Improve getting started page by adding steps (#904)
This commit is contained in:
Sidharth Mohanty
@@ -6,7 +6,7 @@ openapi: post /create
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```bash Request
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curl --request POST \
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--url http://localhost:8080/create \
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--url http://localhost:8080/create?app_id=app1 \
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-F "config=@/path/to/config.yaml"
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```
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@@ -15,7 +15,81 @@ curl --request POST \
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<ResponseExample>
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```json Response
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{ "response": "App created successfully. App ID: {app_id}" }
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{ "response": "App created successfully. App ID: app1" }
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```
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</ResponseExample>
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By default we will use the opensource **gpt4all** model to get started. You can also specify your own config by uploading a config YAML file.
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For example, create a `config.yaml` file (adjust according to your requirements):
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```yaml
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app:
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config:
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id: "default-app"
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llm:
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provider: openai
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config:
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model: "gpt-3.5-turbo"
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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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template: |
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Use the following pieces of context to answer the query at the end.
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If you don't know the answer, just say that you don't know, don't try to make up an answer.
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$context
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Query: $query
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Helpful Answer:
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vectordb:
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provider: chroma
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config:
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collection_name: "rest-api-app"
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dir: db
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allow_reset: true
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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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To learn more about custom configurations, check out the [custom configurations docs](https://docs.embedchain.ai/advanced/configuration). To explore more examples of config yamls for embedchain, visit [embedchain/configs](https://github.com/embedchain/embedchain/tree/main/configs).
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Now, you can upload this config file in the request body.
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For example,
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```bash Request
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curl --request POST \
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--url http://localhost:8080/create?app_id=my-app \
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-F "config=@/path/to/config.yaml"
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```
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**Note:** To use custom models, an **API key** might be required. Refer to the table below to determine the necessary API key for your provider.
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| Keys | Providers |
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| -------------------------- | ------------------------------ |
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| `OPENAI_API_KEY ` | OpenAI, Azure OpenAI, Jina etc |
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| `OPENAI_API_TYPE` | Azure OpenAI |
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| `OPENAI_API_BASE` | Azure OpenAI |
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| `OPENAI_API_VERSION` | Azure OpenAI |
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| `COHERE_API_KEY` | Cohere |
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| `ANTHROPIC_API_KEY` | Anthropic |
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| `JINACHAT_API_KEY` | Jina |
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| `HUGGINGFACE_ACCESS_TOKEN` | Huggingface |
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| `REPLICATE_API_TOKEN` | LLAMA2 |
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To add env variables, you can simply run the docker command with the `-e` flag.
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For example,
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```bash
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docker run --name embedchain -p 8080:8080 -e OPENAI_API_KEY=<YOUR_OPENAI_API_KEY> embedchain/rest-api:latest
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```
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@@ -14,73 +14,280 @@ Navigate to [http://localhost:8080/docs](http://localhost:8080/docs) to interact
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## Creating your first App
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## ⚡ Steps to get started
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App requires an `app_id` to be created. The `app_id` is a unique identifier for your app. By default we will use the opensource **gpt4all** model to get started. You can also specify your own config by uploading a config YAML file.
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<Steps>
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<Step title="⚙️ Create an app">
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<Tabs>
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<Tab title="cURL">
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```bash
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curl --request POST "http://localhost:8080/create?app_id=my-app" \
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-H "accept: application/json"
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```
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</Tab>
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<Tab title="python">
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```python
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import requests
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For example, create a `config.yaml` file (adjust according to your requirements):
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url = "http://localhost:8080/create?app_id=my-app"
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```yaml
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app:
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config:
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id: "default-app"
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payload={}
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llm:
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provider: openai
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config:
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model: "gpt-3.5-turbo"
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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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template: |
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Use the following pieces of context to answer the query at the end.
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If you don't know the answer, just say that you don't know, don't try to make up an answer.
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response = requests.request("POST", url, data=payload)
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$context
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print(response)
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```
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</Tab>
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<Tab title="javascript">
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```javascript
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const data = fetch("http://localhost:8080/create?app_id=my-app", {
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method: "POST",
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}).then((res) => res.json());
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Query: $query
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console.log(data);
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```
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</Tab>
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<Tab title="go">
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```go
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package main
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Helpful Answer:
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import (
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"fmt"
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"net/http"
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"io/ioutil"
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)
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vectordb:
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provider: chroma
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config:
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collection_name: "rest-api-app"
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dir: db
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allow_reset: true
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func main() {
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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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url := "http://localhost:8080/create?app_id=my-app"
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To learn more about custom configurations, check out the [custom configurations docs](https://docs.embedchain.ai/advanced/configuration). To explore more examples of config yamls for embedchain, visit [embedchain/configs](https://github.com/embedchain/embedchain/tree/main/configs).
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payload := strings.NewReader("")
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Now, you can upload this config file in the request body.
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req, _ := http.NewRequest("POST", url, payload)
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**Note:** To use custom models, an **API key** might be required. Refer to the table below to determine the necessary API key for your provider.
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req.Header.Add("Content-Type", "application/json")
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| Keys | Providers |
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| -------------------------- | ------------------------------ |
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| `OPENAI_API_KEY ` | OpenAI, Azure OpenAI, Jina etc |
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| `OPENAI_API_TYPE` | Azure OpenAI |
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| `OPENAI_API_BASE` | Azure OpenAI |
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| `OPENAI_API_VERSION` | Azure OpenAI |
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| `COHERE_API_KEY` | Cohere |
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| `ANTHROPIC_API_KEY` | Anthropic |
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| `JINACHAT_API_KEY` | Jina |
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| `HUGGINGFACE_ACCESS_TOKEN` | Huggingface |
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| `REPLICATE_API_TOKEN` | LLAMA2 |
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res, _ := http.DefaultClient.Do(req)
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To add env variables, you can simply run the docker command with the `-e` flag.
