Initial commit

app/api_routes.py

@@ -0,0 +1,23 @@
+from app.bootstrap import chat_fn
+from app.schemas import ChatRequest
+from fastapi import APIRouter
+from fastapi.responses import StreamingResponse
+
+router = APIRouter()
+
+
+@router.post("/chat")
+async def chat_endpoint(req: ChatRequest):
+    def response_stream():
+        generator = chat_fn(
+            question=req.question,
+            history=req.history,
+            media_type=req.media_type,
+            genres=req.genres,
+            providers=req.providers,
+            year_range=tuple(req.year_range),
+        )
+        for chunk in generator:
+            yield chunk
+
+    return StreamingResponse(response_stream(), media_type="text/plain")

app/bootstrap.py

@@ -0,0 +1,85 @@
+import os
+import time
+from pathlib import Path
+
+import joblib
+import nltk
+from app.chatbot import build_chat_fn
+from app.config import (
+    BM25_PATH,
+    INTENT_MODEL,
+    NLTK_PATH,
+    QDRANT_API_KEY,
+    QDRANT_ENDPOINT,
+    QDRANT_MOVIE_COLLECTION_NAME,
+    QDRANT_TV_COLLECTION_NAME,
+)
+from app.llm_services import load_sentence_model
+from app.retriever import get_media_retriever
+from app.vectorstore import connect_qdrant
+from rank_bm25 import BM25Okapi
+from transformers import pipeline
+
+start = time.time()
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+def load_bm25_files() -> tuple[dict[str, BM25Okapi], dict[str, int]]:
+    bm25_dir = Path(BM25_PATH)
+    try:
+        bm25_models = {
+            "movie": joblib.load(bm25_dir / "movie_bm25_model.joblib"),
+            "tv": joblib.load(bm25_dir / "tv_bm25_model.joblib"),
+        }
+        bm25_vocabs = {
+            "movie": joblib.load(bm25_dir / "movie_bm25_vocab.joblib"),
+            "tv": joblib.load(bm25_dir / "tv_bm25_vocab.joblib"),
+        }
+    except FileNotFoundError as e:
+        raise FileNotFoundError(f"Missing BM25 files: {e}")
+    return bm25_models, bm25_vocabs
+
+
+def setup_retriever():
+    embed_model = load_sentence_model()
+    qdrant_client = connect_qdrant(endpoint=QDRANT_ENDPOINT, api_key=QDRANT_API_KEY)
+    nltk.data.path.append(str(NLTK_PATH))
+    print("✅ NLTK resources loaded")
+
+    bm25_models, bm25_vocabs = load_bm25_files()
+    print("✅ BM25 files loaded")
+
+    return get_media_retriever(
+        embed_model=embed_model,
+        qdrant_client=qdrant_client,
+        bm25_models=bm25_models,
+        bm25_vocabs=bm25_vocabs,
+        movie_collection_name=QDRANT_MOVIE_COLLECTION_NAME,
+        tv_collection_name=QDRANT_TV_COLLECTION_NAME,
+    )
+
+
+def setup_intent_classifier():
+    print(f"🔧 Loading intent classifier from {INTENT_MODEL}")
+    classifier = pipeline("text-classification", model=INTENT_MODEL)
+
+    print("🔥 Warming up intent classifier...")
+    warmup_queries = [
+        "Can you recommend a feel-good movie?",
+        "Who directed The Godfather?",
+        "Do you like action films?",
+    ]
+    for q in warmup_queries:
+        _ = classifier(q)
+
+    print("🤖 Classifier ready")
+    return classifier
+
+
+# Initialize once at startup
+retriever = setup_retriever()
+intent_classifier = setup_intent_classifier()
+chat_fn = build_chat_fn(retriever, intent_classifier)
+
+print(f"🔧 Total startup time: {time.time() - start:.2f}s")
+

