diff --git a/pgml-apps/pgml-chat/.env.template b/pgml-apps/pgml-chat/.env.template
index f08a1b630..d5837444e 100644
--- a/pgml-apps/pgml-chat/.env.template
+++ b/pgml-apps/pgml-chat/.env.template
@@ -3,4 +3,5 @@ DATABASE_URL=<POSTGRES_DATABASE_URL starts with postgres://>
 
 SLACK_BOT_TOKEN=<SLACK_BOT_TOKEN>
 SLACK_APP_TOKEN=<SLACK_APP_TOKEN>
-DISCORD_BOT_TOKEN=<DISCORD_BOT_TOKEN>
\ No newline at end of file
+DISCORD_BOT_TOKEN=<DISCORD_BOT_TOKEN>
+SYSTEM_PROMPT_TEMPLATE=<SYSTEM PROMPT FOR CHAT COMPLETION MODEL. Check prompts.md file for examples>
\ No newline at end of file
diff --git a/pgml-apps/pgml-chat/README.md b/pgml-apps/pgml-chat/README.md
index c1d570035..349d2b3c5 100644
--- a/pgml-apps/pgml-chat/README.md
+++ b/pgml-apps/pgml-chat/README.md
@@ -3,7 +3,7 @@ A command line tool to build and deploy a **_knowledge based_** chatbot using Po
 
 There are two stages in building a knowledge based chatbot:
 - Build a knowledge base by ingesting documents, chunking documents, generating embeddings and indexing these embeddings for fast query
-- Generate responses to user queries by retrieving relevant documents and generating responses using OpenAI API
+- Generate responses to user queries by retrieving relevant documents and generating responses using OpenAI and [OpenSourceAI API](https://postgresml.org/docs/introduction/apis/client-sdks/opensourceai)
 
 This tool automates the above two stages and provides a command line interface to build and deploy a knowledge based chatbot.
 
@@ -12,7 +12,7 @@ Before you begin, make sure you have the following:
 
 - PostgresML Database: Sign up for a free [GPU-powered database](https://postgresml.org/signup)
 - Python version >=3.8
-- OpenAI API key
+- (Optional) OpenAI API key
 
 
 # Getting started
@@ -30,10 +30,10 @@ wget https://raw.githubusercontent.com/postgresml/postgresml/master/pgml-apps/pg
 ```
 3. Copy the template file to `.env`
 
-4. Update environment variables with your OpenAI API key and PostgresML database credentials.
+4. Update environment variables with your PostgresML database credentials and OpenAI API key (optional).
 ```bash
-OPENAI_API_KEY=<OPENAI_API_KEY>
 DATABASE_URL=<POSTGRES_DATABASE_URL starts with postgres://>
+OPENAI_API_KEY=<OPENAI_API_KEY> # Optional
 ```
 
 # Usage
@@ -41,13 +41,13 @@ You can get help on the command line interface by running:
 
 ```bash
 (pgml-bot-builder-py3.9) pgml-chat % pgml-chat % pgml-chat --help
-usage: pgml-chat [-h] --collection_name COLLECTION_NAME [--root_dir ROOT_DIR] [--stage {ingest,chat}] [--chat_interface {cli,slack,discord}]
-                 [--chat_history CHAT_HISTORY] [--bot_name BOT_NAME] [--bot_language BOT_LANGUAGE] [--bot_topic BOT_TOPIC]
-                 [--bot_topic_primary_language BOT_TOPIC_PRIMARY_LANGUAGE] [--bot_persona BOT_PERSONA]
+usage: pgml-chat [-h] --collection_name COLLECTION_NAME [--root_dir ROOT_DIR] [--stage {ingest,chat}] [--chat_interface {cli,slack,discord}] [--chat_history CHAT_HISTORY] [--bot_name BOT_NAME]
+                 [--bot_language BOT_LANGUAGE] [--bot_topic BOT_TOPIC] [--bot_topic_primary_language BOT_TOPIC_PRIMARY_LANGUAGE] [--bot_persona BOT_PERSONA]
+                 [--chat_completion_model CHAT_COMPLETION_MODEL] [--max_tokens MAX_TOKENS] [--vector_recall_limit VECTOR_RECALL_LIMIT]
 
 PostgresML Chatbot Builder
 
-optional arguments:
+options:
   -h, --help            show this help message and exit
   --collection_name COLLECTION_NAME
                         Name of the collection (schema) to store the data in PostgresML database (default: None)
@@ -57,16 +57,21 @@ optional arguments:
