bump version to 0.0.48 (#503)

Co-authored-by: Taranjeet Singh <reachtotj@gmail.com>

.github/ISSUE_TEMPLATE/config.yml

@@ -1,5 +1,8 @@
 blank_issues_enabled: true
 contact_links:
+  - name: 1-on-1 Session
+    url: https://cal.com/taranjeetio/ec
+    about: Speak directly with Taranjeet, the founder, to discuss issues, share feedback, or explore improvements for Embedchain
   - name: Discord
     url: https://discord.gg/6PzXDgEjG5
-    about: General community discussions
+    about: General community discussions

.github/workflows/cd.yml

@@ -19,12 +19,16 @@ jobs:
         with:
           python-version: '3.11'
 
-      - name: Install pep517
+      - name: Install Poetry
         run: |
-          python -m pip install pep517 --user
-
+          curl -sSL https://install.python-poetry.org | python3 -
+          echo "$HOME/.local/bin" >> $GITHUB_PATH
+  
+      - name: Install dependencies
+        run: poetry install
+  
       - name: Build a binary wheel and a source tarball
-        run: python -m pep517.build .
+        run: poetry build
 
       - name: Publish distribution 📦 to Test PyPI
         uses: pypa/gh-action-pypi-publish@release/v1

README.md

@@ -1,42 +1,23 @@
 # embedchain
 
 [![PyPI](https://img.shields.io/pypi/v/embedchain)](https://pypi.org/project/embedchain/)
-[![Discord](https://dcbadge.vercel.app/api/server/6PzXDgEjG5?style=flat)](https://discord.gg/6PzXDgEjG5)
+[![Discord](https://dcbadge.vercel.app/api/server/6PzXDgEjG5?style=flat)](https://discord.gg/CUU9FPhRNt)
 [![Twitter](https://img.shields.io/twitter/follow/embedchain)](https://twitter.com/embedchain)
 [![Substack](https://img.shields.io/badge/Substack-%23006f5c.svg?logo=substack)](https://embedchain.substack.com/)
 [![Open in Colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/drive/138lMWhENGeEu7Q1-6lNbNTHGLZXBBz_B?usp=sharing)
 
 Embedchain is a framework to easily create LLM powered bots over any dataset. If you want a javascript version, check out [embedchain-js](https://github.com/embedchain/embedchainjs)
 
+## 🤝 Schedule a 1-on-1 Session
+
+Book a [1-on-1 Session](https://cal.com/taranjeetio/ec) with Taranjeet, the founder, to discuss any issues, provide feedback, or explore how we can improve Embedchain for you.
+
 ## 🔧 Quick install
 
 ```bash
 pip install embedchain
 ```
 
-## 🔥 Latest
-
-- **[2023/07/19]** Released support for 🦙 `llama2` model. Start creating your `llama2` based bots like this:
-
-  ```python
-  import os
-
-  from embedchain import Llama2App
-
-  os.environ['REPLICATE_API_TOKEN'] = "REPLICATE API TOKEN"
-
-  zuck_bot = Llama2App()
-
-  # Embed your data
-  zuck_bot.add("https://www.youtube.com/watch?v=Ff4fRgnuFgQ")
-  zuck_bot.add("https://en.wikipedia.org/wiki/Mark_Zuckerberg")
-
-  # Nice, your bot is ready now. Start asking questions to your bot.
-  zuck_bot.query("Who is Mark Zuckerberg?")
-  # Answer: Mark Zuckerberg is an American internet entrepreneur and business magnate. He is the co-founder and CEO of Facebook.
-  ```
-
-
 ## 🔍 Demo
 
 Try out embedchain in your browser:
@@ -51,6 +32,16 @@ The documentation for embedchain can be found at [docs.embedchain.ai](https://do
 
 Embedchain empowers you to create chatbot models similar to ChatGPT, using your own evolving dataset.
 
