[docs]: Revamp embedchain docs (#799)
This commit is contained in:
Deshraj Yadav
135
docs/components/embedding-models.mdx
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135
docs/components/embedding-models.mdx
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---
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title: 🧩 Embedding models
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---
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## Overview
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Embedchain supports several embedding models from the following providers:
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<CardGroup cols={4}>
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<Card title="OpenAI" href="#openai"></Card>
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<Card title="GPT4All" href="#gpt4all"></Card>
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<Card title="Hugging Face" href="#hugging-face"></Card>
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<Card title="Vertex AI" href="#vertex-ai"></Card>
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</CardGroup>
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## OpenAI
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To use OpenAI embedding function, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
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Once you have obtained the key, you can use it like this:
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<CodeGroup>
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```python main.py
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import os
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from embedchain import App
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os.environ['OPENAI_API_KEY'] = 'xxx'
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# load embedding model configuration from openai.yaml file
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app = App.from_config(yaml_path="openai.yaml")
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app.add("https://en.wikipedia.org/wiki/OpenAI")
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app.query("What is OpenAI?")
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```
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```yaml openai.yaml
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embedder:
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provider: openai
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config:
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model: 'text-embedding-ada-002'
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```
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</CodeGroup>
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## GPT4ALL
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GPT4All supports generating high quality embeddings of arbitrary length documents of text using a CPU optimized contrastively trained Sentence Transformer.
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load embedding model configuration from gpt4all.yaml file
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app = App.from_config(yaml_path="gpt4all.yaml")
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```
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```yaml gpt4all.yaml
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llm:
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provider: gpt4all
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model: 'orca-mini-3b.ggmlv3.q4_0.bin'
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config:
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temperature: 0.5
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max_tokens: 1000
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top_p: 1
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stream: false
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embedder:
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provider: gpt4all
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config:
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model: 'all-MiniLM-L6-v2'
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```
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</CodeGroup>
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## Hugging Face
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Hugging Face supports generating embeddings of arbitrary length documents of text using Sentence Transformer library. Example of how to generate embeddings using hugging face is given below:
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load embedding model configuration from huggingface.yaml file
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app = App.from_config(yaml_path="huggingface.yaml")
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```
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```yaml huggingface.yaml
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llm:
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provider: huggingface
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model: 'google/flan-t5-xxl'
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config:
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temperature: 0.5
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max_tokens: 1000
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top_p: 0.5
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stream: false
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embedder:
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provider: huggingface
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config:
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model: 'sentence-transformers/all-mpnet-base-v2'
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```
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</CodeGroup>
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## Vertex AI
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Embedchain supports Google's VertexAI embeddings model through a simple interface. You just have to pass the `model_name` in the config yaml and it would work out of the box.
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load embedding model configuration from vertexai.yaml file
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app = App.from_config(yaml_path="vertexai.yaml")
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```
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```yaml vertexai.yaml
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llm:
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provider: vertexai
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model: 'chat-bison'
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config:
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temperature: 0.5
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top_p: 0.5
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embedder:
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provider: vertexai
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config:
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model: 'textembedding-gecko'
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```
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</CodeGroup>
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280
docs/components/llms.mdx
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280
docs/components/llms.mdx
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---
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title: 🤖 Large language models (LLMs)
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---
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## Overview
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Embedchain comes with built-in support for various popular large language models. We handle the complexity of integrating these models for you, allowing you to easily customize your language model interactions through a user-friendly interface.
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<CardGroup cols={4}>
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<Card title="OpenAI" href="#openai"></Card>
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<Card title="Azure OpenAI" href="#azure-openai"></Card>
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<Card title="Anthropic" href="#anthropic"></Card>
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<Card title="Cohere" href="#cohere"></Card>
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<Card title="GPT4All" href="#gpt4all"></Card>
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<Card title="JinaChat" href="#jinachat"></Card>
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<Card title="Hugging Face" href="#hugging-face"></Card>
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<Card title="Llama2" href="#llama2"></Card>
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<Card title="Vertex AI" href="#vertex-ai"></Card>
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</CardGroup>
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## OpenAI
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To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
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Once you have obtained the key, you can use it like this:
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```python
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import os
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from embedchain import App
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os.environ['OPENAI_API_KEY'] = 'xxx'
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app = App()
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app.add("https://en.wikipedia.org/wiki/OpenAI")
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app.query("What is OpenAI?")
