[![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/aws/rag_titan-text-express-v1_bedrock.ipynb)

## Dependencies

```python
!pip install weaviate-client
```

## Configuration

```python
import weaviate, os

# Connect to your local Weaviate instance deployed with Docker
client = weaviate.connect_to_local(
    headers={
        "X-AWS-Access-Key": os.getenv("AWS_ACCESS_KEY"), # Replace with your AWS access key - recommended: use env var
        "X-AWS-Secret-Key": os.getenv("AWS_SECRET_KEY"), # Replace with your AWS secret key - recommended: use env var
    }
)

# Option 2
# Connect to your Weaviate Client Service cluster
# client = weaviate.connect_to_wcs(
#     cluster_url="WCS-CLUSTER-ID",                             # Replace with your WCS cluster ID
#     auth_credentials=weaviate.auth.AuthApiKey("WCS-API-KEY"), # Replace with your WCS API KEY - recommended: use env var
#     headers={
#         "X-AWS-Access-Key": os.getenv("AWS_ACCESS_KEY"), # Replace with your AWS access key - recommended: use env var
#         "X-AWS-Secret-Key": os.getenv("AWS_SECRET_KEY"), # Replace with your AWS secret key - recommended: use env var
#     }
# )

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.
import weaviate.classes.config as wc

# 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_aws(
        service="bedrock",   #this is crucial
        model="cohere.embed-english-v3", # select the model, make sure it is enabled for your account
        # model="amazon.titan-embed-text-v1", # select the model, make sure it is enabled for your account
        region="eu-west-2"               # select your region
    ),

    # Enable generative model from AWS
    generative_config=wc.Configure.Generative.aws(
        service="bedrock",   #this is crucial
        model="amazon.titan-text-express-v1", # select the model, make sure it is enabled for your account
        region="eu-west-2"               # select your region
    ),

    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 the Data

```python
import requests, json
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.get("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.")
```

## Generative Search Queries

### Single Result

Single Result makes a generation for each individual search result.

In the below example, I want to create a Facebook ad from the Jeopardy question about Elephants.

```python
generatePrompt = "Turn the following Jeogrady question into a Facebook Ad: {question}"

jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.generate.near_text(
    query="Elephants",
    limit=2,
    single_prompt=generatePrompt
)

for item in response.objects:
    print(json.dumps(item.properties, indent=1))
    print("-----vvvvvv-----")
    print(item.generated)
    print("-----^^^^^^-----")
```

### Grouped Result

Grouped Result generates a single response from all the search results.

The below example is creating a Facebook ad from the 2 retrieved Jeoprady questions about animals.

```python
generateTask = "Explain why these Jeopardy questions are under the Animals category."

jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.generate.near_text(
    query="Animals",
    limit=3,
    grouped_task=generateTask
)

print(response.generated)
```

## 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=<what-you-are-trying-to-do>` steers the answer toward your
  objective (e.g. `&goal=write+a+python+client`).
- `&version=<label>` scopes the answer to a mounted version when the
  portal publishes more than one.

The response is `text/markdown`: the answer followed by a `# Sources` list
of the portal pages it was grounded in. Status codes are the contract:

- `200` — the answer; `402` — the portal owner’s plan or answer credits are
  exhausted (surface this to your operator; do NOT retry); `429` — you are
  rate-limited; back off for the `Retry-After` seconds; `503` — the answer
  lane is temporarily unavailable; fall back to crawling the `.md` pages.

For the full corpus map read `llms.txt` at the site root; for the tool
surface (search + page fetch as MCP tools) see `/mcp`.