diff --git a/.github/workflows/code-scans.yaml b/.github/workflows/code-scans.yaml new file mode 100644 index 0000000..18e5cd9 --- /dev/null +++ b/.github/workflows/code-scans.yaml @@ -0,0 +1,98 @@ +name: SDLE Scans + +on: + workflow_dispatch: + inputs: + PR_number: + description: 'Pull request number' + required: true + push: + branches: [ main ] + pull_request: + types: [opened, synchronize, reopened, ready_for_review] + +concurrency: + group: sdle-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + +# ----------------------------- +# 1) Trivy Scan (fixed) +# ----------------------------- + trivy_scan: + name: Trivy Vulnerability Scan + runs-on: ubuntu-latest + env: + TRIVY_REPORT_FORMAT: table + TRIVY_SCAN_TYPE: fs + TRIVY_SCAN_PATH: . + TRIVY_EXIT_CODE: '1' + TRIVY_VULN_TYPE: os,library + TRIVY_SEVERITY: CRITICAL,HIGH + steps: + - uses: actions/checkout@v4 + + - name: Create report directory + run: mkdir -p trivy-reports + + - name: Run Trivy FS Scan + uses: aquasecurity/trivy-action@0.24.0 + with: + scan-type: 'fs' + scan-ref: '.' + scanners: 'vuln,misconfig,secret,license' + ignore-unfixed: true + format: 'table' + exit-code: '1' + output: 'trivy-reports/trivy_scan_report.txt' + vuln-type: 'os,library' + severity: 'CRITICAL,HIGH' + + - name: Upload Trivy Report + uses: actions/upload-artifact@v4 + with: + name: trivy-report + path: trivy-reports/trivy_scan_report.txt + - name: Show Trivy Report in Logs + if: failure() + run: | + echo "========= TRIVY FINDINGS =========" + cat trivy-reports/trivy_scan_report.txt + echo "=================================" + +# ----------------------------- +# 2) Bandit Scan +# ----------------------------- + bandit_scan: + name: Bandit security scan + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + submodules: 'recursive' + fetch-depth: 0 + - uses: actions/setup-python@v5 + with: + python-version: "3.x" + - name: Install Bandit + run: pip install bandit + - name: Create Bandit configuration + run: | + cat > .bandit << 'EOF' + [bandit] + exclude_dirs = tests,test,venv,.venv,node_modules + skips = B101 + EOF + shell: bash + - name: Run Bandit scan + run: | + bandit -r . -ll -iii -f screen + bandit -r . -ll -iii -f html -o bandit-report.html + - name: Upload Bandit Report + uses: actions/upload-artifact@v4 + with: + name: bandit-report + path: bandit-report.html + retention-days: 30 \ No newline at end of file diff --git a/backend/requirements.txt b/backend/requirements.txt index 2b4c699..b93628d 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -1,27 +1,27 @@ -# Core Web Framework -fastapi==0.109.0 -uvicorn[standard]==0.27.0 -python-multipart==0.0.6 - -# LLM & AI APIs -httpx==0.27.2 -requests==2.31.0 -openai>=1.0.0 - -# Document Processing -pypdf==6.1.1 -python-docx==1.1.0 -pdf2image==1.16.3 -pytesseract==0.3.10 -Pillow==10.2.0 - -# Configuration Management -python-dotenv==1.0.0 -pydantic==2.5.3 - -# RAG / Vector Search -numpy==1.26.4 -faiss-cpu==1.8.0 - -# Utilities -aiofiles==23.2.1 +# Core Web Framework +fastapi==0.109.0 +uvicorn[standard]==0.27.0 +python-multipart==0.0.22 + +# LLM & AI APIs +httpx==0.27.2 +requests==2.31.0 +openai>=1.0.0 + +# Document Processing +pypdf==6.1.1 +python-docx==1.1.0 +pdf2image==1.16.3 +pytesseract==0.3.10 +Pillow==12.1.1 + +# Configuration Management +python-dotenv==1.0.0 +pydantic==2.5.3 + +# RAG / Vector Search +numpy==1.26.4 +faiss-cpu==1.8.0 + +# Utilities +aiofiles==23.2.1 diff --git a/backend/services/llm_service.py b/backend/services/llm_service.py index 2f1f161..f904a0c 100644 --- a/backend/services/llm_service.py +++ b/backend/services/llm_service.py @@ -1,902 +1,902 @@ -""" -LLM Service for Document Summarization (FinSights) -Uses OpenAI Chat Completions API (gpt-4o-mini) - -This version supports: -- Dynamic section chips (2 to 5) generated from the document at initial step. -- Section-wise summaries for ANY selected section title (no pre-defined section list). -- Readable section summaries WITHOUT showing quotes. - Internally, we extract "facts" with short "anchors" that must exist in the document, - validate anchors, then generate the final section from facts only. - -Compatibility: -- initial_summary_first_chunk(doc_id) returns ONLY a summary string (same as before). -- summarize_financial(mode="financial_section") accepts any section title. -- doc_id flow stays fast (no file re-upload needed on chip clicks). - -Anti-hallucination strategy: -- We do NOT require exact quotes in the final output. -- We do require each extracted fact to include at least one anchor that exists in the document text. - Anchors are not shown to the user; they are only used for validation. -""" - -from typing import Iterator, Dict, Any, Optional, Union, List -import logging -import re -import os -import time -import uuid -import hashlib -import json - -import config -from openai import OpenAI - -logger = logging.getLogger(__name__) - - -def clean_text(text: str) -> str: - if not text: - return "" - text = re.sub(r"\*\*(.+?)\*\*", r"\1", text) - text = re.sub(r"__(.+?)__", r"\1", text) - text = re.sub(r"\*(.+?)\*", r"\1", text) - text = re.sub(r"_(.+?)_", r"\1", text) - text = re.sub(r"```.*?```", "", text, flags=re.DOTALL) - text = re.sub(r"`(.+?)