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```python
print(f"vector 0: {len(res.data[0].embedding)}\nvector 1: {len(res.data[1].embedding)}")
```

    vector 0: 1536
    vector 1: 1536



```python
# we can extract embeddings to a list
embeds = [record.embedding for record in res.data]
len(embeds)
```




    2



Next, we initialize our index to store vector embeddings with Pinecone.


```python
len(embeds[0])
```




    1536



Initialize connection to Pinecone, you can get a free API key in the [Pinecone dashboard](https://app.pinecone.io).


```python
from pinecone import Pinecone

pc = Pinecone(api_key="...")
```


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{"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"}


key: chroma:document
string_value:
-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))




```python
print(f"vector 0: {len(res.data[0].embedding)}\nvector 1: {len(res.data[1].embedding)}")
```

    vector 0: 1536
    vector 1: 1536



```python
# we can extract embeddings to a list
embeds = [record.embedding for record in res.data]
len(embeds)
```




    2



Next, we initialize our index to store vector embeddings with Pinecone.


```python
len(embeds[0])
```




    1536



Initialize connection to Pinecone, you can get a free API key in the [Pinecone dashboard](https://app.pinecone.io).


```python
from pinecone import Pinecone

pc = Pinecone(api_key="...")
```




```python
import time
from pinecone import ServerlessSpec

spec = ServerlessSpec(cloud="aws", region="us-west-2")

index_name = 'semantic-search-openai'

# check if index already exists (if shouldn't if this is your first run)
if index_name not in pc.list_indexes().names():
    # if does not exist, create index
    pc.create_index(
        index_name,
        dimension=len(embeds[0]),  # dimensionality of text-embed-3-small
        metric='dotproduct',
        spec=spec
    )
    # wait for index to be initialized
    while not pc.describe_index(index_name).status['ready']:
        time.sleep(1)

# connect to index
index = pc.Index(index_name)
time.sleep(1)
# view index stats
index.describe_index_stats()
```




    {'dimension': 1536,
     'index_fullness': 0.0,
     'namespaces': {},
     'total_vector_count': 0}



## Populating the Index

Now we will take 1K questions from the TREC dataset


key: _node_content
string_value:
{"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"}


key: chroma:document
string_value:
```python
# we can extract embeddings to a list
embeds = [record.embedding for record in res.data]
len(embeds)
```




    2



Next, we initialize our index to store vector embeddings with Pinecone.


```python
len(embeds[0])
```




    1536



Initialize connection to Pinecone, you can get a free API key in the [Pinecone dashboard](https://app.pinecone.io).


```python
from pinecone import Pinecone

pc = Pinecone(api_key="...")
```




```python
import time
from pinecone import ServerlessSpec

spec = ServerlessSpec(cloud="aws", region="us-west-2")

index_name = 'semantic-search-openai'

# check if index already exists (if shouldn't if this is your first run)
if index_name not in pc.list_indexes().names():
    # if does not exist, create index
    pc.create_index(
        index_name,
        dimension=len(embeds[0]),  # dimensionality of text-embed-3-small
        metric='dotproduct',
        spec=spec
    )
    # wait for index to be initialized
    while not pc.describe_index(index_name).status['ready']:
        time.sleep(1)

# connect to index
index = pc.Index(index_name)
time.sleep(1)
# view index stats
index.describe_index_stats()
```




    {'dimension': 1536,
     'index_fullness': 0.0,
     'namespaces': {},
     'total_vector_count': 0}



## Populating the Index

Now we will take 1K questions from the TREC dataset


```python
from datasets import load_dataset

# load the first 1K rows of the TREC dataset
trec = load_dataset('trec', split='train[:1000]')
trec
```

    /usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: 
    The secret `HF_TOKEN` does not exist in your Colab secrets.
    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.
    You will be able to reuse this secret in all of your notebooks.
    Please note that authentication is recommended but still optional to access public models or datasets.
      warnings.warn(



    Downloading data:   0%|          | 0.00/213k [00:00<?, ?B/s]



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    Dataset({
        features: ['text', 'coarse_label', 'fine_label'],
        num_rows: 1000
    })




```python
trec[0]
```




    {'text': 'How did serfdom develop in and then leave Russia ?',
     'coarse_label': 2,
     'fine_label': 26}



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.


key: _node_content
string_value:
{"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"}


key: chroma:document
string_value:
```python
from datasets import load_dataset

# load the first 1K rows of the TREC dataset
trec = load_dataset('trec', split='train[:1000]')
trec
```

    /usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: 
    The secret `HF_TOKEN` does not exist in your Colab secrets.
    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.
    You will be able to reuse this secret in all of your notebooks.
    Please note that authentication is recommended but still optional to access public models or datasets.
      warnings.warn(



    Downloading data:   0%|          | 0.00/213k [00:00<?, ?B/s]



    Downloading data:   0%|          | 0.00/17.1k [00:00<?, ?B/s]



    Generating train split:   0%|          | 0/5452 [00:00<?, ? examples/s]



    Generating test split:   0%|          | 0/500 [00:00<?, ? examples/s]





    Dataset({
        features: ['text', 'coarse_label', 'fine_label'],
        num_rows: 1000
    })




```python
trec[0]
```




    {'text': 'How did serfdom develop in and then leave Russia ?',
     'coarse_label': 2,
     'fine_label': 26}



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.


```python
from tqdm.auto import tqdm

count = 0  # we'll use the count to create unique IDs
batch_size = 32  # process everything in batches of 32
for i in tqdm(range(0, len(trec['text']), batch_size)):
    # set end position of batch
    i_end = min(i+batch_size, len(trec['text']))
    # get batch of lines and IDs
    lines_batch = trec['text'][i: i+batch_size]
    ids_batch = [str(n) for n in range(i, i_end)]
    # create embeddings
    res = client.embeddings.create(input=lines_batch, model=MODEL)
    embeds = [record.embedding for record in res.data]
    # prep metadata and upsert batch
    meta = [{'text': line} for line in lines_batch]
    to_upsert = zip(ids_batch, embeds, meta)
    # upsert to Pinecone
    index.upsert(vectors=list(to_upsert))
```


      0%|          | 0/32 [00:00<?, ?it/s]


---

# Querying

With 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.


```python
query = "What caused the 1929 Great Depression?"

xq = client.embeddings.create(input=query, model=MODEL).data[0].embedding
```

Now query...


key: _node_content
string_value:
{"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"}


key: chroma:document
string_value:
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.


```python
from tqdm.auto import tqdm

count = 0  # we'll use the count to create unique IDs
batch_size = 32  # process everything in batches of 32
for i in tqdm(range(0, len(trec['text']), batch_size)):
    # set end position of batch
    i_end = min(i+batch_size, len(trec['text']))
    # get batch of lines and IDs
    lines_batch = trec['text'][i: i+batch_size]
    ids_batch = [str(n) for n in range(i, i_end)]
    # create embeddings
    res = client.embeddings.create(input=lines_batch, model=MODEL)
    embeds = [record.embedding for record in res.data]
    # prep metadata and upsert batch
    meta = [{'text': line} for line in lines_batch]
    to_upsert = zip(ids_batch, embeds, meta)
    # upsert to Pinecone
    index.upsert(vectors=list(to_upsert))
```


      0%|          | 0/32 [00:00<?, ?it/s]


---

# Querying

With 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.


```python
query = "What caused the 1929 Great Depression?"

xq = client.embeddings.create(input=query, model=MODEL).data[0].embedding
```

Now query...


