bump version to 0.0.51 (#540)

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

@@ -1,24 +1,40 @@
-name: cd
+name: Publish Python 🐍 distributions 📦 to PyPI and TestPyPI
 
 on:
   release:
-    types:
-      - published
-
-permissions:
-  id-token: write
-  contents: read
+    types: [published] # This will trigger the workflow when you create a new release
 
 jobs:
-  publish_to_pypi:
-    name: publish to pypi on new release
+  build-n-publish:
+    name: Build and publish Python 🐍 distributions 📦 to PyPI and TestPyPI
     runs-on: ubuntu-latest
+    permissions:
+      # IMPORTANT: this permission is mandatory for trusted publishing
+      id-token: write
     steps:
-      - uses: actions/checkout@v3
-      - uses: JRubics/poetry-publish@v1.16
-        name: Build and publish to PyPI
+      - uses: actions/checkout@v2
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
         with:
-          pypi_token: ${{ secrets.PYPI_TOKEN }}
-          ignore_dev_requirements: "yes"
-          repository_url: https://upload.pypi.org/legacy/
-          repository_name: embedchain
+          python-version: '3.11'
+
+      - name: Install Poetry
+        run: |
+          curl -sSL https://install.python-poetry.org | python3 -
+          echo "$HOME/.local/bin" >> $GITHUB_PATH
+  
+      - name: Install dependencies
+        run: poetry install
+  
+      - name: Build a binary wheel and a source tarball
+        run: poetry build
+
+      - name: Publish distribution 📦 to Test PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          repository_url: https://test.pypi.org/legacy/
+
+      - name: Publish distribution 📦 to PyPI
+        if: startsWith(github.ref, 'refs/tags')
+        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:
@@ -78,7 +69,7 @@ elon_bot.query("How many companies does Elon Musk run?")
 Contributions are welcome! Please check out the issues on the repository, and feel free to open a pull request.
 For more information, please see the [contributing guidelines](CONTRIBUTING.md).
 
-For more refrence, please go through [Development Guide](https://docs.embedchain.ai/contribution/dev) and [Documentation Guide](https://docs.embedchain.ai/contribution/docs).
+For more reference, please go through [Development Guide](https://docs.embedchain.ai/contribution/dev) and [Documentation Guide](https://docs.embedchain.ai/contribution/docs).
 
 
 ## Citation

docs/advanced/showcase.mdx

@@ -38,6 +38,9 @@ Embedchain community has been super active in creating demos on top of Embedchai
 - [Understanding what the LLM framework embedchain does](https://zenn.dev/hijikix/articles/4bc8d60156a436) by Daisuke Hashimoto
 - [In bed with GPT and Node.js](https://dev.to/worldlinetech/in-bed-with-gpt-and-nodejs-4kh2) by Raphaël Semeteys, ([LinkedIn Post](https://www.linkedin.com/posts/raphaelsemeteys_in-bed-with-gpt-and-nodejs-activity-7088113552326029313-nn87/))
 - [Using Embedchain — A powerful LangChain Python wrapper to build Chat Bots even faster!⚡](https://medium.com/@avra42/using-embedchain-a-powerful-langchain-python-wrapper-to-build-chat-bots-even-faster-35c12994a360) by Avra, ([Tweet](https://twitter.com/Avra_b/status/1686767751560310784/))
+- [What is the Embedchain library?](https://jahaniwww.com/%da%a9%d8%aa%d8%a7%d8%a8%d8%ae%d8%a7%d9%86%d9%87-embedchain/) by Ali Jahani, ([LinkedIn Post](https://www.linkedin.com/posts/ajahani_aepaetaeqaexaggahyaeu-aetaexaesabraeaaeqaepaeu-activity-7097605202135904256-ppU-/))
+- [LangChain is Nice, But Have You Tried EmbedChain ?](https://medium.com/thoughts-on-machine-learning/langchain-is-nice-but-have-you-tried-embedchain-215a34421cde) by FS Ndzomga, ([Tweet](https://twitter.com/ndzfs/status/1695583640372035951/))
+- [Simplest Method to Build a Custom Chatbot with GPT-3.5 (via Embedchain)](https://www.ainewsletter.today/p/simplest-method-to-build-a-custom) by Arjun, ([Tweet](https://twitter.com/aiguy_arjun/status/1696393808467091758/))
 
