Rename embedchain to mem0 and open sourcing code for long term memory (#1474)

Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>

embedchain/embedchain/deployment/fly.io/.dockerignore

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+db/

embedchain/embedchain/deployment/fly.io/.env.example

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+OPENAI_API_KEY=sk-xxx

embedchain/embedchain/deployment/fly.io/Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt /app/
+
+RUN pip install -r requirements.txt
+
+COPY . /app
+
+EXPOSE 8080
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8080"]

embedchain/embedchain/deployment/fly.io/app.py

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+from dotenv import load_dotenv
+from fastapi import FastAPI, responses
+from pydantic import BaseModel
+
+from embedchain import App
+
+load_dotenv(".env")
+
+app = FastAPI(title="Embedchain FastAPI App")
+embedchain_app = App()
+
+
+class SourceModel(BaseModel):
+    source: str
+
+
+class QuestionModel(BaseModel):
+    question: str
+
+
+@app.post("/add")
+async def add_source(source_model: SourceModel):
+    """
+    Adds a new source to the EmbedChain app.
+    Expects a JSON with a "source" key.
+    """
+    source = source_model.source
+    embedchain_app.add(source)
+    return {"message": f"Source '{source}' added successfully."}
+
+
+@app.post("/query")
+async def handle_query(question_model: QuestionModel):
+    """
+    Handles a query to the EmbedChain app.
+    Expects a JSON with a "question" key.
+    """
+    question = question_model.question
+    answer = embedchain_app.query(question)
+    return {"answer": answer}
+
+
+@app.post("/chat")
+async def handle_chat(question_model: QuestionModel):
+    """
+    Handles a chat request to the EmbedChain app.
+    Expects a JSON with a "question" key.
+    """
+    question = question_model.question
+    response = embedchain_app.chat(question)
+    return {"response": response}
+
+
+@app.get("/")
+async def root():
+    return responses.RedirectResponse(url="/docs")

embedchain/embedchain/deployment/fly.io/requirements.txt

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+fastapi==0.104.0
+uvicorn==0.23.2
+embedchain
+beautifulsoup4

embedchain/embedchain/deployment/gradio.app/app.py

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+import os
+
+import gradio as gr
+
+from embedchain import App
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+app = App()
+
+
+def query(message, history):
+    return app.chat(message)
+
+
+demo = gr.ChatInterface(query)
+
+demo.launch()

embedchain/embedchain/deployment/gradio.app/requirements.txt

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+gradio==4.11.0
+embedchain

embedchain/embedchain/deployment/modal.com/.env.example

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+OPENAI_API_KEY=sk-xxx

embedchain/embedchain/deployment/modal.com/.gitignore

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+.env

embedchain/embedchain/deployment/modal.com/app.py

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+from dotenv import load_dotenv
+from fastapi import Body, FastAPI, responses
+from modal import Image, Secret, Stub, asgi_app
+
+from embedchain import App
+
+load_dotenv(".env")
+
+image = Image.debian_slim().pip_install(
+    "embedchain",
+    "lanchain_community==0.2.6",
+    "youtube-transcript-api==0.6.1",
+    "pytube==15.0.0",
+    "beautifulsoup4==4.12.3",
+    "slack-sdk==3.21.3",
+    "huggingface_hub==0.23.0",
+    "gitpython==3.1.38",
+    "yt_dlp==2023.11.14",
+    "PyGithub==1.59.1",
+    "feedparser==6.0.10",
+    "newspaper3k==0.2.8",
+    "listparser==0.19",
+)
+
+stub = Stub(
+    name="embedchain-app",
+    image=image,
+    secrets=[Secret.from_dotenv(".env")],
+)
+
+web_app = FastAPI()
+embedchain_app = App(name="embedchain-modal-app")
+
+
+@web_app.post("/add")
+async def add(
+    source: str = Body(..., description="Source to be added"),
+    data_type: str | None = Body(None, description="Type of the data source"),
+):
+    """
+    Adds a new source to the EmbedChain app.
+    Expects a JSON with a "source" and "data_type" key.
+    "data_type" is optional.
+    """
+    if source and data_type:
+        embedchain_app.add(source, data_type)
+    elif source:
+        embedchain_app.add(source)
+    else:
+        return {"message": "No source provided."}
+    return {"message": f"Source '{source}' added successfully."}
+
+
+@web_app.post("/query")
+async def query(question: str = Body(..., description="Question to be answered")):
+    """
+    Handles a query to the EmbedChain app.
+    Expects a JSON with a "question" key.
+    """
+    if not question:
+        return {"message": "No question provided."}
+    answer = embedchain_app.query(question)
+    return {"answer": answer}
+
+
+@web_app.get("/chat")
+async def chat(question: str = Body(..., description="Question to be answered")):
+    """
+    Handles a chat request to the EmbedChain app.
+    Expects a JSON with a "question" key.
+    """
+    if not question:
+        return {"message": "No question provided."}
+    response = embedchain_app.chat(question)
+    return {"response": response}
+
+
+@web_app.get("/")
+async def root():
+    return responses.RedirectResponse(url="/docs")
+
+
+@stub.function(image=image)
+@asgi_app()
+def fastapi_app():
+    return web_app

