Using Chroma for Embeddings Search
Source: openai_cookbooks

# Using Chroma for Embeddings SearchThis 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 DatabaseA 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 DatabaseVector 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 FlowThe demo flow is:- **Setup**: Import packages and set any required variables- **Load data**: Load a dataset and embed it using OpenAI embeddings- **Chroma**:    - *Setup*: Here we'll set up the Python client for Chroma. For more details go [here](https://docs.trychroma.com/usage-guide)    - *Index Data*: We'll create collections with vectors for __titles__ and __content__    - *Search Data*: We'll run a few searches to confirm it worksOnce 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.## SetupImport the required libraries and set the embedding model that we'd like to use.```python# Make sure the OpenAI library is installed%pip install openai# We'll need to install the Chroma client%pip install chromadb# Install wget to pull zip file%pip install wget# Install numpy for data manipulation%pip install numpy```    Collecting openai      Obtaining dependency information for openai from https://files.pythonhosted.org/packages/67/78/7588a047e458cb8075a4089d721d7af5e143ff85a2388d4a28c530be0494/openai-0.27.8-py3-none-any.whl.metadata      Downloading openai-0.27.8-py3-none-any.whl.metadata (13 kB)    Collecting requests>=2.20 (from openai)      Obtaining dependency information for requests>=2.20 from https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl.metadata      Using cached requests-2.31.0-py3-none-any.whl.metadata (4.6 kB)    Collecting tqdm (from openai)      Using cached tqdm-4.65.0-py3-none-any.whl (77 kB)    Collecting aiohttp (from openai)      Obtaining dependency information for aiohttp from https://files.pythonhosted.org/packages/fa/9e/49002fde2a97d7df0e162e919c31cf13aa9f184537739743d1239edd0e67/aiohttp-3.8.5-cp310-cp310-macosx_11_0_arm64.whl.metadata      Downloading aiohttp-3.8.5-cp310-cp310-macosx_11_0_arm64.whl.metadata (7.7 kB)    Collecting charset-normalizer<4,>=2 (from requests>=2.20->openai)      Obtaining dependency information for charset-normalizer<4,>=2 from https://files.pythonhosted.org/packages/ec/a7/96835706283d63fefbbbb4f119d52f195af00fc747e67cc54397c56312c8/charset_normalizer-3.2.0-cp310-cp310-macosx_11_0_arm64.whl.metadata      Using cached charset_normalizer-3.2.0-cp310-cp310-macosx_11_0_arm64.whl.metadata (31 kB)    Collecting idna<4,>=2.5 (from requests>=2.20->openai)      Using cached idna-3.4-py3-none-any.whl (61 kB)    Collecting urllib3<3,>=1.21.1 (from requests>=2.20->openai)      Obtaining dependency information for urllib3<3,>=1.21.1 from https://files.pythonhosted.org/packages/9b/81/62fd61001fa4b9d0df6e31d47ff49cfa9de4af03adecf339c7bc30656b37/urllib3-2.0.4-py3-none-any.whl.metadata      Downloading urllib3-2.0.4-py3-none-any.whl.metadata (6.6 kB)    Collecting certifi>=2017.4.17 (from requests>=2.20->openai)      Using cached certifi-2023.5.7-py3-none-any.whl (156 kB)    Collecting attrs>=17.3.0 (from aiohttp->openai)      Using cached attrs-23.1.0-py3-none-any.whl (61 kB)    Collecting multidict<7.0,>=4.5 (from aiohttp->openai)      Using cached multidict-6.0.4-cp310-cp310-macosx_11_0_arm64.whl (29 kB)    Collecting async-timeout<5.0,>=4.0.0a3 (from aiohttp->openai)      Using cached async_timeout-4.0.2-py3-none-any.whl (5.8 kB)    Collecting yarl<2.0,>=1.0 (from aiohttp->openai)      Using cached yarl-1.9.2-cp310-cp310-macosx_11_0_arm64.whl (62 kB)    Collecting frozenlist>=1.1.1 (from aiohttp->openai)      Obtaining dependency information for frozenlist>=1.1.1 from https://files.pythonhosted.org/packages/67/6a/55a49da0fa373ac9aa49ccd5b6393ecc183e2a0904d9449ea3ee1163e0b1/frozenlist-1.4.0-cp310-cp310-macosx_11_0_arm64.whl.metadata      Downloading frozenlist-1.4.0-cp310-cp310-macosx_11_0_arm64.whl.metadata (5.2 kB)    Collecting aiosignal>=1.1.2 (from aiohttp->openai)      Using cached aiosignal-1.3.1-py3-none-any.whl (7.6 kB)    Using cached