```python
res = index.query(vector=[xq], top_k=5, include_metadata=True)
res
```




    {'matches': [{'id': '932',
                  'metadata': {'text': 'Why did the world enter a global '
                                       'depression in 1929 ?'},
                  'score': 0.751888752,
                  'values': []},
                 {'id': '787',
                  'metadata': {'text': "When was `` the Great Depression '' ?"},
                  'score': 0.597448647,
                  'values': []},
                 {'id': '400',
                  'metadata': {'text': 'What crop failure caused the Irish Famine '
                                       '?'},
                  'score': 0.367482603,
                  'values': []},
                 {'id': '835',
                  'metadata': {'text': 'What were popular songs and types of songs '
                                       'in the 1920s ?'},
                  'score': 0.324545294,
                  'values': []},
                 {'id': '262',
                  'metadata': {'text': 'When did World War I start ?'},
                  'score': 0.320995867,
                  'values': []}],
     'namespace': '',
     'usage': {'read_units': 6}}



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.


key: _node_content
string_value:
{"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"}


key: chroma:document
string_value:
```python
query = "What caused the 1929 Great Depression?"

xq = client.embeddings.create(input=query, model=MODEL).data[0].embedding
```

Now query...


```python
res = index.query(vector=[xq], top_k=5, include_metadata=True)
res
```




    {'matches': [{'id': '932',
                  'metadata': {'text': 'Why did the world enter a global '
                                       'depression in 1929 ?'},
                  'score': 0.751888752,
                  'values': []},
                 {'id': '787',
                  'metadata': {'text': "When was `` the Great Depression '' ?"},
                  'score': 0.597448647,
                  'values': []},
                 {'id': '400',
                  'metadata': {'text': 'What crop failure caused the Irish Famine '
                                       '?'},
                  'score': 0.367482603,
                  'values': []},
                 {'id': '835',
                  'metadata': {'text': 'What were popular songs and types of songs '
                                       'in the 1920s ?'},
                  'score': 0.324545294,
                  'values': []},
                 {'id': '262',
                  'metadata': {'text': 'When did World War I start ?'},
                  'score': 0.320995867,
                  'values': []}],
     'namespace': '',
     'usage': {'read_units': 6}}



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.


```python
for match in res['matches']:
    print(f"{match['score']:.2f}: {match['metadata']['text']}")
```

    0.75: Why did the world enter a global depression in 1929 ?
    0.60: When was `` the Great Depression '' ?
    0.37: What crop failure caused the Irish Famine ?
    0.32: What were popular songs and types of songs in the 1920s ?
    0.32: When did World War I start ?


Looks good, let's make it harder and replace *"depression"* with the incorrect term *"recession"*.


```python
query = "What was the cause of the major recession in the early 20th century?"

# create the query embedding
xq = client.embeddings.create(input=query, model=MODEL).data[0].embedding

# query, returning the top 5 most similar results
res = index.query(vector=[xq], top_k=5, include_metadata=True)

for match in res['matches']:
    print(f"{match['score']:.2f}: {match['metadata']['text']}")
```

    0.63: Why did the world enter a global depression in 1929 ?
    0.55: When was `` the Great Depression '' ?
    0.34: What were popular songs and types of songs in the 1920s ?
    0.33: What crop failure caused the Irish Famine ?
    0.29: What is considered the costliest disaster the insurance industry has ever faced ?


And again...


key: _node_content
string_value:
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key: chroma:document
string_value:
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.


```python
for match in res['matches']:
    print(f"{match['score']:.2f}: {match['metadata']['text']}")
```

    0.75: Why did the world enter a global depression in 1929 ?
    0.60: When was `` the Great Depression '' ?
    0.37: What crop failure caused the Irish Famine ?
    0.32: What were popular songs and types of songs in the 1920s ?
    0.32: When did World War I start ?


Looks good, let's make it harder and replace *"depression"* with the incorrect term *"recession"*.


```python
query = "What was the cause of the major recession in the early 20th century?"

# create the query embedding
xq = client.embeddings.create(input=query, model=MODEL).data[0].embedding

# query, returning the top 5 most similar results
res = index.query(vector=[xq], top_k=5, include_metadata=True)

for match in res['matches']:
    print(f"{match['score']:.2f}: {match['metadata']['text']}")
```

    0.63: Why did the world enter a global depression in 1929 ?
    0.55: When was `` the Great Depression '' ?
    0.34: What were popular songs and types of songs in the 1920s ?
    0.33: What crop failure caused the Irish Famine ?
    0.29: What is considered the costliest disaster the insurance industry has ever faced ?


And again...


```python
query = "Why was there a long-term economic downturn in the early 20th century?"

# create the query embedding
xq = client.embeddings.create(input=query, model=MODEL).data[0].embedding

# query, returning the top 5 most similar results
res = index.query(vector=[xq], top_k=5, include_metadata=True)

for match in res['matches']:
    print(f"{match['score']:.2f}: {match['metadata']['text']}")
```

    0.62: Why did the world enter a global depression in 1929 ?
    0.54: When was `` the Great Depression '' ?
    0.34: What were popular songs and types of songs in the 1920s ?
    0.33: What crop failure caused the Irish Famine ?
    0.32: What do economists do ?


Looks 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.

Once we're finished with the index we delete it to save resources.


```python
pc.delete_index(index_name)
```

---


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# Pinecone Vector Database

[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.

[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.

## Examples

This folder contains examples of using Pinecone and OpenAI together. More will be added over time so check back for updates!

| Name | Description | Google Colab |
| --- | --- | --- |
| [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) |
| [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) |
| [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) |


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https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/pinecone/Using_Pinecone_for_embeddings_search.ipynb


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key: chroma:document
string_value:
# Using Pinecone for Embeddings Search

This 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.

### What is a Vector Database

A 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.

### Why use a Vector Database

Vector 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.


### Demo Flow
The demo flow is:
- **Setup**: Import packages and set any required variables
- **Load data**: Load a dataset and embed it using OpenAI embeddings
- **Pinecone**
    - *Setup*: Here we'll set up the Python client for Pinecone. For more details go [here](https://docs.pinecone.io/docs/quickstart)
    - *Index Data*: We'll create an index with namespaces for __titles__ and __content__
    - *Search Data*: We'll test out both namespaces with search queries to confirm it works

Once 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.

## Setup

Import the required libraries and set the embedding model that we'd like to use.


```python
# We'll need to install the Pinecone client
!pip install pinecone-client

#Install wget to pull zip file
!pip install wget
```

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key: chroma:document
string_value:
```python
# We'll need to install the Pinecone client
!pip install pinecone-client

#Install wget to pull zip file
!pip install wget
```

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```python
import openai

from typing import List, Iterator
import pandas as pd
import numpy as np
import os
import wget
from ast import literal_eval

# Pinecone's client library for Python
import pinecone

# I've set this to our new embeddings model, this can be changed to the embedding model of your choice
EMBEDDING_MODEL = "text-embedding-3-small"

# Ignore unclosed SSL socket warnings - optional in case you get these errors
import warnings

warnings.filterwarnings(action="ignore", message="unclosed", category=ResourceWarning)
warnings.filterwarnings("ignore", category=DeprecationWarning) 
```

    /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
      from tqdm.autonotebook import tqdm


## Load data

In this section we'll load embedded data that we've prepared [in this article](../../Embedding_Wikipedia_articles_for_search.ipynb).


