diff --git a/pers/langchain/tools/browser.py b/pers/langchain/tools/browser.py
index 957ea1d..640ae65 100644
--- a/pers/langchain/tools/browser.py
+++ b/pers/langchain/tools/browser.py
@@ -32,7 +32,7 @@ def render_webpage(url: str):
 
 @tool('render_webpage')
 def render_webpage_tool(url: str, reasoning: str):
-    """Fetches the raw HTML of a webpage for use with the `retrieve_from_faiss` tool."""
+    """Fetches the raw HTML of a webpage for use with the `retrieve_from_chroma` tool."""
     if PRINT_USAGE:
         _print_func_call('render_webpage', {'url': url, 'reasoning': reasoning})
     html_source = render_webpage(url)
diff --git a/pers/langchain/tools/document_manager.py b/pers/langchain/tools/document_manager.py
index c36a894..f217669 100644
--- a/pers/langchain/tools/document_manager.py
+++ b/pers/langchain/tools/document_manager.py
@@ -1,6 +1,6 @@
 from langchain.chains import RetrievalQA
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores.faiss import FAISS
+from langchain_community.vectorstores.chroma import Chroma
 from langchain_core.tools import tool
 from langchain_openai import OpenAIEmbeddings
 
@@ -11,7 +11,7 @@ class DocumentManager:
     """
     A class to manage loading large documents into the chain and giving the agent the ability to read it on subsequent loops.
     """
-    index: FAISS
+    index: Chroma
     qa_chain: RetrievalQA
 
     def __init__(self):
@@ -21,7 +21,7 @@ class DocumentManager:
         assert isinstance(data, str)
         text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
         splits = text_splitter.split_text(data)
-        self.index = FAISS.from_texts(splits, self.embeddings)
+        self.index = Chroma.from_texts(splits, self.embeddings)
 
     def create_retrieval(self):
         self.qa_chain = RetrievalQA.from_chain_type(
@@ -29,13 +29,13 @@ class DocumentManager:
             chain_type="map_reduce",
             retriever=self.index.as_retriever(),
         )
-        return {'success': True, 'msg': 'Use the `retrieve_from_faiss` to parse the result'}
+        return {'success': True, 'msg': 'Use the `retrieve_from_chroma` to parse the result'}
 
     def retrieve(self, question: str):
         return self.qa_chain.invoke({"query": question})
 
 
 @tool
-def retrieve_from_faiss(question: str):
-    """Retrieve data from data embedded in FAISS"""
+def retrieve_from_chroma(question: str):
+    """Retrieve data from data embedded in the Chroma DB."""
     return GLOBALS.DocumentManager.retrieve(question)
diff --git a/requirements.txt b/requirements.txt
index a657953..f0e5252 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,4 @@
 termcolor==2.4.0
-faiss-cpu==1.7.4
 serpapi==0.1.5
 langchain==0.1.5
 langchain-openai==0.0.5
@@ -15,3 +14,4 @@ async-timeout==4.0.3
 pyyaml==6.0.1
 py-cpuinfo==9.0.0
 psutil==5.9.8
+chromadb==0.4.22
\ No newline at end of file
diff --git a/run.py b/run.py
index 4bad0d0..b209aa8 100755
--- a/run.py
+++ b/run.py
@@ -24,7 +24,7 @@ from pers.langchain.history import HistoryManager
 from pers.langchain.tools.agent_end_response import end_my_response
 from pers.langchain.tools.bash import run_bash
 from pers.langchain.tools.browser import render_webpage_tool
-from pers.langchain.tools.document_manager import DocumentManager, retrieve_from_faiss
+from pers.langchain.tools.document_manager import DocumentManager, retrieve_from_chroma
 from pers.langchain.tools.google import search_google, search_google_news, search_google_maps
 from pers.langchain.tools.python import run_python
 from pers.langchain.tools.terminate import terminate_chat
@@ -84,7 +84,7 @@ def main():
         ]
     )
 
-    tools = [end_my_response, run_bash, run_python, search_google, search_google_maps, search_google_news, retrieve_from_faiss, read_webpage, render_webpage_tool, terminate_chat]
+    tools = [end_my_response, run_bash, run_python, search_google, search_google_maps, search_google_news, retrieve_from_chroma, read_webpage, render_webpage_tool, terminate_chat]
     llm_with_tools = pers.GLOBALS.OpenAI.bind(functions=[convert_to_openai_function(t) for t in tools])
 
     agent = (