[add] migrate chromadb to plugin

.gitignore

@@ -2,6 +2,7 @@ __pycache__
 .DS_Store
 .direnv
 data
+datasets
 venv
 openai_key
 ha_key

README.md

@@ -13,12 +13,44 @@
 
 ---
 
-AI Chat Bot with Plugins (Home Assistant, Vehicle Lookup, DuckDuckGo Search)
+AI Chat Bot with Plugins (RAG VectorDB - ChromaDB, DuckDuckGo Search, Home Assistant, Vehicle Lookup)
 
 [![Build Status](https://drone.va.reichard.io/api/badges/evan/minyma/status.svg)](https://drone.va.reichard.io/evan/minyma)
 
 ## Plugins
 
+### ChromeDB Embeddings / Vectors
+
+This utilizes a local embeddings DB. This allows you to ask the assistant
+about local information. [Utilizes Retrieval-Augmented Generation (RAG)](https://arxiv.org/abs/2005.11401).
+
+```
+User:      What are some common symptoms of COVID-19?
+Assistant: Some common symptoms of COVID-19 mentioned in the context are
+           fatigue, headache, dyspnea (shortness of breath), anosmia (loss of
+           sense of smell), lower respiratory symptoms, cardiac symptoms,
+           concentration or memory issues, tinnitus and earache, and peripheral
+           neuropathy symptoms.
+```
+
+**NOTE:** Instructions on how to load this with your own information are in the
+"Normalizing & Loading Data" section. We include a PubMed data normalizer as an
+example.
+
+### DuckDuckGo
+
+This utilizes DuckDuckGo Search by scraping the top 5 results.
+
+```
+User:      Tell me about Evan Reichard
+Assistant: Evan Reichard is a Principal Detection and Response Engineer based
+           in the Washington DC-Baltimore Area. He has been in this role since
+           August 2022. Evan has created a browser extension that helps SOC
+           analysts and saves them over 300 hours per month. Additionally,
+           there are three professionals named Evan Reichard on LinkedIn and
+           there are also profiles of people named Evan Reichard on Facebook.
+```
+
 ### Vehicle Lookup API
 
 This utilizes Carvana's undocumented API to lookup details on a vehicle.
@@ -41,25 +73,12 @@ User:      Turn on the living room lights
 Assistant: The living room lights have been turned on successfully.
 ```
 
-### DuckDuckGo
-
-This utilizes DuckDuckGo Search by scraping the top 5 results.
-
-```
-User:      Tell me about Evan Reichard
-Assistant: Evan Reichard is a Principal Detection and Response Engineer based
-           in the Washington DC-Baltimore Area. He has been in this role since
-           August 2022. Evan has created a browser extension that helps SOC
-           analysts and saves them over 300 hours per month. Additionally,
-           there are three professionals named Evan Reichard on LinkedIn and
-           there are also profiles of people named Evan Reichard on Facebook.
-```
-
 ## Running Server
 
 ```bash
 # Locally (See "Development" Section)
 export OPENAI_API_KEY=`cat openai_key`
+export CHROMA_DATA_PATH=/data
 export HOME_ASSISTANT_API_KEY=`cat ha_key`
 export HOME_ASSISTANT_URL=https://some-url.com
 
@@ -69,7 +88,7 @@ minyma server run
 docker run \
     -p 5000:5000 \
     -e OPENAI_API_KEY=`cat openai_key` \
-    -e DATA_PATH=/data \
+    -e CHROMA_DATA_PATH=/data \
     -v ./data:/data \
     gitea.va.reichard.io/evan/minyma:latest
 ```
@@ -87,10 +106,10 @@ To normalize data, you can use Minyma's `normalize` CLI command:
 
