diff --git a/Pipfile.lock b/Pipfile.lock
index e69de29..3d2c569 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -0,0 +1,2950 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "cda77fdd85b31665e0628bb05e1be8de7fdd87a543addf808b5ce41587367a02"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_full_version": "3.10.12",
+            "python_version": "3.10"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
+        "aiohappyeyeballs": {
+            "hashes": [
+                "sha256:40a16ceffcf1fc9e142fd488123b2e218abc4188cf12ac20c67200e1579baa42",
+                "sha256:7e1ae8399c320a8adec76f6c919ed5ceae6edd4c3672f4d9eae2b27e37c80ff6"
+            ],
+            "markers": "python_version >= '3.8' and python_version < '4.0'",
+            "version": "==2.3.4"
+        },
+        "aiohttp": {
+            "hashes": [
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+            "markers": "python_version >= '3.8'",
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+                "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"
+            ],
+            "markers": "python_version < '3.11'",
+            "version": "==2.0.1"
+        },
+        "tomlkit": {
+            "hashes": [
+                "sha256:08ad192699734149f5b97b45f1f18dad7eb1b6d16bc72ad0c2335772650d7b72",
+                "sha256:7075d3042d03b80f603482d69bf0c8f345c2b30e41699fd8883227f89972b264"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==0.13.0"
+        },
+        "typing-extensions": {
+            "hashes": [
+                "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d",
+                "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==4.12.2"
+        }
+    }
+}
diff --git a/config_util.py b/config_util.py
deleted file mode 100644
index 6afad63..0000000
--- a/config_util.py
+++ /dev/null
@@ -1,39 +0,0 @@
-""" utility functions supporting server.py routes for pipeline.py (Pipeline class) """
-import json
-
-import app_logger as log
-
-DEFAULT_TOP_N = 5
-
-def ui_to_pipeline(ui_json):
-    # note:  takes in json, returns an obj
-
-    ui_obj = json.loads(ui_json)
-    log.debug('config_util.py ui_to_pipeline: ui_obj', ui_obj)
-
-    pipeline_obj = {
-        'id': ui_obj['id'],
-        'name': ui_obj['name'],
-        'knowledgebases': ui_obj['knowledge_bases'],
-        'postprocessing': {
-            'similarity': {
-                'on': ui_obj['similarity']['on'],
-                'similarity_cutoff': ui_obj['similarity'].get('cutoff', 0.7),
-            },
-            'colbertRerank': {
-                'on': ui_obj['colbert_rerank']['on'],
-                'top_n': ui_obj['colbert_rerank'].get('top_n', DEFAULT_TOP_N)
-            },
-            'longContextReorder': {
-                'on': ui_obj['long_context_reorder'],
-            }
-        },
-        'generative_model': ui_obj['generative_model'],
-        'prompt': {
-            'on': 'True' if ui_obj.get('prompt') else 'False',
-            'template_str': ui_obj.get('prompt', '')
-        }
-    }
-
-    log.debug('config_util.py ui_to_pipeline: pipeline_obj', pipeline_obj)
-    return pipeline_obj
diff --git a/config_util_test.py b/config_util_test.py
deleted file mode 100644
index e4fe4c9..0000000
--- a/config_util_test.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import json
-
-# import unittest
-import config_util as cutil
-
-# class TestConfigUtil(unittest.TestCase):
-
-def test_1():
-    request = {
-        'id': 'string',
-        'name': 'string',
-        'knowledge_bases': ['string'],
-        'generative_model': 'string',
-        'similarity': {
-            'on': True,
-            'cutoff': 0.3,
-        },
-        'colbert_rerank': {
-            'on': True,
-            'top_n': 0.4,
-        },
-        'long_context_reorder': True,
-        'prompt': 'string',
-    }
-
-    output = cutil.ui_to_pipeline(json.dumps(request))
-    assert "colbertRerank" in output['postprocessing']
-
-# if __name__ == '__main__':
-#     unittest.main()
diff --git a/pipeline/config_util.py b/pipeline/config_util.py
index 6afad63..2e10461 100644
--- a/pipeline/config_util.py
+++ b/pipeline/config_util.py
@@ -1,7 +1,7 @@
 """ utility functions supporting server.py routes for pipeline.py (Pipeline class) """
 import json
 
-import app_logger as log
+import db.app_logger as log
 
 DEFAULT_TOP_N = 5
 
diff --git a/pipeline/config_util_test.py b/pipeline/config_util_test.py
index e4fe4c9..69bccca 100644
--- a/pipeline/config_util_test.py
+++ b/pipeline/config_util_test.py
@@ -1,7 +1,7 @@
 import json
 
 # import unittest
-import config_util as cutil
+import db.pipeline.config_util as cutil
 
 # class TestConfigUtil(unittest.TestCase):
 
diff --git a/pipeline/hybridSearch/__init__.py b/pipeline/hybrid_search/__init__.py
similarity index 100%
rename from pipeline/hybridSearch/__init__.py
rename to pipeline/hybrid_search/__init__.py
diff --git a/pipeline/hybridSearch/keyword.py b/pipeline/hybrid_search/keyword.py
similarity index 98%
rename from pipeline/hybridSearch/keyword.py
rename to pipeline/hybrid_search/keyword.py
index be8d39c..8e36603 100644
--- a/pipeline/hybridSearch/keyword.py
+++ b/pipeline/hybrid_search/keyword.py
@@ -8,7 +8,7 @@
 from dotenv import load_dotenv
 import Stemmer
 