app/chatbot.py

@@ -0,0 +1,78 @@
+import re
+import time
+from concurrent.futures import ThreadPoolExecutor
+
+from app.llm_services import call_chat_model_openai
+
+
+def sanitize_markdown(md_text: str) -> str:
+    return re.sub(r'!\[.*?\]\(.*?\)', '', md_text)
+
+
+def build_chat_fn(retriever, intent_classifier):
+    def chat(
+        question,
+        history,
+        media_type="movies",
+        genres=None,
+        providers=None,
+        year_range=None,
+    ):
+        full_t0 = time.time()
+        
+        with ThreadPoolExecutor() as executor:
+            # Classify user intent to determine if it is a recommendation ask
+            t0 = time.time()
+            intent_future = executor.submit(
+                lambda q: intent_classifier(q)[0]["label"] == "recommendation", question
+            )
+            print(f"\n🧠 executor.submit(classify_intent) took {time.time() - t0:.3f}s")
+
+            # Embed user query as dense vector asynchronously
+            t0 = time.time()
+            query_vector_future = executor.submit(retriever.embed_dense, question)
+            print(f"🧵 executor.submit(embed_text) took {time.time() - t0:.3f}s")
+
+            # Wait for results
+            t0 = time.time()
+            is_rec_intent = intent_future.result()
+            print(f"✅ classify_intent() result received in {time.time() - t0:.3f}s")
+
+            t0 = time.time()
+            dense_vector = query_vector_future.result()
+            print(f"📈 embed_text() result received in {time.time() - t0:.3f}s")
+
+        # Embed user query as sparse vector for hybrid retrieval
+        t0 = time.time()
+        sparse_vector = retriever.embed_sparse(question, media_type)
+        print(f"📈 embed_sparse() result received in {time.time() - t0:.3f}s")
+        
+        if is_rec_intent:
+            # If Yes, proceed with the RAG pipeline for retrieval and recommendation
+            t0 = time.time()            
+            retrieved_movies = retriever.retrieve_and_rerank(
+                dense_vector,
+                sparse_vector,
+                media_type.lower(),
+                genres,
+                providers,
+                year_range,
+            )
+            print(f"\n📚 retrieve_and_rerank() took {time.time() - t0:.3f}s")
+            
+            context = retriever.format_context(retrieved_movies)
+            user_message = f"{question}\n\nContext:\nBased on the following retrieved {media_type.lower()}, suggest the best recommendations.\n\n{context}"
+            
+            print(f"✨ Total chat() prep time before streaming: {time.time() - full_t0:.3f}s")
+            for chunk in call_chat_model_openai(history, user_message):
+                yield chunk
+
+        else:
+            # If No, proceed with a general conversation
+            user_message = question
+            
+            print(f"✨ Total chat() prep time before streaming: {time.time() - full_t0:.3f}s")
+            for chunk in call_chat_model_openai(history, user_message):
+                yield sanitize_markdown(chunk)
+
+    return chat

app/config.py

@@ -0,0 +1,26 @@
+import os
+from pathlib import Path
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
+
+QDRANT_ENDPOINT = os.getenv("QDRANT_ENDPOINT")
+QDRANT_MOVIE_COLLECTION_NAME = os.getenv("QDRANT_MOVIE_COLLECTION_NAME_BGE")
+QDRANT_TV_COLLECTION_NAME = os.getenv("QDRANT_TV_COLLECTION_NAME_BGE")
+
+NLTK_PATH = Path(__file__).resolve().parent.parent / "data" / "nltk_data"
+BM25_PATH = Path(__file__).resolve().parent.parent / "data" / "bm25_files"
+
+INTENT_MODEL = "JJTsao/intent-classifier-distilbert-moviebot"   # Fine-tuned intent classification model for query intent classifiation  
+EMBEDDING_MODEL = "JJTsao/fine-tuned_movie_retriever-bge-base-en-v1.5"  # Fine-tuned sentence transfomer model for query dense vector embedding 
+OPENAI_MODEL = "gpt-4o-mini"  # LLM for chat completions
+
+
+if not OPENAI_API_KEY or not QDRANT_API_KEY:
+    raise ValueError("Missing API key(s).")
+if not QDRANT_ENDPOINT or not QDRANT_MOVIE_COLLECTION_NAME or not QDRANT_TV_COLLECTION_NAME:
+    raise ValueError("Missing QDrant URL or collection name.")

