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

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

embedchain/examples/unacademy-ai/README.md

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+## Unacademy UPSC AI
+
+This directory contains the code used to implement [Unacademy UPSC AI](https://unacademy-ai.streamlit.app/) using Embedchain. It is built on 16K+ youtube videos and 800+ course pages from Unacademy website. You can find the full list of data sources [here](https://gist.github.com/deshraj/7714feadccca13cefe574951652fa9b2).
+
+## Run locally
+
+You can run Unacademy AI locally as a streamlit app using the following command:
+
+```bash
+export OPENAI_API_KEY=sk-xxx
+pip install -r requirements.txt
+streamlit run app.py
+```
+
+Note: Remember to set your `OPENAI_API_KEY`.
+
+## Deploy to production
+
+You can create your own Unacademy AI or similar RAG applications in production using one of the several deployment methods provided in [our docs](https://docs.embedchain.ai/get-started/deployment).

embedchain/examples/unacademy-ai/app.py

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+import queue
+
+import streamlit as st
+
+from embedchain import App
+from embedchain.config import BaseLlmConfig
+from embedchain.helpers.callbacks import (StreamingStdOutCallbackHandlerYield,
+                                          generate)
+
+
+@st.cache_resource
+def unacademy_ai():
+    app = App()
+    return app
+
+
+app = unacademy_ai()
+
+assistant_avatar_url = "https://cdn-images-1.medium.com/v2/resize:fit:1200/1*LdFNhpOe7uIn-bHK9VUinA.jpeg"
+
+st.markdown(f"# <img src='{assistant_avatar_url}' width={35} /> Unacademy UPSC AI", unsafe_allow_html=True)
+
+styled_caption = """
+<p style="font-size: 17px; color: #aaa;">
+🚀 An <a href="https://github.com/embedchain/embedchain">Embedchain</a> app powered with Unacademy\'s UPSC data!
+</p>
+"""
+st.markdown(styled_caption, unsafe_allow_html=True)
+
+with st.expander(":grey[Want to create your own Unacademy UPSC AI?]"):
+    st.write(
+        """
+    ```bash
+    pip install embedchain
+    ```
+
+    ```python
+    from embedchain import App
+    unacademy_ai_app = App()
+    unacademy_ai_app.add(
+        "https://unacademy.com/content/upsc/study-material/plan-policy/atma-nirbhar-bharat-3-0/",
+        data_type="web_page"
+    )
+    unacademy_ai_app.chat("What is Atma Nirbhar 3.0?")
+    ```
+
+    For more information, checkout the [Embedchain docs](https://docs.embedchain.ai/get-started/quickstart).
+    """
+    )
+
+if "messages" not in st.session_state:
+    st.session_state.messages = [
+        {
+            "role": "assistant",
+            "content": """Hi, I'm Unacademy UPSC AI bot, who can answer any questions related to UPSC preparation.
+            Let me help you prepare better for UPSC.\n
+Sample questions:
+- What are the subjects in UPSC CSE?
+- What is the CSE scholarship price amount?
+- What are different indian calendar forms?
+            """,
+        }
+    ]
+
+for message in st.session_state.messages:
+    role = message["role"]
+    with st.chat_message(role, avatar=assistant_avatar_url if role == "assistant" else None):
+        st.markdown(message["content"])
+
+if prompt := st.chat_input("Ask me anything!"):
+    with st.chat_message("user"):
+        st.markdown(prompt)
+        st.session_state.messages.append({"role": "user", "content": prompt})
+
+    with st.chat_message("assistant", avatar=assistant_avatar_url):
+        msg_placeholder = st.empty()
+        msg_placeholder.markdown("Thinking...")
+        full_response = ""
+
+        q = queue.Queue()
+
+        def app_response(result):
+            llm_config = app.llm.config.as_dict()
+            llm_config["callbacks"] = [StreamingStdOutCallbackHandlerYield(q=q)]
+            config = BaseLlmConfig(**llm_config)
+            answer, citations = app.chat(prompt, config=config, citations=True)
+            result["answer"] = answer
+            result["citations"] = citations
+
+        results = {}
+
+        for answer_chunk in generate(q):
+            full_response += answer_chunk
+            msg_placeholder.markdown(full_response)
+
+        answer, citations = results["answer"], results["citations"]
+
+        if citations:
+            full_response += "\n\n**Sources**:\n"
+            sources = list(set(map(lambda x: x[1], citations)))
+            for i, source in enumerate(sources):
+                full_response += f"{i+1}. {source}\n"
+
+        msg_placeholder.markdown(full_response)
+        st.session_state.messages.append({"role": "assistant", "content": full_response})

embedchain/examples/unacademy-ai/requirements.txt

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+embedchain
+streamlit
+pysqlite3-binary