openai-0.27.8-py3-none-any.whl (73 kB)    Using cached requests-2.31.0-py3-none-any.whl (62 kB)    Downloading aiohttp-3.8.5-cp310-cp310-macosx_11_0_arm64.whl (343 kB)    [2K   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m343.9/343.9 kB[0m [31m11.4 MB/s[0m eta [36m0:00:00[0m    [?25hUsing cached charset_normalizer-3.2.0-cp310-cp310-macosx_11_0_arm64.whl (124 kB)    Downloading frozenlist-1.4.0-cp310-cp310-macosx_11_0_arm64.whl (46 kB)    [2K   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m46.0/46.0 kB[0m [31m4.4 MB/s[0m eta [36m0:00:00[0m    [?25hDownloading urllib3-2.0.4-py3-none-any.whl (123 kB)    [2K   [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m123.9/123.9 kB[0m [31m20.0 MB/s[0m eta [36m0:00:00[0m    [?25hInstalling collected packages: urllib3, tqdm, multidict, idna, frozenlist, charset-normalizer, certifi, attrs, async-timeout, yarl, requests, aiosignal, aiohttp, openai    Successfully installed aiohttp-3.8.5 aiosignal-1.3.1 async-timeout-4.0.2 attrs-23.1.0 certifi-2023.5.7 charset-normalizer-3.2.0 frozenlist-1.4.0 idna-3.4 multidict-6.0.4 openai-0.27.8 requests-2.31.0 tqdm-4.65.0 urllib3-2.0.4 yarl-1.9.2    Note: you may need to restart the kernel to use updated packages.    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flatbuffers-23.5.26 h11-0.14.0 httptools-0.6.0 humanfriendly-10.0 importlib-resources-6.0.0 monotonic-1.6 mpmath-1.3.0 numpy-1.25.1 onnxruntime-1.15.1 overrides-7.3.1 pandas-2.0.3 posthog-3.0.1 protobuf-4.23.4 pulsar-client-3.2.0 pydantic-1.10.11 pypika-0.48.9 python-dotenv-1.0.0 pytz-2023.3 pyyaml-6.0.1 sniffio-1.3.0 starlette-0.27.0 sympy-1.12 tokenizers-0.13.3 tzdata-2023.3 uvicorn-0.23.1 uvloop-0.17.0 watchfiles-0.19.0 websockets-11.0.3    Note: you may need to restart the kernel to use updated packages.    Collecting wget      Using cached wget-3.2.zip (10 kB)      Preparing metadata (setup.py) ... [?25ldone    [?25hBuilding wheels for collected packages: wget      Building wheel for wget (setup.py) ... [?25ldone    [?25h  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9657 sha256=b2d83c5fcdeab398d0a4e9808a470bbf725fffea4a6130e731c6097b9561005b      Stored in directory: /Users/antontroynikov/Library/Caches/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769    Successfully built wget    Installing collected packages: wget    Successfully installed wget-3.2    Note: you may need to restart the kernel to use updated packages.    Requirement already satisfied: numpy in /Users/antontroynikov/miniforge3/envs/chroma-openai-cookbook/lib/python3.10/site-packages (1.25.1)    Note: you may need to restart the kernel to use updated packages.```pythonimport openaiimport pandas as pdimport osimport wgetfrom ast import literal_eval# Chroma's client library for Pythonimport chromadb# I've set this to our new embeddings model, this can be changed to the embedding model of your choiceEMBEDDING_MODEL = "text-embedding-3-small"# Ignore unclosed SSL socket warnings - optional in case you get these errorsimport warningswarnings.filterwarnings(action="ignore", message="unclosed", category=ResourceWarning)warnings.filterwarnings("ignore", category=DeprecationWarning) ```## Load dataIn this section we'll load embedded data that we've prepared previous to this session.```pythonembeddings_url = 'https://cdn.openai.com/API/examples/data/vector_database_wikipedia_articles_embedded.zip'# The file is ~700 MB so this will take some timewget.download(embeddings_url)```    'vector_database_wikipedia_articles_embedded.zip'```pythonimport zipfilewith zipfile.ZipFile("vector_database_wikipedia_articles_embedded.zip","r") as zip_ref:    zip_ref.extractall("../data")``````pythonarticle_df = pd.read_csv('../data/vector_database_wikipedia_articles_embedded.csv')``````pythonarticle_df.head()```<div><style scoped>    .dataframe tbody tr th:only-of-type {        vertical-align: middle;    }    .dataframe tbody tr th {        vertical-align: top;    }    .dataframe thead th {        text-align: right;    }</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.) is the eighth month of the year ...