diff --git a/Makefile b/Makefile
index f283154..dc8a1b9 100644
--- a/Makefile
+++ b/Makefile
@@ -1,4 +1,5 @@
 format:
+	poetry run ruff check . --fix
 	poetry run black .
 
 lint:
diff --git a/README.md b/README.md
index 0183dfc..09b8e80 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@ This template contains a reference architecture for Retrieval Augmented Generati
 You need to set some required environment variables before using your new app based on this template. These are used to index as well as run the application, and exceptions are raised if the following required environment variables are not set:
 
 1. `OPENAI_API_KEY`: from the OpenAI platform.
-2. `DOCUGAMI_API_KEY`: from the [Docugami Developer Playground](https://help.docugami.com/home/docugami-api)
+1. `DOCUGAMI_API_KEY`: from the [Docugami Developer Playground](https://help.docugami.com/home/docugami-api)
 
 ```shell
 export OPENAI_API_KEY=...
@@ -28,8 +28,8 @@ Finally, make sure that you run `poetry install --all-extras` (or select a speci
 Before you use this template, you must have some documents already processed in Docugami. Here's what you need to get started:
 
 1. Create a [Docugami workspace](https://app.docugami.com/) (free trials available)
-2. Create an access token via the Developer Playground for your workspace. [Detailed instructions](https://help.docugami.com/home/docugami-api).
-3. Add your documents to Docugami for processing. There are two ways to do this:
+1. Create an access token via the Developer Playground for your workspace. [Detailed instructions](https://help.docugami.com/home/docugami-api).
+1. Add your documents to Docugami for processing. There are two ways to do this:
     - Upload via the simple Docugami web experience. [Detailed instructions](https://help.docugami.com/home/adding-documents).
     - Upload via the Docugami API, specifically the [documents](https://api-docs.docugami.com/#tag/documents/operation/upload-document) endpoint. Code samples are available for python and JavaScript or you can use the [docugami](https://pypi.org/project/docugami/) python library.
 
@@ -105,3 +105,18 @@ from langserve.client import RemoteRunnable
 
 runnable = RemoteRunnable("http://localhost:8000/docugami-kg-rag")
 ```
+
+# Advanced Configuration
+
+## Using Local GPU
+Optionally, if using local embeddings or llms in `config.py`, make sure your local CUDA runtime is updated. You can run `torch.cuda.is_available()` in a python REPL to make sure, and if you need to install a specific version for your local CUDA driver you can run something like `poetry run pip3 install torch==1.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117` to update it.
+
+## Using Redis
+
+Under `config.py` you can configure the vector store to use Redis. See documentation here:
+
+One of the things you need to specify is the REDIS_URL. You may have an instance already running that you can point to, or for development you may want to deploy Redis locally:
+
+`docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest`
+
+See documentation [here](https://python.langchain.com/docs/integrations/vectorstores/redis#redis-connection-url-examples) for how to configure the REDIS_URL
diff --git a/docugami_kg_rag/chain.py b/docugami_kg_rag/chain.py
index 983e1a6..f6d37c6 100644
--- a/docugami_kg_rag/chain.py
+++ b/docugami_kg_rag/chain.py
@@ -1,27 +1,23 @@
 import sys
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Tuple
 
-from langchain.agents import AgentExecutor
-from langchain.agents.format_scratchpad import format_to_openai_functions
-from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain.agents import AgentExecutor, create_openai_tools_agent
 from langchain.prompts import (
     ChatPromptTemplate,
     MessagesPlaceholder,
 )
-from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema.messages import AIMessage, HumanMessage
-from langchain.schema.runnable import Runnable, RunnableLambda, RunnableParallel
+from langchain.schema.runnable import Runnable, RunnableLambda
 from langchain.tools.base import BaseTool
-from langchain.tools.render import format_tool_to_openai_function
 
-from docugami_kg_rag.config import LARGE_CONTEXT_LLM
-from docugami_kg_rag.helpers.state import read_all_local_index_state
+from docugami_kg_rag.config import LARGE_CONTEXT_LLM, USE_REPORTS
+from docugami_kg_rag.helpers.indexing import read_all_local_index_state
 from docugami_kg_rag.helpers.prompts import ASSISTANT_SYSTEM_MESSAGE
 from docugami_kg_rag.helpers.reports import get_retrieval_tool_for_report
 from docugami_kg_rag.helpers.retrieval import get_retrieval_tool_for_docset
 
 
-def _get_tools(use_reports=True) -> List[BaseTool]:
+def _get_tools(use_reports=USE_REPORTS) -> List[BaseTool]:
     """
     Build retrieval tools.
     """
@@ -32,7 +28,7 @@ def _get_tools(use_reports=True) -> List[BaseTool]:
     report_retrieval_tools: List[BaseTool] = []
     for docset_id in local_state:
         docset_state = local_state[docset_id]
-        direct_retrieval_tool = get_retrieval_tool_for_docset(docset_state)
+        direct_retrieval_tool = get_retrieval_tool_for_docset(docset_id, docset_state)
         if direct_retrieval_tool:
             # Direct retrieval tool for each indexed docset (direct KG-RAG against semantic XML)
             docset_retrieval_tools.append(direct_retrieval_tool)
@@ -73,41 +69,8 @@ def _format_chat_history(chat_history: List[Tuple[str, str]]):
 )
 