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```
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If you are looking to configure the different parameters of the LLM, you can do so by loading the app using a [yaml config](https://github.com/embedchain/embedchain/blob/main/embedchain/yaml/chroma.yaml) file.
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<CodeGroup>
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```python main.py
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import os
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from embedchain import App
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os.environ['OPENAI_API_KEY'] = 'xxx'
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# load llm configuration from openai.yaml file
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app = App.from_config(yaml_path="openai.yaml")
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```
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```yaml openai.yaml
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llm:
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provider: openai
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model: 'gpt-3.5-turbo'
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config:
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temperature: 0.5
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max_tokens: 1000
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top_p: 1
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stream: false
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```
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</CodeGroup>
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## Azure OpenAI
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_Coming soon_
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## Anthropic
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To use anthropic's model, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys).
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<CodeGroup>
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```python main.py
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import os
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from embedchain import App
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os.environ["ANTHROPIC_API_KEY"] = "xxx"
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# load llm configuration from anthropic.yaml file
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app = App.from_config(yaml_path="anthropic.yaml")
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```
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```yaml anthropic.yaml
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llm:
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provider: anthropic
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model: 'claude-instant-1'
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config:
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temperature: 0.5
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max_tokens: 1000
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top_p: 1
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stream: false
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```
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</CodeGroup>
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<br />
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<Tip>
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You may also have to set the `OPENAI_API_KEY` if you use the OpenAI's embedding model.
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</Tip>
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## Cohere
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Set the `COHERE_API_KEY` as environment variable which you can find on their [Account settings page](https://dashboard.cohere.com/api-keys).
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Once you have the API key, you are all set to use it with Embedchain.
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<CodeGroup>
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```python main.py
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import os
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from embedchain import App
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os.environ["COHERE_API_KEY"] = "xxx"
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# load llm configuration from cohere.yaml file
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app = App.from_config(yaml_path="cohere.yaml")
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```
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```yaml cohere.yaml
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llm:
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provider: cohere
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model: large
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config:
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temperature: 0.5
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max_tokens: 1000
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top_p: 1
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```
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</CodeGroup>
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## GPT4ALL
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GPT4all is a free-to-use, locally running, privacy-aware chatbot. No GPU or internet required. You can use this with Embedchain using the following code:
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load llm configuration from gpt4all.yaml file
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app = App.from_config(yaml_path="gpt4all.yaml")
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```
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```yaml gpt4all.yaml
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llm:
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provider: gpt4all
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model: 'orca-mini-3b.ggmlv3.q4_0.bin'
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config:
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temperature: 0.5
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max_tokens: 1000
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top_p: 1
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stream: false
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embedder:
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provider: gpt4all
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config:
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model: 'all-MiniLM-L6-v2'
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```
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</CodeGroup>
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## JinaChat
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First, set `JINACHAT_API_KEY` in environment variable which you can obtain from [their platform](https://chat.jina.ai/api).
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Once you have the key, load the app using the config yaml file:
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<CodeGroup>
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```python main.py
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import os
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from embedchain import App
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os.environ["JINACHAT_API_KEY"] = "xxx"
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# load llm configuration from jina.yaml file
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app = App.from_config(yaml_path="jina.yaml")
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```
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```yaml jina.yaml
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llm:
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provider: jina
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config:
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temperature: 0.5
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max_tokens: 1000
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top_p: 1
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stream: false
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```
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</CodeGroup>
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## Hugging Face
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First, set `HUGGINGFACE_ACCESS_TOKEN` in environment variable which you can obtain from [their platform](https://huggingface.co/settings/tokens).
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Once you have the token, load the app using the config yaml file:
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<CodeGroup>
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```python main.py
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import os
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from embedchain import App
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os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "xxx"
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# load llm configuration from huggingface.yaml file
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app = App.from_config(yaml_path="huggingface.yaml")
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```
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```yaml huggingface.yaml
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llm:
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provider: huggingface
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model: 'google/flan-t5-xxl'
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config:
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temperature: 0.5
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max_tokens: 1000
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top_p: 0.5
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stream: false
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```
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</CodeGroup>
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## Llama2
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Llama2 is integrated through [Replicate](https://replicate.com/). Set `REPLICATE_API_TOKEN` in environment variable which you can obtain from [their platform](https://replicate.com/account/api-tokens).
