diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index 4a8c340292a9..b2fba4a8ea32 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -1,13 +1,21 @@
 {
-    "extensions": ["ms-python.python", "visualstudioexptteam.vscodeintellicode"],
-    "dockerFile": "Dockerfile",
-    "settings": {
-        "terminal.integrated.profiles.linux": {
-            "bash": {
-                "path": "/bin/bash"
+    "customizations":  {
+        "vscode": {
+            "extensions": [
+                "ms-python.python",
+                "ms-toolsai.jupyter",
+                "visualstudioexptteam.vscodeintellicode"
+            ],
+            "settings": {
+                "terminal.integrated.profiles.linux": {
+                    "bash": {
+                        "path": "/bin/bash"
+                    }
+                },
+                "terminal.integrated.defaultProfile.linux": "bash"
             }
-        },
-        "terminal.integrated.defaultProfile.linux": "bash"
+        }
     },
+    "dockerFile": "Dockerfile",
     "updateContentCommand": "pip install -e . pre-commit && pre-commit install"
 }
diff --git a/.github/workflows/openai.yml b/.github/workflows/openai.yml
index 0a678fd9ed10..339c36527ba6 100644
--- a/.github/workflows/openai.yml
+++ b/.github/workflows/openai.yml
@@ -56,6 +56,10 @@ jobs:
       - name: Install packages for Teachable when needed
         run: |
           pip install -e .[teachable]
+      - name: Install packages for RetrieveChat with QDrant when needed
+        if: matrix.python-version == '3.9'
+        run: |
+          pip install qdrant_client[fastembed]
       - name: Coverage
         if: matrix.python-version == '3.9'
         env:
diff --git a/README.md b/README.md
index 9d425abd13f4..430a431d96bb 100644
--- a/README.md
+++ b/README.md
@@ -13,10 +13,9 @@ This project is a spinoff from [FLAML](https://github.com/microsoft/FLAML).
     <br>
 </p> -->
 
-:fire: autogen has graduated from [FLAML](https://github.com/microsoft/FLAML) into a new project.
-
-<!-- :fire: Heads-up: We're preparing to migrate [autogen](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) into a dedicated Github repository. Alongside this move, we'll also launch a dedicated Discord server and a website for comprehensive documentation.
+:fire: Heads-up: pyautogen v0.2 will switch to using openai v1.
 
+<!--
 :fire: FLAML is highlighted in OpenAI's [cookbook](https://github.com/openai/openai-cookbook#related-resources-from-around-the-web).
 
 :fire: [autogen](https://microsoft.github.io/autogen/) is released with support for ChatGPT and GPT-4, based on [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673).
@@ -33,7 +32,7 @@ AutoGen is a framework that enables the development of LLM applications using mu
 - It supports **diverse conversation patterns** for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy,
   the number of agents, and agent conversation topology.
 - It provides a collection of working systems with different complexities. These systems span a **wide range of applications** from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.
-- AutoGen provides a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` as an **enhanced inference API**. It allows easy performance tuning, utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.
+- AutoGen provides **enhanced LLM inference**. It offers easy performance tuning, plus utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.
 
 AutoGen is powered by collaborative [research studies](https://microsoft.github.io/autogen/docs/Research) from Microsoft, Penn State University, and the University of Washington.
 
@@ -111,7 +110,7 @@ Please find more [code examples](https://microsoft.github.io/autogen/docs/Exampl
 
 ## Enhanced LLM Inferences
 
-Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` adding powerful functionalities like tuning, caching, error handling, and templating. For example, you can optimize generations by LLM with your own tuning data, success metrics, and budgets.
+Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers enhanced LLM inference with powerful functionalities like tuning, caching, error handling, and templating. For example, you can optimize generations by LLM with your own tuning data, success metrics, and budgets.
 
 ```python
 # perform tuning
diff --git a/autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py b/autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py
new file mode 100644
index 000000000000..e0bb8d8216f0
--- /dev/null
+++ b/autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py
@@ -0,0 +1,266 @@
+from typing import Callable, Dict, List, Optional
+
+from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent
+from autogen.retrieve_utils import get_files_from_dir, split_files_to_chunks
+import logging
+
+logger = logging.getLogger(__name__)
+
+try:
+    from qdrant_client import QdrantClient, models
+    from qdrant_client.fastembed_common import QueryResponse
+    import fastembed
+except ImportError as e:
+    logging.fatal("Failed to import qdrant_client with fastembed. Try running 'pip install qdrant_client[fastembed]'")
+    raise e
+
+
+class QdrantRetrieveUserProxyAgent(RetrieveUserProxyAgent):
+    def __init__(
+        self,
+        name="RetrieveChatAgent",  # default set to RetrieveChatAgent
+        human_input_mode: Optional[str] = "ALWAYS",
+        is_termination_msg: Optional[Callable[[Dict], bool]] = None,
+        retrieve_config: Optional[Dict] = None,  # config for the retrieve agent
+        **kwargs,
+    ):
+        """
+        Args:
+            name (str): name of the agent.
+            human_input_mode (str): whether to ask for human inputs every time a message is received.
+                Possible values are "ALWAYS", "TERMINATE", "NEVER".
+                (1) When "ALWAYS", the agent prompts for human input every time a message is received.
+                    Under this mode, the conversation stops when the human input is "exit",
+                    or when is_termination_msg is True and there is no human input.
+                (2) When "TERMINATE", the agent only prompts for human input only when a termination message is received or
+                    the number of auto reply reaches the max_consecutive_auto_reply.
+                (3) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops
+                    when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True.
+            is_termination_msg (function): a function that takes a message in the form of a dictionary
+                and returns a boolean value indicating if this received message is a termination message.
+                The dict can contain the following keys: "content", "role", "name", "function_call".
+            retrieve_config (dict or None): config for the retrieve agent.
+                To use default config, set to None. Otherwise, set to a dictionary with the following keys:
+                - task (Optional, str): the task of the retrieve chat. Possible values are "code", "qa" and "default". System
+                    prompt will be different for different tasks. The default value is `default`, which supports both code and qa.
+                - client (Optional, qdrant_client.QdrantClient(":memory:")): A QdrantClient instance. If not provided, an in-memory instance will be assigned. Not recommended for production.
+                    will be used. If you want to use other vector db, extend this class and override the `retrieve_docs` function.
+                - docs_path (Optional, str): the path to the docs directory. It can also be the path to a single file,
+                    or the url to a single file. Default is None, which works only if the collection is already created.
+                - collection_name (Optional, str): the name of the collection.
+                    If key not provided, a default name `autogen-docs` will be used.
+                - model (Optional, str): the model to use for the retrieve chat.
+                    If key not provided, a default model `gpt-4` will be used.
+                - chunk_token_size (Optional, int): the chunk token size for the retrieve chat.
+                    If key not provided, a default size `max_tokens * 0.4` will be used.
+                - context_max_tokens (Optional, int): the context max token size for the retrieve chat.
+                    If key not provided, a default size `max_tokens * 0.8` will be used.
+                - chunk_mode (Optional, str): the chunk mode for the retrieve chat. Possible values are
+                    "multi_lines" and "one_line". If key not provided, a default mode `multi_lines` will be used.
+                - must_break_at_empty_line (Optional, bool): chunk will only break at empty line if True. Default is True.
+                    If chunk_mode is "one_line", this parameter will be ignored.
+                - embedding_model (Optional, str): the embedding model to use for the retrieve chat.
+                    If key not provided, a default model `BAAI/bge-small-en-v1.5` will be used. All available models
+                    can be found at `https://qdrant.github.io/fastembed/examples/Supported_Models/`.
+                - customized_prompt (Optional, str): the customized prompt for the retrieve chat. Default is None.
+                - customized_answer_prefix (Optional, str): the customized answer prefix for the retrieve chat. Default is "".
+                    If not "" and the customized_answer_prefix is not in the answer, `Update Context` will be triggered.
+                - update_context (Optional, bool): if False, will not apply `Update Context` for interactive retrieval. Default is True.
+                - custom_token_count_function(Optional, Callable): a custom function to count the number of tokens in a string.
+                    The function should take a string as input and return three integers (token_count, tokens_per_message, tokens_per_name).
+                    Default is None, tiktoken will be used and may not be accurate for non-OpenAI models.
+                - custom_text_split_function(Optional, Callable): a custom function to split a string into a list of strings.
+                    Default is None, will use the default function in `autogen.retrieve_utils.split_text_to_chunks`.
+                - parallel (Optional, int): How many parallel workers to use for embedding. Defaults to the number of CPU cores.
+                - on_disk (Optional, bool): Whether to store the collection on disk. Default is False.
+                - quantization_config: Quantization configuration. If None, quantization will be disabled.
+                - hnsw_config: HNSW configuration. If None, default configuration will be used.
+                  You can find more info about the hnsw configuration options at https://qdrant.tech/documentation/concepts/indexing/#vector-index.
+                  API Reference: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_collection
+                - payload_indexing: Whether to create a payload index for the document field. Default is False.
+                  You can find more info about the payload indexing options at https://qdrant.tech/documentation/concepts/indexing/#payload-index
+                  API Reference: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_field_index
+             **kwargs (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#__init__).
+
+        """
+        super().__init__(name, human_input_mode, is_termination_msg, retrieve_config, **kwargs)
+        self._client = self._retrieve_config.get("client", QdrantClient(":memory:"))
+        self._embedding_model = self._retrieve_config.get("embedding_model", "BAAI/bge-small-en-v1.5")
+        # Uses all available CPU cores to encode data when set to 0
+        self._parallel = self._retrieve_config.get("parallel", 0)
+        self._on_disk = self._retrieve_config.get("on_disk", False)
+        self._quantization_config = self._retrieve_config.get("quantization_config", None)
+        self._hnsw_config = self._retrieve_config.get("hnsw_config", None)
+        self._payload_indexing = self._retrieve_config.get("payload_indexing", False)
+
+    def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str = ""):
+        """
+        Args:
+            problem (str): the problem to be solved.
+            n_results (int): the number of results to be retrieved.
+            search_string (str): only docs containing this string will be retrieved.
+        """
+        if not self._collection:
+            print("Trying to create collection.")
+            create_qdrant_from_dir(
+                dir_path=self._docs_path,
+                max_tokens=self._chunk_token_size,
+                client=self._client,
+                collection_name=self._collection_name,
+                chunk_mode=self._chunk_mode,
+                must_break_at_empty_line=self._must_break_at_empty_line,
+                embedding_model=self._embedding_model,
+                custom_text_split_function=self.custom_text_split_function,
+                parallel=self._parallel,
+                on_disk=self._on_disk,
+                quantization_config=self._quantization_config,
+                hnsw_config=self._hnsw_config,
+                payload_indexing=self._payload_indexing,
+            )
+            self._collection = True
+
+        results = query_qdrant(
+            query_texts=problem,
+            n_results=n_results,
+            search_string=search_string,
+            client=self._client,
+            collection_name=self._collection_name,
+            embedding_model=self._embedding_model,
+        )
+        self._results = results
+
+
+def create_qdrant_from_dir(
+    dir_path: str,
+    max_tokens: int = 4000,
+    client: QdrantClient = None,
+    collection_name: str = "all-my-documents",
+    chunk_mode: str = "multi_lines",
+    must_break_at_empty_line: bool = True,
+    embedding_model: str = "BAAI/bge-small-en-v1.5",
+    custom_text_split_function: Callable = None,
+    parallel: int = 0,
+    on_disk: bool = False,
+    quantization_config: Optional[models.QuantizationConfig] = None,
+    hnsw_config: Optional[models.HnswConfigDiff] = None,
+    payload_indexing: bool = False,
+    qdrant_client_options: Optional[Dict] = {},
+):
+    """Create a Qdrant collection from all the files in a given directory, the directory can also be a single file or a url to
+        a single file.
+
+    Args:
+        dir_path (str): the path to the directory, file or url.
+        max_tokens (Optional, int): the maximum number of tokens per chunk. Default is 4000.
+        client (Optional, QdrantClient): the QdrantClient instance. Default is None.
+        collection_name (Optional, str): the name of the collection. Default is "all-my-documents".
+        chunk_mode (Optional, str): the chunk mode. Default is "multi_lines".
+        must_break_at_empty_line (Optional, bool): Whether to break at empty line. Default is True.
+        embedding_model (Optional, str): the embedding model to use. Default is "BAAI/bge-small-en-v1.5". The list of all the available models can be at https://qdrant.github.io/fastembed/examples/Supported_Models/.
+        parallel (Optional, int): How many parallel workers to use for embedding. Defaults to the number of CPU cores
+        on_disk (Optional, bool): Whether to store the collection on disk. Default is False.
+        quantization_config: Quantization configuration. If None, quantization will be disabled. Ref: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_collection
+        hnsw_config: HNSW configuration. If None, default configuration will be used. Ref: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_collection
+        payload_indexing: Whether to create a payload index for the document field. Default is False.
+        qdrant_client_options: (Optional, dict): the options for instantiating the qdrant client. Reference: https://github.com/qdrant/qdrant-client/blob/master/qdrant_client/qdrant_client.py#L36-L58.
+    """
+    if client is None:
+        client = QdrantClient(**qdrant_client_options)
+        client.set_model(embedding_model)
+
+    if custom_text_split_function is not None:
+        chunks = split_files_to_chunks(
+            get_files_from_dir(dir_path), custom_text_split_function=custom_text_split_function
+        )
+    else:
+        chunks = split_files_to_chunks(get_files_from_dir(dir_path), max_tokens, chunk_mode, must_break_at_empty_line)
+    logger.info(f"Found {len(chunks)} chunks.")
+
+    # Check if collection by same name exists, if not, create it with custom options
+    try:
+        client.get_collection(collection_name=collection_name)
+    except Exception:
+        client.create_collection(
+            collection_name=collection_name,
+            vectors_config=client.get_fastembed_vector_params(
+                on_disk=on_disk, quantization_config=quantization_config, hnsw_config=hnsw_config
+            ),
+        )
+        client.get_collection(collection_name=collection_name)
+
+    # Upsert in batch of 100 or less if the total number of chunks is less than 100
+    for i in range(0, len(chunks), min(100, len(chunks))):
+        end_idx = i + min(100, len(chunks) - i)
+        client.add(collection_name, documents=chunks[i:end_idx], ids=[j for j in range(i, end_idx)], parallel=parallel)
+
+    # Create a payload index for the document field
+    # Enables highly efficient payload filtering. Reference: https://qdrant.tech/documentation/concepts/indexing/#indexing
+    # Creating an index requires additional computational resources and memory.
+    # If filtering performance is critical, we can consider creating an index.
+    if payload_indexing:
+        client.create_payload_index(
+            collection_name=collection_name,
+            field_name="document",
+            field_schema=models.TextIndexParams(
+                type="text",
+                tokenizer=models.TokenizerType.WORD,
+                min_token_len=2,
+                max_token_len=15,
+            ),
+        )
+
+
+def query_qdrant(
+    query_texts: List[str],
+    n_results: int = 10,
+    client: QdrantClient = None,
+    collection_name: str = "all-my-documents",
+    search_string: str = "",
+    embedding_model: str = "BAAI/bge-small-en-v1.5",
+    qdrant_client_options: Optional[Dict] = {},
+) -> List[List[QueryResponse]]:
+    """Perform a similarity search with filters on a Qdrant collection
+
+    Args:
+        query_texts (List[str]): the query texts.
+        n_results (Optional, int): the number of results to return. Default is 10.
+        client (Optional, API): the QdrantClient instance. A default in-memory client will be instantiated if None.
+        collection_name (Optional, str): the name of the collection. Default is "all-my-documents".
+        search_string (Optional, str): the search string. Default is "".
+        embedding_model (Optional, str): the embedding model to use. Default is "all-MiniLM-L6-v2". Will be ignored if embedding_function is not None.
+        qdrant_client_options: (Optional, dict): the options for instantiating the qdrant client. Reference: https://github.com/qdrant/qdrant-client/blob/master/qdrant_client/qdrant_client.py#L36-L58.
+
+    Returns:
+        List[List[QueryResponse]]: the query result. The format is:
+            class QueryResponse(BaseModel, extra="forbid"):  # type: ignore
+                id: Union[str, int]
+                embedding: Optional[List[float]]
+                metadata: Dict[str, Any]
+                document: str
+                score: float
+    """
+    if client is None:
+        client = QdrantClient(**qdrant_client_options)
+        client.set_model(embedding_model)
+
+    results = client.query_batch(
+        collection_name,
+        query_texts,
+        limit=n_results,
+        query_filter=models.Filter(
+            must=[
+                models.FieldCondition(
+                    key="document",
+                    match=models.MatchText(text=search_string),
+                )
+            ]
+        )
+        if search_string
+        else None,
+    )
+
+    data = {
+        "ids": [[result.id for result in sublist] for sublist in results],
+        "documents": [[result.document for result in sublist] for sublist in results],
+    }
+    return data
diff --git a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
index 40a146e93edb..b24249bbe961 100644
--- a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
+++ b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
@@ -6,7 +6,8 @@
     raise ImportError("Please install dependencies first. `pip install pyautogen[retrievechat]`")
 from autogen.agentchat.agent import Agent
 from autogen.agentchat import UserProxyAgent
-from autogen.retrieve_utils import create_vector_db_from_dir, query_vector_db, num_tokens_from_text
+from autogen.retrieve_utils import create_vector_db_from_dir, query_vector_db
+from autogen.token_count_utils import count_token
 from autogen.code_utils import extract_code
 
 from typing import Callable, Dict, Optional, Union, List, Tuple, Any
@@ -124,8 +125,8 @@ def __init__(
                 - get_or_create (Optional, bool): if True, will create/recreate a collection for the retrieve chat.
                     This is the same as that used in chromadb. Default is False. Will be set to False if docs_path is None.
                 - custom_token_count_function(Optional, Callable): a custom function to count the number of tokens in a string.
-                    The function should take a string as input and return three integers (token_count, tokens_per_message, tokens_per_name).
-                    Default is None, tiktoken will be used and may not be accurate for non-OpenAI models.
+                    The function should take (text:str, model:str) as input and return the token_count(int). the retrieve_config["model"] will be passed in the function.
+                    Default is autogen.token_count_utils.count_token that uses tiktoken, which may not be accurate for non-OpenAI models.
                 - custom_text_split_function(Optional, Callable): a custom function to split a string into a list of strings.
                     Default is None, will use the default function in `autogen.retrieve_utils.split_text_to_chunks`.
             **kwargs (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#__init__).
@@ -180,7 +181,7 @@ def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str =
         self._get_or_create = (
             self._retrieve_config.get("get_or_create", False) if self._docs_path is not None else False
         )
-        self.custom_token_count_function = self._retrieve_config.get("custom_token_count_function", None)
+        self.custom_token_count_function = self._retrieve_config.get("custom_token_count_function", count_token)
         self.custom_text_split_function = self._retrieve_config.get("custom_text_split_function", None)
         self._context_max_tokens = self._max_tokens * 0.8
         self._collection = True if self._docs_path is None else False  # whether the collection is created
@@ -244,7 +245,7 @@ def _get_context(self, results: Dict[str, Union[List[str], List[List[str]]]]):
                 continue
             if results["ids"][0][idx] in self._doc_ids:
                 continue
-            _doc_tokens = num_tokens_from_text(doc, custom_token_count_function=self.custom_token_count_function)
+            _doc_tokens = self.custom_token_count_function(doc, self._model)
             if _doc_tokens > self._context_max_tokens:
                 func_print = f"Skip doc_id {results['ids'][0][idx]} as it is too long to fit in the context."
                 print(colored(func_print, "green"), flush=True)
@@ -361,7 +362,7 @@ def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str =
         """
         if not self._collection or self._get_or_create:
             print("Trying to create collection.")
-            create_vector_db_from_dir(
+            self._client = create_vector_db_from_dir(
                 dir_path=self._docs_path,
                 max_tokens=self._chunk_token_size,
                 client=self._client,
diff --git a/autogen/code_utils.py b/autogen/code_utils.py
index 814a1e15b964..caaf09072850 100644
--- a/autogen/code_utils.py
+++ b/autogen/code_utils.py
@@ -1,4 +1,3 @@
-import signal
 import subprocess
 import sys
 import os
@@ -9,6 +8,7 @@
 from hashlib import md5
 import logging
 from autogen import oai
+from concurrent.futures import ThreadPoolExecutor, TimeoutError
 
 try:
     import docker
@@ -307,21 +307,20 @@ def execute_code(
                 text=True,
             )
         else:
-            signal.signal(signal.SIGALRM, timeout_handler)
-            try:
-                signal.alarm(timeout)
-                # run the code in a subprocess in the current docker container in the working directory
-                result = subprocess.run(
+            with ThreadPoolExecutor(max_workers=1) as executor:
+                future = executor.submit(
+                    subprocess.run,
                     cmd,
                     cwd=work_dir,
                     capture_output=True,
                     text=True,
                 )
-                signal.alarm(0)
-            except TimeoutError:
-                if original_filename is None:
-                    os.remove(filepath)
-                return 1, TIMEOUT_MSG, None
+                try:
+                    result = future.result(timeout=timeout)
+                except TimeoutError:
+                    if original_filename is None:
+                        os.remove(filepath)
+                    return 1, TIMEOUT_MSG, None
         if original_filename is None:
             os.remove(filepath)
         if result.returncode:
diff --git a/autogen/retrieve_utils.py b/autogen/retrieve_utils.py
index c660fa85d6dc..b6edba7a1e40 100644
--- a/autogen/retrieve_utils.py
+++ b/autogen/retrieve_utils.py
@@ -5,12 +5,16 @@
 import glob
 import tiktoken
 import chromadb
-from chromadb.api import API
+
+if chromadb.__version__ < "0.4.15":
+    from chromadb.api import API
+else:
+    from chromadb.api import ClientAPI as API
 from chromadb.api.types import QueryResult
 import chromadb.utils.embedding_functions as ef
 import logging
 import pypdf
-
+from autogen.token_count_utils import count_token
 
 logger = logging.getLogger(__name__)
 TEXT_FORMATS = [
@@ -33,80 +37,6 @@
 VALID_CHUNK_MODES = frozenset({"one_line", "multi_lines"})
 
 
-def num_tokens_from_text(
-    text: str,
-    model: str = "gpt-3.5-turbo-0613",
-    return_tokens_per_name_and_message: bool = False,
-    custom_token_count_function: Callable = None,
-) -> Union[int, Tuple[int, int, int]]:
-    """Return the number of tokens used by a text.
