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feat: One App (#635)

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Co-authored-by: Taranjeet Singh <reachtotj@gmail.com>
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cachho
2023-09-30 08:30:43 +02:00
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parent 2db07cdb1f
commit 9ecf2e9feb
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5
docs/advanced/adding_data.mdx
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@@ -4,13 +4,12 @@ title: '➕ Adding Data'
## Add Dataset
- This step assumes that you have already created an `app` instance by either using `App`, `OpenSourceApp` or `CustomApp`. We are calling our app instance as `naval_chat_bot` 🤖
- This step assumes that you have already created an `App`. We are calling our app instance as `naval_chat_bot` 🤖
- Now use `.add` method to add any dataset.
```python
# naval_chat_bot = App() or
# naval_chat_bot = OpenSourceApp()
naval_chat_bot = App()
# Embed Online Resources
naval_chat_bot.add("https://www.youtube.com/watch?v=3qHkcs3kG44")

249
docs/advanced/app_types.mdx
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@@ -4,108 +4,120 @@ title: '📱 App types'
## App Types
We have three types of App.
Embedchain supports a variety of LLMs, embedding functions/models and vector databases.
### App
Our app gives you full control over which components you want to use, you can mix and match them to your hearts content.
<Tip>
Out of the box, if you just use `app = App()`, Embedchain uses what we believe to be the best configuration available. This might include paid/proprietary components. Currently, this is
* LLM: OpenAi (gpt-3.5-turbo-0613)
* Embedder: OpenAi (text-embedding-ada-002)
* Database: ChromaDB
</Tip>
### LLM
#### Choosing an LLM
The following LLM providers are supported by Embedchain:
- OPENAI
- ANTHPROPIC
- VERTEX_AI
- GPT4ALL
- AZURE_OPENAI
- LLAMA2
You can choose one by importing it from `embedchain.llm`. E.g.:
```python
from embedchain import App
app = App()
from embedchain.llm.llama2 import Llama2Llm
app = App(llm=Llama2Llm())
```
- `App` uses OpenAI's model, so these are paid models. 💸 You will be charged for embedding model usage and LLM usage.
- `App` uses OpenAI's embedding model to create embeddings for chunks and ChatGPT API as LLM to get answer given the relevant docs. Make sure that you have an OpenAI account and an API key. If you don't have an API key, you can create one by visiting [this link](https://platform.openai.com/account/api-keys).
- `App` is opinionated. It uses the best embedding model and LLM on the market.
- Once you have the API key, set it in an environment variable called `OPENAI_API_KEY`
#### Configuration
The LLMs can be configured by passing an LlmConfig object.
The config options can be found [here](/advanced/query_configuration#llmconfig)
```python
import os
os.environ["OPENAI_API_KEY"] = "sk-xxxx"
from embedchain import App
from embedchain.llm.llama2 import Llama2Llm
from embedchain.config import LlmConfig
app = App(llm=Llama2Llm(), llm_config=LlmConfig(number_documents=3, temperature=0))
```
### Llama2App
### Embedder
#### Choosing an Embedder
The following providers for embedding functions are supported by Embedchain:
- OPENAI
- HUGGING_FACE
- VERTEX_AI
- GPT4ALL
- AZURE_OPENAI
You can choose one by importing it from `embedchain.embedder`. E.g.:
```python
import os
from embedchain import Llama2App
os.environ['REPLICATE_API_TOKEN'] = "REPLICATE API TOKEN"
zuck_bot = Llama2App()
# Embed your data
zuck_bot.add("https://www.youtube.com/watch?v=Ff4fRgnuFgQ")
zuck_bot.add("https://en.wikipedia.org/wiki/Mark_Zuckerberg")
# Nice, your bot is ready now. Start asking questions to your bot.
zuck_bot.query("Who is Mark Zuckerberg?")
# Answer: Mark Zuckerberg is an American internet entrepreneur and business magnate. He is the co-founder and CEO of Facebook. Born in 1984, he dropped out of Harvard University to focus on his social media platform, which has since grown to become one of the largest and most influential technology companies in the world.
# Enable web search for your bot
zuck_bot.online = True # enable internet access for the bot
zuck_bot.query("Who owns the new threads app and when it was founded?")
# Answer: Based on the context provided, the new Threads app is owned by Meta, the parent company of Facebook, Instagram, and WhatsApp.
```
- `Llama2App` uses Replicate's LLM model, so these are paid models. You can get the `REPLICATE_API_TOKEN` by registering on [their website](https://replicate.com/account).
- `Llama2App` uses OpenAI's embedding model to create embeddings for chunks. Make sure that you have an OpenAI account and an API key. If you don't have an API key, you can create one by visiting [this link](https://platform.openai.com/account/api-keys).
### OpenSourceApp
```python
from embedchain import OpenSourceApp
app = OpenSourceApp()
```
- `OpenSourceApp` uses open source embedding and LLM model. It uses `all-MiniLM-L6-v2` from Sentence Transformers library as the embedding model and `gpt4all` as the LLM.
- Here there is no need to setup any api keys. You just need to install embedchain package and these will get automatically installed. 📦
- Once you have imported and instantiated the app, every functionality from here onwards is the same for either type of app. 📚
- `OpenSourceApp` is opinionated. It uses the best open source embedding model and LLM on the market.
- extra dependencies are required for this app type. Install them with `pip install --upgrade embedchain[opensource]`.
