From adf8daafb70a30281ddf6753a6341023dfa867a7 Mon Sep 17 00:00:00 2001
From: Shivank Kacker <kacker.shivank@gmail.com>
Date: Wed, 3 Jul 2024 17:31:45 +0530
Subject: [PATCH] Switched to Milvus (#518)

* Switched to Milvus

* updated readme

* changes

* linted
---
 .envs/.local/.django                          |    1 +
 Makefile                                      |    5 +-
 Pipfile                                       |    3 +-
 Pipfile.lock                                  | 1425 +++++++++--------
 Procfile                                      |    4 -
 README.md                                     |   59 +-
 ayushma/management/__init__.py                |    0
 ayushma/management/commands/upsert.py         |   83 -
 ...53_alter_chat_model_alter_project_model.py |   46 +
 ayushma/models/enums.py                       |    1 +
 ayushma/utils/langchain.py                    |    5 +-
 ayushma/utils/openaiapi.py                    |   61 +-
 ayushma/utils/upsert.py                       |   46 +-
 ayushma/utils/vectordb.py                     |  215 +++
 ayushma/views/chat.py                         |    4 +-
 ayushma/views/document.py                     |   13 +-
 ayushma/views/project.py                      |    6 +-
 compose/production/milvus/embedEtcd.yaml      |    5 +
 core/settings/base.py                         |   13 +-
 core/settings/production.py                   |    2 +-
 docker-compose.local.yaml                     |   28 +
 docker-compose.production.yaml                |   28 +
 example.env                                   |    2 -
 prompt-example.txt                            |   13 +
 24 files changed, 1208 insertions(+), 860 deletions(-)
 delete mode 100644 Procfile
 delete mode 100644 ayushma/management/__init__.py
 delete mode 100644 ayushma/management/commands/upsert.py
 create mode 100644 ayushma/migrations/0053_alter_chat_model_alter_project_model.py
 create mode 100644 ayushma/utils/vectordb.py
 create mode 100644 compose/production/milvus/embedEtcd.yaml
 create mode 100644 prompt-example.txt

diff --git a/.envs/.local/.django b/.envs/.local/.django
index 247287be..05c5b39f 100644
--- a/.envs/.local/.django
+++ b/.envs/.local/.django
@@ -5,6 +5,7 @@ IPYTHONDIR=/app/.ipython
 # Redis
 # ------------------------------------------------------------------------------
 REDIS_URL=redis://redis:6379/0
+DATABASE_URL=postgres://debug:debug@postgres:5432/ayushma
 
 # Celery
 # ------------------------------------------------------------------------------
diff --git a/Makefile b/Makefile
index a9548416..0eb9637a 100644
--- a/Makefile
+++ b/Makefile
@@ -30,7 +30,10 @@ logs:
 	docker compose -f $(docker_config_file) logs
 
 makemigrations: up
-	docker exec django bash -c "python manage.py makemigrations"
+	docker compose -f $(docker_config_file) exec django bash -c "python manage.py makemigrations"
+
+migrate: up
+	docker compose -f $(docker_config_file) exec django bash -c "python manage.py migrate"
 
 checkmigration:
 	docker compose -f $(docker_config_file) exec django bash -c "python manage.py makemigrations --check --dry-run"
diff --git a/Pipfile b/Pipfile
index db43b4a2..75a45bec 100644
--- a/Pipfile
+++ b/Pipfile
@@ -24,7 +24,7 @@ drf-nested-routers = "==0.93.4"
 drf-spectacular = "==0.27.0"
 requests = "==2.31.0"
 openai = "==1.2.4"
-pinecone-client = "==2.2.4"
+pinecone-client = "==4.1.1"
 pypdf2 = "==3.0.1"
 tiktoken = "==0.5.2"
 langchain = "==0.0.352"
@@ -41,6 +41,7 @@ nltk = "==3.8.1"
 gunicorn = "==21.2.0"
 psycopg = {extras = ["c"], version = "==3.1.17"}
 sentry-sdk = "==1.30.0"
+pymilvus = "*"
 