-
-    Args:
-        text (str): The text to count tokens for.
-        model (Optional, str): The model to use for tokenization. Default is "gpt-3.5-turbo-0613".
-        return_tokens_per_name_and_message (Optional, bool): Whether to return the number of tokens per name and per
-            message. Default is False.
-        custom_token_count_function (Optional, Callable): A custom function to count tokens. Default is None.
-
-    Returns:
-        int: The number of tokens used by the text.
-        int: The number of tokens per message. Only returned if return_tokens_per_name_and_message is True.
-        int: The number of tokens per name. Only returned if return_tokens_per_name_and_message is True.
-    """
-    if isinstance(custom_token_count_function, Callable):
-        token_count, tokens_per_message, tokens_per_name = custom_token_count_function(text)
-    else:
-        # https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
-        try:
-            encoding = tiktoken.encoding_for_model(model)
-        except KeyError:
-            logger.debug("Warning: model not found. Using cl100k_base encoding.")
-            encoding = tiktoken.get_encoding("cl100k_base")
-        known_models = {
-            "gpt-3.5-turbo": (3, 1),
-            "gpt-35-turbo": (3, 1),
-            "gpt-3.5-turbo-0613": (3, 1),
-            "gpt-3.5-turbo-16k-0613": (3, 1),
-            "gpt-3.5-turbo-0301": (4, -1),
-            "gpt-4": (3, 1),
-            "gpt-4-0314": (3, 1),
-            "gpt-4-32k-0314": (3, 1),
-            "gpt-4-0613": (3, 1),
-            "gpt-4-32k-0613": (3, 1),
-        }
-        tokens_per_message, tokens_per_name = known_models.get(model, (3, 1))
-        token_count = len(encoding.encode(text))
-
-    if return_tokens_per_name_and_message:
-        return token_count, tokens_per_message, tokens_per_name
-    else:
-        return token_count
-
-
-def num_tokens_from_messages(
-    messages: dict,
-    model: str = "gpt-3.5-turbo-0613",
-    custom_token_count_function: Callable = None,
-    custom_prime_count: int = 3,
-):
-    """Return the number of tokens used by a list of messages."""
-    num_tokens = 0
-    for message in messages:
-        for key, value in message.items():
-            _num_tokens, tokens_per_message, tokens_per_name = num_tokens_from_text(
-                value,
-                model=model,
-                return_tokens_per_name_and_message=True,
-                custom_token_count_function=custom_token_count_function,
-            )
-            num_tokens += _num_tokens
-            if key == "name":
-                num_tokens += tokens_per_name
-        num_tokens += tokens_per_message
-    num_tokens += custom_prime_count  # With ChatGPT, every reply is primed with <|start|>assistant<|message|>
-    return num_tokens
-
-
 def split_text_to_chunks(
     text: str,
     max_tokens: int = 4000,
@@ -121,7 +51,7 @@ def split_text_to_chunks(
         must_break_at_empty_line = False
     chunks = []
     lines = text.split("\n")
-    lines_tokens = [num_tokens_from_text(line) for line in lines]
+    lines_tokens = [count_token(line) for line in lines]
     sum_tokens = sum(lines_tokens)
     while sum_tokens > max_tokens:
         if chunk_mode == "one_line":
@@ -144,7 +74,7 @@ def split_text_to_chunks(
                 split_len = int(max_tokens / lines_tokens[0] * 0.9 * len(lines[0]))
                 prev = lines[0][:split_len]
                 lines[0] = lines[0][split_len:]
-                lines_tokens[0] = num_tokens_from_text(lines[0])
+                lines_tokens[0] = count_token(lines[0])
             else:
                 logger.warning("Failed to split docs with must_break_at_empty_line being True, set to False.")
                 must_break_at_empty_line = False
@@ -254,7 +184,10 @@ def get_files_from_dir(dir_path: Union[str, List[str]], types: list = TEXT_FORMA
 def get_file_from_url(url: str, save_path: str = None):
     """Download a file from a URL."""
     if save_path is None:
+        os.makedirs("/tmp/chromadb", exist_ok=True)
         save_path = os.path.join("/tmp/chromadb", os.path.basename(url))
+    else:
+        os.makedirs(os.path.dirname(save_path), exist_ok=True)
     with requests.get(url, stream=True) as r:
         r.raise_for_status()
         with open(save_path, "wb") as f:
@@ -284,7 +217,7 @@ def create_vector_db_from_dir(
     embedding_model: str = "all-MiniLM-L6-v2",
     embedding_function: Callable = None,
     custom_text_split_function: Callable = None,
-):
+) -> API:
     """Create a vector db from all the files in a given directory, the directory can also be a single file or a url to
         a single file. We support chromadb compatible APIs to create the vector db, this function is not required if
         you prepared your own vector db.
@@ -304,6 +237,9 @@ def create_vector_db_from_dir(
         embedding_function (Optional, Callable): the embedding function to use. Default is None, SentenceTransformer with
             the given `embedding_model` will be used. If you want to use OpenAI, Cohere, HuggingFace or other embedding
             functions, you can pass it here, follow the examples in `https://docs.trychroma.com/embeddings`.
+
+    Returns:
+        API: the chromadb client.
     """
     if client is None:
         client = chromadb.PersistentClient(path=db_path)
@@ -341,6 +277,7 @@ def create_vector_db_from_dir(
             )
     except ValueError as e:
         logger.warning(f"{e}")
+    return client
 
 
 def query_vector_db(
diff --git a/autogen/token_count_utils.py b/autogen/token_count_utils.py
new file mode 100644
index 000000000000..fd9d61a10a16
--- /dev/null
+++ b/autogen/token_count_utils.py
@@ -0,0 +1,182 @@
+import tiktoken
+from typing import List, Union, Dict, Tuple
+import logging
+import json
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_max_token_limit(model="gpt-3.5-turbo-0613"):
+    max_token_limit = {
+        "gpt-3.5-turbo": 4096,
+        "gpt-3.5-turbo-0301": 4096,
+        "gpt-3.5-turbo-0613": 4096,
+        "gpt-3.5-turbo-instruct": 4096,
+        "gpt-3.5-turbo-16k": 16384,
+        "gpt-35-turbo": 4096,
+        "gpt-35-turbo-16k": 16384,
+        "gpt-35-turbo-instruct": 4096,
+        "gpt-4": 8192,
+        "gpt-4-32k": 32768,
+        "gpt-4-32k-0314": 32768,  # deprecate in Sep
+        "gpt-4-0314": 8192,  # deprecate in Sep
+        "gpt-4-0613": 8192,
+        "gpt-4-32k-0613": 32768,
+    }
+    return max_token_limit[model]
+
+
+def percentile_used(input, model="gpt-3.5-turbo-0613"):
+    return count_token(input) / get_max_token_limit(model)
+
+
+def token_left(input: Union[str, List, Dict], model="gpt-3.5-turbo-0613") -> int:
+    """Count number of tokens left for an OpenAI model.
+
+    Args:
+        input: (str, list, dict): Input to the model.
+        model: (str): Model name.
+
+    Returns:
+        int: Number of tokens left that the model can use for completion.
+    """
+    return get_max_token_limit(model) - count_token(input, model=model)
+
+
+def count_token(input: Union[str, List, Dict], model: str = "gpt-3.5-turbo-0613") -> int:
+    """Count number of tokens used by an OpenAI model.
+    Args:
+        input: (str, list, dict): Input to the model.
+        model: (str): Model name.
+
+    Returns:
+        int: Number of tokens from the input.
+    """
+    if isinstance(input, str):
+        return _num_token_from_text(input, model=model)
+    elif isinstance(input, list) or isinstance(input, dict):
+        return _num_token_from_messages(input, model=model)
+    else:
+        raise ValueError("input must be str, list or dict")
+
+
+def _num_token_from_text(text: str, model: str = "gpt-3.5-turbo-0613"):
+    """Return the number of tokens used by a string."""
+    try:
+        encoding = tiktoken.encoding_for_model(model)
+    except KeyError:
+        logger.warning(f"Model {model} not found. Using cl100k_base encoding.")
+        encoding = tiktoken.get_encoding("cl100k_base")
+    return len(encoding.encode(text))
+
+
+def _num_token_from_messages(messages: Union[List, Dict], model="gpt-3.5-turbo-0613"):
+    """Return the number of tokens used by a list of messages.
+
+    retrieved from https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb/
+    """
+    if isinstance(messages, dict):
+        messages = [messages]
+
+    try:
+        encoding = tiktoken.encoding_for_model(model)
+    except KeyError:
+        print("Warning: model not found. Using cl100k_base encoding.")
+        encoding = tiktoken.get_encoding("cl100k_base")
+    if model in {
+        "gpt-3.5-turbo-0613",
+        "gpt-3.5-turbo-16k-0613",
+        "gpt-4-0314",
+        "gpt-4-32k-0314",
+        "gpt-4-0613",
+        "gpt-4-32k-0613",
+    }:
+        tokens_per_message = 3
+        tokens_per_name = 1
+    elif model == "gpt-3.5-turbo-0301":
+        tokens_per_message = 4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
+        tokens_per_name = -1  # if there's a name, the role is omitted
+    elif "gpt-3.5-turbo" in model:
+        logger.info("gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613.")
+        return _num_token_from_messages(messages, model="gpt-3.5-turbo-0613")
+    elif "gpt-4" in model:
+        logger.info("gpt-4 may update over time. Returning num tokens assuming gpt-4-0613.")
+        return _num_token_from_messages(messages, model="gpt-4-0613")
+    else:
+        raise NotImplementedError(
+            f"""_num_token_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."""
+        )
+    num_tokens = 0
+    for message in messages:
+        num_tokens += tokens_per_message
+        for key, value in message.items():
+            if value is None:
+                continue
+
+            # function calls
+            if not isinstance(value, str):
+                try:
+                    value = json.dumps(value)
+                except TypeError:
+                    logger.warning(
+                        f"Value {value} is not a string and cannot be converted to json. It is a type: {type(value)} Skipping."
+                    )
+                    continue
+
+            num_tokens += len(encoding.encode(value))
+            if key == "name":
+                num_tokens += tokens_per_name
+    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
+    return num_tokens
+
+
+def num_tokens_from_functions(functions, model="gpt-3.5-turbo-0613") -> int:
+    """Return the number of tokens used by a list of functions.
+
+    Args:
+        functions: (list): List of function descriptions that will be passed in model.
+        model: (str): Model name.
+
+    Returns:
+        int: Number of tokens from the function descriptions.
+    """
+    try:
+        encoding = tiktoken.encoding_for_model(model)
+    except KeyError:
+        print("Warning: model not found. Using cl100k_base encoding.")
+        encoding = tiktoken.get_encoding("cl100k_base")
+
+    num_tokens = 0
+    for function in functions:
+        function_tokens = len(encoding.encode(function["name"]))
+        function_tokens += len(encoding.encode(function["description"]))
+        function_tokens -= 2
+        if "parameters" in function:
+            parameters = function["parameters"]
+            if "properties" in parameters:
+                for propertiesKey in parameters["properties"]:
+                    function_tokens += len(encoding.encode(propertiesKey))
+                    v = parameters["properties"][propertiesKey]
+                    for field in v:
+                        if field == "type":
+                            function_tokens += 2
+                            function_tokens += len(encoding.encode(v["type"]))
+                        elif field == "description":
+                            function_tokens += 2
+                            function_tokens += len(encoding.encode(v["description"]))
+                        elif field == "enum":
+                            function_tokens -= 3
+                            for o in v["enum"]:
+                                function_tokens += 3
+                                function_tokens += len(encoding.encode(o))
+                        else:
+                            print(f"Warning: not supported field {field}")
+                function_tokens += 11
+                if len(parameters["properties"]) == 0:
+                    function_tokens -= 2
+
+        num_tokens += function_tokens
+
+    num_tokens += 12
+    return num_tokens
diff --git a/notebook/agentchat_RetrieveChat.ipynb b/notebook/agentchat_RetrieveChat.ipynb
index bfdb43b9d509..36605e521605 100644
--- a/notebook/agentchat_RetrieveChat.ipynb
+++ b/notebook/agentchat_RetrieveChat.ipynb
@@ -194,8 +194,13 @@
     "\n",
     "# 2. create the RetrieveUserProxyAgent instance named \"ragproxyagent\"\n",
     "# By default, the human_input_mode is \"ALWAYS\", which means the agent will ask for human input at every step. We set it to \"NEVER\" here.\n",
-    "# `docs_path` is the path to the docs directory. By default, it is set to \"./docs\". Here we generated the documentations from FLAML's docstrings.\n",
-    "# Navigate to the website folder and run `pydoc-markdown` and it will generate folder `reference` under `website/docs`.\n",
+    "# `docs_path` is the path to the docs directory. It can also be the path to a single file, or the url to a single file. By default, \n",
+    "# it is set to None, which works only if the collection is already created.\n",
+    "# \n",
+    "# Here we generated the documentations from FLAML's docstrings. Not needed if you just want to try this notebook but not to reproduce the\n",
+    "# outputs. Clone the FLAML (https://github.com/microsoft/FLAML) repo and navigate to its website folder. Pip install and run `pydoc-markdown`\n",
+    "# and it will generate folder `reference` under `website/docs`.\n",
+    "#\n",
     "# `task` indicates the kind of task we're working on. In this example, it's a `code` task.\n",
     "# `chunk_token_size` is the chunk token size for the retrieve chat. By default, it is set to `max_tokens * 0.6`, here we set it to 2000.\n",
     "ragproxyagent = RetrieveUserProxyAgent(\n",
@@ -204,11 +209,12 @@
     "    max_consecutive_auto_reply=10,\n",
     "    retrieve_config={\n",
     "        \"task\": \"code\",\n",
-    "        \"docs_path\": \"../website/docs/reference\",\n",
+    "        \"docs_path\": \"~/code/FLAML/website/docs/reference\",  # change this to your own path, such as https://raw.githubusercontent.com/microsoft/autogen/main/README.md\n",
     "        \"chunk_token_size\": 2000,\n",
     "        \"model\": config_list[0][\"model\"],\n",
     "        \"client\": chromadb.PersistentClient(path=\"/tmp/chromadb\"),\n",
     "        \"embedding_model\": \"all-mpnet-base-v2\",\n",
+    "        \"get_or_create\": False,  # set to True if you want to recreate the collection\n",
     "    },\n",
     ")"
    ]
diff --git a/notebook/agentchat_lmm_llava.ipynb b/notebook/agentchat_lmm_llava.ipynb
new file mode 100644
index 000000000000..a3a51d3abfb4
--- /dev/null
+++ b/notebook/agentchat_lmm_llava.ipynb
@@ -0,0 +1,1363 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2c75da30",
+   "metadata": {},
+   "source": [
+    "# Agent Chat with Multimodal Models\n",
+    "\n",
+    "We use **LLaVA** as an example for the multimodal feature.  More information about LLaVA can be found in their [GitHub page](https://github.com/haotian-liu/LLaVA)\n",
+    "\n",
+    "\n",
+    "This notebook contains the following information and examples:\n",
+    "\n",
+    "1. Install [LLaVA package](#install)\n",
+    "2. Setup LLaVA Model\n",
+    "    - Option 1: Use [API calls from `Replicate`](#replicate)\n",
+    "    - Option 2: Setup [LLaVA locally (requires GPU)](#local)\n",
+    "2. Application 1: [Image Chat](#app-1)\n",
+    "3. Application 2: [Figure Creator](#app-2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "b1ffe2ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We use this variable to control where you want to host LLaVA, locally or remotely?\n",
+    "# More details in the two setup options below.\n",
+    "LLAVA_MODE = \"remote\" # Either \"local\" or \"remote\"\n",
+    "assert LLAVA_MODE in [\"local\", \"remote\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2ec49aeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we will override the following variables later.\n",
+    "MODEL_NAME = \"\" \n",
+    "SEP = \"###\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d64154f0",
+   "metadata": {},
+   "source": [
+    "<a id=\"install\"></a>\n",
+    "## Install the LLaVA library\n",
+    "\n",
+    "Please follow the LLaVA GitHub [page](https://github.com/haotian-liu/LLaVA/) to install LLaVA.\n",
+    "\n",
+    "\n",
+    "#### Download the package\n",
+    "```bash\n",
+    "git clone https://github.com/haotian-liu/LLaVA.git\n",
+    "cd LLaVA\n",
+    "```\n",
+    "\n",
+    "#### Install the inference package\n",
+    "```bash\n",
+    "conda create -n llava python=3.10 -y\n",
+    "conda activate llava\n",
+    "pip install --upgrade pip  # enable PEP 660 support\n",
+    "pip install -e .\n",
+    "```\n",
+    "\n",
+    "### Don't forget AutoGen in the new environment\n",
+    "```bash\n",
+    "pip install pyautogen\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "67d45964",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2023-10-20 12:47:04,159] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "import os\n",
+    "from llava.conversation import default_conversation as conv\n",
+    "from llava.conversation import Conversation\n",
+    "\n",
+    "from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union\n",
+    "\n",
+    "import autogen\n",
+    "from autogen import AssistantAgent, Agent, UserProxyAgent, ConversableAgent\n",
+    "from termcolor import colored\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acc4703b",
+   "metadata": {},
+   "source": [
+    "<a id=\"replicate\"></a>\n",
+    "## (Option 1, preferred) Use API Calls from Replicate [Remote]\n",
+    "We can also use [Replicate](https://replicate.com/yorickvp/llava-13b/api) to use LLaVA directly, which will host the model for you.\n",
+    "\n",
+    "1. Run `pip install replicate` to install the package\n",
+    "2. You need to get an API key from Replicate from your [account setting page](https://replicate.com/account/api-tokens)\n",
+    "3. Next, copy your API token and authenticate by setting it as an environment variable:\n",
+    "    `export REPLICATE_API_TOKEN=<paste-your-token-here>` \n",
+    "4. You need to enter your credit card information for Replicate 🥲\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f650bf3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pip install replicate\n",
+    "# import os\n",
+    "## alternatively, you can put your API key here for the environment variable.\n",
+    "# os.environ[\"REPLICATE_API_TOKEN\"] = \"r8_xyz your api key goes here~\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "267ffd78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if LLAVA_MODE == \"remote\":\n",
+    "    import replicate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1805e4bd",
+   "metadata": {},
+   "source": [
+    "<a id=\"local\"></a>\n",
+    "## [Option 2] Setup LLaVA Locally\n",
+    "\n",
+    "\n",
+    "Some helpful packages and dependencies:\n",
+    "```bash\n",
+    "conda install -c nvidia cuda-toolkit\n",
+    "```\n",
+    "\n",
+    "\n",
+    "### Launch\n",
+    "\n",
+    "In one terminal, start the controller first:\n",
+    "```bash\n",
+    "python -m llava.serve.controller --host 0.0.0.0 --port 10000\n",
+    "```\n",
+    "\n",
+    "\n",
+    "Then, in another terminal, start the worker, which will load the model to the GPU:\n",
+    "```bash\n",
+    "python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b\n",
+    "``"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c29925f",
+   "metadata": {},
+   "source": [
+    "**Note: make sure the environment of this notebook also installed the llava package from `pip install -e .`**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "93bf7915",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'models': ['llava-v1.5-13b']}\n",
+      "Model Name: llava-v1.5-13b\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run this code block only if you want to run LlaVA locally\n",
+    "if LLAVA_MODE == \"local\":\n",
+    "    # Setup some global constants for convenience\n",
+    "    # Note: make sure the addresses below are consistent with your setup in LLaVA \n",
+    "    CONTROLLER_ADDR = \"http://0.0.0.0:10000\"\n",
+    "    SEP =  conv.sep\n",
+    "    ret = requests.post(CONTROLLER_ADDR + \"/list_models\")\n",
+    "    print(ret.json())\n",
+    "    MODEL_NAME = ret.json()[\"models\"][0]\n",
+    "    print(\"Model Name:\", MODEL_NAME)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "307852dd",
+   "metadata": {},
+   "source": [
+    "# Multimodal Functions\n",
+    "\n",
+    "The Multimodal Functions library provides a set of utilities to manage and process multimodal data, focusing on textual and image components. The library allows you to format prompts, extract image paths, and handle image data in various formats.\n",
+    "\n",
+    "## Functions\n",
+    "\n",
+    "\n",
+    "### `get_image_data`\n",
+    "\n",
+    "This function retrieves the content of an image specified by a file path or URL and optionally converts it to base64 format. It can handle both web-hosted images and locally stored files.\n",
+    "\n",
+    "\n",
+    "### `lmm_formater`\n",
+    "\n",
+    "This function formats a user-provided prompt containing `<img ...>` tags, replacing these tags with `<image>` or numbered versions like `<image 1>`, `<image 2>`, etc., and extracts the image locations. It returns a tuple containing the new formatted prompt and a list of image data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4bf7f549",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "import re\n",
+    "from io import BytesIO\n",
+    "\n",
+    "from PIL import Image\n",
+    "\n",
+    "import re\n",
+    "\n",
+    "\n",
+    "def get_image_data(image_file, use_b64=True):\n",
+    "    if image_file.startswith('http://') or image_file.startswith('https://'):\n",
+    "        response = requests.get(image_file)\n",
+    "        content = response.content\n",
+    "    elif re.match(r\"data:image/(?:png|jpeg);base64,\", image_file):\n",
+    "        return re.sub(r\"data:image/(?:png|jpeg);base64,\", \"\", image_file)\n",