-import app_logger as log
+import db.app_logger as log
 
 load_dotenv(dotenv_path='../.env', override=True)
 
diff --git a/pipeline/hybridSearch/search.py b/pipeline/hybrid_search/search.py
similarity index 93%
rename from pipeline/hybridSearch/search.py
rename to pipeline/hybrid_search/search.py
index 59333ec..d61f7ea 100644
--- a/pipeline/hybridSearch/search.py
+++ b/pipeline/hybrid_search/search.py
@@ -1,5 +1,5 @@
-from pipeline.hybridSearch import keyword
-from pipeline.hybridSearch import vector
+from db.pipeline.hybrid_search import keyword
+from db.pipeline.hybrid_search import vector
 
 # defaults
 default_top_k = 5
diff --git a/pipeline/hybridSearch/vector.py b/pipeline/hybrid_search/vector.py
similarity index 99%
rename from pipeline/hybridSearch/vector.py
rename to pipeline/hybrid_search/vector.py
index 29c545a..d84ffe4 100644
--- a/pipeline/hybridSearch/vector.py
+++ b/pipeline/hybrid_search/vector.py
@@ -8,7 +8,7 @@
 from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
 import pymongo
 
-import app_logger as log
+import db.app_logger as log
 
 load_dotenv(override=True)
 
diff --git a/pipeline/pipelineTest.py b/pipeline/pipelineTest.py
deleted file mode 100644
index 5b3c70e..0000000
--- a/pipeline/pipelineTest.py
+++ /dev/null
@@ -1,114 +0,0 @@
-from llama_index.core import QueryBundle
-from llama_index.core.postprocessor import SimilarityPostprocessor
-from llama_index.core.postprocessor import LongContextReorder
-from llama_index.postprocessor.colbert_rerank import ColbertRerank
-from llama_index.core import get_response_synthesizer, PromptTemplate
-from llama_index.core.response_synthesizers import ResponseMode
-
-import hybridSearch.search as search
-
-def print_nodes(nodes):
-    for node in nodes:
-        print(node)
-
-
-query = 'tell me about promises'
-
-# get all nodes
-nodes = search.hybrid_get_nodes(query, top_k=3)
-all_nodes = nodes['keyword'] + nodes['vector']
-
-
-# similarity
-similarity_pp = SimilarityPostprocessor(
-    nodes=all_nodes,
-    similarity_cutoff=0.5
-)
-
-nodes_similar = similarity_pp.postprocess_nodes(all_nodes)
-
-
-
-
-
-# Colbert rerank
-reranker = ColbertRerank(top_n=4)
-query_bundle = QueryBundle(query)
-
-nodes_rerank = reranker.postprocess_nodes(all_nodes, query_bundle)
-
-print('='*20)
-print_nodes(nodes_rerank)
-
-
-
-# LongContextReorder
-reorder = LongContextReorder()
-
-nodes_reorder = reorder.postprocess_nodes(nodes_rerank)
-
-print('='*20)
-print_nodes(nodes_reorder)
-
-
-
-# Response synthesizer
-synth = get_response_synthesizer(
-    response_mode=ResponseMode.SIMPLE_SUMMARIZE
-)
-
-response = synth.synthesize(query, nodes=nodes_reorder)
-print(response)
-
-print('*'*20)
-
-
-# Custom Prompt
-new_prompt = (
-    "Context information is below.\n"
-    "-----------------------------\n"
-    "{context_str}\n"
-    "-----------------------------\n"
-    "Given the context information and not prior knowledge, "
-    "answer the query in French.\n"
-    "Query: {query_str}\n"
-    "Answer: "
-)
-new_template = PromptTemplate(new_prompt)
-
-synth.update_prompts(
-    {"text_qa_template": new_template}
-)
-
-response = synth.synthesize(query, nodes=nodes_reorder)
-print(response)
-
-
-
-
-
-'''
-Notes:
-
-incorporate post-processing modules:
-- created `pipelineTest.py` based upon ‘hybridTest.py’
-- added similarity
-- adding ColbertRerank
-- found Colbert import statement from https://docs.llamaindex.ai/en/stable/examples/pipeline/query_pipeline_memory/?h=colbertr
-- found reranker syntax from https://docs.llamaindex.ai/en/stable/examples/node_postprocessor/LLMReranker-Lyft-10k/?h=reranker
-- adding LongContextReorder
-- https://docs.llamaindex.ai/en/stable/module_guides/querying/node_postprocessors/node_postprocessors/?h=
-
-- post-processing modules all seem to work
-- need to go from nodes to query response now
-- llamaindex uses a “response synthesizer”
-- https://docs.llamaindex.ai/en/stable/api_reference/response_synthesizers/
-- “simple_summarize” merges all text chunks from nodes into 1 and makes an LLM call
-- it will fail if the merged text chunk exceeds the context window size
-
-- Accessing and customizing prompts
-- https://docs.llamaindex.ai/en/stable/examples/prompts/prompt_mixin/
-- `synthesizer.get_prompts()` returns a dictionary of prompts
-- key is a template (e.g., “text_qa_template”)
-- see promptTest.py to access returned dict and display prompt content
-'''
\ No newline at end of file
diff --git a/pipeline/pipeline_class.py b/pipeline/pipeline_class.py
index 286122e..5beedd4 100644
--- a/pipeline/pipeline_class.py
+++ b/pipeline/pipeline_class.py
@@ -8,9 +8,9 @@
 from llama_index.postprocessor.colbert_rerank import ColbertRerank
 from llama_index.core.response_synthesizers import  ResponseMode
 
