> ## Documentation Index
> Fetch the complete documentation index at: https://morphik.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# query

> Generate completion using relevant chunks as context

# query

Generate completion using relevant chunks as context.

<Tabs>
  <Tab title="Sync">
    ```python theme={null}
    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,
        use_reranking: Optional[bool] = None,
        prompt_overrides: Optional[Union[QueryPromptOverrides, Dict[str, Any]]] = None,
        folder_name: Optional[Union[str, List[str]]] = None,
        folder_depth: Optional[int] = None,
        chat_id: Optional[str] = None,
        schema: Optional[Union[Type[BaseModel], Dict[str, Any]]] = None,
        llm_config: Optional[Dict[str, Any]] = None,
        padding: int = 0,
    ) -> CompletionResponse
    ```
  </Tab>

  <Tab title="Async">
    ```python theme={null}
    async 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,
        use_reranking: Optional[bool] = None,
        prompt_overrides: Optional[Union[QueryPromptOverrides, Dict[str, Any]]] = None,
        folder_name: Optional[Union[str, List[str]]] = None,
        folder_depth: Optional[int] = None,
        chat_id: Optional[str] = None,
        schema: Optional[Union[Type[BaseModel], Dict[str, Any]]] = None,
        llm_config: Optional[Dict[str, Any]] = None,
        padding: int = 0,
    ) -> CompletionResponse
    ```
  </Tab>
</Tabs>

## 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.
* `use_reranking` (bool, optional): Override workspace reranking configuration for this request.
* `prompt_overrides` (QueryPromptOverrides | Dict\[str, Any], optional): Optional customizations for entity extraction, resolution, and query prompts
* `folder_name` (str | List\[str], optional): Optional folder scope. Accepts canonical paths (e.g., `/projects/alpha/specs`) or a list of paths/names.
* `folder_depth` (int, optional): Folder scope depth. `None`/`0` = exact match, `-1` = include all descendants, `n > 0` = include descendants up to `n` levels deep.
* `chat_id` (str, optional): Optional chat session ID for persisting conversation history.
* `schema` (Type\[BaseModel] | Dict\[str, Any], optional): Optional schema for structured output, can be a Pydantic model or a JSON schema dict
* `llm_config` (Dict\[str, Any], optional): Optional LiteLLM-compatible model configuration (e.g., model name, API key, base URL). Allows overriding the default LLM configuration on a per-query basis. Defaults to None.
* `padding` (int, optional): Number of additional chunks/pages to retrieve before and after matched chunks (ColPali only). Defaults to 0.

## Metadata Filters

Use the same JSON filters described in the [Metadata Filtering concept](/concepts/metadata-filtering) to restrict which documents feed into the completion. Example:

```python theme={null}
filters = {
    "$and": [
        {"project": {"$eq": "delta"}},
        {"start_date": {"$lte": "2024-06-01T00:00:00Z"}},
        {"end_date": {"$gte": "2024-06-01T00:00:00Z"}}
    ]
}

response = db.query(
    "Summarize current scope changes",
    filters=filters,
    k=6,
    temperature=0.3
)
```

You can also filter by folder name and use expressive operators like `$in`, `$regex`, and `$nin`:

```python theme={null}
# Query across multiple folders
filters = {
    "$and": [
        {"folder_name": {"$in": ["reports", "invoices"]}},
        {"year": 2024},
        {"priority": {"$gte": 50}}
    ]
}

response = db.query("What are the key financial highlights?", filters=filters)
```

For more advanced filtering patterns, see the [Complex Metadata Filtering cookbook](/cookbooks/complex-metadata-filtering).

## Returns

* `CompletionResponse`: Response containing the completion, source information, and potentially structured output.

## Examples

### Standard Query

<Tabs>
  <Tab title="Sync">
    ```python theme={null}
    from morphik import Morphik

    db = Morphik()

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

    nested = db.query(
        "List open design questions",
        folder_name="/projects/alpha",
        folder_depth=-1,
        k=6,
    )

    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}")
    ```
  </Tab>

  <Tab title="Async">
    ```python theme={null}
    from morphik import AsyncMorphik

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

        nested = await db.query(
            "List open design questions",
            folder_name="/projects/alpha",
            folder_depth=-1,
            k=6,
        )
        
        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}")
    ```
  </Tab>
</Tabs>

### With Custom Prompt Overrides

<Tabs>
  <Tab title="Sync">
    ```python theme={null}
    from morphik import Morphik
    from morphik.models import QueryPromptOverride, QueryPromptOverrides

    db = Morphik()