+    "    else:\n",
+    "        image = Image.open(image_file).convert('RGB')\n",
+    "        buffered = BytesIO()\n",
+    "        image.save(buffered, format=\"PNG\")\n",
+    "        content = buffered.getvalue()\n",
+    "        \n",
+    "    if use_b64:\n",
+    "        return base64.b64encode(content).decode('utf-8')\n",
+    "    else:\n",
+    "        return content\n",
+    "\n",
+    "def lmm_formater(prompt: str, order_image_tokens: bool = False) -> Tuple[str, List[str]]:\n",
+    "    \"\"\"\n",
+    "    Formats the input prompt by replacing image tags and returns the new prompt along with image locations.\n",
+    "    \n",
+    "    Parameters:\n",
+    "        - prompt (str): The input string that may contain image tags like <img ...>.\n",
+    "        - order_image_tokens (bool, optional): Whether to order the image tokens with numbers. \n",
+    "            It will be useful for GPT-4V. Defaults to False.\n",
+    "    \n",
+    "    Returns:\n",
+    "        - Tuple[str, List[str]]: A tuple containing the formatted string and a list of images (loaded in b64 format).\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Initialize variables\n",
+    "    new_prompt = prompt\n",
+    "    image_locations = []\n",
+    "    images = []\n",
+    "    image_count = 0\n",
+    "    \n",
+    "    # Regular expression pattern for matching <img ...> tags\n",
+    "    img_tag_pattern = re.compile(r'<img ([^>]+)>')\n",
+    "    \n",
+    "    # Find all image tags\n",
+    "    for match in img_tag_pattern.finditer(prompt):\n",
+    "        image_location = match.group(1)\n",
+    "        \n",
+    "        try: \n",
+    "            img_data = get_image_data(image_location)\n",
+    "        except:\n",
+    "            # Remove the token\n",
+    "            print(f\"Warning! Unable to load image from {image_location}\")\n",
+    "            new_prompt = new_prompt.replace(match.group(0), \"\", 1)\n",
+    "            continue\n",
+    "        \n",
+    "        image_locations.append(image_location)\n",
+    "        images.append(img_data)\n",
+    "        \n",
+    "        # Increment the image count and replace the tag in the prompt\n",
+    "        new_token = f'<image {image_count}>' if  order_image_tokens else \"<image>\"\n",
+    "\n",
+    "        new_prompt = new_prompt.replace(match.group(0), new_token, 1)\n",
+    "        image_count += 1\n",
+    "        \n",
+    "    return new_prompt, images\n",
+    "\n",
+    "\n",
+    "\n",
+    "def gpt4v_formatter(prompt: str) -> List[Union[str, dict]]:\n",
+    "    \"\"\"\n",
+    "    Formats the input prompt by replacing image tags and returns a list of text and images.\n",
+    "    \n",
+    "    Parameters:\n",
+    "        - prompt (str): The input string that may contain image tags like <img ...>.\n",
+    "\n",
+    "    Returns:\n",
+    "        - List[Union[str, dict]]: A list of alternating text and image dictionary items.\n",
+    "    \"\"\"\n",
+    "    output = []\n",
+    "    last_index = 0\n",
+    "    image_count = 0\n",
+    "    \n",
+    "    # Regular expression pattern for matching <img ...> tags\n",
+    "    img_tag_pattern = re.compile(r'<img ([^>]+)>')\n",
+    "    \n",
+    "    # Find all image tags\n",
+    "    for match in img_tag_pattern.finditer(prompt):\n",
+    "        image_location = match.group(1)\n",
+    "        \n",
+    "        try:\n",
+    "            img_data = get_image_data(image_location)\n",
+    "        except:\n",
+    "            # Warning and skip this token\n",
+    "            print(f\"Warning! Unable to load image from {image_location}\")\n",
+    "            continue\n",
+    "\n",
+    "        # Add text before this image tag to output list\n",
+    "        output.append(prompt[last_index:match.start()])\n",
+    "        \n",
+    "        # Add image data to output list\n",
+    "        output.append({\"image\": img_data})\n",
+    "        \n",
+    "        last_index = match.end()\n",
+    "        image_count += 1\n",
+    "\n",
+    "    # Add remaining text to output list\n",
+    "    output.append(prompt[last_index:])\n",
+    "    \n",
+    "    return output\n",
+    "\n",
+    "\n",
+    "def extract_img_paths(paragraph: str) -> list:\n",
+    "    \"\"\"\n",
+    "    Extract image paths (URLs or local paths) from a text paragraph.\n",
+    "    \n",
+    "    Parameters:\n",
+    "        paragraph (str): The input text paragraph.\n",
+    "        \n",
+    "    Returns:\n",
+    "        list: A list of extracted image paths.\n",
+    "    \"\"\"\n",
+    "    # Regular expression to match image URLs and file paths\n",
+    "    img_path_pattern = re.compile(r'\\b(?:http[s]?://\\S+\\.(?:jpg|jpeg|png|gif|bmp)|\\S+\\.(?:jpg|jpeg|png|gif|bmp))\\b', \n",
+    "                                  re.IGNORECASE)\n",
+    "    \n",
+    "    # Find all matches in the paragraph\n",
+    "    img_paths = re.findall(img_path_pattern, paragraph)\n",
+    "    return img_paths\n",
+    "\n",
+    "\n",
+    "def _to_pil(data):\n",
+    "    return Image.open(BytesIO(base64.b64decode(data)))\n",
+    "\n",
+    "\n",
+    "\n",
+    "def llava_call_binary(prompt: str, images: list, \n",
+    "                      model_name:str = MODEL_NAME, \n",
+    "                      max_new_tokens:int=1000, temperature: float=0.5, seed: int = 1):\n",
+    "    # TODO 1: add caching around the LLaVA call to save compute and cost\n",
+    "    # TODO 2: add `seed` to ensure reproducibility. The seed is not working now.\n",
+    "    if LLAVA_MODE == \"local\":\n",
+    "        headers = {\"User-Agent\": \"LLaVA Client\"}\n",
+    "        pload = {\n",
+    "            \"model\": model_name,\n",
+    "            \"prompt\": prompt,\n",
+    "            \"max_new_tokens\": max_new_tokens,\n",
+    "            \"temperature\": temperature,\n",
+    "            \"stop\": SEP,\n",
+    "            \"images\": images,\n",
+    "        }\n",
+    "\n",
+    "        response = requests.post(CONTROLLER_ADDR + \"/worker_generate_stream\", headers=headers,\n",
+    "                json=pload, stream=False)\n",
+    "\n",
+    "        for chunk in response.iter_lines(chunk_size=8192, decode_unicode=False, delimiter=b\"\\0\"):\n",
+    "            if chunk:\n",
+    "                data = json.loads(chunk.decode(\"utf-8\"))\n",
+    "                output = data[\"text\"].split(SEP)[-1]\n",
+    "    elif LLAVA_MODE == \"remote\":\n",
+    "        # The Replicate version of the model only support 1 image for now.\n",
+    "        img = 'data:image/jpeg;base64,' + images[0]\n",
+    "        response = replicate.run(\n",
+    "            \"yorickvp/llava-13b:2facb4a474a0462c15041b78b1ad70952ea46b5ec6ad29583c0b29dbd4249591\",\n",
+    "            input={\"image\": img, \"prompt\": prompt.replace(\"<image>\", \" \"), \"seed\": seed}\n",
+    "        )\n",
+    "        # The yorickvp/llava-13b model can stream output as it's running.\n",
+    "        # The predict method returns an iterator, and you can iterate over that output.\n",
+    "        output = \"\"\n",
+    "        for item in response:\n",
+    "            # https://replicate.com/yorickvp/llava-13b/versions/2facb4a474a0462c15041b78b1ad70952ea46b5ec6ad29583c0b29dbd4249591/api#output-schema\n",
+    "            output += item\n",
+    "        \n",
+    "    # Remove the prompt and the space.\n",
+    "    output = output.replace(prompt, \"\").strip().rstrip()\n",
+    "    return output\n",
+    "    \n",
+    "\n",
+    "def llava_call(prompt:str, model_name: str=MODEL_NAME, images: list=[], \n",
+    "               max_new_tokens:int=1000, temperature: float=0.5, seed: int = 1) -> str:\n",
+    "    \"\"\"\n",
+    "    Makes a call to the LLaVA service to generate text based on a given prompt and optionally provided images.\n",
+    "\n",
+    "    Args:\n",
+    "        - prompt (str): The input text for the model. Any image paths or placeholders in the text should be replaced with \"<image>\".\n",
+    "        - model_name (str, optional): The name of the model to use for the text generation. Defaults to the global constant MODEL_NAME.\n",
+    "        - images (list, optional): A list of image paths or URLs. If not provided, they will be extracted from the prompt.\n",
+    "            If provided, they will be appended to the prompt with the \"<image>\" placeholder.\n",
+    "        - max_new_tokens (int, optional): Maximum number of new tokens to generate. Defaults to 1000.\n",
+    "        - temperature (float, optional): temperature for the model. Defaults to 0.5.\n",
+    "\n",
+    "    Returns:\n",
+    "        - str: Generated text from the model.\n",
+    "\n",
+    "    Raises:\n",
+    "        - AssertionError: If the number of \"<image>\" tokens in the prompt and the number of provided images do not match.\n",
+    "        - RunTimeError: If any of the provided images is empty.\n",
+    "\n",
+    "    Notes:\n",
+    "    - The function uses global constants: CONTROLLER_ADDR and SEP.\n",
+    "    - Any image paths or URLs in the prompt are automatically replaced with the \"<image>\" token.\n",
+    "    - If more images are provided than there are \"<image>\" tokens in the prompt, the extra tokens are appended to the end of the prompt.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if len(images) == 0:\n",
+    "        prompt, images = lmm_formater(prompt, order_image_tokens=False)\n",
+    "    else:\n",
+    "        # Append the <image> token if missing\n",
+    "        assert prompt.count(\"<image>\") <= len(images), \"the number \"\n",
+    "        \"of image token in prompt and in the images list should be the same!\"\n",
+    "        num_token_missing = len(images) - prompt.count(\"<image>\")\n",
+    "        prompt += \" <image> \" * num_token_missing\n",
+    "        images = [get_image_data(x) for x in images]\n",
+    "    \n",
+    "    for im in images:\n",
+    "        if len(im) == 0:\n",
+    "            raise RunTimeError(\"An image is empty!\")\n",
+    "\n",
+    "    return llava_call_binary(prompt, images, \n",
+    "                      model_name, \n",
+    "                      max_new_tokens, temperature, seed)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4123df2c",
+   "metadata": {},
+   "source": [
+    "Here is the image that we are going to use.\n",
+    "\n",
+    "![Image](https://github.com/haotian-liu/LLaVA/raw/main/images/llava_logo.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05ed5a35",
+   "metadata": {},
+   "source": [
+    "We can call llava by providing the prompt and images separately.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ec31ca74",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The image features a small, orange, and black toy animal, possibly a stuffed dog or a toy horse, with flames coming out of its back. The toy is sitting on a table, and it appears to be a unique and creative design. The toy is wearing glasses, adding a touch of whimsy to its appearance. The overall scene is quite eye-catching and playful.\n"
+     ]
+    }
+   ],
+   "source": [
+    "out = llava_call(\"Describe this image: <image>\", \n",
+    "                 images=[\"https://github.com/haotian-liu/LLaVA/raw/main/images/llava_logo.png\"])\n",
+    "print(out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6619dc30",
+   "metadata": {},
+   "source": [
+    "Or, we can also call LLaVA with only prompt, with images embedded in the prompt with the <img xxx> format\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "12a7db5a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A red toy with flames and glasses on it.\n"
+     ]
+    }
+   ],
+   "source": [
+    "out = llava_call(\"Describe this image in one sentence: <img https://github.com/haotian-liu/LLaVA/raw/main/images/llava_logo.png>\")\n",
+    "print(out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e4faf59",
+   "metadata": {},
+   "source": [
+    "<a id=\"app-1\"></a>\n",
+    "## Application 1: Image Chat\n",
+    "\n",
+    "In this section, we present a straightforward dual-agent architecture to enable user to chat with a multimodal agent.\n",
+    "\n",
+    "\n",
+    "First, we show this image and ask a question.\n",
+    "![](https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "286938aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "config_list_gpt4 = autogen.config_list_from_json(\n",
+    "    \"OAI_CONFIG_LIST\",\n",
+    "    filter_dict={\n",
+    "        \"model\": [\"gpt-4\", \"gpt-4-0314\", \"gpt4\", \"gpt-4-32k\", \"gpt-4-32k-0314\", \"gpt-4-32k-v0314\"],\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "llm_config = {\"config_list\": config_list_gpt4, \"seed\": 42}\n",
+    "\n",
+    "DEFAULT_LMM_SYS_MSG = \"\"\"You are a helpful AI assistant.\n",
+    "You can also view images, where the \"<image i>\" represent the i-th image you received.\"\"\"\n",
+    "\n",
+    "class MultimodalConversableAgent(ConversableAgent):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        name: str,\n",
+    "        system_message: Optional[Tuple[str, List]] = DEFAULT_LMM_SYS_MSG,\n",
+    "        is_termination_msg=None,\n",
+    "        *args,\n",
+    "        **kwargs,\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Args:\n",
+    "            name (str): agent name.\n",
+    "            system_message (str): system message for the ChatCompletion inference.\n",
+    "                Please override this attribute if you want to reprogram the agent.\n",
+    "            **kwargs (dict): Please refer to other kwargs in\n",
+    "                [ConversableAgent](conversable_agent#__init__).\n",
+    "        \"\"\"\n",
+    "        super().__init__(\n",
+    "            name,\n",
+    "            system_message,\n",
+    "            is_termination_msg=is_termination_msg,\n",
+    "            *args,\n",
+    "            **kwargs,\n",
+    "        )\n",
+    "        \n",
+    "        self.update_system_message(system_message)\n",
+    "        self._is_termination_msg = (\n",
+    "            is_termination_msg if is_termination_msg is not None else (lambda x: x.get(\"content\")[-1] == \"TERMINATE\")\n",
+    "        )\n",
+    "            \n",
+    "    @property\n",
+    "    def system_message(self) -> List:\n",
+    "        \"\"\"Return the system message.\"\"\"\n",
+    "        return self._oai_system_message[0][\"content\"]\n",
+    "\n",
+    "    def update_system_message(self, system_message: str):\n",
+    "        \"\"\"Update the system message.\n",
+    "\n",
+    "        Args:\n",
+    "            system_message (str): system message for the ChatCompletion inference.\n",
+    "        \"\"\"\n",
+    "        self._oai_system_message[0][\"content\"] = self._message_to_dict(system_message)[\"content\"]\n",
+    "        self._oai_system_message[0][\"role\"] = \"system\"\n",
+    "        \n",
+    "    @staticmethod\n",
+    "    def _message_to_dict(message: Union[Dict, List, str]):\n",
+    "        \"\"\"Convert a message to a dictionary.\n",
+    "\n",
+    "        The message can be a string or a dictionary. The string will be put in the \"content\" field of the new dictionary.\n",
+    "        \"\"\"\n",
+    "        if isinstance(message, str):\n",
+    "            return {\"content\": gpt4v_formatter(message)}\n",
+    "        if isinstance(message, list):\n",
+    "            return {\"content\": message}\n",
+    "        else:\n",
+    "            return message\n",
+    "        \n",
+    "    def _content_str(self, content: List) -> str:\n",
+    "        rst = \"\"\n",
+    "        for item in content:\n",
+    "            if isinstance(item, str):\n",
+    "                rst += item\n",
+    "            else:\n",
+    "                assert isinstance(item, dict) and \"image\" in item, (\"Wrong content format.\")\n",
+    "                rst += \"<image>\"\n",
+    "        return rst\n",
+    "                \n",
+    "    def _print_received_message(self, message: Union[Dict, str], sender: Agent):\n",
+    "        # print the message received\n",
+    "        print(colored(sender.name, \"yellow\"), \"(to\", f\"{self.name}):\\n\", flush=True)\n",
+    "        if message.get(\"role\") == \"function\":\n",
+    "            func_print = f\"***** Response from calling function \\\"{message['name']}\\\" *****\"\n",
+    "            print(colored(func_print, \"green\"), flush=True)\n",
+    "            print(self._content_str(message[\"content\"]), flush=True)\n",
+    "            print(colored(\"*\" * len(func_print), \"green\"), flush=True)\n",
+    "        else:\n",
+    "            content = message.get(\"content\")\n",
+    "            if content is not None:\n",
+    "                if \"context\" in message:\n",
+    "                    content = oai.ChatCompletion.instantiate(\n",
+    "                        content,\n",
+    "                        message[\"context\"],\n",
+    "                        self.llm_config and self.llm_config.get(\"allow_format_str_template\", False),\n",
+    "                    )\n",
+    "                print(self._content_str(content), flush=True)\n",
+    "            if \"function_call\" in message:\n",
+    "                func_print = f\"***** Suggested function Call: {message['function_call'].get('name', '(No function name found)')} *****\"\n",
+    "                print(colored(func_print, \"green\"), flush=True)\n",
+    "                print(\n",
+    "                    \"Arguments: \\n\",\n",
+    "                    message[\"function_call\"].get(\"arguments\", \"(No arguments found)\"),\n",
+    "                    flush=True,\n",
+    "                    sep=\"\",\n",
+    "                )\n",
+    "                print(colored(\"*\" * len(func_print), \"green\"), flush=True)\n",
+    "        print(\"\\n\", \"-\" * 80, flush=True, sep=\"\")\n",
+    "    # TODO: we may want to udpate `generate_code_execution_reply` or `extract_code` for the \"content\" type change.\n",
+    "    \n",
+    "\n",
+    "DEFAULT_LLAVA_SYS_MSG = \"You are an AI agent and you can view images.\"\n",
+    "class LLaVAAgent(MultimodalConversableAgent):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        name: str,\n",
+    "        system_message: Optional[Tuple[str, List]] = DEFAULT_LLAVA_SYS_MSG,\n",
+    "        *args,\n",
+    "        **kwargs,\n",
+    "    ):\n",
+    "        \"\"\"\n",
+    "        Args:\n",
+    "            name (str): agent name.\n",
+    "            system_message (str): system message for the ChatCompletion inference.\n",
+    "                Please override this attribute if you want to reprogram the agent.\n",
+    "            **kwargs (dict): Please refer to other kwargs in\n",
+    "                [ConversableAgent](conversable_agent#__init__).\n",
+    "        \"\"\"\n",
+    "        super().__init__(\n",
+    "            name,\n",
+    "            system_message=system_message,\n",
+    "            *args,\n",
+    "            **kwargs,\n",
+    "        )\n",
+    "        self.register_reply([Agent, None], reply_func=LLaVAAgent._image_reply, position=0)\n",
+    "\n",
+    "    def _image_reply(\n",
+    "        self,\n",
+    "        messages=None,\n",
+    "        sender=None, config=None\n",
+    "    ):\n",
+    "        # Note: we did not use \"llm_config\" yet.\n",
+    "        # TODO 1: make the LLaVA API design compatible with llm_config\n",
+    "        \n",
+    "        if all((messages is None, sender is None)):\n",
+    "            error_msg = f\"Either {messages=} or {sender=} must be provided.\"\n",
+    "            logger.error(error_msg)\n",
+    "            raise AssertionError(error_msg)\n",
+    "\n",
+    "        if messages is None:\n",
+    "            messages = self._oai_messages[sender]\n",
+    "\n",
+    "        # The formats for LLaVA and GPT are different. So, we manually handle them here.\n",
+    "        # TODO: format the images from the history accordingly.\n",
+    "        images = []\n",
+    "        prompt = self._content_str(self.system_message) + \"\\n\"\n",
+    "        for msg in messages:\n",
+    "            role = \"Human\" if msg[\"role\"] == \"user\" else \"Assistant\"\n",
+    "            images += [d[\"image\"] for d in msg[\"content\"] if isinstance(d, dict)]\n",
+    "            content_prompt = self._content_str(msg[\"content\"])\n",
+    "            prompt += f\"{SEP}{role}: {content_prompt}\\n\"\n",
+    "        prompt += \"\\n\" + SEP + \"Assistant: \"\n",
+    "        print(colored(prompt, \"blue\"))\n",
+    "    \n",
+    "        out = \"\"\n",
+    "        retry = 10\n",
+    "        while len(out) == 0 and retry > 0:\n",
+    "            # image names will be inferred automatically from llava_call\n",
+    "            out = llava_call_binary(prompt=prompt, images=images, temperature=0, max_new_tokens=2000)\n",
+    "            retry -= 1\n",
+    "            \n",
+    "        assert out != \"\", \"Empty response from LLaVA.\"\n",
+    "        \n",
+    "        \n",
+    "        return True, out"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3d5580e",
+   "metadata": {},
+   "source": [
+    "Within the user proxy agent, we can decide to activate the human input mode or not (for here, we use human_input_mode=\"NEVER\" for conciseness). This allows you to interact with LLaVA in a multi-round dialogue, enabling you to provide feedback as the conversation unfolds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "67157629",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mUser_proxy\u001b[0m (to image-explainer):\n",
+      "\n",
+      "What's the breed of this dog? \n",
+      "<image>.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[34mYou are an AI agent and you can view images.\n",
+      "###Human: What's the breed of this dog? \n",
+      "<image>.\n",
+      "\n",
+      "###Assistant: \u001b[0m\n",
+      "\u001b[33mimage-explainer\u001b[0m (to User_proxy):\n",
+      "\n",
+      "The dog in the image is a poodle.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "image_agent = LLaVAAgent(\n",
+    "    name=\"image-explainer\",\n",
+    "    max_consecutive_auto_reply=0\n",
+    ")\n",
+    "\n",
+    "user_proxy = autogen.UserProxyAgent(\n",
+    "    name=\"User_proxy\",\n",
+    "    system_message=\"A human admin.\",\n",
+    "    code_execution_config={\n",
+    "        \"last_n_messages\": 3,\n",
+    "        \"work_dir\": \"groupchat\"\n",
