Merge pull request #95 from ClickHouse/streamlit-app

Streamlit app for hackernews

blog-examples/llama-index/hacknernews_app/.gitignore

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+.streamlit/secrets.toml

blog-examples/llama-index/hacknernews_app/.streamlit/secrets.sample.toml

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+[clickhouse]
+username = "default"
+password = ""
+host = "localhost"
+native_port = 9000
+http_port = 8123
+secure = false
+database = "default"
+hackernews_table = "hackernews"
+stackoverflow_table = "surveys"

blog-examples/llama-index/hacknernews_app/hacker_insights.py

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+"""
+# My first app
+Here's our first attempt at using data to create a table:
+"""
+import logging
+import sys
+
+import streamlit as st
+from clickhouse_connect import common
+from llama_index.core.settings import Settings
+from llama_index.embeddings.fastembed import FastEmbedEmbedding
+from llama_index.llms.openai import OpenAI
+
+from llama_index.core import VectorStoreIndex, PromptTemplate
+from llama_index.core.indices.struct_store import NLSQLTableQueryEngine
+from llama_index.core.indices.vector_store import VectorIndexAutoRetriever
+from llama_index.core.indices.vector_store.retrievers.auto_retriever.prompts import PREFIX, EXAMPLES
+from llama_index.core.prompts import PromptType
+from llama_index.core.query_engine import RetrieverQueryEngine, SQLAutoVectorQueryEngine
+from llama_index.core.tools import QueryEngineTool
+from llama_index.core.vector_stores.types import VectorStoreInfo, MetadataInfo
+from llama_index.vector_stores.clickhouse import ClickHouseVectorStore
+import clickhouse_connect
+import openai
+from sqlalchemy import (
+    create_engine,
+)
+from llama_index.core import SQLDatabase
+
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+host = st.secrets.clickhouse.host
+password = st.secrets.clickhouse.password
+username = st.secrets.clickhouse.username
+secure = st.secrets.clickhouse.secure
+http_port = st.secrets.clickhouse.http_port
+native_port = st.secrets.clickhouse.native_port
+open_ai_model = "gpt-4"
+
+database = st.secrets.clickhouse.database
+hackernews_table = st.secrets.clickhouse.hackernews_table
+stackoverflow_table = st.secrets.clickhouse.stackoverflow_table
+database = st.secrets.clickhouse.database
+
+st.set_page_config(
+    page_title="Get summaries of Hacker News posts enriched with Stackoverflow survey results, powered by LlamaIndex and CLickHouse",
+    page_icon="🦙🚀", layout="centered", initial_sidebar_state="auto", menu_items=None)
+st.title("💬HackBot powered by LlamaIndex 🦙 and ClickHouse 🚀")
+st.info(
+    "Check out the full [blog post](https://blog.streamlit.io/build-a-chatbot-with-custom-data-sources-powered-by-llamaindex/) for this app",
+    icon="📃")
+st.caption("A streamlit chatbot for Hacker News powered by 💬🦙 and ClickHouse 🚀")
+
+
+@st.cache_resource
+def load_embedding():
+    return FastEmbedEmbedding(
+        model_name="sentence-transformers/all-MiniLM-L6-v2",
+        max_length=384,
+    )
+
+
+Settings.embed_model = load_embedding()
+
+CLICKHOUSE_TEXT_TO_SQL_TMPL = (
+    "Given an input question, first create a syntactically correct ClickHouse SQL "
+    "query to run, then look at the results of the query and return the answer. "
+    "You can order the results by a relevant column to return the most "
+    "interesting examples in the database.\n\n"
+    "Never query for all the columns from a specific table, only ask for a "
+    "few relevant columns given the question.\n\n"
+    "Pay attention to use only the column names that you can see in the schema "
+    "description. "
+    "Be careful to not query for columns that do not exist. "
+    "Pay attention to which column is in which table. "
+    "Also, qualify column names with the table name when needed. \n"
+    "If needing to group on Array Columns use the ClickHouse function arrayJoin e.g. arrayJoin(columnName) \n"
+    "For example, the following query identifies the most popular database:\n"
+    "SELECT d, count(*) AS count FROM so_surveys GROUP BY "
+    "arrayJoin(database_want_to_work_with) AS d ORDER BY count DESC LIMIT 1\n"
+    "You are required to use the following format, each taking one line:\n\n"
+    "Question: Question here\n"
+    "SQLQuery: SQL Query to run\n"
+    "SQLResult: Result of the SQLQuery\n"
+    "Answer: Final answer here\n\n"
+    "Only use tables listed below.\n"
+    "{schema}\n\n"
+    "Question: {query_str}\n"
+    "SQLQuery: "
+)
+
+CLICKHOUSE_TEXT_TO_SQL_PROMPT = PromptTemplate(
+    CLICKHOUSE_TEXT_TO_SQL_TMPL,
+    prompt_type=PromptType.TEXT_TO_SQL,
+)
+CLICKHOUSE_CUSTOM_SUFFIX = """
+
+The following is the datasource schema to work with. 
+IMPORTANT: Make sure that filters are only used as needed and only suggest filters for fields in the data source.
+
+Data Source:
+```json
+{info_str}
+```
+
+User Query:
+{query_str}
+
+Structured Request:
+"""
+
+CLICKHOUSE_VECTOR_STORE_QUERY_PROMPT_TMPL = PREFIX + EXAMPLES + CLICKHOUSE_CUSTOM_SUFFIX
+
+
+@st.cache_resource
+def clickhouse():
+    common.set_setting('autogenerate_session_id', False)
+    return clickhouse_connect.get_client(
