# embeddings_queue

created_at: 2024-07-28 17:45:51
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created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
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encoding: FLOAT32
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created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
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encoding: FLOAT32
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created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: 8fb68880-2ea9-4fba-8ad4-1805248b2410
encoding: FLOAT32
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created_at: 2024-07-28 17:45:51
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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_\": \"281951c3-4489-4fdf-8a98-26bec4d50b2e\", \"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\": \"daeab515-6b4f-40c7-a1a5-a97592b75399\", \"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\": \"d7cfae8881809a7e19fe70260803d9ad55217e7fdcfba2e97c643bf2b4919e8e\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"ca1b6ca8-eb61-4546-8434-c1c2b683a10a\", \"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\": \"c97ff4135f6a37229034acf72bb201be5e6ab2aaf32c52ab75cdacfec2f27a79\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"ec0d622b-ae62-4310-8a74-ba41e645e632\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"ab1c655e499d9a518b7e01ab19cb6c8a003e7a4c0b2accab96faadf53a027622\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 71768, \"end_char_idx\": 73816, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "doc_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "ref_doc_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "chroma:document": "-0.007614186033606529, -0.00643457705155015, -0.02566220425069332, 0.012298375368118286, 0.017123356461524963, 0.01261801179498434, -0.001627670251764357, -0.003949787467718124, -0.00968040432780981, -0.03981751203536987, 0.020197950303554535, -0.01837145909667015, -0.0007795884157530963, 0.014581489376723766, 0.04188753664493561, 0.006232901941984892, -0.017001591622829437, 0.019178159534931183, -0.006887394469231367], index=1, object='embedding')], model='text-embedding-3-small', object='list', usage=Usage(prompt_tokens=13, total_tokens=13))\n\n\n\n\n```python\nprint(f\"vector 0: {len(res.data[0].embedding)}\\nvector 1: {len(res.data[1].embedding)}\")\n```\n\n    vector 0: 1536\n    vector 1: 1536\n\n\n\n```python\n# we can extract embeddings to a list\nembeds = [record.embedding for record in res.data]\nlen(embeds)\n```\n\n\n\n\n    2\n\n\n\nNext, we initialize our index to store vector embeddings with Pinecone.\n\n\n```python\nlen(embeds[0])\n```\n\n\n\n\n    1536\n\n\n\nInitialize connection to Pinecone, you can get a free API key in the [Pinecone dashboard](https://app.pinecone.io).\n\n\n```python\nfrom pinecone import Pinecone\n\npc = Pinecone(api_key=\"...\")\n```\n\n\n\n\n```python\nimport time\nfrom pinecone import ServerlessSpec\n\nspec = ServerlessSpec(cloud=\"aws\", region=\"us-west-2\")\n\nindex_name = 'semantic-search-openai'\n\n# check if index already exists (if shouldn't if this is your first run)\nif index_name not in pc.list_indexes().names():\n    # if does not exist, create index\n    pc.create_index(\n        index_name,\n        dimension=len(embeds[0]),  # dimensionality of text-embed-3-small\n        metric='dotproduct',\n        spec=spec\n    )\n    # wait for index to be initialized\n    while not pc.describe_index(index_name).status['ready']:\n        time.sleep(1)\n\n# connect to index\nindex = pc.Index(index_name)\ntime.sleep(1)\n# view index stats\nindex.describe_index_stats()\n```\n\n\n\n\n    {'dimension': 1536,\n     'index_fullness': 0.0,\n     'namespaces': {},\n     'total_vector_count': 0}\n\n\n\n## Populating the Index\n\nNow we will take 1K questions from the TREC dataset"}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: ec0d622b-ae62-4310-8a74-ba41e645e632
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_\": \"ec0d622b-ae62-4310-8a74-ba41e645e632\", \"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\": \"daeab515-6b4f-40c7-a1a5-a97592b75399\", \"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\": \"d7cfae8881809a7e19fe70260803d9ad55217e7fdcfba2e97c643bf2b4919e8e\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"281951c3-4489-4fdf-8a98-26bec4d50b2e\", \"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\": \"5d759ce4fe54c17221a95bc446dfd7f621c29190a58ebaf1c2f9fce85f71d271\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"9a08b00b-94ef-4618-a17e-aa737fbfc16c\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"9e6befbadf57f71353b5f661a6939effdefc4571c7e19deb46014bdfef7bbec4\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 72467, \"end_char_idx\": 75208, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "doc_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "ref_doc_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "chroma:document": "```python\n# we can extract embeddings to a list\nembeds = [record.embedding for record in res.data]\nlen(embeds)\n```\n\n\n\n\n    2\n\n\n\nNext, we initialize our index to store vector embeddings with Pinecone.\n\n\n```python\nlen(embeds[0])\n```\n\n\n\n\n    1536\n\n\n\nInitialize connection to Pinecone, you can get a free API key in the [Pinecone dashboard](https://app.pinecone.io).\n\n\n```python\nfrom pinecone import Pinecone\n\npc = Pinecone(api_key=\"...\")\n```\n\n\n\n\n```python\nimport time\nfrom pinecone import ServerlessSpec\n\nspec = ServerlessSpec(cloud=\"aws\", region=\"us-west-2\")\n\nindex_name = 'semantic-search-openai'\n\n# check if index already exists (if shouldn't if this is your first run)\nif index_name not in pc.list_indexes().names():\n    # if does not exist, create index\n    pc.create_index(\n        index_name,\n        dimension=len(embeds[0]),  # dimensionality of text-embed-3-small\n        metric='dotproduct',\n        spec=spec\n    )\n    # wait for index to be initialized\n    while not pc.describe_index(index_name).status['ready']:\n        time.sleep(1)\n\n# connect to index\nindex = pc.Index(index_name)\ntime.sleep(1)\n# view index stats\nindex.describe_index_stats()\n```\n\n\n\n\n    {'dimension': 1536,\n     'index_fullness': 0.0,\n     'namespaces': {},\n     'total_vector_count': 0}\n\n\n\n## Populating the Index\n\nNow we will take 1K questions from the TREC dataset\n\n\n```python\nfrom datasets import load_dataset\n\n# load the first 1K rows of the TREC dataset\ntrec = load_dataset('trec', split='train[:1000]')\ntrec\n```\n\n    /usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n    The secret `HF_TOKEN` does not exist in your Colab secrets.\n    To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n    You will be able to reuse this secret in all of your notebooks.\n    Please note that authentication is recommended but still optional to access public models or datasets.\n      warnings.warn(\n\n\n\n    Downloading data:   0%|          | 0.00/213k [00:00<?, ?B/s]\n\n\n\n    Downloading data:   0%|          | 0.00/17.1k [00:00<?, ?B/s]\n\n\n\n    Generating train split:   0%|          | 0/5452 [00:00<?, ? examples/s]\n\n\n\n    Generating test split:   0%|          | 0/500 [00:00<?, ? examples/s]\n\n\n\n\n\n    Dataset({\n        features: ['text', 'coarse_label', 'fine_label'],\n        num_rows: 1000\n    })\n\n\n\n\n```python\ntrec[0]\n```\n\n\n\n\n    {'text': 'How did serfdom develop in and then leave Russia ?',\n     'coarse_label': 2,\n     'fine_label': 26}\n\n\n\nThen we create a vector embedding for each phrase using OpenAI, and `upsert` the ID, vector embedding, and original text for each phrase to Pinecone."}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: 9a08b00b-94ef-4618-a17e-aa737fbfc16c