    # 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)
    ```
  </Tab>

  <Tab title="Async">
    ```python theme={null}
    from morphik import AsyncMorphik
    from morphik.models import QueryPromptOverride, EntityExtractionPromptOverride, QueryPromptOverrides

    async with AsyncMorphik() as db:
        # Example with both query and entity extraction customization
        response = await db.query(
            "How does the medication affect diabetes?",
            prompt_overrides=QueryPromptOverrides(
                # Customize how responses are generated
                query=QueryPromptOverride(
                    prompt_template="Provide a concise, medically accurate answer: {question}\n\nContext:\n{context}\n\nAnswer:"
                ),
                # Customize entity extraction
                entity_extraction=EntityExtractionPromptOverride(
                    examples=[
                        {"label": "Insulin", "type": "MEDICATION"},
                        {"label": "Diabetes", "type": "CONDITION"}
                    ]
                )
            )
        )
        
        print(response.completion)
    ```
  </Tab>
</Tabs>

## CompletionResponse Properties

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

* `completion` (str | Dict\[str, Any] | None): The generated completion text or the structured output dictionary.
* `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 (if provided by the server).
* `finish_reason` (Optional\[str]): Reason the generation finished (e.g., 'stop', 'length')

### 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)

### Using Custom LLM Configuration

<Note>
  The `llm_config` parameter is available in SDK version 0.2.5 and later.
</Note>

Use the `llm_config` parameter to override the default LLM configuration on a per-query basis. This allows you to use different models, API keys, or other LiteLLM-compatible settings for specific queries.

<Tabs>
  <Tab title="Sync">
    ```python theme={null}
    from morphik import Morphik

    db = Morphik()

    # Use GPT-4 for a specific query
    response = db.query(
        "What are the key findings?",
        llm_config={
            "model": "gpt-4",
            "api_key": "sk-...",
            "base_url": "https://api.openai.com/v1",
            "temperature": 0.7,
            "max_tokens": 2000
        }
    )

    print(response.completion)

    # Use Claude for another query
    response = db.query(
        "Summarize the research findings",
        llm_config={
            "model": "claude-3-opus-20240229",
            "api_key": "your-anthropic-key"
        }
    )

    print(response.completion)
    ```
  </Tab>

  <Tab title="Async">
    ```python theme={null}
    from morphik import AsyncMorphik

    async with AsyncMorphik() as db:
        # Use GPT-4 for a specific query
        response = await db.query(
            "What are the key findings?",
            llm_config={
                "model": "gpt-4",
                "api_key": "sk-...",
                "base_url": "https://api.openai.com/v1",
                "temperature": 0.7,
                "max_tokens": 2000
            }
        )
        
        print(response.completion)
        
        # Use Claude for another query
        response = await db.query(
            "Summarize the research findings",
            llm_config={
                "model": "claude-3-opus-20240229",
                "api_key": "your-anthropic-key"
            }
        )
        
        print(response.completion)
    ```
  </Tab>
</Tabs>

### Using Structured Output

Use the `schema` parameter to get the completion response in a structured format according to a Pydantic model or a JSON schema dictionary.

<Tabs>
  <Tab title="Sync">
    ```python theme={null}
    from morphik import Morphik
    from pydantic import BaseModel
    from typing import List

    # Define the desired output structure
    class ResearchFindings(BaseModel):
        main_finding: str
        supporting_evidence: List[str]
        limitations: List[str]

    db = Morphik()

    response = db.query(
        "Summarize the key research findings from these documents",
        filters={"department": "research"},
        schema=ResearchFindings
    )

    # Check if the completion is a dictionary (structured output)
    if isinstance(response.completion, dict):
        try:
            # Parse the dictionary into the Pydantic model
            findings = ResearchFindings(**response.completion)
            print(f"Main finding: {findings.main_finding}")
            print("Supporting evidence:")
            for evidence in findings.supporting_evidence:
                print(f"- {evidence}")
            print("Limitations:")
            for limitation in findings.limitations:
                print(f"- {limitation}")
        except Exception as e:
            print(f"Error parsing structured output: {e}")
            # Fallback: print the raw dictionary
            print(response.completion)
    elif isinstance(response.completion, str):
        # Fallback to text completion
        print(response.completion)
    ```
  </Tab>

  <Tab title="Async">
    ```python theme={null}
    from morphik import AsyncMorphik
    from pydantic import BaseModel
    from typing import List

    # Define the desired output structure
    class ResearchFindings(BaseModel):
        main_finding: str
        supporting_evidence: List[str]
        limitations: List[str]

    async with AsyncMorphik() as db:
        response = await db.query(
            "Summarize the key research findings from these documents",
            filters={"department": "research"},
            schema=ResearchFindings
        )

        # Check if the completion is a dictionary (structured output)
        if isinstance(response.completion, dict):
            try:
                # Parse the dictionary into the Pydantic model
                findings = ResearchFindings(**response.completion)
                print(f"Main finding: {findings.main_finding}")
                print("Supporting evidence:")
                for evidence in findings.supporting_evidence:
                    print(f"- {evidence}")
                print("Limitations:")
                for limitation in findings.limitations:
                    print(f"- {limitation}")
            except Exception as e:
                print(f"Error parsing structured output: {e}")
                # Fallback: print the raw dictionary
                print(response.completion)
        elif isinstance(response.completion, str):
            # Fallback to text completion
            print(response.completion)
    ```
  </Tab>
</Tabs>