+        host=host, port=http_port, username=username, password=password,
+        secure=secure, settings={"max_parallel_replicas": "3", "use_hedged_requests": "0",
+                                 "allow_experimental_parallel_reading_from_replicas": "1"}
+    )
+
+
+def sql_auto_vector_query_engine():
+    with st.spinner(text="Preparing indexes. This should take a few seconds. No time to make 🫖"):
+        engine = create_engine(
+            f'clickhouse+native://{username}:{password}@{host}:' +
+            f'{native_port}/{database}?compression=lz4&secure={secure}'
+        )
+        sql_database = SQLDatabase(engine, include_tables=[stackoverflow_table], view_support=True)
+        vector_store = ClickHouseVectorStore(clickhouse_client=clickhouse(), table=hackernews_table)
+        vector_index = VectorStoreIndex.from_vector_store(vector_store)
+        return sql_database, vector_index
+
+
+def get_engine(min_length, score, min_date):
+    sql_database, vector_index = sql_auto_vector_query_engine()
+
+    nl_sql_engine = NLSQLTableQueryEngine(
+        sql_database=sql_database,
+        tables=[stackoverflow_table],
+        text_to_sql_prompt=CLICKHOUSE_TEXT_TO_SQL_PROMPT,
+        llm=OpenAI(model=open_ai_model)
+    )
+    vector_store_info = VectorStoreInfo(
+        content_info="Social news posts and comments from users",
+        metadata_info=[
+            MetadataInfo(
+                name="post_score", type="int", description="Score of the comment or post",
+            ),
+            MetadataInfo(
+                name="by", type="str", description="the author or person who posted the comment",
+            ),
+            MetadataInfo(
+                name="time", type="date", description="the time at which the post or comment was made",
+            ),
+        ]
+    )
+
+    vector_auto_retriever = VectorIndexAutoRetriever(
+        vector_index, vector_store_info=vector_store_info, similarity_top_k=10,
+        prompt_template_str=CLICKHOUSE_VECTOR_STORE_QUERY_PROMPT_TMPL, llm=OpenAI(model=open_ai_model),
+        vector_store_kwargs={"where": f"length >= {min_length} AND post_score >= {score} AND time >= '{min_date}'"}
+    )
+
+    retriever_query_engine = RetrieverQueryEngine.from_args(vector_auto_retriever, llm=OpenAI(model=open_ai_model))
+
+    sql_tool = QueryEngineTool.from_defaults(
+        query_engine=nl_sql_engine,
+        description=(
+            "Useful for translating a natural language query into a SQL query over"
+            f" a table: {stackoverflow_table}, containing the survey responses on"
+            f" different types of technology users currently use and want to use"
+        ),
+    )
+    vector_tool = QueryEngineTool.from_defaults(
+        query_engine=retriever_query_engine,
+        description=(
+            f"Useful for answering semantic questions abouts users comments and posts"
+        ),
+    )
+
+    return SQLAutoVectorQueryEngine(
+        sql_tool, vector_tool, llm=OpenAI(model=open_ai_model)
+    )
+
+
+if "max_score" not in st.session_state.keys():
+    client = clickhouse()
+    st.session_state.max_score = int(
+        client.query("SELECT max(post_score) FROM default.hackernews_llama").first_row[0])
+    st.session_state.max_length = int(
+        client.query("SELECT max(length) FROM default.hackernews_llama").first_row[0])
+    st.session_state.min_date, st.session_state.max_date = client.query(
+        "SELECT min(toDate(time)), max(toDate(time)) FROM default.hackernews_llama WHERE time != '1970-01-01 00:00:00'").first_row
+
+if "messages" not in st.session_state:
+    st.session_state.messages = [
+        {"role": "assistant", "content": "Ask me a question about opinions on Hacker News and Stackoverflow!"}]
+
+with st.sidebar:
+    score = st.slider('Min Score', 0, st.session_state.max_score, value=0)
+    min_length = st.slider('Min comment Length (tokens)', 0, st.session_state.max_length, value=20)
+    min_date = st.date_input('Min comment date', value=st.session_state.min_date, min_value=st.session_state.min_date,
+                             max_value=st.session_state.max_date)
+    openai_api_key = st.text_input("Open API Key", key="chatbot_api_key", type="password")
+    openai.api_key = openai_api_key
+    "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
+    "[View the source code](https://github.com/clickhouse/examples/blob/main/Chatbot.py)"
+    "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/ClickHouse/examples?quickstart=1)"
+
+if not openai_api_key:
+    st.info("Please add your OpenAI API key to continue.")
+    st.stop()
+
+if prompt := st.chat_input(placeholder="Your question about Hacker News"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+for message in st.session_state.messages:  # Display the prior chat messages
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+# If last message is not from assistant, generate a new response
+if st.session_state.messages[-1]["role"] != "assistant":
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..."):
+            response = str(get_engine(min_length, score, min_date).query(prompt))
+            st.write(response)
+            st.session_state.messages.append({"role": "assistant", "content": response})