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defer res.Body.Close()
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body, _ := ioutil.ReadAll(res.Body)
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For example,
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fmt.Println(res)
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fmt.Println(string(body))
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```bash
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docker run --name embedchain -p 8080:8080 -e OPENAI_API_KEY=<YOUR_OPENAI_API_KEY> embedchain/rest-api:latest
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```
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}
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```
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</Tab>
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</Tabs>
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</Step>
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<Step title="🗃️ Add data sources">
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<Tabs>
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<Tab title="cURL">
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```bash
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curl --request POST \
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--url http://localhost:8080/my-app/add \
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-d "source=https://www.forbes.com/profile/elon-musk" \
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-d "data_type=web_page"
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```
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</Tab>
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<Tab title="python">
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```python
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import requests
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url = "http://localhost:8080/my-app/add"
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payload = "source=https://www.forbes.com/profile/elon-musk&data_type=web_page"
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headers = {}
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response = requests.request("POST", url, headers=headers, data=payload)
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print(response)
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```
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</Tab>
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<Tab title="javascript">
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```javascript
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const data = fetch("http://localhost:8080/my-app/add", {
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method: "POST",
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body: "source=https://www.forbes.com/profile/elon-musk&data_type=web_page",
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}).then((res) => res.json());
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console.log(data);
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```
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</Tab>
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<Tab title="go">
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```go
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package main
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import (
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"fmt"
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"strings"
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"net/http"
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"io/ioutil"
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)
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func main() {
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url := "http://localhost:8080/my-app/add"
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payload := strings.NewReader("source=https://www.forbes.com/profile/elon-musk&data_type=web_page")
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req, _ := http.NewRequest("POST", url, payload)
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req.Header.Add("Content-Type", "application/x-www-form-urlencoded")
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res, _ := http.DefaultClient.Do(req)
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defer res.Body.Close()
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body, _ := ioutil.ReadAll(res.Body)
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fmt.Println(res)
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fmt.Println(string(body))
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}
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```
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</Tab>
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</Tabs>
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</Step>
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<Step title="💬 Query on your data">
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<Tabs>
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<Tab title="cURL">
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```bash
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curl --request POST \
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--url http://localhost:8080/my-app/query \
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-d "query=Who is Elon Musk?"
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```
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</Tab>
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<Tab title="python">
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```python
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import requests
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url = "http://localhost:8080/my-app/query"
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payload = "query=Who is Elon Musk?"
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headers = {}
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response = requests.request("POST", url, headers=headers, data=payload)
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print(response)
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```
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</Tab>
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<Tab title="javascript">
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```javascript
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const data = fetch("http://localhost:8080/my-app/query", {
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method: "POST",
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body: "query=Who is Elon Musk?",
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}).then((res) => res.json());
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console.log(data);
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```
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</Tab>
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<Tab title="go">
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```go
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package main
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import (
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"fmt"
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"strings"
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"net/http"
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"io/ioutil"
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)
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func main() {
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url := "http://localhost:8080/my-app/query"
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payload := strings.NewReader("query=Who is Elon Musk?")
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req, _ := http.NewRequest("POST", url, payload)
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req.Header.Add("Content-Type", "application/x-www-form-urlencoded")
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res, _ := http.DefaultClient.Do(req)
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defer res.Body.Close()
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body, _ := ioutil.ReadAll(res.Body)
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fmt.Println(res)
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fmt.Println(string(body))
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}
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```
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</Tab>
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</Tabs>
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</Step>
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<Step title="🚀 (Optional) Deploy your app to Embedchain Platform">
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<Tabs>
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<Tab title="cURL">
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```bash
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curl --request POST \
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--url http://localhost:8080/my-app/deploy \
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-d "api_key=ec-xxxx"
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```
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</Tab>
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<Tab title="python">
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```python
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import requests
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url = "http://localhost:8080/my-app/deploy"
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payload = "api_key=ec-xxxx"
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response = requests.request("POST", url, data=payload)
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print(response)
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```
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</Tab>
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<Tab title="javascript">
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```javascript
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const data = fetch("http://localhost:8080/my-app/deploy", {
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method: "POST",
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body: "api_key=ec-xxxx",
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}).then((res) => res.json());
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console.log(data);
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```
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</Tab>
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<Tab title="go">
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```go
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package main
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import (
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"fmt"
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"strings"
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"net/http"
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"io/ioutil"
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)
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func main() {
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url := "http://localhost:8080/my-app/deploy"
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payload := strings.NewReader("api_key=ec-xxxx")
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req, _ := http.NewRequest("POST", url, payload)
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req.Header.Add("Content-Type", "application/x-www-form-urlencoded")
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res, _ := http.DefaultClient.Do(req)
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defer res.Body.Close()
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body, _ := ioutil.ReadAll(res.Body)
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fmt.Println(res)
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fmt.Println(string(body))
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}
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```
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</Tab>
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</Tabs>
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</Step>
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</Steps>
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And you're ready! 🎉
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If you run into issues, please feel free to contact us using below links:
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Reference in New Issue
Block a user