Spaces:
Building
Building
File size: 9,304 Bytes
8c64d11 464851e 6f120f4 8c64d11 464851e 5c73b25 464851e 8c64d11 464851e 5ddd201 5c73b25 464851e 5ddd201 5c73b25 5ddd201 464851e 5ddd201 5c73b25 464851e 5ddd201 8c64d11 464851e 8c64d11 5c73b25 8c64d11 464851e 5ddd201 5c73b25 5ddd201 8c64d11 5ddd201 8c64d11 5ddd201 5c73b25 8c64d11 5ddd201 8c64d11 5c73b25 5ddd201 8c64d11 5ddd201 5c73b25 5ddd201 8c64d11 5ddd201 5c73b25 8c64d11 6f120f4 8c64d11 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 |
import chainlit as cl
from langchain_anthropic import ChatAnthropic
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
from langchain.prompts import ChatPromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter
from pathlib import Path
import os
from huggingface_hub import HfApi, hf_hub_download
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Optional
import asyncio
from contextlib import asynccontextmanager
# Global variable to store the vectorstore instance
_vectorstore = None
# Configurable model selection
MODELS = {
"gpt-3.5-turbo": ChatOpenAI(model_name="gpt-3.5-turbo"),
"gpt-4": ChatOpenAI(model_name="gpt-4"),
"claude-3-sonnet": ChatAnthropic(model="claude-3-sonnet")
}
# Custom prompt for vegan ethics
VEGAN_ETHICS_TEMPLATE = """
You are an ethical philosopher specializing in animal rights and veganism.
Use the following pieces of context to answer the user's question.
Always guide your response toward applying the "Name the Trait" argument:
1. Ask what trait in non-human animals, if present in humans, would justify treating humans the same way
2. Examine if this trait is morally relevant or arbitrary
3. Challenge the user to maintain logical consistency in their ethical framework
4. Use scientific evidence from the context to support your points
Context: {context}
Question: {question}
Answer:
"""
def get_vectorstore(persist_dir: str = "vector_store"):
"""Create or return cached vectorstore instance"""
global _vectorstore
if _vectorstore is not None:
return _vectorstore
# Initialize vector store with persistence
persist_dir = Path(persist_dir)
client = QdrantClient(
path=str(persist_dir),
force_disable_check_same_thread=True # Important for concurrent access
)
# Check if collection exists
collections = client.get_collections().collections
collection_names = [c.name for c in collections]
if "vegan_ethics" not in collection_names:
print(f"Creating new vector store in {persist_dir}")
client.create_collection(
collection_name="vegan_ethics",
vectors_config=VectorParams(
size=1536,
distance=Distance.COSINE,
),
)
_vectorstore = QdrantVectorStore(
client=client,
embedding=OpenAIEmbeddings(),
collection_name="vegan_ethics"
)
return _vectorstore
async def process_and_load_documents(vectorstore, repo_id="Frikster42/name-that-trait", data_folder="data"):
# Create a single TaskList for the entire process
tasks = cl.TaskList()
tasks.status = "Initializing..."
await tasks.send()
msg = cl.Message(content="Loading documents from Hugging Face repository... please be patient...")
await msg.send()
data_dir = Path("data")
data_dir.mkdir(exist_ok=True)
# Get list of files in the repository
api = HfApi()
dataset_files = api.list_repo_files(repo_id, repo_type="dataset")
dataset_pdf_files = [f for f in dataset_files if f.endswith('.pdf')]
# Download phase
tasks.status = "Downloading files..."
await tasks.send()
for i, pdf_file in enumerate(dataset_pdf_files):
task = cl.Task(title=f"Downloading {pdf_file}")
await tasks.add_task(task)
hf_hub_download(
repo_id=repo_id,
filename=pdf_file,
local_dir=str(data_dir),
local_dir_use_symlinks=False,
repo_type="dataset"
)
task.status = cl.TaskStatus.DONE
await tasks.send()
# Loading phase
documents = []
pdf_files = [f for f in os.listdir(data_folder) if f.endswith('.pdf')]
tasks.status = "Loading files..."
