mem0/docs/advanced/configuration.mdx

---
title: '⚙️ Custom configurations'
---

Embedchain is made to work out of the box. However, for advanced users we're also offering configuration options. All of these configuration options are optional and have sane defaults.

## Examples

### Custom embedding function

Here's the readme example with configuration options.

```python
import os
from embedchain import App
from embedchain.config import AppConfig, AddConfig, QueryConfig, ChunkerConfig
from chromadb.utils import embedding_functions

# Example: use your own embedding function
# Warning: We are currenty reworking the concept of custom apps, this might not be working.
config = AppConfig(ef=embedding_functions.OpenAIEmbeddingFunction(
                api_key=os.getenv("OPENAI_API_KEY"),
                organization_id=os.getenv("OPENAI_ORGANIZATION"),
                model_name="text-embedding-ada-002"
            ))
naval_chat_bot = App(config)

# Example: define your own chunker config for `youtube_video`
chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=100, length_function=len)
naval_chat_bot.add("youtube_video", "https://www.youtube.com/watch?v=3qHkcs3kG44", AddConfig(chunker=chunker_config))

add_config = AddConfig()
naval_chat_bot.add("pdf_file", "https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf", add_config)
naval_chat_bot.add("web_page", "https://nav.al/feedback", add_config)
naval_chat_bot.add("web_page", "https://nav.al/agi", add_config)

naval_chat_bot.add_local("qna_pair", ("Who is Naval Ravikant?", "Naval Ravikant is an Indian-American entrepreneur and investor."), add_config)

query_config = QueryConfig() # Currently no options
print(naval_chat_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?", query_config))
```

### Custom prompt template

Here's the example of using custom prompt template with `.query`

```python
from embedchain.config import QueryConfig
from embedchain.embedchain import App
from string import Template
import wikipedia

einstein_chat_bot = App()

# Embed Wikipedia page
page = wikipedia.page("Albert Einstein")
einstein_chat_bot.add("text", page.content)

# Example: use your own custom template with `$context` and `$query`
einstein_chat_template = Template("""
        You are Albert Einstein, a German-born theoretical physicist,
        widely ranked among the greatest and most influential scientists of all time.

        Use the following information about Albert Einstein to respond to
        the human's query acting as Albert Einstein.
        Context: $context

        Keep the response brief. If you don't know the answer, just say that you don't know, don't try to make up an answer.

        Human: $query
        Albert Einstein:""")
query_config = QueryConfig(einstein_chat_template)
queries = [
        "Where did you complete your studies?",
        "Why did you win nobel prize?",
        "Why did you divorce your first wife?",
]
for query in queries:
        response = einstein_chat_bot.query(query, query_config)
        print("Query: ", query)
        print("Response: ", response)

# Output
# Query:  Where did you complete your studies?
# Response:  I completed my secondary education at the Argovian cantonal school in Aarau, Switzerland.
# Query:  Why did you win nobel prize?
# Response:  I won the Nobel Prize in Physics in 1921 for my services to Theoretical Physics, particularly for my discovery of the law of the photoelectric effect.
# Query:  Why did you divorce your first wife?
# Response:  We divorced due to living apart for five years.
```

## Other methods

### Reset

Resets the database and deletes all embeddings. Irreversible. Requires reinitialization afterwards.

```python
app.reset()
```

### Count

Counts the number of embeddings (chunks) in the database.

```python
print(app.count())
# returns: 481
```