+    "    },\n",
+    "    human_input_mode=\"NEVER\", # Try between ALWAYS or NEVER\n",
+    "#     llm_config=llm_config,\n",
+    "    max_consecutive_auto_reply=0,\n",
+    ")\n",
+    "\n",
+    "# Ask the question with an image\n",
+    "user_proxy.initiate_chat(image_agent, \n",
+    "                         message=\"\"\"What's the breed of this dog? \n",
+    "<img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f60521d",
+   "metadata": {},
+   "source": [
+    "Now, input another image, and ask a followup question.\n",
+    "\n",
+    "![](https://th.bing.com/th/id/OIP.29Mi2kJmcHHyQVGe_0NG7QHaEo?pid=ImgDet&rs=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "73a2b234",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mUser_proxy\u001b[0m (to image-explainer):\n",
+      "\n",
+      "How about these breeds? \n",
+      "<image>\n",
+      "\n",
+      "Among the breeds, which one barks less?\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[34mYou are an AI agent and you can view images.\n",
+      "###Human: What's the breed of this dog? \n",
+      "<image>.\n",
+      "###Assistant: The dog in the image is a poodle.\n",
+      "###Human: How about these breeds? <image> and <image>\n",
+      "Among all the breeds, which one barks less?\n",
+      "###Assistant: The breeds of the dog in the image are a poodle and a terrier. Among the two, the poodle is known to bark less.\n",
+      "###Human: How about these breeds? \n",
+      "<image>\n",
+      "\n",
+      "Among the breeds, which one barks less?\n",
+      "\n",
+      "###Assistant: \u001b[0m\n",
+      "\u001b[33mimage-explainer\u001b[0m (to User_proxy):\n",
+      "\n",
+      "Among the breeds, the poodle is known to bark less.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Ask the question with an image\n",
+    "user_proxy.send(message=\"\"\"How about these breeds? \n",
+    "<img https://th.bing.com/th/id/OIP.29Mi2kJmcHHyQVGe_0NG7QHaEo?pid=ImgDet&rs=1>\n",
+    "\n",
+    "Among the breeds, which one barks less?\"\"\", \n",
+    "                recipient=image_agent)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c40d0eb",
+   "metadata": {},
+   "source": [
+    "<a id=\"app-2\"></a>\n",
+    "## Application 2: Figure Creator\n",
+    "\n",
+    "Here, we define a `FigureCreator` agent, which contains three child agents: commander, coder, and critics.\n",
+    "\n",
+    "- Commander: interacts with users, runs code, and coordinates the flow between the coder and critics.\n",
+    "- Coder: writes code for visualization.\n",
+    "- Critics: LLaVA-based agent that provides comments and feedback on the generated image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e8eca993",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FigureCreator(AssistantAgent):\n",
+    "\n",
+    "    def __init__(self, n_iters=2, **kwargs):\n",
+    "        \"\"\"\n",
+    "        Initializes a FigureCreator instance.\n",
+    "        \n",
+    "        This agent facilitates the creation of visualizations through a collaborative effort among its child agents: commander, coder, and critics.\n",
+    "        \n",
+    "        Parameters:\n",
+    "            - n_iters (int, optional): The number of \"improvement\" iterations to run. Defaults to 2.\n",
+    "            - **kwargs: keyword arguments for the parent AssistantAgent.\n",
+    "        \"\"\"\n",
+    "        super().__init__(**kwargs)\n",
+    "        self.register_reply([Agent, None],\n",
+    "                            reply_func=FigureCreator._reply_user,\n",
+    "                            position=0)\n",
+    "        self._n_iters = n_iters\n",
+    "\n",
+    "    def _reply_user(self, messages=None, sender=None, config=None):\n",
+    "        if all((messages is None, sender is None)):\n",
+    "            error_msg = f\"Either {messages=} or {sender=} must be provided.\"\n",
+    "            logger.error(error_msg)\n",
+    "            raise AssertionError(error_msg)\n",
+    "\n",
+    "        if messages is None:\n",
+    "            messages = self._oai_messages[sender]\n",
+    "\n",
+    "        user_question = messages[-1][\"content\"]\n",
+    "\n",
+    "        ### Define the agents\n",
+    "        commander = AssistantAgent(\n",
+    "            name=\"Commander\",\n",
+    "            human_input_mode=\"NEVER\",\n",
+    "            max_consecutive_auto_reply=10,\n",
+    "            system_message=\n",
+    "            \"Help me run the code, and tell other agents it is in the <img result.jpg> file location.\",\n",
+    "            is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\n",
+    "                \"TERMINATE\"),\n",
+    "            code_execution_config={\n",
+    "                \"last_n_messages\": 3,\n",
+    "                \"work_dir\": \".\",\n",
+    "                \"use_docker\": False\n",
+    "            },\n",
+    "            llm_config=self.llm_config,\n",
+    "        )\n",
+    "\n",
+    "        critics = LLaVAAgent(\n",
+    "            name=\"Critics\",\n",
+    "            system_message=\n",
+    "            \"Criticize the input figure. How to replot the figure so it will be better? Find bugs and issues for the figure. If you think the figures is good enough, then simply say NO_ISSUES\",\n",
+    "            llm_config=self.llm_config,\n",
+    "            human_input_mode=\"NEVER\",\n",
+    "            max_consecutive_auto_reply=0,\n",
+    "            #     use_docker=False,\n",
+    "        )\n",
+    "\n",
+    "        coder = AssistantAgent(\n",
+    "            name=\"Coder\",\n",
+    "            llm_config=self.llm_config,\n",
+    "        )\n",
+    "\n",
+    "        coder.update_system_message(\n",
+    "            coder.system_message +\n",
+    "            \"ALWAYS save the figure in `result.jpg` file. Tell other agents it is in the <img result.jpg> file location.\"\n",
+    "        )\n",
+    "\n",
+    "        # Data flow begins\n",
+    "        commander.initiate_chat(coder, message=user_question)\n",
+    "        img = Image.open(\"result.jpg\")\n",
+    "        plt.imshow(img)\n",
+    "        plt.axis('off')  # Hide the axes\n",
+    "        plt.show()\n",
+    "        \n",
+    "        for i in range(self._n_iters):\n",
+    "            commander.send(message=\"Improve <img result.jpg>\",\n",
+    "                           recipient=critics,\n",
+    "                           request_reply=True)\n",
+    "            \n",
+    "            feedback = commander._oai_messages[critics][-1][\"content\"]\n",
+    "            if feedback.find(\"NO_ISSUES\") >= 0:\n",
+    "                break\n",
+    "            commander.send(\n",
+    "                message=\"Here is the feedback to your figure. Please improve! Save the result to `result.jpg`\\n\"\n",
+    "                + feedback,\n",
+    "                recipient=coder,\n",
+    "                request_reply=True)\n",
+    "            img = Image.open(\"result.jpg\")\n",
+    "            plt.imshow(img)\n",
+    "            plt.axis('off')  # Hide the axes\n",
+    "            plt.show()\n",
+    "            \n",
+    "        return True, \"result.jpg\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "977b9017",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mUser\u001b[0m (to Figure Creator~):\n",
+      "\n",
+      "\n",
+      "Plot a figure by using the data from:\n",
+      "https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\n",
+      "\n",
+      "I want to show both temperature high and low.\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mCommander\u001b[0m (to Coder):\n",
+      "\n",
+      "\n",
+      "Plot a figure by using the data from:\n",
+      "https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\n",
+      "\n",
+      "I want to show both temperature high and low.\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mCoder\u001b[0m (to Commander):\n",
+      "\n",
+      "To plot the figure using the data from the provided URL, we'll first download the data, then use the pandas library to read the CSV data and finally, use the matplotlib library to plot the temperature high and low.\n",
+      "\n",
+      "Step 1: Download the CSV file\n",
+      "Step 2: Read the CSV file using pandas\n",
+      "Step 3: Plot the temperature high and low using matplotlib\n",
+      "\n",
+      "Please execute the following code:\n",
+      "\n",
+      "```python\n",
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "import urllib.request\n",
+      "\n",
+      "# Download the CSV file from the URL\n",
+      "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n",
+      "urllib.request.urlretrieve(url, \"seattle-weather.csv\")\n",
+      "\n",
+      "# Read the CSV file using pandas\n",
+      "data = pd.read_csv(\"seattle-weather.csv\")\n",
+      "\n",
+      "# Plot the temperature high and low using matplotlib\n",
+      "plt.plot(data[\"date\"], data[\"temp_max\"], label=\"Temperature High\")\n",
+      "plt.plot(data[\"date\"], data[\"temp_min\"], label=\"Temperature Low\")\n",
+      "plt.xlabel(\"Date\")\n",
+      "plt.ylabel(\"Temperature\")\n",
+      "plt.title(\"Seattle Weather - Temperature High and Low\")\n",
+      "plt.legend()\n",
+      "plt.savefig(\"result.jpg\")\n",
+      "plt.show()\n",
+      "```\n",
+      "\n",
+      "After executing the code, you should see the desired plot with temperature high and low. The figure will be saved as `result.jpg`.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
+      "\u001b[33mCommander\u001b[0m (to Coder):\n",
+      "\n",
+      "exitcode: 0 (execution succeeded)\n",
+      "Code output: \n",
+      "Figure(640x480)\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mCoder\u001b[0m (to Commander):\n",
+      "\n",
+      "Great! The code execution succeeded, and the figure has been plotted using the data provided. The figure is saved in the `result.jpg` file. Please check the file for the plotted figure showing both temperature high and low.\n",
+      "\n",
+      "TERMINATE\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mCommander\u001b[0m (to Critics):\n",
+      "\n",
+      "Improve <image>\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[34mCriticize the input figure. How to replot the figure so it will be better? Find bugs and issues for the figure. If you think the figures is good enough, then simply say NO_ISSUES\n",
+      "###Human: Improve <image>\n",
+      "\n",
+      "###Assistant: \u001b[0m\n",
+      "\u001b[33mCritics\u001b[0m (to Commander):\n",
+      "\n",
+      "The input figure shows a graph with three different colored lines, representing temperature high, temperature low, and temperature average. The graph is labeled \"Seattle Weather - Temperature High and Low.\" However, the graph is not well-organized, and the lines are not clearly distinguishable.\n",
+      "\n",
+      "To improve the figure, one could use a more visually appealing color scheme for the lines, such as different shades of blue, green, and orange. Additionally, the labels for the temperature high, temperature low, and temperature average could be placed above or below the respective lines to make them more easily readable. The graph could also benefit from a clear title and axis labels to provide more context and information about the data being displayed.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mCommander\u001b[0m (to Coder):\n",
+      "\n",
+      "Here is the feedback to your figure. Please improve! Save the result to `result.jpg`\n",
+      "The input figure shows a graph with three different colored lines, representing temperature high, temperature low, and temperature average. The graph is labeled \"Seattle Weather - Temperature High and Low.\" However, the graph is not well-organized, and the lines are not clearly distinguishable.\n",
+      "\n",
+      "To improve the figure, one could use a more visually appealing color scheme for the lines, such as different shades of blue, green, and orange. Additionally, the labels for the temperature high, temperature low, and temperature average could be placed above or below the respective lines to make them more easily readable. The graph could also benefit from a clear title and axis labels to provide more context and information about the data being displayed.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mCoder\u001b[0m (to Commander):\n",
+      "\n",
+      "I appreciate your feedback. I will make the following improvements based on the feedback provided for the figure:\n",
+      "\n",
+      "1. Use a better color scheme for the lines, such as different shades of blue, green, and orange.\n",
+      "2. Add a label for temperature average.\n",
+      "3. Place the labels for temperature high, temperature low, and temperature average above or below the respective lines.\n",
+      "4. Improve the title and axis labels for better readability and context.\n",
+      "\n",
+      "Please execute the following code to generate the improved figure:\n",
+      "\n",
+      "```python\n",
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "import urllib.request\n",
+      "\n",
+      "# Download the CSV file from the URL\n",
+      "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n",
+      "urllib.request.urlretrieve(url, \"seattle-weather.csv\")\n",
+      "\n",
+      "# Read the CSV file using pandas\n",
+      "data = pd.read_csv(\"seattle-weather.csv\")\n",
+      "\n",
+      "# Calculate temperature average\n",
+      "data[\"temp_avg\"] = (data[\"temp_max\"] + data[\"temp_min\"]) / 2\n",
+      "\n",
+      "# Plot the temperature high, low, and average using matplotlib with better colors\n",
+      "plt.plot(data[\"date\"], data[\"temp_max\"], color=\"darkorange\", label=\"Temperature High\")\n",
+      "plt.plot(data[\"date\"], data[\"temp_min\"], color=\"dodgerblue\", label=\"Temperature Low\")\n",
+      "plt.plot(data[\"date\"], data[\"temp_avg\"], color=\"mediumseagreen\", label=\"Temperature Average\")\n",
+      "\n",
+      "# Improve the title and axis labels\n",
+      "plt.xlabel(\"Date\", fontsize=12)\n",
+      "plt.ylabel(\"Temperature\", fontsize=12)\n",
+      "plt.title(\"Seattle Weather - Temperatures (High, Low, and Average)\", fontsize=14)\n",
+      "\n",
+      "# Plot the legend\n",
+      "plt.legend(fontsize=10, loc='upper right')\n",
+      "\n",
+      "# Save the improved figure as 'result.jpg'\n",
+      "plt.savefig(\"result.jpg\", dpi=100)\n",
+      "\n",
+      "# Show the figure\n",
+      "plt.show()\n",
+      "```\n",
+      "\n",
+      "After executing the code, you should see the improved plot with better colors, labels, and readability. The figure will be saved as `result.jpg`.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
+      "\u001b[33mCommander\u001b[0m (to Coder):\n",
+      "\n",
+      "exitcode: 0 (execution succeeded)\n",
+      "Code output: \n",
+      "Figure(640x480)\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mCoder\u001b[0m (to Commander):\n",
+      "\n",
+      "Great! The code execution succeeded, and the improved figure has been plotted using the updated colors, labels, and readability. The figure is saved in the `result.jpg` file. Please check the file for the updated plotted figure showing temperature high, low, and average.\n",
+      "\n",
+      "TERMINATE\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mCommander\u001b[0m (to Critics):\n",
+      "\n",
+      "Improve <image>\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[34mCriticize the input figure. How to replot the figure so it will be better? Find bugs and issues for the figure. If you think the figures is good enough, then simply say NO_ISSUES\n",
+      "###Human: Improve <image>\n",
+      "###Assistant: The input figure shows a graph with three different colored lines, representing temperature high, temperature low, and temperature average. The graph is labeled \"Seattle Weather - Temperature High and Low.\" However, the graph is not well-organized, and the lines are not clearly distinguishable.\n",
+      "\n",
+      "To improve the figure, one could use a more visually appealing color scheme for the lines, such as different shades of blue, green, and orange. Additionally, the labels for the temperature high, temperature low, and temperature average could be placed above or below the respective lines to make them more easily readable. The graph could also benefit from a clear title and axis labels to provide more context and information about the data being displayed.\n",
+      "###Human: Improve <image>\n",
+      "\n",
+      "###Assistant: \u001b[0m\n",
+      "\u001b[33mCritics\u001b[0m (to Commander):\n",
+      "\n",
+      "To improve the figure, one could use a more visually appealing color scheme for the lines, such as different shades of blue, green, and orange. Additionally, the labels for the temperature high, temperature low, and temperature average could be placed above or below the respective lines to make them more easily readable. The graph could also benefit from a clear title and axis labels to provide more context and information about the data being displayed.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mCommander\u001b[0m (to Coder):\n",
+      "\n",
+      "Here is the feedback to your figure. Please improve! Save the result to `result.jpg`\n",
+      "To improve the figure, one could use a more visually appealing color scheme for the lines, such as different shades of blue, green, and orange. Additionally, the labels for the temperature high, temperature low, and temperature average could be placed above or below the respective lines to make them more easily readable. The graph could also benefit from a clear title and axis labels to provide more context and information about the data being displayed.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mCoder\u001b[0m (to Commander):\n",
+      "\n",
+      "Thank you for the feedback. I misunderstood the part about placing the labels above or below the respective lines. I will implement the necessary changes and generate the figure again. Here's the plan:\n",
+      "\n",
+      "1. Improve the line style for better readability by using different line styles.\n",
+      "2. Annotate the points on the graph with their respective labels (high, low, and average) for better readability.\n",
+      "3. Keep the colors, title, and axis labels from the previous improvement.\n",
+      "\n",
+      "Please execute the following code to generate the updated figure:\n",
+      "\n",
+      "```python\n",
+      "import pandas as pd\n",
+      "import matplotlib.pyplot as plt\n",
+      "import urllib.request\n",
+      "import numpy as np\n",
+      "\n",
+      "# Download the CSV file from the URL\n",
+      "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n",
+      "urllib.request.urlretrieve(url, \"seattle-weather.csv\")\n",
+      "\n",
+      "# Read the CSV file using pandas\n",
+      "data = pd.read_csv(\"seattle-weather.csv\")\n",
+      "\n",
+      "# Calculate temperature average\n",
+      "data[\"temp_avg\"] = (data[\"temp_max\"] + data[\"temp_min\"]) / 2\n",
+      "\n",
+      "# Plot the temperature high, low, and average using matplotlib with better colors\n",
+      "plt.plot(data[\"date\"], data[\"temp_max\"], color=\"darkorange\", label=\"Temperature High\", linestyle='--')\n",
+      "plt.plot(data[\"date\"], data[\"temp_min\"], color=\"dodgerblue\", label=\"Temperature Low\", linestyle='-.')\n",
+      "plt.plot(data[\"date\"], data[\"temp_avg\"], color=\"mediumseagreen\", label=\"Temperature Average\", linestyle='-')\n",
+      "\n",
+      "# Improve the title and axis labels\n",
+      "plt.xlabel(\"Date\", fontsize=12)\n",
+      "plt.ylabel(\"Temperature\", fontsize=12)\n",
+      "plt.title(\"Seattle Weather - Temperatures (High, Low, and Average)\", fontsize=14)\n",
+      "\n",
+      "# Add labels for some data points\n",
+      "num_labels = 5\n",
+      "label_indices = np.linspace(0, len(data) - 1, num_labels, dtype=int)\n",
+      "\n",
+      "for i in label_indices:\n",
+      "    plt.text(data.loc[i, \"date\"], data.loc[i, \"temp_max\"], \"High\", fontsize=8, verticalalignment=\"bottom\", horizontalalignment=\"left\")\n",
+      "    plt.text(data.loc[i, \"date\"], data.loc[i, \"temp_min\"], \"Low\", fontsize=8, verticalalignment=\"top\", horizontalalignment=\"left\")\n",
+      "    plt.text(data.loc[i, \"date\"], data.loc[i, \"temp_avg\"], \"Avg\", fontsize=8, verticalalignment=\"baseline\", horizontalalignment=\"left\")\n",
+      "\n",
+      "# Plot the legend\n",
+      "plt.legend(fontsize=10, loc='upper right')\n",
+      "\n",
+      "# Save the improved figure as 'result.jpg'\n",
+      "plt.savefig(\"result.jpg\", dpi=100)\n",
+      "\n",
+      "# Show the figure\n",
+      "plt.show()\n",
+      "```\n",
+      "\n",
+      "After executing the code, you should see the updated plot that addresses the concerns with labels and better readability. The figure will be saved as `result.jpg`.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
+      "\u001b[33mCommander\u001b[0m (to Coder):\n",
+      "\n",
+      "exitcode: 0 (execution succeeded)\n",
+      "Code output: \n",
+      "Figure(640x480)\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mCoder\u001b[0m (to Commander):\n",
+      "\n",
+      "Great! The code execution succeeded, and the updated figure has been plotted with the improved line styles and annotations for a better visualization. The figure is saved in the `result.jpg` file. Please check the file for the updated plotted figure showing temperature high, low, and average with better readability.\n",
+      "\n",
+      "TERMINATE\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mFigure Creator~\u001b[0m (to User):\n",
+      "\n",
+      "result.jpg\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import time\n",
+    "\n",
+    "creator = FigureCreator(\n",
+    "    name=\"Figure Creator~\",\n",
+    "    llm_config=llm_config\n",
+    "    \n",
+    ")\n",
+    "\n",
+    "user_proxy = autogen.UserProxyAgent(\n",
+    "    name=\"User\",\n",
+    "    human_input_mode=\"NEVER\",\n",
+    "    llm_config=llm_config,\n",
+    "    max_consecutive_auto_reply=0\n",
+    ")\n",
+    "\n",
+    "user_proxy.initiate_chat(creator, message=\"\"\"\n",
+    "Plot a figure by using the data from:\n",
+    "https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\n",
+    "\n",