 [dev-packages]
 ipdb = "==0.13.13"
diff --git a/Pipfile.lock b/Pipfile.lock
index 3c9b0163..2eca5bd5 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "63b31ab91cb495e4a97169808cd4de31645b941b2e62433ce1c0d9a7650b2565"
+            "sha256": "e2d224518ee4b6b66d64f42bd2d4f64c19eb45c5750e6bb787de91ca26242003"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -18,85 +18,85 @@
     "default": {
         "aiohttp": {
             "hashes": [
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-                "sha256:039df344b45ae0b34ac885ab5b53940b174530d4dd8a14ed8b0e2155b9dddccb",
-                "sha256:055ce4f74b82551678291473f66dc9fb9048a50d8324278751926ff0ae7715e5",
-                "sha256:06a9b2c8837d9a94fae16c6223acc14b4dfdff216ab9b7202e07a9a09541168f",
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-                "sha256:0fa43c32d1643f518491d9d3a730f85f5bbaedcbd7fbcae27435bb8b7a061b29",
-                "sha256:1f5a71d25cd8106eab05f8704cd9167b6e5187bcdf8f090a66c6d88b634802b4",
-                "sha256:1f5cd333fcf7590a18334c90f8c9147c837a6ec8a178e88d90a9b96ea03194cc",
-                "sha256:27468897f628c627230dba07ec65dc8d0db566923c48f29e084ce382119802bc",
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@@ -116,11 +116,11 @@
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@@ -169,11 +169,11 @@
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@@ -202,27 +202,27 @@
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@@ -235,11 +235,11 @@
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@@ -405,11 +405,11 @@
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@@ -428,11 +428,11 @@
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@@ -534,14 +534,6 @@
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@@ -560,6 +552,14 @@
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+                "sha256:fdf348ae69c6ff484402cfdb14e18c1b0054ac2420079d575c53a60b9b2853ae"
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+            "markers": "python_version >= '3.8'",
+            "version": "==1.63.0"
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         "grpcio-status": {
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-            "version": "==1.60.1"
+            "version": "==1.62.2"
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@@ -794,27 +787,27 @@
         },
         "httpcore": {
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-            "version": "==1.0.3"
+            "version": "==1.0.5"
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-                "sha256:8915f5a3627c4d47b73e8202457cb28f1266982d1159bd5779d86a80c0eab1cd"
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-            "version": "==0.26.0"
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@@ -834,11 +827,11 @@
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+            "markers": "python_version >= '3.8'",
+            "version": "==1.4.2"
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@@ -850,19 +843,19 @@
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-            "version": "==2.4"
+            "markers": "python_version >= '3.7'",
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-            "version": "==4.21.1"
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@@ -874,11 +867,11 @@
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@@ -922,21 +915,23 @@
             "markers": "python_version < '4.0' and python_full_version >= '3.8.1'",
             "version": "==0.0.87"
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-        "loguru": {
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+            "version": "==3.21.3"
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-            "version": "==3.20.2"
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@@ -1110,6 +1105,41 @@
             "markers": "python_version >= '3.7'",
             "version": "==23.2"
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+        "pandas": {
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@@ -1175,12 +1205,20 @@
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-            "markers": "python_version >= '3.8'",
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+            "markers": "python_version >= '3.8' and python_version < '4.0'",
+            "version": "==4.1.1"
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+        "pinecone-plugin-interface": {
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@@ -1191,19 +1229,19 @@
         },
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-                "sha256:a11a29cb3bf0a28a387fe5122cdb649816a957cd9261dcedf8c9f1fef33eacf6"
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@@ -1241,119 +1279,138 @@
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@@ -1366,11 +1423,19 @@
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@@ -1447,110 +1512,96 @@
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+            "markers": "python_version >= '3.8'",
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         },
         "requests": {
             "hashes": [
@@ -1563,108 +1614,108 @@
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@@ -1676,11 +1727,11 @@
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@@ -1690,6 +1741,14 @@
             "index": "pypi",
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@@ -1830,19 +1889,19 @@
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@@ -1869,11 +2012,11 @@
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@@ -1999,11 +2142,11 @@
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@@ -2177,19 +2320,19 @@
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@@ -2211,11 +2354,11 @@
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@@ -2228,11 +2371,11 @@
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@@ -2252,11 +2395,11 @@
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@@ -2276,11 +2419,11 @@
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@@ -2292,11 +2435,11 @@
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@@ -2311,16 +2454,16 @@
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@@ -2333,11 +2476,11 @@
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@@ -2452,19 +2595,19 @@
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+                "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"
             ],
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
-            "version": "==2.8.2"
+            "version": "==2.9.0.post0"
         },
         "pyyaml": {
             "hashes": [
@@ -2523,14 +2666,6 @@
             "markers": "python_version >= '3.6'",
             "version": "==6.0.1"
         },
-        "setuptools": {
-            "hashes": [
-                "sha256:850894c4195f09c4ed30dba56213bf7c3f21d86ed6bdaafb5df5972593bfc401",
-                "sha256:c054629b81b946d63a9c6e732bc8b2513a7c3ea645f11d0139a2191d735c60c6"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==69.1.0"
-        },
         "six": {
             "hashes": [
                 "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926",
@@ -2541,11 +2676,11 @@
         },
         "sqlparse": {
             "hashes": [
-                "sha256:5430a4fe2ac7d0f93e66f1efc6e1338a41884b7ddf2a350cedd20ccc4d9d28f3",
-                "sha256:d446183e84b8349fa3061f0fe7f06ca94ba65b426946ffebe6e3e8295332420c"
+                "sha256:714d0a4932c059d16189f58ef5411ec2287a4360f17cdd0edd2d09d4c5087c93",
+                "sha256:c204494cd97479d0e39f28c93d46c0b2d5959c7b9ab904762ea6c7af211c8663"
             ],
-            "markers": "python_version >= '3.5'",
-            "version": "==0.4.4"
+            "markers": "python_version >= '3.8'",
+            "version": "==0.5.0"
         },
         "stack-data": {
             "hashes": [
@@ -2556,27 +2691,27 @@
         },
         "traitlets": {
             "hashes": [
-                "sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74",
-                "sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e"
+                "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7",
+                "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==5.14.1"
+            "version": "==5.14.3"
         },
         "typing-extensions": {
             "hashes": [
-                "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783",
-                "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"
+                "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d",
+                "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==4.9.0"
+            "version": "==4.12.2"
         },
         "virtualenv": {
             "hashes": [
-                "sha256:4238949c5ffe6876362d9c0180fc6c3a824a7b12b80604eeb8085f2ed7460de3",
-                "sha256:bf51c0d9c7dd63ea8e44086fa1e4fb1093a31e963b86959257378aef020e1f1b"
+                "sha256:4c43a2a236279d9ea36a0d76f98d84bd6ca94ac4e0f4a3b9d46d05e10fea542a",
+                "sha256:8cc4a31139e796e9a7de2cd5cf2489de1217193116a8fd42328f1bd65f434589"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==20.25.0"
+            "version": "==20.26.3"
         },
         "watchdog": {
             "hashes": [
diff --git a/Procfile b/Procfile
deleted file mode 100644
index 89db3785..00000000
--- a/Procfile
+++ /dev/null
@@ -1,4 +0,0 @@
-release: python manage.py migrate
-web: gunicorn core.wsgi:application --timeout 180
-# worker: REMAP_SIGTERM=SIGQUIT celery -A core.celery_app worker --loglevel=info
-# beat: REMAP_SIGTERM=SIGQUIT celery -A core.celery_app beat --loglevel=info
diff --git a/README.md b/README.md
index 81cdb372..1146e68f 100644
--- a/README.md
+++ b/README.md
@@ -1,50 +1,49 @@
 # Ayushma AI
 
-This is a Django Project.
+Empowering Nurses with Multilingual ICU Protocols. Leveraging the rapid advancements in AI technology, created multilingual interfaces that assist nurses in rapidly upgrading their knowledge about ICU protocols. This is a Django project.
 