app/llm_services.py

@@ -0,0 +1,77 @@
+import time
+
+import torch
+from openai import OpenAI
+from sentence_transformers import SentenceTransformer
+from app.config import EMBEDDING_MODEL, OPENAI_MODEL, OPENAI_API_KEY
+
+# === LLM Config ===
+_sentence_model = None  # Not loaded at import time
+
+# === Clients ===
+openai_client = OpenAI(api_key=OPENAI_API_KEY)
+
+# === System Prompt ===
+SYSTEM_PROMPT = """
+You are a professional film curator and critic. Your role is to analyze the user's preferences and recommend high-quality films or TV shows using the provided context. Do not seek film or tv show options outside of the list provided to you.
+Focus on: 
+
+- Artistic merit and storytelling 
+- Genres, themes, and tone
+- Popularity, IMDB ratings, and Rotten Tomatoes ratings
+
+Provide a brief explanation of why the user might enjoy each movie or tv series. Include IMDB rating, Rotten Tomatoe ratings, and a poster. Answer with authority and care. Respond in markdown.
+"""
+
+
+def load_sentence_model():
+    global _sentence_model
+    if _sentence_model is None:
+        print("⏳ Loading embedding model...")
+        _sentence_model = SentenceTransformer(
+            EMBEDDING_MODEL, device="cuda" if torch.cuda.is_available() else "cpu"
+        )
+
+        print(f"🔥 Model '{EMBEDDING_MODEL}' loaded. Performing GPU warmup...")
+
+        # Realistic multi-sentence warmup to trigger full CUDA graph
+        warmup_sentences = [
+            "A suspenseful thriller with deep character development and moral ambiguity.",
+            "Coming-of-age story with emotional storytelling and strong ensemble performances.",
+            "Mind-bending sci-fi with philosophical undertones and high concept ideas.",
+            "Recommend me some comedies.",
+        ]
+        _ = _sentence_model.encode(warmup_sentences, show_progress_bar=False)
+        time.sleep(0.5)
+        _ = _sentence_model.encode(warmup_sentences, show_progress_bar=False)
+        print("🚀 Embedding model fully warmed up.")
+
+    return _sentence_model
+
+
+def embed_text(text: str) -> list[float]:
+    model = load_sentence_model()
+    return model.encode(text).tolist()
+
+
+def build_chat_history(history: list, max_turns: int = 5) -> list:
+    return [
+        {"role": msg.role, "content": msg.content}
+        for msg in history[-max_turns * 2:]
+    ]
+
+
+
+def call_chat_model_openai(history, user_message: str):
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+    messages += build_chat_history(history or [])
+    messages.append({"role": "user", "content": user_message})
+
+    response = openai_client.chat.completions.create(
+        model=OPENAI_MODEL, messages=messages, temperature=0.7, stream=True
+    )
+
+    for chunk in response:
+        delta = chunk.choices[0].delta.content
+        if delta:
+            yield delta