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    Requirement already satisfied: certifi>=2017.4.17 in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (from requests>=2.19.0->pinecone-client) (2023.5.7)
    Requirement already satisfied: wget in /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages (3.2)



```python
import openai

from typing import List, Iterator
import pandas as pd
import numpy as np
import os
import wget
from ast import literal_eval

# Pinecone's client library for Python
import pinecone

# I've set this to our new embeddings model, this can be changed to the embedding model of your choice
EMBEDDING_MODEL = "text-embedding-3-small"

# Ignore unclosed SSL socket warnings - optional in case you get these errors
import warnings

warnings.filterwarnings(action="ignore", message="unclosed", category=ResourceWarning)
warnings.filterwarnings("ignore", category=DeprecationWarning) 
```

    /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
      from tqdm.autonotebook import tqdm


## Load data

In this section we'll load embedded data that we've prepared [in this article](../../Embedding_Wikipedia_articles_for_search.ipynb).


```python
embeddings_url = 'https://cdn.openai.com/API/examples/data/vector_database_wikipedia_articles_embedded.zip'

# The file is ~700 MB so this will take some time
wget.download(embeddings_url)
```


```python
import zipfile
with zipfile.ZipFile("vector_database_wikipedia_articles_embedded.zip","r") as zip_ref:
    zip_ref.extractall("../data")
```


```python
article_df = pd.read_csv('../data/vector_database_wikipedia_articles_embedded.csv')
```


```python
article_df.head()
```


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key: chroma:document
string_value:
10/site-packages (3.2)



```python
import openai

from typing import List, Iterator
import pandas as pd
import numpy as np
import os
import wget
from ast import literal_eval

# Pinecone's client library for Python
import pinecone

# I've set this to our new embeddings model, this can be changed to the embedding model of your choice
EMBEDDING_MODEL = "text-embedding-3-small"

# Ignore unclosed SSL socket warnings - optional in case you get these errors
import warnings

warnings.filterwarnings(action="ignore", message="unclosed", category=ResourceWarning)
warnings.filterwarnings("ignore", category=DeprecationWarning) 
```

    /Users/colin.jarvis/Documents/dev/cookbook/openai-cookbook/vector_db/lib/python3.10/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
      from tqdm.autonotebook import tqdm


## Load data

In this section we'll load embedded data that we've prepared [in this article](../../Embedding_Wikipedia_articles_for_search.ipynb).


```python
embeddings_url = 'https://cdn.openai.com/API/examples/data/vector_database_wikipedia_articles_embedded.zip'

# The file is ~700 MB so this will take some time
wget.download(embeddings_url)
```


```python
import zipfile
with zipfile.ZipFile("vector_database_wikipedia_articles_embedded.zip","r") as zip_ref:
    zip_ref.extractall("../data")
```


```python
article_df = pd.read_csv('../data/vector_database_wikipedia_articles_embedded.csv')
```


```python
article_df.head()
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
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    .dataframe tbody tr th {
        vertical-align: top;
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        text-align: right;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>url</th>
      <th>title</th>
      <th>text</th>
      <th>title_vector</th>
      <th>content_vector</th>
      <th>vector_id</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>https://simple.wikipedia.org/wiki/April</td>
      <td>April</td>
      <td>April is the fourth month of the year in the J...</td>
      <td>[0.001009464613161981, -0.020700545981526375, ...</td>
      <td>[-0.011253940872848034, -0.013491976074874401,...</td>
      <td>0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>https://simple.wikipedia.org/wiki/August</td>
      <td>August</td>
      <td>August (Aug.)


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key: chroma:document
string_value:
Air is a...</td>
      <td>[0.02224554680287838, -0.02044147066771984, -0...</td>
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      <td>4</td>
    </tr>
  </tbody>
</table>
</div>




```python
# Read vectors from strings back into a list
article_df['title_vector'] = article_df.title_vector.apply(literal_eval)
article_df['content_vector'] = article_df.content_vector.apply(literal_eval)

# Set vector_id to be a string
article_df['vector_id'] = article_df['vector_id'].apply(str)
```


```python
article_df.info(show_counts=True)
```

    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 25000 entries, 0 to 24999
    Data columns (total 7 columns):
     #   Column          Non-Null Count  Dtype 
    ---  ------          --------------  ----- 
     0   id              25000 non-null  int64 
     1   url             25000 non-null  object
     2   title           25000 non-null  object
     3   text            25000 non-null  object
     4   title_vector    25000 non-null  object
     5   content_vector  25000 non-null  object
     6   vector_id       25000 non-null  object
    dtypes: int64(1), object(6)
    memory usage: 1.3+ MB


## Pinecone

The next option we'll look at is **Pinecone**, a managed vector database which offers a cloud-native option.

Before you proceed with this step you'll need to navigate to [Pinecone](pinecone.io), sign up and then save your API key as an environment variable titled ```PINECONE_API_KEY```.

For section we will:
- Create an index with multiple namespaces for article titles and content
- Store our data in the index with separate searchable "namespaces" for article **titles** and **content**
- Fire some similarity search queries to verify our setup is working


```python
api_key = os.getenv("PINECONE_API_KEY")
pinecone.init(api_key=api_key)
```

### Create Index

First we will need to create an index, which we'll call `wikipedia-articles`. 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.).

If 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).


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key: chroma:document
string_value:
## Pinecone

The next option we'll look at is **Pinecone**, a managed vector database which offers a cloud-native option.

Before you proceed with this step you'll need to navigate to [Pinecone](pinecone.io), sign up and then save your API key as an environment variable titled ```PINECONE_API_KEY```.

For section we will:
- Create an index with multiple namespaces for article titles and content
- Store our data in the index with separate searchable "namespaces" for article **titles** and **content**
- Fire some similarity search queries to verify our setup is working


```python
api_key = os.getenv("PINECONE_API_KEY")
pinecone.init(api_key=api_key)
```

### Create Index

First we will need to create an index, which we'll call `wikipedia-articles`. 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.).

If 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).


```python
# Models a simple batch generator that make chunks out of an input DataFrame
class BatchGenerator:
    
    
    def __init__(self, batch_size: int = 10) -> None:
        self.batch_size = batch_size
    
    # Makes chunks out of an input DataFrame
    def to_batches(self, df: pd.DataFrame) -> Iterator[pd.DataFrame]:
        splits = self.splits_num(df.shape[0])
        if splits <= 1:
            yield df
        else:
            for chunk in np.array_split(df, splits):
                yield chunk

    # Determines how many chunks DataFrame contains
    def splits_num(self, elements: int) -> int:
        return round(elements / self.batch_size)
    
    __call__ = to_batches

df_batcher = BatchGenerator(300)
```


```python
# Pick a name for the new index
index_name = 'wikipedia-articles'

# Check whether the index with the same name already exists - if so, delete it
if index_name in pinecone.list_indexes():
    pinecone.delete_index(index_name)
    
# Creates new index
pinecone.create_index(name=index_name, dimension=len(article_df['content_vector'][0]))
index = pinecone.Index(index_name=index_name)

# Confirm our index was created
pinecone.list_indexes()
```




    ['podcasts', 'wikipedia-articles']




```python
# Upsert content vectors in content namespace - this can take a few minutes
print("Uploading vectors to content namespace..")
for batch_df in df_batcher(article_df):
    index.upsert(vectors=zip(batch_df.vector_id, batch_df.content_vector), namespace='content')
```

    Uploading vectors to content namespace..



```python
# Upsert title vectors in title namespace - this can also take a few minutes
print("Uploading vectors to title namespace..")
for batch_df in df_batcher(article_df):
    index.upsert(vectors=zip(batch_df.vector_id, batch_df.title_vector), namespace='title')
```

    Uploading vectors to title namespace..



```python
# Check index size for each namespace to confirm all of our docs have loaded
index.describe_index_stats()
```


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key: chroma:document
string_value:
```python
# Pick a name for the new index
index_name = 'wikipedia-articles'

# Check whether the index with the same name already exists - if so, delete it
if index_name in pinecone.list_indexes():
    pinecone.delete_index(index_name)
    
# Creates new index
pinecone.create_index(name=index_name, dimension=len(article_df['content_vector'][0]))
index = pinecone.Index(index_name=index_name)

# Confirm our index was created
pinecone.list_indexes()
```




    ['podcasts', 'wikipedia-articles']




```python
# Upsert content vectors in content namespace - this can take a few minutes
print("Uploading vectors to content namespace..")
for batch_df in df_batcher(article_df):
    index.upsert(vectors=zip(batch_df.vector_id, batch_df.content_vector), namespace='content')
```

    Uploading vectors to content namespace..