 ### LinkedIn
 
@@ -50,6 +53,7 @@ Embedchain community has been super active in creating demos on top of Embedchai
 - [About embedchain](https://www.linkedin.com/feed/update/urn:li:activity:7080984218914189312/) by Morris Lee
 - [How to use Embedchain](https://www.linkedin.com/posts/nehaabansal_github-embedchainembedchain-framework-activity-7085830340136595456-kbW5/) by Neha Bansal
 - [Youtube/Webpage summary for Energy Study](https://www.linkedin.com/posts/bar%C4%B1%C5%9F-sanl%C4%B1-34b82715_enerji-python-activity-7082735341563977730-Js0U/) by Barış Sanlı, ([Tweet](https://twitter.com/barissanli/status/1676968784979193857/)) 
+- [Demo: How to use Embedchain? (Contains Collab Notebook link)](https://www.linkedin.com/posts/liorsinclair_embedchain-is-getting-a-lot-of-traction-because-activity-7103044695995424768-RckT/) by Lior Sinclair
 
 ### Twitter
 
@@ -79,9 +83,31 @@ Embedchain community has been super active in creating demos on top of Embedchai
 - [AI ChatBot in 5 lines Python Code](https://www.youtube.com/watch?v=zjWvLJLksv8) by Data Engineering
 - [Interview with Karl Marx](https://www.youtube.com/watch?v=5Y4Tscwj1xk) by Alexander Ray Williams
 - [Vlog where we try to build a bot based on our content on the internet](https://www.youtube.com/watch?v=I2w8CWM3bx4) by DV, ([Tweet](https://twitter.com/dvcoolster/status/1688387017544261632))
+- [CHAT with ANY ONLINE RESOURCES using EMBEDCHAIN|STREAMLIT with MEMORY |All OPENSOURCE](https://www.youtube.com/watch?v=TqQIHWoWTDQ&pp=ygUKZW1iZWRjaGFpbg%3D%3D) by DataInsightEdge
+- [Build POWERFUL LLM Bots EASILY with Your Own Data - Embedchain - Langchain 2.0? (Tutorial)](https://www.youtube.com/watch?v=jE24Y_GasE8) by WorldofAI, ([Tweet](https://twitter.com/intheworldofai/status/1696229166922780737))
+- [Embedchain: An AI knowledge base assistant for customizing enterprise private data, which can be connected to discord, whatsapp, slack, tele and other terminals (with gradio to build a request interface) in Chinese](https://www.youtube.com/watch?v=5RZzCJRk-d0) by AIGC LINK
+- [Embedchain Introduction](https://www.youtube.com/watch?v=Jet9zAqyggI) by Fahd Mirza 
 
 ## Mentions
 
 ### Github repos
 
+- [Awesome-LLM](https://github.com/Hannibal046/Awesome-LLM)
+- [awesome-chatgpt-api](https://github.com/reorx/awesome-chatgpt-api)
+- [awesome-langchain](https://github.com/kyrolabs/awesome-langchain)
+- [Awesome-Prompt-Engineering](https://github.com/promptslab/Awesome-Prompt-Engineering)
+- [awesome-chatgpt](https://github.com/eon01/awesome-chatgpt)
+- [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps)
+- [awesome-generative-ai](https://github.com/filipecalegario/awesome-generative-ai)
+- [awesome-gpt](https://github.com/formulahendry/awesome-gpt)
 - [awesome-ChatGPT-repositories](https://github.com/taishi-i/awesome-ChatGPT-repositories)
+- [awesome-gpt-prompt-engineering](https://github.com/snwfdhmp/awesome-gpt-prompt-engineering)
+- [awesome-chatgpt](https://github.com/awesome-chatgpt/awesome-chatgpt)
+- [awesome-llm-and-aigc](https://github.com/sjinzh/awesome-llm-and-aigc)
+- [awesome-compbio-chatgpt](https://github.com/csbl-br/awesome-compbio-chatgpt)
+- [Awesome-LLM4Tool](https://github.com/OpenGVLab/Awesome-LLM4Tool)
+
+## Meetups
+
+- [Dash and ChatGPT: Future of AI-enabled apps 30/08/23](https://go.plotly.com/dash-chatgpt)
+- [Pie & AI: Bangalore - Build end-to-end LLM app using Embedchain 01/09/23](https://www.eventbrite.com/e/pie-ai-bangalore-build-end-to-end-llm-app-using-embedchain-tickets-698045722547)

