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| from llama_index.core.agent import ReActAgent | |
| from llama_index.llms.openai import OpenAI | |
| from llama_index.core.tools import FunctionTool | |
| from opensearchpy import OpenSearch | |
| from gradio_client import Client | |
| import streamlit as st | |
| import json | |
| import openai | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| import os | |
| openai_api_key = os.getenv("OPENAI_API_KEY") | |
| job_id = os.getenv('job_id') | |
| user = os.getenv('USERNAME') | |
| password = os.getenv('PASSWORD') | |
| host = os.getenv('HOST') | |
| port = int(os.getenv('PORT')) | |
| auth = (user,password) | |
| client = openai.OpenAI(api_key=openai_api_key) | |
| # os_client = OpenSearch( | |
| # hosts = [{'host': host, 'port': port}], | |
| # http_auth = auth, | |
| # use_ssl = True, | |
| # verify_certs = False | |
| # ) | |
| # indices = os_client.cat.indices(format="json") | |
| # list_of_indeces = [] | |
| # for index in indices: | |
| # list_of_indeces.append(index['index']) | |
| def rag_app(user_input: str) -> str: | |
| gr_client = Client("anasmkh/QdrantVectorStore_Llamaindex") | |
| result = gr_client.predict( | |
| user_input=user_input, | |
| api_name="/chat_with_ai" | |
| ) | |
| return result | |
| rag_tool = FunctionTool.from_defaults(fn=rag_app) | |
| def query_generator(user_input:str) -> str: | |
| job = job_id | |
| response = client.fine_tuning.jobs.retrieve(job) | |
| completion = client.chat.completions.create( | |
| model=response.fine_tuned_model, | |
| messages=[ | |
| {"role": "system", "content": """don't add any other text to sql query;You are a highly skilled assistant trained to translate natural language requests | |
| into accurate and efficient OpenSearch JSON queries """ | |
| }, | |
| {"role": "user", "content": user_input} | |
| ]) | |
| return completion.choices[0].message | |
| query_tool = FunctionTool.from_defaults(fn=query_generator) | |
| llm = OpenAI(model="gpt-3.5-turbo", temperature=0) | |
| agent = ReActAgent.from_tools([query_tool,rag_tool], llm=llm, verbose=True) | |
| def implement_query(generated_query): | |
| # st.write("Raw Query:", generated_query) | |
| if isinstance(generated_query, str): | |
| generated_query = generated_query.replace("'", '"') | |
| query = json.loads(generated_query) | |
| else: | |
| query = generated_query | |
| # st.write("Validated Query:", query) | |
| # response = os_client.search(body=query) | |
| # return response | |
| # except json.JSONDecodeError as e: | |
| # st.error("Error: The generated query is not valid JSON.") | |
| # st.write(f"JSONDecodeError Details: {e}") | |
| # except Exception as e: | |
| # st.error(f"Error executing OpenSearch query: {e}") | |
| # st.write(f"Exception Details: {e}") | |
| st.subheader('OpenSearch Assistant') | |
| user_input = st.text_input("Enter your query:", "") | |
| if st.button("Submit"): | |
| if user_input: | |
| with st.spinner("Processing..."): | |
| try: | |
| response = agent.chat(user_input) | |
| st.success("Query Processed Successfully!") | |
| st.subheader("Agent Response:") | |
| sources = response.sources | |
| for source in sources: | |
| st.write('Used Tool: ',source.tool_name) | |
| if source.tool_name =='query_generator': | |
| st.write(source.raw_output.content) | |
| os_response = implement_query(source.raw_output.content) | |
| st.subheader('OS Response') | |
| st.write(os_response) | |
| else: | |
| st.write(source.raw_output[0][0][1]) | |
| except Exception as e: | |
| st.error(f"Error: {e}") | |
| else: | |
| st.warning("Please enter a query to process.") | |