# embeddings_queue

created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: e6c7b816-6a7f-4515-8bef-a139ae15db5f
encoding: FLOAT32
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We'll explain more about this operator later when we implement our match function.\n\nLet's also follow security best practices by enabling row level security on the table:\n\n```sql\nalter table documents enable row level security;\n```\n\nThis will prevent unauthorized access to this table through the auto-generated REST API (more on this shortly).\n\n## Generate OpenAI embeddings\n\nThis guide uses JavaScript to generate embeddings, but you can easily modify it to use any [language supported by OpenAI](https://platform.openai.com/docs/libraries).\n\nIf you are using JavaScript, feel free to use whichever server-side JavaScript runtime that you prefer (Node.js, Deno, Supabase Edge Functions).\n\nIf you're using Node.js, first install `openai` as a dependency:\n\n```shell\nnpm install openai\n```\n\nthen import it:\n\n```js\nimport OpenAI from \"openai\";\n```\n\nIf you're using Deno or Supabase Edge Functions, you can import `openai` directly from a URL:\n\n```js\nimport OpenAI from \"https://esm.sh/openai@4\";\n```\n\n> In this example we import from https://esm.sh which is a CDN that automatically fetches the respective NPM module for you and serves it over HTTP.\n\nNext we'll generate an OpenAI embedding using [`text-embedding-3-small`](https://platform.openai.com/docs/guides/embeddings/embedding-models):\n\n```js\nconst openai = new OpenAI();\n\nconst input = \"The cat chases the mouse\";\n\nconst result = await openai.embeddings.create({\n  input,\n  model: \"text-embedding-3-small\",\n});\n\nconst [{ embedding }] = result.data;\n```\n\nRemember that you will need an [OpenAI API key](https://platform.openai.com/api-keys) to interact with the OpenAI API. You can pass this as an environment variable called `OPENAI_API_KEY`, or manually set it when you instantiate your OpenAI client:\n\n```js\nconst openai = new OpenAI({\n  apiKey: \"<openai-api-key>\",\n});\n```\n\n_**Remember:** Never hard-code API keys in your code. Best practice is to either store it in a `.env` file and load it using a library like [`dotenv`](https://github.com/motdotla/dotenv) or load it from an external key management system._\n\n## Store embeddings in database\n\nSupabase comes with an [auto-generated REST API](https://postgrest.org/) that dynamically builds REST endpoints for each of your tables. This means you don't need to establish a direct Postgres connection to your database - instead you can interact with it simply using by the REST API. This is especially useful in serverless environments that run short-lived processes where re-establishing a database connection every time can be expensive.\n\nSupabase comes with a number of [client libraries](https://supabase.com/docs#client-libraries) to simplify interaction with the REST API."}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 285fa2ef-2cf7-47fd-bd4f-12cf7660e1c5
encoding: FLOAT32
metadata:
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You can pass this as an environment variable called `OPENAI_API_KEY`, or manually set it when you instantiate your OpenAI client:\n\n```js\nconst openai = new OpenAI({\n  apiKey: \"<openai-api-key>\",\n});\n```\n\n_**Remember:** Never hard-code API keys in your code. Best practice is to either store it in a `.env` file and load it using a library like [`dotenv`](https://github.com/motdotla/dotenv) or load it from an external key management system._\n\n## Store embeddings in database\n\nSupabase comes with an [auto-generated REST API](https://postgrest.org/) that dynamically builds REST endpoints for each of your tables. This means you don't need to establish a direct Postgres connection to your database - instead you can interact with it simply using by the REST API. This is especially useful in serverless environments that run short-lived processes where re-establishing a database connection every time can be expensive.