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/apps/App.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 import openai
 
 from embedchain.config import AppConfig, ChatConfig
@@ -14,19 +16,27 @@ class App(EmbedChain):
     dry_run(query): test your prompt without consuming tokens.
     """
 
-    def __init__(self, config: AppConfig = None):
+    def __init__(self, config: AppConfig = None, system_prompt: Optional[str] = None):
         """
         :param config: AppConfig instance to load as configuration. Optional.
+        :param system_prompt: System prompt string. Optional.
         """
         if config is None:
             config = AppConfig()
 
-        super().__init__(config)
+        super().__init__(config, system_prompt)
 
     def get_llm_model_answer(self, prompt, config: ChatConfig):
         messages = []
-        if config.system_prompt:
-            messages.append({"role": "system", "content": config.system_prompt})
+        system_prompt = (
+            self.system_prompt
+            if self.system_prompt is not None
+            else config.system_prompt
+            if config.system_prompt is not None
+            else None
+        )
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
         messages.append({"role": "user", "content": prompt})
         response = openai.ChatCompletion.create(
             model=config.model or "gpt-3.5-turbo-0613",

embedchain/apps/CustomApp.py

@@ -18,10 +18,11 @@ class CustomApp(EmbedChain):
     dry_run(query): test your prompt without consuming tokens.
     """
 
-    def __init__(self, config: CustomAppConfig = None):
+    def __init__(self, config: CustomAppConfig = None, system_prompt: Optional[str] = None):
         """
         :param config: Optional. `CustomAppConfig` instance to load as configuration.
         :raises ValueError: Config must be provided for custom app
+        :param system_prompt: Optional. System prompt string.
         """
         if config is None:
             raise ValueError("Config must be provided for custom app")
@@ -34,7 +35,7 @@ class CustomApp(EmbedChain):
             # Because these models run locally, they should have an instance running when the custom app is created
             self.open_source_app = OpenSourceApp(config=config.open_source_app_config)
 
-        super().__init__(config)
+        super().__init__(config, system_prompt)
 
     def set_llm_model(self, provider: Providers):
         self.provider = provider
@@ -51,6 +52,9 @@ class CustomApp(EmbedChain):
                 "Streaming responses have not been implemented for this model yet. Please disable."
             )
 
+        if config.system_prompt is None and self.system_prompt is not None:
+            config.system_prompt = self.system_prompt
+
         try:
             if self.provider == Providers.OPENAI:
                 return CustomApp._get_openai_answer(prompt, config)

embedchain/apps/Llama2App.py

@@ -1,4 +1,5 @@
 import os
+from typing import Optional
 
 from langchain.llms import Replicate
 
@@ -15,9 +16,10 @@ class Llama2App(EmbedChain):
     query(query): finds answer to the given query using vector database and LLM.
     """
 
-    def __init__(self, config: AppConfig = None):
+    def __init__(self, config: AppConfig = None, system_prompt: Optional[str] = None):
         """
         :param config: AppConfig instance to load as configuration. Optional.
+        :param system_prompt: System prompt string. Optional.
         """
         if "REPLICATE_API_TOKEN" not in os.environ:
             raise ValueError("Please set the REPLICATE_API_TOKEN environment variable.")
@@ -25,11 +27,11 @@ class Llama2App(EmbedChain):
         if config is None:
             config = AppConfig()
 
-        super().__init__(config)
+        super().__init__(config, system_prompt)
 
     def get_llm_model_answer(self, prompt, config: ChatConfig = None):
         # TODO: Move the model and other inputs into config
-        if config.system_prompt:
+        if self.system_prompt or config.system_prompt:
             raise ValueError("Llama2App does not support `system_prompt`")
         llm = Replicate(
             model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",

embedchain/apps/OpenSourceApp.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Iterable, Union
+from typing import Iterable, Union, Optional
 
 from embedchain.config import ChatConfig, OpenSourceAppConfig
 from embedchain.embedchain import EmbedChain
@@ -18,10 +18,11 @@ class OpenSourceApp(EmbedChain):
     query(query): finds answer to the given query using vector database and LLM.
     """
 