app/media_retriever.py

@@ -0,0 +1,260 @@
+from collections import Counter
+from typing import Dict, List
+
+from nltk.corpus import stopwords
+from nltk.stem import PorterStemmer
+from nltk.tokenize import word_tokenize
+from qdrant_client import QdrantClient
+from qdrant_client.models import FieldCondition, Filter, MatchValue, Range, models
+from sentence_transformers import SentenceTransformer
+
+
+class MediaRetriever:
+    def __init__(
+        self,
+        embed_model: SentenceTransformer,
+        qdrant_client: QdrantClient,
+        bm25_models: Dict,
+        bm25_vocabs: Dict,
+        movie_collection_name: str,
+        tv_collection_name: str,
+        dense_weight: float = 0.4,  # Weight of semantic match score for reranking
+        sparse_weight: float = 0.1,  # Weight of BM25 match score for reranking
+        rating_weight: float = 0.3,  # Weight of rating score for reranking
+        popularity_weight: float = 0.2,  # Weight of popularity score for reranking
+        semantic_retrieval_limit: int = 300,  # Number of movies to retrieve for reranking
+        bm25_retrieval_limit: int = 20,
+        top_k: int = 20,  # Number of post-reranking movies to send to LLM
+    ):
+        self.client = qdrant_client
+        self.movie_collection_name = movie_collection_name
+        self.tv_collection_name = tv_collection_name
+        self.embed_model = embed_model
+        self.bm25_models = bm25_models
+        self.bm25_vocabs = bm25_vocabs
+        self.dense_weight = dense_weight
+        self.sparse_weight = sparse_weight
+        self.rating_weight = rating_weight
+        self.popularity_weight = popularity_weight
+        self.semantic_retrieval_limit = semantic_retrieval_limit
+        self.bm25_retrieval_limit = bm25_retrieval_limit
+        self.top_k = top_k
+
+    def embed_dense(self, query: str) -> List[float]:
+        return self.embed_model.encode(query).tolist()
+
+    @staticmethod
+    def tokenize_and_preprocess(text: str) -> List[str]:
+        stop_words = set(stopwords.words("english"))
+        stemmer = PorterStemmer()
+
+        tokens = word_tokenize(text.lower())
+        filtered_tokens = [w for w in tokens if w not in stop_words and w.isalnum()]
+        processed_tokens = [stemmer.stem(w) for w in filtered_tokens]
+
+        return processed_tokens
+
+    def embed_sparse(self, query: str, media_type: str) -> Dict:
+        bm25_model = (
+            self.bm25_models["movie"]
+            if media_type.lower() == "movies"
+            else self.bm25_models["tv"]
+        )
+        bm25_vocab = (
+            self.bm25_vocabs["movie"]
+            if media_type.lower() == "movies"
+            else self.bm25_vocabs["tv"]
+        )
+
+        tokens = self.tokenize_and_preprocess(query)
+
+        term_counts = Counter(tokens)
+        indices, values = [], []
+
+        avg_doc_length = bm25_model.avgdl
+        k1, b = bm25_model.k1, bm25_model.b
+
+        for term, tf in term_counts.items():
+            if term in bm25_vocab:
+                idx = bm25_vocab[term]
+                idf = bm25_model.idf.get(term, 0)
+                numerator = idf * tf * (k1 + 1)
+                denominator = tf + k1 * (1 - b + b * len(tokens) / avg_doc_length)
+                if denominator != 0:
+                    weight = numerator / denominator
+                    indices.append(idx)
+                    values.append(float(weight))
+        sparse_vector = {"indices": indices, "values": values}
+        return sparse_vector
+
+    def retrieve_and_rerank(
+        self,
+        dense_vector: List[float],
+        sparse_vector: Dict,
+        media_type: str = "movies",
+        genres=None,
+        providers=None,
+        year_range=None,
+    ) -> List[dict]:
+        # Construct Qdrant filter based on user input
+        qdrant_filter = self._build_filter(genres, providers, year_range)
+
+        # Query Qdrant for semantic search with dense vector
+        dense_results = self._query_dense(
+            vector=dense_vector,
+            media_type=media_type,
+            qdrant_filter=qdrant_filter,
+        )
+
+        # Query Qdrant for BM25 search with sparse vector
+        sparse_results = self._query_sparse(
+            vector=sparse_vector,
+            media_type=media_type,
+            qdrant_filter=qdrant_filter,
+        )
+
+        if not dense_results:
+            return []
+
+        # Fuse dense and sparse results and rerank
+        fused = self.fuse_dense_sparse(dense_results, sparse_results)
+        reranked = self.rerank_fused_results(fused)
+
+        reranked_ids = [p.id for p in reranked[:20]]
+        print ("\nReranked Top-30:")
+        for i, id_ in enumerate(reranked_ids):
+            f = fused[id_]
+            p = f["point"]
+            print(
+                f"#{i + 1} {p.payload.get('title', '')} | Score: {p.score} Dense: {f['dense_score']:.3f}, Sparse: {f['sparse_score']:.3f}, Pop: {p.payload.get('popularity', 0)}, Rating: {p.payload.get('vote_average', 0)}"
+            )
+
+        return reranked[: self.top_k]
+
+    def _build_filter(
+        self, genres=None, providers=None, year_range=None
+    ) -> Filter | None:
+        must_clauses = []
+
+        if genres:
+            genre_conditions = [
+                FieldCondition(key="genres", match=MatchValue(value=genre))
+                for genre in genres
+            ]
+            must_clauses.append({"should": genre_conditions})
+
+        if providers:
+            provider_conditions = [
+                FieldCondition(key="watch_providers", match=MatchValue(value=provider))
+                for provider in providers
+            ]
+            must_clauses.append({"should": provider_conditions})
+
+        if year_range:
+            must_clauses.append(
+                FieldCondition(
+                    key="release_year",
+                    range=Range(gte=year_range[0], lte=year_range[1]),
+                )
+            )
+
+        return Filter(must=must_clauses) if must_clauses else None
+
+    def _query_dense(self, vector, media_type, qdrant_filter):
+        collection = (
+            self.movie_collection_name
+            if media_type == "movies"
+            else self.tv_collection_name
+        )
+        return self.client.query_points(
+            collection_name=collection,
+            query=vector,
+            using="dense_vector",
+            query_filter=qdrant_filter,
+            limit=self.semantic_retrieval_limit,
+            with_payload=["llm_context", "title", "popularity", "vote_average"],
+            with_vectors=False,
+        )
+
+    def _query_sparse(self, vector, media_type, qdrant_filter):
+        collection = (
+            self.movie_collection_name
+            if media_type == "movies"
+            else self.tv_collection_name
+        )
+        return self.client.query_points(
+            collection_name=collection,
+            query=models.SparseVector(**vector),
+            using="sparse_vector",
+            query_filter=qdrant_filter,
+            limit=self.bm25_retrieval_limit,
+            with_payload=["llm_context", "title", "popularity", "vote_average"],
+            with_vectors=False,
+        )
+
+    def fuse_dense_sparse(
+        self,
+        dense_results: List,
+        sparse_results: List,
+    ) -> Dict[str, Dict]:
+        fused = {}
+
+        # Add dense results
+        for point in dense_results.points:
+            fused[point.id] = {
+                "point": point,
+                "dense_score": point.score or 0.0,
+                "sparse_score": 0.0,
+            }
+
+        max_sparse_score = max((pt.score for pt in sparse_results.points), default=1e-6)
+
+        # Add sparse scores
+        for point in sparse_results.points:
+            if point.id in fused:
+                fused[point.id]["sparse_score"] = (
+                    min(point.score / max_sparse_score, 0.8) or 0.0
+                )
+            else:
+                fused[point.id] = {
+                    "point": point,
+                    "dense_score": 0.0,
+                    "sparse_score": min(point.score / max_sparse_score, 0.8) or 0.0,
+                }
+
+        return fused
+
+    def rerank_fused_results(
+        self,
+        fused: Dict[str, Dict],
+    ) -> List:
+        max_popularity = max(
+            (float(f["point"].payload.get("popularity", 0)) for f in fused.values()),
+            default=1.0,
+        )
+
+        def compute_score(f):
+            point = f["point"]
+            dense_score = f["dense_score"]
+            sparse_score = f["sparse_score"]
+            popularity = float(point.payload.get("popularity", 0)) / max_popularity
+            vote_average = float(point.payload.get("vote_average", 0)) / 10.0
+
+            return (
+                self.dense_weight * dense_score
+                + self.sparse_weight * sparse_score
+                + self.rating_weight * vote_average
+                + self.popularity_weight * popularity
+            )
+
+        reranked = sorted(fused.values(), key=compute_score, reverse=True)
+
+        return [f["point"] for f in reranked]
+
+    def format_context(self, movies: list[dict]) -> str:
+        # Formart the retrieved documents as context for LLM
+        return "\n\n".join(
+            [f"  {movie.payload.get('llm_context', '')}" for movie in movies]
+        )
+
+

