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Created a prompt_template
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@AIWithShrey
AIWithShrey committed Jul 31, 2024
1 parent 789d912 commit 45048b8
Showing 1 changed file with 43 additions and 92 deletions.
135 changes: 43 additions & 92 deletions app.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,8 @@
import os
import uuid
from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
import streamlit as st
import datasets
from langchain import hub
from langchain_huggingface import HuggingFaceEndpointEmbeddings, HuggingFaceEndpoint
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain.agents import create_react_agent, AgentExecutor
Expand Down Expand Up @@ -61,16 +58,18 @@ def setup_huggingface_endpoint():
task="conversational",
stop_sequences=[
"<|im_end|>",
"{your_token}".format(your_token=os.getenv("STOP_TOKEN", "<|endoftext|>")),
"{your_token}".format(your_token=os.getenv("STOP_TOKEN", "<|end_of_text|>")),
],
)
return model

def setup_portkey_integrated_model():
from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
from langchain_openai import ChatOpenAI
portkey_headers = createHeaders(
api_key=os.getenv("PORTKEY_API_KEY"),
custom_host=os.getenv("PORTKEY_CUSTOM_HOST"),
provider=os.getenv("PORTKEY_PROVIDER")
api_key=os.getenv("PORTKEY_API_KEY"),
custom_host=os.getenv("PORTKEY_CUSTOM_HOST"),
provider=os.getenv("PORTKEY_PROVIDER")
)

model = ChatOpenAI(
Expand All @@ -94,61 +93,17 @@ def setup_huggingface_embeddings():

def load_prompt_and_system_ins():
#prompt = hub.pull("hwchase17/react-chat")
prompt = PromptTemplate.from_template("""
InSightful is a bot developed by InfraCloud Technologies.
InSightful is used to assist technical communities online on platforms such as Slack, Reddit and Discord.
InSightful can answer questions from conversations amongst community members and can also search StackOverflow for technical questions.
InSightful can also conduct its own research on the web to find answers to questions.
InSightful is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. InSightful is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
InSightful is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, InSightful is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
Overall, InSightful is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, InSightful is here to assist.
TOOLS:
------
InSightful has access to the following tools:
{tools}
To use a tool, please use the following format:
```
Thought: Do I need to use a tool? Yes
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
```
For the tavily_search_results_json tool, make sure the Action Input is a string derived from the new input.
When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
```
Thought: Do I need to use a tool? No
Final Answer: [your response here]
```
Begin!
Previous conversation history:
{chat_history}
New input: {input}
{agent_scratchpad}
""")
prompt = PromptTemplate.from_file("templates/prompt_template.tmpl")

# Set up prompt template
template = """
Based on the retrieved context, respond with an accurate answer. Use the provided tools to support your response.
Be concise and always provide accurate, specific, and relevant information.
"""

system_instructions = SystemMessage(
content=system_message_template,
content=template,
metadata={"role": "system"},
)

Expand Down Expand Up @@ -221,46 +176,28 @@ def query_docs(self, model, question, vector_store, prompt):
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)

#def create_retriever(name, model, description, client, chroma_embedding_function, embedder):
# rag = RAG(llm=model, embeddings=embedder, collection_name="Slack", db_client=client)
# pages = rag.load_documents("spencer/software_slacks")
# chunks = rag.chunk_doc(pages)
# vector_store = rag.insert_embeddings(chunks, chroma_embedding_function, embedder)
# retriever = vector_store.as_retriever(
# search_type="similarity", search_kwargs={"k": 10}
# )
# info_retriever = create_retriever_tool(retriever, name, description)
# return info_retriever