 ```bash
 minyma normalize \
-    --filename ./pubmed_manuscripts.jsonl \
     --normalizer pubmed \
     --database chroma \
-    --datapath ./chroma
+    --datapath ./data \
+    --filename ./datasets/pubmed_manuscripts.jsonl
 ```
 
 The above example does the following:
@@ -105,10 +124,12 @@ The above example does the following:
 
 ## Configuration
 
-| Environment Variable | Default Value | Description                                                                        |
-| -------------------- | ------------- | ---------------------------------------------------------------------------------- |
-| OPENAI_API_KEY       | NONE          | Required OpenAI API Key for ChatGPT access.                                        |
-| DATA_PATH            | ./data        | The path to the data directory. Chroma will store its data in the `chroma` subdir. |
+| Environment Variable   | Default Value | Description                         |
+| ---------------------- | ------------- | ----------------------------------- |
+| OPENAI_API_KEY         | NONE          | Required OpenAI API Key for ChatGPT |
+| CHROMA_DATA_PATH       | NONE          | ChromaDB Persistent Data Director   |
+| HOME_ASSISTANT_API_KEY | NONE          | Home Assistant API Key              |
+| HOME_ASSISTANT_URL     | NONE          | Home Assistant Instance URL         |
 
 # Development
 
@@ -120,31 +141,9 @@ python3 -m venv venv
 # Local Development
 pip install -e .
 
-# Creds
+# Creds & Other Environment Variables
 export OPENAI_API_KEY=`cat openai_key`
 
 # Docker
 make docker_build_local
 ```
-
-# Notes
-
-This is the first time I'm doing anything LLM related, so it was an adventure.
-Initially I was entertaining OpenAI's Embedding API with plans to load embeddings
-into Pinecone, however initial calculations with `tiktoken` showed that generating
-embeddings would cost roughly $250 USD.
-
-Fortunately I found [Chroma](https://www.trychroma.com/), which basically solved
-both of those issues. It allowed me to load in the normalized data and automatically
-generated embeddings for me.
-
-In order to fit into OpenAI ChatGPT's token limit, I limited each document to roughly
-1000 words. I wanted to make sure I could add the top two matches as context while
-still having enough headroom for the actual question from the user.
-
-A few notes:
-
-- Context is not carried over from previous messages
-- I "stole" the prompt that is used in LangChain (See `oai.py`). I tried some variations without much (subjective) improvement.
-- A generalized normalizer format. This should make it fairly easy to use completely different data. Just add a new normalizer that implements the super class.
-- Basic web front end with TailwindCSS

minyma/__init__.py

@@ -16,15 +16,14 @@ def signal_handler(sig, frame):
 
 
 def create_app():
-    global oai, vdb, plugins
+    global oai, plugins
 
     from minyma.config import Config
     import minyma.api.common as api_common
     import minyma.api.v1 as api_v1
 
     app = Flask(__name__)
-    vdb = ChromaDB(path.join(Config.DATA_PATH, "chroma"))
-    oai = OpenAIConnector(Config.OPENAI_API_KEY, vdb)
+    oai = OpenAIConnector(Config.OPENAI_API_KEY)
     plugins = PluginLoader(Config)
 
     app.register_blueprint(api_common.bp)
@@ -70,7 +69,7 @@ def normalize(filename, normalizer, database, datapath):
         return print("INVALID NORMALIZER:", normalizer)
 
     # Process Data
-    vdb.load_documents(norm)
+    vdb.load_documents(norm.name, norm)
 
 
 signal.signal(signal.SIGINT, signal_handler)

minyma/config.py

@@ -19,7 +19,7 @@ class Config:
         OpenAI API Key - Required
     """
 
-    DATA_PATH: str = get_env("DATA_PATH", default="./data")
-    OPENAI_API_KEY: str = get_env("OPENAI_API_KEY", required=True)
+    CHROMA_DATA_PATH: str = get_env("CHROMA_DATA_PATH", required=False)
     HOME_ASSISTANT_API_KEY: str = get_env("HOME_ASSISTANT_API_KEY", required=False)
     HOME_ASSISTANT_URL: str = get_env("HOME_ASSISTANT_URL", required=False)
+    OPENAI_API_KEY: str = get_env("OPENAI_API_KEY", required=True)

minyma/normalizer.py

@@ -18,6 +18,7 @@ class PubMedNormalizer(DataNormalizer):
     normalized inside the iterator.
     """
     def __init__(self, file: TextIOWrapper):
+        self.name = "pubmed"
         self.file = file
         self.length = 0
 

minyma/oai.py

@@ -3,22 +3,8 @@ from textwrap import indent
 from dataclasses import dataclass
 from typing import Any, List
 import openai
-
-from minyma.vdb import VectorDB
 import minyma
 
-# Stolen LangChain Prompt
-PROMPT_TEMPLATE = """
-Use the following pieces of context to answer the question at the end.
-If you don't know the answer, just say that you don't know, don't try to
-make up an answer.
-
-{context}
-
-Question: {question}
-Helpful Answer:
-"""
-
 INITIAL_PROMPT_TEMPLATE = """
 You are a helpful assistant. You are connected to various external functions that can provide you with more personalized and up-to-date information and have already been granted the permissions to execute these functions at will. DO NOT say you don't have access to real time information, instead attempt to call one or more of the listed functions:
 