-    def __init__(self, config: OpenSourceAppConfig = None):
+    def __init__(self, config: OpenSourceAppConfig = None, system_prompt: Optional[str] = None):
         """
         :param config: OpenSourceAppConfig instance to load as configuration. Optional.
         `ef` defaults to open source.
+        :param system_prompt: System prompt string. Optional.
         """
         logging.info("Loading open source embedding model. This may take some time...")  # noqa:E501
         if not config:
@@ -33,7 +34,7 @@ class OpenSourceApp(EmbedChain):
         self.instance = OpenSourceApp._get_instance(config.model)
 
         logging.info("Successfully loaded open source embedding model.")
-        super().__init__(config)
+        super().__init__(config, system_prompt)
 
     def get_llm_model_answer(self, prompt, config: ChatConfig):
         return self._get_gpt4all_answer(prompt=prompt, config=config)
@@ -55,7 +56,7 @@ class OpenSourceApp(EmbedChain):
                 "OpenSourceApp does not support switching models at runtime. Please create a new app instance."
             )
 
-        if config.system_prompt:
+        if self.system_prompt or config.system_prompt:
             raise ValueError("OpenSourceApp does not support `system_prompt`")
 
         response = self.instance.generate(

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
@@ -25,31 +27,56 @@ 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:
-    def __init__(self, config: BaseAppConfig):
+    def __init__(self, config: BaseAppConfig, system_prompt: Optional[str] = None):
         """
         Initializes the EmbedChain instance, sets up a vector DB client and
         creates a collection.
 
         :param config: BaseAppConfig instance to load as configuration.
+        :param system_prompt: Optional. System prompt string.
         """
 
         self.config = config
+        self.system_prompt = system_prompt
         self.collection = self.config.db._get_or_create_collection(self.config.collection_name)
         self.db = self.config.db
         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 +389,7 @@ 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 +402,14 @@ 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 +421,7 @@ 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 +471,11 @@ 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}")

embedchain/utils.py

@@ -93,15 +93,6 @@ def use_pysqlite3():
                 "Error:",
                 e,
             )
-        __import__("pysqlite3")
-        sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")
-        # Let the user know what happened.
-        current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S,%f")[:-3]
-        print(
-            f"{current_time} [embedchain] [INFO]",
-            "Swapped std-lib sqlite3 with pysqlite3 for ChromaDb compatibility.",
-            f"Your original version was {sqlite3.sqlite_version}.",
-        )
 
 
 def format_source(source: str, limit: int = 20) -> str:

embedchain/vectordb/chroma_db.py

@@ -28,8 +28,7 @@ class ChromaDB(BaseVectorDB):
 
         if host and port:
             logging.info(f"Connecting to ChromaDB server: {host}:{port}")
-            self.settings = Settings(chroma_server_host=host, chroma_server_http_port=port)
-            self.client = chromadb.HttpClient(self.settings)
+            self.client = chromadb.HttpClient(host=host, port=8000)
         else:
             if db_dir is None:
                 db_dir = "db"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "embedchain"
-version = "0.0.42"
+version = "0.0.51"
 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)

tests/embedchain/test_query.py

@@ -43,7 +43,7 @@ class TestApp(unittest.TestCase):
         mock_answer.assert_called_once()
 
     @patch("openai.ChatCompletion.create")
-    def test_query_config_passing(self, mock_create):
+    def test_query_config_app_passing(self, mock_create):
         mock_create.return_value = {"choices": [{"message": {"content": "response"}}]}  # Mock response
 
         config = AppConfig()
@@ -52,9 +52,24 @@ class TestApp(unittest.TestCase):
 
         app.get_llm_model_answer("Test query", chat_config)
 
-        # Test systemp_prompt: Check that the 'create' method was called with the correct 'messages' argument
+        # Test system_prompt: Check that the 'create' method was called with the correct 'messages' argument
         messages_arg = mock_create.call_args.kwargs["messages"]
         self.assertEqual(messages_arg[0]["role"], "system")
         self.assertEqual(messages_arg[0]["content"], "Test system prompt")
 
         # TODO: Add tests for other config variables
+
+    @patch("openai.ChatCompletion.create")
+    def test_app_passing(self, mock_create):
+        mock_create.return_value = {"choices": [{"message": {"content": "response"}}]}  # Mock response
+
+        config = AppConfig()
+        chat_config = QueryConfig()
+        app = App(config=config, system_prompt="Test system prompt")
+
+        app.get_llm_model_answer("Test query", chat_config)
+
+        # Test system_prompt: Check that the 'create' method was called with the correct 'messages' argument
+        messages_arg = mock_create.call_args.kwargs["messages"]
+        self.assertEqual(messages_arg[0]["role"], "system")
+        self.assertEqual(messages_arg[0]["content"], "Test system prompt")