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_\": \"9a08b00b-94ef-4618-a17e-aa737fbfc16c\", \"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\": \"daeab515-6b4f-40c7-a1a5-a97592b75399\", \"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\": \"d7cfae8881809a7e19fe70260803d9ad55217e7fdcfba2e97c643bf2b4919e8e\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"ec0d622b-ae62-4310-8a74-ba41e645e632\", \"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\": \"0bde2a6601df5201d2b3b32dc6b008434b69f6fc3b3fe4b430438d50b98b3045\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"07e5f11e-47ee-4195-bc45-acb219403d5b\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"69d5087af3cc2eb5e74b6318ec6bca788889aa5cf20d869c5c43d845eb58cd1a\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 73819, \"end_char_idx\": 76542, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "doc_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "ref_doc_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "chroma:document": "```python\nfrom datasets import load_dataset\n\n# load the first 1K rows of the TREC dataset\ntrec = load_dataset('trec', split='train[:1000]')\ntrec\n```\n\n    /usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n    The secret `HF_TOKEN` does not exist in your Colab secrets.\n    To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n    You will be able to reuse this secret in all of your notebooks.\n    Please note that authentication is recommended but still optional to access public models or datasets.\n      warnings.warn(\n\n\n\n    Downloading data:   0%|          | 0.00/213k [00:00<?, ?B/s]\n\n\n\n    Downloading data:   0%|          | 0.00/17.1k [00:00<?, ?B/s]\n\n\n\n    Generating train split:   0%|          | 0/5452 [00:00<?, ? examples/s]\n\n\n\n    Generating test split:   0%|          | 0/500 [00:00<?, ? examples/s]\n\n\n\n\n\n    Dataset({\n        features: ['text', 'coarse_label', 'fine_label'],\n        num_rows: 1000\n    })\n\n\n\n\n```python\ntrec[0]\n```\n\n\n\n\n    {'text': 'How did serfdom develop in and then leave Russia ?',\n     'coarse_label': 2,\n     'fine_label': 26}\n\n\n\nThen we create a vector embedding for each phrase using OpenAI, and `upsert` the ID, vector embedding, and original text for each phrase to Pinecone.\n\n\n```python\nfrom tqdm.auto import tqdm\n\ncount = 0  # we'll use the count to create unique IDs\nbatch_size = 32  # process everything in batches of 32\nfor i in tqdm(range(0, len(trec['text']), batch_size)):\n    # set end position of batch\n    i_end = min(i+batch_size, len(trec['text']))\n    # get batch of lines and IDs\n    lines_batch = trec['text'][i: i+batch_size]\n    ids_batch = [str(n) for n in range(i, i_end)]\n    # create embeddings\n    res = client.embeddings.create(input=lines_batch, model=MODEL)\n    embeds = [record.embedding for record in res.data]\n    # prep metadata and upsert batch\n    meta = [{'text': line} for line in lines_batch]\n    to_upsert = zip(ids_batch, embeds, meta)\n    # upsert to Pinecone\n    index.upsert(vectors=list(to_upsert))\n```\n\n\n      0%|          | 0/32 [00:00<?, ?it/s]\n\n\n---\n\n# Querying\n\nWith our data indexed, we're now ready to move onto performing searches. This follows a similar process to indexing. We start with a text `query`, that we would like to use to find similar sentences. As before we encode this with OpenAI's text similarity Babbage model to create a *query vector* `xq`. We then use `xq` to query the Pinecone index.\n\n\n```python\nquery = \"What caused the 1929 Great Depression?\"\n\nxq = client.embeddings.create(input=query, model=MODEL).data[0].embedding\n```\n\nNow query..."}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: 07e5f11e-47ee-4195-bc45-acb219403d5b
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_\": \"07e5f11e-47ee-4195-bc45-acb219403d5b\", \"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\": \"daeab515-6b4f-40c7-a1a5-a97592b75399\", \"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\": \"d7cfae8881809a7e19fe70260803d9ad55217e7fdcfba2e97c643bf2b4919e8e\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"9a08b00b-94ef-4618-a17e-aa737fbfc16c\", \"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\": \"d1b8452917069d235c2969576a7ee7aa82e7fd3754747ff67c7cc8482e9f3e71\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"eab46e92-ce8e-4c6c-80cd-f11860790b60\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"a4fae49221d2e416814407e8ac90d84d6e9c874cd0d949c7ce6b489fe80f8e76\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 75059, \"end_char_idx\": 77942, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "doc_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "ref_doc_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "chroma:document": "Then we create a vector embedding for each phrase using OpenAI, and `upsert` the ID, vector embedding, and original text for each phrase to Pinecone.\n\n\n```python\nfrom tqdm.auto import tqdm\n\ncount = 0  # we'll use the count to create unique IDs\nbatch_size = 32  # process everything in batches of 32\nfor i in tqdm(range(0, len(trec['text']), batch_size)):\n    # set end position of batch\n    i_end = min(i+batch_size, len(trec['text']))\n    # get batch of lines and IDs\n    lines_batch = trec['text'][i: i+batch_size]\n    ids_batch = [str(n) for n in range(i, i_end)]\n    # create embeddings\n    res = client.embeddings.create(input=lines_batch, model=MODEL)\n    embeds = [record.embedding for record in res.data]\n    # prep metadata and upsert batch\n    meta = [{'text': line} for line in lines_batch]\n    to_upsert = zip(ids_batch, embeds, meta)\n    # upsert to Pinecone\n    index.upsert(vectors=list(to_upsert))\n```\n\n\n      0%|          | 0/32 [00:00<?, ?it/s]\n\n\n---\n\n# Querying\n\nWith our data indexed, we're now ready to move onto performing searches. This follows a similar process to indexing. We start with a text `query`, that we would like to use to find similar sentences. As before we encode this with OpenAI's text similarity Babbage model to create a *query vector* `xq`. We then use `xq` to query the Pinecone index.\n\n\n```python\nquery = \"What caused the 1929 Great Depression?\"\n\nxq = client.embeddings.create(input=query, model=MODEL).data[0].embedding\n```\n\nNow query...\n\n\n```python\nres = index.query(vector=[xq], top_k=5, include_metadata=True)\nres\n```\n\n\n\n\n    {'matches': [{'id': '932',\n                  'metadata': {'text': 'Why did the world enter a global '\n                                       'depression in 1929 ?'},\n                  'score': 0.751888752,\n                  'values': []},\n                 {'id': '787',\n                  'metadata': {'text': \"When was `` the Great Depression '' ?\"},\n                  'score': 0.597448647,\n                  'values': []},\n                 {'id': '400',\n                  'metadata': {'text': 'What crop failure caused the Irish Famine '\n                                       '?'},\n                  'score': 0.367482603,\n                  'values': []},\n                 {'id': '835',\n                  'metadata': {'text': 'What were popular songs and types of songs '\n                                       'in the 1920s ?'},\n                  'score': 0.324545294,\n                  'values': []},\n                 {'id': '262',\n                  'metadata': {'text': 'When did World War I start ?'},\n                  'score': 0.320995867,\n                  'values': []}],\n     'namespace': '',\n     'usage': {'read_units': 6}}\n\n\n\nThe response from Pinecone includes our original text in the `metadata` field, let's print out the `top_k` most similar questions and their respective similarity scores."}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: eab46e92-ce8e-4c6c-80cd-f11860790b60