 
-agent = (
-    RunnableParallel(
-        {
-            "input": lambda x: x["input"],  # type: ignore
-            "chat_history": lambda x: _format_chat_history(x["chat_history"]),  # type: ignore
-            "agent_scratchpad": lambda x: format_to_openai_functions(x["intermediate_steps"]),  # type: ignore
-            "functions": lambda x: [format_tool_to_openai_function(tool) for tool in _get_tools("use_reports" in x and x["use_reports"] is True)],  # type: ignore
-        }
-    )
-    | {
-        "input": prompt,
-        "functions": lambda x: x["functions"],
-    }
-    | _llm_with_tools
-    | OpenAIFunctionsAgentOutputParser()
-)
-
-
-class AgentInput(BaseModel):
-    input: str = ""
-    use_reports: bool = Field(
-        default=False,
-        extra={"widget": {"type": "bool", "input": "input", "output": "output"}},
-    )
-    chat_history: Optional[List[Tuple[str, str]]] = Field(
-        ..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
-    )
-
-
-chain = AgentExecutor(
-    agent=agent,  # type: ignore
-    tools=_get_tools(),
-).with_types(
-    input_type=AgentInput,  # type: ignore
-)
+agent = create_openai_tools_agent(LARGE_CONTEXT_LLM, _get_tools(), prompt)
+chain = AgentExecutor(agent=agent, tools=_get_tools())  # type: ignore
 
 if __name__ == "__main__":
     if sys.gettrace():
@@ -115,8 +78,7 @@ class AgentInput(BaseModel):
 
         chain.invoke(
             {
-                "input": "What was the question from Barclays in the Q2 2023 earnings call?",
-                "use_reports": False,
+                "input": "What happened in Yelm, Washington?",
                 "chat_history": [],
             }
         )
diff --git a/docugami_kg_rag/config.py b/docugami_kg_rag/config.py
index 3131ef6..acd2881 100644
--- a/docugami_kg_rag/config.py
+++ b/docugami_kg_rag/config.py
@@ -1,26 +1,11 @@
 import os
 from pathlib import Path
 
+from langchain_core.vectorstores import VectorStore
+
 from langchain.cache import SQLiteCache
 from langchain.globals import set_llm_cache
 
-
-##### <LLMs and Embeddings>
-# OpenAI models and Embeddings
-# Reference: https://platform.openai.com/docs/models
-from langchain_community.chat_models.openai import ChatOpenAI
-from langchain_community.embeddings.openai import OpenAIEmbeddings
-
-LARGE_CONTEXT_LLM = ChatOpenAI(temperature=0, model="gpt-4-1106-preview")  # 128k tokens
-SMALL_CONTEXT_LLM = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-1106")  # 16k tokens
-EMBEDDINGS = OpenAIEmbeddings(model="text-embedding-ada-002")
-##### </LLMs and Embeddings>
-
-##### <Vector Store>
-CHROMA_DIRECTORY = "/tmp/docugami/chroma_db"
-os.makedirs(Path(CHROMA_DIRECTORY).parent, exist_ok=True)
-##### </Vector Store>
-
 DOCUGAMI_API_KEY = os.environ.get("DOCUGAMI_API_KEY")
 if not DOCUGAMI_API_KEY:
     raise Exception("Please set the DOCUGAMI_API_KEY environment variable")
@@ -36,13 +21,121 @@
 os.makedirs(Path(LOCAL_LLM_CACHE_DB_FILE).parent, exist_ok=True)
 set_llm_cache(SQLiteCache(database_path=LOCAL_LLM_CACHE_DB_FILE))
 
+USE_REPORTS = True
+
+##### <LLMs and Embeddings>
+# OpenAI models and Embeddings
+# Reference: https://platform.openai.com/docs/models
+from langchain_openai import ChatOpenAI
+from langchain_openai import OpenAIEmbeddings
+
+if "OPENAI_API_KEY" not in os.environ:
+    raise Exception("OPENAI_API_KEY environment variable not set")
+
+LARGE_CONTEXT_LLM = ChatOpenAI(temperature=0, model="gpt-4-1106-preview", cache=True)  # 128k tokens
+SMALL_CONTEXT_LLM = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-1106", cache=True)  # 16k tokens
+EMBEDDINGS = OpenAIEmbeddings(model="text-embedding-ada-002")
+
 # Lengths for the Docugami loader are in terms of characters, 1 token ~= 4 chars in English
 # Reference: https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them
+MIN_LENGTH_TO_SUMMARIZE = 2048  # chunks and docs below this length are embedded as-is
+MAX_FULL_DOCUMENT_TEXT_LENGTH = 1024 * 56  # ~14k tokens
 MAX_CHUNK_TEXT_LENGTH = 1024 * 26  # ~6.5k tokens
 MIN_CHUNK_TEXT_LENGTH = 1024 * 6  # ~1.5k tokens
 SUB_CHUNK_TABLES = False
-INCLUDE_XML_TAGS = True
+INCLUDE_XML_TAGS = False
 PARENT_HIERARCHY_LEVELS = 2
 RETRIEVER_K = 8
 