-import app_logger as log
-from pipeline.hybridSearch import search
-import pipeline.mongo_util as mg
+import db.app_logger as log
+from db.pipeline.hybrid_search import search
+import db.pipeline.mongo_util as mg
 
 class Pipeline:
     """
diff --git a/pipeline/query.py b/pipeline/query.py
index d356007..a743a2c 100644
--- a/pipeline/query.py
+++ b/pipeline/query.py
@@ -5,8 +5,8 @@
 from pydantic import BaseModel
 # from dotenv import load_dotenv
 
-import app_logger as log
-from pipeline.pipeline_class import Pipeline
+import db.app_logger as log
+from db.pipeline.pipeline_class import Pipeline
 # import pipeline.mongo_util as mutil
 # import pipeline.config_util as cutil
 
diff --git a/random/docdbtest/README.md b/random/docdbtest/README.md
deleted file mode 100644
index 3b2e417..0000000
--- a/random/docdbtest/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
-# test files for connecting with AWS DocumentDB instance
-- a set of test files to confirm a connection with AWS DocumentDB and vector embedding persistence
-- Note:  the LlamaIndex DocDB integration uses `pymongo`
-  - when running the various files, there may be errors from the pymongo package, but these do not seem to impact vector storage / retrieval
-
-
-
-## to use
-- create a `.env` file (can use env.template as a starter)
-  - MONGO_URI will come from the AWS Console for DocumentDB
-- ensure you download the global-bundle.pem from the AWS Console
-- ensure you've run `pipenv shell` in the root project folder
-
-
-
-## overview of files
-- `test_nodb.py` : will create a VectorStoreIndex from the same files with no persistence
-  - note that running this file should give you a 'baseline' of how llamaIndex will perform
-  - note also that since being first created, it appears an underlying LangChain method has changed and is now deprecated (as of Jul 21, 2024)
-
-- `store_vectors.py` : this will vectorize the same files and store the index in DocDB
-
-- `list_vectors.py` : this program queries the contents of the 'testdb', 'testcollection' directly and prints the values to screen
-  - note: you can pipe the output to a file to examine output more closely
-    - e.g., `python list_vectors.py > output.txt`
-
-- `load_vectors.py` : this will load the vector embeddings from DocDB and then run a query against them
-
diff --git a/random/docdbtest/env.template b/random/docdbtest/env.template
deleted file mode 100644
index 7f14155..0000000
--- a/random/docdbtest/env.template
+++ /dev/null
@@ -1,2 +0,0 @@
-OPENAI_API_KEY=
-MONGO_URI=
diff --git a/random/docdbtest/files/file1.txt b/random/docdbtest/files/file1.txt
deleted file mode 100644
index 13f6cc4..0000000
--- a/random/docdbtest/files/file1.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-In the mystical land of Rudenza:
-Apples are purple.
-Clouds are silver, tinged with gold and bronze.
-Pianos will bite your fingers if you don't wear stripes when you practice.
-
-
diff --git a/random/docdbtest/files/file2.txt b/random/docdbtest/files/file2.txt
deleted file mode 100644
index db01e83..0000000
--- a/random/docdbtest/files/file2.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-PotatoPecanPie is the secret word.
-James' favourite food is pizza.
diff --git a/random/docdbtest/list_vectors.py b/random/docdbtest/list_vectors.py
deleted file mode 100644
index ba5f050..0000000
--- a/random/docdbtest/list_vectors.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# this mini program is to list the vectors within the documentDB instance that were written by llamaIndex
-# Note:  the db and collection as named below - these can be changed when the vector_store is instantiated
-
-
-import pymongo
-import pprint
-from dotenv import load_dotenv
-import os
-
-load_dotenv(override=True)
-
-mongo_uri = os.environ["MONGO_URI"]
-client = pymongo.MongoClient(mongo_uri)
-
-
-db = client[os.environ["DOCDB_NAME"]]
-collection = db[os.environ["DOCDB_COLLECTION"]]
-
-for post in collection.find():
-  pprint.pprint(post)
-
-
-print('==========')
diff --git a/random/docdbtest/load_vectors.py b/random/docdbtest/load_vectors.py
deleted file mode 100644
index 2ac3755..0000000
--- a/random/docdbtest/load_vectors.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# based loosely upon LlamaIndex demo
-# https://docs.llamaindex.ai/en/stable/examples/vector_stores/AWSDocDBDemo/
-# 
-# key goal here was to retrieve the stored vectors from DocumentDB rather than re-create them
-
-
-
-import pymongo
-from dotenv import load_dotenv
-
-from llama_index.vector_stores.awsdocdb import AWSDocDbVectorStore
-from llama_index.core import VectorStoreIndex
-from llama_index.core import StorageContext
-# from llama_index.core import SimpleDirectoryReader
-import os
-
-load_dotenv(override=True)
-
-mongo_uri = os.environ["MONGO_URI"]
-mongodb_client = pymongo.MongoClient(mongo_uri)
-store = AWSDocDbVectorStore(mongodb_client, db_name='testdb', collection_name='testcollection')
-storage_context = StorageContext.from_defaults(vector_store=store)
-
-
-
-index = VectorStoreIndex.from_vector_store(
-  vector_store=store,
-  storage_context=storage_context
-)
-
-
-response = index.as_query_engine().query('Tell me about Rudenza')
-print(f"{response}")
-
-
diff --git a/random/docdbtest/not_test_nodb.py b/random/docdbtest/not_test_nodb.py
deleted file mode 100644
index 047ae53..0000000
--- a/random/docdbtest/not_test_nodb.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Testing vector store - no persistence
-
