haiku.rag/docs/python.md

# Python API

Use `haiku.rag` directly in your Python applications.

## Basic Usage

```python
from pathlib import Path
from haiku.rag.client import HaikuRAG

# Create a new database
async with HaikuRAG("path/to/database.lancedb", create=True) as client:
    # Your code here
    pass

# Open an existing database (will fail if database doesn't exist)
async with HaikuRAG("path/to/database.lancedb") as client:
    # Your code here
    pass

# Open in read-only mode (blocks writes)
async with HaikuRAG("path/to/database.lancedb", read_only=True) as client:
    results = await client.search("query")  # Read operations work
    # await client.create_document(...)  # Would raise ReadOnlyError
```

!!! note
    Databases must be explicitly created with `create=True` or via `haiku-rag init` before use. Operations on non-existent databases will raise `FileNotFoundError`.

!!! note
    Read-only mode is useful for safely accessing databases without risk of modification. It blocks all write operations and prevents settings from being saved.

!!! warning "Database Migrations"
    When upgrading haiku.rag to a version with schema changes, opening an existing database will raise `MigrationRequiredError`. Run `haiku-rag migrate` to apply pending migrations before using the database. See [CLI Database Management](cli.md#migrate-database) for details.

## Document Management

### Creating Documents

From text:
```python
doc = await client.create_document(
    content="Your document content here",
    uri="doc://example",
    title="My Example Document",  # optional human‑readable title
    metadata={"source": "manual", "topic": "example"}
)
```

From HTML content (preserves document structure):
```python
html_content = "<h1>Title</h1><p>Paragraph</p><ul><li>Item 1</li></ul>"
doc = await client.create_document(
    content=html_content,
    uri="doc://html-example",
    format="html"  # parse as HTML instead of markdown
)
```

The `format` parameter controls how text content is parsed:

- `"md"` (default) - Parse as Markdown
- `"html"` - Parse as HTML, preserving semantic structure (headings, lists, tables)
- `"plain"` - Plain text, no parsing (creates a simple text document)

!!! note
    The document's `content` field stores the markdown export of the parsed document for consistent display. The original DoclingDocument structure is preserved in the `docling_document` field (zstd-compressed, without page images). Page images are stored separately in `docling_pages`.

From file:
```python
doc = await client.create_document_from_source(
    "path/to/document.pdf", title="Project Brief"
)
```

From URL:
```python
doc = await client.create_document_from_source(
    "https://example.com/article.html", title="Example Article"
)
```

### Importing Pre-Processed Documents

If you process documents externally or need custom processing, use `import_document()`:

```python
from haiku.rag.store.models.chunk import Chunk

# Convert your source to a DoclingDocument
docling_doc = await client.convert("path/to/document.pdf")

# Create chunks (embeddings optional - will be generated if missing)
chunks = [
    Chunk(
        content="This is the first chunk",
        metadata={"section": "intro"},
        order=0,
    ),
    Chunk(
        content="This is the second chunk",
        metadata={"section": "body"},
        embedding=[0.1] * 1024,  # Optional: pre-computed embedding
        order=1,
    ),
]

# Import document with custom chunks
doc = await client.import_document(
    docling_document=docling_doc,
    chunks=chunks,
    uri="doc://custom",
    title="Custom Document",
    metadata={"source": "external-pipeline"},
)
```

The `docling_document` provides rich metadata for visual grounding, page numbers, and section headings. Content is automatically extracted from the DoclingDocument.

See [Custom Processing Pipelines](custom-pipelines.md) for building pipelines with `convert()`, `chunk()`, and `embed_chunks()`.

### Retrieving Documents

By ID:
```python
doc = await client.get_document_by_id("document-id-string")
```

By URI:
```python
doc = await client.get_document_by_uri("file:///path/to/document.pdf")
```

List all documents:
```python
docs = await client.list_documents(limit=10, offset=0)

# Include full content and docling document (not loaded by default)
docs = await client.list_documents(include_content=True)
```

Filter documents by properties:
```python
# Filter by URI pattern
docs = await client.list_documents(filter="uri LIKE '%arxiv%'")

# Filter by exact title
docs = await client.list_documents(filter="title = 'My Document'")

# Combine multiple conditions
docs = await client.list_documents(
    limit=10,
    filter="uri LIKE '%.pdf' AND title LIKE '%paper%'"
)
```

Count documents:
```python
# Count all documents
total = await client.count_documents()

# Count with filter
pdf_count = await client.count_documents(filter="uri LIKE '%.pdf'")
```

### Updating Documents

```python
# Update content (triggers re-chunking)
await client.update_document(document_id=doc.id, content="New content")

# Update metadata only (no re-chunking)
await client.update_document(
    document_id=doc.id,
    metadata={"version": "2.0", "updated_by": "admin"}
)

# Update title only (no re-chunking)
await client.update_document(document_id=doc.id, title="New Title")

# Update multiple fields at once
await client.update_document(
    document_id=doc.id,
    content="New content",
    title="Updated Title",
    metadata={"status": "final"}
)

# Use custom chunks (embeddings optional - will be generated if missing)
custom_chunks = [
    Chunk(content="Custom chunk 1"),
    Chunk(content="Custom chunk 2", embedding=[...]),  # Pre-computed embedding
]
await client.update_document(document_id=doc.id, chunks=custom_chunks)
```