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_\": \"eab46e92-ce8e-4c6c-80cd-f11860790b60\", \"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\": \"daeab515-6b4f-40c7-a1a5-a97592b75399\", \"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\": \"d7cfae8881809a7e19fe70260803d9ad55217e7fdcfba2e97c643bf2b4919e8e\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"07e5f11e-47ee-4195-bc45-acb219403d5b\", \"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\": \"b0bda47e1960eef03abaf3f34d6997d49fa0d6e584f53397b071727aa01cc1bf\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"7f4cb939-6f23-49f3-a7d8-84f856ad0d07\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"5013e1c8c1c5943f09b53b1b236b4fa872689bf4b7fc03a3fc3469757c99bb42\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 76391, \"end_char_idx\": 79175, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "doc_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "ref_doc_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "chroma:document": "```python\nquery = \"What caused the 1929 Great Depression?\"\n\nxq = client.embeddings.create(input=query, model=MODEL).data[0].embedding\n```\n\nNow query...\n\n\n```python\nres = index.query(vector=[xq], top_k=5, include_metadata=True)\nres\n```\n\n\n\n\n    {'matches': [{'id': '932',\n                  'metadata': {'text': 'Why did the world enter a global '\n                                       'depression in 1929 ?'},\n                  'score': 0.751888752,\n                  'values': []},\n                 {'id': '787',\n                  'metadata': {'text': \"When was `` the Great Depression '' ?\"},\n                  'score': 0.597448647,\n                  'values': []},\n                 {'id': '400',\n                  'metadata': {'text': 'What crop failure caused the Irish Famine '\n                                       '?'},\n                  'score': 0.367482603,\n                  'values': []},\n                 {'id': '835',\n                  'metadata': {'text': 'What were popular songs and types of songs '\n                                       'in the 1920s ?'},\n                  'score': 0.324545294,\n                  'values': []},\n                 {'id': '262',\n                  'metadata': {'text': 'When did World War I start ?'},\n                  'score': 0.320995867,\n                  'values': []}],\n     'namespace': '',\n     'usage': {'read_units': 6}}\n\n\n\nThe response from Pinecone includes our original text in the `metadata` field, let's print out the `top_k` most similar questions and their respective similarity scores.\n\n\n```python\nfor match in res['matches']:\n    print(f\"{match['score']:.2f}: {match['metadata']['text']}\")\n```\n\n    0.75: Why did the world enter a global depression in 1929 ?\n    0.60: When was `` the Great Depression '' ?\n    0.37: What crop failure caused the Irish Famine ?\n    0.32: What were popular songs and types of songs in the 1920s ?\n    0.32: When did World War I start ?\n\n\nLooks good, let's make it harder and replace *\"depression\"* with the incorrect term *\"recession\"*.\n\n\n```python\nquery = \"What was the cause of the major recession in the early 20th century?\"\n\n# create the query embedding\nxq = client.embeddings.create(input=query, model=MODEL).data[0].embedding\n\n# query, returning the top 5 most similar results\nres = index.query(vector=[xq], top_k=5, include_metadata=True)\n\nfor match in res['matches']:\n    print(f\"{match['score']:.2f}: {match['metadata']['text']}\")\n```\n\n    0.63: Why did the world enter a global depression in 1929 ?\n    0.55: When was `` the Great Depression '' ?\n    0.34: What were popular songs and types of songs in the 1920s ?\n    0.33: What crop failure caused the Irish Famine ?\n    0.29: What is considered the costliest disaster the insurance industry has ever faced ?\n\n\nAnd again..."}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: 7f4cb939-6f23-49f3-a7d8-84f856ad0d07
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_\": \"7f4cb939-6f23-49f3-a7d8-84f856ad0d07\", \"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\": \"daeab515-6b4f-40c7-a1a5-a97592b75399\", \"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\": \"d7cfae8881809a7e19fe70260803d9ad55217e7fdcfba2e97c643bf2b4919e8e\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"eab46e92-ce8e-4c6c-80cd-f11860790b60\", \"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\": \"b490b8f4a9cfb5b4ff522f80073c6ff38b4e6a6a10bff286e428cb435e2feb4e\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 77773, \"end_char_idx\": 80169, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "doc_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "ref_doc_id": "daeab515-6b4f-40c7-a1a5-a97592b75399", "chroma:document": "The response from Pinecone includes our original text in the `metadata` field, let's print out the `top_k` most similar questions and their respective similarity scores.\n\n\n```python\nfor match in res['matches']:\n    print(f\"{match['score']:.2f}: {match['metadata']['text']}\")\n```\n\n    0.75: Why did the world enter a global depression in 1929 ?\n    0.60: When was `` the Great Depression '' ?\n    0.37: What crop failure caused the Irish Famine ?\n    0.32: What were popular songs and types of songs in the 1920s ?\n    0.32: When did World War I start ?\n\n\nLooks good, let's make it harder and replace *\"depression\"* with the incorrect term *\"recession\"*.\n\n\n```python\nquery = \"What was the cause of the major recession in the early 20th century?\"\n\n# create the query embedding\nxq = client.embeddings.create(input=query, model=MODEL).data[0].embedding\n\n# query, returning the top 5 most similar results\nres = index.query(vector=[xq], top_k=5, include_metadata=True)\n\nfor match in res['matches']:\n    print(f\"{match['score']:.2f}: {match['metadata']['text']}\")\n```\n\n    0.63: Why did the world enter a global depression in 1929 ?\n    0.55: When was `` the Great Depression '' ?\n    0.34: What were popular songs and types of songs in the 1920s ?\n    0.33: What crop failure caused the Irish Famine ?\n    0.29: What is considered the costliest disaster the insurance industry has ever faced ?\n\n\nAnd again...\n\n\n```python\nquery = \"Why was there a long-term economic downturn in the early 20th century?\"\n\n# create the query embedding\nxq = client.embeddings.create(input=query, model=MODEL).data[0].embedding\n\n# query, returning the top 5 most similar results\nres = index.query(vector=[xq], top_k=5, include_metadata=True)\n\nfor match in res['matches']:\n    print(f\"{match['score']:.2f}: {match['metadata']['text']}\")\n```\n\n    0.62: Why did the world enter a global depression in 1929 ?\n    0.54: When was `` the Great Depression '' ?\n    0.34: What were popular songs and types of songs in the 1920s ?\n    0.33: What crop failure caused the Irish Famine ?\n    0.32: What do economists do ?\n\n\nLooks great, our semantic search pipeline is clearly able to identify the meaning between each of our queries and return the most semantically similar questions from the already indexed questions.\n\nOnce we're finished with the index we delete it to save resources.\n\n\n```python\npc.delete_index(index_name)\n```\n\n---"}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: 69c82264-f452-4040-9f6f-ebe472abdfcf
encoding: FLOAT32
metadata:
{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/README.ipynb", "title": "Pinecone Vector Database", "tokens": 512, "retrieve_doc": true, "source": "OpenAI_Cookbooks", "_node_content": "{\"id_\": \"69c82264-f452-4040-9f6f-ebe472abdfcf\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/README.ipynb\", \"title\": \"Pinecone Vector Database\", \"tokens\": 512, \"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\": \"781954e6-3cf7-47bb-b607-6f1f4e5ee54f\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/README.ipynb\", \"title\": \"Pinecone Vector Database\", \"tokens\": 512, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"e5f53811c65c60c9c98b662d4ca3f25c91cd352c9883ce121a53023effb8edaf\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 0, \"end_char_idx\": 2245, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "781954e6-3cf7-47bb-b607-6f1f4e5ee54f", "doc_id": "781954e6-3cf7-47bb-b607-6f1f4e5ee54f", "ref_doc_id": "781954e6-3cf7-47bb-b607-6f1f4e5ee54f", "chroma:document": "# Pinecone Vector Database\n\n[Vector search](https://www.pinecone.io/learn/vector-search-basics/) is an innovative technology that enables developers and engineers to efficiently store, search, and recommend information by representing complex data as mathematical vectors. By comparing the similarities between these vectors, you can quickly retrieve relevant information in a seamless and intuitive manner.\n\n[Pinecone](https://pinecone.io/) is a [vector database](https://www.pinecone.io/learn/vector-database/) designed with developers and engineers in mind. As a managed service, it alleviates the burden of maintenance and engineering, allowing you to focus on extracting valuable insights from your data. The free tier supports up to 5 million vectors, making it an accessible and cost-effective way to experiment with vector search capabilities. With Pinecone, you'll experience impressive speed, accuracy, and scalability, as well as access to advanced features like single-stage metadata filtering and the cutting-edge sparse-dense index.\n\n## Examples\n\nThis folder contains examples of using Pinecone and OpenAI together. More will be added over time so check back for updates!\n\n| Name | Description | Google Colab |\n| --- | --- | --- |\n| [GPT-4 Retrieval Augmentation](./GPT4_Retrieval_Augmentation.ipynb) | How to supercharge GPT-4 with retrieval augmentation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openai/openai-cookbook/blob/master/examples/vector_databases/pinecone/GPT4_Retrieval_Augmentation.ipynb) |\n| [Generative Question-Answering](./Gen_QA.ipynb) | A simple walkthrough demonstrating the use of Generative Question-Answering | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openai/openai-cookbook/blob/master/examples/vector_databases/pinecone/Gen_QA.ipynb) |\n| [Semantic Search](./Semantic_Search.ipynb) | A guide to building a simple semantic search process | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openai/openai-cookbook/blob/master/examples/vector_databases/pinecone/Semantic_Search.ipynb) |"}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: 251eabb7-83aa-4b4d-9f94-c586547fb57a
encoding: FLOAT32
metadata:
{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Using_Pinecone_for_embeddings_search.ipynb", "title": "Using Pinecone for Embeddings Search", "tokens": 4156, "retrieve_doc": true, "source": "OpenAI_Cookbooks", "_node_content": "{\"id_\": \"251eabb7-83aa-4b4d-9f94-c586547fb57a\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Using_Pinecone_for_embeddings_search.ipynb\", \"title\": \"Using Pinecone for Embeddings Search\", \"tokens\": 4156, \"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\": \"14dcb9f8-5c6b-4845-bfcd-714b8cb126be\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Using_Pinecone_for_embeddings_search.ipynb\", \"title\": \"Using Pinecone for Embeddings Search\", \"tokens\": 4156, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"4b2b4e69c5cda35a3093ed9101592972f85e722e0d2119bf50c9adb026462e68\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"fe0e5096-aa79-4987-b314-ad99ae82ded5\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"373a4a79d637360e114455f80825418f3ef64c0ccdbe8d98290205b3d8f7581b\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 0, \"end_char_idx\": 3350, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "14dcb9f8-5c6b-4845-bfcd-714b8cb126be", "doc_id": "14dcb9f8-5c6b-4845-bfcd-714b8cb126be", "ref_doc_id": "14dcb9f8-5c6b-4845-bfcd-714b8cb126be", "chroma:document": "# Using Pinecone for Embeddings Search\n\nThis notebook takes you through a simple flow to download some data, embed it, and then index and search it using a selection of vector databases. This is a common requirement for customers who want to store and search our embeddings with their own data in a secure environment to support production use cases such as chatbots, topic modelling and more.\n\n### What is a Vector Database\n\nA vector database is a database made to store, manage and search embedding vectors. The use of embeddings to encode unstructured data (text, audio, video and more) as vectors for consumption by machine-learning models has exploded in recent years, due to the increasing effectiveness of AI in solving use cases involving natural language, image recognition and other unstructured forms of data. Vector databases have emerged as an effective solution for enterprises to deliver and scale these use cases.\n\n### Why use a Vector Database\n\nVector databases enable enterprises to take many of the embeddings use cases we've shared in this repo (question and answering, chatbot and recommendation services, for example), and make use of them in a secure, scalable environment. Many of our customers make embeddings solve their problems at small scale but performance and security hold them back from going into production - we see vector databases as a key component in solving that, and in this guide we'll walk through the basics of embedding text data, storing it in a vector database and using it for semantic search.\n\n\n### Demo Flow\nThe demo flow is:\n- **Setup**: Import packages and set any required variables\n- **Load data**: Load a dataset and embed it using OpenAI embeddings\n- **Pinecone**\n    - *Setup*: Here we'll set up the Python client for Pinecone. For more details go [here](https://docs.pinecone.io/docs/quickstart)\n    - *Index Data*: We'll create an index with namespaces for __titles__ and __content__\n    - *Search Data*: We'll test out both namespaces with search queries to confirm it works\n\nOnce you've run through this notebook you should have a basic understanding of how to setup and use vector databases, and can move on to more complex use cases making use of our embeddings.\n\n## Setup\n\nImport the required libraries and set the embedding model that we'd like to use.\n\n\n```python\n# We'll need to install the Pinecone client\n!pip install pinecone-client\n\n#Install wget to pull zip file\n!pip install wget\n```\n\n    Requirement already satisfied: pinecone-client in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (2.2.2)\n    Requirement already satisfied: requests>=2.19.0 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (2.31.0)\n    Requirement already satisfied: pyyaml>=5.4 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (6.0)\n    Requirement already satisfied: loguru>=0.5.0 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (0.7.0)\n    Requirement already satisfied: typing-extensions>=3.7.4 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (4.5."}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: fe0e5096-aa79-4987-b314-ad99ae82ded5
encoding: FLOAT32
metadata:
{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Using_Pinecone_for_embeddings_search.ipynb", "title": "Using Pinecone for Embeddings Search", "tokens": 4156, "retrieve_doc": true, "source": "OpenAI_Cookbooks", "_node_content": "{\"id_\": \"fe0e5096-aa79-4987-b314-ad99ae82ded5\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Using_Pinecone_for_embeddings_search.ipynb\", \"title\": \"Using Pinecone for Embeddings Search\", \"tokens\": 4156, \"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\": \"14dcb9f8-5c6b-4845-bfcd-714b8cb126be\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Using_Pinecone_for_embeddings_search.ipynb\", \"title\": \"Using Pinecone for Embeddings Search\", \"tokens\": 4156, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"4b2b4e69c5cda35a3093ed9101592972f85e722e0d2119bf50c9adb026462e68\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"251eabb7-83aa-4b4d-9f94-c586547fb57a\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Using_Pinecone_for_embeddings_search.ipynb\", \"title\": \"Using Pinecone for Embeddings Search\", \"tokens\": 4156, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"592ac940a694aec13e059b468abb7c3e14f5237120026cd5df4de21c4dc53593\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"db359a96-896f-44af-8456-4bebefe3d6a4\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"30fc4eddb067f979f28f1d146c041365475c760fa5524650a25e2e696d6e274a\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 2320, \"end_char_idx\": 4952, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "14dcb9f8-5c6b-4845-bfcd-714b8cb126be", "doc_id": "14dcb9f8-5c6b-4845-bfcd-714b8cb126be", "ref_doc_id": "14dcb9f8-5c6b-4845-bfcd-714b8cb126be", "chroma:document": "```python\n# We'll need to install the Pinecone client\n!pip install pinecone-client\n\n#Install wget to pull zip file\n!pip install wget\n```\n\n    Requirement already satisfied: pinecone-client in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (2.2.2)\n    Requirement already satisfied: requests>=2.19.0 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (2.31.0)\n    Requirement already satisfied: pyyaml>=5.4 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (6.0)\n    Requirement already satisfied: loguru>=0.5.0 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (0.7.0)\n    Requirement already satisfied: typing-extensions>=3.7.4 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (4.5.0)\n    Requirement already satisfied: dnspython>=2.0.0 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (2.3.0)\n    Requirement already satisfied: python-dateutil>=2.5.3 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (2.8.2)\n    Requirement already satisfied: urllib3>=1.21.1 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (1.26.16)\n    Requirement already satisfied: tqdm>=4.64.1 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (4.65.0)\n    Requirement already satisfied: numpy>=1.22.0 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from pinecone-client) (1.25.0)\n    Requirement already satisfied: six>=1.5 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from python-dateutil>=2.5.3->pinecone-client) (1.16.0)\n    Requirement already satisfied: charset-normalizer<4,>=2 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from requests>=2.19.0->pinecone-client) (3.1.0)\n    Requirement already satisfied: idna<4,>=2.5 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from requests>=2.19.0->pinecone-client) (3.4)\n    Requirement already satisfied: certifi>=2017.4.17 in /Users/colin."}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: db359a96-896f-44af-8456-4bebefe3d6a4
encoding: FLOAT32
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Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n      from tqdm.autonotebook import tqdm\n\n\n## Load data\n\nIn this section we'll load embedded data that we've prepared [in this article](../../Embedding_Wikipedia_articles_for_search.ipynb)."}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: 963a4f04-5ac7-4b4c-b814-f0b7d88bb663
encoding: FLOAT32
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created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: 39f7926f-d4d0-46c6-b8ca-20120470294b
encoding: FLOAT32
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Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n      from tqdm.autonotebook import tqdm\n\n\n## Load data\n\nIn this section we'll load embedded data that we've prepared [in this article](../../Embedding_Wikipedia_articles_for_search.ipynb).\n\n\n```python\nembeddings_url = 'https://cdn.openai.com/API/examples/data/vector_database_wikipedia_articles_embedded.zip'\n\n# The file is ~700 MB so this will take some time\nwget.download(embeddings_url)\n```\n\n\n```python\nimport zipfile\nwith zipfile.ZipFile(\"vector_database_wikipedia_articles_embedded.zip\",\"r\") as zip_ref:\n    zip_ref.extractall(\"../data\")\n```\n\n\n```python\narticle_df = pd.read_csv('../data/vector_database_wikipedia_articles_embedded.csv')\n```\n\n\n```python\narticle_df.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>url</th>\n      <th>title</th>\n      <th>text</th>\n      <th>title_vector</th>\n      <th>content_vector</th>\n      <th>vector_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>https://simple.wikipedia.org/wiki/April</td>\n      <td>April</td>\n      <td>April is the fourth month of the year in the J...</td>\n      <td>[0.001009464613161981, -0.020700545981526375, ...</td>\n      <td>[-0.011253940872848034, -0.013491976074874401,...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>https://simple.wikipedia.org/wiki/August</td>\n      <td>August</td>\n      <td>August (Aug.)"}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: b00082d0-05d2-4711-b152-8402b1e895e7
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created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
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created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
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Once we have an index, we can create multiple namespaces, which can make a single index searchable for various use cases. For more details, consult [Pinecone documentation](https://docs.pinecone.io/docs/namespaces#:~:text=Pinecone%20allows%20you%20to%20partition,different%20subsets%20of%20your%20index.).\n\nIf you want to batch insert to your index in parallel to increase insertion speed then there is a great guide in the Pinecone documentation on [batch inserts in parallel](https://docs.pinecone.io/docs/insert-data#sending-upserts-in-parallel)."}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
