feat: add new custom app (#313)
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cachho
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docs/advanced/adding_data.mdx
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docs/advanced/adding_data.mdx
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---
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title: '➕ Adding Data'
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---
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## Add Dataset
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- 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` 🤖
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- Now use `.add()` function to add any dataset.
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```python
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# naval_chat_bot = App() or
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# naval_chat_bot = OpenSourceApp()
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# Embed Online Resources
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naval_chat_bot.add("youtube_video", "https://www.youtube.com/watch?v=3qHkcs3kG44")
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naval_chat_bot.add("pdf_file", "https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf")
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naval_chat_bot.add("web_page", "https://nav.al/feedback")
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naval_chat_bot.add("web_page", "https://nav.al/agi")
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# Embed Local Resources
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naval_chat_bot.add_local("qna_pair", ("Who is Naval Ravikant?", "Naval Ravikant is an Indian-American entrepreneur and investor."))
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```
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The possible formats to add data can be found on the [Supported Data Formats](/advanced/data_types) page.
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@@ -2,12 +2,6 @@
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title: '📱 App types'
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---
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Creating a chatbot involves 3 steps:
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- ⚙️ Import the App instance
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- 🗃️ Add Dataset
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- 💬 Query or Chat on the dataset and get answers (Interface Types)
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## App Types
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We have three types of App.
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@@ -16,13 +10,12 @@ We have three types of App.
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```python
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from embedchain import App
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naval_chat_bot = App()
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app = App()
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```
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- `App` uses OpenAI's model, so these are paid models. 💸 You will be charged for embedding model usage and LLM usage.
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- `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 have don't have an API key, you can create one by visiting [this link](https://platform.openai.com/account/api-keys).
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- `App` is opinionated. It uses the best embedding model and LLM on the market.
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- Once you have the API key, set it in an environment variable called `OPENAI_API_KEY`
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```python
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@@ -34,12 +27,31 @@ os.environ["OPENAI_API_KEY"] = "sk-xxxx"
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```python
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from embedchain import OpenSourceApp
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naval_chat_bot = OpenSourceApp()
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app = OpenSourceApp()
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```
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- `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.
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- Here there is no need to setup any api keys. You just need to install embedchain package and these will get automatically installed. 📦
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- Once you have imported and instantiated the app, every functionality from here onwards is the same for either type of app. 📚
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- `OpenSourceApp` is opinionated. It uses the best open source embedding model and LLM on the market.
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### CustomApp
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```python
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from embedchain import CustomApp
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from embedchain.config import CustomAppConfig
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from embedchain.models import Providers, EmbeddingFunctions
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config = CustomAppConfig(embedding_fn=EmbeddingFunctions.OPENAI, provider=Providers.OPENAI)
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app = CustomApp()
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```
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- `CustomApp` is not opinionated.
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- Configuration required. It's for advanced users who want to mix and match different embedding models and LLMs. Configuration required.
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- while it's doing that, it's still providing abstractions through `Providers`.
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- paid and free/open source providers included.
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- Once you have imported and instantiated the app, every functionality from here onwards is the same for either type of app. 📚
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### PersonApp
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@@ -57,25 +69,7 @@ import os
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os.environ["OPENAI_API_KEY"] = "sk-xxxx"
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```
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## Add Dataset
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- This step assumes that you have already created an `app` instance by either using `App` or `OpenSourceApp`. We are calling our app instance as `naval_chat_bot` 🤖
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- Now use `.add()` function to add any dataset.
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```python
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# naval_chat_bot = App() or
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# naval_chat_bot = OpenSourceApp()
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# Embed Online Resources
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naval_chat_bot.add("youtube_video", "https://www.youtube.com/watch?v=3qHkcs3kG44")
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naval_chat_bot.add("pdf_file", "https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf")
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naval_chat_bot.add("web_page", "https://nav.al/feedback")
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naval_chat_bot.add("web_page", "https://nav.al/agi")
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# Embed Local Resources
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naval_chat_bot.add_local("qna_pair", ("Who is Naval Ravikant?", "Naval Ravikant is an Indian-American entrepreneur and investor."))