### CustomApp
```python
from embedchain import CustomApp
from embedchain.config import (CustomAppConfig, ElasticsearchDBConfig,
EmbedderConfig, LlmConfig)
from embedchain import App
from embedchain.embedder.vertexai import VertexAiEmbedder
from embedchain.llm.vertex_ai import VertexAiLlm
from embedchain.models import EmbeddingFunctions, Providers
from embedchain.vectordb.elasticsearch import Elasticsearch
# short
app = CustomApp(llm=VertexAiLlm(), db=Elasticsearch(), embedder=VertexAiEmbedder())
# with configs
app = CustomApp(
config=CustomAppConfig(log_level="INFO"),
llm=VertexAiLlm(config=LlmConfig(number_documents=5)),
db=Elasticsearch(config=ElasticsearchDBConfig(es_url="...")),
embedder=VertexAiEmbedder(config=EmbedderConfig()),
)
app = App(embedder=VertexAiEmbedder())
```
- `CustomApp` is not opinionated.
- Configuration required. It's for advanced users who want to mix and match different embedding models and LLMs.
- while it's doing that, it's still providing abstractions by allowing you to import Classes from `embedchain.llm`, `embedchain.vectordb`, and `embedchain.embedder`.
- paid and free/open source providers included.
- Once you have imported and instantiated the app, every functionality from here onwards is the same for either type of app. 📚
- Following providers are available for an LLM
- OPENAI
- ANTHPROPIC
- VERTEX_AI
- GPT4ALL
- AZURE_OPENAI
- LLAMA2
- Following embedding functions are available for an embedding function
- OPENAI
- HUGGING_FACE
- VERTEX_AI
- GPT4ALL
- AZURE_OPENAI
#### Configuration
The LLMs can be configured by passing an EmbedderConfig object.
```python
from embedchain import App
from embedchain.embedder.openai import OpenAiEmbedder
from embedchain.config import EmbedderConfig
app = App(embedder=OpenAiEmbedder(), embedder_config=EmbedderConfig(model="text-embedding-ada-002"))
```
<Tip>
You can also pass an `LlmConfig` instance directly to the `query` or `chat` method.
This creates a temporary config for that request alone, so you could, for example, use a different model (from the same provider) or get more context documents for a specific query.
</Tip>
### Vector Database
#### Choosing a Vector Database
The following vector databases are supported by Embedchain:
- ChromaDB
- Elasticsearch
You can choose one by importing it from `embedchain.vectordb`. E.g.:
```python
from embedchain import App
from embedchain.vectordb.elasticsearch import ElasticsearchDB
app = App(db=ElasticsearchDB())
```
#### Configuration
The vector databases can be configured by passing a specific config object.
These vary greatly between the different vector databases.
```python
from embedchain import App
from embedchain.vectordb.elasticsearch import ElasticsearchDB
from embedchain.config import ElasticsearchDBConfig
app = App(db=ElasticsearchDB(), db_config=ElasticsearchDBConfig())
```
### PersonApp
@@ -123,18 +135,79 @@ import os
os.environ["OPENAI_API_KEY"] = "sk-xxxx"
```
#### Compatibility with other apps
### Full Configuration Examples
Embedchain previously offered fully configured classes, namely `App`, `OpenSourceApp`, `CustomApp` and `Llama2App`.
We deprecated these apps. The reason for this decision was that it was hard to switch from to a different LLM, embedder or vector db, if you one day decided that that's what you want to do.
The new app allows drop-in replacements, such as changing `App(llm=OpenAiLlm())` to `App(llm=Llama2Llm())`.
To make the switch to our new, fully configurable app easier, we provide you with full examples for what the old classes would look like implemented as a new app.
You can swap these in, and if you decide you want to try a different model one day, you don't have to rewrite your whole bot.
#### App
App without any configuration is still using the best options available, so you can keep using:
```python
from embedchain import App
app = App()
```
#### OpenSourceApp
Use this snippet to run an open source app.
```python
from embedchain import App
from embedchain.llm.gpt4all import GPT4ALLLlm
from embedchain.embedder.gpt4all import GPT4AllEmbedder
from embedchain.vectordb.chroma import ChromaDB
app = App(llm=GPT4ALLLlm(), embedder=GPT4AllEmbedder(), db=ChromaDB())
```
#### Llama2App
```python
from embedchain import App
from embedchain.llm.llama2 import Llama2Llm
app = App(llm=Llama2Llm())
```
#### CustomApp
Every app is a custom app now.
If you were previously using a `CustomApp`, you can now just change it to `App`.
Here's one example, what you could do if we combined everything shown on this page.
```python
from embedchain import App
from embedchain.config import ElasticsearchDBConfig, EmbedderConfig, LlmConfig
from embedchain.embedder.openai import OpenAiEmbedder
from embedchain.llm.llama2 import Llama2Llm
from embedchain.vectordb.elasticsearch import ElasticsearchDB
app = App(
llm=Llama2Llm(),
llm_config=LlmConfig(number_documents=3, temperature=0),
embedder=OpenAiEmbedder(),
embedder_config=EmbedderConfig(model="text-embedding-ada-002"),
db=ElasticsearchDB(),
db_config=ElasticsearchDBConfig(),
)
```
### Compatibility with other apps
- If there is any other app instance in your script or app, you can change the import as
```python
from embedchain import App as EmbedChainApp
from embedchain import OpenSourceApp as EmbedChainOSApp
from embedchain import PersonApp as EmbedChainPersonApp
# or
from embedchain import App as ECApp
from embedchain import OpenSourceApp as ECOSApp
from embedchain import PersonApp as ECPApp
```