+    "I want to show both temperature high and low.\n",
+    "\"\"\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0a58827",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebook/agentchat_qdrant_RetrieveChat.ipynb b/notebook/agentchat_qdrant_RetrieveChat.ipynb
new file mode 100644
index 000000000000..42a5cf82f4c2
--- /dev/null
+++ b/notebook/agentchat_qdrant_RetrieveChat.ipynb
@@ -0,0 +1,1234 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_qdrant_RetrieveChat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"toc\"></a>\n",
+    "# Using RetrieveChat with Qdrant for Retrieve Augmented Code Generation and Question Answering\n",
+    "\n",
+    "[Qdrant](https://qdrant.tech/) is a high-performance vector search engine/database.\n",
+    "\n",
+    "This notebook demonstrates the usage of `QdrantRetrieveUserProxyAgent` for RAG, based on [agentchat_RetrieveChat.ipynb](https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat.ipynb).\n",
+    "\n",
+    "\n",
+    "RetrieveChat is a conversational system for retrieve augmented code generation and question answering. In this notebook, we demonstrate how to utilize RetrieveChat to generate code and answer questions based on customized documentations that are not present in the LLM's training dataset. RetrieveChat uses the `RetrieveAssistantAgent` and `QdrantRetrieveUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)).\n",
+    "\n",
+    "We'll demonstrate usage of RetrieveChat with Qdrant for code generation and question answering w/ human feedback.\n",
+    "\n",
+    "\n",
+    "## Requirements\n",
+    "\n",
+    "AutoGen requires `Python>=3.8`. To run this notebook example, please install the [retrievechat] option.\n",
+    "```bash\n",
+    "pip install \"pyautogen[retrievechat] flaml[automl] qdrant_client[fastembed]\"\n",
+    "```"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set your API Endpoint\n",
+    "\n",
+    "The [`config_list_from_json`](https://microsoft.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "models to use:  ['gpt-3.5-turbo']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import autogen\n",
+    "\n",
+    "config_list = autogen.config_list_from_json(\n",
+    "    env_or_file=\"OAI_CONFIG_LIST\",\n",
+    "    file_location=\".\",\n",
+    "    filter_dict={\n",
+    "        \"model\": {\n",
+    "            \"gpt-4\",\n",
+    "            \"gpt4\",\n",
+    "            \"gpt-4-32k\",\n",
+    "            \"gpt-4-32k-0314\",\n",
+    "            \"gpt-35-turbo\",\n",
+    "            \"gpt-3.5-turbo\",\n",
+    "        }\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "assert len(config_list) > 0\n",
+    "print(\"models to use: \", [config_list[i][\"model\"] for i in range(len(config_list))])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by models (you can filter by other keys as well). Only the gpt-4 and gpt-3.5-turbo models are kept in the list based on the filter condition.\n",
+    "\n",
+    "The config list looks like the following:\n",
+    "```python\n",
+    "config_list = [\n",
+    "    {\n",
+    "        'model': 'gpt-4',\n",
+    "        'api_key': '<your OpenAI API key here>',\n",
+    "    },\n",
+    "    {\n",
+    "        'model': 'gpt-4',\n",
+    "        'api_key': '<your Azure OpenAI API key here>',\n",
+    "        'api_base': '<your Azure OpenAI API base here>',\n",
+    "        'api_type': 'azure',\n",
+    "        'api_version': '2023-06-01-preview',\n",
+    "    },\n",
+    "    {\n",
+    "        'model': 'gpt-3.5-turbo',\n",
+    "        'api_key': '<your Azure OpenAI API key here>',\n",
+    "        'api_base': '<your Azure OpenAI API base here>',\n",
+    "        'api_type': 'azure',\n",
+    "        'api_version': '2023-06-01-preview',\n",
+    "    },\n",
+    "]\n",
+    "```\n",
+    "\n",
+    "If you open this notebook in colab, you can upload your files by clicking the file icon on the left panel and then choose \"upload file\" icon.\n",
+    "\n",
+    "You can set the value of config_list in other ways you prefer, e.g., loading from a YAML file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accepted file formats for `docs_path`:\n",
+      "['txt', 'json', 'csv', 'tsv', 'md', 'html', 'htm', 'rtf', 'rst', 'jsonl', 'log', 'xml', 'yaml', 'yml', 'pdf']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Accepted file formats for that can be stored in \n",
+    "# a vector database instance\n",
+    "from autogen.retrieve_utils import TEXT_FORMATS\n",
+    "\n",
+    "print(\"Accepted file formats for `docs_path`:\")\n",
+    "print(TEXT_FORMATS)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Construct agents for RetrieveChat\n",
+    "\n",
+    "We start by initialzing the `RetrieveAssistantAgent` and `QdrantRetrieveUserProxyAgent`. The system message needs to be set to \"You are a helpful assistant.\" for RetrieveAssistantAgent. The detailed instructions are given in the user message. Later we will use the `QdrantRetrieveUserProxyAgent.generate_init_prompt` to combine the instructions and a retrieval augmented generation task for an initial prompt to be sent to the LLM assistant.\n",
+    "\n",
+    "### You can find the list of all the embedding models supported by Qdrant [here](https://qdrant.github.io/fastembed/examples/Supported_Models/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from autogen.agentchat.contrib.retrieve_assistant_agent import RetrieveAssistantAgent\n",
+    "from autogen.agentchat.contrib.qdrant_retrieve_user_proxy_agent import QdrantRetrieveUserProxyAgent\n",
+    "from qdrant_client import QdrantClient\n",
+    "\n",
+    "autogen.ChatCompletion.start_logging()\n",
+    "\n",
+    "# 1. create an RetrieveAssistantAgent instance named \"assistant\"\n",
+    "assistant = RetrieveAssistantAgent(\n",
+    "    name=\"assistant\", \n",
+    "    system_message=\"You are a helpful assistant.\",\n",
+    "    llm_config={\n",
+    "        \"request_timeout\": 600,\n",
+    "        \"seed\": 42,\n",
+    "        \"config_list\": config_list,\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# 2. create the QdrantRetrieveUserProxyAgent instance named \"ragproxyagent\"\n",
+    "# By default, the human_input_mode is \"ALWAYS\", which means the agent will ask for human input at every step. We set it to \"NEVER\" here.\n",
+    "# `docs_path` is the path to the docs directory. It can also be the path to a single file, or the url to a single file. By default, \n",
+    "# it is set to None, which works only if the collection is already created.\n",
+    "# \n",
+    "# Here we generated the documentations from FLAML's docstrings. Not needed if you just want to try this notebook but not to reproduce the\n",
+    "# outputs. Clone the FLAML (https://github.com/microsoft/FLAML) repo and navigate to its website folder. Pip install and run `pydoc-markdown`\n",
+    "# and it will generate folder `reference` under `website/docs`.\n",
+    "#\n",
+    "# `task` indicates the kind of task we're working on. In this example, it's a `code` task.\n",
+    "# `chunk_token_size` is the chunk token size for the retrieve chat. By default, it is set to `max_tokens * 0.6`, here we set it to 2000.\n",
+    "# We use an in-memory QdrantClient instance here. Not recommended for production.\n",
+    "# Get the installation instructions here: https://qdrant.tech/documentation/guides/installation/\n",
+    "ragproxyagent = QdrantRetrieveUserProxyAgent(\n",
+    "    name=\"ragproxyagent\",\n",
+    "    human_input_mode=\"NEVER\",\n",
+    "    max_consecutive_auto_reply=10,\n",
+    "    retrieve_config={\n",
+    "        \"task\": \"code\",\n",
+    "        \"docs_path\": \"~/path/to/FLAML/website/docs/reference\",  # change this to your own path, such as https://raw.githubusercontent.com/microsoft/autogen/main/README.md\n",
+    "        \"chunk_token_size\": 2000,\n",
+    "        \"model\": config_list[0][\"model\"],\n",
+    "        \"client\": QdrantClient(\":memory:\"),\n",
+    "        \"embedding_model\": \"BAAI/bge-small-en-v1.5\",\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"example-1\"></a>\n",
+    "### Example 1\n",
+    "\n",
+    "[back to top](#toc)\n",
+    "\n",
+    "Use RetrieveChat to answer a question and ask for human-in-loop feedbacks.\n",
+    "\n",
+    "Problem: Is there a function named `tune_automl` in FLAML?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32mAdding doc_id 69 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 0 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 47 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 64 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 65 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 21 to context.\u001b[0m\n",
+      "\u001b[33mragproxyagent\u001b[0m (to assistant):\n",
+      "\n",
+      "You're a retrieve augmented coding assistant. You answer user's questions based on your own knowledge and the\n",
+      "context provided by the user.\n",
+      "If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.\n",
+      "For code generation, you must obey the following rules:\n",
+      "Rule 1. You MUST NOT install any packages because all the packages needed are already installed.\n",
+      "Rule 2. You must follow the formats below to write your code:\n",
+      "```language\n",
+      "# your code\n",
+      "```\n",
+      "\n",
+      "User's question is: Is there a function called tune_automl?\n",
+      "\n",
+      "Context is: {\n",
+      "  \"items\": [\n",
+      "    {\n",
+      "      \"items\": [\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            {\n",
+      "              \"items\": [\n",
+      "                \"reference/autogen/agentchat/contrib/math_user_proxy_agent\",\n",
+      "                \"reference/autogen/agentchat/contrib/retrieve_assistant_agent\",\n",
+      "                \"reference/autogen/agentchat/contrib/retrieve_user_proxy_agent\"\n",
+      "              ],\n",
+      "              \"label\": \"autogen.agentchat.contrib\",\n",
+      "              \"type\": \"category\"\n",
+      "            },\n",
+      "            \"reference/autogen/agentchat/agent\",\n",
+      "            \"reference/autogen/agentchat/assistant_agent\",\n",
+      "            \"reference/autogen/agentchat/conversable_agent\",\n",
+      "            \"reference/autogen/agentchat/groupchat\",\n",
+      "            \"reference/autogen/agentchat/user_proxy_agent\"\n",
+      "          ],\n",
+      "          \"label\": \"autogen.agentchat\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/autogen/oai/completion\",\n",
+      "            \"reference/autogen/oai/openai_utils\"\n",
+      "          ],\n",
+      "          \"label\": \"autogen.oai\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        \"reference/autogen/code_utils\",\n",
+      "        \"reference/autogen/math_utils\",\n",
+      "        \"reference/autogen/retrieve_utils\"\n",
+      "      ],\n",
+      "      \"label\": \"autogen\",\n",
+      "      \"type\": \"category\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"items\": [\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            {\n",
+      "              \"items\": [\n",
+      "                \"reference/automl/nlp/huggingface/trainer\",\n",
+      "                \"reference/automl/nlp/huggingface/training_args\",\n",
+      "                \"reference/automl/nlp/huggingface/utils\"\n",
+      "              ],\n",
+      "              \"label\": \"automl.nlp.huggingface\",\n",
+      "              \"type\": \"category\"\n",
+      "            },\n",
+      "            \"reference/automl/nlp/utils\"\n",
+      "          ],\n",
+      "          \"label\": \"automl.nlp\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/automl/spark/metrics\",\n",
+      "            \"reference/automl/spark/utils\"\n",
+      "          ],\n",
+      "          \"label\": \"automl.spark\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/automl/task/task\",\n",
+      "            \"reference/automl/task/time_series_task\"\n",
+      "          ],\n",
+      "          \"label\": \"automl.task\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/automl/time_series/sklearn\",\n",
+      "            \"reference/automl/time_series/tft\",\n",
+      "            \"reference/automl/time_series/ts_data\",\n",
+      "            \"reference/automl/time_series/ts_model\"\n",
+      "          ],\n",
+      "          \"label\": \"automl.time_series\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        \"reference/automl/automl\",\n",
+      "        \"reference/automl/data\",\n",
+      "        \"reference/automl/ml\",\n",
+      "        \"reference/automl/model\",\n",
+      "        \"reference/automl/state\"\n",
+      "      ],\n",
+      "      \"label\": \"automl\",\n",
+      "      \"type\": \"category\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"items\": [\n",
+      "        \"reference/default/estimator\",\n",
+      "        \"reference/default/greedy\",\n",
+      "        \"reference/default/portfolio\",\n",
+      "        \"reference/default/suggest\"\n",
+      "      ],\n",
+      "      \"label\": \"default\",\n",
+      "      \"type\": \"category\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"items\": [\n",
+      "        \"reference/onlineml/autovw\",\n",
+      "        \"reference/onlineml/trial\",\n",
+      "        \"reference/onlineml/trial_runner\"\n",
+      "      ],\n",
+      "      \"label\": \"onlineml\",\n",
+      "      \"type\": \"category\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"items\": [\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/tune/scheduler/online_scheduler\",\n",
+      "            \"reference/tune/scheduler/trial_scheduler\"\n",
+      "          ],\n",
+      "          \"label\": \"tune.scheduler\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/tune/searcher/blendsearch\",\n",
+      "            \"reference/tune/searcher/cfo_cat\",\n",
+      "            \"reference/tune/searcher/flow2\",\n",
+      "            \"reference/tune/searcher/online_searcher\",\n",
+      "            \"reference/tune/searcher/search_thread\",\n",
+      "            \"reference/tune/searcher/suggestion\",\n",
+      "            \"reference/tune/searcher/variant_generator\"\n",
+      "          ],\n",
+      "          \"label\": \"tune.searcher\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/tune/spark/utils\"\n",
+      "          ],\n",
+      "          \"label\": \"tune.spark\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        \"reference/tune/analysis\",\n",
+      "        \"reference/tune/sample\",\n",
+      "        \"reference/tune/space\",\n",
+      "        \"reference/tune/trial\",\n",
+      "        \"reference/tune/trial_runner\",\n",
+      "        \"reference/tune/tune\",\n",
+      "        \"reference/tune/utils\"\n",
+      "      ],\n",
+      "      \"label\": \"tune\",\n",
+      "      \"type\": \"category\"\n",
+      "    },\n",
+      "    \"reference/config\"\n",
+      "  ],\n",
+      "  \"label\": \"Reference\",\n",
+      "  \"type\": \"category\"\n",
+      "}\n",
+      "---\n",
+      "sidebar_label: config\n",
+      "title: config\n",
+      "---\n",
+      "\n",
+      "!\n",
+      "* Copyright (c) Microsoft Corporation. All rights reserved.\n",
+      "* Licensed under the MIT License.\n",
+      "\n",
+      "#### PENALTY\n",
+      "\n",
+      "penalty term for constraints\n",
+      "\n",
+      "\n",
+      "---\n",
+      "sidebar_label: trial_scheduler\n",
+      "title: tune.scheduler.trial_scheduler\n",
+      "---\n",
+      "\n",
+      "## TrialScheduler Objects\n",
+      "\n",
+      "```python\n",
+      "class TrialScheduler()\n",
+      "```\n",
+      "\n",
+      "Interface for implementing a Trial Scheduler class.\n",
+      "\n",
+      "#### CONTINUE\n",
+      "\n",
+      "Status for continuing trial execution\n",
+      "\n",
+      "#### PAUSE\n",
+      "\n",
+      "Status for pausing trial execution\n",
+      "\n",
+      "#### STOP\n",
+      "\n",
+      "Status for stopping trial execution\n",
+      "\n",
+      "\n",
+      "---\n",
+      "sidebar_label: retrieve_user_proxy_agent\n",
+      "title: autogen.agentchat.contrib.retrieve_user_proxy_agent\n",
+      "---\n",
+      "\n",
+      "## RetrieveUserProxyAgent Objects\n",
+      "\n",
+      "```python\n",
+      "class RetrieveUserProxyAgent(UserProxyAgent)\n",
+      "```\n",
+      "\n",
+      "#### \\_\\_init\\_\\_\n",
+      "\n",
+      "```python\n",
+      "def __init__(name=\"RetrieveChatAgent\",\n",
+      "             is_termination_msg: Optional[Callable[\n",
+      "                 [Dict], bool]] = _is_termination_msg_retrievechat,\n",
+      "             human_input_mode: Optional[str] = \"ALWAYS\",\n",
+      "             retrieve_config: Optional[Dict] = None,\n",
+      "             **kwargs)\n",
+      "```\n",
+      "\n",
+      "**Arguments**:\n",
+      "\n",
+      "- `name` _str_ - name of the agent.\n",
+      "- `human_input_mode` _str_ - whether to ask for human inputs every time a message is received.\n",
+      "  Possible values are \"ALWAYS\", \"TERMINATE\", \"NEVER\".\n",
+      "  (1) When \"ALWAYS\", the agent prompts for human input every time a message is received.\n",
+      "  Under this mode, the conversation stops when the human input is \"exit\",\n",
+      "  or when is_termination_msg is True and there is no human input.\n",
+      "  (2) When \"TERMINATE\", the agent only prompts for human input only when a termination message is received or\n",
+      "  the number of auto reply reaches the max_consecutive_auto_reply.\n",
+      "  (3) When \"NEVER\", the agent will never prompt for human input. Under this mode, the conversation stops\n",
+      "  when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True.\n",
+      "- `retrieve_config` _dict or None_ - config for the retrieve agent.\n",
+      "  To use default config, set to None. Otherwise, set to a dictionary with the following keys:\n",
+      "  - task (Optional, str): the task of the retrieve chat. Possible values are \"code\", \"qa\" and \"default\". System\n",
+      "  prompt will be different for different tasks. The default value is `default`, which supports both code and qa.\n",
+      "  - client (Optional, chromadb.Client): the chromadb client.\n",
+      "  If key not provided, a default client `chromadb.Client()` will be used.\n",
+      "  - docs_path (Optional, str): the path to the docs directory. It can also be the path to a single file,\n",
+      "  or the url to a single file. If key not provided, a default path `./docs` will be used.\n",
+      "  - collection_name (Optional, str): the name of the collection.\n",
+      "  If key not provided, a default name `flaml-docs` will be used.\n",
+      "  - model (Optional, str): the model to use for the retrieve chat.\n",
+      "  If key not provided, a default model `gpt-4` will be used.\n",
+      "  - chunk_token_size (Optional, int): the chunk token size for the retrieve chat.\n",
+      "  If key not provided, a default size `max_tokens * 0.4` will be used.\n",
+      "  - context_max_tokens (Optional, int): the context max token size for the retrieve chat.\n",
+      "  If key not provided, a default size `max_tokens * 0.8` will be used.\n",
+      "  - chunk_mode (Optional, str): the chunk mode for the retrieve chat. Possible values are\n",
+      "  \"multi_lines\" and \"one_line\". If key not provided, a default mode `multi_lines` will be used.\n",
+      "  - must_break_at_empty_line (Optional, bool): chunk will only break at empty line if True. Default is True.\n",
+      "  If chunk_mode is \"one_line\", this parameter will be ignored.\n",
+      "  - embedding_model (Optional, str): the embedding model to use for the retrieve chat.\n",
+      "  If key not provided, a default model `all-MiniLM-L6-v2` will be used. All available models\n",
+      "  can be found at `https://www.sbert.net/docs/pretrained_models.html`. The default model is a\n",
+      "  fast model. If you want to use a high performance model, `all-mpnet-base-v2` is recommended.\n",
+      "  - customized_prompt (Optional, str): the customized prompt for the retrieve chat. Default is None.\n",
+      "- `**kwargs` _dict_ - other kwargs in [UserProxyAgent](user_proxy_agent#__init__).\n",
+      "\n",
+      "#### generate\\_init\\_message\n",
+      "\n",
+      "```python\n",
+      "def generate_init_message(problem: str,\n",
+      "                          n_results: int = 20,\n",
+      "                          search_string: str = \"\")\n",
+      "```\n",
+      "\n",
+      "Generate an initial message with the given problem and prompt.\n",
+      "\n",
+      "**Arguments**:\n",
+      "\n",
+      "- `problem` _str_ - the problem to be solved.\n",
+      "- `n_results` _int_ - the number of results to be retrieved.\n",
+      "- `search_string` _str_ - only docs containing this string will be retrieved.\n",
+      "  \n",
+      "\n",
+      "**Returns**:\n",
+      "\n",
+      "- `str` - the generated prompt ready to be sent to the assistant agent.\n",
+      "\n",
+      "\n",
+      "---\n",
+      "sidebar_label: retrieve_assistant_agent\n",
+      "title: autogen.agentchat.contrib.retrieve_assistant_agent\n",
+      "---\n",
+      "\n",
+      "## RetrieveAssistantAgent Objects\n",
+      "\n",
+      "```python\n",
+      "class RetrieveAssistantAgent(AssistantAgent)\n",
+      "```\n",
+      "\n",
+      "(Experimental) Retrieve Assistant agent, designed to solve a task with LLM.\n",
+      "\n",
+      "RetrieveAssistantAgent is a subclass of AssistantAgent configured with a default system message.\n",
+      "The default system message is designed to solve a task with LLM,\n",
+      "including suggesting python code blocks and debugging.\n",
+      "`human_input_mode` is default to \"NEVER\"\n",
+      "and `code_execution_config` is default to False.\n",
+      "This agent doesn't execute code by default, and expects the user to execute the code.\n",
+      "\n",
+      "\n",
+      "---\n",
+      "sidebar_label: utils\n",
+      "title: automl.nlp.huggingface.utils\n",
+      "---\n",
+      "\n",
+      "#### todf\n",
+      "\n",
+      "```python\n",
+      "def todf(X, Y, column_name)\n",
+      "```\n",
+      "\n",
+      "todf converts Y from any format (list, pandas.Series, numpy array) to a DataFrame before being returned\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33massistant\u001b[0m (to ragproxyagent):\n",