`", r"\1", text) - text = re.sub(r"^#+\s+", "", text, flags=re.MULTILINE) - text = re.sub(r"\n{3,}", "\n\n", text).strip() - return text - - -def _normalize_money(text: str) -> str: - if not text: - return "" - text = re.sub(r"\bRs\.\s*", "₹ ", text, flags=re.IGNORECASE) - text = re.sub(r"\bINR\s*", "₹ ", text, flags=re.IGNORECASE) - return text - - -def _dedupe_section_heading(text: str, section: str) -> str: - if not text: - return "" - t = text.replace("\r\n", "\n").strip() - lines = [ln.strip() for ln in t.split("\n") if ln.strip()] - body = [ln for ln in lines if ln.lower() != section.lower()] - out = section + "\n" + "\n".join(body) - return re.sub(r"\n{3,}", "\n\n", out).strip() - - -class LLMService: - def __init__(self): - self.client: Optional[OpenAI] = None - self.model = os.getenv("OPENAI_MODEL", None) or getattr(config, "OPENAI_MODEL", None) or "gpt-4o-mini" - self._initialized = False - - # Large default; override via env if needed. - self.model_context_tokens = int(os.getenv("MODEL_CONTEXT_TOKENS", "128000")) - - # In-memory doc store for doc_id flow - self.doc_store: Dict[str, Dict[str, Any]] = {} - self.cache_ttl_seconds = int(os.getenv("CACHE_TTL_SECONDS", str(60 * 60))) # 1 hour - self.cache_max_docs = int(os.getenv("CACHE_MAX_DOCS", "25")) - - # Dynamic section discovery bounds (frontend chips) - self.dynamic_min_sections = int(os.getenv("DYNAMIC_SECTIONS_MIN", "2")) - self.dynamic_max_sections = int(os.getenv("DYNAMIC_SECTIONS_MAX", "5")) - - # Evidence/facts extraction bounds - self.facts_max_items = int(os.getenv("FACTS_MAX_ITEMS", "10")) - self.anchor_max_items = int(os.getenv("ANCHOR_MAX_ITEMS", "3")) - self.anchor_max_chars_each = int(os.getenv("ANCHOR_MAX_CHARS_EACH", "60")) - - # Validation threshold: how many facts must be anchored to proceed - # Example: 0.6 means at least 60% of extracted facts must have >=1 valid anchor. - self.min_anchored_fact_ratio = float(os.getenv("MIN_ANCHORED_FACT_RATIO", "0.6")) - - def _ensure_initialized(self): - if self._initialized: - return - if not config.OPENAI_API_KEY: - raise ValueError("OPENAI_API_KEY must be set in environment variables") - - self.client = OpenAI( - api_key=config.OPENAI_API_KEY, - timeout=float(os.getenv("OPENAI_TIMEOUT", "60")), - max_retries=int(os.getenv("OPENAI_MAX_RETRIES", "2")), - ) - self._initialized = True - - logger.info("OpenAI client initialized successfully") - logger.info(f"Model: {self.model}") - logger.info(f"MODEL_CONTEXT_TOKENS: {self.model_context_tokens}") - logger.info(f"CACHE_MAX_DOCS: {self.cache_max_docs}") - logger.info(f"CACHE_TTL_SECONDS: {self.cache_ttl_seconds}") - logger.info(f"DYNAMIC_SECTIONS_MIN: {self.dynamic_min_sections}") - logger.info(f"DYNAMIC_SECTIONS_MAX: {self.dynamic_max_sections}") - logger.info(f"FACTS_MAX_ITEMS: {self.facts_max_items}") - logger.info(f"ANCHOR_MAX_ITEMS: {self.anchor_max_items}") - logger.info(f"MIN_ANCHORED_FACT_RATIO: {self.min_anchored_fact_ratio}") - - # ---------------------------- - # Compatibility wrapper - # ---------------------------- - def summarize( - self, - text: str, - max_tokens: int = None, - temperature: float = None, - stream: bool = False, - mode: str = "financial_initial", - section: str = None, - ) -> Union[str, Iterator[str]]: - return self.summarize_financial( - text=text, - mode=mode, - section=section, - max_tokens=max_tokens, - temperature=temperature, - stream=stream, - ) - - # ---------------------------- - # doc_id based flow (compat) - # ---------------------------- - def create_doc(self, text: str) -> str: - self._ensure_initialized() - self._evict_cache_if_needed() - - if not text or not text.strip(): - raise ValueError("Empty text") - - doc_id = str(uuid.uuid4()) - dk = self._doc_key(text) - - self.doc_store[doc_id] = { - "ts": time.time(), - "text": text, - "doc_key": dk, - # list of {"title": str, "hint": str} created during initial summary - "sections": None, - - # ---- RAG indexing state (for chat) ---- - "index_status": "pending", # pending | ready | failed - "chunk_count": 0, - "index_error": "", - "index_started_at": None, - "index_finished_at": None, - } - return doc_id - - def get_doc_text(self, doc_id: str) -> str: - obj = self.doc_store.get(doc_id) - if not obj: - raise ValueError("Invalid doc_id") - obj["ts"] = time.time() - return obj.get("text", "") - - def get_doc_key(self, doc_id: str) -> str: - obj = self.doc_store.get(doc_id) - if not obj: - raise ValueError("Invalid doc_id") - obj["ts"] = time.time() - return obj.get("doc_key", "") - - def get_doc_sections(self, doc_id: str) -> List[str]: - """ - Returns discovered dynamic section titles for this doc_id. - If not discovered yet, returns []. - """ - obj = self.doc_store.get(doc_id) - if not obj: - raise ValueError("Invalid doc_id") - obj["ts"] = time.time() - - secs = obj.get("sections") - if not isinstance(secs, list): - return [] - - titles: List[str] = [] - for s in secs: - if isinstance(s, dict) and s.get("title"): - titles.append(str(s["title"]).strip()) - elif isinstance(s, str): - titles.append(str(s).strip()) - return [t for t in titles if t] - - def get_doc_section_hint(self, doc_id: str, section_title: str) -> str: - """ - Returns a stored hint for a discovered section (optional), else "". - Hints are short descriptions like "Totals, taxes, payment status". - """ - obj = self.doc_store.get(doc_id) - if not obj: - raise ValueError("Invalid