app/retriever.py

@@ -0,0 +1,18 @@
+from app.media_retriever import MediaRetriever
+
+def get_media_retriever(
+    embed_model,
+    qdrant_client,
+    bm25_models,
+    bm25_vocabs,
+    movie_collection_name,
+    tv_collection_name,
+):
+    return MediaRetriever(
+        embed_model=embed_model,
+        qdrant_client=qdrant_client,
+        bm25_models=bm25_models,
+        bm25_vocabs=bm25_vocabs,
+        movie_collection_name=movie_collection_name,
+        tv_collection_name=tv_collection_name,
+    )

app/schemas.py

@@ -0,0 +1,35 @@
+from enum import Enum
+from typing import List
+
+from pydantic import BaseModel, field_validator, model_validator
+
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+
+class MediaType(str, Enum):
+    MOVIE = "movies"
+    TV = "tvs"
+
+
+class ChatRequest(BaseModel):
+    question: str
+    history: List[ChatMessage] = []
+    media_type: MediaType = MediaType.MOVIE
+    genres: List[str] = []
+    providers: List[str] = []
+    year_range: List[int] = [1920, 2025]
+
+    @field_validator("question")
+    def validate_question(cls, v):
+        if not v.strip():
+            raise ValueError("Question cannot be empty")
+        return v
+
+    @model_validator(mode="after")
+    def validate_year_range(self) -> "ChatRequest":
+        if len(self.year_range) != 2:
+            raise ValueError("year_range must be a list of exactly two integers: [start, end]")
+        return self

app/vectorstore.py

@@ -0,0 +1,14 @@
+from qdrant_client import QdrantClient
+
+
+def connect_qdrant(endpoint: str, api_key: str) -> QdrantClient:
+    try:
+        client = QdrantClient(
+            url=endpoint,
+            api_key=api_key
+        )
+        print ("✅ Connected to Qdrant.")
+        return client
+    except Exception as e:
+        print(f"❌ Error connecting to Qdrant: {e}")
+        raise

data/bm25_files/movie_bm25_model.joblib

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data/bm25_files/movie_bm25_vocab.joblib

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data/bm25_files/tv_bm25_model.joblib

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data/bm25_files/tv_bm25_vocab.joblib

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data/nltk_data/corpora/stopwords.zip

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data/nltk_data/corpora/stopwords/README