</td>      <td>[0.0009286514250561595, 0.000820168002974242, ...</td>      <td>[0.0003609954728744924, 0.007262262050062418, ...</td>      <td>1</td>    </tr>    <tr>      <th>2</th>      <td>6</td>      <td>https://simple.wikipedia.org/wiki/Art</td>      <td>Art</td>      <td>Art is a creative activity that expresses imag...</td>      <td>[0.003393713850528002, 0.0061537534929811954, ...</td>      <td>[-0.004959689453244209, 0.015772193670272827, ...</td>      <td>2</td>    </tr>    <tr>      <th>3</th>      <td>8</td>      <td>https://simple.wikipedia.org/wiki/A</td>      <td>A</td>      <td>A or a is the first letter of the English alph...</td>      <td>[0.0153952119871974, -0.013759135268628597, 0....</td>      <td>[0.024894846603274345, -0.022186409682035446, ...</td>      <td>3</td>    </tr>    <tr>      <th>4</th>      <td>9</td>      <td>https://simple.wikipedia.org/wiki/Air</td>      <td>Air</td>      <td>Air refers to the Earth's atmosphere. Air is a...</td>      <td>[0.02224554680287838, -0.02044147066771984, -0...</td>      <td>[0.021524671465158463, 0.018522677943110466, -...</td>      <td>4</td>    </tr>  </tbody></table></div>```python# Read vectors from strings back into a listarticle_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 stringarticle_df['vector_id'] = article_df['vector_id'].apply(str)``````pythonarticle_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# ChromaWe'll index these embedded documents in a vector database and search them. The first option we'll look at is **Chroma**, an easy to use open-source self-hosted in-memory vector database, designed for working with embeddings together with LLMs. In this section, we will:- Instantiate the Chroma client- Create collections for each class of embedding - Query each collection ### Instantiate the Chroma clientCreate the Chroma client. By default, Chroma is ephemeral and runs in memory. However, you can easily set up a persistent configuration which writes to disk.```pythonchroma_client = chromadb.EphemeralClient() # Equivalent to chromadb.Client(), ephemeral.# Uncomment for persistent client# chroma_client = chromadb.PersistentClient()```### Create collectionsChroma collections allow you to store and filter with arbitrary metadata, making it easy to query subsets of the embedded data. Chroma is already integrated with OpenAI's embedding functions. The best way to use them is on construction of a collection, as follows.Alternatively, you can 'bring your own embeddings'. More information can be found [here](https://docs.trychroma.com/embeddings)```pythonfrom chromadb.utils.embedding_functions import OpenAIEmbeddingFunction# Test that your OpenAI API key is correctly set as an environment variable# Note. if you run this notebook locally, you will need to reload your terminal and the notebook for the env variables to be live.# Note. alternatively you can set a temporary env variable like this:# os.environ["OPENAI_API_KEY"] = 'sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'if os.getenv("OPENAI_API_KEY") is not None:    openai.api_key = os.getenv("OPENAI_API_KEY")    print ("OPENAI_API_KEY is ready")else:    print ("OPENAI_API_KEY environment variable not found")embedding_function = OpenAIEmbeddingFunction(api_key=os.environ.get('OPENAI_API_KEY'), model_name=EMBEDDING_MODEL)wikipedia_content_collection = chroma_client.create_collection(name='wikipedia_content', embedding_function=embedding_function)wikipedia_title_collection = chroma_client.create_collection(name='wikipedia_titles', embedding_function=embedding_function)```    OPENAI_API_KEY is ready### Populate the collectionsChroma collections allow you to populate, and filter on, whatever metadata you like. Chroma can also store the text alongside the vectors, and return everything in a single `query` call, when this is more convenient. For this use-case, we'll just store the embeddings and IDs, and use these to index the original dataframe. ```python# Add the content vectorswikipedia_content_collection.add(    ids=article_df.vector_id.tolist(),    embeddings=article_df.content_vector.tolist(),)# Add the title vectorswikipedia_title_collection.add(    ids=article_df.vector_id.tolist(),    embeddings=article_df.title_vector.tolist(),)```### Search the collectionsChroma handles embedding queries for you if an embedding function is set, like in this example.