-from dotenv import load_dotenv
-from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
-
-load_dotenv()
-
-documents = SimpleDirectoryReader("files").load_data()
-
-index = VectorStoreIndex.from_documents(documents)
-
-print("Index created successfully!")
diff --git a/random/docdbtest/store_vectors.py b/random/docdbtest/store_vectors.py
deleted file mode 100644
index fd16d01..0000000
--- a/random/docdbtest/store_vectors.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# based upon LlamaIndex demo
-# https://docs.llamaindex.ai/en/stable/examples/vector_stores/AWSDocDBDemo/
-
-
-import pymongo
-from dotenv import load_dotenv
-
-from llama_index.vector_stores.awsdocdb import AWSDocDbVectorStore
-from llama_index.core import VectorStoreIndex
-from llama_index.core import StorageContext
-from llama_index.core import SimpleDirectoryReader
-import os
-
-load_dotenv(override=True)
-
-
-mongo_uri = os.environ["MONGO_URI"]
-mongodb_client = pymongo.MongoClient(mongo_uri)
-store = AWSDocDbVectorStore(mongodb_client, db_name='testdb', collection_name='testcollection')
-storage_context = StorageContext.from_defaults(vector_store=store)
-
-documents = SimpleDirectoryReader("files").load_data()
-
-index = VectorStoreIndex.from_documents(
-    documents, storage_context=storage_context
-)
-
-
-response = index.as_query_engine().query('Tell me about Rudenza')
-print(f"{response}")
-
-
diff --git a/random/hybridTest.py b/random/hybridTest.py
deleted file mode 100644
index e845cb0..0000000
--- a/random/hybridTest.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import hybridSearch.search as search
-
-def print_nodes(nodes):
-    for node in nodes:
-        print(node)
-
-
-kb_file_path = './tmpfiles/giraffes.pdf'
-search.hybrid_write('giraffes', kb_file_path) # only need to do this the first time
-
-query = 'how long are giraffe necks?'
-
-# get nodes
-nodes = search.hybrid_get_nodes('giraffes', query, top_k=5)
-
-all_nodes = nodes['keyword'] + nodes['vector']
-
-print_nodes(all_nodes)
-
-
-
diff --git a/random/not_test_kb.py b/random/not_test_kb.py
deleted file mode 100644
index 063a32e..0000000
--- a/random/not_test_kb.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import json
-
-import refactor1.db.knowledge_base.kb_config as kbClass
-
-kb_name = 'giraffe2'
-
-config_template = {
-    "id": kb_name,
-    "kb_name": kb_name,
-    "ingest_method": "Simple",
-    "splitter": "Sentence",
-    "embed_config": {
-        "embed_provider": "OpenAI",
-        "embed_model": "text-embedding-3-small"
-    },
-    "splitter_config": {
-        "chunk_size": 1024,
-        "chunk_overlap": 200
-    }
-}
-
-json_config = json.dumps(config_template)
-print(json_config)
-
-kbClass.KnowledgeBase.create(json_config)
-
-kb = kbClass.KnowledgeBase('giraffe2')
-kb.ingest_file_path('./tmpfiles/giraffes.pdf')
-
-
-
diff --git a/random/pipelineTest.py b/random/pipelineTest.py
deleted file mode 100644
index 5b3c70e..0000000
--- a/random/pipelineTest.py
+++ /dev/null
@@ -1,114 +0,0 @@
-from llama_index.core import QueryBundle
-from llama_index.core.postprocessor import SimilarityPostprocessor
-from llama_index.core.postprocessor import LongContextReorder
-from llama_index.postprocessor.colbert_rerank import ColbertRerank
-from llama_index.core import get_response_synthesizer, PromptTemplate
-from llama_index.core.response_synthesizers import ResponseMode
-
-import hybridSearch.search as search
-
-def print_nodes(nodes):
-    for node in nodes:
-        print(node)
-
-
-query = 'tell me about promises'
-
-# get all nodes
-nodes = search.hybrid_get_nodes(query, top_k=3)
-all_nodes = nodes['keyword'] + nodes['vector']
-
-
-# similarity
-similarity_pp = SimilarityPostprocessor(
-    nodes=all_nodes,
-    similarity_cutoff=0.5
-)
-
-nodes_similar = similarity_pp.postprocess_nodes(all_nodes)
-
-
-
-
-
-# Colbert rerank
-reranker = ColbertRerank(top_n=4)
-query_bundle = QueryBundle(query)
-
-nodes_rerank = reranker.postprocess_nodes(all_nodes, query_bundle)
-
-print('='*20)
-print_nodes(nodes_rerank)
-
-
-
-# LongContextReorder
-reorder = LongContextReorder()
-
-nodes_reorder = reorder.postprocess_nodes(nodes_rerank)
-
-print('='*20)
-print_nodes(nodes_reorder)
-
-
-
-# Response synthesizer
-synth = get_response_synthesizer(
-    response_mode=ResponseMode.SIMPLE_SUMMARIZE
-)
-
-response = synth.synthesize(query, nodes=nodes_reorder)
-print(response)
-
-print('*'*20)
-
-
-# Custom Prompt
-new_prompt = (
-    "Context information is below.\n"
-    "-----------------------------\n"
-    "{context_str}\n"
-    "-----------------------------\n"
-    "Given the context information and not prior knowledge, "
-    "answer the query in French.\n"
-    "Query: {query_str}\n"
-    "Answer: "
-)
-new_template = PromptTemplate(new_prompt)
-
-synth.update_prompts(
-    {"text_qa_template": new_template}
-)
-
-response = synth.synthesize(query, nodes=nodes_reorder)
-print(response)
-
-
-
-
-
-'''
-Notes:
-
-incorporate post-processing modules:
-- created `pipelineTest.py` based upon ‘hybridTest.py’
-- added similarity
-- adding ColbertRerank
-- found Colbert import statement from https://docs.llamaindex.ai/en/stable/examples/pipeline/query_pipeline_memory/?h=colbertr
-- found reranker syntax from https://docs.llamaindex.ai/en/stable/examples/node_postprocessor/LLMReranker-Lyft-10k/?h=reranker
-- adding LongContextReorder
-- https://docs.llamaindex.ai/en/stable/module_guides/querying/node_postprocessors/node_postprocessors/?h=
-
