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

# Knowledge Base

> Ingest documents and chunks that are searched alongside episodic memories at recall time.

## How it works

Episodic memories (from conversations) and static knowledge (from documents) have different lifecycles. MemWire keeps them separate but surfaces both at recall time.

When you call `memory.recall()`, the engine:

1. Traverses the memory graph for relevant conversation paths
2. **Also searches knowledge chunks** using the same query embedding
3. Merges both results into a single `RecallResult`

You can also search knowledge directly without going through the memory graph.

***

## Adding a knowledge base

```python theme={null}
from memwire import MemWire, MemWireConfig

config = MemWireConfig(qdrant_path="./memwire_data")
memory = MemWire(config=config)

USER_ID = "alice"

memory.add_knowledge(
    name="Company Handbook",
    chunks=[
        {"content": "All employees must complete onboarding within the first 30 days."},
        {"content": "Remote work is allowed up to 3 days per week."},
        {"content": "Expenses above $500 require manager approval."},
    ],
    user_id=USER_ID,
)
```

Chunks are embedded and stored in a dedicated vector store collection, separate from memory vectors.

***

## Recall with knowledge

Knowledge chunks appear automatically in recall results:

```python theme={null}
result = memory.recall("What is the expense approval policy?", user_id=USER_ID)

# Conversation memories
print(result.formatted)

# Knowledge chunks (also included in formatted)
for chunk in result.knowledge:
    print(chunk.content, chunk.score)
```

***

## Direct knowledge search

```python theme={null}
chunks = memory.search_knowledge(
    "remote work policy",
    user_id=USER_ID,
    top_k=5,
)
for chunk in chunks:
    print(f"[{chunk.score:.2f}] {chunk.content}")
```

***

## Deleting a knowledge base

```python theme={null}
# List knowledge bases via stats
stats = memory.get_stats(user_id=USER_ID)
print(stats["knowledge_bases"])  # count

# Delete by kb_id
memory.delete_knowledge(kb_id="kb_abc123")
```

***

## Scoping knowledge

Knowledge bases respect the same multi-tenancy hierarchy as memories:

```python theme={null}
memory.add_knowledge(
    name="Team Runbook",
    chunks=[{"content": "Deploy with: ./scripts/deploy.sh production"}],
    user_id=USER_ID,
    workspace_id="team_engineering",
    app_id="ops_assistant",
)
```

***

## KnowledgeChunk fields

| Field      | Type    | Description                           |
| ---------- | ------- | ------------------------------------- |
| `chunk_id` | `str`   | Unique identifier for the chunk       |
| `kb_id`    | `str`   | Parent knowledge base ID              |
| `content`  | `str`   | Chunk text                            |
| `score`    | `float` | Similarity score from the search      |
| `metadata` | `dict`  | Optional metadata passed at ingestion |