+### Data Types Supported
+
+* Youtube video
+* PDF file
+* Web page
+* Sitemap
+* Doc file
+* Code documentation website loader
+* Notion
+
 ### Queries
 
 For example, you can use Embedchain to create an Elon Musk bot using the following code:

docs/examples/poe_bot.mdx

@@ -0,0 +1,48 @@
+---
+title: '🔮 Poe Bot'
+---
+
+### 🚀 Getting started
+
+1. Install embedchain python package:
+
+```bash
+pip install embedchain[poe]
+```
+
+2. Create a free account on [Poe](https://www.poe.com?utm_source=embedchain).
+3. Click "Create Bot" button on top left
+4. Give it a handle and an optional description.
+5. Select `Use API`.
+6. Under `API URL` enter your server or ngrok address. You can use your machine's public IP or DNS. Otherwise, employ a proxy server like [ngrok](https://ngrok.com/) to make your local bot accessible.
+7. Copy your api key and paste it in `.env` as `POE_API_KEY`.
+8. Start the bot.
+
+```bash
+python -m embedchain.bots.poe
+```
+
+If you want to run the bot on another port, you can pass `--port option` like
+
+```bash
+python -m embedchain.bots.poe --port 5000
+```
+
+9. Click `Run check` to make sure your machine can be reached.
+10. Make sure your bot is private if that's what you want.
+11. Click `Create bot` at the bottom to finally create the bot
+12. Now you bot is created.
+
+### 💬 How to use
+
+- To include data sources, use this command:
+```text
+/add <url_or_text>
+```
+
+- You can refer the [Supported Data formats](https://docs.embedchain.ai/advanced/data_types) section to refer the supported data types in embedchain.
+
+- To ask the bot questions, just type your query:
+```text
+<your-question-here>
+```

docs/mint.json

@@ -36,7 +36,7 @@
     },
     {
       "group": "Examples",
-      "pages": ["examples/full_stack", "examples/api_server", "examples/discord_bot", "examples/slack_bot", "examples/telegram_bot", "examples/whatsapp_bot"]
+      "pages": ["examples/full_stack", "examples/api_server", "examples/discord_bot", "examples/slack_bot", "examples/telegram_bot", "examples/whatsapp_bot", "examples/poe_bot"]
     },
     {
       "group": "Contribution Guidelines",
@@ -47,7 +47,8 @@
   "footerSocials": {
     "twitter": "https://twitter.com/embedchain",
     "github": "https://github.com/embedchain/embedchain",
-    "linkedin": "https://www.linkedin.com/company/embedchain"
+    "linkedin": "https://www.linkedin.com/company/embedchain",
+    "website": "https://embedchain.ai"
   },
   "backgroundImage": "/background.png",
   "isWhiteLabeled": true

embedchain/bots/poe.py

@@ -0,0 +1,79 @@
+import argparse
+import logging
+import os
+from typing import List, Optional
+
+from fastapi_poe import PoeBot, run
+
+from embedchain.config import QueryConfig
+
+from .base import BaseBot
+
+
+class EcPoeBot(BaseBot, PoeBot):
+    def __init__(self):
+        self.history_length = 5
+        super().__init__()
+
+    async def get_response(self, query):
+        last_message = query.query[-1].content
+        try:
+            history = (
+                [f"{m.role}: {m.content}" for m in query.query[-(self.history_length + 1) : -1]]
+                if len(query.query) > 0
+                else None
+            )
+        except Exception as e:
+            logging.error(f"Error when processing the chat history. Message is being sent without history. Error: {e}")
+        logging.warning(history)
+        answer = self.handle_message(last_message, history)
+        yield self.text_event(answer)
+
+    def handle_message(self, message, history: Optional[List[str]] = None):
+        if message.startswith("/add "):
+            response = self.add_data(message)
+        else:
+            response = self.ask_bot(message, history)
+        return response
+
+    def add_data(self, message):
+        data = message.split(" ")[-1]
+        try:
+            self.add(data)
+            response = f"Added data from: {data}"
+        except Exception:
+            logging.exception(f"Failed to add data {data}.")
+            response = "Some error occurred while adding data."
+        return response
+
+    def ask_bot(self, message, history: List[str]):
+        try:
+            config = QueryConfig(history=history)
+            response = self.query(message, config)
+        except Exception:
+            logging.exception(f"Failed to query {message}.")
+            response = "An error occurred. Please try again!"
+        return response
+
+
+def start_command():
+    parser = argparse.ArgumentParser(description="EmbedChain PoeBot command line interface")
+    # parser.add_argument("--host", default="0.0.0.0", help="Host IP to bind")
+    parser.add_argument("--port", default=8080, type=int, help="Port to bind")
+    parser.add_argument("--api-key", type=str, help="Poe API key")
+    # parser.add_argument(
+    #     "--history-length",
+    #     default=5,
+    #     type=int,
+    #     help="Set the max size of the chat history. Multiplies cost, but improves conversation awareness.",
+    # )
+    args = parser.parse_args()
+
+    # FIXME: Arguments are automatically loaded by Poebot's ArgumentParser which causes it to fail.
+    # the port argument here is also just for show, it actually works because poe has the same argument.
+
+    run(EcPoeBot(), api_key=args.api_key or os.environ.get("POE_API_KEY"))
+
+
+if __name__ == "__main__":
+    start_command()