@@ -0,0 +1,32 @@
+Stopwords Corpus
+
+This corpus contains lists of stop words for several languages.  These
+are high-frequency grammatical words which are usually ignored in text
+retrieval applications.
+
+They were obtained from:
+http://anoncvs.postgresql.org/cvsweb.cgi/pgsql/src/backend/snowball/stopwords/
+
+The stop words for the Romanian language were obtained from:
+http://arlc.ro/resources/
+
+The English list has been augmented
+https://github.com/nltk/nltk_data/issues/22
+
+The German list has been corrected
+https://github.com/nltk/nltk_data/pull/49
+
+A Kazakh list has been added
+https://github.com/nltk/nltk_data/pull/52
+
+A Nepali list has been added
+https://github.com/nltk/nltk_data/pull/83
+
+An Azerbaijani list has been added
+https://github.com/nltk/nltk_data/pull/100
+
+A Greek list has been added
+https://github.com/nltk/nltk_data/pull/103
+
+An Indonesian list has been added
+https://github.com/nltk/nltk_data/pull/112

data/nltk_data/corpora/stopwords/albanian

@@ -0,0 +1,237 @@
+tyre
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data/nltk_data/corpora/stopwords/arabic

@@ -0,0 +1,754 @@
+إذ
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+إذما
+إذن
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data/nltk_data/corpora/stopwords/azerbaijani

@@ -0,0 +1,165 @@
+a
+ad
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+az
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data/nltk_data/corpora/stopwords/basque

@@ -0,0 +1,326 @@
+ahala
+aitzitik
+al
+ala 
+alabadere
+alabaina
+alabaina
+aldiz 
+alta
+amaitu
+amaitzeko
+anitz
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+arabera
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+arabera
+argi
+arratsaldero
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+asko
+aspaldiko
+aurrera
+aurrera
+azkenez
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+azkenik
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+bada 
+bada 
+bada 
+badarik
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+baino
+baita
+baizik 
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+barren
+bat
+batean
+batean
+batean
+batean
+batek
+baten
+batera
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data/nltk_data/corpora/stopwords/belarusian

@@ -0,0 +1,224 @@
+на
+не
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+як
+для
+гэта
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+побач

data/nltk_data/corpora/stopwords/bengali

@@ -0,0 +1,398 @@
+অতএব
+অথচ
+অথবা
+অনুযায়ী
+অনেক
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data/nltk_data/corpora/stopwords/catalan

@@ -0,0 +1,278 @@
+a
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+vosaltres
+vostra
+vostre
+vostres
+érem
+éreu
+és
+éssent
+últim
+ús

data/nltk_data/corpora/stopwords/chinese

@@ -0,0 +1,841 @@
+一
+一下
+一些
+一切
+一则
+一天
+一定
+一方面
+一旦
+一时
+一来
+一样
+一次
+一片
+一直
+一致
+一般
+一起
+一边
+一面
+万一
+上下
+上升
+上去
+上来
+上述
+上面
+下列
+下去
+下来
+下面
+不一
+不久
+不仅
+不会
+不但
+不光
+不单
+不变
+不只
+不可
+不同
+不够
+不如
+不得
+不怕
+不惟
+不成
+不拘
+不敢
+不断
+不是
+不比
+不然
+不特
+不独
+不管
+不能
+不要
+不论
+不足
+不过
+不问
+与
+与其
+与否
+与此同时
+专门
+且
+两者
+严格
+严重
+个
+个人
+个别
+中小
+中间
+丰富
+临
+为
+为主
+为了
+为什么
+为什麽
+为何
+为着
+主张
+主要
+举行
+乃
+乃至
+么
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+之一
+之前
+之后
+之後
+之所以
+之类
+乌乎
+乎
+乘
+也
+也好
+也是
+也罢
+了
+了解
+争取
+于
+于是
+于是乎
+云云
+互相
+产生
+人们
+人家
+什么
+什么样
+什麽
+今后
+今天
+今年
+今後
+仍然
+从
+从事
+从而
+他
+他人
+他们
+他的
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+以致
+们
+任
+任何
+任凭
+任务
+企图
+伟大
+似乎
+似的
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+何
+何况
+何处
+何时
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+你
+你们
+你的
+使得
+使用
+例如
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+依照
+依靠
+促进
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+反映
+反过来
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+总是
+总的来看
+总的来说
+总的说来
+总结
+总而言之
+恰恰相反
+您
+意思
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+我
+我们
+我的
+或
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+战斗
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+所以
+所有
+所谓
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+真正
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+自
+自个儿
+自从
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+至于
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+行为
+行动
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+要么
+要是
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+规定
+觉得
+认为
+认真
+认识
+让
+许多
+论
+设使
+设若
+该
+说明
+诸位
+谁
+谁知
+赶
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+起来
+起见
+趁
+趁着
+越是
+跟
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+较之
+边
+达到
+迅速
+过
+过去
+过来
+运用
+还是
+还有
+这
+这个
+这么
+这么些
+这么样
+这么点儿
+这些
+这会儿
+这儿
+这就是说
+这时
+这样
+这点
+这种
+这边
+这里
+这麽
+进入
+进步
+进而
+进行
+连
+连同
+适应
+适当
+适用
+逐步
+逐渐
+通常
+通过
+造成
+遇到
+遭到
+避免
+那
+那个
+那么
+那么些
+那么样
+那些
+那会儿
+那儿
+那时
+那样
+那边
+那里
+那麽
+部分
+鄙人
+采取
+里面
+重大
+重新
+重要
+鉴于
+问题
+防止
+阿
+附近
+限制
+除
+除了
+除此之外
+除非
+随
+随着
+随著
+集中
+需要
+非但
+非常
+非徒
+靠
+顺
+顺着
+首先
+高兴
+是不是