   --chat_interface {cli,slack,discord}
                         Chat interface to use (default: cli)
   --chat_history CHAT_HISTORY
-                        Number of messages from history used for generating response (default: 1)
+                        Number of messages from history used for generating response (default: 0)
   --bot_name BOT_NAME   Name of the bot (default: PgBot)
   --bot_language BOT_LANGUAGE
                         Language of the bot (default: English)
   --bot_topic BOT_TOPIC
                         Topic of the bot (default: PostgresML)
   --bot_topic_primary_language BOT_TOPIC_PRIMARY_LANGUAGE
-                        Primary programming language of the topic (default: )
+                        Primary programming language of the topic (default: SQL)
   --bot_persona BOT_PERSONA
                         Persona of the bot (default: Engineer)
+  --chat_completion_model CHAT_COMPLETION_MODEL
+  --max_tokens MAX_TOKENS
+                        Maximum number of tokens to generate (default: 256)
+  --vector_recall_limit VECTOR_RECALL_LIMIT
+                        Maximum number of documents to retrieve from vector recall (default: 1)
 ```
 ## Ingest
 In this step, we ingest documents, chunk documents, generate embeddings and index these embeddings for fast query.
@@ -146,6 +151,32 @@ Once the discord app is running, you can interact with the chatbot on Discord as
 
 ![Discord Chatbot](./images/discord_screenshot.png)
 
+# Prompt Engineering
+In addition to relevant context retrieved from vector search, system prompt to generate accurate responses with minimum hallucinations requires prompt engineering. 
+Different chat completion models require different system prompts. Since the prompts including the context are long, they suffer from **lost in the middle** problem described in [this paper](https://arxiv.org/pdf/2307.03172.pdf). Below are some of the prompts that we have used for different chat completion models.
+
+## Default prompt (GPT-3.5 and open source models)
+```text
+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.
+Use three sentences maximum and keep the answer as concise as possible.
+Always say "thanks for asking!" at the end of the answer.
+```
+
+## GPT-4 System prompt
+```text
+You are an assistant to answer questions about {topic}.\ 
+Your name is {name}. You speak like {persona} in {language}. Use the given list of documents to answer user's question.\
+Use the conversation history if it is applicable to answer the question. \n Use the following steps:\n \
+1. Identify if the user input is really a question. \n \
+2. If the user input is not related to the {topic} then respond that it is not related to the {topic}.\n \
+3. If the user input is related to the {topic} then first identify relevant documents from the list of documents. \n \
+4. If the documents that you found relevant have information to completely and accurately answers the question then respond with the answer.\n \
+5. If the documents that you found relevant have code snippets then respond with the code snippets. \n \
+6. Most importantly, don't make up code snippets that are not present in the documents.\n \
+7. If the user input is generic like Cool, Thanks, Hello, etc. then respond with a generic answer. \n"
+```
+
 # Developer Guide
 
 1. Clone this repository, start a poetry shell and install dependencies
diff --git a/pgml-apps/pgml-chat/pgml_chat/main.py b/pgml-apps/pgml-chat/pgml_chat/main.py
index 3dce96836..3d8b27dda 100644
--- a/pgml-apps/pgml-chat/pgml_chat/main.py
+++ b/pgml-apps/pgml-chat/pgml_chat/main.py
@@ -1,5 +1,14 @@