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```
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#### Compatibility with other apps
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- If there is any other app instance in your script or app, you can change the import as
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@@ -90,49 +84,3 @@ from embedchain import App as ECApp
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from embedchain import OpenSourceApp as ECOSApp
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from embedchain import PersonApp as ECPApp
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```
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## Interface Types
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### Query Interface
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- This interface is like a question answering bot. It takes a question and gets the answer. It does not maintain context about the previous chats.❓
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- To use this, call `.query()` function to get the answer for any query.
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```python
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print(naval_chat_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?"))
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# answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
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```
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### Chat Interface
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- This interface is chat interface where it remembers previous conversation. Right now it remembers 5 conversation by default. 💬
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- To use this, call `.chat` function to get the answer for any query.
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```python
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print(naval_chat_bot.chat("How to be happy in life?"))
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# answer: The most important trick to being happy is to realize happiness is a skill you develop and a choice you make. You choose to be happy, and then you work at it. It's just like building muscles or succeeding at your job. It's about recognizing the abundance and gifts around you at all times.
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print(naval_chat_bot.chat("who is naval ravikant?"))
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# answer: Naval Ravikant is an Indian-American entrepreneur and investor.
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print(naval_chat_bot.chat("what did the author say about happiness?"))
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# answer: The author, Naval Ravikant, believes that happiness is a choice you make and a skill you develop. He compares the mind to the body, stating that just as the body can be molded and changed, so can the mind. He emphasizes the importance of being present in the moment and not getting caught up in regrets of the past or worries about the future. By being present and grateful for where you are, you can experience true happiness.
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```
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### Stream Response
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- You can add config to your query method to stream responses like ChatGPT does. You would require a downstream handler to render the chunk in your desirable format. Supports both OpenAI model and OpenSourceApp. 📊
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- To use this, instantiate a `QueryConfig` or `ChatConfig` object with `stream=True`. Then pass it to the `.chat()` or `.query()` method. The following example iterates through the chunks and prints them as they appear.
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```python
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app = App()
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query_config = QueryConfig(stream = True)
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resp = app.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?", query_config)
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for chunk in resp:
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print(chunk, end="", flush=True)
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# answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
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```
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@@ -6,7 +6,7 @@ Embedchain is made to work out of the box. However, for advanced users we're als
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## Examples
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### Custom embedding function
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### General
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Here's the readme example with configuration options.
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@@ -16,13 +16,8 @@ from embedchain import App
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from embedchain.config import AppConfig, AddConfig, QueryConfig, ChunkerConfig
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from chromadb.utils import embedding_functions
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# Example: use your own embedding function
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# Warning: We are currenty reworking the concept of custom apps, this might not be working.
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config = AppConfig(ef=embedding_functions.OpenAIEmbeddingFunction(
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api_key=os.getenv("OPENAI_API_KEY"),
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organization_id=os.getenv("OPENAI_ORGANIZATION"),
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model_name="text-embedding-ada-002"
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))
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# Example: set the log level for debugging
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config = AppConfig(log_level="DEBUG")
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naval_chat_bot = App(config)
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# Example: define your own chunker config for `youtube_video`
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@@ -36,7 +31,7 @@ naval_chat_bot.add("web_page", "https://nav.al/agi", add_config)
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naval_chat_bot.add_local("qna_pair", ("Who is Naval Ravikant?", "Naval Ravikant is an Indian-American entrepreneur and investor."), add_config)
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query_config = QueryConfig() # Currently no options
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query_config = QueryConfig()
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print(naval_chat_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?", query_config))
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```
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@@ -88,22 +83,3 @@ for query in queries:
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# Query: Why did you divorce your first wife?
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# Response: We divorced due to living apart for five years.
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```
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## Other methods
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### Reset
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Resets the database and deletes all embeddings. Irreversible. Requires reinitialization afterwards.
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```python
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app.reset()
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```
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### Count
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Counts the number of embeddings (chunks) in the database.
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```python
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print(app.count())
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# returns: 481
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```
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74
docs/advanced/interface_types.mdx
Normal file
74
docs/advanced/interface_types.mdx
Normal file
@@ -0,0 +1,74 @@
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---
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title: '🤝 Interface types'
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---
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## Interface Types
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The embedchain app exposes the following methods.
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### Query Interface
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- This interface is like a question answering bot. It takes a question and gets the answer. It does not maintain context about the previous chats.❓
|
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|
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- To use this, call `.query()` function to get the answer for any query.