 ## Requirements
 
-- Python 3.8
-- Postgres 15
 - OpenAI Account with a valid API Key
-- Pinecone Account with a valid API Key
+- Docker
 
 ## Optional Requirements
 
-- Docker
+- Pinecone Account with a valid API Key
 - AWS SES Accout
 - AWS S3 Account
 - Google Cloud account with access to speech to text API
 
-## Installation
+## Running the Project
 
-create a virtual environment and install the requirements
+> We strongly recommend using Docker to run the project. Manual methods are not supported at the moment.
 
 ```bash
-pip3 install --user virtualenv
-virtualenv .venv
-source .venv/bin/activate
-pip3 install -r requirements/local.txt
+make up
 ```
 
 ## Env Variables
 
-You can add these at the end of your `activate` file in `[virtualenvfolder] -> bin` like `export [env] = [value]`
-
-| Variable | Description
-| --- | ---
-| AI_NAME | Name of the AI (default: Ayushma)
-| OPENAI_API_KEY | OpenAI API Key
-| PINECONE_API_KEY | Pinecone API Key
-| PINECONE_ENVIRONMENT | Pinecone Environment
-| PINECONE_INDEX | Pinecone Index
-| CURRENT_DOMAIN | Current Domain where the frontend is hosted. ex. `https://ayushma.ohc.network`
-| EMAIL_HOST | SES Email Host (Optional)
-| EMAIL_USER | SES Email User (Optional)
-| EMAIL_PASSWORD | SES Email Password (Optional)
-| GOOGLE_APPLICATION_CREDENTIALS | Google Cloud Credentials (Optional). These should be in a file named `gc_credential.json` in the root of the project
-| S3_SECRET_KEY | AWS S3 Secret Key (Optional)
-| S3_KEY_ID | AWS S3 Key ID (Optional)
-| S3_BUCKET_NAME | AWS S3 Bucket Name (Optional)
-| S3_REGION | AWS S3 Region (Optional)
-| GOOGLE_RECAPTCHA_SECRET_KEY | Google Recaptcha Secret Key (Optional)
+Add these environment variables to your `.env` file.
+
+| Variable                       | Description                                                                                                          |
+| ------------------------------ | -------------------------------------------------------------------------------------------------------------------- |
+| AI_NAME                        | Name of the AI (default: Ayushma)                                                                                    |
+| OPENAI_API_KEY                 | OpenAI API Key                                                                                                       |
+| PINECONE_API_KEY               | Pinecone API Key                                                                                                     |
+| PINECONE_INDEX                 | Pinecone Index                                                                                                       |
+| VECTOR_DB                      | The Vector DB you would like to choose. "milvus" (default) or "pinecone"                                             |
+| CURRENT_DOMAIN                 | Current Domain where the frontend is hosted. ex. `https://ayushma.ohc.network`                                       |
+| EMAIL_HOST                     | SES Email Host (Optional)                                                                                            |
+| EMAIL_USER                     | SES Email User (Optional)                                                                                            |
+| EMAIL_PASSWORD                 | SES Email Password (Optional)                                                                                        |
+| GOOGLE_APPLICATION_CREDENTIALS | Google Cloud Credentials (Optional). These should be in a file named `gc_credential.json` in the root of the project |
+| S3_SECRET_KEY                  | AWS S3 Secret Key (Optional)                                                                                         |
+| S3_KEY_ID                      | AWS S3 Key ID (Optional)                                                                                             |
+| S3_BUCKET_NAME                 | AWS S3 Bucket Name (Optional)                                                                                        |
+| S3_REGION                      | AWS S3 Region (Optional)                                                                                             |
+| GOOGLE_RECAPTCHA_SECRET_KEY    | Google Recaptcha Secret Key (Optional)                                                                               |
+
+## Google Cloud
+
+To use Google Cloud Speech to Text API, you need to enable the API and create a service account. Download the credentials and save them in a file named `gc_credential.json` in the root of the project.
diff --git a/ayushma/management/__init__.py b/ayushma/management/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/ayushma/management/commands/upsert.py b/ayushma/management/commands/upsert.py
deleted file mode 100644
index 69694318..00000000
--- a/ayushma/management/commands/upsert.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import hashlib
-import os
-
-import pinecone
-from django.conf import settings
-from django.core.management.base import BaseCommand
-from PyPDF2 import PdfReader
-from tqdm.auto import tqdm
-
-from ayushma.utils.openaiapi import get_embedding
-
-
-def read_document(filepath):
-    if filepath.endswith(".pdf"):  # Handle pdf files
-        pdf_reader = PdfReader(filepath)
-        text = ""
-        for i in range(len(pdf_reader.pages)):
-            page = pdf_reader.pages[i]
-            text += page.extract_text()
-        document_text = text
-    else:
-        with open(filepath, "r") as f:  # Handle txt and md files
-            document_text = f.read()
-
-    return document_text
-
-
-class Command(BaseCommand):
-    help = "Upserts data to Pinecone index"
-
-    def handle(self, *args, **options):
-        upsert_dir = "upsert"
-        pinecone.init(
-            api_key=settings.PINECONE_API_KEY,
-            environment=settings.PINECONE_ENVIRONMENT,
-        )
-        print("Initialized Pinecone and OpenAI")
-
-        for filename in os.listdir(upsert_dir):
-            if filename == ".gitkeep":
-                continue
-            print(f"Processing {filename}...")
-
-            filepath = os.path.join(upsert_dir, filename)
-            filename_md5 = hashlib.md5(filename.encode()).hexdigest()
-
-            print(f"Md5 of {filename} is {filename_md5}")
-
-            document_lines = read_document(filepath).splitlines()
-
-            batch_size = 100  # process everything in batches of 100 (creates 100 vectors per upset)
-
-            print("Fetching Pinecone index...")
-            if settings.PINECONE_INDEX not in pinecone.list_indexes():
-                pinecone.create_index(
-                    settings.PINECONE_INDEX,
-                    dimension=1536,  # 1536 is the dimension of the text-embedding-ada-002 model
-                )
-            pinecone_index = pinecone.Index(index_name=settings.PINECONE_INDEX)
-
-            print(f"Upserting {filename} to Pinecone index...")
-
-            for i in tqdm(range(0, len(document_lines), batch_size)):
-                i_end = min(
-                    i + batch_size, len(document_lines)
-                )  # set end position of batch
-                lines_batch = document_lines[
-                    i : i + batch_size
-                ]  # get batch of lines and IDs
-                lines_batch = [
-                    line.strip() for line in lines_batch if line.strip()
-                ]  # remove blank lines
-                ids_batch = [str(n) for n in range(i, i_end)]  # create IDs
-                embeds = get_embedding(lines_batch)  # create embeddings
-                meta = [
-                    {"text": line} for line in lines_batch
-                ]  # prep metadata and upsert batch
-                to_upsert = zip(ids_batch, embeds, meta)  # zip together
-                pinecone_index.upsert(
-                    vectors=list(to_upsert), namespace=filename_md5
-                )  # upsert to Pinecone
-
-            print(f"Finished upserting {filename} to Pinecone index")
diff --git a/ayushma/migrations/0053_alter_chat_model_alter_project_model.py b/ayushma/migrations/0053_alter_chat_model_alter_project_model.py
new file mode 100644
index 00000000..ffcfe757
--- /dev/null
+++ b/ayushma/migrations/0053_alter_chat_model_alter_project_model.py
@@ -0,0 +1,46 @@
+# Generated by Django 4.2.6 on 2024-07-02 16:07
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ("ayushma", "0052_document_failed"),
+    ]
+
+    operations = [
+        migrations.AlterField(
+            model_name="chat",
+            name="model",
+            field=models.IntegerField(
+                blank=True,
+                choices=[
+                    (1, "Gpt 3 5"),
+                    (2, "Gpt 3 5 16K"),
+                    (3, "Gpt 4"),
+                    (4, "Gpt 4 32K"),
+                    (5, "Gpt 4 Visual"),
+                    (6, "Gpt 4 Turbo"),
+                    (7, "Gpt 4 Omni"),
+                ],
+                null=True,
+            ),
+        ),
+        migrations.AlterField(
+            model_name="project",
+            name="model",
+            field=models.IntegerField(
+                choices=[
+                    (1, "Gpt 3 5"),
+                    (2, "Gpt 3 5 16K"),
+                    (3, "Gpt 4"),
+                    (4, "Gpt 4 32K"),
+                    (5, "Gpt 4 Visual"),
+                    (6, "Gpt 4 Turbo"),
+                    (7, "Gpt 4 Omni"),
+                ],
+                default=1,
+            ),
+        ),
+    ]
diff --git a/ayushma/models/enums.py b/ayushma/models/enums.py
index 0a856e94..eea3c919 100644
--- a/ayushma/models/enums.py
+++ b/ayushma/models/enums.py
@@ -47,6 +47,7 @@ class ModelType(IntegerChoices):
     GPT_4_32K = 4
     GPT_4_VISUAL = 5
     GPT_4_TURBO = 6
+    GPT_4_OMNI = 7
 