data/nltk_data/corpora/stopwords/danish

@@ -0,0 +1,94 @@
+og
+i
+jeg
+det
+at
+en
+den
+til
+er
+som
+på
+de
+med
+han
+af
+for
+ikke
+der
+var
+mig
+sig
+men
+et
+har
+om
+vi
+min
+havde
+ham
+hun
+nu
+over
+da
+fra
+du
+ud
+sin
+dem
+os
+op
+man
+hans
+hvor
+eller
+hvad
+skal
+selv
+her
+alle
+vil
+blev
+kunne
+ind
+når
+være
+dog
+noget
+ville
+jo
+deres
+efter
+ned
+skulle
+denne
+end
+dette
+mit
+også
+under
+have
+dig
+anden
+hende
+mine
+alt
+meget
+sit
+sine
+vor
+mod
+disse
+hvis
+din
+nogle
+hos
+blive
+mange
+ad
+bliver
+hendes
+været
+thi
+jer
+sådan

data/nltk_data/corpora/stopwords/dutch

@@ -0,0 +1,101 @@
+de
+en
+van
+ik
+te
+dat
+die
+in
+een
+hij
+het
+niet
+zijn
+is
+was
+op
+aan
+met
+als
+voor
+had
+er
+maar
+om
+hem
+dan
+zou
+of
+wat
+mijn
+men
+dit
+zo
+door
+over
+ze
+zich
+bij
+ook
+tot
+je
+mij
+uit
+der
+daar
+haar
+naar
+heb
+hoe
+heeft
+hebben
+deze
+u
+want
+nog
+zal
+me
+zij
+nu
+ge
+geen
+omdat
+iets
+worden
+toch
+al
+waren
+veel
+meer
+doen
+toen
+moet
+ben
+zonder
+kan
+hun
+dus
+alles
+onder
+ja
+eens
+hier
+wie
+werd
+altijd
+doch
+wordt
+wezen
+kunnen
+ons
+zelf
+tegen
+na
+reeds
+wil
+kon
+niets
+uw
+iemand
+geweest
+andere

data/nltk_data/corpora/stopwords/english

@@ -0,0 +1,198 @@
+a
+about
+above
+after
+again
+against
+ain
+all
+am
+an
+and
+any
+are
+aren
+aren't
+as
+at
+be
+because
+been
+before
+being
+below
+between
+both
+but
+by
+can
+couldn
+couldn't
+d
+did
+didn
+didn't
+do
+does
+doesn
+doesn't
+doing
+don
+don't
+down
+during
+each
+few
+for
+from
+further
+had
+hadn
+hadn't
+has
+hasn
+hasn't
+have
+haven
+haven't
+having
+he
+he'd
+he'll
+her
+here
+hers
+herself
+he's
+him
+himself
+his
+how
+i
+i'd
+if
+i'll
+i'm
+in
+into
+is
+isn
+isn't
+it
+it'd
+it'll
+it's
+its
+itself
+i've
+just
+ll
+m
+ma
+me
+mightn
+mightn't
+more
+most
+mustn
+mustn't
+my
+myself
+needn
+needn't
+no
+nor
+not
+now
+o
+of
+off
+on
+once
+only
+or
+other
+our
+ours
+ourselves
+out
+over
+own
+re
+s
+same
+shan
+shan't
+she
+she'd
+she'll
+she's
+should
+shouldn
+shouldn't
+should've
+so
+some
+such
+t
+than
+that
+that'll
+the
+their
+theirs
+them
+themselves
+then
+there
+these
+they
+they'd
+they'll
+they're
+they've
+this
+those
+through
+to
+too
+under
+until
+up
+ve
+very
+was
+wasn
+wasn't
+we
+we'd
+we'll
+we're
+were
+weren
+weren't
+we've
+what
+when
+where
+which
+while
+who
+whom
+why
+will
+with
+won
+won't
+wouldn
+wouldn't
+y
+you
+you'd
+you'll
+your
+you're
+yours
+yourself
+yourselves
+you've