embedchain/embedchain.py

@@ -1,9 +1,11 @@
 import hashlib
 import importlib.metadata
+import json
 import logging
 import os
 import threading
 import uuid
+from pathlib import Path
 from typing import Dict, Optional
 
 import requests
@@ -25,8 +27,9 @@ load_dotenv()
 
 ABS_PATH = os.getcwd()
 DB_DIR = os.path.join(ABS_PATH, "db")
-
-memory = ConversationBufferMemory()
+HOME_DIR = str(Path.home())
+CONFIG_DIR = os.path.join(HOME_DIR, ".embedchain")
+CONFIG_FILE = os.path.join(CONFIG_DIR, "config.json")
 
 
 class EmbedChain:
@@ -44,12 +47,34 @@ class EmbedChain:
         self.user_asks = []
         self.is_docs_site_instance = False
         self.online = False
+        self.memory = ConversationBufferMemory()
 
         # Send anonymous telemetry
         self.s_id = self.config.id if self.config.id else str(uuid.uuid4())
+        self.u_id = self._load_or_generate_user_id()
         thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("init",))
         thread_telemetry.start()
 
+    def _load_or_generate_user_id(self):
+        """
+        Loads the user id from the config file if it exists, otherwise generates a new
+        one and saves it to the config file.
+        """
+        if not os.path.exists(CONFIG_DIR):
+            os.makedirs(CONFIG_DIR)
+
+        if os.path.exists(CONFIG_FILE):
+            with open(CONFIG_FILE, "r") as f:
+                data = json.load(f)
+                if "user_id" in data:
+                    return data["user_id"]
+
+        u_id = str(uuid.uuid4())
+        with open(CONFIG_FILE, "w") as f:
+            json.dump({"user_id": u_id}, f)
+
+        return u_id
+
     def add(
         self,
         source,
@@ -362,8 +387,7 @@ class EmbedChain:
             k["web_search_result"] = self.access_search_and_get_results(input_query)
         contexts = self.retrieve_from_database(input_query, config)
 
-        global memory
-        chat_history = memory.load_memory_variables({})["history"]
+        chat_history = self.memory.load_memory_variables({})["history"]
 
         if chat_history:
             config.set_history(chat_history)
@@ -376,14 +400,14 @@ class EmbedChain:
 
         answer = self.get_answer_from_llm(prompt, config)
 
-        memory.chat_memory.add_user_message(input_query)
+        self.memory.chat_memory.add_user_message(input_query)
 
         # Send anonymous telemetry
         thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("chat",))
         thread_telemetry.start()
 
         if isinstance(answer, str):
-            memory.chat_memory.add_ai_message(answer)
+            self.memory.chat_memory.add_ai_message(answer)
             logging.info(f"Answer: {answer}")
             return answer
         else:
@@ -395,7 +419,7 @@ class EmbedChain:
         for chunk in answer:
             streamed_answer = streamed_answer + chunk
             yield chunk
-        memory.chat_memory.add_ai_message(streamed_answer)
+        self.memory.chat_memory.add_ai_message(streamed_answer)
         logging.info(f"Answer: {streamed_answer}")
 
     def set_collection(self, collection_name):
@@ -445,9 +469,11 @@ class EmbedChain:
                 "version": importlib.metadata.version(__package__ or __name__),
                 "method": method,
                 "language": "py",
+                "u_id": self.u_id,
             }
             if extra_metadata:
                 metadata.update(extra_metadata)
 
             response = requests.post(url, json={"metadata": metadata})
-            response.raise_for_status()
+            if response.status_code != 200:
+                logging.warning(f"Telemetry event failed with status code {response.status_code}")

