copied all files

.gitignore

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+.env
+__pycache__
+.cache
+.chainlit

Dockerfile

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+FROM python:3.9
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+COPY ./requirements.txt ~/app/requirements.txt
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

README.md

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 ---
-title: NBA Assist
-emoji: 🦀
-colorFrom: pink
-colorTo: gray
+title: Barbenheimer RAQA Application Chainlit Demo
+emoji: 🔥
+colorFrom: red
+colorTo: red
 sdk: docker
 pinned: false
-license: bigscience-openrail-m
+license: apache-2.0
+duplicated_from: ai-maker-space/Barbie-RAQA-Application-Chainlit-Demo
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import openai
+import chainlit as cl
+import pandas as pd
+import chromadb
+
+from chainlit import user_session
+from sqlalchemy import create_engine
+from typing import List, Tuple, Any
+from pydantic import BaseModel, Field
+from llama_index import Document
+from llama_index import SQLDatabase
+from llama_index.agent import OpenAIAgent
+from llama_index.tools.query_engine import QueryEngineTool
+from llama_index.indices.struct_store.sql_query import NLSQLTableQueryEngine
+from llama_index import ServiceContext
+from llama_index.llms import OpenAI
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index import VectorStoreIndex
+from llama_index.vector_stores import ChromaVectorStore
+from llama_index.storage.storage_context import StorageContext
+from llama_index.tools import FunctionTool
+from llama_index.retrievers import VectorIndexRetriever
+from llama_index.query_engine import RetrieverQueryEngine
+from llama_index.vector_stores.types import (
+    VectorStoreInfo,
+    MetadataInfo,
+    ExactMatchFilter,
+    MetadataFilters,
+)
+
+openai.api_key = os.environ["OPENAI_API_KEY"]
+
+# preparation
+def get_df_from_workbook(sheet_name,
+                         workbook_id = '1MB1ZsQul4AB262AsaY4fHtGW4HWp2-56zB-E5xTbs2A'):
+    url = f'https://docs.google.com/spreadsheets/d/{workbook_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}'
+    return pd.read_csv(url)
+
+docEmailSample = Document(
+    text="Hey KD, let's grab dinner after our next game, Steph", 
+    metadata={'from_to': 'Stephen Curry to Kevin Durant',}
+)
+docEmailSample2 = Document(
+    text="Yo Joker, you were a monster last year, can't wait to play against you in the opener! Draymond", 
+    metadata={'from_to': 'Draymond Green to Nikola Jokic',}
+)
+docAdditionalSamples = [docEmailSample, docEmailSample2]
+
+class AutoRetrieveModel(BaseModel):
+    query: str = Field(..., description="natural language query string")
+    filter_key_list: List[str] = Field(
+        ..., description="List of metadata filter field names"
+    )
+    filter_value_list: List[str] = Field(
+        ...,
+        description=(
+            "List of metadata filter field values (corresponding to names specified in filter_key_list)"
+        )
+    )
+    
+def auto_retrieve_fn(
+    query: str, filter_key_list: List[str], filter_value_list: List[str]
+):
+    """Auto retrieval function.
+
+    Performs auto-retrieval from a vector database, and then applies a set of filters.
+
+    """
+    query = query or "Query"
+    
+    # for i, (k, v) in enumerate(zip(filter_key_list, filter_value_list)):
+    #     if k == 'token_list':
+    #         if token not in v:
+    #             v = ''
+
+    exact_match_filters = [
+        ExactMatchFilter(key=k, value=v)
+        for k, v in zip(filter_key_list, filter_value_list)
+    ]
+    retriever = VectorIndexRetriever(
+        vector_index, filters=MetadataFilters(filters=exact_match_filters), top_k=top_k
+    )
+    # query_engine = vector_index.as_query_engine(filters=MetadataFilters(filters=exact_match_filters))
+    query_engine = RetrieverQueryEngine.from_args(retriever)
+
+    response = query_engine.query(query)
+    return str(response)
+
+# loading CSV data
+sheet_names = ['Teams', 'Players', 'Schedule', 'Player_Stats']