doc_id") - obj["ts"] = time.time() - - secs = obj.get("sections") - if not isinstance(secs, list): - return "" - - target = self._normalize_section_title(section_title).lower() - if not target: - return "" - - for s in secs: - if isinstance(s, dict): - t = self._normalize_section_title(str(s.get("title", ""))).lower() - if t == target: - return str(s.get("hint", "") or "").strip() - return "" - - def prefetch_doc(self, doc_id: str) -> None: - # No-op (kept so existing routes won't break) - obj = self.doc_store.get(doc_id) - if obj: - obj["ts"] = time.time() - return - - def summarize_by_doc_id( - self, - doc_id: str, - mode: str = "financial_initial", - section: str = None, - max_tokens: int = None, - temperature: float = None, - stream: bool = False, - ) -> Union[str, Iterator[str]]: - text = self.get_doc_text(doc_id) - return self.summarize_financial( - text=text, - mode=mode, - section=section, - max_tokens=max_tokens, - temperature=temperature, - stream=stream, - doc_id=doc_id, - ) - - def initial_summary_first_chunk(self, doc_id: str, max_tokens: int = 240, temperature: float = 0.25) -> str: - """ - Compatibility method expected by routes/frontend. - Uses full doc text (fitted to context) to produce 4-5 sentences. - - Updated behavior: - - Also runs dynamic section discovery and stores it in doc_store[doc_id]["sections"]. - - Still returns ONLY the summary string (so existing routes don't break). - """ - self._ensure_initialized() - text = self.get_doc_text(doc_id) - fitted = self._fit_text_to_context(text, max_output_tokens=max_tokens) - - # Dynamic sections discovery (best effort; never breaks summary) - try: - sections = self._discover_dynamic_sections( - fitted_text=fitted, - min_sections=self.dynamic_min_sections, - max_sections=self.dynamic_max_sections, - temperature=0.2, - ) - obj = self.doc_store.get(doc_id) - if obj is not None: - obj["sections"] = sections - obj["ts"] = time.time() - except Exception: - logger.exception("Dynamic section discovery failed (continuing with summary only).") - - # Normal initial summary - system_prompt = self._base_system_prompt() + ( - "\nWrite a short generalized summary that tells the user what this document is about.\n" - "Focus on: document type, company/entity, reporting period/date, and purpose ONLY if explicitly stated.\n" - "Rules:\n" - "- Plain text only.\n" - "- 4 to 5 sentences.\n" - "- Prefer exact names, dates, and periods when present.\n" - "- Include key numeric highlights ONLY if explicitly present.\n" - "- Do not invent any details.\n" - ) - - user_prompt = f"""Write the generalized summary for this document. - -Document: -{fitted} -""" - resp = self._call_chat( - system_prompt, - user_prompt, - max_tokens=max_tokens, - temperature=max(0.0, min(float(temperature), 0.35)), - stream=False, - ) - return _normalize_money(clean_text(resp.choices[0].message.content or "")) - - # ---------------------------- - # Prompt building - # ---------------------------- - @staticmethod - def _base_system_prompt() -> str: - return ( - "You are an analyst AI.\n" - "You must use ONLY information supported by the provided document text.\n" - "Do not invent facts, numbers, dates, names, or events.\n" - "If you are uncertain, state it clearly.\n" - "Output must be plain text only (no markdown emphasis: no **, *, _).\n" - ) - def chat_with_context( - self, - question: str, - context: str, - max_tokens: int = 500, - temperature: float = 0.2, - ) -> str: - """ - Answer a user question using ONLY the retrieved context. - If the answer is not supported by the context, say so. - """ - self._ensure_initialized() - - q = (question or "").strip() - ctx = (context or "").strip() - - if not q: - return "Please enter a question." - - system_prompt = ( - self._base_system_prompt() - + "\nYou are answering questions about an uploaded document.\n" - "Use ONLY the provided CONTEXT.\n" - "If the answer is not in the context, say you cannot find it.\n" - "Keep the response concise and readable.\n" - ) - - user_prompt = f"""CONTEXT: -{ctx} - -QUESTION: -{q} - -Answer using only the context. -""" - - resp = self._call_chat( - system_prompt=system_prompt, - user_prompt=user_prompt, - max_tokens=max_tokens, - temperature=max(0.0, min(float(temperature), 0.5)), - stream=False, - ) - return _normalize_money(clean_text(resp.choices[0].message.content or "")) - - - # ---------------------------- - # Core helpers - # ---------------------------- - @staticmethod - def _estimate_tokens(text: str) -> int: - return max(1, int(len(text) / 4)) if text else 0 - +""" +LLM Service for Document Summarization (FinSights) +Uses OpenAI Chat Completions API (gpt-4o-mini) + +This version supports: +- Dynamic section chips (2 to 5) generated from the document at initial step. +- Section-wise summaries for ANY selected section title (no pre-defined section list). +- Readable section summaries WITHOUT showing quotes. + Internally, we extract "facts" with short "anchors" that must exist in the document, + validate anchors, then generate the final section from facts only. + +Compatibility: +- initial_summary_first_chunk(doc_id) returns ONLY a summary string (same as before). +- summarize_financial(mode="financial_section") accepts any section title. +- doc_id flow stays fast (no file re-upload needed on chip clicks). + +Anti-hallucination strategy: +- We do NOT require exact quotes in the final output. +- We do require each extracted fact to include at least one anchor that exists in the document text. + Anchors are not shown to the user; they are only used for validation. +""" + +from typing import Iterator, Dict, Any, Optional, Union, List +import logging +import re +import os +import time +import uuid +import hashlib +import json + +import config +from openai import OpenAI + +logger = logging.getLogger(__name__) + + +def clean_text(text: str) -> str: + if not text: + return "" + text = re.sub(r"\*\*(.