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "embedchain"
-version = "0.0.44"
+version = "0.0.48"
 description = "embedchain is a framework to easily create LLM powered bots over any dataset"
 authors = ["Taranjeet Singh"]
 license = "Apache License"
@@ -85,6 +85,7 @@ python-dotenv = "^1.0.0"
 langchain = "^0.0.237"
 requests = "^2.31.0"
 openai = "^0.27.5"
+tiktoken = "^0.4.0"
 chromadb ="^0.4.2"
 youtube-transcript-api = "^0.6.1"
 beautifulsoup4 = "^4.12.2"
@@ -98,6 +99,7 @@ gpt4all = { version = "^1.0.8", optional = true }
 elasticsearch = { version = "^8.9.0", optional = true }
 flask = "^2.3.3"
 twilio = "^8.5.0"
+fastapi-poe = { version = "0.0.16", optional = true }
 
 
 
@@ -116,10 +118,10 @@ streamlit = ["streamlit"]
 community = ["llama-index"]
 opensource = ["sentence-transformers", "torch", "gpt4all"]
 elasticsearch = ["elasticsearch"]
+poe = ["fastapi-poe"]
 
 [tool.poetry.group.docs.dependencies]
 
 
 
 [tool.poetry.scripts]
-

tests/embedchain/test_chat.py

@@ -7,15 +7,13 @@ from embedchain.config import AppConfig
 
 
 class TestApp(unittest.TestCase):
-    os.environ["OPENAI_API_KEY"] = "test_key"
-
     def setUp(self):
+        os.environ["OPENAI_API_KEY"] = "test_key"
         self.app = App(config=AppConfig(collect_metrics=False))
 
-    @patch("embedchain.embedchain.memory", autospec=True)
     @patch.object(App, "retrieve_from_database", return_value=["Test context"])
     @patch.object(App, "get_answer_from_llm", return_value="Test answer")
-    def test_chat_with_memory(self, mock_answer, mock_retrieve, mock_memory):
+    def test_chat_with_memory(self, mock_get_answer, mock_retrieve):
         """
         This test checks the functionality of the 'chat' method in the App class with respect to the chat history
         memory.
@@ -23,27 +21,17 @@ class TestApp(unittest.TestCase):
         The second call is expected to use the chat history from the first call.
 
         Key assumptions tested:
-        - After the first call, 'memory.chat_memory.add_user_message' and 'memory.chat_memory.add_ai_message' are
             called with correct arguments, adding the correct chat history.
+        - After the first call, 'memory.chat_memory.add_user_message' and 'memory.chat_memory.add_ai_message' are
         - During the second call, the 'chat' method uses the chat history from the first call.
 
         The test isolates the 'chat' method behavior by mocking out 'retrieve_from_database', 'get_answer_from_llm' and
         'memory' methods.
         """
-        mock_memory.load_memory_variables.return_value = {"history": []}
         app = App()
-
-        # First call to chat
         first_answer = app.chat("Test query 1")
         self.assertEqual(first_answer, "Test answer")
-        mock_memory.chat_memory.add_user_message.assert_called_once_with("Test query 1")
-        mock_memory.chat_memory.add_ai_message.assert_called_once_with("Test answer")
-
-        mock_memory.chat_memory.add_user_message.reset_mock()
-        mock_memory.chat_memory.add_ai_message.reset_mock()
-
-        # Second call to chat
+        self.assertEqual(len(app.memory.chat_memory.messages), 2)
         second_answer = app.chat("Test query 2")
         self.assertEqual(second_answer, "Test answer")
-        mock_memory.chat_memory.add_user_message.assert_called_once_with("Test query 2")
-        mock_memory.chat_memory.add_ai_message.assert_called_once_with("Test answer")
+        self.assertEqual(len(app.memory.chat_memory.messages), 4)