
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{ title: "Semantic Caching", path: "/user_guide/llmcache_03.html" },
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{ title: "Semantic Routing", path: "/user_guide/semantic_router_08.html" },
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]},
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];
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document.addEventListener('DOMContentLoaded', function() {

‎docs/api/cache.rst

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*********
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LLM Cache
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*********

‎docs/api/index.md

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vectorizer
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reranker
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cache
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session_manager
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router
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```
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‎docs/api/session_manager.rst

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*******************
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LLM Session Manager
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*******************
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SemanticSessionManager
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======================
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.. _semantic_session_manager_api:
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.. currentmodule:: redisvl.extensions.session_manager
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.. autoclass:: SemanticSessionManager
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:show-inheritance:
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:members:
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:inherited-members:
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StandardSessionManager
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======================
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.. _standard_session_manager_api:
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.. currentmodule:: redisvl.extensions.session_manager
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.. autoclass:: StandardSessionManager
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:show-inheritance:
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:members:
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:inherited-members:

‎docs/api/vectorizer.rst

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:members:
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AzureOpenAITextVectorizer
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=========================
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.. _azureopenaitextvectorizer_api:
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.. currentmodule:: redisvl.utils.vectorize.text.azureopenai
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.. autoclass:: AzureOpenAITextVectorizer
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:show-inheritance:
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:members:
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VertexAITextVectorizer
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======================
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:show-inheritance:
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:members:
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CustomTextVectorizer
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====================
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.. _customtextvectorizer_api:
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.. currentmodule:: redisvl.utils.vectorize.text.custom
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.. autoclass:: CustomTextVectorizer
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:show-inheritance:
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:members:

‎docs/overview/cli.ipynb

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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32m09:44:14\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m RedisVL version 0.2.3\n"
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"\u001b[32m22:33:29\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m RedisVL version 0.3.0\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Overwriting schema.yaml\n"
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]
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}
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],
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"source": [
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"%%writefile schema.yaml\n",
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"\n",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32m14:22:33\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Index created successfully\n"
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"\u001b[32m22:33:31\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Index created successfully\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32m14:22:34\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n",
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"\u001b[32m14:22:34\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m 1. vectorizers\n"
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"\u001b[32m22:33:33\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n",
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"\u001b[32m22:33:33\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m 1. vectorizers\n"
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]
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}
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],
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"│ vectorizers │ HASH │ ['doc'] │ [] │ 0 │\n",
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"╰──────────────┴────────────────┴────────────┴─────────────────┴────────────╯\n",
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"Index Fields:\n",
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"╭───────────┬─────────────┬────────┬────────────────┬────────────────╮\n",
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"│ Name │ Attribute │ Type │ Field Option │ Option Value │\n",
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"├───────────┼─────────────┼────────┼────────────────┼────────────────┤\n",
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"│ sentence │ sentence │ TEXT │ WEIGHT │ 1 │\n",
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"│ embedding │ embedding │ VECTOR │ │ │\n",
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"╰───────────┴─────────────┴────────┴────────────────┴────────────────╯\n"
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"╭───────────┬─────────────┬────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬────────────────┬─────────────────┬────────────────╮\n",
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"│ Name │ Attribute │ Type │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │ Field Option │ Option Value │\n",
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"├───────────┼─────────────┼────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼─────────────────┼────────────────┤\n",
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"│ sentence │ sentence │ TEXT │ WEIGHT │ 1 │ │ │ │ │ │ │\n",
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"│ embedding │ embedding │ VECTOR │ algorithm │ FLAT │ data_type │ FLOAT32 │ dim │ 768 │ distance_metric │ COSINE │\n",
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"╰───────────┴─────────────┴────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴────────────────┴─────────────────┴────────────────╯\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32m14:22:36\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Index deleted successfully\n"
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"\u001b[32m22:33:36\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Index deleted successfully\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32m14:22:38\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n"
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"\u001b[32m22:33:37\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32m14:22:39\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Index created successfully\n"
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"\u001b[32m22:33:40\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Index created successfully\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32m14:22:40\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n",
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"\u001b[32m14:22:40\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m 1. vectorizers\n"
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"\u001b[32m22:33:42\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n",
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"\u001b[32m22:33:42\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m 1. vectorizers\n"
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]
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}
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"output_type": "stream",
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"text": [
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"\u001b[32m14:22:42\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Index deleted successfully\n"
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"\u001b[32m22:33:45\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Index deleted successfully\n"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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"version": "3.11.9"
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}
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"nbformat": 4,

‎pyproject.toml

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[tool.poetry]
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name = "redisvl"
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version = "0.2.3"
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version = "0.3.0"
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description = "Python client library and CLI for using Redis as a vector database"
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authors = ["Redis Inc. <[email protected]>"]
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license = "MIT"

‎redisvl/extensions/llmcache/base.py

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metadata."""
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raise NotImplementedError
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def hash_input(self, prompt: str):
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"""Hashes the input using SHA256."""
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def hash_input(self, prompt: str) -> str:
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"""Hashes the input prompt using SHA256.
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Args:
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prompt (str): Input string to be hashed.
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Returns:
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str: Hashed string.
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"""
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‎redisvl/extensions/llmcache/semantic.py

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Args:
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ids (Optional[str]): The document ID or IDs to remove from the cache.
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keys (Optional[str]):
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keys (Optional[str]): The Redis keys to remove from the cache.
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self._refresh_ttl(key)
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print(cache_search_result, flush=True)
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# Create cache hit
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cache_hit = CacheHit(**cache_search_result)
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cache_hit_dict = {

‎redisvl/extensions/session_manager/semantic_session.py

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as_text (bool): Whether to return the prompts and responses as text
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or as JSON
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top_k (int): The number of previous messages to return. Default is 5.
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fallback (bool): Whether to drop back to recent conversation history
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fall_back (bool): Whether to drop back to recent conversation history
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if no relevant context is found.
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session_tag (Optional[str]): Tag to be added to entries to link to a specific
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session. Defaults to instance uuid.

‎redisvl/utils/vectorize/text/custom.py

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embed_many: Optional[Callable] = None,
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aembed: Optional[Callable] = None,
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aembed_many: Optional[Callable] = None,
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model: str = "custom_vectorizer",
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):
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super().__init__(model=model, dims=self._set_model_dims())
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super().__init__(model=self.type, dims=self._set_model_dims())
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def _validate_embed(self, func: Callable):
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"""calls the func with dummy input and validates that it returns a vector"""

‎redisvl/version.py

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__version__ = "0.2.3"
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__version__ = "0.3.0"

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