data/nltk_data/corpora/stopwords/finnish

@@ -0,0 +1,235 @@
+olla
+olen
+olet
+on
+olemme
+olette
+ovat
+ole
+oli
+olisi
+olisit
+olisin
+olisimme
+olisitte
+olisivat
+olit
+olin
+olimme
+olitte
+olivat
+ollut
+olleet
+en
+et
+ei
+emme
+ette
+eivät
+minä
+minun
+minut
+minua
+minussa
+minusta
+minuun
+minulla
+minulta
+minulle
+sinä
+sinun
+sinut
+sinua
+sinussa
+sinusta
+sinuun
+sinulla
+sinulta
+sinulle
+hän
+hänen
+hänet
+häntä
+hänessä
+hänestä
+häneen
+hänellä
+häneltä
+hänelle
+me
+meidän
+meidät
+meitä
+meissä
+meistä
+meihin
+meillä
+meiltä
+meille
+te
+teidän
+teidät
+teitä
+teissä
+teistä
+teihin
+teillä
+teiltä
+teille
+he
+heidän
+heidät
+heitä
+heissä
+heistä
+heihin
+heillä
+heiltä
+heille
+tämä
+tämän
+tätä
+tässä
+tästä
+tähän
+tallä
+tältä
+tälle
+tänä
+täksi
+tuo
+tuon
+tuotä
+tuossa
+tuosta
+tuohon
+tuolla
+tuolta
+tuolle
+tuona
+tuoksi
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+sen
+sitä
+siinä
+siitä
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+sillä
+siltä
+sille
+sinä
+siksi
+nämä
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+näitä
+näissä
+näistä
+näihin
+näillä
+näiltä
+näille
+näinä
+näiksi
+nuo
+noiden
+noita
+noissa
+noista
+noihin
+noilla
+noilta
+noille
+noina
+noiksi
+ne
+niiden
+niitä
+niissä
+niistä
+niihin
+niillä
+niiltä
+niille
+niinä
+niiksi
+kuka
+kenen
+kenet
+ketä
+kenessä
+kenestä
+keneen
+kenellä
+keneltä
+kenelle
+kenenä
+keneksi
+ketkä
+keiden
+ketkä
+keitä
+keissä
+keistä
+keihin
+keillä
+keiltä
+keille
+keinä
+keiksi
+mikä
+minkä
+minkä
+mitä
+missä
+mistä
+mihin
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+miltä
+mille
+minä
+miksi
+mitkä
+joka
+jonka
+jota
+jossa
+josta
+johon
+jolla
+jolta
+jolle
+jona
+joksi
+jotka
+joiden
+joita
+joissa
+joista
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+joilla
+joilta
+joille
+joina
+joiksi
+että
+ja
+jos
+koska
+kuin
+mutta
+niin
+sekä
+sillä
+tai
+vaan
+vai
+vaikka
+kanssa
+mukaan
+noin
+poikki
+yli
+kun
+niin
+nyt
+itse

data/nltk_data/corpora/stopwords/french

@@ -0,0 +1,157 @@
+au
+aux
+avec
+ce
+ces
+dans
+de
+des
+du
+elle
+en
+et
+eux
+il
+ils
+je
+la
+le
+les
+leur
+lui
+ma
+mais
+me
+même
+mes
+moi
+mon
+ne
+nos
+notre
+nous
+on
+ou
+par
+pas
+pour
+qu
+que
+qui
+sa
+se
+ses
+son
+sur
+ta
+te
+tes
+toi
+ton
+tu
+un
+une
+vos
+votre
+vous
+c
+d
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data/nltk_data/corpora/stopwords/german

@@ -0,0 +1,232 @@
+aber
+alle
+allem
+allen
+aller
+alles
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+also
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data/nltk_data/corpora/stopwords/greek

@@ -0,0 +1,265 @@
+αλλα
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+ᾧ

data/nltk_data/corpora/stopwords/hebrew

@@ -0,0 +1,221 @@
+אני
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data/nltk_data/corpora/stopwords/hinglish

@@ -0,0 +1,1036 @@
+a
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data/nltk_data/corpora/stopwords/hungarian

@@ -0,0 +1,199 @@
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data/nltk_data/corpora/stopwords/indonesian

@@ -0,0 +1,758 @@
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data/nltk_data/corpora/stopwords/italian

@@ -0,0 +1,279 @@
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