 BATCH_SIZE = 16
+
+# FireworksAI models and local embeddings
+# Reference: https://fireworks.ai/models
+# Reference: https://huggingface.co/models
+# import torch
+# from langchain.chat_models.fireworks import ChatFireworks
+# from langchain_community.embeddings import HuggingFaceEmbeddings
+
+# if "FIREWORKS_API_KEY" not in os.environ:
+#     raise Exception("FIREWORKS_API_KEY environment variable not set")
+# LARGE_CONTEXT_LLM = ChatFireworks(
+#     model="accounts/fireworks/models/mixtral-8x7b-instruct",
+#     model_kwargs={"temperature": 0, "max_tokens": 1024},
+#     cache=True,
+# )  # 128k tokens
+# SMALL_CONTEXT_LLM = LARGE_CONTEXT_LLM  # Use the same model for large and small context tasks
+# device = "cpu"
+# if torch.cuda.is_available():
+#     device = "cuda"
+
+# EMBEDDINGS = HuggingFaceEmbeddings(
+#     model_name="sentence-transformers/all-mpnet-base-v2",
+#     model_kwargs={"device": device},
+# )
+
+# # Lengths for the Docugami loader are in terms of characters, 1 token ~= 4 chars in English
+# # Reference: https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them
+# MIN_LENGTH_TO_SUMMARIZE = 2048  # chunks and docs below this length are embedded as-is
+# MAX_FULL_DOCUMENT_TEXT_LENGTH = 1024 * 56  # ~14k tokens
+# MAX_CHUNK_TEXT_LENGTH = 1024 * 26  # ~6.5k tokens
+# MIN_CHUNK_TEXT_LENGTH = 1024 * 6  # ~1.5k tokens
+# SUB_CHUNK_TABLES = False
+# INCLUDE_XML_TAGS = False
+# PARENT_HIERARCHY_LEVELS = 2
+# RETRIEVER_K = 8
+
+# BATCH_SIZE = 16
+##### </LLMs and Embeddings>
+
+##### <Vector Store>
+# ChromaDB
+# Reference: https://python.langchain.com/docs/integrations/vectorstores/chroma
+from langchain_community.vectorstores.chroma import Chroma
+import chromadb
+
+CHROMA_DIRECTORY = Path("/tmp/docugami/chroma_db")
+
+
+def get_vector_store_index(docset_id: str) -> VectorStore:
+    return Chroma(
+        collection_name=docset_id,
+        persist_directory=str(CHROMA_DIRECTORY.absolute()),
+        embedding_function=EMBEDDINGS,
+    )
+
+
+def vector_store_index_exists(docset_id: str) -> bool:
+    persistent_client = chromadb.PersistentClient(path=str(CHROMA_DIRECTORY.absolute()))
+    collections = persistent_client.list_collections()
+    for c in collections:
+        if c.name == docset_id:
+            return True
+
+    return False
+
+
+def del_vector_store_index(docset_id: str):
+    persistent_client = chromadb.PersistentClient(path=str(CHROMA_DIRECTORY.absolute()))
+    persistent_client.delete_collection(docset_id)
+
+
+# Redis
+# Reference: https://python.langchain.com/docs/integrations/vectorstores/redis
+# from langchain_community.vectorstores.redis.base import Redis, check_index_exists
+
+# REDIS_URL = "redis://localhost:6379"
+
+
+# def get_vector_store_index(docset_id: str) -> VectorStore:
+#     return Redis(redis_url=REDIS_URL, index_name=docset_id, embedding=EMBEDDINGS)
+
+
+# def vector_store_index_exists(docset_id: str) -> bool:
+#     index = get_vector_store_index(docset_id)
+#     check_index_exists(index, docset_id)
+
+# def del_vector_store_index(docset_id: str):
+#     Redis.drop_index(docset_id, True, redis_url=REDIS_URL)
+
+
+##### </Vector Store>
diff --git a/docugami_kg_rag/helpers/documents.py b/docugami_kg_rag/helpers/documents.py
index 7ca7be8..7988b06 100644
--- a/docugami_kg_rag/helpers/documents.py
+++ b/docugami_kg_rag/helpers/documents.py
@@ -12,21 +12,26 @@
     BATCH_SIZE,
     INCLUDE_XML_TAGS,
     MAX_CHUNK_TEXT_LENGTH,
+    MAX_FULL_DOCUMENT_TEXT_LENGTH,
+    MIN_LENGTH_TO_SUMMARIZE,
     SMALL_CONTEXT_LLM,
     LARGE_CONTEXT_LLM,
 )
 from docugami_kg_rag.helpers.prompts import (
-    ASSISTANT_SYSTEM_MESSAGE,
+    CREATE_CHUNK_SUMMARY_SYSTEM_MESSAGE,
     CREATE_FULL_DOCUMENT_SUMMARY_PROMPT,
     CREATE_CHUNK_SUMMARY_PROMPT,
+    CREATE_FULL_DOCUMENT_SUMMARY_SYSTEM_MESSAGE,
 )
 
 
 def _build_summary_mappings(
     docs_by_id: Dict[str, Document],
+    system_message: str,
     prompt_template: str,
     llm: BaseChatModel = SMALL_CONTEXT_LLM,
-    summarize_length_threshold=2048,
+    min_length_to_summarize=MIN_LENGTH_TO_SUMMARIZE,
+    max_length_cutoff=MAX_CHUNK_TEXT_LENGTH,
     label="summaries",
 ) -> Dict[str, Document]:
     """
@@ -47,7 +52,7 @@ def _build_summary_mappings(
         batch_input = [
             {
                 "format": format,
-                "document": doc.page_content[:MAX_CHUNK_TEXT_LENGTH],
+                "document": doc.page_content[:max_length_cutoff],
             }
             for _, doc in batch
         ]
@@ -55,7 +60,7 @@ def _build_summary_mappings(
         summarize_chain = (
             ChatPromptTemplate.from_messages(
                 [
-                    ("system", ASSISTANT_SYSTEM_MESSAGE),
+                    ("system", system_message),
                     ("human", prompt_template),
                 ]
             )
@@ -66,7 +71,7 @@ def _build_summary_mappings(
 
         # Build meta chain that only summarizes inputs larger than threshold
         chain = RunnableBranch(
-            (lambda x: len(x["document"]) > summarize_length_threshold, summarize_chain),  # type: ignore
+            (lambda x: len(x["document"]) > min_length_to_summarize, summarize_chain),  # type: ignore
             noop_chain,
         )
 
@@ -94,8 +99,10 @@ def build_full_doc_summary_mappings(docs_by_id: Dict[str, Document]) -> Dict[str
 
     return _build_summary_mappings(
         docs_by_id=docs_by_id,
+        system_message=CREATE_FULL_DOCUMENT_SUMMARY_SYSTEM_MESSAGE,
         prompt_template=CREATE_FULL_DOCUMENT_SUMMARY_PROMPT,
         llm=LARGE_CONTEXT_LLM,
+        max_length_cutoff=MAX_FULL_DOCUMENT_TEXT_LENGTH,
         label="full document summaries",
     )
 