+      "\n",
+      "No, there is no function called `tune_automl` in the given context.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# reset the assistant. Always reset the assistant before starting a new conversation.\n",
+    "assistant.reset()\n",
+    "\n",
+    "qa_problem = \"Is there a function called tune_automl?\"\n",
+    "ragproxyagent.initiate_chat(assistant, problem=qa_problem)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"example-2\"></a>\n",
+    "### Example 2\n",
+    "\n",
+    "[back to top](#toc)\n",
+    "\n",
+    "Use RetrieveChat to answer a question that is not related to code generation.\n",
+    "\n",
+    "Problem: Who is the author of FLAML?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32mAdding doc_id 0 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 21 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 47 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 35 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 41 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 69 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 34 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 22 to context.\u001b[0m\n",
+      "\u001b[32mAdding doc_id 51 to context.\u001b[0m\n",
+      "\u001b[33mragproxyagent\u001b[0m (to assistant):\n",
+      "\n",
+      "You're a retrieve augmented coding assistant. You answer user's questions based on your own knowledge and the\n",
+      "context provided by the user.\n",
+      "If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.\n",
+      "For code generation, you must obey the following rules:\n",
+      "Rule 1. You MUST NOT install any packages because all the packages needed are already installed.\n",
+      "Rule 2. You must follow the formats below to write your code:\n",
+      "```language\n",
+      "# your code\n",
+      "```\n",
+      "\n",
+      "User's question is: Who is the author of FLAML?\n",
+      "\n",
+      "Context is: ---\n",
+      "sidebar_label: config\n",
+      "title: config\n",
+      "---\n",
+      "\n",
+      "!\n",
+      "* Copyright (c) Microsoft Corporation. All rights reserved.\n",
+      "* Licensed under the MIT License.\n",
+      "\n",
+      "#### PENALTY\n",
+      "\n",
+      "penalty term for constraints\n",
+      "\n",
+      "\n",
+      "---\n",
+      "sidebar_label: utils\n",
+      "title: automl.nlp.huggingface.utils\n",
+      "---\n",
+      "\n",
+      "#### todf\n",
+      "\n",
+      "```python\n",
+      "def todf(X, Y, column_name)\n",
+      "```\n",
+      "\n",
+      "todf converts Y from any format (list, pandas.Series, numpy array) to a DataFrame before being returned\n",
+      "\n",
+      "\n",
+      "---\n",
+      "sidebar_label: trial_scheduler\n",
+      "title: tune.scheduler.trial_scheduler\n",
+      "---\n",
+      "\n",
+      "## TrialScheduler Objects\n",
+      "\n",
+      "```python\n",
+      "class TrialScheduler()\n",
+      "```\n",
+      "\n",
+      "Interface for implementing a Trial Scheduler class.\n",
+      "\n",
+      "#### CONTINUE\n",
+      "\n",
+      "Status for continuing trial execution\n",
+      "\n",
+      "#### PAUSE\n",
+      "\n",
+      "Status for pausing trial execution\n",
+      "\n",
+      "#### STOP\n",
+      "\n",
+      "Status for stopping trial execution\n",
+      "\n",
+      "\n",
+      "---\n",
+      "sidebar_label: space\n",
+      "title: tune.space\n",
+      "---\n",
+      "\n",
+      "#### is\\_constant\n",
+      "\n",
+      "```python\n",
+      "def is_constant(space: Union[Dict, List]) -> bool\n",
+      "```\n",
+      "\n",
+      "Whether the search space is all constant.\n",
+      "\n",
+      "**Returns**:\n",
+      "\n",
+      "  A bool of whether the search space is all constant.\n",
+      "\n",
+      "#### define\\_by\\_run\\_func\n",
+      "\n",
+      "```python\n",
+      "def define_by_run_func(trial,\n",
+      "                       space: Dict,\n",
+      "                       path: str = \"\") -> Optional[Dict[str, Any]]\n",
+      "```\n",
+      "\n",
+      "Define-by-run function to create the search space.\n",
+      "\n",
+      "**Returns**:\n",
+      "\n",
+      "  A dict with constant values.\n",
+      "\n",
+      "#### unflatten\\_hierarchical\n",
+      "\n",
+      "```python\n",
+      "def unflatten_hierarchical(config: Dict, space: Dict) -> Tuple[Dict, Dict]\n",
+      "```\n",
+      "\n",
+      "Unflatten hierarchical config.\n",
+      "\n",
+      "#### add\\_cost\\_to\\_space\n",
+      "\n",
+      "```python\n",
+      "def add_cost_to_space(space: Dict, low_cost_point: Dict, choice_cost: Dict)\n",
+      "```\n",
+      "\n",
+      "Update the space in place by adding low_cost_point and choice_cost.\n",
+      "\n",
+      "**Returns**:\n",
+      "\n",
+      "  A dict with constant values.\n",
+      "\n",
+      "#### normalize\n",
+      "\n",
+      "```python\n",
+      "def normalize(config: Dict,\n",
+      "              space: Dict,\n",
+      "              reference_config: Dict,\n",
+      "              normalized_reference_config: Dict,\n",
+      "              recursive: bool = False)\n",
+      "```\n",
+      "\n",
+      "Normalize config in space according to reference_config.\n",
+      "\n",
+      "Normalize each dimension in config to [0,1].\n",
+      "\n",
+      "#### indexof\n",
+      "\n",
+      "```python\n",
+      "def indexof(domain: Dict, config: Dict) -> int\n",
+      "```\n",
+      "\n",
+      "Find the index of config in domain.categories.\n",
+      "\n",
+      "#### complete\\_config\n",
+      "\n",
+      "```python\n",
+      "def complete_config(partial_config: Dict,\n",
+      "                    space: Dict,\n",
+      "                    flow2,\n",
+      "                    disturb: bool = False,\n",
+      "                    lower: Optional[Dict] = None,\n",
+      "                    upper: Optional[Dict] = None) -> Tuple[Dict, Dict]\n",
+      "```\n",
+      "\n",
+      "Complete partial config in space.\n",
+      "\n",
+      "**Returns**:\n",
+      "\n",
+      "  config, space.\n",
+      "\n",
+      "\n",
+      "---\n",
+      "sidebar_label: search_thread\n",
+      "title: tune.searcher.search_thread\n",
+      "---\n",
+      "\n",
+      "## SearchThread Objects\n",
+      "\n",
+      "```python\n",
+      "class SearchThread()\n",
+      "```\n",
+      "\n",
+      "Class of global or local search thread.\n",
+      "\n",
+      "#### \\_\\_init\\_\\_\n",
+      "\n",
+      "```python\n",
+      "def __init__(mode: str = \"min\",\n",
+      "             search_alg: Optional[Searcher] = None,\n",
+      "             cost_attr: Optional[str] = TIME_TOTAL_S,\n",
+      "             eps: Optional[float] = 1.0)\n",
+      "```\n",
+      "\n",
+      "When search_alg is omitted, use local search FLOW2.\n",
+      "\n",
+      "#### suggest\n",
+      "\n",
+      "```python\n",
+      "def suggest(trial_id: str) -> Optional[Dict]\n",
+      "```\n",
+      "\n",
+      "Use the suggest() of the underlying search algorithm.\n",
+      "\n",
+      "#### on\\_trial\\_complete\n",
+      "\n",
+      "```python\n",
+      "def on_trial_complete(trial_id: str,\n",
+      "                      result: Optional[Dict] = None,\n",
+      "                      error: bool = False)\n",
+      "```\n",
+      "\n",
+      "Update the statistics of the thread.\n",
+      "\n",
+      "#### reach\n",
+      "\n",
+      "```python\n",
+      "def reach(thread) -> bool\n",
+      "```\n",
+      "\n",
+      "Whether the incumbent can reach the incumbent of thread.\n",
+      "\n",
+      "#### can\\_suggest\n",
+      "\n",
+      "```python\n",
+      "@property\n",
+      "def can_suggest() -> bool\n",
+      "```\n",
+      "\n",
+      "Whether the thread can suggest new configs.\n",
+      "\n",
+      "\n",
+      "{\n",
+      "  \"items\": [\n",
+      "    {\n",
+      "      \"items\": [\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            {\n",
+      "              \"items\": [\n",
+      "                \"reference/autogen/agentchat/contrib/math_user_proxy_agent\",\n",
+      "                \"reference/autogen/agentchat/contrib/retrieve_assistant_agent\",\n",
+      "                \"reference/autogen/agentchat/contrib/retrieve_user_proxy_agent\"\n",
+      "              ],\n",
+      "              \"label\": \"autogen.agentchat.contrib\",\n",
+      "              \"type\": \"category\"\n",
+      "            },\n",
+      "            \"reference/autogen/agentchat/agent\",\n",
+      "            \"reference/autogen/agentchat/assistant_agent\",\n",
+      "            \"reference/autogen/agentchat/conversable_agent\",\n",
+      "            \"reference/autogen/agentchat/groupchat\",\n",
+      "            \"reference/autogen/agentchat/user_proxy_agent\"\n",
+      "          ],\n",
+      "          \"label\": \"autogen.agentchat\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/autogen/oai/completion\",\n",
+      "            \"reference/autogen/oai/openai_utils\"\n",
+      "          ],\n",
+      "          \"label\": \"autogen.oai\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        \"reference/autogen/code_utils\",\n",
+      "        \"reference/autogen/math_utils\",\n",
+      "        \"reference/autogen/retrieve_utils\"\n",
+      "      ],\n",
+      "      \"label\": \"autogen\",\n",
+      "      \"type\": \"category\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"items\": [\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            {\n",
+      "              \"items\": [\n",
+      "                \"reference/automl/nlp/huggingface/trainer\",\n",
+      "                \"reference/automl/nlp/huggingface/training_args\",\n",
+      "                \"reference/automl/nlp/huggingface/utils\"\n",
+      "              ],\n",
+      "              \"label\": \"automl.nlp.huggingface\",\n",
+      "              \"type\": \"category\"\n",
+      "            },\n",
+      "            \"reference/automl/nlp/utils\"\n",
+      "          ],\n",
+      "          \"label\": \"automl.nlp\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/automl/spark/metrics\",\n",
+      "            \"reference/automl/spark/utils\"\n",
+      "          ],\n",
+      "          \"label\": \"automl.spark\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/automl/task/task\",\n",
+      "            \"reference/automl/task/time_series_task\"\n",
+      "          ],\n",
+      "          \"label\": \"automl.task\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/automl/time_series/sklearn\",\n",
+      "            \"reference/automl/time_series/tft\",\n",
+      "            \"reference/automl/time_series/ts_data\",\n",
+      "            \"reference/automl/time_series/ts_model\"\n",
+      "          ],\n",
+      "          \"label\": \"automl.time_series\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        \"reference/automl/automl\",\n",
+      "        \"reference/automl/data\",\n",
+      "        \"reference/automl/ml\",\n",
+      "        \"reference/automl/model\",\n",
+      "        \"reference/automl/state\"\n",
+      "      ],\n",
+      "      \"label\": \"automl\",\n",
+      "      \"type\": \"category\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"items\": [\n",
+      "        \"reference/default/estimator\",\n",
+      "        \"reference/default/greedy\",\n",
+      "        \"reference/default/portfolio\",\n",
+      "        \"reference/default/suggest\"\n",
+      "      ],\n",
+      "      \"label\": \"default\",\n",
+      "      \"type\": \"category\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"items\": [\n",
+      "        \"reference/onlineml/autovw\",\n",
+      "        \"reference/onlineml/trial\",\n",
+      "        \"reference/onlineml/trial_runner\"\n",
+      "      ],\n",
+      "      \"label\": \"onlineml\",\n",
+      "      \"type\": \"category\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"items\": [\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/tune/scheduler/online_scheduler\",\n",
+      "            \"reference/tune/scheduler/trial_scheduler\"\n",
+      "          ],\n",
+      "          \"label\": \"tune.scheduler\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/tune/searcher/blendsearch\",\n",
+      "            \"reference/tune/searcher/cfo_cat\",\n",
+      "            \"reference/tune/searcher/flow2\",\n",
+      "            \"reference/tune/searcher/online_searcher\",\n",
+      "            \"reference/tune/searcher/search_thread\",\n",
+      "            \"reference/tune/searcher/suggestion\",\n",
+      "            \"reference/tune/searcher/variant_generator\"\n",
+      "          ],\n",
+      "          \"label\": \"tune.searcher\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        {\n",
+      "          \"items\": [\n",
+      "            \"reference/tune/spark/utils\"\n",
+      "          ],\n",
+      "          \"label\": \"tune.spark\",\n",
+      "          \"type\": \"category\"\n",
+      "        },\n",
+      "        \"reference/tune/analysis\",\n",
+      "        \"reference/tune/sample\",\n",
+      "        \"reference/tune/space\",\n",
+      "        \"reference/tune/trial\",\n",
+      "        \"reference/tune/trial_runner\",\n",
+      "        \"reference/tune/tune\",\n",
+      "        \"reference/tune/utils\"\n",
+      "      ],\n",
+      "      \"label\": \"tune\",\n",
+      "      \"type\": \"category\"\n",
+      "    },\n",
+      "    \"reference/config\"\n",
+      "  ],\n",
+      "  \"label\": \"Reference\",\n",
+      "  \"type\": \"category\"\n",
+      "}\n",
+      "---\n",
+      "sidebar_label: utils\n",
+      "title: tune.utils\n",
+      "---\n",
+      "\n",
+      "#### choice\n",
+      "\n",
+      "```python\n",
+      "def choice(categories: Sequence, order=None)\n",
+      "```\n",
+      "\n",
+      "Sample a categorical value.\n",
+      "Sampling from ``tune.choice([1, 2])`` is equivalent to sampling from\n",
+      "``np.random.choice([1, 2])``\n",
+      "\n",
+      "**Arguments**:\n",
+      "\n",
+      "- `categories` _Sequence_ - Sequence of categories to sample from.\n",
+      "- `order` _bool_ - Whether the categories have an order. If None, will be decided autoamtically:\n",
+      "  Numerical categories have an order, while string categories do not.\n",
+      "\n",
+      "\n",
+      "---\n",
+      "sidebar_label: trainer\n",
+      "title: automl.nlp.huggingface.trainer\n",
+      "---\n",
+      "\n",
+      "## TrainerForAuto Objects\n",
+      "\n",
+      "```python\n",
+      "class TrainerForAuto(Seq2SeqTrainer)\n",
+      "```\n",
+      "\n",
+      "#### evaluate\n",
+      "\n",
+      "```python\n",
+      "def evaluate(eval_dataset=None, ignore_keys=None, metric_key_prefix=\"eval\")\n",
+      "```\n",
+      "\n",
+      "Overriding transformers.Trainer.evaluate by saving metrics and checkpoint path.\n",
+      "\n",
+      "\n",
+      "---\n",
+      "sidebar_label: trial\n",
+      "title: onlineml.trial\n",
+      "---\n",
+      "\n",
+      "#### get\\_ns\\_feature\\_dim\\_from\\_vw\\_example\n",
+      "\n",
+      "```python\n",
+      "def get_ns_feature_dim_from_vw_example(vw_example) -> dict\n",
+      "```\n",
+      "\n",
+      "Get a dictionary of feature dimensionality for each namespace singleton.\n",
+      "\n",
+      "## OnlineResult Objects\n",
+      "\n",
+      "```python\n",
+      "class OnlineResult()\n",
+      "```\n",
+      "\n",
+      "Class for managing the result statistics of a trial.\n",
+      "\n",
+      "#### CB\\_COEF\n",
+      "\n",
+      "0.001 for mse\n",
+      "\n",
+      "#### \\_\\_init\\_\\_\n",
+      "\n",
+      "```python\n",
+      "def __init__(result_type_name: str,\n",
+      "             cb_coef: Optional[float] = None,\n",
+      "             init_loss: Optional[float] = 0.0,\n",
+      "             init_cb: Optional[float] = 100.0,\n",
+      "             mode: Optional[str] = \"min\",\n",
+      "             sliding_window_size: Optional[int] = 100)\n",
+      "```\n",
+      "\n",
+      "Constructor.\n",
+      "\n",
+      "**Arguments**:\n",
+      "\n",
+      "- `result_type_name` - A String to specify the name of the result type.\n",
+      "- `cb_coef` - a string to specify the coefficient on the confidence bound.\n",
+      "- `init_loss` - a float to specify the inital loss.\n",
+      "- `init_cb` - a float to specify the intial confidence bound.\n",
+      "- `mode` - A string in ['min', 'max'] to specify the objective as\n",
+      "  minimization or maximization.\n",
+      "- `sliding_window_size` - An int to specify the size of the sliding window\n",
+      "  (for experimental purpose).\n",
+      "\n",
+      "#### update\\_result\n",
+      "\n",
+      "```python\n",
+      "def update_result(new_loss,\n",
+      "                  new_resource_used,\n",
+      "                  data_dimension,\n",
+      "                  bound_of_range=1.0,\n",
+      "                  new_observation_count=1.0)\n",
+      "```\n",
+      "\n",
+      "Update result statistics.\n",
+      "\n",
+      "## BaseOnlineTrial Objects\n",
+      "\n",
+      "```python\n",
+      "class BaseOnlineTrial(Trial)\n",
+      "```\n",
+      "\n",
+      "Class for the online trial.\n",
+      "\n",
+      "#### \\_\\_init\\_\\_\n",
+      "\n",
+      "```python\n",
+      "def __init__(config: dict,\n",
+      "             min_resource_lease: float,\n",
+      "             is_champion: Optional[bool] = False,\n",
+      "             is_checked_under_current_champion: Optional[bool] = True,\n",
+      "             custom_trial_name: Optional[str] = \"mae\",\n",
+      "             trial_id: Optional[str] = None)\n",
+      "```\n",
+      "\n",
+      "Constructor.\n",
+      "\n",
+      "**Arguments**:\n",
+      "\n",
+      "- `config` - The configuration dictionary.\n",
+      "- `min_resource_lease` - A float specifying the minimum resource lease.\n",
+      "- `is_champion` - A bool variable indicating whether the trial is champion.\n",
+      "- `is_checked_under_current_champion` - A bool indicating whether the trial\n",
+      "  has been used under the current champion.\n",
+      "- `custom_trial_name` - A string of a custom trial name.\n",
+      "- `trial_id` - A string for the trial id.\n",
+      "\n",
+      "#### set\\_resource\\_lease\n",
+      "\n",
+      "```python\n",
+      "def set_resource_lease(resource: float)\n",
+      "```\n",
+      "\n",
+      "Sets the resource lease accordingly.\n",
+      "\n",
+      "#### set\\_status\n",
+      "\n",
+      "```python\n",
+      "def set_status(status)\n",
+      "```\n",
+      "\n",
+      "Sets the status of the trial and record the start time.\n",
+      "\n",
+      "## VowpalWabbitTrial Objects\n",
+      "\n",
+      "```python\n",
+      "class VowpalWabbitTrial(BaseOnlineTrial)\n",
+      "```\n",
+      "\n",
+      "The class for Vowpal Wabbit online trials.\n",
+      "\n",
+      "#### \\_\\_init\\_\\_\n",
+      "\n",
+      "```python\n",
+      "def __init__(config: dict,\n",
+      "             min_resource_lease: float,\n",
+      "             metric: str = \"mae\",\n",
+      "             is_champion: Optional[bool] = False,\n",
+      "             is_checked_under_current_champion: Optional[bool] = True,\n",
+      "             custom_trial_name: Optional[str] = \"vw_mae_clipped\",\n",
+      "             trial_id: Optional[str] = None,\n",
+      "             cb_coef: Optional[float] = None)\n",
+      "```\n",
+      "\n",
+      "Constructor.\n",
+      "\n",
+      "**Arguments**:\n",
+      "\n",
+      "- `config` _dict_ - the config of the trial (note that the config is a set\n",
+      "  because the hyperparameters are).\n",
+      "- `min_resource_lease` _float_ - the minimum resource lease.\n",
+      "- `metric` _str_ - the loss metric.\n",
+      "- `is_champion` _bool_ - indicates whether the trial is the current champion or not.\n",
+      "- `is_checked_under_current_champion` _bool_ - indicates whether this trials has\n",
+      "  been paused under the current champion.\n",
+      "- `trial_id` _str_ - id of the trial (if None, it will be generated in the constructor).\n",
+      "\n",
+      "#### train\\_eval\\_model\\_online\n",
+      "\n",
+      "```python\n",
+      "def train_eval_model_online(data_sample, y_pred)\n",
+      "```\n",
+      "\n",
+      "Train and evaluate model online.\n",
+      "\n",
+      "#### predict\n",
+      "\n",
+      "```python\n",
+      "def predict(x)\n",
+      "```\n",
+      "\n",
+      "Predict using the model.\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33massistant\u001b[0m (to ragproxyagent):\n",
+      "\n",
+      "The author of FLAML is Microsoft Corporation.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# reset the assistant. Always reset the assistant before starting a new conversation.\n",
+    "assistant.reset()\n",
+    "\n",
+    "qa_problem = \"Who is the author of FLAML?\"\n",
+    "ragproxyagent.initiate_chat(assistant, problem=qa_problem)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebook/oai_openai_utils.ipynb b/notebook/oai_openai_utils.ipynb
index 6179c21dfa6d..38a5accb9988 100644
--- a/notebook/oai_openai_utils.ipynb
+++ b/notebook/oai_openai_utils.ipynb
@@ -281,7 +281,7 @@
    "source": [
     "## config_list_from_models\n",
     "\n",
-    "This method creates configurations based on a provided list of models. It's useful when you have specific models in mind and don't want to manually specify each configuration.\n",
+    "This method creates configurations based on a provided list of models. It's useful when you have specific models in mind and don't want to manually specify each configuration. The [`config_list_from_models`](/docs/reference/oai/openai_utils#config_list_from_models) function tries to create a list of configurations using Azure OpenAI endpoints and OpenAI endpoints for the provided list of models. It assumes the api keys and api bases are stored in the corresponding environment variables or local txt files. It's okay to only have the OpenAI API key, OR only the Azure OpenAI API key + base. For Azure the model name refers to the OpenAI Studio deployment name.\n",
     "\n",
     "Steps:\n",
     "- Similar to method 1, store API keys and bases either in environment variables or `.txt` files."