-- post-processing modules all seem to work
-- need to go from nodes to query response now
-- llamaindex uses a “response synthesizer”
-- https://docs.llamaindex.ai/en/stable/api_reference/response_synthesizers/
-- “simple_summarize” merges all text chunks from nodes into 1 and makes an LLM call
-- it will fail if the merged text chunk exceeds the context window size
-
-- Accessing and customizing prompts
-- https://docs.llamaindex.ai/en/stable/examples/prompts/prompt_mixin/
-- `synthesizer.get_prompts()` returns a dictionary of prompts
-- key is a template (e.g., “text_qa_template”)
-- see promptTest.py to access returned dict and display prompt content
-'''
\ No newline at end of file
diff --git a/random/promptTest.py b/random/promptTest.py
deleted file mode 100644
index 0b2820a..0000000
--- a/random/promptTest.py
+++ /dev/null
@@ -1,41 +0,0 @@
-
-from llama_index.core import get_response_synthesizer, PromptTemplate
-from llama_index.core.response_synthesizers import ResponseMode
-
-
-def display_prompts(prompts_dict):
-    for k, p in prompts_dict.items():
-        print(f"Prompt Key:  {k}")
-        print("Text: ")
-        print(p.get_template())
-        print("-"*30)
-
-
-synth = get_response_synthesizer(
-    response_mode=ResponseMode.SIMPLE_SUMMARIZE
-)
-
-prompt = synth.get_prompts()
-
-display_prompts(prompt)
-
-
-new_prompt = (
-    "Context information is below.\n"
-    "-----------------------------\n"
-    "{context_str}\n"
-    "-----------------------------\n"
-    "Given the context information and not prior knowledge, "
-    "answer the query in French.\n"
-    "Query: {query_str}\n"
-    "Answer: "
-)
-new_template = PromptTemplate(new_prompt)
-
-synth.update_prompts(
-    {"text_qa_template": new_template}
-)
-
-prompt = synth.get_prompts()
-
-display_prompts(prompt)
diff --git a/random/setup_test_not.py b/random/setup_test_not.py
deleted file mode 100644
index d11caa1..0000000
--- a/random/setup_test_not.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# helper scripts to populate test kbs
-import os
-
-import pymongo
-from dotenv import load_dotenv
-
-import hybridSearch.search as search
-
-load_dotenv(override=True)
-
-mongo_uri = os.environ["MONGO_URI"]
-mongo = pymongo.MongoClient(mongo_uri)
-
-
-# kb1
-kb1_file_path = './tmpfiles/AsyncJS.md'
-search.keyword_write('kb1', kb1_file_path)
-search.vector_write('kb1', kb1_file_path)
-
-#kb2
-kb2_file_path = './tmpfiles/cpumemory.pdf'
-search.hybrid_write('kb2', kb2_file_path)
-
-#kb3
-kb3_file_path = './tmpfiles/newfile.txt'
-search.hybrid_write('kb3', kb3_file_path)
-
-
-# config db setup
-
-kb_config1 = {
-    'id': 'kb1',
-    'name': 'AsyncJS',
-    'files': [{ 'filename': './tmpfiles/AsyncJS.md'}],
-    'ingest': {
-        'method': 'simple_ingest',
-        'splitter': {
-            'type': 'sentence',
-            'chunk_size': '',
-            'chunk_overlap': '',
-            'separator': '',
-        },
-    },
-    'embedding_model': 'gpt-3.5-turbo',
-    'vector_store': {
-        'name': 'idstring',
-        'collection': 'vector_index',
-    },
-    'keyword_store': {
-        'name': 'idstring',
-        'collections': ['docstore/ref_doc_info', 'docstore/data', 'docstore/metadata']
-    }
-}
-
-kb_config2 = kb_config1.copy()
-kb_config2['id'] = 'kb2'
-kb_config2['name'] = 'cpumemory'
-kb_config2['files'] = [{ 'filename': './tmpfiles/cpumemory.pdf'}],
-
-
-kb_config3 = kb_config1.copy()
-kb_config3['id'] = 'kb3'
-kb_config3['name'] = 'newfile'
-kb_config3['files'] = [{ 'filename': './tmpfiles/newfile.txt'}],
-
-
-config_db = mongo[ os.environ["CONFIG_DB"] ]
-config_kb_col = config_db[ os.environ["CONFIG_KB_COL"] ]
-config_kb_col.insert_one(kb_config1)
-config_kb_col.insert_one(kb_config2)
-config_kb_col.insert_one(kb_config3)
-
-
-
-
-# Pipeline config
-
-pipeline_config1 = {
-    'id': 'pipeline1',
-    'name': 'pipelineConfigName',
-    'knowledgebases': ['kb1', 'kb2', 'kb3'],
-    'retrieval': {
-        'vector': 'llm_model_name',
-    },
-    'postprocessing': {
-        'similarity': {
-            'on': False,
-            'similarity_cutoff': 0.7
-        },
-        'colbertRerank': {
-            'on': False,
-            'top_n': 5
-        },
-        'longContextReorder': {
-            'on': True,
-        }
-    },
-    'generative_model': 'gpt-3.5-turbo',
-    'prompt': {
-        'on': True,
-        'template_str': 'answer the question - {query_str} - in French'
-    }
-}
-
-pipeline_config2 = pipeline_config1.copy()
-pipeline_config2['id'] = 'pipeline2'
-pipeline_config2['name'] = 'kb1 only (async)'
-pipeline_config2['knowledgebases'] = ['kb1']
-
-
-pipeline_config3 = pipeline_config1.copy()
-pipeline_config3['id'] = 'pipeline3'
-pipeline_config3['name'] = 'kb2 only (cpumemory)'
-pipeline_config3['knowledgebases'] = ['kb2']
-
-pipeline_config4 = pipeline_config1.copy()
-pipeline_config4['id'] = 'pipeline4'
-pipeline_config4['name'] = 'kb1 (async) and kb2 (cpumemory)'
-pipeline_config4['knowledgebases'] = ['kb2', 'kb1']
-
-config_pipeline_col = config_db[ os.environ["CONFIG_PIPELINE_COL"] ]
-config_pipeline_col.insert_one(pipeline_config1)
-config_pipeline_col.insert_one(pipeline_config2)
-config_pipeline_col.insert_one(pipeline_config3)
-config_pipeline_col.insert_one(pipeline_config4)
-
-
diff --git a/routers/chatbots.py b/routers/chatbots.py
index db38477..a4c70a1 100644
--- a/routers/chatbots.py
+++ b/routers/chatbots.py
@@ -5,9 +5,9 @@
 from pydantic import BaseModel
 from dotenv import load_dotenv
 