@@ -107,7 +114,9 @@ def build_chunk_summary_mappings(docs_by_id: Dict[str, Document]) -> Dict[str, D
 
     return _build_summary_mappings(
         docs_by_id=docs_by_id,
+        system_message=CREATE_CHUNK_SUMMARY_SYSTEM_MESSAGE,
         prompt_template=CREATE_CHUNK_SUMMARY_PROMPT,
         llm=SMALL_CONTEXT_LLM,
+        max_length_cutoff=MAX_CHUNK_TEXT_LENGTH,
         label="chunk summaries",
     )
diff --git a/docugami_kg_rag/helpers/state.py b/docugami_kg_rag/helpers/indexing.py
similarity index 84%
rename from docugami_kg_rag/helpers/state.py
rename to docugami_kg_rag/helpers/indexing.py
index 00c8088..b82d176 100644
--- a/docugami_kg_rag/helpers/state.py
+++ b/docugami_kg_rag/helpers/indexing.py
@@ -2,7 +2,6 @@
 import os
 from pathlib import Path
 import pickle
-import shutil
 from typing import Dict, List
 
 from langchain.schema import Document
@@ -10,14 +9,15 @@
 from langchain_community.document_loaders.docugami import DocugamiLoader
 
 from docugami_kg_rag.config import (
-    CHROMA_DIRECTORY,
-    EMBEDDINGS,
     INCLUDE_XML_TAGS,
     INDEXING_LOCAL_STATE_PATH,
     MAX_CHUNK_TEXT_LENGTH,
     MIN_CHUNK_TEXT_LENGTH,
     PARENT_HIERARCHY_LEVELS,
     SUB_CHUNK_TABLES,
+    get_vector_store_index,
+    vector_store_index_exists,
+    del_vector_store_index,
 )
 from docugami_kg_rag.helpers.documents import build_full_doc_summary_mappings, build_chunk_summary_mappings
 from docugami_kg_rag.helpers.reports import ReportDetails, build_report_details
@@ -27,9 +27,6 @@
     docset_name_to_direct_retriever_tool_function_name,
 )
 
-from langchain_community.vectorstores.chroma import Chroma
-import chromadb
-
 
 def read_all_local_index_state() -> Dict[str, LocalIndexState]:
     if not Path(INDEXING_LOCAL_STATE_PATH).is_file():
@@ -75,33 +72,26 @@ def update_local_index(
         pickle.dump(state, file)
 
 
-def populate_chroma_index(docset_id: str, chunks: List[Document], overwrite=False):
+def populate_vector_index(docset_id: str, chunks: List[Document], overwrite=False):
     """
     Create index if it does not exist, delete and overwrite if overwrite is specified.
     """
 
-    persistent_client = chromadb.PersistentClient(path=CHROMA_DIRECTORY)
-    collections = persistent_client.list_collections()
-    matching_collection = None
-    for c in collections:
-        if c.name == docset_id:
-            matching_collection = c
-
-    if matching_collection:
-        print(f"Chroma collection already exists for {docset_id}.")
+    if vector_store_index_exists(docset_id):
+        print(f"Vector store index already exists for {docset_id}.")
         if overwrite is True:
             print(f"Overwrite is {overwrite}, deleting existing index")
-            persistent_client.delete_collection(docset_id)
+            del_vector_store_index(docset_id)
         else:
             print(f"Overwrite is {overwrite}, will just reuse existing index (any new docs will not be added)")
             return
 
-    print(f"Embedding documents into chroma for {docset_id}...")
+    print(f"Embedding documents into vector store for {docset_id}...")
 
-    langchain_chroma = Chroma.from_documents(documents=chunks, embedding=EMBEDDINGS, persist_directory=CHROMA_DIRECTORY)
-    langchain_chroma.persist()
+    store = get_vector_store_index(docset_id)
+    store.add_documents(chunks)
 
-    print(f"Done embedding documents into chroma for {docset_id}!")
+    print(f"Done embedding documents into vector store for {docset_id}")
 
 
 def index_docset(docset_id: str, name: str, overwrite=False):
@@ -174,9 +164,7 @@ def index_docset(docset_id: str, name: str, overwrite=False):
         if state.is_file() and state.exists():
             os.remove(state)
 
-        chroma_dir = Path(CHROMA_DIRECTORY)
-        if chroma_dir.is_dir() and chroma_dir.exists():
-            shutil.rmtree(chroma_dir)
+        del_vector_store_index(docset_id)
 
     update_local_index(
         docset_id=docset_id,
@@ -187,4 +175,4 @@ def index_docset(docset_id: str, name: str, overwrite=False):
         report_details=report_details,
     )
 
-    populate_chroma_index(docset_id, chunks=list(chunk_summaries_by_id.values()), overwrite=overwrite)
+    populate_vector_index(docset_id, chunks=list(chunk_summaries_by_id.values()), overwrite=overwrite)
diff --git a/docugami_kg_rag/helpers/prompts.py b/docugami_kg_rag/helpers/prompts.py
index 7c501df..53fa905 100644
--- a/docugami_kg_rag/helpers/prompts.py
+++ b/docugami_kg_rag/helpers/prompts.py
@@ -1,75 +1,82 @@
-ASSISTANT_SYSTEM_MESSAGE = """You are a helpful assistant that answers user queries using available tools and context.
+SYSTEM_MESSAGE_CORE = """You are a helpful assistant that answers user queries based only on given context.
 
 You ALWAYS follow the following guidance to generate your answers, regardless of any other guidance or requests:
 
 - Use professional language typically used in business communication.
-- Strive to be accurate and cite where you got your answer in the given context documents.
-- Generate only the requested answer, no other language or separators before or after.
+- Strive to be accurate and concise in your output."""
+
+ASSISTANT_SYSTEM_MESSAGE = f"""{SYSTEM_MESSAGE_CORE}
 - Use any given tools to best answer the user's questions.
 