\n\nSupabase comes with a number of [client libraries](https://supabase.com/docs#client-libraries) to simplify interaction with the REST API. In this guide we'll use the [JavaScript client library](https://supabase.com/docs/reference/javascript), but feel free to adjust this to your preferred language.\n\nIf you're using Node.js, install `@supabase/supabase-js` as a dependency:\n\n```shell\nnpm install @supabase/supabase-js\n```\n\nthen import it:\n\n```js\nimport { createClient } from \"@supabase/supabase-js\";\n```\n\nIf you're using Deno or Supabase Edge Functions, you can import `@supabase/supabase-js` directly from a URL:\n\n```js\nimport { createClient } from \"https://esm.sh/@supabase/supabase-js@2\";\n```\n\nNext we'll instantiate our Supabase client and configure it so that it points to your Supabase project. In this guide we'll store a reference to your Supabase URL and key in a `.env` file, but feel free to modify this based on how your application handles configuration.\n\nIf you are using Node.js or Deno, add your Supabase URL and service role key to a `.env` file. If you are using the cloud platform, you can find these from your Supabase dashboard [settings page](https://supabase.com/dashboard/project/_/settings/api). If you're running Supabase locally, you can find these by running `npx supabase status` in a terminal.\n\n_.env_\n\n```\nSUPABASE_URL=<supabase-url>\nSUPABASE_SERVICE_ROLE_KEY=<supabase-service-role-key>\n```\n\nIf you are using Supabase Edge Functions, these environment variables are automatically injected into your function for you so you can skip the above step.\n\nNext we'll pull these environment variables into our app."}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: eb121261-d8e1-4df6-a4db-197df3f7473a
encoding: FLOAT32
metadata:
{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/supabase/semantic-search.ipynb", "title": "Semantic search using Supabase Vector", "tokens": 2931, "retrieve_doc": true, "source": "openai_cookbooks", "_node_content": "{\"id_\": \"eb121261-d8e1-4df6-a4db-197df3f7473a\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/supabase/semantic-search.ipynb\", \"title\": \"Semantic search using Supabase Vector\", \"tokens\": 2931, \"retrieve_doc\": true, \"source\": \"openai_cookbooks\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"194d89f3-3014-4f51-b486-e26acb62c304\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/supabase/semantic-search.ipynb\", \"title\": \"Semantic search using Supabase Vector\", \"tokens\": 2931, \"retrieve_doc\": true, \"source\": \"openai_cookbooks\"}, \"hash\": \"a2bad350f3b11c3364f449d8ace39db42dfdfcb0d328c863612badce410f5ef6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"285fa2ef-2cf7-47fd-bd4f-12cf7660e1c5\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/supabase/semantic-search.ipynb\", \"title\": \"Semantic search using Supabase Vector\", \"tokens\": 2931, \"retrieve_doc\": true, \"source\": \"openai_cookbooks\"}, \"hash\": \"c39aed1bbfe966b8131836282888c9823cfebb2c300f0024c0223706dd579201\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"3b232e0d-a5bd-4188-809a-4e9214d43138\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"36e8d4af2824e722835469176fdaee287ab8add1530d639a1b941a27f999b524\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 6408, \"end_char_idx\": 9395, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "194d89f3-3014-4f51-b486-e26acb62c304", "doc_id": "194d89f3-3014-4f51-b486-e26acb62c304", "ref_doc_id": "194d89f3-3014-4f51-b486-e26acb62c304", "chroma:document": "In this guide we'll use the [JavaScript client library](https://supabase.com/docs/reference/javascript), but feel free to adjust this to your preferred language.\n\nIf you're using Node.js, install `@supabase/supabase-js` as a dependency:\n\n```shell\nnpm install @supabase/supabase-js\n```\n\nthen import it:\n\n```js\nimport { createClient } from \"@supabase/supabase-js\";\n```\n\nIf you're using Deno or Supabase Edge Functions, you can import `@supabase/supabase-js` directly from a URL:\n\n```js\nimport { createClient } from \"https://esm.sh/@supabase/supabase-js@2\";\n```\n\nNext we'll instantiate our Supabase client and configure it so that it points to your Supabase project. In this guide we'll store a reference to your Supabase URL and key in a `.env` file, but feel free to modify this based on how your application handles configuration.