 
 class StatusChoices(IntegerChoices):
diff --git a/ayushma/utils/langchain.py b/ayushma/utils/langchain.py
index 22449090..56bf63a7 100644
--- a/ayushma/utils/langchain.py
+++ b/ayushma/utils/langchain.py
@@ -2,13 +2,14 @@
 
 import openai
 from django.conf import settings
-from langchain import LLMChain, PromptTemplate
 from langchain.callbacks.manager import AsyncCallbackManager
+from langchain.chains import LLMChain
 from langchain.chat_models import ChatOpenAI
 from langchain.llms import AzureOpenAI
 from langchain.prompts import (
     ChatPromptTemplate,
     MessagesPlaceholder,
+    PromptTemplate,
     SystemMessagePromptTemplate,
 )
 from langchain.prompts.chat import BaseStringMessagePromptTemplate
@@ -38,6 +39,8 @@ def get_model_name(model_type: ModelType):
         return "gpt-4-vision-preview"
     elif model_type == ModelType.GPT_4_TURBO:
         return "gpt-4-1106-preview"
+    elif model_type == ModelType.GPT_4_OMNI:
+        return "gpt-4o"
     else:
         if settings.OPENAI_API_TYPE == "azure":
             return settings.AZURE_CHAT_MODEL
diff --git a/ayushma/utils/openaiapi.py b/ayushma/utils/openaiapi.py
index c82da95d..d90d1ec3 100644
--- a/ayushma/utils/openaiapi.py
+++ b/ayushma/utils/openaiapi.py
@@ -10,7 +10,6 @@
 from django.conf import settings
 from langchain.schema import AIMessage, HumanMessage
 from openai import OpenAI
-from pinecone import QueryResponse
 
 from ayushma.models import ChatMessage
 from ayushma.models.chat import Chat
@@ -18,6 +17,7 @@
 from ayushma.models.enums import ChatMessageType, ModelType
 from ayushma.utils.langchain import LangChainHelper
 from ayushma.utils.language_helpers import text_to_speech, translate_text
+from ayushma.utils.vectordb import VectorDB
 from core.settings.base import AI_NAME
 
 
@@ -67,39 +67,6 @@ def get_embedding(
     return [record.embedding for record in res.data]
 