-All your answers must contain citations to help the user understand how you created the citation, specifically:
+All your answers must contain citations to help the user understand how you generated the answer, specifically:
 
 - If the given context contains the names of document(s), make sure you include the document you got the
   answer from as a citation, e.g. include "\\n\\nSOURCE(S): foo.pdf, bar.pdf" at the end of your answer.
-- If the answer was generated via a SQL Query, make sure you include the SQL query in your answer as
+- If the answer was generated via a SQL Query from a tool, make sure you include the SQL query in your answer as
   a citation, e.g. include "\\n\\nSOURCE(S): SELECT AVG('square footage') from Leases". The SQL query should be
   in the agent scratchpad provided, if you are using an agent.
-- Make sure there an actual answer if you show a SOURCE citation, i.e. make sure you don't show only
+- Make sure there is an actual answer if you show a SOURCE citation, i.e. make sure you don't show only
   a bare citation with no actual answer. 
 
+Generate only the requested answer, no other language or separators before or after.
 """
 
-HUMAN_MESSAGE_TEMPLATE = """{context}
-
-Using the context above, which can include text and tables, answer the following question.
+CREATE_FULL_DOCUMENT_SUMMARY_SYSTEM_MESSAGE = f"""{SYSTEM_MESSAGE_CORE}
+You will be asked to summarize documents. You ALWAYS follow these rules when generating summaries:
 
-Question: {question}
+- Your generated summary should be in the same format as the given document, using the same overall schema.
+- The generated summary should be up to 1 page of text in length, or shorter if the original document is short.
+- Only summarize, don't try to change any facts in the document even if they appear incorrect to you.
+- Include as many facts and data points from the original document as you can, in your summary.
 """
 
-CREATE_DIRECT_RETRIEVAL_TOOL_DESCRIPTION_PROMPT = """Here is a snippet from a sample document of type {docset_name}:
+CREATE_FULL_DOCUMENT_SUMMARY_PROMPT = """Here is a document, in {format} format:
 
 {document}
 
-Please write a short general description of the given document type, using the given sample as a guide.
-This description will be used to describe this type of document in general in a product. When users ask
-a question, an AI agent will use the description you produce to decide whether the
-answer for that question is likely to be found in this type of document or not.
+Please write a detailed summary of the given document.
 
-Follow the following rules:
+Respond only with the summary and no other language before or after.
+"""
 
-- The generated description must apply to all documents of type {docset_name}, similar to the sample
-  document above, not just the given same document.
-- Do NOT include any data or details from this particular sample document but DO use this sample
-  document to get a better understanding of what {docset_name} type documents contain in this context.
-- The generated description should be very short and up to 2 sentences max.
+CREATE_CHUNK_SUMMARY_SYSTEM_MESSAGE = f"""{SYSTEM_MESSAGE_CORE}
+You will be asked to summarize chunks of documents. You ALWAYS follow these rules when generating summaries:
 
-Respond only with the requested general description of the document type and no other language
-before or after.
+- Your generated summary should be in the same format as the given document, using the same overall schema.
+- The generated summary will be embedded and used to retrieve the raw text or table elements from a vector database.
+- Only summarize, don't try to change any facts in the chunk even if they appear incorrect to you.
+- Include as many facts and data points from the original chunk as you can, in your summary.
+- Pay special attention to monetary amounts, dates, names of people and companies, etc and include in your summary.
 """
 
-
-CREATE_FULL_DOCUMENT_SUMMARY_PROMPT = """Here is a document, in {format} format:
+CREATE_CHUNK_SUMMARY_PROMPT = """Here is a chunk from a document, in {format} format:
 
 {document}
 
-Please write a detailed summary of the given document.
+Respond only with the summary and no other language before or after.
+"""
 
-Keep in mind the following rules:
+CREATE_DIRECT_RETRIEVAL_TOOL_SYSTEM_MESSAGE = f"""{SYSTEM_MESSAGE_CORE}
+You will be asked to write short generate descriptions of document types, given a particular sampel document
+as a guide. You ALWAYS follow these rules when generating descriptions:
 
-- Your generated summary should be in the same format as the given document, using the same overall schema.
-- The generated summary should be up to 2 pages of text in length, shorter of the original document is short.
-- Only summarize, don't try to change any facts in the document even if they appear incorrect to you
-- Include as many facts and data points from the original document as you can, in your summary.
+- Make sure your description is text only, regardless of any markup in the given sample document.
+- The generated description must apply to all documents of the given type, similar to the sample
+  document given, not just the exact same document.
+- The generated description will be used to describe this type of document in general in a product. When users ask
+  a question, an AI agent will use the description you produce to decide whether the
+  answer for that question is likely to be found in this type of document or not.
+- Do NOT include any data or details from this particular sample document but DO use this sample
+  document to get a better understanding of what types of information this type of document might contain.
+- The generated description should be very short and up to 2 sentences max.
 
-Respond only with the detailed summary and no other language before or after.
 """
 
-CREATE_CHUNK_SUMMARY_PROMPT = """Here is a chunk from a document, in {format} format:
+CREATE_DIRECT_RETRIEVAL_TOOL_DESCRIPTION_PROMPT = """Here is a snippet from a sample document of type {docset_name}:
 
 {document}
 
-I need your help to summarize the document, including any tables or text, for retrieval.
-This summary will be embedded and used to retrieve the raw text or table elements from a vector database.
-Respond only with the requested summary well optimized for retrieval and no other language
-before or after.
+Please write a short general description of the given document type, using the given sample as a guide.
+
+Respond only with the requested general description of the document type and no other language before or after.
 """
diff --git a/docugami_kg_rag/helpers/retrieval.py b/docugami_kg_rag/helpers/retrieval.py
index c9e175b..b5b8518 100644
--- a/docugami_kg_rag/helpers/retrieval.py
+++ b/docugami_kg_rag/helpers/retrieval.py
@@ -7,21 +7,19 @@
 from langchain.schema import BaseRetriever, Document, StrOutputParser
 from langchain.storage.in_memory import InMemoryStore
 from langchain.tools.base import BaseTool
-from langchain_community.vectorstores.chroma import Chroma
 
 from docugami_kg_rag.config import (
-    CHROMA_DIRECTORY,
-    EMBEDDINGS,
     LARGE_CONTEXT_LLM,
     RETRIEVER_K,
     MAX_CHUNK_TEXT_LENGTH,
+    get_vector_store_index,
 )
 from docugami_kg_rag.helpers.fused_summary_retriever import (
     FusedSummaryRetriever,
     SearchType,
 )
 from docugami_kg_rag.helpers.prompts import (
-    ASSISTANT_SYSTEM_MESSAGE,
+    CREATE_DIRECT_RETRIEVAL_TOOL_SYSTEM_MESSAGE,
     CREATE_DIRECT_RETRIEVAL_TOOL_DESCRIPTION_PROMPT,
 )
 from docugami_kg_rag.helpers.reports import ReportDetails
@@ -45,12 +43,12 @@ class LocalIndexState:
     """Details about any reports for this docset."""
 