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Once we have an index, we can create multiple namespaces, which can make a single index searchable for various use cases. For more details, consult [Pinecone documentation](https://docs.pinecone.io/docs/namespaces#:~:text=Pinecone%20allows%20you%20to%20partition,different%20subsets%20of%20your%20index.).\n\nIf you want to batch insert to your index in parallel to increase insertion speed then there is a great guide in the Pinecone documentation on [batch inserts in parallel](https://docs.pinecone.io/docs/insert-data#sending-upserts-in-parallel).\n\n\n```python\n# Models a simple batch generator that make chunks out of an input DataFrame\nclass BatchGenerator:\n    \n    \n    def __init__(self, batch_size: int = 10) -> None:\n        self.batch_size = batch_size\n    \n    # Makes chunks out of an input DataFrame\n    def to_batches(self, df: pd.DataFrame) -> Iterator[pd.DataFrame]:\n        splits = self.splits_num(df.shape[0])\n        if splits <= 1:\n            yield df\n        else:\n            for chunk in np.array_split(df, splits):\n                yield chunk\n\n    # Determines how many chunks DataFrame contains\n    def splits_num(self, elements: int) -> int:\n        return round(elements / self.batch_size)\n    \n    __call__ = to_batches\n\ndf_batcher = BatchGenerator(300)\n```\n\n\n```python\n# Pick a name for the new index\nindex_name = 'wikipedia-articles'\n\n# Check whether the index with the same name already exists - if so, delete it\nif index_name in pinecone.list_indexes():\n    pinecone.delete_index(index_name)\n    \n# Creates new index\npinecone.create_index(name=index_name, dimension=len(article_df['content_vector'][0]))\nindex = pinecone.Index(index_name=index_name)\n\n# Confirm our index was created\npinecone.list_indexes()\n```\n\n\n\n\n    ['podcasts', 'wikipedia-articles']\n\n\n\n\n```python\n# Upsert content vectors in content namespace - this can take a few minutes\nprint(\"Uploading vectors to content namespace..\")\nfor batch_df in df_batcher(article_df):\n    index.upsert(vectors=zip(batch_df.vector_id, batch_df.content_vector), namespace='content')\n```\n\n    Uploading vectors to content namespace..\n\n\n\n```python\n# Upsert title vectors in title namespace - this can also take a few minutes\nprint(\"Uploading vectors to title namespace..\")\nfor batch_df in df_batcher(article_df):\n    index.upsert(vectors=zip(batch_df.vector_id, batch_df.title_vector), namespace='title')\n```\n\n    Uploading vectors to title namespace..\n\n\n\n```python\n# Check index size for each namespace to confirm all of our docs have loaded\nindex.describe_index_stats()\n```"}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
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{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Using_Pinecone_for_embeddings_search.ipynb", "title": "Using Pinecone for Embeddings Search", "tokens": 4156, "retrieve_doc": true, "source": "OpenAI_Cookbooks", "_node_content": "{\"id_\": \"5d83b4d2-1cda-4bbc-a92e-7f4257e30aa1\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Using_Pinecone_for_embeddings_search.ipynb\", \"title\": \"Using Pinecone for Embeddings Search\", \"tokens\": 4156, \"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\": \"14dcb9f8-5c6b-4845-bfcd-714b8cb126be\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Using_Pinecone_for_embeddings_search.ipynb\", \"title\": \"Using Pinecone for Embeddings Search\", \"tokens\": 4156, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"4b2b4e69c5cda35a3093ed9101592972f85e722e0d2119bf50c9adb026462e68\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"54a195f0-f608-4074-a7a0-fad86ccb493b\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Using_Pinecone_for_embeddings_search.ipynb\", \"title\": \"Using Pinecone for Embeddings Search\", \"tokens\": 4156, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"daceef67ef73f3f79e64537732e540cecf827d8e369c389c77abd47ee388b5b9\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"ae644be7-a783-49a3-8514-5b35ce725514\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"1a7834183876003050708c032af3cb4596f1001b144c3e1e345778eebd14c495\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 12058, \"end_char_idx\": 15251, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "14dcb9f8-5c6b-4845-bfcd-714b8cb126be", "doc_id": "14dcb9f8-5c6b-4845-bfcd-714b8cb126be", "ref_doc_id": "14dcb9f8-5c6b-4845-bfcd-714b8cb126be", "chroma:document": "```python\n# Pick a name for the new index\nindex_name = 'wikipedia-articles'\n\n# Check whether the index with the same name already exists - if so, delete it\nif index_name in pinecone.list_indexes():\n    pinecone.delete_index(index_name)\n    \n# Creates new index\npinecone.create_index(name=index_name, dimension=len(article_df['content_vector'][0]))\nindex = pinecone.Index(index_name=index_name)\n\n# Confirm our index was created\npinecone.list_indexes()\n```\n\n\n\n\n    ['podcasts', 'wikipedia-articles']\n\n\n\n\n```python\n# Upsert content vectors in content namespace - this can take a few minutes\nprint(\"Uploading vectors to content namespace..\")\nfor batch_df in df_batcher(article_df):\n    index.upsert(vectors=zip(batch_df.vector_id, batch_df.content_vector), namespace='content')\n```\n\n    Uploading vectors to content namespace..\n\n\n\n```python\n# Upsert title vectors in title namespace - this can also take a few minutes\nprint(\"Uploading vectors to title namespace..\")\nfor batch_df in df_batcher(article_df):\n    index.upsert(vectors=zip(batch_df.vector_id, batch_df.title_vector), namespace='title')\n```\n\n    Uploading vectors to title namespace..\n\n\n\n```python\n# Check index size for each namespace to confirm all of our docs have loaded\nindex.describe_index_stats()\n```\n\n\n\n\n    {'dimension': 1536,\n     'index_fullness': 0.1,\n     'namespaces': {'content': {'vector_count': 25000},\n                    'title': {'vector_count': 25000}},\n     'total_vector_count': 50000}\n\n\n\n### Search data\n\nNow we'll enter some dummy searches and check we get decent results back\n\n\n```python\n# First we'll create dictionaries mapping vector IDs to their outputs so we can retrieve the text for our search results\ntitles_mapped = dict(zip(article_df.vector_id,article_df.title))\ncontent_mapped = dict(zip(article_df.vector_id,article_df.text))\n```\n\n\n```python\ndef query_article(query, namespace, top_k=5):\n    '''Queries an article using its title in the specified\n     namespace and prints results.'''\n\n    # Create vector embeddings based on the title column\n    embedded_query = openai.Embedding.create(\n                                            input=query,\n                                            model=EMBEDDING_MODEL,\n                                            )[\"data\"][0]['embedding']\n\n    # Query namespace passed as parameter using title vector\n    query_result = index.query(embedded_query, \n                                      namespace=namespace, \n                                      top_k=top_k)\n\n    # Print query results \n    print(f'\\nMost similar results to {query} in \"{namespace}\" namespace:\\n')\n    if not query_result.matches:\n        print('no query result')\n    \n    matches = query_result.matches\n    ids = [res.id for res in matches]\n    scores = [res.score for res in matches]\n    df = pd.DataFrame({'id':ids, \n                       'score':scores,\n                       'title': [titles_mapped[_id] for _id in ids],\n                       'content': [content_mapped[_id] for _id in ids],\n                       })\n    \n    counter = 0\n    for k,v in df.iterrows():\n        counter += 1\n        print(f'{v.title} (score = {v.score})')\n    \n    print('\\n')\n\n    return df\n```"}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: ae644be7-a783-49a3-8514-5b35ce725514
encoding: FLOAT32
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created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: c80ea04e-004a-44bd-a714-f1b7fe525c48
encoding: FLOAT32
