Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) combines information retrieval with generative AI models.

In Weaviate, a RAG query consists of two parts: a search query, and a prompt for the model. Weaviate first performs the search, then passes both the search results and your prompt to a generative AI model before returning the generated response.

How to configure RAG

To use RAG, a collection must be configured to use a generative model integration.

See the relevant model provider documentation for more details.

Named vectors

Added in v1.24

Any vector-based search on collections with named vectors configured must include a target vector name in the query. This allows Weaviate to find the correct vector to compare with the query vector.

Python Client v4

from weaviate.classes.query import MetadataQuery

reviews = client.collections.get("WineReviewNV")
response = reviews.generate.near_text(
    query="a sweet German white wine",
    limit=2,
    target_vector="title_country",  # Specify the target vector for named vector collections
    single_prompt="Translate this into German: {review_body}",
    grouped_task="Summarize these review",
    return_metadata=MetadataQuery(distance=True)
)

print(response.generated)
for o in response.objects:
    print(o.properties)
    print(o.generated)

Python Client v3

# Unfortunately, named vectors are not suppored in the v3 API / Python client.
# Please upgrade to the v4 API / Python client to use named vectors.

JS/TS Client v3

const myNVCollection = client.collections.get('WineReviewNV');

const result = await myNVCollection.generate.nearText(
  'a sweet German white wine',
  {
    singlePrompt: 'Translate this into German: {review_body}',
    groupedTask: 'Summarize these review',
  },
  {
    limit: 2,
    targetVector: 'title_country',
  }
);

console.log(result.generated); // print groupedTask result

for (let object of result.objects) {
  console.log(JSON.stringify(object.properties, null, 2));
  console.log(object.generated); // print singlePrompt result
}

JS/TS Client v2

result = await client.graphql
  .get()
  .withClassName('WineReviewNV')
  .withNearText({
    concepts: ['a sweet German white wine'],
    targetVectors: ['title_country'],
  })
  .withGenerate({
    singlePrompt: 'Translate this into German: {review_body}',
    groupedTask: 'Summarize these reviews',
  })
  .withLimit(2)
  .withFields('title review_body country')
  .do();

console.log(JSON.stringify(result, null, 2));

GraphQL

{
  Get {
    JeopardyQuestion(
      limit: 2
      nearText: {
        concepts: ["animals in movies"]
      }
      where: {
        path: ["round"]
        operator: Equal
        valueText: "Double Jeopardy!"
      }
    ) {
      question
      answer
      _additional {
        generate(
          singleResult: {
            prompt: """
              Translate this into German: {review_body}
            """
          }
          groupedResult: {
            task: """
              Summarize these reviews
            """
          }
        ) {
          singleResult
          error
        }
      }
    }
  }
}

Single prompt search

Single prompt search returns a generated response for each object in the query results.
Define object properties – using {prop-name} syntax – to interpolate retrieved content in the prompt.
The properties you use in the prompt do not have to be among the properties you retrieve in the query.

Python Client v4

prompt = "Convert this quiz question: {question} and answer: {answer} into a trivia tweet."

jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.generate.near_text(
    query="World history",
    limit=2,
    single_prompt=prompt
)

# print source properties and generated responses
for o in response.objects:
    print(o.properties)
    print(o.generated)

Python Client v3

generate_prompt = "Convert this quiz question: {question} and answer: {answer} into a trivia tweet."

response = (
  client.query
  .get("JeopardyQuestion")
  .with_generate(single_prompt=generate_prompt)
  .with_near_text({
    "concepts": ["World history"]
  })
  .with_limit(2)
).do()

print(json.dumps(response, indent=2))

JS/TS Client v3

const jeopardy = client.collections.get('JeopardyQuestion');

const prompt = `Convert this quiz question: {question} and answer: {answer} into a trivia tweet.`

const result = await jeopardy.generate.nearText('World history',
  { singlePrompt: prompt },
  { limit: 2 }
)

for (let object of result.objects) {
  console.log(JSON.stringify(object.properties, null, 2));
  console.log(object.generated); // print singlePrompt result
}

JS/TS Client v2

generatePrompt = 'Convert this quiz question: {question} and answer: {answer} into a trivia tweet.';

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withGenerate({
    singlePrompt: generatePrompt,
  })
  .withNearText({
    concepts: ['World history'],
  })
  .withFields('round')
  .withLimit(2)
  .do();

console.log(JSON.stringify(result, null, 2));

