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

# Putting Files

> How to add documents to your KnowledgeBase

## Overview

KnowledgeBase accepts multiple types of sources: individual files, directories, or direct string content. It automatically detects file types and uses appropriate loaders.

## Installation

To add files to your KnowledgeBase, you'll need a vector database provider and document loaders for the file types you want to process.

<Note>
  **Example: Setting up for PDF files with Chroma**

  To process PDF files and store them in ChromaDB:

  ```bash theme={null}
  uv pip install "upsonic[chroma]"
  uv pip install "upsonic[pdf-loader]"
  ```

  Or install both at once:

  ```bash theme={null}
  uv pip install "upsonic[chroma,pdf-loader]"
  ```

  **What you need:**

  * A vector database provider (e.g., `chroma`, `qdrant`, `milvus`, `weaviate`, `pinecone`, `faiss`, or `pgvector`)
  * Document loaders for your file types (e.g., `pdf-loader`, `docx-loader`, `csv-loader`, `markdown-loader`, `html-loader`, `json-loader`, `xml-loader`, `yaml-loader`, `text-loader`)

  The examples below use Chroma and PDF loader, but you can use any combination of supported providers and loaders. See [Vector Stores](/concepts/knowledgebase/vector-stores/chroma) and [Document Loaders](/concepts/knowledgebase/document-loaders/pypdf) for all options.
</Note>

## Examples

### Single File

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode
from upsonic.loaders.pdf import PdfLoader
from upsonic.loaders.config import PdfLoaderConfig

# Setup embedding provider
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())

# Setup vector database
config = ChromaConfig(
    collection_name="my_kb",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db")
)
vectordb = ChromaProvider(config)

# Setup PDF loader
loader = PdfLoader(PdfLoaderConfig())

# Create knowledge base
kb = KnowledgeBase(
    sources="document.pdf",
    embedding_provider=embedding,
    vectordb=vectordb,
    loaders=[loader]
)

# Use with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="What was the total revenue in Q3 2024 according to the report?",
    context=[kb]
)

result = agent.do(task)
print(result)
```

### Multiple Files

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Setup dependencies
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())
vectordb = ChromaProvider(ChromaConfig(
    collection_name="my_kb",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db")
))

# Create knowledge base with multiple sources
kb = KnowledgeBase(
    sources=["doc1.pdf", "doc2.md", "doc3.docx"],
    embedding_provider=embedding,
    vectordb=vectordb
)

# Use with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="What are the three main conclusions drawn from the A/B testing results?",
    context=[kb]
)

result = agent.do(task)
print(result)
```

### Directory

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode
from upsonic.loaders.pdf import PdfLoader
from upsonic.loaders.config import PdfLoaderConfig

# Setup dependencies
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())
vectordb = ChromaProvider(ChromaConfig(
    collection_name="my_kb",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db")
))

# Setup PDF loader
loader = PdfLoader(PdfLoaderConfig())

# Create knowledge base from directory
kb = KnowledgeBase(
    sources="data/",
    embedding_provider=embedding,
    vectordb=vectordb,
    loaders=[loader]
)

# Use with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="What are the dimensions and power requirements for the Model X unit?",
    context=[kb]
)

result = agent.do(task)
print(result)
```

### Mixed Sources

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode
from upsonic.loaders.pdf import PdfLoader
from upsonic.loaders.config import PdfLoaderConfig

# Setup dependencies
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())
vectordb = ChromaProvider(ChromaConfig(
    collection_name="my_kb",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db")
))

# Setup PDF loader
loader = PdfLoader(PdfLoaderConfig())

# Create knowledge base with mixed sources
kb = KnowledgeBase(
    sources=["doc1.pdf", "data/", "This is direct content text."],
    embedding_provider=embedding,
    vectordb=vectordb,
    loaders=[loader]
)

# Use with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="Find the warranty terms for the battery component in the text",
    context=[kb]
)

result = agent.do(task)
print(result)
```

## Supported File Types

* **PDF**: `.pdf` (PyPDF, PDFPlumber, PyMuPDF)
* **Markdown**: `.md`, `.markdown`
* **Documents**: `.docx`
* **Spreadsheets**: `.csv`
* **Data**: `.json`, `.jsonl`, `.xml`, `.yaml`, `.yml`
* **Code**: `.py`, `.js`, `.ts`, `.java`, `.c`, `.cpp`, `.h`, `.cs`, `.go`, `.rs`, `.php`, `.rb`
* **Web**: `.html`, `.htm`, `.xhtml`, `.css`
* **Text**: `.txt`, `.log`, `.rst`