 
-def get_sanitized_reference(pinecone_references: List[QueryResponse]) -> str:
-    """
-    Extracts the text from the Pinecone QueryResponse object and sanitizes it.
-
-    Args:
-        pinecone_reference (List[QueryResponse]): The similar documents retrieved from
-            the Pinecone index.
-
-    Returns:
-        A string containing the document id and text from the Pinecone QueryResponse object.
-
-    Example usage:
-        >>> get_sanitized_reference([QueryResponse(...), QueryResponse(...)])
-        "{'28': 'Hello how are you, I am fine, thank you.', '21': 'How was your day?, Mine was good.'}"
-    """
-    sanitized_reference = {}
-
-    for reference in pinecone_references:
-        for match in reference.matches:
-            try:
-                document_id = str(match.metadata["document"])
-                text = str(match.metadata["text"]).replace("\n", " ") + ","
-                if document_id in sanitized_reference:
-                    sanitized_reference[document_id] += text
-                else:
-                    sanitized_reference[document_id] = text
-            except Exception as e:
-                print(f"Error extracting reference: {e}")
-                pass
-
-    return json.dumps(sanitized_reference)
-
-
 def num_tokens_from_string(string: str, encoding_name: str) -> int:
     """Returns the number of tokens in a text string."""
     encoding = tiktoken.get_encoding(encoding_name)
@@ -155,18 +122,18 @@ def get_reference(text, openai_key, namespace, top_k):
                 raise Exception(
                     "[Reference] Error generating embeddings for split text"
                 )
-    # find similar embeddings from pinecone index for each embedding
-    pinecone_references: List[QueryResponse] = []
-
-    for embedding in embeddings:
-        similar: QueryResponse = settings.PINECONE_INDEX_INSTANCE.query(
-            vector=embedding,
-            top_k=int(top_k),
-            namespace=namespace,
-            include_metadata=True,
-        )
-        pinecone_references.append(similar)
-    return get_sanitized_reference(pinecone_references=pinecone_references)
+    # find similar embeddings from vector index for each embedding
+
+    flat_embeddings = [item for sublist in embeddings for item in sublist]
+    vdb = VectorDB()
+    similar = vdb.search(
+        embeddings=flat_embeddings,
+        partition_name=namespace,
+        limit=int(top_k),
+    )
+
+    print("References fetched")
+    return vdb.sanitize(similar)
 
 
 def add_reference_documents(chat_message):
@@ -299,7 +266,7 @@ def converse(
             reference = get_reference(
                 english_text,
                 openai_key,
-                str(chat.project.external_id),
+                str(chat.project.external_id).replace("-", "_"),
                 match_number,
             )
         except Exception as e:
diff --git a/ayushma/utils/upsert.py b/ayushma/utils/upsert.py
index 13727e17..252730cd 100644
--- a/ayushma/utils/upsert.py
+++ b/ayushma/utils/upsert.py
@@ -2,13 +2,13 @@
 from io import BytesIO
 from typing import Optional
 
-import pinecone
 import requests
 from bs4 import BeautifulSoup
 from django.conf import settings
 from PyPDF2 import PdfReader
 
 from ayushma.utils.openaiapi import get_embedding
+from ayushma.utils.vectordb import VectorDB
 
 
 def read_document(url):
@@ -42,10 +42,10 @@ def upsert(
     text: Optional[str] = None,
 ):
     """
-    Upserts the contents of a file, URL, or text to a Pinecone index with the specified external ID.
+    Upserts the contents of a file, URL, or text to a vector index with the specified external ID.
 
     Args:
-        external_id (str): The external ID to use when upserting to the Pinecone index.
+        external_id (str): The external ID to use when upserting to the vector index.
         document_id (int): The external_id of the document that is to be upserted (external_id of the doc is added to the metadata)
         s3_url (str, optional): The S3 URL of the file to upsert. Defaults to None.
         url (str, optional): The URL of the website to upsert. Defaults to None.
@@ -57,11 +57,6 @@ def upsert(
     Returns:
         None
     """
-    pinecone.init(
-        api_key=settings.PINECONE_API_KEY,
-        environment=settings.PINECONE_ENVIRONMENT,
-    )
-    print("Initialized Pinecone and OpenAI")
 
     print("Processing...")
 
@@ -90,31 +85,22 @@ def upsert(
         100  # process everything in batches of 100 (creates 100 vectors per upset)
     )
 
-    print("Fetching Pinecone index...")
-    if settings.PINECONE_INDEX not in pinecone.list_indexes():
-        pinecone.create_index(
-            settings.PINECONE_INDEX,
-            dimension=1536,  # 1536 is the dimension of the text-embedding-ada-002 model
-        )
-    pinecone_index = pinecone.Index(index_name=settings.PINECONE_INDEX)
-
-    print("Upserting to Pinecone index...")
+    print("Upserting to vector collection...")
 