embedchain/embedchain/deployment/modal.com/requirements.txt

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+modal==0.56.4329
+fastapi==0.104.0
+uvicorn==0.23.2
+embedchain

embedchain/embedchain/deployment/render.com/.env.example

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+OPENAI_API_KEY=sk-xxx

embedchain/embedchain/deployment/render.com/.gitignore

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+.env

embedchain/embedchain/deployment/render.com/app.py

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+from fastapi import FastAPI, responses
+from pydantic import BaseModel
+
+from embedchain import App
+
+app = FastAPI(title="Embedchain FastAPI App")
+embedchain_app = App()
+
+
+class SourceModel(BaseModel):
+    source: str
+
+
+class QuestionModel(BaseModel):
+    question: str
+
+
+@app.post("/add")
+async def add_source(source_model: SourceModel):
+    """
+    Adds a new source to the EmbedChain app.
+    Expects a JSON with a "source" key.
+    """
+    source = source_model.source
+    embedchain_app.add(source)
+    return {"message": f"Source '{source}' added successfully."}
+
+
+@app.post("/query")
+async def handle_query(question_model: QuestionModel):
+    """
+    Handles a query to the EmbedChain app.
+    Expects a JSON with a "question" key.
+    """
+    question = question_model.question
+    answer = embedchain_app.query(question)
+    return {"answer": answer}
+
+
+@app.post("/chat")
+async def handle_chat(question_model: QuestionModel):
+    """
+    Handles a chat request to the EmbedChain app.
+    Expects a JSON with a "question" key.
+    """
+    question = question_model.question
+    response = embedchain_app.chat(question)
+    return {"response": response}
+
+
+@app.get("/")
+async def root():
+    return responses.RedirectResponse(url="/docs")

embedchain/embedchain/deployment/render.com/render.yaml

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+services:
+  - type: web
+    name: ec-render-app
+    runtime: python
+    repo: https://github.com/<your-username>/<repo-name>
+    scaling:
+      minInstances: 1
+      maxInstances: 3
+      targetMemoryPercent: 60 # optional if targetCPUPercent is set
+      targetCPUPercent: 60 # optional if targetMemory is set
+    buildCommand: pip install -r requirements.txt
+    startCommand: uvicorn app:app --host 0.0.0.0
+    envVars:
+      - key: OPENAI_API_KEY
+        value: sk-xxx
+    autoDeploy: false # optional

embedchain/embedchain/deployment/render.com/requirements.txt

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+fastapi==0.104.0
+uvicorn==0.23.2
+embedchain
+beautifulsoup4

embedchain/embedchain/deployment/streamlit.io/.streamlit/secrets.toml

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+OPENAI_API_KEY="sk-xxx"

embedchain/embedchain/deployment/streamlit.io/app.py

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+import streamlit as st
+
+from embedchain import App
+
+
+@st.cache_resource
+def embedchain_bot():
+    return App()
+
+
+st.title("💬 Chatbot")
+st.caption("🚀 An Embedchain app powered by OpenAI!")
+if "messages" not in st.session_state:
+    st.session_state.messages = [
+        {
+            "role": "assistant",
+            "content": """
+        Hi! I'm a chatbot. I can answer questions and learn new things!\n
+        Ask me anything and if you want me to learn something do `/add <source>`.\n
+        I can learn mostly everything. :)
+        """,
+        }
+    ]
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+if prompt := st.chat_input("Ask me anything!"):
+    app = embedchain_bot()
+
+    if prompt.startswith("/add"):
+        with st.chat_message("user"):
+            st.markdown(prompt)
+            st.session_state.messages.append({"role": "user", "content": prompt})
+        prompt = prompt.replace("/add", "").strip()
+        with st.chat_message("assistant"):
+            message_placeholder = st.empty()
+            message_placeholder.markdown("Adding to knowledge base...")
+            app.add(prompt)
+            message_placeholder.markdown(f"Added {prompt} to knowledge base!")
+            st.session_state.messages.append({"role": "assistant", "content": f"Added {prompt} to knowledge base!"})
+            st.stop()
+
+    with st.chat_message("user"):
+        st.markdown(prompt)
+        st.session_state.messages.append({"role": "user", "content": prompt})
+
+    with st.chat_message("assistant"):
+        msg_placeholder = st.empty()
+        msg_placeholder.markdown("Thinking...")
+        full_response = ""
+
+        for response in app.chat(prompt):
+            msg_placeholder.empty()
+            full_response += response
+
+        msg_placeholder.markdown(full_response)
+        st.session_state.messages.append({"role": "assistant", "content": full_response})

embedchain/embedchain/deployment/streamlit.io/requirements.txt

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+streamlit==1.29.0
+embedchain