[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/weaviate/recipes/blob/main/weaviate-features/model-providers/mistral/hybrid_search_mistral_embed.ipynb) # Hybrid Search with Mistral This recipe will show you how to run hybrid search with embeddings from Mistral. ## Requirements 1. Weaviate cluster 1. You can create a free cluster on [WCD](/go/console?utm_content=recipe/) 2. [Embedded Weaviate](https://docs.weaviate.io/deploy/installation-guides/embedded) 3. [Local deployment](https://docs.weaviate.io/deploy/installation-guides/docker-installation) 4. [Other options](https://docs.weaviate.io/deploy) 2. Mistral API key. Grab one [here](https://docs.mistral.ai/api/). ```python import weaviate, os from weaviate.embedded import EmbeddedOptions import weaviate.classes as wvc import weaviate.classes.config as wc import requests, json import weaviate.classes.query as wq ``` ## Connect to Weaviate Only choose one option from the below. **Weaviate Cloud Deployment** ```python WCD_URL = os.environ["WEAVIATE_URL"] # Replace with your Weaviate cluster URL WCD_AUTH_KEY = os.environ["WEAVIATE_AUTH"] # Replace with your cluster auth key MISTRAL_KEY = os.environ["MISTRAL_API_KEY"] # Replace with your Mistral key # Weaviate Cloud Deployment client = weaviate.connect_to_wcs( cluster_url=WCD_URL, auth_credentials=weaviate.auth.AuthApiKey(WCD_AUTH_KEY), headers={ "X-Mistral-Api-Key": MISTRAL_KEY} ) print(client.is_ready()) ``` **Embedded Weaviate** ```python # MISTRAL_KEY = os.environ["MISTRAL_API_KEY"] # Replace with your Mistral key # client = weaviate.WeaviateClient( # embedded_options=EmbeddedOptions( # version="1.26.1", # additional_env_vars={ # "ENABLE_MODULES": "text2vec-mistral" # }), # additional_headers={ # "X-Mistral-Api-Key": MISTRAL_KEY # } # ) # client.connect() ``` **Local Deployment** ```python # MISTRAL_KEY = os.environ["MISTRAL_API_KEY"] # Replace with your Mistral key # client = weaviate.connect_to_local( # headers={ # "X-Mistral-Api-Key": MISTRAL_KEY # } # ) # print(client.is_ready()) ``` ## Create a collection > Collection stores your data and vector embeddings. ```python # Note: in practice, you shouldn't rerun this cell, as it deletes your data # in "JeopardyQuestion", and then you need to re-import it again. # Delete the collection if it already exists if (client.collections.exists("JeopardyQuestion")): client.collections.delete("JeopardyQuestion") client.collections.create( name="JeopardyQuestion", vector_config=wc.Configure.Vectors.text2vec_mistral( # specify the vectorizer and model model="mistral-embed", ), properties=[ # defining properties (data schema) is optional wc.Property(name="Question", data_type=wc.DataType.TEXT), wc.Property(name="Answer", data_type=wc.DataType.TEXT), wc.Property(name="Category", data_type=wc.DataType.TEXT, skip_vectorization=True), ] ) print("Successfully created collection: JeopardyQuestion.") ``` ## Import Data ```python url = 'https://raw.githubusercontent.com/weaviate/weaviate-examples/main/jeopardy_small_dataset/jeopardy_tiny.json' resp = requests.get(url) data = json.loads(resp.text) # Get a collection object for "JeopardyQuestion" jeopardy = client.collections.use("JeopardyQuestion") # Insert data objects response = jeopardy.data.insert_many(data) # Note, the `data` array contains 10 objects, which is great to call insert_many with. # However, if you have a milion objects to insert, then you should spit them into smaller batches (i.e. 100-1000 per insert) if (response.has_errors): print(response.errors) else: print("Insert complete.") ``` ## Hybrid Search The `alpha` parameter determines the weight given to the sparse and dense search methods. `alpha = 0` is pure sparse (bm25) search, whereas `alpha = 1` is pure dense (vector) search. Alpha is an optional parameter. The default is set to `0.75`. ### Hybrid Search only The below query is finding Jeopardy questions about animals and is limiting the output to only two results. Notice `alpha` is set to `0.80`, which means it is weighing the vector search results more than bm25. If you were to set `alpha = 0.25`, you would get different results. ```python response = jeopardy.query.hybrid( query="northern beast", query_properties=["question"], alpha=0.8, limit=3 ) for item in response.objects: print("ID:", item.uuid) print("Data:", json.dumps(item.properties, indent=2), "\n") ``` ### Hybrid Search with a `where` filter Find Jeopardy questions about elephants, where the category is set to Animals. ```python response = jeopardy.query.hybrid( query="northern beast", alpha=0.8, filters=wq.Filter.by_property("category").equal("Animals"), limit=3 ) for item in response.objects: print("ID:", item.uuid) print("Data:", json.dumps(item.properties, indent=2), "\n") ``` ## Related pages - [Agents](./agents-index.md) - [AI-assisted Weaviate code generation](./ai-assisted-vibe-coding-index.md) - [APIs](./apis-index.md) - [Authorization and authentication](./authorization-and-authentication-index.md) - [Benchmarks](./benchmarks-index.md) - [Best practices](./best-practices-index.md) - [Client libraries](./clients-index.md) - [Client Libraries / SDKs](./client-libraries-index.md) - [Cloud](./cloud-index.md) - [Cloud account management](./cloud-account-management-index.md) # Agent Instructions This portal answers questions programmatically. To receive a synthesized, source-cited answer instead of crawling page by page, append the `?ask=` query parameter to any page URL on this site: /guides/quickstart?ask=how+do+I+authenticate Optional parameters: - `&goal=` steers the answer toward your objective (e.g. `&goal=write+a+python+client`). - `&version=