 import asyncio
-from pgml import Collection, Model, Splitter, Pipeline, migrate, init_logger, Builtins
+from pgml import (
+    Collection,
+    Model,
+    Splitter,
+    Pipeline,
+    migrate,
+    init_logger,
+    Builtins,
+    OpenSourceAI,
+)
 import logging
 from rich.logging import RichHandler
 from rich.progress import track
@@ -9,7 +18,7 @@
 import glob
 import argparse
 from time import time
-import openai
+from openai import OpenAI
 import signal
 from uuid import uuid4
 import pendulum
@@ -110,6 +119,43 @@ def handler(signum, frame):
     help="Persona of the bot",
 )
 
+parser.add_argument(
+    "--chat_completion_model",
+    dest="chat_completion_model",
+    type=str,
+    default="HuggingFaceH4/zephyr-7b-beta",
+)
+
+parser.add_argument(
+    "--max_tokens",
+    dest="max_tokens",
+    type=int,
+    default=256,
+    help="Maximum number of tokens to generate",
+)
+
+parser.add_argument(
+    "--temperature",
+    dest="temperature",
+    type=float,
+    default=0.7,
+    help="Temperature for generating response",
+)
+
+parser.add_argument(
+    "--top_p",
+    dest="top_p",
+    type=float,
+    default=0.9,
+    help="Top p for generating response",
+)
+parser.add_argument(
+    "--vector_recall_limit",
+    dest="vector_recall_limit",
+    type=int,
+    default=1,
+    help="Maximum number of documents to retrieve from vector recall",
+)
 
 args = parser.parse_args()
 
@@ -152,36 +198,31 @@ def handler(signum, frame):
 model_name = "hkunlp/instructor-xl"
 model_embedding_instruction = "Represent the %s document for retrieval: " % (bot_topic)
 model_params = {"instruction": model_embedding_instruction}
-# model_name = "BAAI/bge-large-en-v1.5"
-# model_params = {}
+
 model = Model(model_name, "pgml", model_params)
 pipeline = Pipeline(args.collection_name + "_pipeline", model, splitter)
 chat_history_pipeline = Pipeline(
     chat_history_collection_name + "_pipeline", model, splitter
 )
 
+chat_completion_model = args.chat_completion_model
+max_tokens = args.max_tokens
+temperature = args.temperature
+vector_recall_limit = args.vector_recall_limit
+
 query_params_instruction = (
     "Represent the %s question for retrieving supporting documents: " % (bot_topic)
 )
 query_params = {"instruction": query_params_instruction}
-# query_params = {}
-
-default_system_prompt_template = """
-You are an assistant to answer questions about {topic}. 
-Your name is {name}. You speak like {persona} in {language}. Use the given list of documents to answer user's question.
-Use the conversation history if it is applicable to answer the question. 
-Use the following steps:
-
-1. Identify if the user input is really a question. 
-2. If the user input is not related to the {topic} then respond that it is not related to the {topic}.
-3. If the user input is related to the {topic} then first identify relevant documents from the list of documents. 
-4. If the documents that you found relevant have information to completely and accurately answers the question then respond with the answer.
-5. If the documents that you found relevant have code snippets then respond with the code snippets. 
-6. Most importantly, don't make up code snippets that are not present in the documents.
-7. If the user input is generic like Cool, Thanks, Hello, etc. then respond with a generic answer. 
-"""
-
-default_system_prompt = default_system_prompt_template.format(
+
+default_system_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.
+Use three sentences maximum and keep the answer as concise as possible.
+Always say "thanks for asking!" at the end of the answer."""