**Notes:**

- Updates to only `metadata` or `title` skip re-chunking
- Updates to `content` trigger re-chunking and re-embedding
- Custom `chunks` with embeddings are stored as-is; missing embeddings are generated automatically

### Deleting Documents

```python
await client.delete_document(doc.id)
```

### Rebuilding the Database

```python
from haiku.rag.client import RebuildMode

# Full rebuild (default) - re-converts from source files, re-chunks, re-embeds
async for doc_id in client.rebuild_database():
    print(f"Processed document {doc_id}")

# Re-chunk from stored content (no source file access)
async for doc_id in client.rebuild_database(mode=RebuildMode.RECHUNK):
    print(f"Processed document {doc_id}")

# Only regenerate embeddings (fastest, keeps existing chunks)
async for doc_id in client.rebuild_database(mode=RebuildMode.EMBED_ONLY):
    print(f"Processed document {doc_id}")

# Add VLM picture descriptions to an existing database — runs the VLM
# over already-stored picture bytes, patches descriptions into the
# docling blob, then re-chunks + re-embeds. Requires
# processing.pictures='description' in the config.
async for doc_id in client.rebuild_database(mode=RebuildMode.DESCRIPTIONS):
    print(f"Described pictures in {doc_id}")
```

**Rebuild modes:**

- `RebuildMode.FULL` - Re-convert from source files, re-chunk, re-embed (default)
- `RebuildMode.RECHUNK` - Re-chunk from existing document content, re-embed
- `RebuildMode.EMBED_ONLY` - Keep existing chunks, only regenerate embeddings
- `RebuildMode.TITLE_ONLY` - Generate titles for untitled documents (no re-chunking or re-embedding)
- `RebuildMode.DESCRIPTIONS` - Run the VLM over picture bytes already stored on `document_items.picture_data`, patch descriptions into the docling blob, re-chunk + re-embed. Skips the docling parse entirely. Idempotent — pictures already carrying `meta.description.text` are not re-described, so the operation is safe to re-run.

### Generating Titles

Generate a title for an existing document on demand:

```python
title = await client.generate_title(doc)
if title:
    await client.update_document(document_id=doc.id, title=title)
```

Uses the same two-tier approach as automatic ingestion: structural extraction from DoclingDocument metadata first, with LLM fallback via `processing.title_model`. Unlike ingestion, this method does not catch exceptions — if the LLM call fails, the error propagates.

To batch-generate titles for all untitled documents, use `RebuildMode.TITLE_ONLY`:

```python
async for doc_id in client.rebuild_database(mode=RebuildMode.TITLE_ONLY):
    print(f"Generated title for {doc_id}")
```

See [Automatic Title Generation](configuration/processing.md#automatic-title-generation) for configuration details.

## Maintenance

Run maintenance to optimize storage and prune old table versions:

```python
await client.vacuum()
```

This compacts tables and removes historical versions to keep disk usage in check. It’s safe to run anytime, for example after bulk imports or periodically in long‑running apps.

### Atomic Writes and Rollback

Document create and update operations take a snapshot of table versions before any write and automatically roll back to that snapshot if something fails (for example, during chunking or embedding). This restores both the `documents` and `chunks` tables to their pre‑operation state using LanceDB’s table versioning.

- Applies to: `create_document(...)`, `create_document_from_source(...)`, `update_document(...)`, and internal rebuild/update flows.
- Scope: Both document rows and all associated chunks are rolled back together.
- Vacuum: Running `vacuum()` later prunes old versions for disk efficiency; rollbacks occur immediately during the failing operation and are not impacted.

## Searching Documents

The search method performs native hybrid search (vector + full-text) using LanceDB with optional reranking for improved relevance:

Basic hybrid search (default):
```python
results = await client.search("machine learning algorithms", limit=5)
for result in results:
    print(f"Score: {result.score:.3f}")
    print(f"Content: {result.content}")
    print(f"Document ID: {result.document_id}")
```

Search with different search types:
```python
# Vector search only
results = await client.search(
    query="machine learning",
    limit=5,
    search_type="vector"
)

# Full-text search only
results = await client.search(
    query="machine learning",
    limit=5,
    search_type="fts"
)

# Hybrid search (default - combines vector + fts with native LanceDB RRF)
results = await client.search(
    query="machine learning",
    limit=5,
    search_type="hybrid"
)

# Process results
for result in results:
    print(f"Relevance: {result.score:.3f}")
    print(f"Content: {result.content}")
    print(f"From document: {result.document_id}")
    print(f"Document URI: {result.document_uri}")
    print(f"Document Title: {result.document_title}")  # when available
```