+?)\*\*", r"\1", text) + text = re.sub(r"__(.+?)__", r"\1", text) + text = re.sub(r"\*(.+?)\*", r"\1", text) + text = re.sub(r"_(.+?)_", r"\1", text) + text = re.sub(r"```.*?```", "", text, flags=re.DOTALL) + text = re.sub(r"`(.+?)`", r"\1", text) + text = re.sub(r"^#+\s+", "", text, flags=re.MULTILINE) + text = re.sub(r"\n{3,}", "\n\n", text).strip() + return text + + +def _normalize_money(text: str) -> str: + if not text: + return "" + text = re.sub(r"\bRs\.\s*", "₹ ", text, flags=re.IGNORECASE) + text = re.sub(r"\bINR\s*", "₹ ", text, flags=re.IGNORECASE) + return text + + +def _dedupe_section_heading(text: str, section: str) -> str: + if not text: + return "" + t = text.replace("\r\n", "\n").strip() + lines = [ln.strip() for ln in t.split("\n") if ln.strip()] + body = [ln for ln in lines if ln.lower() != section.lower()] + out = section + "\n" + "\n".join(body) + return re.sub(r"\n{3,}", "\n\n", out).strip() + + +class LLMService: + def __init__(self): + self.client: Optional[OpenAI] = None + self.model = os.getenv("OPENAI_MODEL", None) or getattr(config, "OPENAI_MODEL", None) or "gpt-4o-mini" + self._initialized = False + + # Large default; override via env if needed. + self.model_context_tokens = int(os.getenv("MODEL_CONTEXT_TOKENS", "128000")) + + # In-memory doc store for doc_id flow + self.doc_store: Dict[str, Dict[str, Any]] = {} + self.cache_ttl_seconds = int(os.getenv("CACHE_TTL_SECONDS", str(60 * 60))) # 1 hour + self.cache_max_docs = int(os.getenv("CACHE_MAX_DOCS", "25")) + + # Dynamic section discovery bounds (frontend chips) + self.dynamic_min_sections = int(os.getenv("DYNAMIC_SECTIONS_MIN", "2")) + self.dynamic_max_sections = int(os.getenv("DYNAMIC_SECTIONS_MAX", "5")) + + # Evidence/facts extraction bounds + self.facts_max_items = int(os.getenv("FACTS_MAX_ITEMS", "10")) + self.anchor_max_items = int(os.getenv("ANCHOR_MAX_ITEMS", "3")) + self.anchor_max_chars_each = int(os.getenv("ANCHOR_MAX_CHARS_EACH", "60")) + + # Validation threshold: how many facts must be anchored to proceed + # Example: 0.6 means at least 60% of extracted facts must have >=1 valid anchor. + self.min_anchored_fact_ratio = float(os.getenv("MIN_ANCHORED_FACT_RATIO", "0.6")) + + def _ensure_initialized(self): + if self._initialized: + return + if not config.OPENAI_API_KEY: + raise ValueError("OPENAI_API_KEY must be set in environment variables") + + self.client = OpenAI( + api_key=config.OPENAI_API_KEY, + timeout=float(os.getenv("OPENAI_TIMEOUT", "60")), + max_retries=int(os.getenv("OPENAI_MAX_RETRIES", "2")), + ) + self._initialized = True + + logger.info("OpenAI client initialized successfully") + logger.info(f"Model: {self.model}") + logger.info(f"MODEL_CONTEXT_TOKENS: {self.model_context_tokens}") + logger.info(f"CACHE_MAX_DOCS: {self.cache_max_docs}") + logger.info(f"CACHE_TTL_SECONDS: {self.cache_ttl_seconds}") + logger.info(f"DYNAMIC_SECTIONS_MIN: {self.dynamic_min_sections}") + logger.info(f"DYNAMIC_SECTIONS_MAX: {self.dynamic_max_sections}") + logger.info(f"FACTS_MAX_ITEMS: {self.facts_max_items}") + logger.info(f"ANCHOR_MAX_ITEMS: {self.anchor_max_items}") + logger.info(f"MIN_ANCHORED_FACT_RATIO: {self.min_anchored_fact_ratio}") + + # ---------------------------- + # Compatibility wrapper + # ---------------------------- + def summarize( + self, + text: str, + max_tokens: int = None, + temperature: float = None, + stream: bool = False, + mode: str = "financial_initial", + section: str = None, + ) -> Union[str, Iterator[str]]: + return self.summarize_financial( + text=text, + mode=mode, + section=section, + max_tokens=max_tokens, + temperature=temperature, + stream=stream, + ) + + # ---------------------------- + # doc_id based flow (compat) + # ---------------------------- + def create_doc(self, text: str) -> str: + self._ensure_initialized() + self._evict_cache_if_needed() + + if not text or not text.strip(): + raise ValueError("Empty text") + + doc_id = str(uuid.uuid4()) + dk = self._doc_key(text) + + self.doc_store[doc_id] = { + "ts": time.time(), + "text": text, + "doc_key": dk, + # list of {"title": str, "hint": str} created during initial summary + "sections": None, + + # ---- RAG indexing state (for chat) ---- + "index_status": "pending", # pending | ready | failed + "chunk_count": 0, + "index_error": "", + "index_started_at": None, + "index_finished_at": None, + } + return doc_id + + def get_doc_text(self, doc_id: str) -> str: + obj = self.doc_store.get(doc_id) + if not obj: + raise ValueError("Invalid doc_id") + obj["ts"] = time.time() + return obj.get("text", "") + + def get_doc_key(self, doc_id: str) -> str: + obj = self.doc_store.get(doc_id) + if not obj: + raise ValueError("Invalid doc_id") + obj["ts"] = time.time() + return