[Pipelines] Improvements in pipelines feature (#861)

docs/mint.json

@@ -71,6 +71,10 @@
       "group": "Examples",
       "pages": ["examples/full_stack", "examples/api_server", "examples/discord_bot", "examples/slack_bot", "examples/telegram_bot", "examples/whatsapp_bot", "examples/poe_bot"]
     },
+    {
+      "group": "Pipelines",
+      "pages": ["pipelines/quickstart"]
+    },
     {
       "group": "Community",
       "pages": [

docs/pipelines/quickstart.mdx

@@ -0,0 +1,44 @@
+---
+title: '🚀 Pipelines'
+description: '💡 Start building LLM powered data pipelines in 1 minute'
+---
+
+Embedchain lets you build data pipelines on your own data sources and deploy it in production in less than a minute. It can load, index, retrieve, and sync any unstructured data.
+
+Install embedchain python package:
+
+```bash
+pip install embedchain
+```
+
+Creating a pipeline involves 3 steps:
+
+<Steps>
+  <Step title="⚙️ Import pipeline instance">
+```python
+from embedchain import Pipeline
+p = Pipeline(name="Elon Musk")
+```
+  </Step>
+
+  <Step title="🗃️ Add data sources">
+```python
+# Add different data sources
+p.add("https://en.wikipedia.org/wiki/Elon_Musk")
+p.add("https://www.forbes.com/profile/elon-musk")
+# You can also add local data sources such as pdf, csv files etc.
+# p.add("/path/to/file.pdf")
+```
+  </Step>
+  <Step title="💬 Deploy your pipeline to Embedchain platform">
+```python
+p.deploy()
+```
+  </Step>
+</Steps>
+
+That's it. Now, head to the [Embedchain platform](https://app.embedchain.ai) and your pipeline is available there. Make sure to set the `OPENAI_API_KEY` 🔑 environment variable in the code.
+
+After you deploy your pipeline to Embedchain platform, you can still add more data sources and update the pipeline multiple times.
+
+Here is a Google Colab notebook for you to get started: [![Open in Colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/drive/1YVXaBO4yqlHZY4ho67GCJ6aD4CHNiScD?usp=sharing)

embedchain/pipeline.py

@@ -34,6 +34,8 @@ class Pipeline(EmbedChain):
 
     def __init__(
         self,
+        id: str = None,
+        name: str = None,
         config: PipelineConfig = None,
         db: BaseVectorDB = None,
         embedding_model: BaseEmbedder = None,
@@ -61,6 +63,15 @@ class Pipeline(EmbedChain):
         :type auto_deploy: bool, optional
         :raises Exception: If an error occurs while creating the pipeline
         """
+        if id and yaml_path:
+            raise Exception("Cannot provide both id and config. Please provide only one of them.")
+
+        if id and name:
+            raise Exception("Cannot provide both id and name. Please provide only one of them.")
+
+        if name and config:
+            raise Exception("Cannot provide both name and config. Please provide only one of them.")
+
         logging.basicConfig(level=log_level, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
         self.logger = logging.getLogger(__name__)
 
@@ -71,16 +82,28 @@ class Pipeline(EmbedChain):
         self.client = None
         # pipeline_id from the backend
         self.id = None
-        if yaml_path:
-            with open(yaml_path, "r") as file:
-                config_data = yaml.safe_load(file)
-                self.yaml_config = config_data
 
         self.config = config or PipelineConfig()
         self.name = self.config.name
 
         self.config.id = self.local_id = str(uuid.uuid4()) if self.config.id is None else self.config.id
 
+        if yaml_path:
+            with open(yaml_path, "r") as file:
+                config_data = yaml.safe_load(file)
+                self.yaml_config = config_data
+
+        if id is not None:
+            # Init client first since user is trying to fetch the pipeline
+            # details from the platform
+            self._init_client()
+            pipeline_details = self._get_pipeline(id)
+            self.config.id = self.local_id = pipeline_details["metadata"]["local_id"]
+            self.id = id
+
+        if name is not None:
+            self.name = name
+
         self.embedding_model = embedding_model or OpenAIEmbedder()
         self.db = db or ChromaDB()
         self.llm = llm or OpenAILlm()
@@ -134,6 +157,24 @@ class Pipeline(EmbedChain):
             )
             self.client = Client(api_key=api_key)
 
+    def _get_pipeline(self, id):
+        """
+        Get existing pipeline
+        """
+        print("🛠️ Fetching pipeline details from the platform...")
+        url = f"{self.client.host}/api/v1/pipelines/{id}/cli/"
+        r = requests.get(
+            url,
+            headers={"Authorization": f"Token {self.client.api_key}"},
+        )
+        if r.status_code == 404:
+            raise Exception(f"❌ Pipeline with id {id} not found!")
+
+        print(
+            f"🎉 Pipeline loaded successfully! Pipeline url: https://app.embedchain.ai/pipelines/{r.json()['id']}\n"  # noqa: E501
+        )
+        return r.json()
+
     def _create_pipeline(self):
         """
         Create a pipeline on the platform.
@@ -154,6 +195,11 @@ class Pipeline(EmbedChain):
         if r.status_code not in [200, 201]:
             raise Exception(f"❌ Error occurred while creating pipeline. API response: {r.text}")
 
+        if r.status_code == 200:
+            print(
+                f"🎉🎉🎉 Existing pipeline found! View your pipeline: https://app.embedchain.ai/pipelines/{r.json()['id']}\n"  # noqa: E501
+            )  # noqa: E501
+        elif r.status_code == 201:
             print(
                 f"🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/{r.json()['id']}\n"  # noqa: E501
             )
@@ -257,7 +303,7 @@ class Pipeline(EmbedChain):
         self.id = pipeline_data["id"]
 
         results = self.cursor.execute(
-            "SELECT * FROM data_sources WHERE pipeline_id = ? AND is_uploaded = 0", (self.local_id,)
+            "SELECT * FROM data_sources WHERE pipeline_id = ? AND is_uploaded = 0", (self.local_id,)  # noqa:E501
         ).fetchall()
 
         if len(results) > 0:

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "embedchain"
-version = "0.0.78"
+version = "0.0.79"
 description = "Data platform for LLMs - Load, index, retrieve and sync any unstructured data"
 authors = ["Taranjeet Singh, Deshraj Yadav"]
 license = "Apache License"