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{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb", "title": "Retrieval Augmented Generative Question Answering with Pinecone", "tokens": 6559, "retrieve_doc": true, "source": "OpenAI_Cookbooks", "_node_content": "{\"id_\": \"c80ea04e-004a-44bd-a714-f1b7fe525c48\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb\", \"title\": \"Retrieval Augmented Generative Question Answering with Pinecone\", \"tokens\": 6559, \"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\": \"de06eb38-789b-4399-b619-bc86e67a253f\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb\", \"title\": \"Retrieval Augmented Generative Question Answering with Pinecone\", \"tokens\": 6559, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"4b300da6c251f86fdd06f6b49f107a0b8937b5cf6b26ed75eb115789fed88b95\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"6f740fa5-ae09-4f92-9862-6bde744095b3\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"a96adf98e64c7662f0aa6d45bc7d47fc79c4a1e0dda582e161091a00a7da52f4\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 0, \"end_char_idx\": 678, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "de06eb38-789b-4399-b619-bc86e67a253f", "doc_id": "de06eb38-789b-4399-b619-bc86e67a253f", "ref_doc_id": "de06eb38-789b-4399-b619-bc86e67a253f", "chroma:document": "# Retrieval Augmented Generative Question Answering with Pinecone\n\n#### Fixing LLMs that Hallucinate\n\nIn this notebook we will learn how to query relevant contexts to our queries from Pinecone, and pass these to a generative OpenAI model to generate an answer backed by real data sources.\n\nA common problem with using GPT-3 to factually answer questions is that GPT-3 can sometimes make things up. The GPT models have a broad range of general knowledge, but this does not necessarily apply to more specific information. For that we use the Pinecone vector database as our _\"external knowledge base\"_ \u2014 like *long-term memory* for GPT-3.\n\nRequired installs for this notebook are:"}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: 6f740fa5-ae09-4f92-9862-6bde744095b3
encoding: FLOAT32
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{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb", "title": "Retrieval Augmented Generative Question Answering with Pinecone", "tokens": 6559, "retrieve_doc": true, "source": "OpenAI_Cookbooks", "_node_content": "{\"id_\": \"6f740fa5-ae09-4f92-9862-6bde744095b3\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb\", \"title\": \"Retrieval Augmented Generative Question Answering with Pinecone\", \"tokens\": 6559, \"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\": \"de06eb38-789b-4399-b619-bc86e67a253f\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb\", \"title\": \"Retrieval Augmented Generative Question Answering with Pinecone\", \"tokens\": 6559, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"4b300da6c251f86fdd06f6b49f107a0b8937b5cf6b26ed75eb115789fed88b95\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"c80ea04e-004a-44bd-a714-f1b7fe525c48\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb\", \"title\": \"Retrieval Augmented Generative Question Answering with Pinecone\", \"tokens\": 6559, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"b1acc88a9adbefcfd8f2fd9e1663a4e5d4c4be2f7cd0f946df87d1847516628b\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"0ce7b2fd-bab2-4838-9a4e-65ba90da04b9\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"b99cd54c1066cfb822bc0d0efb088706a706083892fb5ff41aeab3a5f2cf2024\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 0, \"end_char_idx\": 2121, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "de06eb38-789b-4399-b619-bc86e67a253f", "doc_id": "de06eb38-789b-4399-b619-bc86e67a253f", "ref_doc_id": "de06eb38-789b-4399-b619-bc86e67a253f", "chroma:document": "# Retrieval Augmented Generative Question Answering with Pinecone\n\n#### Fixing LLMs that Hallucinate\n\nIn this notebook we will learn how to query relevant contexts to our queries from Pinecone, and pass these to a generative OpenAI model to generate an answer backed by real data sources.\n\nA common problem with using GPT-3 to factually answer questions is that GPT-3 can sometimes make things up. The GPT models have a broad range of general knowledge, but this does not necessarily apply to more specific information. For that we use the Pinecone vector database as our _\"external knowledge base\"_ \u2014 like *long-term memory* for GPT-3.\n\nRequired installs for this notebook are:\n\n\n```python\n!pip install -qU openai pinecone-client datasets\n```\n\n    \u001b[?25l     \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[32m0.0/55.3 KB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\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[32m55.3/55.3 KB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n    \u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n      Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n      Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\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\u001b[0m \u001b[32m170.6/170.6 KB\u001b[0m \u001b[31m13.7 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\u001b[0m \u001b[32m452.9/452.9 KB\u001b[0m \u001b[31m30.4 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[32m58.3/58.3 KB\u001b[0m \u001b[31m6.8 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\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m17.3 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\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m13.7 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\u001b[0m \u001b[32m182.4/182.4 KB\u001b[0m \u001b[31m18.6 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\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n    \u001b[?25h  Building wheel for openai (pyproject.toml) ... \u001b[?25l\u001b[?25hdone"}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
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{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb", "title": "Retrieval Augmented Generative Question Answering with Pinecone", "tokens": 6559, "retrieve_doc": true, "source": "OpenAI_Cookbooks", "_node_content": "{\"id_\": \"0ce7b2fd-bab2-4838-9a4e-65ba90da04b9\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb\", \"title\": \"Retrieval Augmented Generative Question Answering with Pinecone\", \"tokens\": 6559, \"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\": \"de06eb38-789b-4399-b619-bc86e67a253f\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb\", \"title\": \"Retrieval Augmented Generative Question Answering with Pinecone\", \"tokens\": 6559, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"4b300da6c251f86fdd06f6b49f107a0b8937b5cf6b26ed75eb115789fed88b95\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"6f740fa5-ae09-4f92-9862-6bde744095b3\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb\", \"title\": \"Retrieval Augmented Generative Question Answering with Pinecone\", \"tokens\": 6559, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"0295dd6f287900b705eca32a18b3ef20591d5f37fe269488d649163f934e230f\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"1207f7dd-79b6-4fbd-b541-e87fe2bae4d4\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"25a0ca9c39dc8dabc3823a17b9f9d0c9ca74ba52939574d67224193c86844d72\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 2125, \"end_char_idx\": 4213, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "de06eb38-789b-4399-b619-bc86e67a253f", "doc_id": "de06eb38-789b-4399-b619-bc86e67a253f", "ref_doc_id": "de06eb38-789b-4399-b619-bc86e67a253f", "chroma:document": "```python\nimport openai\n\n# get API key from top-right dropdown on OpenAI website\nopenai.api_key = \"OPENAI_API_KEY\"\n```\n\nFor many questions *state-of-the-art (SOTA)* LLMs are more than capable of answering correctly.\n\n\n```python\nquery = \"who was the 12th person on the moon and when did they land?\"\n\n# now query `gpt-3.5-turbo-instruct` WITHOUT context\nres = openai.Completion.create(\n    engine='gpt-3.5-turbo-instruct',\n    prompt=query,\n    temperature=0,\n    max_tokens=400,\n    top_p=1,\n    frequency_penalty=0,\n    presence_penalty=0,\n    stop=None\n)\n\nres['choices'][0]['text'].strip()\n```\n\n\n\n\n    'The 12th person on the moon was Harrison Schmitt, and he landed on December 11, 1972.'