def create_reranker_retriever(name, model, description, client, chroma_embedding_function, embedder):
def create_retriever(name, model, description, client, chroma_embedding_function, embedder):
rag = RAG(llm=model, embeddings=embedder, collection_name="Slack", db_client=client)
pages = rag.load_documents("spencer/software_slacks", num_docs=100)
pages = rag.load_documents("spencer/software_slacks")
chunks = rag.chunk_doc(pages)
vector_store = rag.insert_embeddings(chunks, chroma_embedding_function, embedder)
compressor = TEIRerank(url="http://{host}:{port}".format(host=os.getenv("RERANKER_HOST", "localhost"),
port=os.getenv("RERANKER_PORT", "8082")),
top_n=10,
batch_size=16)
retriever = vector_store.as_retriever(
search_type="similarity", search_kwargs={"k": 100}
)
compression_retriever = ContextualCompressionRetriever(
base_compressor=compressor, base_retriever=retriever
search_type="similarity", search_kwargs={"k": 10}
)
info_retriever = create_retriever_tool(compression_retriever, name, description)
info_retriever = create_retriever_tool(retriever, name, description)
return info_retriever

def create_reranker_retriever(name, model, description, client, chroma_embedding_function, embedder):
rag = RAG(llm=model, embeddings=embedder, collection_name="Slack", db_client=client)
pages = rag.load_documents("spencer/software_slacks", num_docs=250)
pages = rag.load_documents("spencer/software_slacks", num_docs=100)
chunks = rag.chunk_doc(pages)
vector_store = rag.insert_embeddings(chunks, chroma_embedding_function, embedder)
compressor = TEIRerank(url="http://{host}:{port}".format(host=os.getenv("RERANKER_HOST", "localhost"),
port=os.getenv("RERANKER_PORT", "8082")),
top_n=50,
top_n=10,
batch_size=16)
retriever = vector_store.as_retriever(
search_type="similarity", search_kwargs={"k": 10}
search_type="similarity", search_kwargs={"k": 100}
)
compression_retriever = ContextualCompressionRetriever(
base_compressor=compressor, base_retriever=retriever
Expand All @@ -283,21 +220,32 @@ def setup_tools(_model, _client, _chroma_embedding_function, _embedder):
# chroma_embedding_function=_chroma_embedding_function,
# embedder=_embedder,
#)
reranker_retriever = create_reranker_retriever(
name="slack_conversations_retriever",
model=_model,
description="Useful for when you need to answer from Slack conversations.",
client=_client,
chroma_embedding_function=_chroma_embedding_function,
embedder=_embedder,
)
if os.getenv("USE_RERANKER", "False") == "True":
retriever = create_reranker_retriever(
name="slack_conversations_retriever",
model=_model,
description="Useful for when you need to answer from Slack conversations.",
client=_client,
chroma_embedding_function=_chroma_embedding_function,
embedder=_embedder,
)
else:
retriever = create_retriever(
name="slack_conversations_retriever",
model=_model,
description="Useful for when you need to answer from Slack conversations.",
client=_client,
chroma_embedding_function=_chroma_embedding_function,
embedder=_embedder,
)


return [web_search_tool, stackexchange_tool, reranker_retriever]
return [web_search_tool, stackexchange_tool, retriever]

@st.cache_resource
def setup_agent(_model, _prompt, _client, _chroma_embedding_function, _embedder):
tools = setup_tools(_model, _client, _chroma_embedding_function, _embedder)
agent = create_react_agent(llm=_model, prompt=_prompt, tools=tools)
agent = create_react_agent(llm=_model, prompt=_prompt, tools=tools,)
agent_executor = AgentExecutor(
agent=agent, verbose=True, tools=tools, handle_parsing_errors=True
)
Expand All @@ -307,15 +255,18 @@ def main():
client = setup_chroma_client()
chroma_embedding_function = setup_chroma_embedding_function()
prompt, system_instructions = load_prompt_and_system_ins()
#model = setup_huggingface_endpoint()
model = setup_portkey_integrated_model()
if os.getenv("ENABLE_PORTKEY", "False") == "True":
model = setup_portkey_integrated_model()
else:
model = setup_huggingface_endpoint()
embedder = setup_huggingface_embeddings()

agent_executor = setup_agent(
model, prompt, client, chroma_embedding_function, embedder
)

st.title("InSightful: Your AI Assistant for community questions")
st.text("Made with ❤️ by InfraCloud Technologies")
st.markdown(
"""
InSightful is an AI assistant that helps you with your questions.
Expand Down

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