```pythondef query_collection(collection, query, max_results, dataframe):    results = collection.query(query_texts=query, n_results=max_results, include=['distances'])     df = pd.DataFrame({                'id':results['ids'][0],                 'score':results['distances'][0],                'title': dataframe[dataframe.vector_id.isin(results['ids'][0])]['title'],                'content': dataframe[dataframe.vector_id.isin(results['ids'][0])]['text'],                })        return df``````pythontitle_query_result = query_collection(    collection=wikipedia_title_collection,    query="modern art in Europe",    max_results=10,    dataframe=article_df)title_query_result.head()```<div><style scoped>    .dataframe tbody tr th:only-of-type {        vertical-align: middle;    }    .dataframe tbody tr th {        vertical-align: top;    }    .dataframe thead th {        text-align: right;    }</style><table border="1" class="dataframe">  <thead>    <tr style="text-align: right;">      <th></th>      <th>id</th>      <th>score</th>      <th>title</th>      <th>content</th>    </tr>  </thead>  <tbody>    <tr>      <th>2</th>      <td>23266</td>      <td>0.249646</td>      <td>Art</td>      <td>Art is a creative activity that expresses imag...</td>    </tr>    <tr>      <th>11777</th>      <td>15436</td>      <td>0.271688</td>      <td>Hellenistic art</td>      <td>The art of the Hellenistic time (from 400 B.C....</td>    </tr>    <tr>      <th>12178</th>      <td>23265</td>      <td>0.279306</td>      <td>Byzantine art</td>      <td>Byzantine art is a form of Christian Greek art...</td>    </tr>    <tr>      <th>13215</th>      <td>11777</td>      <td>0.294415</td>      <td>Art film</td>      <td>Art films are a type of movie that is very dif...</td>    </tr>    <tr>      <th>15436</th>      <td>22108</td>      <td>0.305937</td>      <td>Renaissance art</td>      <td>Many of the most famous and best-loved works o...</td>    </tr>  </tbody></table></div>```pythoncontent_query_result = query_collection(    collection=wikipedia_content_collection,    query="Famous battles in Scottish history",    max_results=10,    dataframe=article_df)content_query_result.head()```<div><style scoped>    .dataframe tbody tr th:only-of-type {        vertical-align: middle;    }    .dataframe tbody tr th {        vertical-align: top;    }    .dataframe thead th {        text-align: right;    }</style><table border="1" class="dataframe">  <thead>    <tr style="text-align: right;">      <th></th>      <th>id</th>      <th>score</th>      <th>title</th>      <th>content</th>    </tr>  </thead>  <tbody>    <tr>      <th>2923</th>      <td>13135</td>      <td>0.261328</td>      <td>1651</td>      <td>\n\nEvents \n January 1 – Charles II crowned K...</td>    </tr>    <tr>      <th>3694</th>      <td>13571</td>      <td>0.277058</td>      <td>Stirling</td>      <td>Stirling () is a city in the middle of Scotlan...</td>    </tr>    <tr>      <th>6248</th>      <td>2923</td>      <td>0.294823</td>      <td>841</td>      <td>\n\nEvents \n June 25: Battle of Fontenay – Lo...</td>    </tr>    <tr>      <th>6297</th>      <td>13568</td>      <td>0.300756</td>      <td>1746</td>      <td>\n\nEvents \n January 8 – Bonnie Prince Charli...</td>    </tr>    <tr>      <th>11702</th>      <td>11708</td>      <td>0.307572</td>      <td>William Wallace</td>      <td>William Wallace was a Scottish knight who foug...</td>    </tr>  </tbody></table></div>Now that you've got a basic embeddings search running, you can [hop over to the Chroma docs](https://docs.trychroma.com/usage-guide#using-where-filters) to learn more about how to add filters to your query, update/delete data in your collections, and deploy Chroma.