```python
# Upsert title vectors in title namespace - this can also take a few minutes
print("Uploading vectors to title namespace..")
for batch_df in df_batcher(article_df):
    index.upsert(vectors=zip(batch_df.vector_id, batch_df.title_vector), namespace='title')
```

    Uploading vectors to title namespace..



```python
# Check index size for each namespace to confirm all of our docs have loaded
index.describe_index_stats()
```




    {'dimension': 1536,
     'index_fullness': 0.1,
     'namespaces': {'content': {'vector_count': 25000},
                    'title': {'vector_count': 25000}},
     'total_vector_count': 50000}



### Search data

Now we'll enter some dummy searches and check we get decent results back


```python
# First we'll create dictionaries mapping vector IDs to their outputs so we can retrieve the text for our search results
titles_mapped = dict(zip(article_df.vector_id,article_df.title))
content_mapped = dict(zip(article_df.vector_id,article_df.text))
```


```python
def query_article(query, namespace, top_k=5):
    '''Queries an article using its title in the specified
     namespace and prints results.'''

    # Create vector embeddings based on the title column
    embedded_query = openai.Embedding.create(
                                            input=query,
                                            model=EMBEDDING_MODEL,
                                            )["data"][0]['embedding']

    # Query namespace passed as parameter using title vector
    query_result = index.query(embedded_query, 
                                      namespace=namespace, 
                                      top_k=top_k)

    # Print query results 
    print(f'\nMost similar results to {query} in "{namespace}" namespace:\n')
    if not query_result.matches:
        print('no query result')
    
    matches = query_result.matches
    ids = [res.id for res in matches]
    scores = [res.score for res in matches]
    df = pd.DataFrame({'id':ids, 
                       'score':scores,
                       'title': [titles_mapped[_id] for _id in ids],
                       'content': [content_mapped[_id] for _id in ids],
                       })
    
    counter = 0
    for k,v in df.iterrows():
        counter += 1
        print(f'{v.title} (score = {v.score})')
    
    print('\n')

    return df
```


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key: chroma:document
string_value:
### Search data

Now we'll enter some dummy searches and check we get decent results back


```python
# First we'll create dictionaries mapping vector IDs to their outputs so we can retrieve the text for our search results
titles_mapped = dict(zip(article_df.vector_id,article_df.title))
content_mapped = dict(zip(article_df.vector_id,article_df.text))
```


```python
def query_article(query, namespace, top_k=5):
    '''Queries an article using its title in the specified
     namespace and prints results.'''

    # Create vector embeddings based on the title column
    embedded_query = openai.Embedding.create(
                                            input=query,
                                            model=EMBEDDING_MODEL,
                                            )["data"][0]['embedding']

    # Query namespace passed as parameter using title vector
    query_result = index.query(embedded_query, 
                                      namespace=namespace, 
                                      top_k=top_k)

    # Print query results 
    print(f'\nMost similar results to {query} in "{namespace}" namespace:\n')
    if not query_result.matches:
        print('no query result')
    
    matches = query_result.matches
    ids = [res.id for res in matches]
    scores = [res.score for res in matches]
    df = pd.DataFrame({'id':ids, 
                       'score':scores,
                       'title': [titles_mapped[_id] for _id in ids],
                       'content': [content_mapped[_id] for _id in ids],
                       })
    
    counter = 0
    for k,v in df.iterrows():
        counter += 1
        print(f'{v.title} (score = {v.score})')
    
    print('\n')

    return df
```


```python
query_output = query_article('modern art in Europe','title')
```

    
    Most similar results to modern art in Europe in "title" namespace:
    
    Museum of Modern Art (score = 0.875177085)
    Western Europe (score = 0.867441177)
    Renaissance art (score = 0.864156306)
    Pop art (score = 0.860346854)
    Northern Europe (score = 0.854658186)
    
    



```python
content_query_output = query_article("Famous battles in Scottish history",'content')
```

    
    Most similar results to Famous battles in Scottish history in "content" namespace:
    
    Battle of Bannockburn (score = 0.869336188)
    Wars of Scottish Independence (score = 0.861470938)
    1651 (score = 0.852588475)
    First War of Scottish Independence (score = 0.84962213)
    Robert I of Scotland (score = 0.846214116)
    
    



```python

```


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key: chroma:document
string_value:
# Retrieval Augmented Generative Question Answering with Pinecone

#### Fixing LLMs that Hallucinate

In 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.

A 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"_ — like *long-term memory* for GPT-3.

Required installs for this notebook are:


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key: chroma:document
string_value:
# Retrieval Augmented Generative Question Answering with Pinecone

#### Fixing LLMs that Hallucinate

In 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.

A 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"_ — like *long-term memory* for GPT-3.

Required installs for this notebook are:


```python
!pip install -qU openai pinecone-client datasets
```

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    [?25h  Building wheel for openai (pyproject.toml) ... [?25l[?25hdone


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key: chroma:document
string_value:
```python
import openai

# get API key from top-right dropdown on OpenAI website
openai.api_key = "OPENAI_API_KEY"
```

For many questions *state-of-the-art (SOTA)* LLMs are more than capable of answering correctly.


```python
query = "who was the 12th person on the moon and when did they land?"

# now query `gpt-3.5-turbo-instruct` WITHOUT context
res = openai.Completion.create(
    engine='gpt-3.5-turbo-instruct',
    prompt=query,
    temperature=0,
    max_tokens=400,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0,
    stop=None
)

res['choices'][0]['text'].strip()
```




    'The 12th person on the moon was Harrison Schmitt, and he landed on December 11, 1972.'



However, 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.


```python
def complete(prompt):
    res = openai.Completion.create(
        engine='gpt-3.5-turbo-instruct',
        prompt=prompt,
        temperature=0,
        max_tokens=400,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0,
        stop=None
    )
    return res['choices'][0]['text'].strip()
```

Now 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"_.

Don't worry if this is a new term to you, it isn't required to understand what we're doing or demoing here.


```python
query = (
    "Which training method should I use for sentence transformers when " +
    "I only have pairs of related sentences?"
)

complete(query)
```




    '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.'


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key: chroma:document
string_value:
'The 12th person on the moon was Harrison Schmitt, and he landed on December 11, 1972.'



However, 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.


```python
def complete(prompt):
    res = openai.Completion.create(
        engine='gpt-3.5-turbo-instruct',
        prompt=prompt,
        temperature=0,
        max_tokens=400,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0,
        stop=None
    )
    return res['choices'][0]['text'].strip()
```

Now 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"_.

Don't worry if this is a new term to you, it isn't required to understand what we're doing or demoing here.