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```python
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print(naval_chat_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?"))
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# answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
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```
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### Chat Interface
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- This interface is chat interface where it remembers previous conversation. Right now it remembers 5 conversation by default. 💬
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- To use this, call `.chat` function to get the answer for any query.
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```python
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print(naval_chat_bot.chat("How to be happy in life?"))
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# answer: The most important trick to being happy is to realize happiness is a skill you develop and a choice you make. You choose to be happy, and then you work at it. It's just like building muscles or succeeding at your job. It's about recognizing the abundance and gifts around you at all times.
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print(naval_chat_bot.chat("who is naval ravikant?"))
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# answer: Naval Ravikant is an Indian-American entrepreneur and investor.
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print(naval_chat_bot.chat("what did the author say about happiness?"))
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# answer: The author, Naval Ravikant, believes that happiness is a choice you make and a skill you develop. He compares the mind to the body, stating that just as the body can be molded and changed, so can the mind. He emphasizes the importance of being present in the moment and not getting caught up in regrets of the past or worries about the future. By being present and grateful for where you are, you can experience true happiness.
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```
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### Stream Response
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- You can add config to your query method to stream responses like ChatGPT does. You would require a downstream handler to render the chunk in your desirable format. Supports both OpenAI model and OpenSourceApp. 📊
|
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- To use this, instantiate a `QueryConfig` or `ChatConfig` object with `stream=True`. Then pass it to the `.chat()` or `.query()` method. The following example iterates through the chunks and prints them as they appear.
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```python
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app = App()
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query_config = QueryConfig(stream = True)
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resp = app.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?", query_config)
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for chunk in resp:
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print(chunk, end="", flush=True)
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# answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
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```
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### Other Methods
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#### Dry Run
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Dry run has all the options that `query` has, it just doesn't send the prompt to the LLM, to save money. It's used for [testing](/advanced/testing#dry-run).
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#### Reset
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Resets the database and deletes all embeddings. Irreversible. Requires reinitialization afterwards.
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```python
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app.reset()
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```
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#### Count
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Counts the number of embeddings (chunks) in the database.
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```python
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print(app.count())
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# returns: 481
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```
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@@ -4,11 +4,11 @@ title: '🔍 Query configurations'
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## AppConfig
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| option | description | type | default |
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|-----------|-----------------------|---------------------------------|------------------------|
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| log_level | log level | string | WARNING |
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| ef | embedding function | chromadb.utils.embedding_functions | \{text-embedding-ada-002\} |
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| db | vector database (experimental) | BaseVectorDB | ChromaDB |
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| option | description | type | default |
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|-------------|-----------------------|---------------------------------|------------------------|
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| log_level | log level | string | WARNING |
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| embedding_fn| embedding function | chromadb.utils.embedding_functions | \{text-embedding-ada-002\} |
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| db | vector database (experimental) | BaseVectorDB | ChromaDB |
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## AddConfig
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@@ -2,6 +2,10 @@
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title: '🧪 Testing'
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---
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## Methods for testing
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### Dry Run
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Before you consume valueable tokens, you should make sure that the embedding you have done works and that it's receiving the correct document from the database.
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For this you can use the `dry_run` method.
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@@ -32,7 +32,7 @@
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},
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{
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"group": "Advanced",
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"pages": ["advanced/app_types", "advanced/data_types", "advanced/query_configuration", "advanced/configuration", "advanced/testing", "advanced/showcase"]
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"pages": ["advanced/app_types", "advanced/interface_types", "advanced/adding_data","advanced/data_types", "advanced/query_configuration", "advanced/configuration", "advanced/testing", "advanced/showcase"]
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},
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{
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"group": "Contribution Guidelines",
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@@ -9,6 +9,12 @@ Install embedchain python package:
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pip install embedchain
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```
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Creating a chatbot involves 3 steps:
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|
||||
- ⚙️ Import the App instance
|
||||
- 🗃️ Add Dataset
|
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- 💬 Query or Chat on the dataset and get answers (Interface Types)
|
||||
|
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Run your first bot in python using the following code. Make sure to set the `OPENAI_API_KEY` 🔑 environment variable in the code.
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```python
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Reference in New Issue
Block a user