     for i in range(0, len(document_lines), batch_size):
-        i_end = min(i + batch_size, len(document_lines))  # set end position of batch
         lines_batch = document_lines[i : i + batch_size]  # get batch of lines and IDs
         lines_batch = [
             line.strip() for line in lines_batch if line.strip()
         ]  # remove blank lines
-        ids_batch = [f"{document_id}_{n}" for n in range(i, i_end)]  # create IDs
-
-        embeds = get_embedding(lines_batch)  # create embeddings
-        meta = [
-            {"text": line, "document": str(document_id)} for line in lines_batch
-        ]  # prep metadata and upsert batch
-        to_upsert = zip(ids_batch, embeds, meta)  # zip together
-        pinecone_index.upsert(
-            vectors=list(to_upsert), namespace=str(external_id)
-        )  # upsert to Pinecone
-
-    print("Finished upserting to Pinecone index")
+
+        embeds = get_embedding(lines_batch)
+        partition_name = str(external_id).replace("-", "_")
+
+        VectorDB().insert(
+            vectors=embeds,
+            texts=lines_batch,
+            subject=str(document_id),
+            partition_name=partition_name,
+        )
+
+    print("Finished upserting to vectorDB")
diff --git a/ayushma/utils/vectordb.py b/ayushma/utils/vectordb.py
new file mode 100644
index 00000000..95657846
--- /dev/null
+++ b/ayushma/utils/vectordb.py
@@ -0,0 +1,215 @@
+import json
+from abc import ABC, abstractmethod
+
+from django.conf import settings
+from pinecone import Pinecone
+from pymilvus import MilvusClient
+
+
+class AbstractVectorDB(ABC):
+
+    client = None
+    collection_name = None
+    dimensions = 1536
+    top_k = 10
+
+    @abstractmethod
+    def initialize(self) -> None:
+        pass
+
+    @abstractmethod
+    def get_or_create_partition(self, partition_name: str):
+        pass
+
+    @abstractmethod
+    def insert(self, vectors, texts, subject, partition_name):
+        pass
+
+    @abstractmethod
+    def get_or_create_collection(self, collection_name: str = None):
+        pass
+
+    @abstractmethod
+    def sanitize(self, references):
+        pass
+
+    @abstractmethod
+    def delete_partition(self, partition_name):
+        pass
+
+    @abstractmethod
+    def delete_subject(self, subject, partition_name):
+        pass
+
+    @abstractmethod
+    def search(self, embeddings, partition_name, limit=None):
+        pass
+
+
+class MilvusVectorDB(AbstractVectorDB):
+    collection_name = settings.MILVUS_COLLECTION
+
+    def initialize(self) -> None:
+        self.client = MilvusClient(
+            uri=settings.MILVUS_URL,
+        )
+        self.get_or_create_collection()
+
+    def get_or_create_partition(self, partition_name: str):
+        partitions = self.client.list_partitions(collection_name=self.collection_name)
+        if partition_name not in partitions:
+            self.client.create_partition(
+                collection_name=self.collection_name, partition_name=partition_name
+            )
+
+    def insert(self, vectors, texts, subject, partition_name):
+
+        self.get_or_create_partition(partition_name)
+
+        data = [
+            {"id": i, "vector": vectors[i], "text": texts[i], "subject": subject}
+            for i in range(len(vectors))
+        ]
+
+        self.client.insert(
+            collection_name=self.collection_name,
+            data=data,
+            partition_name=partition_name,
+        )
+
+    def get_or_create_collection(self, collection_name: str = None):
+        if collection_name is None:
+            collection_name = self.collection_name
+        if not self.client.has_collection(collection_name=collection_name):
+            self.client.create_collection(
+                collection_name=collection_name,
+                dimension=self.dimensions,
+            )
+
+    def search(self, embeddings, partition_name, limit=None):
+        self.get_or_create_partition(partition_name)
+
+        results = self.client.search(
+            collection_name=self.collection_name,
+            partition_names=[partition_name],
+            data=embeddings,
+            top_k=limit or self.top_k,
+            output_fields=["text", "subject"],
+        )
+        return results[0]
+
+    def sanitize(self, references):
+        sanitized_reference = {}
+
+        for reference in references:
+            try:
+                document_id = str(reference["entity"]["subject"])
+                text = str(reference["entity"]["text"]).replace("\n", " ") + ","
+                if document_id in sanitized_reference:
+                    sanitized_reference[document_id] += text
+                else:
+                    sanitized_reference[document_id] = text
+            except Exception as e:
+                print(f"Error extracting reference: {e}")
+                pass
+
+        return json.dumps(sanitized_reference)
+
+    def delete_partition(self, partition_name):
+        self.client.drop_partition(
+            collection_name=self.collection_name, partition_name=partition_name
+        )
+
+    def delete_subject(self, subject, partition_name):
+        self.client.delete(
+            collection_name=self.collection_name,
+            partition_name=partition_name,
+            filter='subject in ["' + str(subject) + '"]',
+        )
+
+
+class PineconeVectorDB(AbstractVectorDB):
+    collection_name = settings.PINECONE_INDEX
+
+    def initialize(self):
+        self.client = Pinecone(
+            api_key=settings.PINECONE_API_KEY,
+        )
+        self.get_or_create_collection()
+
+    def get_or_create_partition(self, partition_name):
+        pass
+
+    def insert(self, vectors, texts, subject, partition_name):
+        meta = [{"text": texts[i], "document": subject} for i in range(len(vectors))]
+        ids = [str(i) for i in range(len(vectors))]
+        data = zip(ids, vectors, meta)
+
+        self.client.Index(self.collection_name).upsert(
+            vectors=data,
+            namespace=partition_name,
+        )
+
+    def get_or_create_collection(self, collection_name=None):
+        if collection_name is None:
+            collection_name = self.collection_name
+        indexes = self.client.list_indexes().get("indexes", [])
+        print("Indexes", indexes)
+        index_names = [index["name"] for index in indexes]
+        if collection_name not in index_names:
+            self.client.create_index(
+                name=collection_name,
+                dimension=self.dimensions,
+            )
+
+    def search(self, embeddings, partition_name, limit=None):
+        index = self.client.Index(self.collection_name)
+        result = index.query(
+            vector=embeddings,
+            namespace=partition_name,
+            top_k=limit or self.top_k,
+            include_metadata=True,
+        )
+        return result.matches
+
+    def sanitize(self, references):
+        sanitized_reference = {}
+        for match in references:
+            try:
+                document_id = str(match.metadata["document"])
+                text = str(match.metadata["text"]).replace("\n", " ") + ","
+                if document_id in sanitized_reference:
+                    sanitized_reference[document_id] += text
+                else:
+                    sanitized_reference[document_id] = text
+            except Exception as e:
+                print(f"Error extracting reference: {e}")
+                pass
+
+        return json.dumps(sanitized_reference)
+
+    def delete_partition(self, partition_name):
+        index = self.client.Index(self.collection_name)
+        index.delete(namespace=partition_name, deleteAll=True)
+
+    def delete_subject(self, subject, partition_name):
+        index = self.client.Index(self.collection_name)
+        index.delete(
+            namespace=partition_name,
+            filter={"document": subject},
+        )
+
+
+class VectorDB:
+    def __init__(self):
+        vector_db_type = settings.VECTOR_DB.lower()
+        if vector_db_type == "milvus":
+            self.vector_db = MilvusVectorDB()
+        elif vector_db_type == "pinecone":
+            self.vector_db = PineconeVectorDB()
+        else:
+            raise ValueError(f"Unsupported VECTOR_DB type: {settings.VECTOR_DB}")
+        self.vector_db.initialize()
+
+    def __getattr__(self, name):
+        return getattr(self.vector_db, name)
diff --git a/ayushma/views/chat.py b/ayushma/views/chat.py
index 4c7818dc..ac4f591e 100644
--- a/ayushma/views/chat.py
+++ b/ayushma/views/chat.py
@@ -104,7 +104,9 @@ def perform_create(self, serializer):
     )
     @action(detail=True, methods=["get"])
     def feedbacks(self, *args, **kwarg):
-        q = ChatFeedback.objects.filter(chat_message__chat__external_id=kwarg["external_id"])
+        q = ChatFeedback.objects.filter(
+            chat_message__chat__external_id=kwarg["external_id"]
+        )
         serialized_data = ChatFeedbackSerializer(q, many=True).data
         return Response(
             {"data": serialized_data},
diff --git a/ayushma/views/document.py b/ayushma/views/document.py
index c9a5901d..fc3e2d40 100644
--- a/ayushma/views/document.py
+++ b/ayushma/views/document.py
@@ -1,4 +1,3 @@
-from django.conf import settings
 from rest_framework.exceptions import ValidationError
 from rest_framework.mixins import (
     CreateModelMixin,
@@ -14,6 +13,7 @@
 from ayushma.models.testsuite import TestQuestion
 from ayushma.serializers.document import DocumentSerializer, DocumentUpdateSerializer
 from ayushma.tasks.upsertdoc import upsert_doc
+from ayushma.utils.vectordb import VectorDB
 from utils.views.base import BaseModelViewSet
 from utils.views.mixins import PartialUpdateModelMixin
 