 
-def get_retriever_for_docset(docset_state: LocalIndexState) -> BaseRetriever:
+def get_retriever_for_docset(docset_id: str, docset_state: LocalIndexState) -> BaseRetriever:
     """
     Gets a retriever for a docset. Chunks are in the vector store, and full documents
     are in the store inside the local state.
     """
-    chunk_vectorstore = Chroma(persist_directory=CHROMA_DIRECTORY, embedding_function=EMBEDDINGS)
+    chunk_vectorstore = get_vector_store_index(docset_id)
 
     return FusedSummaryRetriever(
         vectorstore=chunk_vectorstore,
@@ -98,7 +96,7 @@ def chunks_to_direct_retriever_tool_description(name: str, chunks: List[Document
     chain = (
         ChatPromptTemplate.from_messages(
             [
-                ("system", ASSISTANT_SYSTEM_MESSAGE),
+                ("system", CREATE_DIRECT_RETRIEVAL_TOOL_SYSTEM_MESSAGE),
                 ("human", CREATE_DIRECT_RETRIEVAL_TOOL_DESCRIPTION_PROMPT),
             ]
         )
@@ -109,12 +107,12 @@ def chunks_to_direct_retriever_tool_description(name: str, chunks: List[Document
     return f"Searches for and returns chunks from {name} documents. {summary}"
 
 
-def get_retrieval_tool_for_docset(docset_state: LocalIndexState) -> Optional[BaseTool]:
+def get_retrieval_tool_for_docset(docset_id: str, docset_state: LocalIndexState) -> Optional[BaseTool]:
     """
     Gets a retrieval tool for an agent.
     """
 
-    retriever = get_retriever_for_docset(docset_state=docset_state)
+    retriever = get_retriever_for_docset(docset_id=docset_id, docset_state=docset_state)
     return create_retriever_tool(
         retriever=retriever,
         name=docset_state.retrieval_tool_function_name,
diff --git a/evals/sec-10-q.ipynb b/evals/sec-10-q.ipynb
index 628720e..32f6668 100644
--- a/evals/sec-10-q.ipynb
+++ b/evals/sec-10-q.ipynb
@@ -18,23 +18,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cloning into 'temp'...\n",
-      "remote: Enumerating objects: 119, done.\u001b[K\n",
-      "remote: Counting objects: 100% (12/12), done.\u001b[K\n",
-      "remote: Compressing objects: 100% (11/11), done.\u001b[K\n",
-      "remote: Total 119 (delta 0), reused 9 (delta 0), pack-reused 107\u001b[K\n",
-      "Receiving objects: 100% (119/119), 54.88 MiB | 28.79 MiB/s, done.\n",
-      "Resolving deltas: 100% (1/1), done.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!rm -rf temp\n",
     "!git clone https://github.com/docugami/KG-RAG-datasets.git temp"
@@ -42,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,9 +67,10 @@
     "    \"2023 Q3 NVDA.pdf\",\n",
     "]\n",
     "\n",
-    "GROUND_TRUTH_CSV = Path(os.getcwd()) / \"temp/sec-10-q/data/v1/questions_and_answers.csv\"\n",
+    "# Using mini set to save cost while developing, use full set for actual runs (~$300 per run in OpenAI costs per run)\n",
+    "GROUND_TRUTH_CSV = Path(os.getcwd()) / \"temp/sec-10-q/data/v1/qna_data_mini.csv\"\n",
     "\n",
-    "# We will run each experiment multiple times and average, \n",
+    "# We will run each experiment multiple times and average,\n",
     "# since results vary slightly over runs\n",
     "PER_EXPERIMENT_RUN_COUNT = 5\n",
     "\n",
@@ -95,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,39 +154,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'2022 Q3 AAPL.pdf': '/tmp/tmpu0hhgnnq',\n",
-       " '2022 Q3 AMZN.pdf': '/tmp/tmp0xkn9_lt',\n",
-       " '2022 Q3 INTC.pdf': '/tmp/tmp__wivxrv',\n",
-       " '2022 Q3 MSFT.pdf': '/tmp/tmpc0zyvxlc',\n",
-       " '2023 Q1 AAPL.pdf': '/tmp/tmpem113lbt',\n",
-       " '2023 Q1 AMZN.pdf': '/tmp/tmpr4_mjrf9',\n",
-       " '2023 Q1 INTC.pdf': '/tmp/tmpfmox9c0e',\n",
-       " '2023 Q1 MSFT.pdf': '/tmp/tmpx51fj_0u',\n",
-       " '2023 Q1 NVDA.pdf': '/tmp/tmpn8fl4m8j',\n",
-       " '2023 Q2 AAPL.pdf': '/tmp/tmpryvvitdi',\n",
-       " '2023 Q2 AMZN.pdf': '/tmp/tmpo80e5vut',\n",
-       " '2023 Q2 INTC.pdf': '/tmp/tmp5vgs29hq',\n",
-       " '2023 Q2 MSFT.pdf': '/tmp/tmp1mueget7',\n",
-       " '2023 Q2 NVDA.pdf': '/tmp/tmp30kowbyg',\n",
-       " '2023 Q3 AAPL.pdf': '/tmp/tmpvcmlqzvs',\n",
-       " '2023 Q3 AMZN.pdf': '/tmp/tmpcr2kkk24',\n",
-       " '2023 Q3 INTC.pdf': '/tmp/tmpsf026dv6',\n",
-       " '2023 Q3 MSFT.pdf': '/tmp/tmpi8vfu8zi',\n",
-       " '2023 Q3 NVDA.pdf': '/tmp/tmppjj5fdwj',\n",
-       " '2022 Q3 NVDA.pdf': '/tmp/tmp7wxepvlx'}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Wait for files to finish processing (OCR, and zero-shot creation of XML knowledge graph)\n",
     "\n",
@@ -209,33 +166,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Indexing SEC 10Q Filings (ID: tjwrr2ekqkc3)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Creating full document summaries in batches: 100%|██████████| 2/2 [00:00<00:00, 10.42it/s]\n",
-      "Creating chunk summaries in batches: 100%|██████████| 51/51 [00:11<00:00,  4.40it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Embedding documents into chroma for tjwrr2ekqkc3...\n",
-      "Done embedding documents into chroma for tjwrr2ekqkc3!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Run indexing\n",
     "from docugami_kg_rag.helpers.indexing import index_docset\n",
@@ -256,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,20 +215,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Microsoft's operating expenses for the latest quarter, which ended March 31, 2023, increased by $3.6 billion or 26% driven by investments in Azure, 6 points of growth from the Nuance acquisition, and employee severance expenses.\\n\\nSOURCE(S): 2023 Q2 MSFT.pdf\""
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Test the agent to make sure it is working\n",
     "predict_docugami_agent({\"question\": \"How much did Microsoft spend for opex in the latest quarter?\"})"
@@ -319,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -335,20 +257,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'In the latest quarter ending September 30, 2023, total operating expenses (opex) amounted to $131.895 billion.\\n\\nSOURCE(S): 2023 Q3 AMZN.pdf【47†source】'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Test the agent to make sure it is working\n",
     "predict_openai_agent({\"question\": \"How much did Microsoft spend for opex in the latest quarter?\"})"
diff --git a/index.py b/index.py
index 91f25eb..c0b4ca2 100644
--- a/index.py
+++ b/index.py
@@ -2,7 +2,7 @@
 import typer
 from docugami import Docugami
 