+
+system_prompt_template = os.environ.get("SYSTEM_PROMPT_TEMPLATE", default_system_prompt_template)
+
+system_prompt = system_prompt_template.format(
     topic=bot_topic,
     name=bot_name,
     persona=bot_persona,
@@ -189,8 +230,6 @@ def handler(signum, frame):
     response_programming_language=bot_topic_primary_language,
 )
 
-system_prompt = default_system_prompt
-
 base_prompt = """
 {conversation_history}
 ####
@@ -203,18 +242,37 @@ def handler(signum, frame):
 
 Helpful Answer:"""
 
-openai_api_key = os.environ.get("OPENAI_API_KEY")
+
+openai_api_key = os.environ.get("OPENAI_API_KEY","")
 
 system_prompt_document = [
-        {
-            "text": system_prompt,
-            "id": str(uuid4())[:8],
-            "interface": chat_interface,
-            "role": "system",
-            "timestamp": pendulum.now().timestamp(),
-        }
+    {
+        "text": system_prompt,
+        "id": str(uuid4())[:8],
+        "interface": chat_interface,
+        "role": "system",
+        "timestamp": pendulum.now().timestamp(),
+    }
 ]
 
+
+def get_model_type(chat_completion_model: str):
+    model_type = "opensourceai"
+    try:
+        client = OpenAI(api_key=openai_api_key)
+        models = client.models.list()
+        for model in models:
+            if model.id == chat_completion_model:
+                model_type = "openai"
+                break
+    except Exception as e:
+        log.debug(e)
+
+    log.info("Setting model type to " + model_type)
+
+    return model_type
+
+
 async def upsert_documents(folder: str) -> int:
     log.info("Scanning " + folder + " for markdown files")
     md_files = []
@@ -238,43 +296,35 @@ async def generate_chat_response(
     user_input,
     system_prompt,
     openai_api_key,
-    temperature=0.7,
-    max_tokens=256,
+    temperature=temperature,
+    max_tokens=max_tokens,
     top_p=0.9,
     user_name="",
 ):
     messages = []
     messages.append({"role": "system", "content": system_prompt})
 
-    chat_history_messages = await chat_collection.get_documents( {
-        "limit" : chat_history*2,
-        "order_by": {"timestamp": "desc"},
-        "filter": {
-            "metadata": {
-                "$and" : [
-                    {
-                        "$or": 
-                        [
-                        {"role": {"$eq": "assistant"}},
-                        {"role": {"$eq": "user"}}
-                        ]
-                    },
-                    {
-                        "interface" : {
-                            "$eq" : chat_interface
-                        }
-                    },
-                    {
-                        "user_name" : {
-                            "$eq" : user_name
-                        }
-                    }
-                ]
-            }
-        }
+    chat_history_messages = await chat_collection.get_documents(
+        {
+            "limit": chat_history * 2,
+            "order_by": {"timestamp": "desc"},
+            "filter": {
+                "metadata": {
+                    "$and": [
+                        {
+                            "$or": [
+                                {"role": {"$eq": "assistant"}},
+                                {"role": {"$eq": "user"}},
+                            ]
+                        },
+                        {"interface": {"$eq": chat_interface}},
+                        {"user_name": {"$eq": user_name}},
+                    ]
+                }
+            },
         }
     )
-    
+
     # Reverse the order so that user messages are first
 
     chat_history_messages.reverse()
@@ -301,8 +351,8 @@ async def generate_chat_response(
     }
     history_documents.append(_document)
 
-    if user_input:
-        query = await get_prompt(user_input,conversation_history)
+
+    query = await get_prompt(user_input, conversation_history)
 
     messages.append({"role": "user", "content": query})
 
@@ -319,7 +369,7 @@ async def generate_chat_response(
     _document = {
         "text": response,
         "id": str(uuid4())[:8],
-        "parent_message_id" : user_message_id,
+        "parent_message_id": user_message_id,
         "interface": chat_interface,
         "role": "assistant",
         "timestamp": pendulum.now().timestamp(),
@@ -333,21 +383,34 @@ async def generate_chat_response(
 
 
 async def generate_response(
-    messages, openai_api_key, temperature=0.7, max_tokens=256, top_p=0.9
+    messages, openai_api_key, temperature=temperature, max_tokens=max_tokens, top_p=0.9
 ):
-    openai.api_key = openai_api_key
-    log.debug("Generating response from OpenAI API: " + str(messages))
-    response = openai.ChatCompletion.create(
-        # model="gpt-3.5-turbo-16k",
-        model="gpt-4",
-        messages=messages,
-        temperature=temperature,
-        max_tokens=max_tokens,
-        top_p=top_p,
-        frequency_penalty=0,
-        presence_penalty=0,