\n\nIf you are using Node.js or Deno, add your Supabase URL and service role key to a `.env` file. If you are using the cloud platform, you can find these from your Supabase dashboard [settings page](https://supabase.com/dashboard/project/_/settings/api). If you're running Supabase locally, you can find these by running `npx supabase status` in a terminal.\n\n_.env_\n\n```\nSUPABASE_URL=<supabase-url>\nSUPABASE_SERVICE_ROLE_KEY=<supabase-service-role-key>\n```\n\nIf you are using Supabase Edge Functions, these environment variables are automatically injected into your function for you so you can skip the above step.\n\nNext we'll pull these environment variables into our app.\n\nIn Node.js, install the `dotenv` dependency:\n\n```shell\nnpm install dotenv\n```\n\nAnd retrieve the environment variables from `process.env`:\n\n```js\nimport { config } from \"dotenv\";\n\n// Load .env file\nconfig();\n\nconst supabaseUrl = process.env[\"SUPABASE_URL\"];\nconst supabaseServiceRoleKey = process.env[\"SUPABASE_SERVICE_ROLE_KEY\"];\n```\n\nIn Deno, load the `.env` file using the `dotenv` standard library:\n\n```js\nimport { load } from \"https://deno.land/std@0.208.0/dotenv/mod.ts\";\n\n// Load .env file\nconst env = await load();\n\nconst supabaseUrl = env[\"SUPABASE_URL\"];\nconst supabaseServiceRoleKey = env[\"SUPABASE_SERVICE_ROLE_KEY\"];\n```\n\nIn Supabase Edge Functions, simply load the injected environment variables directly:\n\n```js\nconst supabaseUrl = Deno.env.get(\"SUPABASE_URL\");\nconst supabaseServiceRoleKey = Deno.env.get(\"SUPABASE_SERVICE_ROLE_KEY\");\n```\n\nNext let's instantiate our `supabase` client:\n\n```js\nconst supabase = createClient(supabaseUrl, supabaseServiceRoleKey, {\n  auth: { persistSession: false },\n});\n```\n\nFrom here we use the `supabase` client to insert our text and embedding (generated earlier) into the database:\n\n```js\nconst { error } = await supabase.from(\"documents\").insert({\n  content: input,\n  embedding,\n});\n```\n\n> In production, best practice would be to check the response `error` to see if there were any problems inserting the data and handle it accordingly.\n\n## Semantic search\n\nFinally let's perform semantic search over the embeddings in our database."}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 3b232e0d-a5bd-4188-809a-4e9214d43138
encoding: FLOAT32
metadata:
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At this point we'll assume your `documents` table has been filled with multiple records that we can search over.\n\nLet's create a match function in Postgres that performs the semantic search query. Execute the following in the [SQL Editor](https://supabase.com/dashboard/project/_/sql/new):\n\n```sql\ncreate function match_documents (\n  query_embedding vector (1536),\n  match_threshold float,\n)\nreturns setof documents\nlanguage plpgsql\nas $$\nbegin\n  return query\n  select *\n  from documents\n  where documents.embedding <#> query_embedding < -match_threshold\n  order by documents.embedding <#> query_embedding;\nend;\n$$;\n```\n\nThis function accepts a `query_embedding` which represents the embedding generated from the search query text (more on this shortly). It also accepts a `match_threshold` which specifies how similar the document embeddings have to be in order for `query_embedding` to count as a match.\n\nInside the function we implement the query which does two things:\n\n- Filters the documents to only include those who's embeddings match within the above `match_threshold`. Since the `<#>` operator performs the negative inner product (versus positive inner product), we negate the similarity threshold before comparing. This means a `match_threshold` of 1 is most similar, and -1 is most dissimilar.\n- Orders the documents by negative inner product (`<#>`) ascending. This allows us to retrieve documents that match closest first.\n\n> Since OpenAI embeddings are normalized, we opted to use inner product (`<#>`) because it is slightly more performant than other operators like cosine distance (`<=>`). It is important to note though this only works because the embeddings are normalized - if they weren't, cosine distance should be used."}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 11d92a12-b785-4d94-b6ac-5fb4e17a04ed
