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[Feature]: Add posthog anonymous telemetry and update docs (#867)

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This commit is contained in:
Deshraj Yadav
2023-10-29 01:20:21 -07:00
committed by GitHub
parent 35c2b83015
commit 81336668b3
34 changed files with 242 additions and 195 deletions
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10
docs/components/embedding-models.mdx
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@@ -24,7 +24,7 @@ Once you have obtained the key, you can use it like this:
```python main.py
import os
from embedchain import App
from embedchain import Pipeline as App
os.environ['OPENAI_API_KEY'] = 'xxx'
@@ -52,7 +52,7 @@ To use Azure OpenAI embedding model, you have to set some of the azure openai re
```python main.py
import os
from embedchain import App
from embedchain import Pipeline as App
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://xxx.openai.azure.com/"
@@ -90,7 +90,7 @@ GPT4All supports generating high quality embeddings of arbitrary length document
<CodeGroup>
```python main.py
from embedchain import App
from embedchain import Pipeline as App
# load embedding model configuration from config.yaml file
app = App.from_config(yaml_path="config.yaml")
@@ -119,7 +119,7 @@ Hugging Face supports generating embeddings of arbitrary length documents of tex
<CodeGroup>
```python main.py
from embedchain import App
from embedchain import Pipeline as App
# load embedding model configuration from config.yaml file
app = App.from_config(yaml_path="config.yaml")
@@ -150,7 +150,7 @@ Embedchain supports Google's VertexAI embeddings model through a simple interfac
<CodeGroup>
```python main.py
from embedchain import App
from embedchain import Pipeline as App
# load embedding model configuration from config.yaml file
app = App.from_config(yaml_path="config.yaml")

20
docs/components/llms.mdx
View File

@@ -26,7 +26,7 @@ Once you have obtained the key, you can use it like this:
```python
import os
from embedchain import App
from embedchain import Pipeline as App
os.environ['OPENAI_API_KEY'] = 'xxx'
@@ -41,7 +41,7 @@ If you are looking to configure the different parameters of the LLM, you can do
```python main.py
import os
from embedchain import App
from embedchain import Pipeline as App
os.environ['OPENAI_API_KEY'] = 'xxx'
@@ -71,7 +71,7 @@ To use Azure OpenAI model, you have to set some of the azure openai related envi
```python main.py
import os
from embedchain import App
from embedchain import Pipeline as App
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://xxx.openai.azure.com/"
@@ -110,7 +110,7 @@ To use anthropic's model, please set the `ANTHROPIC_API_KEY` which you find on t
```python main.py
import os
from embedchain import App
from embedchain import Pipeline as App
os.environ["ANTHROPIC_API_KEY"] = "xxx"
@@ -147,7 +147,7 @@ Once you have the API key, you are all set to use it with Embedchain.
```python main.py
import os
from embedchain import App
from embedchain import Pipeline as App
os.environ["COHERE_API_KEY"] = "xxx"
@@ -180,7 +180,7 @@ GPT4all is a free-to-use, locally running, privacy-aware chatbot. No GPU or inte
<CodeGroup>
```python main.py
from embedchain import App
from embedchain import Pipeline as App
# load llm configuration from config.yaml file
app = App.from_config(yaml_path="config.yaml")
@@ -212,7 +212,7 @@ Once you have the key, load the app using the config yaml file:
```python main.py
import os
from embedchain import App
from embedchain import Pipeline as App
os.environ["JINACHAT_API_KEY"] = "xxx"
# load llm configuration from config.yaml file
@@ -248,7 +248,7 @@ Once you have the token, load the app using the config yaml file:
```python main.py
import os
from embedchain import App
from embedchain import Pipeline as App
os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "xxx"
@@ -278,7 +278,7 @@ Once you have the token, load the app using the config yaml file:
```python main.py
import os
from embedchain import App
from embedchain import Pipeline as App
os.environ["REPLICATE_API_TOKEN"] = "xxx"
@@ -305,7 +305,7 @@ Setup Google Cloud Platform application credentials by following the instruction
<CodeGroup>
```python main.py
from embedchain import App
from embedchain import Pipeline as App
# load llm configuration from config.yaml file
app = App.from_config(yaml_path="config.yaml")

14
docs/components/vector-databases.mdx
View File

@@ -22,7 +22,7 @@ Utilizing a vector database alongside Embedchain is a seamless process. All you
<CodeGroup>
```python main.py
from embedchain import App
from embedchain import Pipeline as App
# load chroma configuration from yaml file
app = App.from_config(yaml_path="config1.yaml")
@@ -61,7 +61,7 @@ pip install --upgrade 'embedchain[elasticsearch]'
<CodeGroup>
```python main.py
from embedchain import App
from embedchain import Pipeline as App
# load elasticsearch configuration from yaml file
app = App.from_config(yaml_path="config.yaml")
@@ -89,7 +89,7 @@ pip install --upgrade 'embedchain[opensearch]'
<CodeGroup>
```python main.py
from embedchain import App
from embedchain import Pipeline as App
# load opensearch configuration from yaml file
app = App.from_config(yaml_path="config.yaml")
@@ -125,7 +125,7 @@ Set the Zilliz environment variables `ZILLIZ_CLOUD_URI` and `ZILLIZ_CLOUD_TOKEN`
```python main.py
import os
from embedchain import App
from embedchain import Pipeline as App
os.environ['ZILLIZ_CLOUD_URI'] = 'https://xxx.zillizcloud.com'
os.environ['ZILLIZ_CLOUD_TOKEN'] = 'xxx'
@@ -164,7 +164,7 @@ In order to use Pinecone as vector database, set the environment variables `PINE
<CodeGroup>
```python main.py
from embedchain import App
from embedchain import Pipeline as App
# load pinecone configuration from yaml file
app = App.from_config(yaml_path="config.yaml")
@@ -187,7 +187,7 @@ In order to use Qdrant as a vector database, set the environment variables `QDRA
<CodeGroup>
```python main.py
from embedchain import App
from embedchain import Pipeline as App
# load qdrant configuration from yaml file
app = App.from_config(yaml_path="config.yaml")
@@ -207,7 +207,7 @@ In order to use Weaviate as a vector database, set the environment variables `WE
<CodeGroup>
```python main.py
from embedchain import App
from embedchain import Pipeline as App
# load weaviate configuration from yaml file
app = App.from_config(yaml_path="config.yaml")