Spaces:
Sleeping
Sleeping
File size: 7,719 Bytes
dbce286 b04e992 dbce286 b04e992 dbce286 b04e992 dbce286 b04e992 dbce286 b04e992 dbce286 |
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 |
from fastapi import FastAPI, HTTPException
import os
from typing import List, Dict
from dotenv import load_dotenv
import logging
from pathlib import Path
from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Qdrant as QdrantVectorStore
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_groq import ChatGroq
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
from qdrant_client.models import PointIdsList
from langgraph.graph import MessagesState, StateGraph
from langchain_core.messages import SystemMessage, HumanMessage
from langgraph.prebuilt import ToolNode
from langgraph.graph import END
from langgraph.prebuilt import tools_condition
from langgraph.checkpoint.memory import MemorySaver
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
load_dotenv()
GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')
GROQ_API_KEY = os.getenv('GROQ_API_KEY')
if not GOOGLE_API_KEY or not GROQ_API_KEY:
raise ValueError("API keys not set in environment variables")
app = FastAPI()
class QASystem:
def __init__(self):
self.vector_store = None
self.graph = None
self.memory = None
self.embeddings = None
self.client = None
self.pdf_dir = "pdfs"
def load_pdf_documents(self):
documents = []
pdf_dir = Path(self.pdf_dir)
if not pdf_dir.exists():
raise FileNotFoundError(f"PDF directory not found: {self.pdf_dir}")
for pdf_path in pdf_dir.glob("*.pdf"):
try:
loader = PyPDFLoader(str(pdf_path))
documents.extend(loader.load())
logger.info(f"Loaded PDF: {pdf_path}")
except Exception as e:
logger.error(f"Error loading PDF {pdf_path}: {str(e)}")
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=100
)
split_docs = text_splitter.split_documents(documents)
logger.info(f"Split documents into {len(split_docs)} chunks")
return split_docs
def initialize_system(self):
try:
self.client = QdrantClient(":memory:")
try:
self.client.get_collection("pdf_data")
except Exception:
self.client.create_collection(
collection_name="pdf_data",
vectors_config=VectorParams(size=768, distance=Distance.COSINE),
)
logger.info("Created new collection: pdf_data")
self.embeddings = GoogleGenerativeAIEmbeddings(
model="models/embedding-001",
google_api_key=GOOGLE_API_KEY
)
self.vector_store = QdrantVectorStore(
client=self.client,
collection_name="pdf_data",
embeddings=self.embeddings,
)
documents = self.load_pdf_documents()
if documents:
try:
points = self.client.scroll(collection_name="pdf_data", limit=100)[0]
if points:
self.client.delete(
collection_name="pdf_data",
points_selector=PointIdsList(
points=[p.id for p in points]
)
)
except Exception as e:
logger.error(f"Error clearing vectors: {str(e)}")
self.vector_store.add_documents(documents)
logger.info(f"Added {len(documents)} documents to vector store")
llm = ChatGroq(
model="llama3-8b-8192",
api_key=GROQ_API_KEY,
temperature=0.7
)
graph_builder = StateGraph(MessagesState)
def query_or_respond(state: MessagesState):
retrieved_docs = [m for m in state["messages"] if m.type == "tool"]
if retrieved_docs:
context = ' '.join(m.content for m in retrieved_docs)
else:
context = "Legal knowledge system. Use Indian judiciary references."
system_prompt = (
"You are a senior legal assistant with expertise in Indian law. "
"Always provide legally accurate responses with references to Indian judiciary principles. "
"If the user query is not legal-specific, still respond from a legal perspective."
f"\n\nContext:\n{context}"
)
messages = [SystemMessage(content=system_prompt)] + state["messages"]
logger.info(f"Sending to LLM: {[m.content for m in messages]}") # Debugging log
response = llm.invoke(messages)
return {"messages": [response]}
def generate(state: MessagesState):
retrieved_docs = [m for m in reversed(state["messages"]) if m.type == "tool"][::-1]
context = ' '.join(m.content for m in retrieved_docs) if retrieved_docs else "Legal knowledge system."
system_prompt = (
"You are a senior legal assistant specializing in Indian judiciary matters. "
"Your responses MUST be legally accurate, concise (5 sentences max), and reference Indian laws when applicable."
f"\n\nContext:\n{context}"
)
messages = [SystemMessage(content=system_prompt)] + state["messages"]
logger.info(f"Sending to LLM: {[m.content for m in messages]}") # Debugging log
response = llm.invoke(messages)
return {"messages": [response]}
graph_builder.add_node("query_or_respond", query_or_respond)
graph_builder.add_node("generate", generate)
graph_builder.set_entry_point("query_or_respond")
graph_builder.add_edge("query_or_respond", "generate")
graph_builder.add_edge("generate", END)
self.memory = MemorySaver()
self.graph = graph_builder.compile(checkpointer=self.memory)
return True
except Exception as e:
logger.error(f"System initialization error: {str(e)}")
return False
def process_query(self, query: str) -> List[Dict[str, str]]:
try:
responses = []
for step in self.graph.stream(
{"messages": [HumanMessage(content=query)]},
stream_mode="values",
config={"configurable": {"thread_id": "abc123"}}
):
if step["messages"]:
responses.append({
'content': step["messages"][-1].content,
'type': step["messages"][-1].type
})
return responses
except Exception as e:
logger.error(f"Query processing error: {str(e)}")
return [{'content': f"Query processing error: {str(e)}", 'type': 'error'}]
qa_system = QASystem()
if qa_system.initialize_system():
logger.info("QA System Initialized Successfully")
else:
raise RuntimeError("Failed to initialize QA System")
@app.post("/query")
async def query_api(query: str):
responses = qa_system.process_query(query)
return {"responses": responses}
|