\n\n\n\nHowever, that isn't always the case. First let's first rewrite the above into a simple function so we're not rewriting this every time.\n\n\n```python\ndef complete(prompt):\n    res = openai.Completion.create(\n        engine='gpt-3.5-turbo-instruct',\n        prompt=prompt,\n        temperature=0,\n        max_tokens=400,\n        top_p=1,\n        frequency_penalty=0,\n        presence_penalty=0,\n        stop=None\n    )\n    return res['choices'][0]['text'].strip()\n```\n\nNow let's ask a more specific question about training a type of transformer model called a *sentence transformer*. The ideal answer we'd be looking for is _\"Multiple Negatives Ranking (MNR) loss\"_.\n\nDon't worry if this is a new term to you, it isn't required to understand what we're doing or demoing here.\n\n\n```python\nquery = (\n    \"Which training method should I use for sentence transformers when \" +\n    \"I only have pairs of related sentences?\"\n)\n\ncomplete(query)\n```\n\n\n\n\n    'If you only have pairs of related sentences, then the best training method to use for sentence transformers is the supervised learning approach. This approach involves providing the model with labeled data, such as pairs of related sentences, and then training the model to learn the relationships between the sentences. This approach is often used for tasks such as natural language inference, semantic similarity, and paraphrase identification.'"}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: 1207f7dd-79b6-4fbd-b541-e87fe2bae4d4
encoding: FLOAT32
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{"url": "https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb", "title": "Retrieval Augmented Generative Question Answering with Pinecone", "tokens": 6559, "retrieve_doc": true, "source": "OpenAI_Cookbooks", "_node_content": "{\"id_\": \"1207f7dd-79b6-4fbd-b541-e87fe2bae4d4\", \"embedding\": null, \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb\", \"title\": \"Retrieval Augmented Generative Question Answering with Pinecone\", \"tokens\": 6559, \"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\": \"de06eb38-789b-4399-b619-bc86e67a253f\", \"node_type\": \"4\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb\", \"title\": \"Retrieval Augmented Generative Question Answering with Pinecone\", \"tokens\": 6559, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"4b300da6c251f86fdd06f6b49f107a0b8937b5cf6b26ed75eb115789fed88b95\", \"class_name\": \"RelatedNodeInfo\"}, \"2\": {\"node_id\": \"0ce7b2fd-bab2-4838-9a4e-65ba90da04b9\", \"node_type\": \"1\", \"metadata\": {\"url\": \"https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Gen_QA.ipynb\", \"title\": \"Retrieval Augmented Generative Question Answering with Pinecone\", \"tokens\": 6559, \"retrieve_doc\": true, \"source\": \"OpenAI_Cookbooks\"}, \"hash\": \"57a6704189a5ee37db7b32f7f8278cb57f982fa4f87102f6d8adc8acfd590555\", \"class_name\": \"RelatedNodeInfo\"}, \"3\": {\"node_id\": \"2f95c610-05f3-4a2d-88f9-fa3c216d3554\", \"node_type\": \"1\", \"metadata\": {}, \"hash\": \"907b27554daebaefaff6a79e5767ece3c24d8422edd08ec684f233a5ca46405a\", \"class_name\": \"RelatedNodeInfo\"}}, \"text\": \"\", \"mimetype\": \"text/plain\", \"start_char_idx\": 2728, \"end_char_idx\": 6379, \"text_template\": \"{metadata_str}\\n\\n{content}\", \"metadata_template\": \"{key}: {value}\", \"metadata_seperator\": \"\\n\", \"class_name\": \"TextNode\"}", "_node_type": "TextNode", "document_id": "de06eb38-789b-4399-b619-bc86e67a253f", "doc_id": "de06eb38-789b-4399-b619-bc86e67a253f", "ref_doc_id": "de06eb38-789b-4399-b619-bc86e67a253f", "chroma:document": "'The 12th person on the moon was Harrison Schmitt, and he landed on December 11, 1972.'\n\n\n\nHowever, that isn't always the case. First let's first rewrite the above into a simple function so we're not rewriting this every time.\n\n\n```python\ndef complete(prompt):\n    res = openai.Completion.create(\n        engine='gpt-3.5-turbo-instruct',\n        prompt=prompt,\n        temperature=0,\n        max_tokens=400,\n        top_p=1,\n        frequency_penalty=0,\n        presence_penalty=0,\n        stop=None\n    )\n    return res['choices'][0]['text'].strip()\n```\n\nNow let's ask a more specific question about training a type of transformer model called a *sentence transformer*. The ideal answer we'd be looking for is _\"Multiple Negatives Ranking (MNR) loss\"_.\n\nDon't worry if this is a new term to you, it isn't required to understand what we're doing or demoing here.\n\n\n```python\nquery = (\n    \"Which training method should I use for sentence transformers when \" +\n    \"I only have pairs of related sentences?\"\n)\n\ncomplete(query)\n```\n\n\n\n\n    'If you only have pairs of related sentences, then the best training method to use for sentence transformers is the supervised learning approach. This approach involves providing the model with labeled data, such as pairs of related sentences, and then training the model to learn the relationships between the sentences. This approach is often used for tasks such as natural language inference, semantic similarity, and paraphrase identification.'\n\n\n\nOne of the common answers we get to this is:\n\n```\nThe best training method to use for fine-tuning a pre-trained model with sentence transformers is the Masked Language Model (MLM) training. MLM training involves randomly masking some of the words in a sentence and then training the model to predict the masked words. This helps the model to learn the context of the sentence and better understand the relationships between words.\n```\n\nThis answer seems pretty convincing right? Yet, it's wrong. MLM is typically used in the pretraining step of a transformer model but *\"cannot\"* be used to fine-tune a sentence-transformer, and has nothing to do with having _\"pairs of related sentences\"_.\n\nAn alternative answer we receive (and the one we returned above) is about `supervised learning approach` being the most suitable. This is completely true, but it's not specific and doesn't answer the question.\n\nWe have two options for enabling our LLM in understanding and correctly answering this question:\n\n1. We fine-tune the LLM on text data covering the topic mentioned, likely on articles and papers talking about sentence transformers, semantic search training methods, etc.\n\n2. We use **R**etrieval **A**ugmented **G**eneration (RAG), a technique that implements an information retrieval component to the generation process. Allowing us to retrieve relevant information and feed this information into the generation model as a *secondary* source of information.\n\nWe will demonstrate option **2**.\n\n---\n\n## Building a Knowledge Base\n\nWith option **2** the retrieval of relevant information requires an external _\"Knowledge Base\"_, a place where we can store and use to efficiently retrieve information. We can think of this as the external _long-term memory_ of our LLM.\n\nWe will need to retrieve information that is semantically related to our queries, to do this we need to use _\"dense vector embeddings\"_. These can be thought of as numerical representations of the *meaning* behind our sentences.\n\nTo create these dense vectors we use the `text-embedding-3-small` model.\n\nWe have already authenticated our OpenAI connection, to create an embedding we just do:"}


created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
id: 2f95c610-05f3-4a2d-88f9-fa3c216d3554
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created_at: 2024-07-28 17:45:51
topic: persistent://default/default/308e7593-7e45-4080-bc68-ad4f88ce5b4b
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