​
query

Generate completion using relevant chunks as context.

def query(
    query: str,
    filters: Optional[Dict[str, Any]] = None,
    k: int = 4,
    min_score: float = 0.0,
    max_tokens: Optional[int] = None,
    temperature: Optional[float] = None,
    use_colpali: bool = True,
    graph_name: Optional[str] = None,
    hop_depth: int = 1,
    include_paths: bool = False,
    prompt_overrides: Optional[Union[QueryPromptOverrides, Dict[str, Any]]] = None,
) -> CompletionResponse

​
Parameters

  • query (str): Query text
  • filters (Dict[str, Any], optional): Optional metadata filters
  • k (int, optional): Number of chunks to use as context. Defaults to 4.
  • min_score (float, optional): Minimum similarity threshold. Defaults to 0.0.
  • max_tokens (int, optional): Maximum tokens in completion
  • temperature (float, optional): Model temperature
  • use_colpali (bool, optional): Whether to use ColPali-style embedding model to generate the completion (only works for documents ingested with use_colpali=True). Defaults to True.
  • graph_name (str, optional): Optional name of the graph to use for knowledge graph-enhanced retrieval
  • hop_depth (int, optional): Number of relationship hops to traverse in the graph (1-3). Defaults to 1.
  • include_paths (bool, optional): Whether to include relationship paths in the response. Defaults to False.
  • prompt_overrides (QueryPromptOverrides | Dict[str, Any], optional): Optional customizations for entity extraction, resolution, and query prompts

​
Returns

  • CompletionResponse: Response containing the completion and source information

​
Examples

​
Standard Query

from databridge import DataBridge

db = DataBridge()

response = db.query(
    "What are the key findings about customer satisfaction?",
    filters={"department": "research"},
    temperature=0.7
)

print(response.completion)

# Print the sources used for the completion
for source in response.sources:
    print(f"Document ID: {source.document_id}, Chunk: {source.chunk_number}, Score: {source.score}")

​
Knowledge Graph Enhanced Query

from databridge import DataBridge

db = DataBridge()

# Use a knowledge graph to enhance the query
response = db.query(
    "How does product X relate to customer segment Y?",
    graph_name="market_graph",
    hop_depth=2,
    include_paths=True
)

print(response.completion)

# If include_paths=True, you can inspect the graph paths
if response.metadata and "graph" in response.metadata:
    for path in response.metadata["graph"]["paths"]:
        print(" -> ".join(path))

​
With Custom Prompt Overrides

from databridge import DataBridge
from databridge.models import QueryPromptOverride, QueryPromptOverrides

db = DataBridge()

# Using the QueryPromptOverrides object
response = db.query(
    "What are the key findings?",
    filters={"category": "research"},
    prompt_overrides=QueryPromptOverrides(
        query=QueryPromptOverride(
            prompt_template="Answer the question in a formal, academic tone: {question}\n\nContext:\n{context}\n\nAnswer:"
        )
    )
)

# Alternatively, using a dictionary
response = db.query(
    "What are the key findings?",
    filters={"category": "research"},
    prompt_overrides={
        "query": {
            "prompt_template": "Answer the question in a formal, academic tone: {question}\n\nContext:\n{context}\n\nAnswer:"
        }
    }
)

print(response.completion)

​
CompletionResponse Properties

The CompletionResponse object returned by this method has the following properties:

  • completion (str): The generated completion text
  • usage (Dict[str, int]): Token usage information
  • sources (List[ChunkSource]): Sources of chunks used in the completion
  • metadata (Dict[str, Any], optional): Additional metadata about the completion. When using a knowledge graph with include_paths=True, this contains graph traversal information.

​
ChunkSource Properties

Each ChunkSource object in the sources list has the following properties:

  • document_id (str): ID of the source document
  • chunk_number (int): Chunk number within the document
  • score (Optional[float]): Relevance score (if available)