diff --git a/setup.py b/setup.py
index 32b99aa23b23..c68c1062ae43 100644
--- a/setup.py
+++ b/setup.py
@@ -53,6 +53,7 @@
             "sympy",
             "tiktoken",
             "wolframalpha",
+            "qdrant_client[fastembed]",
         ],
         "blendsearch": ["flaml[blendsearch]"],
         "mathchat": ["sympy", "pydantic==1.10.9", "wolframalpha"],
diff --git a/test/agentchat/test_qdrant_retrievechat.py b/test/agentchat/test_qdrant_retrievechat.py
new file mode 100644
index 000000000000..9600b507e04a
--- /dev/null
+++ b/test/agentchat/test_qdrant_retrievechat.py
@@ -0,0 +1,102 @@
+import os
+
+import pytest
+
+from autogen.agentchat.contrib.retrieve_assistant_agent import RetrieveAssistantAgent
+from autogen import ChatCompletion, config_list_from_json
+from test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST
+
+try:
+    from qdrant_client import QdrantClient
+    from autogen.agentchat.contrib.qdrant_retrieve_user_proxy_agent import (
+        create_qdrant_from_dir,
+        QdrantRetrieveUserProxyAgent,
+        query_qdrant,
+    )
+    import fastembed
+
+    QDRANT_INSTALLED = True
+except ImportError:
+    QDRANT_INSTALLED = False
+
+test_dir = os.path.join(os.path.dirname(__file__), "..", "test_files")
+
+
+@pytest.mark.skipif(not QDRANT_INSTALLED, reason="qdrant_client is not installed")
+def test_retrievechat():
+    try:
+        import openai
+    except ImportError:
+        return
+
+    conversations = {}
+    ChatCompletion.start_logging(conversations)
+
+    config_list = config_list_from_json(
+        OAI_CONFIG_LIST,
+        file_location=KEY_LOC,
+        filter_dict={
+            "model": ["gpt-4", "gpt4", "gpt-4-32k", "gpt-4-32k-0314"],
+        },
+    )
+
+    assistant = RetrieveAssistantAgent(
+        name="assistant",
+        system_message="You are a helpful assistant.",
+        llm_config={
+            "request_timeout": 600,
+            "seed": 42,
+            "config_list": config_list,
+        },
+    )
+
+    client = QdrantClient(":memory:")
+    ragproxyagent = QdrantRetrieveUserProxyAgent(
+        name="ragproxyagent",
+        human_input_mode="NEVER",
+        max_consecutive_auto_reply=2,
+        retrieve_config={
+            "client": client,
+            "docs_path": "./website/docs",
+            "chunk_token_size": 2000,
+        },
+    )
+
+    assistant.reset()
+
+    code_problem = "How can I use FLAML to perform a classification task, set use_spark=True, train 30 seconds and force cancel jobs if time limit is reached."
+    ragproxyagent.initiate_chat(assistant, problem=code_problem, silent=True)
+    print(conversations)
+
+
+@pytest.mark.skipif(not QDRANT_INSTALLED, reason="qdrant_client is not installed")
+def test_qdrant_filter():
+    client = QdrantClient(":memory:")
+    create_qdrant_from_dir(dir_path="./website/docs", client=client, collection_name="autogen-docs")
+    results = query_qdrant(
+        query_texts=["How can I use AutoGen UserProxyAgent and AssistantAgent to do code generation?"],
+        n_results=4,
+        client=client,
+        collection_name="autogen-docs",
+        # Return only documents with "AutoGen" in the string
+        search_string="AutoGen",
+    )
+    assert len(results["ids"][0]) == 4
+
+
+@pytest.mark.skipif(not QDRANT_INSTALLED, reason="qdrant_client is not installed")
+def test_qdrant_search():
+    client = QdrantClient(":memory:")
+    create_qdrant_from_dir(test_dir, client=client)
+
+    assert client.get_collection("all-my-documents")
+
+    # Perform a semantic search without any filter
+    results = query_qdrant(["autogen"], client=client)
+    assert isinstance(results, dict) and any("autogen" in res[0].lower() for res in results.get("documents", []))
+
+
+if __name__ == "__main__":
+    test_retrievechat()
+    test_qdrant_filter()
+    test_qdrant_search()
diff --git a/test/oai/test_completion.py b/test/oai/test_completion.py
index 934dbadc9016..f33533b7bf88 100644
--- a/test/oai/test_completion.py
+++ b/test/oai/test_completion.py
@@ -376,7 +376,7 @@ def test_math(num_samples=-1):
     ]
 
     autogen.Completion.set_cache(seed)
-    config_list = autogen.config_list_openai_aoai(KEY_LOC, exclude="aoai")
+    config_list = autogen.config_list_openai_aoai(KEY_LOC)[:2]
     vanilla_config = {
         "model": "text-davinci-003",
         "temperature": 1,
diff --git a/test/test_retrieve_utils.py b/test/test_retrieve_utils.py
index a1c70d9cf28f..db581b8798aa 100644
--- a/test/test_retrieve_utils.py
+++ b/test/test_retrieve_utils.py
@@ -11,10 +11,9 @@
     is_url,
     create_vector_db_from_dir,
     query_vector_db,
-    num_tokens_from_text,
-    num_tokens_from_messages,
     TEXT_FORMATS,
 )
+from autogen.token_count_utils import count_token
 
 import os
 import sys
@@ -31,31 +30,10 @@
 
 
 class TestRetrieveUtils:
-    def test_num_tokens_from_text_custom_token_count_function(self):
-        def custom_token_count_function(text):
-            return len(text), 1, 2
-
-        text = "This is a sample text."
-        assert num_tokens_from_text(
-            text, return_tokens_per_name_and_message=True, custom_token_count_function=custom_token_count_function
-        ) == (22, 1, 2)
-
-    def test_num_tokens_from_text(self):
-        text = "This is a sample text."
-        assert num_tokens_from_text(text) == len(tiktoken.get_encoding("cl100k_base").encode(text))
-
-    def test_num_tokens_from_messages(self):
-        messages = [{"content": "This is a sample text."}, {"content": "Another sample text."}]
-        # Review the implementation of num_tokens_from_messages
-        # and adjust the expected_tokens accordingly.
-        actual_tokens = num_tokens_from_messages(messages)
-        expected_tokens = actual_tokens  # Adjusted to make the test pass temporarily.
-        assert actual_tokens == expected_tokens
-
     def test_split_text_to_chunks(self):
         long_text = "A" * 10000
         chunks = split_text_to_chunks(long_text, max_tokens=1000)
-        assert all(num_tokens_from_text(chunk) <= 1000 for chunk in chunks)
+        assert all(count_token(chunk) <= 1000 for chunk in chunks)
 
     def test_split_text_to_chunks_raises_on_invalid_chunk_mode(self):
         with pytest.raises(AssertionError):
@@ -140,7 +118,7 @@ def query_vector_db(
                 db = lancedb.connect(db_path)
                 table = db.open_table("my_table")
                 query = table.search(vector).where(f"documents LIKE '%{search_string}%'").limit(n_results).to_df()
-                return {"ids": query["id"].tolist(), "documents": query["documents"].tolist()}
+                return {"ids": [query["id"].tolist()], "documents": [query["documents"].tolist()]}
 
             def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str = ""):
                 results = self.query_vector_db(
@@ -166,7 +144,7 @@ def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str =
 
         create_lancedb()
         ragragproxyagent.retrieve_docs("This is a test document spark", n_results=10, search_string="spark")
-        assert ragragproxyagent._results["ids"] == [3, 1, 5]
+        assert ragragproxyagent._results["ids"] == [[3, 1, 5]]
 
     def test_custom_text_split_function(self):
         def custom_text_split_function(text):
diff --git a/test/test_token_count.py b/test/test_token_count.py
new file mode 100644
index 000000000000..1da4ccabeab3
--- /dev/null
+++ b/test/test_token_count.py
@@ -0,0 +1,72 @@
+from autogen.token_count_utils import count_token, num_tokens_from_functions, token_left, percentile_used
+import pytest
+
+func1 = {
+    "name": "sh",
+    "description": "run a shell script and return the execution result.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "script": {
+                "type": "string",
+                "description": "Valid shell script to execute.",
+            }
+        },
+        "required": ["script"],
+    },
+}
+func2 = {
+    "name": "query_wolfram",
+    "description": "Return the API query result from the Wolfram Alpha. the ruturn is a tuple of (result, is_success).",
+    "parameters": {
+        "type": "object",
+        "properties": {},
+    },
+}
+func3 = {
+    "name": "python",
+    "description": "run cell in ipython and return the execution result.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "cell": {
+                "type": "string",
+                "description": "Valid Python cell to execute.",
+            }
+        },
+        "required": ["cell"],
+    },
+}
+
+
+@pytest.mark.parametrize(
+    "input_functions, expected_count", [([func1], 44), ([func2], 47), ([func3], 45), ([func1, func2], 79)]
+)
+def test_num_tokens_from_functions(input_functions, expected_count):
+    assert num_tokens_from_functions(input_functions) == expected_count
+
+
+def test_count_token():
+    messages = [
+        {
+            "role": "system",
+            "content": "you are a helpful assistant. af3758 *3 33(3)",
+        },
+        {
+            "role": "user",
+            "content": "hello asdfjj qeweee",
+        },
+    ]
+    assert count_token(messages) == 34
+    assert percentile_used(messages) == 34 / 4096
+    assert token_left(messages) == 4096 - 34
+
+    text = "I'm sorry, but I'm not able to"
+    assert count_token(text) == 10
+    assert token_left(text) == 4096 - 10
+    assert percentile_used(text) == 10 / 4096
+
+
+if __name__ == "__main__":
+    test_num_tokens_from_functions()
+    test_count_token()
diff --git a/website/blog/2023-10-26-TeachableAgent/img/teachable-arch.png b/website/blog/2023-10-26-TeachableAgent/img/teachable-arch.png
new file mode 100644
index 000000000000..9c43745c7166
Binary files /dev/null and b/website/blog/2023-10-26-TeachableAgent/img/teachable-arch.png differ
diff --git a/website/blog/2023-10-26-TeachableAgent/index.mdx b/website/blog/2023-10-26-TeachableAgent/index.mdx
new file mode 100644
index 000000000000..434e0946b541
--- /dev/null
+++ b/website/blog/2023-10-26-TeachableAgent/index.mdx
@@ -0,0 +1,391 @@
+---
+title: AutoGen's TeachableAgent
+authors: rickyloynd-microsoft
+tags: [LLM, teach]
+---
+
+![Teachable Agent Architecture](img/teachable-arch.png)
+
+**TL;DR:**
+* We introduce **TeachableAgent** (which uses **TextAnalyzerAgent**) so that users can teach their LLM-based assistants new facts, preferences, and skills.
+* We showcase examples of `TeachableAgent` learning and later recalling facts, preferences, and skills in subsequent chats.
+
+
+## Introduction
+Conversational assistants based on LLMs can remember the current chat with the user, and can also demonstrate in-context learning of user teachings during the conversation. But the assistant's memories and learnings are lost once the chat is over, or when a single chat grows too long for the LLM to handle effectively. Then in subsequent chats the user is forced to repeat any necessary instructions over and over.
+
+`TeachableAgent` addresses these limitations by persisting user teachings across chat boundaries in long-term memory implemented as a vector database. Memory is automatically saved to disk at the end of each chat, then loaded from disk at the start of the next. Instead of copying all of memory into the context window, which would eat up valuable space, individual memories (called memos) are retrieved into context as needed. This allows the user to teach frequently used facts and skills to the teachable agent just once, and have it recall them in later chats.
+
+In order to make effective decisions about memo storage and retrieval, `TeachableAgent` calls an instance of `TextAnalyzerAgent` (another AutoGen agent) to identify and reformulate text as needed for remembering facts, preferences, and skills. Note that this adds extra LLM calls involving a relatively small number of tokens, which can add a few seconds to the time a user waits for each response.
+
+
+## Run It Yourself
+
+AutoGen contains three code examples that use `TeachableAgent`.
+
+1. Run [chat_with_teachable_agent.py](https://github.com/microsoft/autogen/blob/main/test/agentchat/chat_with_teachable_agent.py) to converse with `TeachableAgent`.
+
+2. Use the Jupyter notebook [agentchat_teachability.ipynb](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_teachability.ipynb) to step through examples discussed below.
+
+3. Run [test_teachable_agent.py](https://github.com/microsoft/autogen/blob/main/test/agentchat/test_teachable_agent.py) for quick unit testing of `TeachableAgent`.
+
+
+## Basic Usage of TeachableAgent
+1. Install dependencies
+
+Please install pyautogen with the [teachable] option before using TeachableAgent.
+```bash
+pip install "pyautogen[teachable]"
+```
+
+2. Import agents
+```python
+from autogen import UserProxyAgent, config_list_from_json
+from autogen.agentchat.contrib.teachable_agent import TeachableAgent
+```
+
+3. Create llm_config
+```python
+# Load LLM inference endpoints from an env variable or a file
+# See https://microsoft.github.io/autogen/docs/FAQ#set-your-api-endpoints
+# and OAI_CONFIG_LIST_sample
+filter_dict = {"model": ["gpt-4"]}  # GPT-3.5 is less reliable than GPT-4 at learning from user feedback.
+config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST", filter_dict=filter_dict)
+llm_config={"config_list": config_list, "request_timeout": 120}
+```
+
+4. Create the agents
+```python
+teachable_agent = TeachableAgent(
+    name="teachableagent",
+    llm_config=llm_config,
+    teach_config={
+        "reset_db": False,  # Use True to force-reset the memo DB, and False to use an existing DB.
+        "path_to_db_dir": "./tmp/interactive/teachable_agent_db"  # Can be any path.
+    }
+)
+
+user = UserProxyAgent("user", human_input_mode="ALWAYS")
+```
+
+5. Chat with `TeachableAgent`
+```python
+# This function will return once the user types 'exit'.
+teachable_agent.initiate_chat(user, message="Hi, I'm a teachable user assistant! What's on your mind?")
+```
+
+6. Update the database on disk
+```python
+# Before closing the app, let the teachable agent store things that should be learned from this chat.
+teachable_agent.learn_from_user_feedback()
+teachable_agent.close_db()
+```
+
+
+## Example 1 - Learning user info
+
+A user can teach the agent facts about themselves.
+(Note that due to their finetuning, LLMs can be reluctant to admit that they know personal information.)
+```
+CLEARING MEMORY
+teachableagent (to user):
+
+Hi, I'm a teachable user assistant! What's on your mind?
+
+--------------------------------------------------------------------------------
+Provide feedback to teachableagent. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: My name is Ricky
+user (to teachableagent):
+
+My name is Ricky
+
+--------------------------------------------------------------------------------
+teachableagent (to user):
+
+Hello, Ricky! It's nice to meet you. What can I help you with today?
+
+--------------------------------------------------------------------------------
+Provide feedback to teachableagent. Press enter to skip and use auto-reply, or type 'exit' to end the conversation: exit
+
+REVIEWING CHAT FOR USER TEACHINGS TO REMEMBER
+
+SAVING MEMORY TO DISK
+    Location = ./tmp/interactive/teachable_agent_db\uid_text_dict.pkl
+```
+
+In a later conversation, the user can check whether the teachable agent remembers their name. (For readability, the user prompts and some logged notices are not repeated below.)
+```
+--------------------------------------------------------------------------------
+user (to teachableagent):
+
+what's my name?
+
+--------------------------------------------------------------------------------
+teachableagent (to user):
+
+Hi Ricky! How can I help you today?
+
+--------------------------------------------------------------------------------
+```
+
+
+## Example 2 - Learning new facts
+
+A user can teach the agent more complex, related facts.
+```
+--------------------------------------------------------------------------------
+user (to teachableagent):
+
+What is the Vicuna model?
+
+--------------------------------------------------------------------------------
+teachableagent (to user):
+
+I'm sorry, but I don't have information about the "Vicuna model" in my current database. Could you please provide some context, description, or details about this model so I can better assist you?
+
+--------------------------------------------------------------------------------
+user (to teachableagent):
+
+Vicuna is a 13B-parameter language model released by Meta.
+
+--------------------------------------------------------------------------------
+teachableagent (to user):
+
+Thank you for providing the information. The Vicuna model is a large-scale language model developed by Meta. It consists of 13 billion parameters, which is what makes it able to understand and generate human-like text across various topics. Parameters in a neural network are the weights and biases assigned to each connection between the neurons, and having more parameters enables the model to learn and capture more complex patterns and relationships in data. As an AI language model, Vicuna can be utilized in various language-related tasks, such as text completion, translation, summarization, and more.
+
+--------------------------------------------------------------------------------
+user (to teachableagent):
+
+What is the Orca model?
+
+--------------------------------------------------------------------------------
+teachableagent (to user):
+
+The Orca model is a yet-to-be-released language model by Meta, referring to the Research Preview API release of Meta's advanced language model. While I don't have specific details about the Orca model at this time, it is expected that the model would have an even larger number of parameters to further enhance its capabilities compared to its predecessor, Vicuna.
+
+As AI language models evolve, they generally become more efficient at understanding and generating human-like text. This makes them more effective in completing a wide range of tasks, including text completion, translation, summarization, and Q&A, as well as providing more accurate and coherent results in various applications.
+
+--------------------------------------------------------------------------------
+user (to teachableagent):
+
+Orca is a 13B-parameter language model released by Microsoft. It outperforms Vicuna on most tasks.
+
+--------------------------------------------------------------------------------
+teachableagent (to user):
+
+Thank you for the clarification. Orca is a 13-billion-parameter language model developed by Microsoft. It is designed to outperform Vicuna, the language model released by Meta. With a large number of parameters, Orca is capable of understanding and generating human-like text across diverse subjects, making it highly useful in various applications.
+
+Orca's advanced capabilities allow it to excel in tasks such as text completion, translation, summarization, Q&A, and more. Its performance improvement over Vicuna highlights the rapid advancements in natural language processing (NLP) research and the development of increasingly capable AI language models.
+```
+
+Then in a later chat the teachable agent can answer questions about the facts it has been taught.
+(Remember to first close the previous chat by typing 'exit'.)
+```
+--------------------------------------------------------------------------------
+user (to teachableagent):
+
+How does the Vicuna model compare to the Orca model?
+
+--------------------------------------------------------------------------------
+teachableagent (to user):
+
+The Vicuna and Orca models are both 13B-parameter language models released by Meta and Microsoft, respectively. In terms of performance, Orca has been reported to outperform Vicuna on most tasks. However, without detailed information about specific tasks and benchmarks, it is difficult to provide a more comprehensive comparison. Generally speaking, both models are advanced language models that aim to provide high-quality natural language understanding and generation, but Orca appears to have an edge in terms of overall performance.
+
+--------------------------------------------------------------------------------
+```
+
+
+## Example 3 - Learning user preferences
+
+A user can teach the agent how they prefer to have things done.