```python
query = (
    "Which training method should I use for sentence transformers when " +
    "I only have pairs of related sentences?"
)

complete(query)
```




    '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.'



One of the common answers we get to this is:

```
The 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.
```

This 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"_.

An 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.

We have two options for enabling our LLM in understanding and correctly answering this question:

1. 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.

2. 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.

We will demonstrate option **2**.

---

## Building a Knowledge Base

With 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.

We 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.

To create these dense vectors we use the `text-embedding-3-small` model.

We have already authenticated our OpenAI connection, to create an embedding we just do:


key: _node_content
string_value:
{"id_": "2f95c610-05f3-4a2d-88f9-fa3c216d3554", "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": "1207f7dd-79b6-4fbd-b541-e87fe2bae4d4", "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": "b5741d88944436134a70b623c7caffcc3d01955f2516ae0c5e16026063ac3201", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "3d364b50-8afe-4764-8d00-c95a1de38149", "node_type": "1", "metadata": {}, "hash": "e167a90b4f37dc6b5700d6bbe1d3c7b88949fb78ab7a22a211106eb6216fd2ea", "class_name": "RelatedNodeInfo"}}, "text": "", "mimetype": "text/plain", "start_char_idx": 6382, "end_char_idx": 8695, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}


key: chroma:document
string_value:
```python
embed_model = "text-embedding-ada-002"

res = openai.Embedding.create(
    input=[
        "Sample document text goes here",
        "there will be several phrases in each batch"
    ], engine=embed_model
)
```

In the response `res` we will find a JSON-like object containing our new embeddings within the `'data'` field.


```python
res.keys()
```




    dict_keys(['object', 'data', 'model', 'usage'])



Inside `'data'` we will find two records, one for each of the two sentences we just embedded. Each vector embedding contains `1536` dimensions (the output dimensionality of the `text-embedding-3-small` model.


```python
len(res['data'])
```




    2




```python
len(res['data'][0]['embedding']), len(res['data'][1]['embedding'])
```




    (1536, 1536)



We will apply this same embedding logic to a dataset containing information relevant to our query (and many other queries on the topics of ML and AI).

### Data Preparation

The dataset we will be using is the `jamescalam/youtube-transcriptions` from Hugging Face _Datasets_. It contains transcribed audio from several ML and tech YouTube channels. We download it with:


```python
from datasets import load_dataset

data = load_dataset('jamescalam/youtube-transcriptions', split='train')
data
```

    Using custom data configuration jamescalam--youtube-transcriptions-6a482f3df0aedcdb
    Reusing dataset json (/Users/jamesbriggs/.cache/huggingface/datasets/jamescalam___json/jamescalam--youtube-transcriptions-6a482f3df0aedcdb/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b)





    Dataset({
        features: ['title', 'published', 'url', 'video_id', 'channel_id', 'id', 'text', 'start', 'end'],
        num_rows: 208619
    })




```python
data[0]
```




    {'title': 'Training and Testing an Italian BERT - Transformers From Scratch #4',
     'published': '2021-07-06 13:00:03 UTC',
     'url': 'https://youtu.be/35Pdoyi6ZoQ',
     'video_id': '35Pdoyi6ZoQ',
     'channel_id': 'UCv83tO5cePwHMt1952IVVHw',
     'id': '35Pdoyi6ZoQ-t0.0',
     'text': 'Hi, welcome to the video.',
     'start': 0.0,
     'end': 9.36}



The dataset contains many small snippets of text data. We will need to merge many snippets from each video to create more substantial chunks of text that contain more information.


key: _node_content
string_value:
{"id_": "3d364b50-8afe-4764-8d00-c95a1de38149", "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": "2f95c610-05f3-4a2d-88f9-fa3c216d3554", "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": "5ca031f2f1049bb9bf8bfc5d7ef30a6b7211a42d1bf752252ca890262b8d6d00", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "44b2a58c-81a3-453a-8329-f5fbc324cef5", "node_type": "1", "metadata": {}, "hash": "6d3b3955d70abac0aab0497de7e99c523cdf5605a3c7affcd437a7f9aa807a7f", "class_name": "RelatedNodeInfo"}}, "text": "", "mimetype": "text/plain", "start_char_idx": 7534, "end_char_idx": 9561, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}


key: chroma:document
string_value:
```python
from datasets import load_dataset

data = load_dataset('jamescalam/youtube-transcriptions', split='train')
data
```

    Using custom data configuration jamescalam--youtube-transcriptions-6a482f3df0aedcdb
    Reusing dataset json (/Users/jamesbriggs/.cache/huggingface/datasets/jamescalam___json/jamescalam--youtube-transcriptions-6a482f3df0aedcdb/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b)





    Dataset({
        features: ['title', 'published', 'url', 'video_id', 'channel_id', 'id', 'text', 'start', 'end'],
        num_rows: 208619
    })




```python
data[0]
```




    {'title': 'Training and Testing an Italian BERT - Transformers From Scratch #4',
     'published': '2021-07-06 13:00:03 UTC',
     'url': 'https://youtu.be/35Pdoyi6ZoQ',
     'video_id': '35Pdoyi6ZoQ',
     'channel_id': 'UCv83tO5cePwHMt1952IVVHw',
     'id': '35Pdoyi6ZoQ-t0.0',
     'text': 'Hi, welcome to the video.',
     'start': 0.0,
     'end': 9.36}



The dataset contains many small snippets of text data. We will need to merge many snippets from each video to create more substantial chunks of text that contain more information.


```python
from tqdm.auto import tqdm

new_data = []

window = 20  # number of sentences to combine
stride = 4  # number of sentences to 'stride' over, used to create overlap

for i in tqdm(range(0, len(data), stride)):
    i_end = min(len(data)-1, i+window)
    if data[i]['title'] != data[i_end]['title']:
        # in this case we skip this entry as we have start/end of two videos
        continue
    text = ' '.join(data[i:i_end]['text'])
    # create the new merged dataset
    new_data.append({
        'start': data[i]['start'],
        'end': data[i_end]['end'],
        'title': data[i]['title'],
        'text': text,
        'id': data[i]['id'],
        'url': data[i]['url'],
        'published': data[i]['published'],
        'channel_id': data[i]['channel_id']
    })
```


      0%|          | 0/52155 [00:00<?, ?it/s]



```python
new_data[0]
```


key: _node_content
string_value:
{"id_": "44b2a58c-81a3-453a-8329-f5fbc324cef5", "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": "3d364b50-8afe-4764-8d00-c95a1de38149", "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": "c2067b343f4937fc88a95d945362e7bc3abfa7b42f62c5778551471e3c80ab48", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "04c862de-45e9-4a13-8ac2-1da3a807cafa", "node_type": "1", "metadata": {}, "hash": "9bec52cb2282aedac099544f087180820d7919e9e5397f42b8b30afc6309fa02", "class_name": "RelatedNodeInfo"}}, "text": "", "mimetype": "text/plain", "start_char_idx": 8516, "end_char_idx": 10941, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}


key: chroma:document
string_value:
The dataset contains many small snippets of text data. We will need to merge many snippets from each video to create more substantial chunks of text that contain more information.


```python
from tqdm.auto import tqdm

new_data = []

window = 20  # number of sentences to combine
stride = 4  # number of sentences to 'stride' over, used to create overlap

for i in tqdm(range(0, len(data), stride)):
    i_end = min(len(data)-1, i+window)
    if data[i]['title'] != data[i_end]['title']:
        # in this case we skip this entry as we have start/end of two videos
        continue
    text = ' '.join(data[i:i_end]['text'])
    # create the new merged dataset
    new_data.append({
        'start': data[i]['start'],
        'end': data[i_end]['end'],
        'title': data[i]['title'],
        'text': text,
        'id': data[i]['id'],
        'url': data[i]['url'],
        'published': data[i]['published'],
        'channel_id': data[i]['channel_id']
    })
```


      0%|          | 0/52155 [00:00<?, ?it/s]