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Once you have the token, load the app using the config yaml file:
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<CodeGroup>
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```python main.py
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import os
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from embedchain import App
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os.environ["REPLICATE_API_TOKEN"] = "xxx"
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# load llm configuration from llama2.yaml file
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app = App.from_config(yaml_path="llama2.yaml")
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```
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```yaml llama2.yaml
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llm:
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provider: llama2
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model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
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config:
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temperature: 0.5
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max_tokens: 1000
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top_p: 0.5
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stream: false
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```
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</CodeGroup>
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## Vertex AI
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Setup Google Cloud Platform application credentials by following the instruction on [GCP](https://cloud.google.com/docs/authentication/external/set-up-adc). Once setup is done, use the following code to create an app using VertexAI as provider:
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load llm configuration from vertexai.yaml file
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app = App.from_config(yaml_path="vertexai.yaml")
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```
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```yaml vertexai.yaml
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llm:
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provider: vertexai
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model: 'chat-bison'
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config:
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temperature: 0.5
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top_p: 0.5
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```
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</CodeGroup>
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<br/ >
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<Snippet file="missing-llm-tip.mdx" />
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142
docs/components/vector-databases.mdx
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142
docs/components/vector-databases.mdx
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---
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title: 🗄️ Vector databases
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---
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## Overview
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Utilizing a vector database alongside Embedchain is a seamless process. All you need to do is configure it within the YAML configuration file. We've provided examples for each supported database below:
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<CardGroup cols={4}>
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<Card title="ChromaDB" href="#chromadb"></Card>
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<Card title="Elasticsearch" href="#elasticsearch"></Card>
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<Card title="OpenSearch" href="#opensearch"></Card>
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<Card title="Zilliz" href="#zilliz"></Card>
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<Card title="LanceDB" href="#lancedb"></Card>
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<Card title="Pinecone" href="#pinecone"></Card>
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<Card title="Qdrant" href="#qdrant"></Card>
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<Card title="Weaviate" href="#weaviate"></Card>
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</CardGroup>
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## ChromaDB
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load chroma configuration from yaml file
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app = App.from_config(yaml_path="chroma-config-1.yaml")
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```
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```yaml chroma-config-1.yaml
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vectordb:
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provider: chroma
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config:
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collection_name: 'my-collection'
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dir: db
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allow_reset: true
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```
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```yaml chroma-config-2.yaml
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vectordb:
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provider: chroma
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config:
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collection_name: 'my-collection'
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host: localhost
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port: 5200
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allow_reset: true
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```
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</CodeGroup>
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## Elasticsearch
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load elasticsearch configuration from yaml file
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app = App.from_config(yaml_path="elasticsearch.yaml")
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```
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```yaml elasticsearch.yaml
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vectordb:
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provider: elasticsearch
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config:
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collection_name: 'es-index'
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es_url: http://localhost:9200
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allow_reset: true
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api_key: xxx
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```
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</CodeGroup>
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## OpenSearch
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load opensearch configuration from yaml file
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app = App.from_config(yaml_path="opensearch.yaml")
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```
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```yaml opensearch.yaml
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vectordb:
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provider: opensearch
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config:
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opensearch_url: 'https://localhost:9200'
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http_auth:
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- admin
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- admin
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vector_dimension: 1536
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collection_name: 'my-app'
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use_ssl: false
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verify_certs: false
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```
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</CodeGroup>
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## Zilliz
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<CodeGroup>
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```python main.py
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from embedchain import App
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# load zilliz configuration from yaml file
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app = App.from_config(yaml_path="zilliz.yaml")
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```
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```yaml zilliz.yaml
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vectordb:
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provider: zilliz
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config:
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collection_name: 'zilliz-app'
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uri: https://xxxx.api.gcp-region.zillizcloud.com
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token: xxx
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vector_dim: 1536
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metric_type: L2
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```
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</CodeGroup>
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## LanceDB
|
||||
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_Coming soon_
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|
||||
## Pinecone
|
||||
|
||||
_Coming soon_
|
||||
|
||||
## Qdrant
|
||||
|
||||
_Coming soon_
|
||||
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||||
## Weaviate
|
||||
|
||||
_Coming soon_
|
||||
|
||||
<Snippet file="missing-vector-db-tip.mdx" />
|
||||
Reference in New Issue
Block a user