### Filtering Search Results

Filter search results to only include chunks from documents matching specific criteria:

```python
# Filter by document URI pattern
results = await client.search(
    query="machine learning",
    limit=5,
    filter="uri LIKE '%arxiv%'"
)

# Filter by exact document title
results = await client.search(
    query="neural networks",
    limit=5,
    filter="title = 'Deep Learning Guide'"
)

# Combine multiple filter conditions
results = await client.search(
    query="AI research",
    limit=5,
    filter="uri LIKE '%.pdf' AND title LIKE '%paper%'"
)

# Filter with any search type
results = await client.search(
    query="transformers",
    limit=5,
    search_type="vector",
    filter="uri LIKE '%huggingface%'"
)
```

**Note:** Filters apply to document properties only. Available columns for filtering:
- `id` - Document ID
- `uri` - Document URI/URL
- `title` - Document title (if set)
- `created_at`, `updated_at` - Timestamps
- `metadata` - Document metadata (as string, use LIKE for pattern matching)

### Image queries

`client.search()` accepts an image instead of a text query when the configured embedder is multimodal (e.g. `provider: vllm` against a vision-language embedding model). The image is embedded once and the chunks table is searched vector-only — full-text search and reranking don't apply without a text query.

```python
from PIL import Image

# Bytes
results = await client.search(
    open("figure.png", "rb").read(),
    limit=5,
)

# PIL.Image works equivalently
results = await client.search(
    Image.open("figure.png"),
    limit=5,
)
```

Image queries surface picture chunks (synthetic per-figure chunks emitted at ingest under a multimodal embedder) and any text chunks whose vectors land near the image vector in the shared embedding space. Calling `client.search(bytes)` against a text-only embedder raises a `ValueError`.

### Expanding Search Context

Expand search results with surrounding content from the document:

```python
# Get initial search results
search_results = await client.search("machine learning", limit=3)

# Expand with section-bounded context
expanded_results = await client.expand_context(search_results)

for result in expanded_results:
    print(f"Expanded content: {result.content}")
```

Context expansion is automatic and section-aware. For structured documents (with section headers), expansion includes the entire section containing the match. For sections that exceed the budget or are too small (e.g., a title+authors area), expansion grows outward item-by-item from the match center, skipping noise labels (footnotes, page headers) — this naturally crosses into adjacent sections until the budget is filled. For unstructured documents, expansion grows outward item-by-item. Results without `doc_item_refs` (e.g., custom chunks passed to `import_document`) pass through unexpanded.

Configuration:

- **search.max_context_chars**: Maximum characters in expanded context. Default: 10000.

**Smart Merging**: When expanded results overlap within the same document, they are automatically merged into a single result with continuous content and the highest relevance score.

## Question Answering

Ask questions about your documents:

```python
answer, citations = await client.ask("Who is the author of haiku.rag?")
print(answer)
for cite in citations:
    print(f"  [{cite.chunk_id}] {cite.document_title or cite.document_uri}")
```

Customize the QA agent's behavior with a custom system prompt:

```python
custom_prompt = """You are a technical support expert for WIX.
Answer questions based on the knowledge base documents provided.
Be concise and helpful."""

answer, citations = await client.ask(
    "How do I create a blog?",
    system_prompt=custom_prompt
)
```

Filter to specific documents:

```python
answer, citations = await client.ask(
    "What are the main findings?",
    filter="uri LIKE '%paper%'"
)
```

The QA agent searches your documents for relevant information and uses the configured LLM to generate an answer. The method returns a tuple of `(answer_text, list[Citation])`. Citations include page numbers, section headings, and document references.

The QA provider and model are configured in `haiku.rag.yaml` or can be passed directly to the client (see [Configuration](configuration/index.md)).

See also: [Agents](agents/index.md) for details on the QA agent and the multi‑agent research workflow.

## Analysis

Answer complex analytical questions via code execution:

```python
# Aggregation across documents
result = await client.analyze("Which quarter had the highest revenue?")
print(result.answer)    # The answer
print(result.program)   # The final consolidated program

# Computation within a document set
result = await client.analyze(
    "What is the average deal size mentioned in these contracts?",
    filter="uri LIKE '%contracts%'"
)

# Multi-document comparison
result = await client.analyze(
    "What changed between these two versions of the policy?",
    documents=["Policy v1.0", "Policy v2.0"]
)
```

The analysis agent writes and executes Python code in a sandboxed environment to solve problems that traditional RAG struggles with: aggregation, computation, and multi-document analysis.

See [Analysis Agent](agents/analysis.md) for details on capabilities and configuration.

## Building Custom Agents

haiku.rag provides a RAG skill built on [haiku.skills](https://github.com/ggozad/haiku.skills) that bundles all capabilities into a composable agent:

```python
from pydantic_ai import Agent
from haiku.rag.skills.rag import create_skill
from haiku.skills.agent import SkillToolset
from haiku.skills.prompts import build_system_prompt

skill = create_skill(db_path=db_path, config=config)
toolset = SkillToolset(skills=[skill])

agent = Agent(
    "openai:gpt-4o",
    instructions=build_system_prompt(toolset.skill_catalog),
    toolsets=[toolset],
)

result = await agent.run("What are the main findings?")
```

See [Toolsets](tools.md) for the full API reference.