```python
new_data[0]
```




    {'start': 0.0,
     'end': 74.12,
     'title': 'Training and Testing an Italian BERT - Transformers From Scratch #4',
     'text': "Hi, welcome to the video. So this is the fourth video in a Transformers from Scratch mini series. So if you haven't been following along, we've essentially covered what you can see on the screen. So we got some data. We built a tokenizer with it. And then we've set up our input pipeline ready to begin actually training our model, which is what we're going to cover in this video. So let's move over to the code. And we see here that we have essentially everything we've done so far. So we've built our input data, our input pipeline. And we're now at a point where we have a data loader, PyTorch data loader, ready. And we can begin training a model with it. So there are a few things to be aware of. So I mean, first, let's just have a quick look at the structure of our data.",
     'id': '35Pdoyi6ZoQ-t0.0',
     'url': 'https://youtu.be/35Pdoyi6ZoQ',
     'published': '2021-07-06 13:00:03 UTC',
     'channel_id': 'UCv83tO5cePwHMt1952IVVHw'}



Now we need a place to store these embeddings and enable a efficient _vector search_ through them all. To do that we use **`Pinecone`**, we can get a [free API key](https://app.pinecone.io) and enter it below where we will initialize our connection to `Pinecone` and create a new index.


key: _node_content
string_value:
{"id_": "04c862de-45e9-4a13-8ac2-1da3a807cafa", "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": "44b2a58c-81a3-453a-8329-f5fbc324cef5", "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": "9e0f0c8c6861b4b1acb3b36fc468dda589ae08e56a5bbae9f782b2e3cd3656a4", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "0a43be83-1241-43e8-bcee-00718ccd3547", "node_type": "1", "metadata": {}, "hash": "b092832e524d952078343ed675cf6e388505c21a45d340783625e4b7d25166c3", "class_name": "RelatedNodeInfo"}}, "text": "", "mimetype": "text/plain", "start_char_idx": 9536, "end_char_idx": 11944, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}


key: chroma:document
string_value:
```python
new_data[0]
```




    {'start': 0.0,
     'end': 74.12,
     'title': 'Training and Testing an Italian BERT - Transformers From Scratch #4',
     'text': "Hi, welcome to the video. So this is the fourth video in a Transformers from Scratch mini series. So if you haven't been following along, we've essentially covered what you can see on the screen. So we got some data. We built a tokenizer with it. And then we've set up our input pipeline ready to begin actually training our model, which is what we're going to cover in this video. So let's move over to the code. And we see here that we have essentially everything we've done so far. So we've built our input data, our input pipeline. And we're now at a point where we have a data loader, PyTorch data loader, ready. And we can begin training a model with it. So there are a few things to be aware of. So I mean, first, let's just have a quick look at the structure of our data.",
     'id': '35Pdoyi6ZoQ-t0.0',
     'url': 'https://youtu.be/35Pdoyi6ZoQ',
     'published': '2021-07-06 13:00:03 UTC',
     'channel_id': 'UCv83tO5cePwHMt1952IVVHw'}



Now we need a place to store these embeddings and enable a efficient _vector search_ through them all. To do that we use **`Pinecone`**, we can get a [free API key](https://app.pinecone.io) and enter it below where we will initialize our connection to `Pinecone` and create a new index.


```python
import pinecone

index_name = 'openai-youtube-transcriptions'

# initialize connection to pinecone (get API key at app.pinecone.io)
pinecone.init(
    api_key="PINECONE_API_KEY",
    environment="us-east1-gcp"  # may be different, check at app.pinecone.io
)

# check if index already exists (it shouldn't if this is first time)
if index_name not in pinecone.list_indexes():
    # if does not exist, create index
    pinecone.create_index(
        index_name,
        dimension=len(res['data'][0]['embedding']),
        metric='cosine',
        metadata_config={'indexed': ['channel_id', 'published']}
    )
# connect to index
index = pinecone.Index(index_name)
# view index stats
index.describe_index_stats()
```




    {'dimension': 1536,
     'index_fullness': 0.0,
     'namespaces': {},
     'total_vector_count': 0}



We can see the index is currently empty with a `total_vector_count` of `0`. We can begin populating it with OpenAI `text-embedding-3-small` built embeddings like so:


key: _node_content
string_value:
{"id_": "0a43be83-1241-43e8-bcee-00718ccd3547", "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": "04c862de-45e9-4a13-8ac2-1da3a807cafa", "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": "eebb11699e650ca53d74bd86cb51c34f2caaac920ec2ab9c8937234f24a04224", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "29ba9637-e468-4f6d-afb5-5e56f3aa6a6f", "node_type": "1", "metadata": {}, "hash": "b00234140bd9677416e527d3d90d73e5ada8bf7f13731e0ae3fca0dba8201c7e", "class_name": "RelatedNodeInfo"}}, "text": "", "mimetype": "text/plain", "start_char_idx": 10655, "end_char_idx": 13467, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}


key: chroma:document
string_value:
Now we need a place to store these embeddings and enable a efficient _vector search_ through them all. To do that we use **`Pinecone`**, we can get a [free API key](https://app.pinecone.io) and enter it below where we will initialize our connection to `Pinecone` and create a new index.


```python
import pinecone

index_name = 'openai-youtube-transcriptions'

# initialize connection to pinecone (get API key at app.pinecone.io)
pinecone.init(
    api_key="PINECONE_API_KEY",
    environment="us-east1-gcp"  # may be different, check at app.pinecone.io
)

# check if index already exists (it shouldn't if this is first time)
if index_name not in pinecone.list_indexes():
    # if does not exist, create index
    pinecone.create_index(
        index_name,
        dimension=len(res['data'][0]['embedding']),
        metric='cosine',
        metadata_config={'indexed': ['channel_id', 'published']}
    )
# connect to index
index = pinecone.Index(index_name)
# view index stats
index.describe_index_stats()
```




    {'dimension': 1536,
     'index_fullness': 0.0,
     'namespaces': {},
     'total_vector_count': 0}



We can see the index is currently empty with a `total_vector_count` of `0`. We can begin populating it with OpenAI `text-embedding-3-small` built embeddings like so:


```python
from tqdm.auto import tqdm
from time import sleep

batch_size = 100  # how many embeddings we create and insert at once

for i in tqdm(range(0, len(new_data), batch_size)):
    # find end of batch
    i_end = min(len(new_data), i+batch_size)
    meta_batch = new_data[i:i_end]
    # get ids
    ids_batch = [x['id'] for x in meta_batch]
    # get texts to encode
    texts = [x['text'] for x in meta_batch]
    # create embeddings (try-except added to avoid RateLimitError)
    done = False
    while not done:
        try:
            res = openai.Embedding.create(input=texts, engine=embed_model)
            done = True
        except:
            sleep(5)
    embeds = [record['embedding'] for record in res['data']]
    # cleanup metadata
    meta_batch = [{
        'start': x['start'],
        'end': x['end'],
        'title': x['title'],
        'text': x['text'],
        'url': x['url'],
        'published': x['published'],
        'channel_id': x['channel_id']
    } for x in meta_batch]
    to_upsert = list(zip(ids_batch, embeds, meta_batch))
    # upsert to Pinecone
    index.upsert(vectors=to_upsert)
```


      0%|          | 0/487 [00:00<?, ?it/s]


Now we search, for this we need to create a _query vector_ `xq`:


```python
res = openai.Embedding.create(
    input=[query],
    engine=embed_model
)

# retrieve from Pinecone
xq = res['data'][0]['embedding']

# get relevant contexts (including the questions)
res = index.query(xq, top_k=2, include_metadata=True)
```


```python
res
```


key: _node_content
string_value:
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key: chroma:document
string_value:
0%|          | 0/487 [00:00<?, ?it/s]


Now we search, for this we need to create a _query vector_ `xq`:


```python
res = openai.Embedding.create(
    input=[query],
    engine=embed_model
)