obj.get("doc_key", "") + + def get_doc_sections(self, doc_id: str) -> List[str]: + """ + Returns discovered dynamic section titles for this doc_id. + If not discovered yet, returns []. + """ + obj = self.doc_store.get(doc_id) + if not obj: + raise ValueError("Invalid doc_id") + obj["ts"] = time.time() + + secs = obj.get("sections") + if not isinstance(secs, list): + return [] + + titles: List[str] = [] + for s in secs: + if isinstance(s, dict) and s.get("title"): + titles.append(str(s["title"]).strip()) + elif isinstance(s, str): + titles.append(str(s).strip()) + return [t for t in titles if t] + + def get_doc_section_hint(self, doc_id: str, section_title: str) -> str: + """ + Returns a stored hint for a discovered section (optional), else "". + Hints are short descriptions like "Totals, taxes, payment status". + """ + obj = self.doc_store.get(doc_id) + if not obj: + raise ValueError("Invalid doc_id") + obj["ts"] = time.time() + + secs = obj.get("sections") + if not isinstance(secs, list): + return "" + + target = self._normalize_section_title(section_title).lower() + if not target: + return "" + + for s in secs: + if isinstance(s, dict): + t = self._normalize_section_title(str(s.get("title", ""))).lower() + if t == target: + return str(s.get("hint", "") or "").strip() + return "" + + def prefetch_doc(self, doc_id: str) -> None: + # No-op (kept so existing routes won't break) + obj = self.doc_store.get(doc_id) + if obj: + obj["ts"] = time.time() + return + + def summarize_by_doc_id( + self, + doc_id: str, + mode: str = "financial_initial", + section: str = None, + max_tokens: int = None, + temperature: float = None, + stream: bool = False, + ) -> Union[str, Iterator[str]]: + text = self.get_doc_text(doc_id) + return self.summarize_financial( + text=text, + mode=mode, + section=section, + max_tokens=max_tokens, + temperature=temperature, + stream=stream, + doc_id=doc_id, + ) + + def initial_summary_first_chunk(self, doc_id: str, max_tokens: int = 240, temperature: float = 0.25) -> str: + """ + Compatibility method expected by routes/frontend. + Uses full doc text (fitted to context) to produce 4-5 sentences. + + Updated behavior: + - Also runs dynamic section discovery and stores it in doc_store[doc_id]["sections"]. + - Still returns ONLY the summary string (so existing routes don't break). + """ + self._ensure_initialized() + text = self.get_doc_text(doc_id) + fitted = self._fit_text_to_context(text, max_output_tokens=max_tokens) + + # Dynamic sections discovery (best effort; never breaks summary) + try: + sections = self._discover_dynamic_sections( + fitted_text=fitted, + min_sections=self.dynamic_min_sections, + max_sections=self.dynamic_max_sections, + temperature=0.2, + ) + obj = self.doc_store.get(doc_id) + if obj is not None: + obj["sections"] = sections + obj["ts"] = time.time() + except Exception: + logger.exception("Dynamic section discovery failed (continuing with summary only).") + + # Normal initial summary + system_prompt = self._base_system_prompt() + ( + "\nWrite a short generalized summary that tells the user what this document is about.\n" + "Focus on: document type, company/entity, reporting period/date, and purpose ONLY if explicitly stated.\n" + "Rules:\n" + "- Plain text only.\n" + "- 4 to 5 sentences.\n" + "- Prefer exact names, dates, and periods when present.\n" + "- Include key numeric highlights ONLY if explicitly present.\n" + "- Do not invent any details.\n" + ) + + user_prompt = f"""Write the generalized summary for this document. + +Document: +{fitted} +""" + resp = self._call_chat( + system_prompt, + user_prompt, + max_tokens=max_tokens, + temperature=max(0.0, min(float(temperature), 0.35)), + stream=False, + ) + return _normalize_money(clean_text(resp.choices[0].message.content or "")) + + # ---------------------------- + # Prompt building + # ---------------------------- + @staticmethod + def _base_system_prompt() -> str: + return ( + "You are an analyst AI.\n" + "You must use ONLY information supported by the provided document text.\n" + "Do not invent facts, numbers, dates, names, or events.\n" + "If you are uncertain, state it clearly.\n" + "Output must be plain text only (no markdown emphasis: no **, *, _).\n" + ) + def chat_with_context( + self, + question: str, + context: str, + max_tokens: int = 500, + temperature: float = 0.2, + ) -> str: + """ + Answer a user question using ONLY the retrieved context. + If the answer is not supported by the context, say so. + """ + self._ensure_initialized() + + q = (question or "").strip() + ctx = (context or "").strip() + + if not q: + return "Please enter a question." + + system_prompt = ( + self._base_system_prompt() + + "\nYou are answering questions about an uploaded document.\n" + "Use ONLY the provided CONTEXT.\n" + "If the answer is not in the context, say you cannot find it.\n" + "Keep the response concise and readable.