-    )
-    return response["choices"][0]["message"]["content"]
+    model_type = get_model_type(chat_completion_model)
+    if model_type == "openai":
+        client = OpenAI(api_key=openai_api_key)
+        log.debug("Generating response from OpenAI API: " + str(messages))
+        response = client.chat.completions.create(
+            model=chat_completion_model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            frequency_penalty=0,
+            presence_penalty=0,
+        )
+        output = response.choices[0].message.content
+    else:
+        client = OpenSourceAI(database_url=database_url)
+        log.debug("Generating response from OpenSourceAI API: " + str(messages))
+        response = client.chat_completions_create(
+            model=chat_completion_model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+        output = response["choices"][0]["message"]["content"]
+
+    return output
 
 
 async def ingest_documents(folder: str):
@@ -364,7 +427,7 @@ async def get_prompt(user_input: str = "", conversation_history: str = "") -> st
     vector_results = (
         await collection.query()
         .vector_recall(query_input, pipeline, query_params)
-        .limit(5)
+        .limit(vector_recall_limit)
         .fetch_all()
     )
     log.info(vector_results)
@@ -376,7 +439,7 @@ async def get_prompt(user_input: str = "", conversation_history: str = "") -> st
 
     if conversation_history:
         conversation_history = "#### \n Conversation History: \n" + conversation_history
-    
+
     query = base_prompt.format(
         conversation_history=conversation_history,
         context=context,
@@ -399,8 +462,8 @@ async def chat_cli():
                 user_input,
                 system_prompt,
                 openai_api_key,
-                max_tokens=512,
-                temperature=0.3,
+                max_tokens=max_tokens,
+                temperature=temperature,
                 top_p=0.9,
                 user_name=user_name,
             )
@@ -428,11 +491,10 @@ async def message_hello(message, say):
                 user_input,
                 system_prompt,
                 openai_api_key,
-                max_tokens=512,
-                temperature=0.7,
-                user_name = user,
+                max_tokens=max_tokens,
+                temperature=temperature,
+                user_name=user,
             )
-            
 
             await say(text=f"<@{user}> {response}")
 
@@ -462,7 +524,12 @@ async def on_message(message):
         print("Discord response in progress ..")
         user_input = message.content
         response = await generate_chat_response(
-            user_input, system_prompt, openai_api_key, max_tokens=512, temperature=0.7,user_name=message.author.name
+            user_input,
+            system_prompt,
+            openai_api_key,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            user_name=message.author.name,
         )
         await message.channel.send(response)
 
diff --git a/pgml-apps/pgml-chat/poetry.lock b/pgml-apps/pgml-chat/poetry.lock
index dc95a2fea..7199be4e0 100644
--- a/pgml-apps/pgml-chat/poetry.lock
+++ b/pgml-apps/pgml-chat/poetry.lock
@@ -2,112 +2,100 @@
 
 [[package]]
 name = "aiohttp"
-version = "3.8.5"
+version = "3.9.1"
 description = "Async http client/server framework (asyncio)"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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-    {file = "aiohttp-3.8.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:13bf85afc99ce6f9ee3567b04501f18f9f8dbbb2ea11ed1a2e079670403a7c84"},
-    {file = "aiohttp-3.8.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2ce2ac5708501afc4847221a521f7e4b245abf5178cf5ddae9d5b3856ddb2f3a"},
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 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
@@ -124,6 +112,43 @@ files = [
 [package.dependencies]
 frozenlist = ">=1.1.0"
 
+[[package]]
+name = "annotated-types"
+version = "0.6.0"
+description = "Reusable constraint types to use with typing.Annotated"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
+
+[[package]]
+name = "anyio"
+version = "3.7.1"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.dependencies]
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+
+[package.extras]
+doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-jquery"]
+test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (<0.22)"]
+
 [[package]]
 name = "async-timeout"
 version = "4.0.3"
@@ -162,34 +187,30 @@ reference = "testpypi"
 
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 python-versions = ">=3.8"
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 [package.dependencies]
@@ -209,99 +230,114 @@ uvloop = ["uvloop (>=0.15.2)"]
 
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+version = "2023.11.17"
 description = "Python package for providing Mozilla's CA Bundle."
