add chroma in memory client

app.py

@@ -9,7 +9,7 @@ from langchain_community.document_loaders import UnstructuredFileLoader
 from langchain_chroma import Chroma
 from chromadb import Documents, EmbeddingFunction, Embeddings  
 from chromadb.config import Settings  
-from chromadb import HttpClient 
+import chromadb #import HttpClient 
 import os  
 import re
 import uuid  
@@ -19,8 +19,8 @@ import torch.nn.functional as F
 from dotenv import load_dotenv
 from utils import load_env_variables, parse_and_route , escape_special_characters 
 from globalvars import API_BASE, intention_prompt, tasks, system_message, model_name , metadata_prompt 
-import time  
-import httpx  
+# import time  
+# import httpx  
   
 
 
@@ -39,7 +39,10 @@ def clear_cuda_cache():
     torch.cuda.empty_cache()  
   
 client = OpenAI(api_key=yi_token, base_url=API_BASE)
-chroma_client = HttpClient(host="localhost", port=8000)  
+
+chroma_client = chromadb.Client(Settings())  
+  
+# Create a collection  
 chroma_collection = chroma_client.create_collection("all-my-documents")  
 
 class EmbeddingGenerator:  
@@ -154,7 +157,7 @@ def query_chroma(query_text: str, embedding_function: MyEmbeddingFunction):
 intention_client = OpenAI(api_key=yi_token, base_url=API_BASE)  
 embedding_generator = EmbeddingGenerator(model_name=model_name, token=hf_token, intention_client=intention_client)  
 embedding_function = MyEmbeddingFunction(embedding_generator=embedding_generator)  
-chroma_client, chroma_collection = initialize_chroma(collection_name="Tonic-instruct", embedding_function=embedding_function)
+chroma_client, chroma_collection = initialize_chroma(collection_name="Tonic-instruct", embedding_function=embedding_function)  
 
 def respond(  
     message,