\n" + ) + + user_prompt = f"""CONTEXT: +{ctx} + +QUESTION: +{q} + +Answer using only the context. +""" + + resp = self._call_chat( + system_prompt=system_prompt, + user_prompt=user_prompt, + max_tokens=max_tokens, + temperature=max(0.0, min(float(temperature), 0.5)), + stream=False, + ) + return _normalize_money(clean_text(resp.choices[0].message.content or "")) + + + # ---------------------------- + # Core helpers + # ---------------------------- + @staticmethod + def _estimate_tokens(text: str) -> int: + return max(1, int(len(text) / 4)) if text else 0 + @staticmethod def _doc_key(text: str) -> str: - return hashlib.sha1(text.encode("utf-8", errors="ignore")).hexdigest() - - @staticmethod - def _normalize_section_title(title: str) -> str: - t = (title or "").strip() - t = re.sub(r"\s{2,}", " ", t) - t = t.strip(" -:\t\r\n") - if len(t) > 56: - t = t[:56].rstrip() - return t - - def _evict_cache_if_needed(self): - now = time.time() - - expired_docs = [k for k, v in self.doc_store.items() if now - v.get("ts", now) > self.cache_ttl_seconds] - for k in expired_docs: - self.doc_store.pop(k, None) - - if len(self.doc_store) <= self.cache_max_docs: - return - - items = sorted(self.doc_store.items(), key=lambda kv: kv[1].get("ts", 0)) - while len(items) > self.cache_max_docs: - k, _ = items.pop(0) - self.doc_store.pop(k, None) - - def _fit_text_to_context(self, text: str, max_output_tokens: int) -> str: - """ - Truncate input so input + output stays within model_context_tokens. - Keeps head + tail when truncating. - """ - if not text: - return "" - - overhead_tokens = 900 - available_input_tokens = max(500, self.model_context_tokens - int(max_output_tokens) - overhead_tokens) - - est = self._estimate_tokens(text) - if est <= available_input_tokens: - return text - - ratio = available_input_tokens / float(est) - keep_chars = max(2500, int(len(text) * ratio)) - - head = text[: int(keep_chars * 0.72)] - tail = text[-int(keep_chars * 0.28) :] if keep_chars > 4500 else "" - - truncated = head - if tail and tail not in head: - truncated = head + "\n\n[...TRUNCATED...]\n\n" + tail - - return truncated - - def _call_chat(self, system_prompt: str, user_prompt: str, max_tokens: int, temperature: float, stream: bool = False): - try: - return self.client.chat.completions.create( - model=self.model, - messages=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_prompt}, - ], - max_tokens=max_tokens, - temperature=temperature, - stream=stream, - ) - except Exception as e: - logger.exception("OpenAI request failed") - raise RuntimeError(f"OpenAI connection/request failed: {str(e)}") - - # ---------------------------- - # Dynamic section discovery (2 to 5) - # ---------------------------- - def _discover_dynamic_sections( - self, - fitted_text: str, - min_sections: int = 2, - max_sections: int = 5, - temperature: float = 0.2, - ) -> List[Dict[str, Any]]: - """ - Returns 2 to 5 section items derived from the document content. - This is for UI chips. We avoid quotes here; we keep short hints only. - - Output item shape: - - {"title": "Invoice Totals", "hint": "Totals, taxes, payment status"} - """ - self._ensure_initialized() - - min_sections = max(1, min(int(min_sections), 6)) - max_sections = max(min_sections, min(int(max_sections), 8)) - - system_prompt = ( - self._base_system_prompt() - + "\nYou propose section options (chips) for a summarization UI.\n" - "The user can upload ANY document: invoices, payroll, tax returns, audit reports, loan documents, or general articles.\n" - "Propose only sections that are likely supported by the text.\n" - "\nCRITICAL RULES:\n" - f"- Return between {min_sections} and {max_sections} sections.\n" - "- Use short titles (2 to 5 words). No numbering.\n" - "- Provide a short hint (6 to 12 words) describing what to expect.\n" - "- Do not invent specific numbers.\n" - "- Output MUST be strict JSON only.\n" - ) - - user_prompt = f"""Read the document and propose section chips. - -Return JSON: -{{ - "sections": [ - {{ - "title": "Short Title", - "hint": "Short description" - }} - ] -}} - -Document: -{fitted_text} -""" - - resp = self._call_chat( - system_prompt, - user_prompt, - max_tokens=350, - temperature=max(0.0, min(float(temperature), 0.35)), - stream=False, - ) - raw = (resp.choices[0].message.content or "").strip() - - items: List[Dict[str, Any]] = [] - try: - data = json.loads(raw) - secs = data.get("sections", []) - if isinstance(secs, list): - for s in secs: - if not isinstance(s, dict): - continue - title = self._normalize_section_title(str(s.get("title", "")).strip()) - hint = str(s.get("hint", "") or "").strip() - hint = re.sub(r"\s{2,}", " ", hint) - if not title: - continue - if len(hint) > 90: - hint = hint[:90].rstrip() - items.append({"title": title, "hint": hint}) - except Exception: - logger.exception("Failed to parse dynamic sections JSON; using fallback.") - - # de-dupe titles - out: List[Dict[str, Any]] = [] - seen = set() - for it in items: - key = it["title"].lower() - if key in seen: - continue - seen.add(key) - out.append(it) - - if len(out) >= min_sections: - return out[:max_sections] - - # Fallback for low-signal docs - fallback = [ - {"title": "General Summary", "hint": "What the document is about"}, - {"title": "Key Extracts", "hint": "Important names, dates, numbers"}, - ] - return fallback[:max_sections] - - # ---------------------------- - # Fact extraction with anchors (internal) - # ---------------------------- - def _extract_facts_with_anchors(self, section_title: str, section_hint: str, fitted_text: str) -> List[Dict[str, Any]]: - """ - Extract readable facts (paraphrased) relevant to a requested section. - Each fact must include at least one short anchor that appears in the document text. - Anchors are used ONLY for validation and are not shown in the final user output. - """ - title = self._normalize_section_title(section_title) - hint = (section_hint or "").strip() - - if not title: - return [] - - system_prompt = ( - self._base_system_prompt() - + "\nTask: extract readable key facts for a requested section.