Go

generatePrompt := "Convert the following into a question for twitter. Include emojis for fun, but do not include the answer: {question}."

gs := graphql.NewGenerativeSearch().SingleResult(generatePrompt)

response, err := client.GraphQL().Get().
  WithClassName("JeopardyQuestion").
  WithFields(
    graphql.Field{Name: "question"},
  ).
  WithGenerativeSearch(gs).
  WithNearText((&graphql.NearTextArgumentBuilder{}).
    WithConcepts([]string{"World history"})).
  WithLimit(2).
  Do(ctx)
generatePrompt := "Convert this quiz question: {question} and answer: {answer} into a trivia tweet."

gs := graphql.NewGenerativeSearch().SingleResult(generatePrompt)

response, err := client.GraphQL().Get().
  WithClassName("JeopardyQuestion").
  WithFields(
    graphql.Field{Name: "question"},
    graphql.Field{Name: "answer"},
  ).
  WithGenerativeSearch(gs).
  WithNearText((&graphql.NearTextArgumentBuilder{}).
    WithConcepts([]string{"World history"})).
  WithLimit(2).
  Do(ctx)

GraphQL

{
  Get {
    JeopardyQuestion (
      nearText: {
        concepts: ["World history"]
      }
      limit: 2
    ) {
      _additional {
        generate(
          singleResult: {
            prompt: """
              Convert this quiz question: {question} and answer: {answer} into a trivia tweet.
            """
          }
        ) {
          singleResult
          error
        }
      }
    }
  }
}

Example response

The output is like this:

{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "_additional": {
            "generate": {
              "error": null,
              "singleResult": "Did you know that in the 19th century, the British Empire included one quarter of the world's land and people? The sun never set on it! #BritishEmpire #TriviaTuesday"
            }
          }
        },
        {
          "_additional": {
            "generate": {
              "error": null,
              "singleResult": "Did you know that Egypt had more kings than any other country in ancient history? From Menes to the Ptolemys, they ruled the land of the Nile. #Egypt #AncientHistory #Kings"
            }
          }
        }
      ]
    }
  }
}

Grouped task search

Grouped task search returns one response that includes all of the query results. By default grouped task search uses all object properties in the prompt.

Python Client v4

task = "What do these animals have in common, if anything?"

jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.generate.near_text(
    query="Cute animals",
    limit=3,
    grouped_task=task
)

# print the generated response
print(response.generated)

Python Client v3

generate_prompt = "What do these animals have in common, if anything?"

response = (
  client.query
  .get("JeopardyQuestion", ["points"])
  .with_generate(grouped_task=generate_prompt)
  .with_near_text({
    "concepts": ["Cute animals"]
  })
  .with_limit(3)
).do()

print(json.dumps(response, indent=2))

JS/TS Client v3

const jeopardy = client.collections.get('JeopardyQuestion');

const generatePrompt = `What do these animals have in common, if anything?`;

const result = await jeopardy.generate.nearText('Cute animals',
  { groupedTask: generatePrompt },
  { limit: 3 }
)

console.log(result.generated);

JS/TS Client v2

generatePrompt = 'What do these animals have in common, if anything?';

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withGenerate({
    groupedTask: generatePrompt,
  })
  .withNearText({
    concepts: ['Cute animals'],
  })
  .withFields('points')
  .withLimit(3)
  .do();

console.log(JSON.stringify(result, null, 2));

Go

generatePrompt := "What do these animals have in common, if anything?"

gs := graphql.NewGenerativeSearch().GroupedResult(generatePrompt)

response, err := client.GraphQL().Get().
  WithClassName("JeopardyQuestion").
  WithFields(
    graphql.Field{Name: "points"},
  ).
  WithGenerativeSearch(gs).
  WithNearText((&graphql.NearTextArgumentBuilder{}).
    WithConcepts([]string{"Cute animals"})).
  WithLimit(3).
  Do(ctx)
generatePrompt := "What do these animals have in common, if anything?"

gs := graphql.NewGenerativeSearch().GroupedResult(generatePrompt, "answer", "question")

response, err := client.GraphQL().Get().
  WithClassName("JeopardyQuestion").
  WithFields(
    graphql.Field{Name: "question"},
    graphql.Field{Name: "points"},

  ).
  WithGenerativeSearch(gs).
  WithNearText((&graphql.NearTextArgumentBuilder{}).
    WithConcepts([]string{"Australian animals"})).
  WithLimit(3).
  Do(ctx)