@@ -47,8 +33,7 @@ class ChatCompletion:
     usage: dict
 
 class OpenAIConnector:
-    def __init__(self, api_key: str, vdb: VectorDB):
-        self.vdb = vdb
+    def __init__(self, api_key: str):
         self.model = "gpt-3.5-turbo"
         self.word_cap = 1000
         openai.api_key = api_key
@@ -102,7 +87,8 @@ class OpenAIConnector:
         # Build Response Text
         response_content_arr = []
         for key, val in func_responses.items():
-            response_content_arr.append("- %s\n%s" % (key, val))
+            indented_val = indent(val, ' ' * 2)
+            response_content_arr.append("- %s\n%s" % (key, indented_val))
         response_content = "\n".join(response_content_arr)
 
         # Create Follow Up Prompt
@@ -137,26 +123,3 @@ class OpenAIConnector:
                 "total_tokens": total_tokens
             }
         }
-
-    def old_query(self, question: str) -> Any:
-        # Get related documents from vector db
-        related = self.vdb.get_related(question)
-
-        # Validate results
-        all_docs = related.get("docs", [])
-        if len(all_docs) == 0:
-            return { "error": "No Context Found" }
-
-        # Join on new line (cap @ word limit), generate main prompt
-        reduced_docs = list(map(lambda x: " ".join(x.split()[:self.word_cap]), all_docs))
-        context = '\n'.join(reduced_docs)
-        prompt = PROMPT_TEMPLATE.format(context = context, question = question)
-
-        # Query OpenAI ChatCompletion
-        response = openai.ChatCompletion.create(
-          model=self.model,
-          messages=[{"role": "user", "content": prompt}]
-        )
-
-        # Return Response
-        return { "llm": response, "vdb": related }

minyma/plugin.py

@@ -38,7 +38,6 @@ class PluginLoader:
 
             for func_obj in plugin.functions:
                 func_name = func_obj.__name__
-                function_name = "%s_%s" % (plugin_name, func_name)
 
                 signature = inspect.signature(func_obj)
                 params = list(
@@ -48,8 +47,12 @@ class PluginLoader:
                     )
                 )
 
-                func_def = "%s(%s)" % (function_name, ", ".join(params))
-                defs[function_name] = { "func": func_obj, "def": func_def }
+                if func_name in defs:
+                    print("[PluginLoader] Error: Duplicate Function : (%s) %s" % (plugin_name, func_name))
+                    continue
+
+                func_def = "%s(%s)" % (func_name, ", ".join(params))
+                defs[func_name] = { "func": func_obj, "def": func_def }
 
         return defs
 

minyma/plugins/chroma_db.py

@@ -0,0 +1,41 @@
+from textwrap import indent
+from minyma.plugin import MinymaPlugin
+from minyma.vdb import ChromaDB
+
+
+class ChromaDBPlugin(MinymaPlugin):
+    """Perform Local VectorDB Lookup
+
+    ChromDB can access multiple "collections". You can add additional functions
+    here that just access a different collection (i.e. different data)
+    """
+
+    def __init__(self, config):
+        self.name = "chroma_db"
+        self.config = config
+
+        if not config.CHROMA_DATA_PATH:
+            self.functions = []
+        else:
+            self.vdb = ChromaDB(config.CHROMA_DATA_PATH)
+            self.functions = [self.lookup_pubmed_data]
+
+
+    def __lookup_data(self, collection_name: str, query: str):
+        # Get Related
+        related = self.vdb.get_related(collection_name, query)
+
+        # Normalize Data
+        return list(
+            map(
+                lambda x: " ".join(x.split()[:self.vdb.word_cap]),
+                related.get("docs", [])
+            )
+        )
+
+
+    def lookup_pubmed_data(self, query: str):
+        COLLECTION_NAME = "pubmed"
+        documents = self.__lookup_data(COLLECTION_NAME, query)
+        context = '\n'.join(documents)
+        return context

minyma/plugins/duckduckgo.py

@@ -14,9 +14,9 @@ class DuckDuckGoPlugin(MinymaPlugin):
     def __init__(self, config):
         self.config = config
         self.name = "duck_duck_go"
-        self.functions = [self.search]
+        self.functions = [self.duck_duck_go_search]
 