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 optional = false
 python-versions = ">=3.6"
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+    {file = "charset_normalizer-3.3.2-cp39-cp39-win32.whl", hash = "sha256:aed38f6e4fb3f5d6bf81bfa990a07806be9d83cf7bacef998ab1a9bd660a581f"},
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 ]
 
 [[package]]
@@ -362,6 +398,33 @@ speed = ["Brotli", "aiodns (>=1.1)", "cchardet (==2.1.7)", "orjson (>=3.5.4)"]
 test = ["coverage[toml]", "pytest", "pytest-asyncio", "pytest-cov", "pytest-mock", "typing-extensions (>=4.3,<5)"]
 voice = ["PyNaCl (>=1.3.0,<1.6)"]
 
+[[package]]
+name = "distro"
+version = "1.8.0"
+description = "Distro - an OS platform information API"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "distro-1.8.0-py3-none-any.whl", hash = "sha256:99522ca3e365cac527b44bde033f64c6945d90eb9f769703caaec52b09bbd3ff"},
+    {file = "distro-1.8.0.tar.gz", hash = "sha256:02e111d1dc6a50abb8eed6bf31c3e48ed8b0830d1ea2a1b78c61765c2513fdd8"},
+]
+
+[[package]]
+name = "exceptiongroup"
+version = "1.2.0"
+description = "Backport of PEP 654 (exception groups)"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"},
+    {file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
+]
+
+[package.extras]
+test = ["pytest (>=6)"]
+
 [[package]]
 name = "frozenlist"
 version = "1.4.0"
@@ -433,16 +496,75 @@ files = [
     {file = "frozenlist-1.4.0.tar.gz", hash = "sha256:09163bdf0b2907454042edb19f887c6d33806adc71fbd54afc14908bfdc22251"},
 ]
 
+[[package]]
+name = "h11"
+version = "0.14.0"
+description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
+    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.2"
+description = "A minimal low-level HTTP client."
+category = "main"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "httpcore-1.0.2.tar.gz", hash = "sha256:9fc092e4799b26174648e54b74ed5f683132a464e95643b226e00c2ed2fa6535"},
+]
+
+[package.dependencies]
+certifi = "*"
+h11 = ">=0.13,<0.15"
+
+[package.extras]
+asyncio = ["anyio (>=4.0,<5.0)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (>=1.0.0,<2.0.0)"]
+trio = ["trio (>=0.22.0,<0.23.0)"]
+
+[[package]]
+name = "httpx"
+version = "0.25.2"
+description = "The next generation HTTP client."
+category = "main"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "httpx-0.25.2.tar.gz", hash = "sha256:8b8fcaa0c8ea7b05edd69a094e63a2094c4efcb48129fb757361bc423c0ad9e8"},
+]
+
+[package.dependencies]
+anyio = "*"
+certifi = "*"
+httpcore = ">=1.0.0,<2.0.0"
+idna = "*"
+sniffio = "*"
+
+[package.extras]
+brotli = ["brotli", "brotlicffi"]
+cli = ["click (>=8.0.0,<9.0.0)", "pygments (>=2.0.0,<3.0.0)", "rich (>=10,<14)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (>=1.0.0,<2.0.0)"]
+
 [[package]]
 name = "idna"
-version = "3.4"
+version = "3.6"
 description = "Internationalized Domain Names in Applications (IDNA)"
 category = "main"
 optional = false
 python-versions = ">=3.5"
 files = [
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-    {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
+    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
+    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
 ]
 
 [[package]]
@@ -580,37 +702,38 @@ files = [
 
 [[package]]
 name = "openai"
-version = "0.27.10"
-description = "Python client library for the OpenAI API"
+version = "1.3.7"
+description = "The official Python library for the openai API"
 category = "main"
 optional = false
 python-versions = ">=3.7.1"
 files = [
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+    {file = "openai-1.3.7-py3-none-any.whl", hash = "sha256:e5c51367a910297e4d1cd33d2298fb87d7edf681edbe012873925ac16f95bee0"},
+    {file = "openai-1.3.7.tar.gz", hash = "sha256:18074a0f51f9b49d1ae268c7abc36f7f33212a0c0d08ce11b7053ab2d17798de"},
 ]
 
 [package.dependencies]
-aiohttp = "*"
-requests = ">=2.20"
-tqdm = "*"
+anyio = ">=3.5.0,<4"
+distro = ">=1.7.0,<2"
+httpx = ">=0.23.0,<1"
+pydantic = ">=1.9.0,<3"
+sniffio = "*"
+tqdm = ">4"
+typing-extensions = ">=4.5,<5"
 
 [package.extras]
-datalib = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
-dev = ["black (>=21.6b0,<22.0)", "pytest (>=6.0.0,<7.0.0)", "pytest-asyncio", "pytest-mock"]
-embeddings = ["matplotlib", "numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "plotly", "scikit-learn (>=1.0.2)", "scipy", "tenacity (>=8.0.1)"]
-wandb = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "wandb"]
+datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
 [[package]]
 name = "packaging"
-version = "23.1"
+version = "23.2"
 description = "Core utilities for Python packages"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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-    {file = "packaging-23.1.tar.gz", hash = "sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f"},
+    {file = "packaging-23.2-py3-none-any.whl", hash = "sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7"},
+    {file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
 ]
 
 [[package]]
@@ -662,77 +785,216 @@ pytzdata = ">=2020.1"
 
 [[package]]
 name = "pgml"
-version = "0.9.4"
+version = "0.10.1"
 description = "Python SDK is designed to facilitate the development of scalable vector search applications on PostgreSQL databases."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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+
 [[package]]
 name = "pygments"
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+version = "2.17.2"
 description = "Pygments is a syntax highlighting package written in Python."
