[Blocking Issue] Add tests dependencies for qdrant and fix chromadb errors

.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:

autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py

@@ -18,10 +18,10 @@
 class QdrantRetrieveUserProxyAgent(RetrieveUserProxyAgent):
     def __init__(
         self,
-        name="RetrieveChatAgent",
-        human_input_mode: str | None = "ALWAYS",
-        is_termination_msg: Callable[[Dict], bool] | None = None,
-        retrieve_config: Dict | None = None,
+        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,
     ):
         """

autogen/agentchat/contrib/retrieve_user_proxy_agent.py

@@ -361,7 +361,7 @@ 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,

autogen/retrieve_utils.py

@@ -5,7 +5,11 @@
 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
@@ -287,7 +291,7 @@ 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.
@@ -307,6 +311,9 @@ 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)
@@ -344,6 +351,7 @@ def create_vector_db_from_dir(
             )
     except ValueError as e:
         logger.warning(f"{e}")
+    return client
 
 
 def query_vector_db(

setup.py

@@ -53,6 +53,7 @@
             "sympy",
             "tiktoken",
             "wolframalpha",
+            "qdrant_client[fastembed]",
         ],
         "blendsearch": ["flaml[blendsearch]"],
         "mathchat": ["sympy", "pydantic==1.10.9", "wolframalpha"],