-import app_logger as log
-import pipeline.mongo_util as mutil
-import pipeline.config_util as cutil
+import db.app_logger as log
+import db.pipeline.mongo_util as mutil
+import db.pipeline.config_util as cutil
 
 router = APIRouter(
     prefix='/api/chatbots'
diff --git a/server-rag.py b/server-rag.py
deleted file mode 100644
index 8935e70..0000000
--- a/server-rag.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-import nest_asyncio
-from dotenv import load_dotenv
-
-import app_logger as log
-import pipeline.query
-from routers import chatbots
-
-# test if we need this w/n the server
-nest_asyncio.apply()
-
-load_dotenv(override=True)
-
-app = FastAPI()
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=['*'],
-    allow_credentials=True,
-    allow_methods=['*'],
-    allow_headers=['*']
-)
-
-app.include_router(chatbots.router)
-
-@app.get('/api')
-async def root():
-    log.info("server running")
-    return {"message": "Server running"}
-
-
-@app.post('/api/query')
-async def post_query(body: pipeline.query.QueryBody):
-    return pipeline.query.post_query(body)
-
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000, loop='asyncio')
diff --git a/server.py b/server.py
index d348a3e..6d77537 100644
--- a/server.py
+++ b/server.py
@@ -13,18 +13,18 @@
 import nest_asyncio
 from dotenv import load_dotenv
 
-import app_logger as log
-# import util.use_s3
-# import evals
-import pipeline.query
-from kb_config import KnowledgeBase
+import db.app_logger as log
+import db.util.use_s3
+import db.evals as evals
+import db.pipeline.query as pq
+import db.knowledge_base.routes as kb
 