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{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/supabase/semantic-search.ipynb", "title": "Semantic search using Supabase Vector", "tokens": 2931, "retrieve_doc": true, "source": "openai_cookbooks", "_node_content": "{\"id_\": \"11d92a12-b785-4d94-b6ac-5fb4e17a04ed\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/supabase/semantic-search.ipynb\", \"title\": \"Semantic search using Supabase Vector\", \"tokens\": 2931, \"retrieve_doc\": true, \"source\": \"openai_cookbooks\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"194d89f3-3014-4f51-b486-e26acb62c304\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/supabase/semantic-search.ipynb\", \"title\": \"Semantic search using Supabase Vector\", \"tokens\": 2931, \"retrieve_doc\": true, \"source\": \"openai_cookbooks\"}, \"hash\": \"a2bad350f3b11c3364f449d8ace39db42dfdfcb0d328c863612badce410f5ef6\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"3b232e0d-a5bd-4188-809a-4e9214d43138\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/supabase/semantic-search.ipynb\", \"title\": \"Semantic search using Supabase Vector\", \"tokens\": 2931, \"retrieve_doc\": true, \"source\": \"openai_cookbooks\"}, \"hash\": \"fc1f98b304b4404f6345dfbfe2688cf2c5d0bbe8e5494b577d0e70cf9a1bc777\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 9301, \"end_char_idx\": 12701, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "194d89f3-3014-4f51-b486-e26acb62c304", "doc_id": "194d89f3-3014-4f51-b486-e26acb62c304", "ref_doc_id": "194d89f3-3014-4f51-b486-e26acb62c304", "chroma:document": "## Semantic search\n\nFinally let's perform semantic search over the embeddings in our database. At this point we'll assume your `documents` table has been filled with multiple records that we can search over.\n\nLet's create a match function in Postgres that performs the semantic search query. Execute the following in the [SQL Editor](https://supabase.com/dashboard/project/_/sql/new):\n\n```sql\ncreate function match_documents (\n  query_embedding vector (1536),\n  match_threshold float,\n)\nreturns setof documents\nlanguage plpgsql\nas $$\nbegin\n  return query\n  select *\n  from documents\n  where documents.embedding <#> query_embedding < -match_threshold\n  order by documents.embedding <#> query_embedding;\nend;\n$$;\n```\n\nThis function accepts a `query_embedding` which represents the embedding generated from the search query text (more on this shortly). It also accepts a `match_threshold` which specifies how similar the document embeddings have to be in order for `query_embedding` to count as a match.\n\nInside the function we implement the query which does two things:\n\n- Filters the documents to only include those who's embeddings match within the above `match_threshold`. Since the `<#>` operator performs the negative inner product (versus positive inner product), we negate the similarity threshold before comparing. This means a `match_threshold` of 1 is most similar, and -1 is most dissimilar.\n- Orders the documents by negative inner product (`<#>`) ascending. This allows us to retrieve documents that match closest first.\n\n> Since OpenAI embeddings are normalized, we opted to use inner product (`<#>`) because it is slightly more performant than other operators like cosine distance (`<=>`). It is important to note though this only works because the embeddings are normalized - if they weren't, cosine distance should be used.\n\nNow we can call this function from our application using the `supabase.rpc()` method:\n\n```js\nconst query = \"What does the cat chase?\";\n\n// First create an embedding on the query itself\nconst result = await openai.embeddings.create({\n  input: query,\n  model: \"text-embedding-3-small\",\n});\n\nconst [{ embedding }] = result.data;\n\n// Then use this embedding to search for matches\nconst { data: documents, error: matchError } = await supabase\n  .rpc(\"match_documents\", {\n    query_embedding: embedding,\n    match_threshold: 0.8,\n  })\n  .select(\"content\")\n  .limit(5);\n```\n\nIn this example, we set a match threshold to 0.8. Adjust this threshold based on what works best with your data.