GraphQL

{
  Get {
    JeopardyQuestion (
      nearText: {
        concepts: ["Cute animals"]
      }
      limit: 3
    ) {
      points
      _additional {
        generate(
          groupedResult: {
            task: """
              What do these animals have in common, if anything?
            """
          }
        ) {
          groupedResult
          error
        }
      }
    }
  }
}

Example response

The output is like this:

{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "_additional": {
            "generate": {
              "error": null,
              "groupedResult": "All of these animals are mammals."
            }
          },
          "points": 400
        },
        {
          "_additional": {
            "generate": null
          },
          "points": 300
        },
        {
          "_additional": {
            "generate": null
          },
          "points": 400
        }
      ]
    }
  }
}

Set grouped task prompt properties

Added in v1.18.3

Define object properties to use in the prompt. This limits the information in the prompt and reduces prompt length.

Python Client v4

task = "What do these animals have in common, if anything?"

jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.generate.near_text(
    query="Australian animals",
    limit=3,
    grouped_task=task,
    grouped_properties=["answer", "question"]
)

# print the generated response
print(response.generated)

Python Client v3

generate_prompt = "What do these animals have in common, if anything?"

response = (
  client.query
  .get("JeopardyQuestion", ["question points"])
  .with_generate(
      grouped_task=generate_prompt,
      grouped_properties=["answer", "question"]  # available since client version 3.19.2
  )
  .with_near_text({
    "concepts": ["Australian animals"]
  })
  .with_limit(3)
).do()

print(json.dumps(response, indent=2))

JS/TS Client v3

const jeopardy = client.collections.get('JeopardyQuestion');

const generatePrompt = `What do these animals have in common, if anything?`;

const result = await jeopardy.generate.nearText('Australian animals',
  {
    groupedTask: generatePrompt,
    groupedProperties: ['answer', 'question'],
  },
  { limit: 3 }
)

console.log(result.generated);

JS/TS Client v2

generatePrompt = 'What do these animals have in common, if anything?';

result = await client.graphql
  .get()
  .withClassName('JeopardyQuestion')
  .withGenerate({
    groupedTask: generatePrompt,
    groupedProperties: ['answer', 'question'],  // available since client version 1.3.2
  })
  .withNearText({
    concepts: ['Australian animals'],
  })
  .withFields('question points')
  .withLimit(3)
  .do();

console.log(JSON.stringify(result, null, 2));

Go

generatePrompt := "What do these animals have in common, if anything?"

gs := graphql.NewGenerativeSearch().GroupedResult(generatePrompt, "answer", "question")

response, err := client.GraphQL().Get().
  WithClassName("JeopardyQuestion").
  WithFields(
    graphql.Field{Name: "question"},
    graphql.Field{Name: "points"},

  ).
  WithGenerativeSearch(gs).
  WithNearText((&graphql.NearTextArgumentBuilder{}).
    WithConcepts([]string{"Australian animals"})).
  WithLimit(3).
  Do(ctx)

GraphQL

{
  Get {
    JeopardyQuestion (
      nearText: {
        concepts: ["Australian animals"]
      }
      limit: 3
    ) {
      question
      points
      _additional {
        generate(
          groupedResult: {
            task: """
              What do these animals have in common, if anything?
            """
            properties: ["answer", "question"]
          }
        ) {
          groupedResult
          error
        }
      }
    }
  }
}

Example response

The output is like this:

{
  "data": {
    "Get": {
      "JeopardyQuestion": [
        {
          "_additional": {
            "generate": {
              "error": null,
              "groupedResult": "All of the animals mentioned are native to Australia."
            }
          },
          "points": 800,
          "question": "Australians call this animal a jumbuck or a monkey"
        },
        {
          "_additional": {
            "generate": null
          },
          "points": 100,
          "question": "An island named for the animal seen <a href=\"http://www.j-archive.com/media/2000-03-10_J_01.jpg\" target=\"_blank\">here</a> belongs to this country [kangaroo]"
        },
        {
          "_additional": {
            "generate": null
          },
          "points": 300,
          "question": "Found chiefly in Australia, the wallaby is a smaller type of this marsupial"
        }
      ]
    }
  }
}

Related pages

• Connect to Weaviate
• Model provider integrations.
• API References: GraphQL: Get

Questions and feedback

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• Technical questions
• Documentation feedback