-    def search(self, query: str):
+    def duck_duck_go_search(self, query: str):
         """Search DuckDuckGo"""
         resp = requests.get("https://html.duckduckgo.com/html/?q=%s" % query, headers=HEADERS)
         soup = BeautifulSoup(resp.text, features="html.parser")

minyma/plugins/home_assistant.py

@@ -9,7 +9,7 @@ class HomeAssistantPlugin(MinymaPlugin):
 
     def __init__(self, config):
         self.config = config
-        self.name = "home_automation"
+        self.name = "home_assistant"
 
 
         if not config.HOME_ASSISTANT_API_KEY or not config.HOME_ASSISTANT_URL:
@@ -20,9 +20,9 @@ class HomeAssistantPlugin(MinymaPlugin):
 
             self.functions = []
         else:
-            self.functions = [self.command]
+            self.functions = [self.home_automation_command]
 
-    def command(self, natural_language_command: str):
+    def home_automation_command(self, natural_language_command: str):
         url = urllib.parse.urljoin(self.config.HOME_ASSISTANT_URL, "/api/conversation/process")
         headers = {
             "Authorization": "Bearer %s" % self.config.HOME_ASSISTANT_API_KEY,

minyma/plugins/vehicle_lookup.py

@@ -14,7 +14,7 @@ class VehicleLookupPlugin(MinymaPlugin):
     def __init__(self, config):
         self.config = config
         self.name = "vehicle_state_plate"
-        self.functions = [self.lookup]
+        self.functions = [self.lookup_vehicle_by_state_plate]
 
     def __query_api(self, url, json=None, headers=None):
         # Perform Request
@@ -39,7 +39,7 @@ class VehicleLookupPlugin(MinymaPlugin):
             return None, text, error
 
 
-    def lookup(self, state_abbreviation: str, licence_plate: str):
+    def lookup_vehicle_by_state_plate(self, state_abbreviation: str, licence_plate: str):
         CARVANA_URL = (
             "https://apim.carvana.io/trades/api/v5/vehicleconfiguration/plateLookup/%s/%s"
             % (state_abbreviation, licence_plate)

minyma/vdb.py

@@ -18,11 +18,11 @@ def chunk(iterable, chunk_size: int):
 VectorDB Interface
 """
 class VectorDB:
-    def load_documents(self, normalizer: DataNormalizer):
-        pass
+    def load_documents(self, name: str, normalizer: DataNormalizer, chunk_size: int = 10):
+        raise NotImplementedError("VectorDB must implement load_documents")
 
-    def get_related(self, question: str) -> Any:
-        pass
+    def get_related(self, name: str, question: str) -> Any:
+        raise NotImplementedError("VectorDB must implement get_related")
 
 """
 ChromaDV VectorDB Type
@@ -31,12 +31,13 @@ class ChromaDB(VectorDB):
     def __init__(self, path: str):
         self.client: API = chromadb.PersistentClient(path=path)
         self.word_cap = 2500
-        self.collection_name: str = "vdb"
-        self.collection: chromadb.Collection = self.client.create_collection(name=self.collection_name, get_or_create=True)
 
-    def get_related(self, question: str) -> Any:
+    def get_related(self, name: str, question: str) -> Any:
+        # Get or Create Collection
+        collection = chromadb.Collection = self.client.create_collection(name=name, get_or_create=True)
+
         """Returns line separated related docs"""
-        results = self.collection.query(
+        results = collection.query(
             query_texts=[question.lower()],
             n_results=2
         )
@@ -53,7 +54,11 @@ class ChromaDB(VectorDB):
             "ids": all_ids
         }
 
-    def load_documents(self, normalizer: DataNormalizer, chunk_size: int = 10):
+    def load_documents(self, name: str, normalizer: DataNormalizer, chunk_size: int = 10):
+        # Get or Create Collection
+        collection = chromadb.Collection = self.client.create_collection(name=name, get_or_create=True)
+
+        # Load Items
         length = len(normalizer) / chunk_size
         for items in tqdm(chunk(normalizer, chunk_size), total=length):
             ids = []
@@ -65,7 +70,7 @@ class ChromaDB(VectorDB):
                 ids.append(item.get("id"))
                 metadatas.append(item.get("metadata", {}))
 
-            self.collection.add(
+            collection.add(
                 ids=ids,
                 documents=documents,
                 metadatas=metadatas,