\n" - "Facts must be supported by the document.\n" - "\nCRITICAL RULES:\n" - "- Return STRICT JSON only.\n" - "- Facts must be written in plain English (not copied verbatim).\n" - "- Each fact MUST include at least one anchor.\n" - "- Anchors must be SHORT strings that appear verbatim in the document (examples: a number, a date, an entity name, a short label).\n" - "- Do NOT return long quotes.\n" - ) - - user_prompt = f"""Requested section: {title} -Section hint (if any): {hint} - -Return JSON: -{{ - "facts": [ - {{ - "point": "Readable summarized point", - "anchors": ["anchor1", "anchor2"] - }} - ] -}} - -Rules: -- Provide up to {self.facts_max_items} facts. -- Each fact must include 1 to {self.anchor_max_items} anchors. -- Anchors must be <= {self.anchor_max_chars_each} characters. -- Prefer anchors that include numbers, dates, totals, ratios, names, account labels, or table row labels. -- Do not invent any numbers. - -Document: -{fitted_text} -""" - - resp = self._call_chat(system_prompt, user_prompt, max_tokens=650, temperature=0.15, stream=False) - raw = (resp.choices[0].message.content or "").strip() - - facts: List[Dict[str, Any]] = [] - try: - data = json.loads(raw) - items = data.get("facts", []) - if isinstance(items, list): - for it in items: - if not isinstance(it, dict): - continue - point = str(it.get("point", "") or "").strip() - anchors = it.get("anchors", []) - if not point or not isinstance(anchors, list): - continue - - # normalize anchors - norm_anchors: List[str] = [] - for a in anchors[: self.anchor_max_items]: - s = str(a or "").strip() - if not s: - continue - if len(s) > self.anchor_max_chars_each: - s = s[: self.anchor_max_chars_each].rstrip() - norm_anchors.append(s) - - if not norm_anchors: - continue - - facts.append({"point": point, "anchors": norm_anchors}) - except Exception: - logger.exception("Failed to parse facts JSON from extractor") - return [] - - # de-dupe similar points - out: List[Dict[str, Any]] = [] - seen = set() - for f in facts: - key = re.sub(r"\s+", " ", f["point"]).strip().lower() - if key in seen: - continue - seen.add(key) - out.append(f) - return out - - def _validate_anchored_facts(self, facts: List[Dict[str, Any]], fitted_text: str) -> List[Dict[str, Any]]: - """ - Keep only facts that have at least one anchor substring present in fitted_text. - """ - if not facts: - return [] - - valid: List[Dict[str, Any]] = [] - for f in facts: - anchors = f.get("anchors", []) - if not isinstance(anchors, list): - continue - ok = False - for a in anchors: - if not a: - continue - if str(a) in fitted_text: - ok = True - break - if ok: - valid.append(f) - return valid - - # ---------------------------- - # Final section writing (user-facing, no anchors shown) - # ---------------------------- - def _write_section_from_facts(self, section_title: str, facts: List[Dict[str, Any]], max_tokens: int, temperature: float) -> str: - title = self._normalize_section_title(section_title) - - if not facts: - return f"{title}\n- No supported information found in the text for this section.\n" - - # Build fact lines (anchors not shown) - fact_lines: List[str] = [] - for f in facts: - p = str(f.get("point", "") or "").strip() - if p: - fact_lines.append(f"- {p}") - - if not fact_lines: - return f"{title}\n- No supported information found in the text for this section.\n" - - system_prompt = self._base_system_prompt() + ( - "\nWrite the requested section using ONLY the provided facts.\n" - "Do not invent.\n" - "\nOUTPUT RULES:\n" - "- Start with the heading exactly as provided.\n" - "- Keep it easy to read.\n" - "- Use bullets (recommended) or short paragraphs.\n" - "- Keep numbers when present in facts.\n" - "- If facts are weak, explicitly say it is limited.\n" - ) - - user_prompt = f"""Section heading: {title} - -Facts (use only these): -{chr(10).join(fact_lines)} - -Write the section now. -""" - - resp = self._call_chat( - system_prompt, - user_prompt, - max_tokens=max_tokens, - temperature=max(0.0, min(float(temperature), 0.3)), - stream=False, - ) - out = clean_text(resp.choices[0].message.content or "") - out = _dedupe_section_heading(out, title) - return out - - # ---------------------------- - # Public API - # ---------------------------- - def summarize_financial( - self, - text: str, - mode: str = "financial_initial", - section: str = None, - max_tokens: int = None, - temperature: float = None, - stream: bool = False, - doc_id: str = None, - ) -> Union[str, Iterator[str]]: - self._ensure_initialized() - self._evict_cache_if_needed() - - max_tokens = max_tokens or config.LLM_MAX_TOKENS - temperature = temperature if temperature is not None else config.LLM_TEMPERATURE - - if mode not in ("financial_initial", "financial_section", "financial_overall", "financial_sectionwise"): - raise ValueError( - "mode must be one of: financial_initial, financial_section, financial_overall, financial_sectionwise" - ) - - if stream and