-from routers import chatbots
+from db.routers import chatbots
 
 # test if we need this w/n the server
 nest_asyncio.apply()
 
-load_dotenv(override=True)
+# load_dotenv(override=True)
 
 app = FastAPI()
 
@@ -44,83 +44,42 @@ async def root():
     log.info("server running")
     return {"message": "Server running"}
 
-
 # knowledge base routes
 @app.get("/api/knowledge-bases")
 async def get_knowledge_bases():
-    knowledge_bases = KnowledgeBase.get_knowledge_bases()
-    return knowledge_bases
+    return kb.get_all()
 
 # consider adding id to the body of the request sent from the client
 # to create a new knowledge base
 # otherwise, we will use the kb_name prop to see if knowledge base exists
 @app.post('/api/knowledge-bases')
-async def create_kb(request: Request):
-    body = await request.json()
-    # ensure that the kb_name is unique
-    if KnowledgeBase.exists(body["kb_name"]):
-        message = f"{body['kb_name']} already exists"
-    else:
-        message = KnowledgeBase.create(body)
-
-    return {"message": message}
+async def create_knowledge_base(request: Request):
+    client_config = await request.json()
+    return kb.create(client_config)
 
 @app.get("/api/knowledge-base/{id}")
 async def get_knowledge_base(id: str):
-    kb_config = KnowledgeBase.exists(id)
-    if kb_config:
-        return kb_config
-    else:
-        return { "message": f"{id} does not exist" }
+    return kb.get_one(id)
 
 # this route adds a file to a knowledge base
 @app.post('/api/knowledge-bases/{id}/upload')
 async def upload_file(id: str, file: UploadFile=File(...)):
-    if KnowledgeBase.exists(id):
-        kb = KnowledgeBase(id)
-        kb.ingest_file(file)
-        return {"message": f"{file.filename} uploaded"}
-    else:
-        return {"message": f"Knowledge base {id} doesn't exist"}
-
+    try:
+        return await kb.upload_file(id, file)
+        
+    except Exception as e:
+        return {"message": f"Error: {e}"}
 
 # query route
 @app.post('/api/query')
-async def post_query(body: pipeline.query.QueryBody):
-    return pipeline.query.post_query(body)
-
-
-# evals routes
-
-# @app.get('/api/evals')
-# async def get_evals():
-#     eval_table = evals.get_running_evals()
-#     return {"message": eval_table}
-#
-# @app.post('/api/csv')
-# async def upload_csv(file: UploadFile=File(...)):
-#     FILE_DIR = 'tmpfiles/csv'
-#
-#     # write file to disk
-#     if not os.path.exists(f"./{FILE_DIR}"):
-#         os.makedirs(f"./{FILE_DIR}")
-#
-#     file_location = f"./{FILE_DIR}/{file.filename}"
-#     with open(file_location, "wb+") as file_object:
-#         shutil.copyfileobj(file.file, file_object)
-#
-#     util.use_s3.ul_file(file.filename, dir=FILE_DIR)
-#
-#     return {"message": f"{file.filename} received"}
-
+async def post_query(body: pq.QueryBody):
+    return pq.post_query(body)
 
 @app.get('/api/history')
 async def get_evals():
     data = evals.get_chat_history()
     return {"table_data": data}
 
-
-
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000, loop='asyncio')
+    uvicorn.run(app, host="0.0.0.0", port=8000, loop='asyncio')
\ No newline at end of file
diff --git a/server_kb.py b/server_kb.py
deleted file mode 100644
index bba5354..0000000
--- a/server_kb.py
+++ /dev/null
@@ -1,73 +0,0 @@
-
-from fastapi import FastAPI, File, UploadFile, Request
-from fastapi.middleware.cors import CORSMiddleware
-import nest_asyncio
-from dotenv import load_dotenv
-
-# import app_logger as log
-# import use_s3
-import db.knowledge_base.routes as kb
-
-nest_asyncio.apply()
-
-# env = os.getenv("ENV")
-# print(env)
-# if env == 'testing':
-#     load_dotenv(override=True, dotenv_path='.env.testing')
-# else:
-#     load_dotenv(override=True)
-
-# MONGO_URI = os.environ["MONGO_URI"]
-# CONFIG_DB = os.environ["CONFIG_DB"]
-# # print(CONFIG_DB)
-# CONFIG_PIPELINE_COL = os.environ["CONFIG_PIPELINE_COL"]
-
-print("Starting server")
-app = FastAPI()
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=['*'],
-    allow_credentials=True,
-    allow_methods=['*'],
-    allow_headers=['*']
-)
-
-@app.get('/api')
-async def root():
-    # log.info("server running")
-    return {"message": "Server running"}
-
-
-# knowledge base routes
-@app.get("/api/knowledge-bases")
-async def get_knowledge_bases():
-    return kb.get_all()
-
-# consider adding id to the body of the request sent from the client
-# to create a new knowledge base
-# otherwise, we will use the kb_name prop to see if knowledge base exists
-@app.post('/api/knowledge-bases')
-async def create_knowledge_base(request: Request):
-    client_config = await request.json()
-    return kb.create(client_config)
-
-@app.get("/api/knowledge-base/{id}")
-async def get_knowledge_base(id: str):
-    return kb.get_one(id)
-
-# this route adds a file to a knowledge base
-@app.post('/api/knowledge-bases/{id}/upload')
-async def upload_file(id: str, file: UploadFile=File(...)):
-    try:
-        return await kb.upload_file(id, file)
-        
-    except Exception as e:
-        return {"message": f"Error: {e}"}
-
-        
-
-    
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000, loop='asyncio')
\ No newline at end of file
diff --git a/test_server_kb.py b/test_server_kb.py
index acbe2b8..44abf06 100644
--- a/test_server_kb.py
+++ b/test_server_kb.py
@@ -1,6 +1,6 @@
 from fastapi.testclient import TestClient
 