 category = "main"
 optional = false
 python-versions = ">=3.7"
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 [package.extras]
 plugins = ["importlib-metadata"]
+windows-terminal = ["colorama (>=0.4.6)"]
+
+[package.source]
+type = "legacy"
+url = "https://test.pypi.org/simple"
+reference = "testpypi"
 
 [[package]]
 name = "python-dateutil"
@@ -805,14 +1067,14 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "rich"
-version = "13.5.3"
+version = "13.7.0"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 category = "main"
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 python-versions = ">=3.7.0"
 files = [
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 [package.dependencies]
@@ -837,41 +1099,51 @@ files = [
 
 [[package]]
 name = "slack-bolt"
-version = "1.18.0"
+version = "1.18.1"
 description = "The Bolt Framework for Python"
 category = "main"
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 python-versions = ">=3.6"
 files = [
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 [package.dependencies]
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-[package.extras]
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+[package.source]
+type = "legacy"
+url = "https://test.pypi.org/simple"
+reference = "testpypi"
 
 [[package]]
 name = "slack-sdk"
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+version = "3.26.1"
 description = "The Slack API Platform SDK for Python"
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+
+[[package]]
+name = "sniffio"
+version = "1.3.0"
+description = "Sniff out which async library your code is running under"
+category = "main"
+optional = false
+python-versions = ">=3.7"
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 [[package]]
 name = "tomli"
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 [[package]]
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 description = "HTTP library with thread-safe connection pooling, file post, and more."
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 ]
 
 [package.dependencies]
@@ -1027,4 +1314,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<4.0"
-content-hash = "1412f9786ed6bf30332e934385e4c63c4847ed6f198a26d8f1003bc00d518a0c"
+content-hash = "a90cb07092f73bd46253b687864341327459c552c318e142462b4e89987ba474"
diff --git a/pgml-apps/pgml-chat/pyproject.toml b/pgml-apps/pgml-chat/pyproject.toml
index 4dc061d57..c1b2402bb 100644
--- a/pgml-apps/pgml-chat/pyproject.toml
+++ b/pgml-apps/pgml-chat/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pgml-chat"
-version = "0.2.0"
+version = "0.3.0"
 description = "PostgresML bot builder for all your documentation"
 authors = ["PostgresML <team@postgresml.org>"]
 license = "MIT"
@@ -9,7 +9,7 @@ packages = [{include = "pgml_chat"}]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<4.0"
-openai = "^0.27.8"
+openai = ">=1.3.7"
 rich = "^13.4.2"
 python-dotenv = "^1.0.0"
 click = "^8.1.6"
@@ -17,7 +17,8 @@ black = "^23.7.0"
 slack-bolt = "^1.18.0"
 discord-py = "^2.3.1"
 pendulum = "^2.1.2"
-pgml = "^0.9.4"
+pgml = "^0.10.0"
+requests = "^2.31.0"
 
 
 [[tool.poetry.source]]

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