# retrieve from Pinecone
xq = res['data'][0]['embedding']

# get relevant contexts (including the questions)
res = index.query(xq, top_k=2, include_metadata=True)
```


```python
res
```




    {'matches': [{'id': 'pNvujJ1XyeQ-t418.88',
                  'metadata': {'channel_id': 'UCv83tO5cePwHMt1952IVVHw',
                               'end': 568.4,
                               'published': datetime.date(2021, 11, 24),
                               'start': 418.88,
                               'text': 'pairs of related sentences you can go '
                                       'ahead and actually try training or '
                                       'fine-tuning using NLI with multiple '
                                       "negative ranking loss. If you don't have "
                                       'that fine. Another option is that you have '
                                       'a semantic textual similarity data set or '
                                       'STS and what this is is you have so you '
                                       'have sentence A here, sentence B here and '
                                       'then you have a score from from 0 to 1 '
                                       'that tells you the similarity between '
                                       'those two scores and you would train this '
                                       'using something like cosine similarity '
                                       "loss. Now if that's not an option and your "
                                       'focus or use case is on building a '
                                       'sentence transformer for another language '
                                       'where there is no current sentence '
                                       'transformer you can use multilingual '
                                       'parallel data. So what I mean by that is '
                                       'so parallel data just means translation '
                                       'pairs so if you have for example a English '
                                       'sentence and then you have another '
                                       'language here so it can it can be anything '
                                       "I'm just going to put XX and that XX is "
                                       'your target language you can fine-tune a '
                                       'model using something called multilingual '
                                       'knowledge distillation and what that does '
                                       'is takes a monolingual model for example '
                                       'in English and using those translation '
                                       'pairs it distills the knowledge the '
                                       'semantic similarity knowledge from that '
                                       'monolingual English model into a '
                                       'multilingual model which can handle both '
                                       'English and your target language. So '
                                       "they're three options quite popular very "
                                       'common that you can go for and as a '
                                       'supervised methods the chances are that '
                                       'probably going to outperform anything you '
                                       'do with unsupervised training at least for '
                                       'now.


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key: chroma:document
string_value:
If you don't have "
                                       'that fine. Another option is that you have '
                                       'a semantic textual similarity data set or '
                                       'STS and what this is is you have so you '
                                       'have sentence A here, sentence B here and '
                                       'then you have a score from from 0 to 1 '
                                       'that tells you the similarity between '
                                       'those two scores and you would train this '
                                       'using something like cosine similarity '
                                       "loss. Now if that's not an option and your "
                                       'focus or use case is on building a '
                                       'sentence transformer for another language '
                                       'where there is no current sentence '
                                       'transformer you can use multilingual '
                                       'parallel data. So what I mean by that is '
                                       'so parallel data just means translation '
                                       'pairs so if you have for example a English '
                                       'sentence and then you have another '
                                       'language here so it can it can be anything '
                                       "I'm just going to put XX and that XX is "
                                       'your target language you can fine-tune a '
                                       'model using something called multilingual '
                                       'knowledge distillation and what that does '
                                       'is takes a monolingual model for example '
                                       'in English and using those translation '
                                       'pairs it distills the knowledge the '
                                       'semantic similarity knowledge from that '
                                       'monolingual English model into a '
                                       'multilingual model which can handle both '
                                       'English and your target language. So '
                                       "they're three options quite popular very "
                                       'common that you can go for and as a '
                                       'supervised methods the chances are that '
                                       'probably going to outperform anything you '
                                       'do with unsupervised training at least for '
                                       'now. So if none of those sound like '
                                       'something',
                               'title': 'Today Unsupervised Sentence Transformers, '
                                        'Tomorrow Skynet (how TSDAE works)',
                               'url': 'https://youtu.be/pNvujJ1XyeQ'},
                  'score': 0.865277052,
                  'sparseValues': {},
                  'values': []},
                 {'id': 'WS1uVMGhlWQ-t737.28',
                  'metadata': {'channel_id': 'UCv83tO5cePwHMt1952IVVHw',
                               'end': 900.72,
                               'published': datetime.date(2021, 10, 20),
                               'start': 737.28,
                               'text': "were actually more accurate. So we can't "
                                       "really do that. We can't use this what is "
                                       'called a mean pooling approach. Or we '
                                       "can't use it in its current form. Now the "
                                       'solution to this problem was introduced by '
                                       'two people in 2019 Nils Reimers and Irenia '
                                       'Gurevich. They introduced what is the '
                                       'first sentence transformer or sentence '
                                       'BERT. And it was found that sentence BERT '
                                       'or S BERT outformed all of the previous '
                                       'Save the Art models on pretty much all '
                                       'benchmarks. Not all of them but most of '
                                       'them. And it did it in a very quick time. '
                                       'So if we compare it to BERT, if we wanted '
                                       'to find the most similar sentence pair '
                                       'from 10,000 sentences in that 2019 paper '
                                       'they found that with BERT that took 65 '
                                       'hours.


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key: chroma:document
string_value:
So if none of those sound like '
                                       'something',
                               'title': 'Today Unsupervised Sentence Transformers, '
                                        'Tomorrow Skynet (how TSDAE works)',
                               'url': 'https://youtu.be/pNvujJ1XyeQ'},
                  'score': 0.865277052,
                  'sparseValues': {},
                  'values': []},
                 {'id': 'WS1uVMGhlWQ-t737.28',
                  'metadata': {'channel_id': 'UCv83tO5cePwHMt1952IVVHw',
                               'end': 900.72,
                               'published': datetime.date(2021, 10, 20),
                               'start': 737.28,
                               'text': "were actually more accurate. So we can't "
                                       "really do that. We can't use this what is "
                                       'called a mean pooling approach. Or we '
                                       "can't use it in its current form. Now the "
                                       'solution to this problem was introduced by '
                                       'two people in 2019 Nils Reimers and Irenia '
                                       'Gurevich. They introduced what is the '
                                       'first sentence transformer or sentence '
                                       'BERT. And it was found that sentence BERT '
                                       'or S BERT outformed all of the previous '
                                       'Save the Art models on pretty much all '
                                       'benchmarks. Not all of them but most of '
                                       'them. And it did it in a very quick time. '
                                       'So if we compare it to BERT, if we wanted '
                                       'to find the most similar sentence pair '
                                       'from 10,000 sentences in that 2019 paper '
                                       'they found that with BERT that took 65 '
                                       'hours. With S BERT embeddings they could '
                                       'create all the embeddings in just around '
                                       'five seconds. And then they could compare '
                                       'all those with cosine similarity in 0.01 '
                                       "seconds. So it's a lot faster. We go from "
                                       '65 hours to just over five seconds which '
                                       'is I think pretty incredible. Now I think '
                                       "that's pretty much all the context we need "
                                       'behind sentence transformers. And what we '
                                       'do now is dive into a little bit of how '
                                       'they actually work. Now we said before we '
                                       'have the core transform models and what S '
                                       'BERT does is fine tunes on sentence pairs '
                                       'using what is called a Siamese '
                                       'architecture or Siamese network. What we '
                                       'mean by a Siamese network is that we have '
                                       'what we can see, what can view as two BERT '
                                       'models that are identical and the weights '
                                       'between those two models are tied. Now in '
                                       'reality when implementing this we just use '
                                       'a single BERT model. And what we do is we '
                                       'process one sentence, a sentence A through '
                                       'the model and then we process another '
                                       'sentence, sentence B through the model. '
                                       "And that's the sentence pair. So with our "
                                       'cross-linked we were processing the '
                                       'sentence pair together. We were putting '
                                       'them both together, processing them all at '
                                       'once. This time we process them '
                                       'separately.


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key: chroma:document
string_value:
With S BERT embeddings they could '
                                       'create all the embeddings in just around '
                                       'five seconds. And then they could compare '
                                       'all those with cosine similarity in 0.01 '
                                       "seconds. So it's a lot faster. We go from "
                                       '65 hours to just over five seconds which '
                                       'is I think pretty incredible. Now I think '
                                       "that's pretty much all the context we need "
                                       'behind sentence transformers. And what we '
                                       'do now is dive into a little bit of how '
                                       'they actually work. Now we said before we '
                                       'have the core transform models and what S '
                                       'BERT does is fine tunes on sentence pairs '
                                       'using what is called a Siamese '
                                       'architecture or Siamese network. What we '
                                       'mean by a Siamese network is that we have '
                                       'what we can see, what can view as two BERT '
                                       'models that are identical and the weights '
                                       'between those two models are tied. Now in '
                                       'reality when implementing this we just use '
                                       'a single BERT model. And what we do is we '
                                       'process one sentence, a sentence A through '
                                       'the model and then we process another '
                                       'sentence, sentence B through the model. '
                                       "And that's the sentence pair. So with our "
                                       'cross-linked we were processing the '
                                       'sentence pair together. We were putting '
                                       'them both together, processing them all at '
                                       'once. This time we process them '
                                       'separately. And during training what '
                                       'happens is the weights',
                               'title': 'Intro to Sentence Embeddings with '
                                        'Transformers',
                               'url': 'https://youtu.be/WS1uVMGhlWQ'},
                  'score': 0.85855335,
                  'sparseValues': {},
                  'values': []}],
     'namespace': ''}