+dict_of_dfs = {sheet: get_df_from_workbook(sheet) for sheet in sheet_names}
+
+engine = create_engine("sqlite+pysqlite:///:memory:")
+
+for df in dict_of_dfs:
+    dict_of_dfs[df].to_sql(df, con=engine)
+
+sql_database = SQLDatabase(
+    engine,
+    include_tables=list(dict_of_dfs.keys())
+    )
+
+# setting up llm & service content
+embed_model = OpenAIEmbedding()
+chunk_size = 1000
+llm = OpenAI(
+    temperature=0, 
+    model="gpt-3.5-turbo",
+    streaming=True
+)
+service_context = ServiceContext.from_defaults(
+    llm=llm, 
+    chunk_size=chunk_size,
+    embed_model=embed_model
+)
+
+# setting up vector store
+chroma_client = chromadb.Client()
+chroma_collection = chroma_client.create_collection("all_data")
+vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+vector_index = VectorStoreIndex([], storage_context=storage_context, service_context=service_context)
+
+vector_index.insert_nodes(docAdditionalSamples)
+
+# setting up metadata
+top_k = 3
+info_emails_players = VectorStoreInfo(
+    content_info="emails exchanged between NBA players",
+    metadata_info=[
+        MetadataInfo(
+            name="from_to",
+            type="str",
+            description="""
+email sent by a player of the Golden State Warriors to any other NBA player, one of [
+Stephen Curry to any NBA player, 
+Klay Thompson to any NBA player, 
+Chris Paul to any NBA player, 
+Andrew Wiggins to any NBA player, 
+Draymond Green to any NBA player, 
+Gary Payton II to any NBA player, 
+Kevon Looney to any NBA player, 
+Jonathan Kuminga to any NBA player, 
+Moses Moody to any NBA player, 
+Brandin Podziemski to any NBA player, 
+Cory Joseph to any NBA player, 
+Dario Šarić to any NBA player]"""
+        ), 
+    ]
+)
+
+@cl.on_chat_start
+def main():
+   
+    sql_query_engine = NLSQLTableQueryEngine(
+        sql_database=sql_database,
+        tables=list(dict_of_dfs.keys())
+    )
+    
+    sql_nba_tool = QueryEngineTool.from_defaults(
+        query_engine=sql_query_engine, # 
+        name='sql_nba_tool', 
+        description=("""Useful for translating a natural language query into a SQL query over tables containing:
+                        1. teams, containing information related to all NBA teams
+                        2. players, containing information about the team that each player plays for
+                        3. schedule, containing information related to the entire NBA game schedule
+                        4. player_stats, containing information related to all NBA player stats
+                        """
+        ),
+    )
+    
+    description_emails = f"""\
+    Use this tool to look up information about emails exchanged betweed players of the Golden State Warriors and any other NBA player.
+    The vector database schema is given below:
+    {info_emails_players.json()}
+    """
+    auto_retrieve_tool_emails = FunctionTool.from_defaults(
+        fn=auto_retrieve_fn, 
+        name='auto_retrieve_tool_emails',
+        description=description_emails, 
+        fn_schema=AutoRetrieveModel
+    )
+    
+    agent = OpenAIAgent.from_tools(
+    # agent = ReActAgent.from_tools(
+        tools = [sql_nba_tool, 
+                 auto_retrieve_tool_emails,
+                ], 
+        llm=llm, 
+        verbose=True,
+    )
+    
+    cl.user_session.set("agent", agent)
+    
+@cl.on_message
+async def main(message):
+    agent = cl.user_session.get("agent") 
+    
+    # response = agent.chat(message.content)
+    response = agent.chat(message)
+    
+    response_message = cl.Message(content="")
+
+    # for token in response.response:
+    #     await response_message.stream_token(token=token)
+
+    if response.response:
+        response_message.content = response.response
+
+    await response_message.send()

requirements.txt

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+openai==0.27.8 
+llama-index==0.8.6 
+nltk==3.8.1
+chromadb==0.4.6 
+tiktoken==0.4.0 
+sentence-transformers==2.2.2 
+pydantic==1.10.11
+chainlit
+pandas