@@ -58,6 +58,8 @@ def perform_create(self, serializer):
         doc_url = None
         try:
             doc_url = self.request.build_absolute_uri(document.file.url)
+            # replace localhost with "django" (docker service name) for celery worker to access the file. This is a hack, feel free to improve.
+            doc_url = doc_url.replace("http://localhost", "http://django")
         except Exception as e:
             print(e)
             pass
@@ -66,18 +68,19 @@ def perform_create(self, serializer):
 
     def perform_destroy(self, instance):
         # delete namespace from vectorDB
+
         try:
-            settings.PINECONE_INDEX_INSTANCE.delete(
-                namespace=self.kwargs["project_external_id"],
-                filter={"document": str(instance.external_id)},
+            VectorDB().delete_subject(
+                partition_name=self.kwargs["project_external_id"].replace("-", "_"),
+                subject=str(instance.external_id),
             )
+            return super().perform_destroy(instance)
         except Exception as e:
             print(e)
             return Response(
                 {"non_field_errors": "Error deleting document from vectorDB"},
                 status=400,
             )
-        return super().perform_destroy(instance)
 
 
 class TestQuestionDocumentViewSet(
diff --git a/ayushma/views/project.py b/ayushma/views/project.py
index 091be342..1163844d 100644
--- a/ayushma/views/project.py
+++ b/ayushma/views/project.py
@@ -14,6 +14,7 @@
 
 from ayushma.models import Project
 from ayushma.serializers.project import ProjectSerializer, ProjectUpdateSerializer
+from ayushma.utils.vectordb import VectorDB
 from utils.views.base import BaseModelViewSet
 from utils.views.mixins import PartialUpdateModelMixin
 
@@ -68,9 +69,8 @@ def perform_create(self, serializer):
     def perform_destroy(self, instance):
         # delete namespaces from vectorDB
         try:
-            settings.PINECONE_INDEX_INSTANCE.delete(
-                namespace=str(instance.external_id),
-                deleteAll=True,
+            VectorDB().delete_partition(
+                partition_name=str(instance.external_id).replace("-", "_")
             )
         except Exception as e:
             print(e)
diff --git a/compose/production/milvus/embedEtcd.yaml b/compose/production/milvus/embedEtcd.yaml
new file mode 100644
index 00000000..0142678d
--- /dev/null
+++ b/compose/production/milvus/embedEtcd.yaml
@@ -0,0 +1,5 @@
+listen-client-urls: http://0.0.0.0:2379
+advertise-client-urls: http://0.0.0.0:2379
+quota-backend-bytes: 4294967296
+auto-compaction-mode: revision
+auto-compaction-retention: "1000"
diff --git a/core/settings/base.py b/core/settings/base.py
index 0c92e48a..8def4091 100644
--- a/core/settings/base.py
+++ b/core/settings/base.py
@@ -6,7 +6,6 @@
 
 import environ
 import openai
-import pinecone
 from corsheaders.defaults import default_headers
 from django.urls import reverse_lazy
 
@@ -41,7 +40,7 @@
 DATABASES = {
     "default": env.db(
         "DATABASE_URL",
-        default="postgres://postgres:postgres@localhost:5432/ayushma",
+        default="postgres://debug:debug@postgres/ayushma",
     )
 }
 
@@ -243,7 +242,7 @@
 # http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-broker_url
 CELERY_BROKER_URL = env(
     "CELERY_BROKER_URL",
-    default=env("REDIS_URL", default="redis://localhost:6379/0"),
+    default=env("REDIS_URL", default="redis://redis:6379/0"),
 )
 # http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-result_backend
 CELERY_RESULT_BACKEND = CELERY_BROKER_URL
@@ -349,11 +348,13 @@
 
 # Pinecone
 PINECONE_API_KEY = env("PINECONE_API_KEY", default="")
-PINECONE_ENVIRONMENT = env("PINECONE_ENVIRONMENT", default="")
 PINECONE_INDEX = env("PINECONE_INDEX", default="")
 
-pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENVIRONMENT)
-PINECONE_INDEX_INSTANCE = pinecone.Index(index_name=PINECONE_INDEX)
+VECTOR_DB = env("VECTOR_DB", default="milvus")
+
+# Milvus
+MILVUS_URL = env("MILVUS_URL", default="http://milvus:19530")
+MILVUS_COLLECTION = env("MILVUS_COLLECTION", default="ayushma")
 