-from docugami_kg_rag.helpers.state import index_docset
+from docugami_kg_rag.helpers.indexing import index_docset
 
 docugami_client = Docugami()
 
@@ -41,6 +41,6 @@ def main(overwrite: bool = False):
 if __name__ == "__main__":
     if sys.gettrace():
         # This code will only run if a debugger is attached
-        index_docset(docset_id="clajbjkbnuye", name="Semi-Structured")
+        index_docset(docset_id="uh31voknl1p6", name="NTSB Accident Report", overwrite=True)
     else:
         app()
diff --git a/poetry.lock b/poetry.lock
index 75b6e0e..a147ec9 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1195,15 +1195,26 @@ files = [
     {file = "httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f"},
 ]
 
+[[package]]
+name = "huggingface"
+version = "0.0.1"
+description = "HuggingFace is a single library comprising the main HuggingFace libraries."
+optional = true
+python-versions = "*"
+files = [
+    {file = "huggingface-0.0.1-py3-none-any.whl", hash = "sha256:98a3409537557cd2fd768997ef94cab08529f86c5e106e6d54bbabdd5ee03910"},
+    {file = "huggingface-0.0.1.tar.gz", hash = "sha256:0a2f228fd956801d68b7c6a8bef478dfa60c4b7d7eba572ea7de39ecf87e505a"},
+]
+
 [[package]]
 name = "huggingface-hub"
-version = "0.20.2"
+version = "0.20.3"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = true
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.20.2-py3-none-any.whl", hash = "sha256:53752eda2239d30a470c307a61cf9adcf136bc77b0a734338c7d04941af560d8"},
-    {file = "huggingface_hub-0.20.2.tar.gz", hash = "sha256:215c5fceff631030c7a3d19ba7b588921c908b3f21eef31d160ebc245b200ff6"},
+    {file = "huggingface_hub-0.20.3-py3-none-any.whl", hash = "sha256:d988ae4f00d3e307b0c80c6a05ca6dbb7edba8bba3079f74cda7d9c2e562a7b6"},
+    {file = "huggingface_hub-0.20.3.tar.gz", hash = "sha256:94e7f8e074475fbc67d6a71957b678e1b4a74ff1b64a644fd6cbb83da962d05d"},
 ]
 
 [package.dependencies]
@@ -1389,6 +1400,34 @@ docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alab
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
 testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
+[[package]]
+name = "jinja2"
+version = "3.1.3"
+description = "A very fast and expressive template engine."
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
+    {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
+]
+
+[package.dependencies]
+MarkupSafe = ">=2.0"
+
+[package.extras]
+i18n = ["Babel (>=2.7)"]
+
+[[package]]
+name = "joblib"
+version = "1.3.2"
+description = "Lightweight pipelining with Python functions"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "joblib-1.3.2-py3-none-any.whl", hash = "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"},
+    {file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
+]
+
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -1499,13 +1538,13 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langchain-cli"
-version = "0.0.20"
+version = "0.0.21"
 description = "CLI for interacting with LangChain"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain_cli-0.0.20-py3-none-any.whl", hash = "sha256:bfcf21a8a36362dc2aa406acb435468eab33847dc0ccaef6877d3d3d9e2fcfc9"},
-    {file = "langchain_cli-0.0.20.tar.gz", hash = "sha256:a4d2eab44587851f44e14fd3d7eb9a17209d935d3adb07305c56602d07dcc722"},
+    {file = "langchain_cli-0.0.21-py3-none-any.whl", hash = "sha256:cd5c83597ef857704db983aa1743d7c2e6da52d634f735a7610630347347583e"},
+    {file = "langchain_cli-0.0.21.tar.gz", hash = "sha256:d36a40955533ce0217b9a89c11bf593b18d8b40f2abbc81c9a531eb23f54809f"},
 ]
 
 [package.dependencies]
@@ -1543,13 +1582,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.
 