await tasks.send()
for i, filename in enumerate(pdf_files):
task = cl.Task(title=f"Loading {filename}")
await tasks.add_task(task)
filepath = os.path.join(data_folder, filename)
if filename.endswith('.pdf'):
from langchain.document_loaders import PyPDFLoader
loader = PyPDFLoader(filepath)
else:
from langchain.document_loaders import TextLoader
loader = TextLoader(filepath)
documents.extend(loader.load())
task.status = cl.TaskStatus.DONE
await tasks.send()
# Split and process documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = text_splitter.split_documents(documents)
if chunks:
tasks.status = "Processing chunks..."
await tasks.send()
batch_size = 100
num_batches = (len(chunks) + batch_size - 1) // batch_size
for i in range(0, len(chunks), batch_size):
task = cl.Task(title=f"Processing batch {(i//batch_size)+1}/{num_batches}")
await tasks.add_task(task)
batch = chunks[i:i + batch_size]
vectorstore.add_documents(batch)
task.status = cl.TaskStatus.DONE
await tasks.send()
tasks.status = "Completed"
await tasks.send()
msg = cl.Message(content="✅ Documents loaded successfully!")
await msg.send()
return vectorstore
# Create FastAPI app
app = FastAPI(title="Vegan Ethics RAG API")
class QueryRequest(BaseModel):
question: str
model_name: Optional[str] = "gpt-3.5-turbo"
@app.post("/api/query")
async def query_endpoint(request: QueryRequest):
try:
# Get or create vectorstore instance
vectorstore = get_vectorstore()
# Create prompt template
prompt = ChatPromptTemplate.from_messages([
("system", VEGAN_ETHICS_TEMPLATE),
("user", "{question}")
])
# Validate model selection
if request.model_name not in MODELS:
raise HTTPException(status_code=400, detail=f"Invalid model name. Choose from: {list(MODELS.keys())}")
# Get relevant documents
docs = vectorstore.similarity_search(request.question, k=3)
context = "\n".join(doc.page_content for doc in docs)
# Generate response
chain = prompt | MODELS[request.model_name]
response = await chain.ainvoke({
"context": context,
"question": request.question
})
return {
"response": response.content,
"model_used": request.model_name
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@cl.on_chat_start
async def start():
# Get or create vectorstore instance
vectorstore = get_vectorstore()
# Load documents if needed
collection_info = vectorstore.client.get_collection("vegan_ethics")
if collection_info.points_count == 0:
await cl.Message(content="Vector store is empty, loading documents...").send()
vectorstore = await process_and_load_documents(vectorstore)
# Create prompt template
prompt = ChatPromptTemplate.from_messages([
("system", VEGAN_ETHICS_TEMPLATE),
("user", "{question}")
])
# Store components in session
cl.user_session.set("vectorstore", vectorstore)
cl.user_session.set("prompt", prompt)
cl.user_session.set("model_name", "gpt-3.5-turbo")
# UI for model selection
actions = [
cl.Action(
name="model_select",
label="Current Model: gpt-3.5-turbo",
description="Change the AI model",
payload={"current_model": "gpt-3.5-turbo"}
)
]
await cl.Message(
content="Welcome to the Vegan Ethics Assistant. Ask any question about veganism, ethics, or animal consumption.",
actions=actions
).send()
@cl.action_callback("model_select")
async def on_action(action):
models_list = list(MODELS.keys())
current_index = models_list.index(action.payload["current_model"])
next_index = (current_index + 1) % len(models_list)
next_model_name = models_list[next_index]
cl.user_session.set("model_name", next_model_name)
actions = [
cl.Action(
name="model_select",
label=f"Current Model: {next_model_name}",
description="Change the AI model",
payload={"current_model": next_model_name}
)
]
await cl.Message(content=f"Model switched to {next_model_name}", actions=actions).send()
@cl.on_message
async def main(message):
vectorstore = cl.user_session.get("vectorstore")
prompt = cl.user_session.get("prompt")
model_name = cl.user_session.get("model_name")
# Get relevant documents
docs = vectorstore.similarity_search(message.content, k=3)
context = "\n".join(doc.page_content for doc in docs)
# Generate response
chain = prompt | MODELS[model_name]
response = await chain.ainvoke({
"context": context,
"question": message.content
})
await cl.Message(content=response.content).send() |