```python
limit = 3750

def retrieve(query):
    res = openai.Embedding.create(
        input=[query],
        engine=embed_model
    )

    # retrieve from Pinecone
    xq = res['data'][0]['embedding']

    # get relevant contexts
    res = index.query(xq, top_k=3, include_metadata=True)
    contexts = [
        x['metadata']['text'] for x in res['matches']
    ]

    # build our prompt with the retrieved contexts included
    prompt_start = (
        "Answer the question based on the context below.\n\n"+
        "Context:\n"
    )
    prompt_end = (
        f"\n\nQuestion: {query}\nAnswer:"
    )
    # append contexts until hitting limit
    for i in range(1, len(contexts)):
        if len("\n\n---\n\n".join(contexts[:i])) >= limit:
            prompt = (
                prompt_start +
                "\n\n---\n\n".join(contexts[:i-1]) +
                prompt_end
            )
            break
        elif i == len(contexts)-1:
            prompt = (
                prompt_start +
                "\n\n---\n\n".join(contexts) +
                prompt_end
            )
    return prompt
```


```python
# first we retrieve relevant items from Pinecone
query_with_contexts = retrieve(query)
query_with_contexts
```


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key: chroma:document
string_value:
This time we process them '
                                       'separately. And during training what '
                                       'happens is the weights',
                               'title': 'Intro to Sentence Embeddings with '
                                        'Transformers',
                               'url': 'https://youtu.be/WS1uVMGhlWQ'},
                  'score': 0.85855335,
                  'sparseValues': {},
                  'values': []}],
     'namespace': ''}




```python
limit = 3750

def retrieve(query):
    res = openai.Embedding.create(
        input=[query],
        engine=embed_model
    )

    # retrieve from Pinecone
    xq = res['data'][0]['embedding']

    # get relevant contexts
    res = index.query(xq, top_k=3, include_metadata=True)
    contexts = [
        x['metadata']['text'] for x in res['matches']
    ]

    # build our prompt with the retrieved contexts included
    prompt_start = (
        "Answer the question based on the context below.\n\n"+
        "Context:\n"
    )
    prompt_end = (
        f"\n\nQuestion: {query}\nAnswer:"
    )
    # append contexts until hitting limit
    for i in range(1, len(contexts)):
        if len("\n\n---\n\n".join(contexts[:i])) >= limit:
            prompt = (
                prompt_start +
                "\n\n---\n\n".join(contexts[:i-1]) +
                prompt_end
            )
            break
        elif i == len(contexts)-1:
            prompt = (
                prompt_start +
                "\n\n---\n\n".join(contexts) +
                prompt_end
            )
    return prompt
```


```python
# first we retrieve relevant items from Pinecone
query_with_contexts = retrieve(query)
query_with_contexts
```




    "Answer the question based on the context below.\n\nContext:\npairs of related sentences you can go ahead and actually try training or fine-tuning using NLI with multiple negative ranking loss. If you don't have that fine. Another option is that you have a semantic textual similarity data set or STS and what this is is you have so you have sentence A here, sentence B here and then you have a score from from 0 to 1 that tells you the similarity between those two scores and you would train this using something like cosine similarity loss. Now if that's not an option and your focus or use case is on building a sentence transformer for another language where there is no current sentence transformer you can use multilingual parallel data. So what I mean by that is so parallel data just means translation pairs so if you have for example a English sentence and then you have another language here so it can it can be anything I'm just going to put XX and that XX is your target language you can fine-tune a model using something called multilingual knowledge distillation and what that does is takes a monolingual model for example in English and using those translation pairs it distills the knowledge the semantic similarity knowledge from that monolingual English model into a multilingual model which can handle both English and your target language. So they're three options quite popular very common that you can go for and as a supervised methods the chances are that probably going to outperform anything you do with unsupervised training at least for now. So if none of those sound like something\n\n---\n\nwere actually more accurate. So we can't really do that. We can't use this what is called a mean pooling approach. Or we can't use it in its current form.


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key: chroma:document
string_value:
```python
# first we retrieve relevant items from Pinecone
query_with_contexts = retrieve(query)
query_with_contexts
```




    "Answer the question based on the context below.\n\nContext:\npairs of related sentences you can go ahead and actually try training or fine-tuning using NLI with multiple negative ranking loss. If you don't have that fine. Another option is that you have a semantic textual similarity data set or STS and what this is is you have so you have sentence A here, sentence B here and then you have a score from from 0 to 1 that tells you the similarity between those two scores and you would train this using something like cosine similarity loss. Now if that's not an option and your focus or use case is on building a sentence transformer for another language where there is no current sentence transformer you can use multilingual parallel data. So what I mean by that is so parallel data just means translation pairs so if you have for example a English sentence and then you have another language here so it can it can be anything I'm just going to put XX and that XX is your target language you can fine-tune a model using something called multilingual knowledge distillation and what that does is takes a monolingual model for example in English and using those translation pairs it distills the knowledge the semantic similarity knowledge from that monolingual English model into a multilingual model which can handle both English and your target language. So they're three options quite popular very common that you can go for and as a supervised methods the chances are that probably going to outperform anything you do with unsupervised training at least for now. So if none of those sound like something\n\n---\n\nwere actually more accurate. So we can't really do that. We can't use this what is called a mean pooling approach. Or we can't use it in its current form. Now the solution to this problem was introduced by two people in 2019 Nils Reimers and Irenia Gurevich. They introduced what is the first sentence transformer or sentence BERT. And it was found that sentence BERT or S BERT outformed all of the previous Save the Art models on pretty much all benchmarks. Not all of them but most of them. And it did it in a very quick time. So if we compare it to BERT, if we wanted to find the most similar sentence pair from 10,000 sentences in that 2019 paper they found that with BERT that took 65 hours. With S BERT embeddings they could create all the embeddings in just around five seconds. And then they could compare all those with cosine similarity in 0.01 seconds. So it's a lot faster. We go from 65 hours to just over five seconds which is I think pretty incredible. Now I think that's pretty much all the context we need behind sentence transformers. And what we do now is dive into a little bit of how they actually work. Now we said before we have the core transform models and what S BERT does is fine tunes on sentence pairs using what is called a Siamese architecture or Siamese network. What we mean by a Siamese network is that we have what we can see, what can view as two BERT models that are identical and the weights between those two models are tied. Now in reality when implementing this we just use a single BERT model. And what we do is we process one sentence, a sentence A through the model and then we process another sentence, sentence B through the model. And that's the sentence pair.


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