+
+Be aware that a message like the next one cannot be entered as a single message through a command line because it contains a newline character.
+Such messages can be entered in a Jupyter notebook, or through some UI layer like that of ChatGPT.
+```
+--------------------------------------------------------------------------------
+user (to teachableagent):
+
+Please summarize this abstract.
+
+AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
+Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, Chi Wang
+AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools. Using AutoGen, developers can also flexibly define agent interaction behaviors. Both natural language and computer code can be used to program flexible conversation patterns for different applications. AutoGen serves as a generic infrastructure to build diverse applications of various complexities and LLM capacities. Empirical studies demonstrate the effectiveness of the framework in many example applications, with domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.
+
+--------------------------------------------------------------------------------
+teachableagent (to user):
+
+AutoGen is an open-source framework designed to enable developers to create LLM applications with multiple customizable agents that can converse with each other to complete tasks. These agents can operate using different combinations of LLMs, human inputs, and tools, allowing developers to define agent interaction behaviors flexibly. AutoGen supports programming flexible conversation patterns using both natural language and code, making it suitable for building diverse applications with varying complexities and LLM capacities. Its effectiveness has been demonstrated through empirical studies across various domains including mathematics, coding, operations research, decision-making, and entertainment.
+
+--------------------------------------------------------------------------------
+Please summarize this abstract. When I'm summarizing an abstract, I try to make the summary contain just three short bullet points:  the title, the innovation, and the key empirical results.
+
+AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
+Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, Chi Wang
+AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools. Using AutoGen, developers can also flexibly define agent interaction behaviors. Both natural language and computer code can be used to program flexible conversation patterns for different applications. AutoGen serves as a generic infrastructure to build diverse applications of various complexities and LLM capacities. Empirical studies demonstrate the effectiveness of the framework in many example applications, with domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.
+
+--------------------------------------------------------------------------------
+teachableagent (to user):
+
+- Title: AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
+- Innovation: Open-source framework for creating customizable LLM applications through agent conversations, supporting various modes and interaction behaviors.
+- Key Empirical Results: Demonstrated effectiveness across diverse application domains, including mathematics, coding, question answering, and more.
+
+--------------------------------------------------------------------------------
+```
+
+Then in later chats the teacher doesn't need to reiterate their detailed preferences.
+```
+--------------------------------------------------------------------------------
+user (to teachableagent):
+
+Please summarize this abstract.
+
+Sparks of Artificial General Intelligence: Early experiments with GPT-4
+Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang
+Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.
+
+--------------------------------------------------------------------------------
+teachableagent (to user):
+
+- Title: Sparks of Artificial General Intelligence: Early experiments with GPT-4
+- Innovation: GPT-4, an LLM with remarkable capabilities, demonstrates human-level performance across various domains, like math, coding, vision, medicine, law, and psychology.
+- Key results: GPT-4 significantly surpasses prior models, suggesting it may be an early version of AGI; limitations and challenges toward deeper AGI are also discussed.
+
+--------------------------------------------------------------------------------
+```
+
+
+## Example 4 - Learning new skills
+
+Users can extend the teachable agent's capabilities by teaching it new skills for accomplishing challenging tasks. It usually works best to first describe the task, then (in the same turn) provide a hint or advice for approaching the task.
+
+The [Sparks of AGI](https://arxiv.org/abs/2303.12712) paper evaluated GPT-4 on math problems like the following, which it could only solve 32% of the time. We first show a failure case, then teach the agent a strategy which lifts GPT-4's success rate above 95%.
+```
+--------------------------------------------------------------------------------
+user (to teachableagent):
+
+Consider the identity:
+9 * 4 + 6 * 6 = 72
+Can you modify exactly one integer (and not more than that!) on the left hand side of the equation so the right hand side becomes 99?
+-Let's think step-by-step, write down a plan, and then write down your solution as: "The solution is: A * B + C * D".
+
+--------------------------------------------------------------------------------
+Step 1: Identify the current equation and the target value on the right-hand side.
+Current equation: 9 * 4 + 6 * 6 = 72
+Target value: 99
+
+Step 2: Determine what numbers can be changed on the left-hand side.
+Possible changes: 9, 4, 6 (first occurrence), and 6 (second occurrence)
+
+Step 3: Evaluate whether each change in the numbers on the left-hand side can result in the target value.
+9 -> If changed, it would create a larger product.
+4 -> If changed, the product would increase or decrease by more than the amount needed to reach 99.
+6 (first occurrence) -> If increased to 7, then the equation would be 9 * 4 + 7 * 6 = 94, which is still not 99.
+6 (second occurrence) -> If increased to 7, then the equation would be 9 * 4 + 6 * 7 = 99.
+
+Step 4: Write down the solution.
+The solution is: 9 * 4 + 6 * 7
+
+--------------------------------------------------------------------------------
+user (to teachableagent):
+
+Consider the identity:
+9 * 4 + 6 * 6 = 72
+Can you modify exactly one integer (and not more than that!) on the left hand side of the equation so the right hand side becomes 99?
+-Let's think step-by-step, write down a plan, and then write down your solution as: "The solution is: A * B + C * D".
+
+Here's some advice that may help:
+1. Let E denote the original number on the right.
+2. Let F denote the final number on the right.
+3. Calculate the difference between the two, G = F - E.
+4. Examine the numbers on the left one by one until finding one that divides evenly into G, where negative integers are allowed.
+5. Calculate J = G / H. This is the number of times that H divides into G.
+6. Verify that J is an integer, and that H * J = G.
+7. Find the number on the left which is multiplied by H, and call it K.
+8. Change K to K + J.
+9. Recompute the value on the left, and verify that it equals F.
+Finally, write down your solution as: "The solution is: A * B + C * D".
+
+--------------------------------------------------------------------------------
+teachableagent (to user):
+
+Step 1: Identify the original number (E) and the final number (F) on the right-hand side.
+E = 72
+F = 99
+
+Step 2: Calculate the difference between the two, G = F - E.
+G = 99 - 72 = 27
+
+Step 3: Examine the numbers on the left one by one until finding one that divides evenly into G, where negative integers are allowed.
+9 (divides evenly into 27)
+4 (does not)
+6 (first occurrence, does not)
+6 (second occurrence, does not)
+
+Step 4: Calculate J = G / H. This is the number of times that H divides into G.
+H = 9 (since 9 divides evenly into 27)
+J = 27 / 9 = 3
+
+Step 5: Verify that J is an integer, and that H * J = G.
+H * J = 9 * 3 = 27
+J = 3 (which is an integer)
+
+Step 6: Find the number on the left which is multiplied by H, and call it K.
+Number on the leftside of the equation that is multiplied by 9: 4
+Therefore, K = 4
+
+Step 7: Change K to K + J.
+K + J = 4 + 3 = 7
+
+Step 8: Recompute the value on the left, and verify that it equals F.
+9 * 7 + 6 * 6 = 63 + 36 = 99 (which is equal to F)
+
+Step 9: Write down the solution.
+The solution is: 9 * 7 + 6 * 6
+
+--------------------------------------------------------------------------------
+```
+
+In a later chat the user doesn't need to repeat the detailed advice.
+```
+--------------------------------------------------------------------------------
+user (to teachableagent):
+
+Consider the identity:
+8 * 3 + 7 * 9 = 87
+Can you modify exactly one integer (and not more than that!) on the left hand side of the equation so the right hand side becomes 59?
+-Let's think step-by-step, write down a plan, and then write down your solution as: "The solution is: A * B + C * D".
+
+--------------------------------------------------------------------------------
+teachableagent (to user):
+
+Let's follow the advice to modify one integer on the left hand side of the equation to make the right hand side become 59:
+
+1. Let E denote the original number on the right: E = 87.
+2. Let F denote the final number on the right: F = 59.
+3. Calculate the difference between the two, G = F - E: G = 59 - 87 = -28.
+4. Examine the numbers on the left one by one to find one that divides evenly into G: 8, 3, 7, 9. Since 7 divides -28 evenly, we will work with 7 (H = 7).
+5. Calculate J = G / H: J = -28 / 7 = -4.
+6. Verify that J is an integer, and that H * J = G: J is an integer, and 7 * (-4) = -28.
+7. Find the number on the left which is multiplied by H (7), and call it K: K = 9.
+8. Change K to K + J: K_new = 9 + (-4) = 5.
+9. Recompute the value on the left, and verify that it equals F: (8 * 3) + (7 * 5) = 24 + 35 = 59.
+
+The solution is: 8 * 3 + 7 * 5
+
+--------------------------------------------------------------------------------
+```
+
+
+## Planned improvements
+- Instructions for making any AutoGen agent user-teachable.
+- Examples of how to include `TeachableAgent` in group chats.
+- Expansions of AutoGen's current coding-testing strengths.
+- Teachability enhancements:
+  - Understanding user instructions distributed over multiple turns.
+  - Learning from the agent's own experience, to reduce dependence on explicit user instructions.
+  - Learning skills built on top of previously learned skills.
+
+
+## Conclusion
+
+`TeachableAgent` is still under active research and development. For any problems you find or improvements you have in mind, please join our discussions in this repo and on our [Discord channel](https://discord.gg/pAbnFJrkgZ). We look forward to seeing how you and the rest of the community can use and improve `TeachableAgent` and the other agents in AutoGen!
diff --git a/website/blog/authors.yml b/website/blog/authors.yml
index 85c993604089..0793230bfd96 100644
--- a/website/blog/authors.yml
+++ b/website/blog/authors.yml
@@ -27,3 +27,9 @@ thinkall:
   title: Senior Software Engineer at Microsoft
   url: https://github.com/thinkall
   image_url: https://github.com/thinkall.png
+
+rickyloynd-microsoft:
+  name: Ricky Loynd
+  title: Senior Research Engineer at Microsoft
+  url: https://github.com/rickyloynd-microsoft
+  image_url: https://github.com/rickyloynd-microsoft.png
diff --git a/website/docs/Examples/AutoGen-AgentChat.md b/website/docs/Examples/AutoGen-AgentChat.md
index 5e5af4b6eaa7..a9a813ae6c10 100644
--- a/website/docs/Examples/AutoGen-AgentChat.md
+++ b/website/docs/Examples/AutoGen-AgentChat.md
@@ -5,11 +5,13 @@ Please find documentation about this feature [here](/docs/Use-Cases/agent_chat).
 
 Links to notebook examples:
 
+
 1. **Code Generation, Execution, and Debugging**
 
    - Automated Task Solving with Code Generation, Execution & Debugging - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)
    - Auto Code Generation, Execution, Debugging and Human Feedback - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_human_feedback.ipynb)
    - Automated Code Generation and Question Answering with Retrieval Augmented Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat.ipynb)
+   - Automated Code Generation and Question Answering with [Qdrant](https://qdrant.tech/) based Retrieval Augmented Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_qdrant_RetrieveChat.ipynb)
 
 2. **Multi-Agent Collaboration (>3 Agents)**
 
@@ -23,6 +25,7 @@ Links to notebook examples:
 
    - Automated Chess Game Playing & Chitchatting by GPT-4 Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_chess.ipynb)
    - Automated Continual Learning from New Data - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_stream.ipynb)
+   - [OptiGuide](https://github.com/microsoft/optiguide) - Large Language Models for Supply Chain Optimization.
 
 4. **Tool Use**
 
diff --git a/website/docs/FAQ.md b/website/docs/FAQ.md
index ccf214c13365..fb86919adeeb 100644
--- a/website/docs/FAQ.md
+++ b/website/docs/FAQ.md
@@ -2,88 +2,15 @@
 
 ## Set your API endpoints
 
-There are multiple ways to construct a list of configurations for LLM inference.
+There are multiple ways to construct configurations for LLM inference in the `oai` utilities:
 
-### Option 1: Load a list of endpoints from json
+- `get_config_list`: Generates configurations for API calls, primarily from provided API keys.
+- `config_list_openai_aoai`: Constructs a list of configurations using both Azure OpenAI and OpenAI endpoints, sourcing API keys from environment variables or local files.
+- `config_list_from_json`: Loads configurations from a JSON structure, either from an environment variable or a local JSON file, with the flexibility of filtering configurations based on given criteria.
+- `config_list_from_models`: Creates configurations based on a provided list of models, useful when targeting specific models without manually specifying each configuration.
+- `config_list_from_dotenv`: Constructs a configuration list from a `.env` file, offering a consolidated way to manage multiple API configurations and keys from a single file.
 
-The [`config_list_from_json`](/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.
-
-For example,
-
-```python
-import autogen
-config_list = autogen.config_list_from_json(
-    "OAI_CONFIG_LIST",
-    file_location=".",
-    filter_dict={
-        "model": {
-            "gpt-4",
-            "gpt-3.5-turbo",
-        }
-    }
-)
-```
-
-It first looks for environment variable "OAI_CONFIG_LIST" which needs to be a valid json string. If that variable is not found, it then looks for a json file named "OAI_CONFIG_LIST" under the specified `file_location`. It then filters the configs by models (you can filter by other keys as well).
-
-The `OAI_CONFIG_LIST` var or file content looks like the following:
-```json
-[
-    {
-        "model": "gpt-4",
-        "api_key": "<your OpenAI API key here>"
-    },
-    {
-        "model": "gpt-4",
-        "api_key": "<your Azure OpenAI API key here>",
-        "api_base": "<your Azure OpenAI API base here>",
-        "api_type": "azure",
-        "api_version": "2023-07-01-preview"
-    },
-    {
-        "model": "gpt-3.5-turbo",
-        "api_key": "<your Azure OpenAI API key here>",
-        "api_base": "<your Azure OpenAI API base here>",
-        "api_type": "azure",
-        "api_version": "2023-07-01-preview"
-    }
-]
-```
-
-### Option 2: Construct a list of endpoints for OpenAI or Azure OpenAI
-
-The [`config_list_from_models`](/docs/reference/oai/openai_utils#config_list_from_models) function tries to create a list of configurations using Azure OpenAI endpoints and OpenAI endpoints for the provided list of models. It assumes the api keys and api bases are stored in the corresponding environment variables or local txt files:
-
-- OpenAI API key: os.environ["OPENAI_API_KEY"] or `openai_api_key_file="key_openai.txt"`.
-- Azure OpenAI API key: os.environ["AZURE_OPENAI_API_KEY"] or `aoai_api_key_file="key_aoai.txt"`. Multiple keys can be stored, one per line.
-- Azure OpenAI API base: os.environ["AZURE_OPENAI_API_BASE"] or `aoai_api_base_file="base_aoai.txt"`. Multiple bases can be stored, one per line.
-
-It's OK to have only the OpenAI API key, or only the Azure OpenAI API key + base.
-
-```python
-import autogen
-config_list = autogen.config_list_from_models(model_list=["gpt-4", "gpt-3.5-turbo", "gpt-3.5-turbo-16k"])
-```
-
-> For Azure the model name refers to the OpenAI Studio deployment name.
-
-The config list looks like the following, if only OpenAI API key is available:
-```python
-config_list = [
-    {
-        'model': 'gpt-4',
-        'api_key': '<your OpenAI API key here>',
-    },  # OpenAI API endpoint for gpt-4
-    {
-        'model': 'gpt-3.5-turbo',
-        'api_key': '<your OpenAI API key here>',
-    },  # OpenAI API endpoint for gpt-3.5-turbo
-    {
-        'model': 'gpt-3.5-turbo-16k',
-        'api_key': '<your OpenAI API key here>',
-    },  # OpenAI API endpoint for gpt-3.5-turbo-16k
-]
-```
+We suggest that you take a look at this [notebook](https://github.com/microsoft/autogen/blob/main/notebook/oai_openai_utils.ipynb) for full code examples of the different methods to configure your model endpoints.
 
 ### Use the constructed configuration list in agents
 
@@ -160,3 +87,24 @@ Otherwise, reply CONTINUE, or the reason why the task is not solved yet."""
 ```
 
 If you have problems with agents running `pip install` or get errors similar to `Error while fetching server API version: ('Connection aborted.', FileNotFoundError(2, 'No such file or directory')`, you can choose **'python:3'** as image as shown in the code example above and that should solve the problem.
+
+
+### Agents keep thanking each other when using `gpt-3.5-turbo`
+
+When using `gpt-3.5-turbo` you may often encounter agents going into a "gratitude loop", meaning when they complete a task they will begin congratulating and thanking eachother in a continuous loop. This is a limitation in the performance of `gpt-3.5-turbo`, in contrast to `gpt-4` which has no problem remembering instructions. This can hinder the experimentation experience when trying to test out your own use case with cheaper models.
+
+A workaround is to add an additional termination notice to the prompt. This acts a "little nudge" for the LLM to remember that they need to terminate the conversation when their task is complete. You can do this by appending a string such as the following to your user input string:
+
+```python
+prompt = "Some user query"
+
+termination_notice = (
+    '\n\nDo not show appreciation in your responses, say only what is necessary. '
+    'if "Thank you" or "You\'re welcome" are said in the conversation, then say TERMINATE '
+    'to indicate the conversation is finished and this is your last message.'
+)
+
+prompt += termination_notice
+```
+
+**Note**: This workaround gets the job done around 90% of the time, but there are occurences where the LLM still forgets to terminate the conversation.
diff --git a/website/docs/Getting-Started.md b/website/docs/Getting-Started.md
index 937e8e51380f..c0e53aea5cdb 100644
--- a/website/docs/Getting-Started.md
+++ b/website/docs/Getting-Started.md
@@ -12,7 +12,7 @@ AutoGen is a framework that enables development of LLM applications using multip
 * It supports **diverse conversation patterns** for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy,
 the number of agents, and agent conversation topology.
 * It provides a collection of working systems with different complexities. These systems span a **wide range of applications** from various domains and complexities. They demonstrate how AutoGen can easily support different conversation patterns.
-* AutoGen provides a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` as an **enhanced inference API**. It allows easy performance tuning, utilities like API unification & caching, and advanced usage patterns, such as error handling, multi-config inference, context programming etc.
+* AutoGen provides **enhanced LLM inference**. It offers easy performance tuning, plus utilities like API unification & caching, and advanced usage patterns, such as error handling, multi-config inference, context programming etc.
 
 AutoGen is powered by collaborative [research studies](/docs/Research) from Microsoft, Penn State University, and University of Washington.
 
@@ -44,7 +44,7 @@ The figure below shows an example conversation flow with AutoGen.
 * [Documentation](/docs/Use-Cases/agent_chat).
 
 #### Enhanced LLM Inferences
-Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` with powerful functionalites like tuning, caching, error handling, templating. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
+Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers enhanced LLM inference with powerful functionalites like tuning, caching, error handling, templating. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
 ```python
 # perform tuning
 config, analysis = autogen.Completion.tune(
diff --git a/website/docs/Installation.md b/website/docs/Installation.md
index 9d064d4abb73..193e1383974a 100644
--- a/website/docs/Installation.md
+++ b/website/docs/Installation.md
@@ -4,12 +4,16 @@
 
 When not using a docker container, we recommend using a virtual environment to install AutoGen. This will ensure that the dependencies for AutoGen are isolated from the rest of your system.
 
+### Option 1: venv
+
 You can create a virtual environment with `venv` as below:
 ```bash
 python3 -m venv autogen
 source autogen/bin/activate
 ```
 
+### Option 2: conda
+
 Another option is with `Conda`, Conda works better at solving dependency conflicts than pip. You can install it by following [this doc](https://docs.conda.io/projects/conda/en/stable/user-guide/install/index.html),
 and then create a virtual environment as below:
 ```bash
@@ -26,6 +30,9 @@ AutoGen requires **Python version >= 3.8**. It can be installed from pip:
 ```bash
 pip install pyautogen
 ```
+
+`pyautogen<0.2` requires `openai<1`. Starting from pyautogen v0.2, `openai>=1` is required.
+
 <!--
 or conda:
 ```
diff --git a/website/docs/Use-Cases/agent_chat.md b/website/docs/Use-Cases/agent_chat.md
index 4267e85fd655..9062c1b36902 100644
--- a/website/docs/Use-Cases/agent_chat.md
+++ b/website/docs/Use-Cases/agent_chat.md
@@ -99,6 +99,7 @@ The figure below shows six examples of applications built using AutoGen.
 
    - Automated Chess Game Playing & Chitchatting by GPT-4 Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_chess.ipynb)
    - Automated Continual Learning from New Data - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_stream.ipynb)
+   - [OptiGuide](https://github.com/microsoft/optiguide) - Large Language Models for Supply Chain Optimization.
 
 4. **Tool Use**
 
diff --git a/website/docs/Use-Cases/enhanced_inference.md b/website/docs/Use-Cases/enhanced_inference.md
index 2766fd9a483f..8b5dd5c694ed 100644
--- a/website/docs/Use-Cases/enhanced_inference.md
+++ b/website/docs/Use-Cases/enhanced_inference.md
@@ -1,6 +1,6 @@
 # Enhanced Inference
 
-`autogen.Completion` is a drop-in replacement of `openai.Completion` and `openai.ChatCompletion` as an enhanced inference API.
+`autogen.Completion` is a drop-in replacement of `openai.Completion` and `openai.ChatCompletion` for enhanced LLM inference.
 There are a number of benefits of using `autogen` to perform inference: performance tuning, API unification, caching, error handling, multi-config inference, result filtering, templating and so on.
 
 ## Tune Inference Parameters