Add rest-api example (mem0ai#889)

.gitignore

@@ -76,7 +76,6 @@ docs/_build/
 target/
 
 # Jupyter Notebook
-*.yaml
 
 # IPython
 profile_default/

README.md

@@ -26,6 +26,14 @@ Book a [1-on-1 Session](https://cal.com/taranjeetio/ec) with Taranjeet, the foun
 pip install --upgrade embedchain
 ```
 
+To run Embedchain as a REST API server use,
+
+```bash
+docker run -d --name embedchain -p 8000:8000 embedchain/app:rest-api-latest
+```
+
+Navigate to http://0.0.0.0:8000/docs to interact with the API.
+
 ## 🔍 Demo
 
 Try out embedchain in your browser:

configs/anthropic.yaml

@@ -1,7 +1,7 @@
 llm:
   provider: anthropic
-  model: 'claude-instant-1'
   config:
+    model: 'claude-instant-1'
     temperature: 0.5
     max_tokens: 1000
     top_p: 1

configs/azure_openai.yaml

@@ -4,8 +4,8 @@ app:
 
 llm:
   provider: azure_openai
-  model: gpt-35-turbo
   config:
+    model: gpt-35-turbo
     deployment_name: your_llm_deployment_name
     temperature: 0.5
     max_tokens: 1000

configs/chroma.yaml

@@ -5,8 +5,8 @@ app:
 
 llm:
   provider: openai
-  model: 'gpt-3.5-turbo'
   config:
+    model: 'gpt-3.5-turbo'
     temperature: 0.5
     max_tokens: 1000
     top_p: 1

configs/cohere.yaml

@@ -1,7 +1,7 @@
 llm:
   provider: cohere
-  model: large
   config:
+    model: large
     temperature: 0.5
     max_tokens: 1000
     top_p: 1

configs/full-stack.yaml

@@ -4,8 +4,8 @@ app:
 
 llm:
   provider: openai
-  model: 'gpt-3.5-turbo'
   config:
+    model: 'gpt-3.5-turbo'
     temperature: 0.5
     max_tokens: 1000
     top_p: 1

configs/huggingface.yaml

@@ -1,7 +1,7 @@
 llm:
   provider: huggingface
-  model: 'google/flan-t5-xxl'
   config:
+    model: 'google/flan-t5-xxl'
     temperature: 0.5
     max_tokens: 1000
     top_p: 0.5

configs/llama2.yaml

@@ -1,7 +1,7 @@
 llm:
   provider: llama2
-  model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
   config:
+    model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
     temperature: 0.5
     max_tokens: 1000
     top_p: 0.5

configs/opensearch.yaml

@@ -7,8 +7,8 @@ app:
 
 llm:
   provider: openai
-  model: 'gpt-3.5-turbo'
   config:
+    model: 'gpt-3.5-turbo'
     temperature: 0.5
     max_tokens: 1000
     top_p: 1

configs/vertexai.yaml

@@ -1,6 +1,6 @@
 llm:
   provider: vertexai
-  model: 'chat-bison'
   config:
+    model: 'chat-bison'
     temperature: 0.5
     top_p: 0.5

docs/api-reference/add-datasource-to-an-app.mdx

@@ -0,0 +1,3 @@
+---
+openapi: post /{app_id}/add
+---

docs/api-reference/chat-with-an-app.mdx

@@ -0,0 +1,3 @@
+---
+openapi: post /{app_id}/chat
+---

docs/api-reference/check-status.mdx

@@ -0,0 +1,3 @@
+---
+openapi: get /ping
+---

docs/api-reference/create-app.mdx

@@ -0,0 +1,3 @@
+---
+openapi: post /create
+---

docs/api-reference/delete-app.mdx

@@ -0,0 +1,3 @@
+---
+openapi: delete /{app_id}/delete
+---

docs/api-reference/deploy-app.mdx

@@ -0,0 +1,3 @@
+---
+openapi: post /{app_id}/deploy
+---

docs/api-reference/get-all-apps.mdx

@@ -0,0 +1,3 @@
+---
+openapi: get /apps
+---

docs/api-reference/get-datasources-associated-with-app-id.mdx

@@ -0,0 +1,3 @@
+---
+openapi: get /{app_id}/data
+---

docs/api-reference/getting-started.mdx

@@ -0,0 +1,102 @@
+---
+title: "🌍 Getting Started"
+---
+
+## Quick Start
+
+To run Embedchain as a REST API server use,
+
+```bash
+docker run -d --name embedchain -p 8000:8000 embedchain/app:rest-api-latest
+```
+
+Open up your browser and navigate to http://0.0.0.0:8000/docs to interact with the API. There is a full-fledged Swagger docs playground with all the information
+about the API endpoints.
+
+![Swagger Docs Screenshot](https://github.com/embedchain/embedchain/assets/73601258/299d81e5-a0df-407c-afc2-6fa2c4286844)
+
+## Creating your first App
+
+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 perform operations. You can also specify your own config by uploading a config YAML file.
+
+For example, create a `config.yaml` file (adjust according to your requirements):
+
+```yaml
+app:
+  config:
+    id: "default-app"
+
+llm:
+  provider: openai
+  config:
+    model: "gpt-3.5-turbo"
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+    template: |
+      Use the following pieces of context to answer the query at the end.
+      If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+      $context
+
+      Query: $query
+
+      Helpful Answer:
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: "rest-api-app"
+    dir: db
+    allow_reset: true
+
+embedder:
+  provider: openai
+  config:
+    model: "text-embedding-ada-002"
+```
+
+To learn more about custom configurations, check out the [Custom configurations](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).
+
+Now, you can upload this config file in the request body.
+
+**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.
+
+| Keys                       | Providers                      |
+| -------------------------- | ------------------------------ |
+| `OPENAI_API_KEY `          | OpenAI, Azure OpenAI, Jina etc |
+| `OPENAI_API_TYPE`          | Azure OpenAI                   |
+| `OPENAI_API_BASE`          | Azure OpenAI                   |
+| `OPENAI_API_VERSION`       | Azure OpenAI                   |
+| `COHERE_API_KEY`           | Cohere                         |
+| `ANTHROPIC_API_KEY`        | Anthropic                      |
+| `JINACHAT_API_KEY`         | Jina                           |
+| `HUGGINGFACE_ACCESS_TOKEN` | Huggingface                    |
+| `REPLICATE_API_TOKEN`      | LLAMA2                         |
+
+To provide them, you can simply run the docker command with the `-e` flag.
+
+For example,
+
+```bash
+docker run -d --name embedchain -p 8000:8000 -e OPENAI_API_KEY=YOUR_API_KEY embedchain/app:rest-api-latest
+```
+
+Cool! This will create a new Embedchain App with the given `app_id`.
+
+## Deploying your App to Embedchain Platform
+
+This feature is very powerful as it allows the creation of a public API endpoint for your app, enabling queries from anywhere. This creates a _pipeline_
+for your app that can sync the data time to time and provide you with the best results.
+
+![My first Pipeline](https://github.com/embedchain/embedchain/assets/73601258/266a66e0-330e-4bb9-aa97-687d826cd3fa)
+
+To utilize this functionality, visit app.embedchain.ai and create an account. Subsequently, generate a new [API KEY](https://app.embedchain.ai/settings/keys/).
+
+![Create Embedchain API Key](https://github.com/embedchain/embedchain/assets/73601258/791e92cc-4a6f-4740-94c2-f11cce13e93b)
+
+Using this API key, you can deploy your app to the platform.

docs/api-reference/query-an-app.mdx

@@ -0,0 +1,3 @@
+---
+openapi: post /{app_id}/query
+---