\n\nNote that since `match_documents` returns a set of `documents`, we can treat this `rpc()` like a regular table query. Specifically this means we can chain additional commands to this query, like `select()` and `limit()`. Here we select just the columns we care about from the `documents` table (`content`), and we limit the number of documents returned (max 5 in this example).\n\nAt this point you have a list of documents that matched the query based on semantic relationship, ordered by most similar first.\n\n## Next steps\n\nYou can use this example as the foundation for other semantic search techniques, like retrieval augmented generation (RAG).\n\nFor more information on OpenAI embeddings, read the [Embedding](https://platform.openai.com/docs/guides/embeddings) docs.\n\nFor more information on Supabase Vector, read the [AI & Vector](https://supabase.com/docs/guides/ai) docs."}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: 4f878117-1729-49e1-9860-c38cd4cd27cd
encoding: FLOAT32
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{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Semantic_Search.ipynb", "title": "Semantic Search with Pinecone and OpenAI", "tokens": 34426, "retrieve_doc": false, "source": "openai_cookbooks", "_node_content": "{\"id_\": \"4f878117-1729-49e1-9860-c38cd4cd27cd\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Semantic_Search.ipynb\", \"title\": \"Semantic Search with Pinecone and OpenAI\", \"tokens\": 34426, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"ee5c68d1-dff6-4673-87a2-e097be084ab5\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Semantic_Search.ipynb\", \"title\": \"Semantic Search with Pinecone and OpenAI\", \"tokens\": 34426, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"a1034ea29a411ebb75c0f53708206ad7a93cc556d4389577ff0ec654637ecfd2\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"bcb4ae6a-c56c-4baf-a96d-9ef004ae2264\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"52e024ab81bc7502195e48b614ce04b5672bb626ad4eed7affbc668a75d19ef1\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 0, \"end_char_idx\": 1346, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "ee5c68d1-dff6-4673-87a2-e097be084ab5", "doc_id": "ee5c68d1-dff6-4673-87a2-e097be084ab5", "ref_doc_id": "ee5c68d1-dff6-4673-87a2-e097be084ab5", "chroma:document": "# Semantic Search with Pinecone and OpenAI\n\nIn this guide you will learn how to use the OpenAI Embedding API to generate language embeddings, and then index those embeddings in the Pinecone vector database for fast and scalable vector search.\n\nThis is a powerful and common combination for building semantic search, question-answering, threat-detection, and other applications that rely on NLP and search over a large corpus of text data.\n\nThe basic workflow looks like this:\n\n**Embed and index**\n\n* Use the OpenAI Embedding API to generate vector embeddings of your documents (or any text data).\n* Upload those vector embeddings into Pinecone, which can store and index millions/billions of these vector embeddings, and search through them at ultra-low latencies.\n\n**Search**\n\n* Pass your query text or document through the OpenAI Embedding API again.\n* Take the resulting vector embedding and send it as a query to Pinecone.\n* Get back semantically similar documents, even if they don't share any keywords with the query.\n\n![Architecture overview](https://files.readme.io/6a3ea5a-pinecone-openai-overview.png)\n\nLet's get started...\n\n## Setup\n\nWe first need to setup our environment and retrieve API keys for OpenAI and Pinecone. Let's start with our environment, we need HuggingFace *Datasets* for our data, and the OpenAI and Pinecone clients:"}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: bcb4ae6a-c56c-4baf-a96d-9ef004ae2264
encoding: FLOAT32
metadata:
{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Semantic_Search.ipynb", "title": "Semantic Search with Pinecone and OpenAI", "tokens": 34426, "retrieve_doc": false, "source": "openai_cookbooks", "_node_content": "{\"id_\": \"bcb4ae6a-c56c-4baf-a96d-9ef004ae2264\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Semantic_Search.ipynb\", \"title\": \"Semantic Search with Pinecone and OpenAI\", \"tokens\": 34426, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"excluded_embed_metadata_keys\": [\"url\", \"tokens\", \"retrieve_doc\", \"source\"], \"excluded_llm_metadata_keys\": [\"title\", \"tokens\", \"retrieve_doc\", \"source\"], \"relationships\": {\"1\": {\"node_id\": \"ee5c68d1-dff6-4673-87a2-e097be084ab5\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Semantic_Search.ipynb\", \"title\": \"Semantic Search with Pinecone and OpenAI\", \"tokens\": 34426, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"a1034ea29a411ebb75c0f53708206ad7a93cc556d4389577ff0ec654637ecfd2\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"4f878117-1729-49e1-9860-c38cd4cd27cd\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Semantic_Search.ipynb\", \"title\": \"Semantic Search with Pinecone and OpenAI\", \"tokens\": 34426, \"retrieve_doc\": false, \"source\": \"openai_cookbooks\"}, \"hash\": \"c4647087655d223d3c7a0674fea0f8afc77c482cf9ae4ef8b24e403393d65e9e\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"e6426888-bf4f-4123-ba40-87f12125afff\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"84af6e0371bbb614d0ad15fcbd59ab87bd87cf598291defb7fa5c0a7bfb9762d\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 0, \"end_char_idx\": 2705, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "ee5c68d1-dff6-4673-87a2-e097be084ab5", "doc_id": "ee5c68d1-dff6-4673-87a2-e097be084ab5", "ref_doc_id": "ee5c68d1-dff6-4673-87a2-e097be084ab5", "chroma:document": "# Semantic Search with Pinecone and OpenAI\n\nIn this guide you will learn how to use the OpenAI Embedding API to generate language embeddings, and then index those embeddings in the Pinecone vector database for fast and scalable vector search.\n\nThis is a powerful and common combination for building semantic search, question-answering, threat-detection, and other applications that rely on NLP and search over a large corpus of text data.\n\nThe basic workflow looks like this:\n\n**Embed and index**\n\n* Use the OpenAI Embedding API to generate vector embeddings of your documents (or any text data).\n* Upload those vector embeddings into Pinecone, which can store and index millions/billions of these vector embeddings, and search through them at ultra-low latencies.\n\n**Search**\n\n* Pass your query text or document through the OpenAI Embedding API again.\n* Take the resulting vector embedding and send it as a query to Pinecone.\n* Get back semantically similar documents, even if they don't share any keywords with the query.\n\n![Architecture overview](https://files.readme.io/6a3ea5a-pinecone-openai-overview.png)\n\nLet's get started...\n\n## Setup\n\nWe first need to setup our environment and retrieve API keys for OpenAI and Pinecone. Let's start with our environment, we need HuggingFace *Datasets* for our data, and the OpenAI and Pinecone clients:\n\n\n```python\n!pip install -qU \\\n    pinecone-client==3.0.2 \\\n    openai==1.10.0 \\\n    datasets==2.16.1\n```\n\n    \u001b[2K     \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m201.4/201.4 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n    \u001b[2K     \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m225.1/225.1 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n    \u001b[2K     \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n    \u001b[2K     \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n    \u001b[2K     \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n    \u001b[2K     \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n    \u001b[2K     \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n    \u001b[2K     \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n    \u001b[2K     \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n    \u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed."}


created_at: 2024-08-26 20:31:21
topic: persistent://default/default/47f0afae-77ee-4e4a-bcd4-0b050f4de62c
id: e6426888-bf4f-4123-ba40-87f12125afff
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