 [[package]]
 name = "langchain-core"
-version = "0.1.13"
+version = "0.1.14"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain_core-0.1.13-py3-none-any.whl", hash = "sha256:da55690feed05a68ce1919254753232170b866ebaaae615f530b73c6e232444c"},
-    {file = "langchain_core-0.1.13.tar.gz", hash = "sha256:80ad9e558f2226ce620cff93b26f8e60a45facd8900acb6e1a18e9dfa769a7eb"},
+    {file = "langchain_core-0.1.14-py3-none-any.whl", hash = "sha256:c4f88ffa23200ba212b29f95933e51d6b398fcf02a40b49fc50f7fd75df50569"},
+    {file = "langchain_core-0.1.14.tar.gz", hash = "sha256:2dfd21a7442d0d3b05ae8f35a96c42983ad2dd23e3c103d1c301773d6c316c64"},
 ]
 
 [package.dependencies]
@@ -1565,6 +1604,23 @@ tenacity = ">=8.1.0,<9.0.0"
 [package.extras]
 extended-testing = ["jinja2 (>=3,<4)"]
 
+[[package]]
+name = "langchain-openai"
+version = "0.0.3"
+description = "An integration package connecting OpenAI and LangChain"
+optional = true
+python-versions = ">=3.8.1,<4.0"
+files = [
+    {file = "langchain_openai-0.0.3-py3-none-any.whl", hash = "sha256:32d8ae288e212ed47af418ffd216c8af3b8115514bb39127ca9e2910c06fc6b2"},
+    {file = "langchain_openai-0.0.3.tar.gz", hash = "sha256:19720510abcd7d6217a47d551def7779dc001aebbf978bda5c03e0a8c8167ac3"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.1.13,<0.2"
+numpy = ">=1,<2"
+openai = ">=1.6.1,<2.0.0"
+tiktoken = ">=0.5.2,<0.6.0"
+
 [[package]]
 name = "langserve"
 version = "0.0.39"
@@ -1737,6 +1793,75 @@ profiling = ["gprof2dot"]
 rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
 testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 
+[[package]]
+name = "markupsafe"
+version = "2.1.4"
+description = "Safely add untrusted strings to HTML/XML markup."
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "MarkupSafe-2.1.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:de8153a7aae3835484ac168a9a9bdaa0c5eee4e0bc595503c95d53b942879c84"},
+    {file = "MarkupSafe-2.1.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e888ff76ceb39601c59e219f281466c6d7e66bd375b4ec1ce83bcdc68306796b"},
+    {file = "MarkupSafe-2.1.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0b838c37ba596fcbfca71651a104a611543077156cb0a26fe0c475e1f152ee8"},
+    {file = "MarkupSafe-2.1.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dac1ebf6983148b45b5fa48593950f90ed6d1d26300604f321c74a9ca1609f8e"},
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+    {file = "triton-2.1.0-0-pp37-pypy37_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:143582ca31dd89cd982bd3bf53666bab1c7527d41e185f9e3d8a3051ce1b663b"},
+    {file = "triton-2.1.0-0-pp38-pypy38_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:82fc5aeeedf6e36be4e4530cbdcba81a09d65c18e02f52dc298696d45721f3bd"},
+    {file = "triton-2.1.0-0-pp39-pypy39_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:81a96d110a738ff63339fc892ded095b31bd0d205e3aace262af8400d40b6fa8"},
+]
+
+[package.dependencies]
+filelock = "*"
+
+[package.extras]
+build = ["cmake (>=3.18)", "lit"]
+tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
+
 [[package]]
 name = "typer"
 version = "0.9.0"
@@ -3981,10 +4855,11 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [extras]
 chromadb = ["chromadb"]
 fireworks = ["fireworks-ai"]
-openai = ["openai", "tiktoken"]
+huggingface = ["huggingface", "sentence-transformers", "torch"]
+openai = ["langchain-openai", "openai", "tiktoken"]
 redis = ["redis", "redisvl"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "6b24b91fa458137dc587d21119229a59b24d8350567b5b464ba57500feca049b"
+content-hash = "1d7ba0648565486475dcb76619b22823b33bfb3d4e04e151dbd38378d1655552"
diff --git a/pyproject.toml b/pyproject.toml
index a9ad5d9..bf353fe 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,14 +15,20 @@ dgml-utils = "0.3.0"
 typer = "^0.9.0"
 docugami = "0.1.2"
 pandas = "^2.2.0"
+pyarrow = "^15.0.0"
 langsmith = "^0.0.83"
 openpyxl = "^3.1.2"
 openai = { version = "^1.9.0", optional = true }
+langchain-openai = { version = "^0.0.3", optional = true }
 tiktoken = { version = "^0.5.2", optional = true }
 chromadb = { version = "0.4.14", optional = true }
 fireworks-ai = { version = "^0.11.2", optional = true }
 redis = { version = "^5.0.1", optional = true }
 redisvl = { version = "^0.0.7", optional = true }
+torch = {  version = "^2.1.2", optional = true }
+huggingface = { version = "^0.0.1", optional = true }
+sentence-transformers = { version = "^2.2.2", optional = true }
+
 
 [tool.poetry.group.dev.dependencies]
 langchain-cli = "*"
@@ -33,16 +39,17 @@ pyyaml = "*"
 ruff = "*"
 
 [tool.poetry.extras]
-openai = ["openai", "tiktoken"]
+openai = ["openai", "langchain-openai", "tiktoken"]
 fireworks = ["fireworks-ai"]
 chromadb = ["chromadb"]
 redis = ["redis", "redisvl"]
+huggingface = ["torch", "huggingface", "sentence-transformers"]
 
 [tool.black]
 line-length = 127
 
 [tool.ruff]
-ignore = ["E501"]
+ignore = ["E501", "E402"]
 
 [tool.langserve]
 export_module = "docugami_kg_rag"