-from .server_kb import app
+from db.server import app
 from db.knowledge_base.kb_test_constants import (
     client_sentence_config,
     client_semantic_config,
@@ -12,73 +12,73 @@
 
 client = TestClient(app)
 
-# def test_read_knowledge_bases():
-
-#     response = client.get("/api/knowledge-bases")
-#     assert response.status_code == 200
-#     assert response.json() == []
-
-# def test_create_simple_splitter_kb():
-#     response = client.post(
-#         "/api/knowledge-bases",
-#         json=client_sentence_config
-#     )
-#     assert response.status_code == 200
-#     assert response.json() == {"message": "Sentence created"}
-
-#     response = client.post(
-#         "/api/knowledge-bases",
-#         json=client_sentence_config
-#     )
-#     assert response.status_code == 200
-#     assert response.json() == {"message": "Sentence already exists"}
-
-# def test_create_semantic_splitter_kb():
-#     response = client.post(
-#         "/api/knowledge-bases",
-#         json=client_semantic_config
-#     )
-#     assert response.status_code == 200
-#     assert response.json() == {"message": "Semantic created"}
-
-#     response = client.post(
-#         "/api/knowledge-bases",
-#         json=client_semantic_config
-#     )
-#     assert response.status_code == 200
-#     assert response.json() == {"message": "Semantic already exists"}
-
-# def test_create_markdown_splitter_kb():
-#     response = client.post(
-#         "/api/knowledge-bases",
-#         json=client_llama_parse_config
-#     )
-#     assert response.status_code == 200
-#     assert response.json() == {"message": "Markdown created"}
-
-#     response = client.post(
-#         "/api/knowledge-bases",
-#         json=client_llama_parse_config
-#     )
-#     assert response.status_code == 200
-#     assert response.json() == {"message": "Markdown already exists"}
-
-# def test_get_kb_config():
-#     response = client.get("/api/knowledge-base/Sentence")
-#     assert response.status_code == 200
-#     assert response.json() == server_sentence_config
-
-#     response = client.get("/api/knowledge-base/unknown_kb")
-#     assert response.status_code == 200
-#     assert response.json() == {"message": "unknown_kb does not exist"}
-
-# def test_upload_file():
-#     response = client.post(
-#         "/api/knowledge-bases/Sentence/upload",
-#         files={"file": ("test.txt", b"test")}
-#     )
-#     assert response.status_code == 200
-#     assert response.json() == {"message": "test.txt uploaded"}
+def test_read_knowledge_bases():
+
+    response = client.get("/api/knowledge-bases")
+    assert response.status_code == 200
+    assert response.json() == []
+
+def test_create_simple_splitter_kb():
+    response = client.post(
+        "/api/knowledge-bases",
+        json=client_sentence_config
+    )
+    assert response.status_code == 200
+    assert response.json() == {"message": "Sentence created"}
+
+    response = client.post(
+        "/api/knowledge-bases",
+        json=client_sentence_config
+    )
+    assert response.status_code == 200
+    assert response.json() == {"message": "Sentence already exists"}
+
+def test_create_semantic_splitter_kb():
+    response = client.post(
+        "/api/knowledge-bases",
+        json=client_semantic_config
+    )
+    assert response.status_code == 200
+    assert response.json() == {"message": "Semantic created"}
+
+    response = client.post(
+        "/api/knowledge-bases",
+        json=client_semantic_config
+    )
+    assert response.status_code == 200
+    assert response.json() == {"message": "Semantic already exists"}
+
+def test_create_markdown_splitter_kb():
+    response = client.post(
+        "/api/knowledge-bases",
+        json=client_llama_parse_config
+    )
+    assert response.status_code == 200
+    assert response.json() == {"message": "Markdown created"}
+
+    response = client.post(
+        "/api/knowledge-bases",
+        json=client_llama_parse_config
+    )
+    assert response.status_code == 200
+    assert response.json() == {"message": "Markdown already exists"}
+
+def test_get_kb_config():
+    response = client.get("/api/knowledge-base/Sentence")
+    assert response.status_code == 200
+    assert response.json() == server_sentence_config
+
+    response = client.get("/api/knowledge-base/unknown_kb")
+    assert response.status_code == 200
+    assert response.json() == {"message": "unknown_kb does not exist"}
+
+def test_upload_file():
+    response = client.post(
+        "/api/knowledge-bases/Sentence/upload",
+        files={"file": ("test.txt", b"test")}
+    )
+    assert response.status_code == 200
+    assert response.json() == {"message": "test.txt uploaded"}
 
     # response = client.get("/api/knowledge-base/test_kb")
     # assert response.status_code == 200