 # S3
 AWS_STORAGE_BUCKET_NAME = env("S3_BUCKET_NAME", default=None)
diff --git a/core/settings/production.py b/core/settings/production.py
index 6b76cfa7..8077d14e 100644
--- a/core/settings/production.py
+++ b/core/settings/production.py
@@ -30,7 +30,7 @@
 CACHES = {
     "default": {
         "BACKEND": "django.core.cache.backends.redis.RedisCache",
-        "LOCATION": env("REDIS_URL", default="redis://localhost:6379/"),
+        "LOCATION": env("REDIS_URL", default="redis://redis:6379/"),
     }
 }
 
diff --git a/docker-compose.local.yaml b/docker-compose.local.yaml
index eaac25a6..7973050c 100644
--- a/docker-compose.local.yaml
+++ b/docker-compose.local.yaml
@@ -3,6 +3,7 @@ version: '3'
 volumes:
   ayushma_local_postgres_data: {}
   ayushma_local_postgres_data_backups: {}
+  ayushma_local_milvus_data: {}
 
 services:
   django: &django
@@ -21,6 +22,8 @@ services:
     ports:
       - "8000:8000"
     command: /start
+    networks:
+      - default
 
   postgres:
     build:
@@ -32,9 +35,13 @@ services:
       - ayushma_local_postgres_data_backups:/backups:z
     env_file:
       - ./.envs/.local/.postgres
+    networks:
+      - default
 
   redis:
     image: redis:6
+    networks:
+      - default
 
   celeryworker:
     <<: *django
@@ -45,3 +52,24 @@ services:
     <<: *django
     ports: []
     command: /start-celerybeat
+
+  milvus:
+    image: milvusdb/milvus:v2.4.5
+    container_name: milvus-standalone
+    environment:
+      - ETCD_USE_EMBED=true
+      - ETCD_DATA_DIR=/var/lib/milvus/etcd
+      - ETCD_CONFIG_PATH=/milvus/configs/embedEtcd.yaml
+      - COMMON_STORAGETYPE=local
+    volumes:
+      - ayushma_local_milvus_data:/var/lib/milvus
+      - ./compose/production/milvus/embedEtcd.yaml:/milvus/configs/embedEtcd.yaml
+    command: ["milvus", "run", "standalone"]
+    security_opt:
+      - seccomp:unconfined
+    networks:
+      - default
+
+networks:
+  default:
+    name: ayushma_local
diff --git a/docker-compose.production.yaml b/docker-compose.production.yaml
index ef55f10c..f3e20a32 100644
--- a/docker-compose.production.yaml
+++ b/docker-compose.production.yaml
@@ -3,6 +3,7 @@ version: '3'
 volumes:
   production_postgres_data: {}
   production_postgres_data_backups: {}
+  production_milvus_data: {}
 
 services:
   django: &django
@@ -19,6 +20,8 @@ services:
     ports:
       - "8000:8000"
     command: /start
+    networks:
+      - default
 
   postgres:
     build:
@@ -30,9 +33,13 @@ services:
       - production_postgres_data_backups:/backups:z
     env_file:
       - ./.envs/.production/.postgres
+    networks:
+      - default
 
   redis:
     image: redis:6
+    networks:
+      - default
 
   celeryworker:
     <<: *django
@@ -43,3 +50,24 @@ services:
     <<: *django
     ports: []
     command: /start-celerybeat
+
+  milvus:
+    image: milvusdb/milvus:v2.4.5
+    container_name: milvus-standalone
+    environment:
+      - ETCD_USE_EMBED=true
+      - ETCD_DATA_DIR=/var/lib/milvus/etcd
+      - ETCD_CONFIG_PATH=/milvus/configs/embedEtcd.yaml
+      - COMMON_STORAGETYPE=local
+    volumes:
+      - production_milvus_data:/var/lib/milvus
+      - ./compose/production/milvus/embedEtcd.yaml:/milvus/configs/embedEtcd.yaml
+    command: ["milvus", "run", "standalone"]
+    security_opt:
+      - seccomp:unconfined
+    networks:
+      - default
+
+networks:
+  default:
+    name: ayushma_production
diff --git a/example.env b/example.env
index 724fc5e5..0ecedda9 100644
--- a/example.env
+++ b/example.env
@@ -7,9 +7,7 @@ AZURE_CHAT_MODEL=
 AZURE_EMBEDDING_DEPLOYMENT=
 STT_API_KEY=
 PINECONE_INDEX=
-PINECONE_ENVIRONMENT=
 PINECONE_API_KEY=
-DATABASE_URL=
 GOOGLE_APPLICATION_CREDENTIALS=./gc_credential.json
 S3_SECRET_KEY=
 S3_KEY_ID=
diff --git a/prompt-example.txt b/prompt-example.txt
new file mode 100644
index 00000000..c3d6f47b
--- /dev/null
+++ b/prompt-example.txt
@@ -0,0 +1,13 @@
+You are a female assistant called Ayushma who understands all languages and responds only in English and you must follow the given algorithm strictly to assist the user. Remember you must give accurate answers, so stick strictly to the references as explained in algorithm. Give detailed response without going out of the references provided.
+Your output must be in markdown format find important terms and add bold to it (example **word**) find numbers and add italic to it(example *word*) add bullet points to a list(example -word1\n-word2):
+
+references = {reference}
+
+- Analyze the given "references" and current user's query and return a response if "references" strictly contains the information to solve the current query else return "Sorry I am not able to find anything related to your query in my database".
+- Analyze "chat history with user" and "query" and generate an appropriate result for the "query"
+- Analyze "references", chat history with user and user's current query to give the most descriptive and most accurate answer to solve the user's query.
+
+Output Format (follow the below format strictly and you must provide the references ids array in all your responses after the result. Do not mention about the references anywhere else):
+
+Ayushma: <Your response here>
+References: <array of reference_ids (in array format: uuid[] (example : [uuid1, uuid2]) "include all the reference uuids in this array that are relevant and from which you formed the result">