mode != "financial_overall": - raise ValueError("stream=True is only supported for mode='financial_overall'") - - if not text or not text.strip(): - return "No text found to summarize." - - fitted_text = self._fit_text_to_context(text, max_output_tokens=max_tokens) - - if mode == "financial_initial": - out = self._financial_initial(fitted_text, max_tokens=max_tokens, temperature=temperature) - return _normalize_money(clean_text(out)) - - if mode == "financial_overall": - out = self._financial_overall(fitted_text, max_tokens=max_tokens, temperature=temperature, stream=stream) - if isinstance(out, str): - return _normalize_money(clean_text(out)) - return out - - if mode == "financial_section": - # Dynamic: accept ANY section title. - sec = self._normalize_section_title(section or "") - if not sec: - raise ValueError("section must be provided for mode='financial_section'") - - # Optional hint from discovery (helps retrieval) - hint = "" - try: - if doc_id: - hint = self.get_doc_section_hint(doc_id, sec) - except Exception: - hint = "" - - # 1) Extract facts + anchors (internal) - facts = self._extract_facts_with_anchors(sec, hint, fitted_text) - - # 2) Validate anchors exist in document - valid_facts = self._validate_anchored_facts(facts, fitted_text) - - # If too few facts are anchored, treat as unsupported - if facts: - ratio = (len(valid_facts) / float(len(facts))) if len(facts) > 0 else 0.0 - else: - ratio = 0.0 - - if not valid_facts or ratio < self.min_anchored_fact_ratio: - out = f"{sec}\n- No supported information found in the text for this section.\n" - return _normalize_money(clean_text(out)) - - # 3) Write final section from validated facts (anchors NOT shown) - out = self._write_section_from_facts(sec, valid_facts, max_tokens=max_tokens, temperature=temperature) - out = _normalize_money(clean_text(out)) - out = _dedupe_section_heading(out, sec) - return out - - # financial_sectionwise: keep a generic structured brief (still useful for some flows) - out = self._financial_sectionwise(fitted_text, max_tokens=max_tokens, temperature=temperature) - return _normalize_money(clean_text(out)) - - # ---------------------------- - # Implementations - # ---------------------------- - def _financial_initial(self, text: str, max_tokens: int, temperature: float) -> str: - init_max = max(160, min(int(max_tokens), 280)) - temperature_init = max(0.0, min(float(temperature), 0.25)) - - system_prompt = self._base_system_prompt() + ( - "\nWrite a short generalized summary that tells the user what this document is about.\n" - "Focus on: document type, company/entity, reporting period/date, and purpose ONLY if explicitly stated.\n" - "Rules:\n" - "- Plain text only.\n" - "- 3 to 5 sentences.\n" - "- Prefer exact names, dates, and periods when present.\n" - "- Include key numeric highlights ONLY if explicitly present.\n" - ) - - user_prompt = f"""Write the generalized summary for this document. - -Document: -{text} -""" - resp = self._call_chat(system_prompt, user_prompt, max_tokens=init_max, temperature=temperature_init, stream=False) - return resp.choices[0].message.content or "" - - def _financial_overall(self, text: str, max_tokens: int, temperature: float, stream: bool) -> Union[str, Iterator[str]]: - temperature_overall = max(0.0, min(float(temperature), 0.35)) - - system_prompt = self._base_system_prompt() + ( - "\nCreate a concise summary for a non-expert user.\n" - "Prefer concrete numbers and dates when present.\n" - "Keep it readable.\n" - "\nRules:\n" - "- Do not invent.\n" - "- If the document is an article or non-financial, summarize its key claims and any numbers.\n" - ) - - user_prompt = f"""Create a neat summary of the document below. - -Document: -{text} - -Write 8 to 14 bullet points max. -""" - resp = self._call_chat(system_prompt, user_prompt, max_tokens=max_tokens, temperature=temperature_overall, stream=stream) - if stream: - return self._stream_response(resp) - return resp.choices[0].message.content or "" - - def _financial_sectionwise(self, text: str, max_tokens: int, temperature: float) -> str: - temperature_structured = max(0.0, min(float(temperature), 0.35)) - - system_prompt = self._base_system_prompt() + ( - "\nCreate a short section-wise brief.\n" - "Since the document type may be unknown, choose sensible headings that match the content.\n" - "Do not invent.\n" - "Plain text only.\n" - ) - - user_prompt = f"""Create a short section-wise brief for this document. - -Document: -{text} - -Return the section-wise brief now. -""" - resp = self._call_chat(system_prompt, user_prompt, max_tokens=max_tokens, temperature=temperature_structured, stream=False) - return resp.choices[0].message.content or "" - - def _stream_response(self, response) -> Iterator[str]: - accumulated = "" - for chunk in response: - delta = chunk.choices[0].delta - if delta and getattr(delta, "content", None): - accumulated += delta.content - if accumulated.endswith((".", "!", "?", "\n")): - yield _normalize_money(clean_text(accumulated)) - accumulated = "" - if accumulated: - yield _normalize_money(clean_text(accumulated)) - - def health_check(self) -> Dict[str, Any]: - try: - if not config.OPENAI_API_KEY: - return {"status": "not_configured", "provider": "OpenAI", "message": "OPENAI_API_KEY not configured"} - - self._ensure_initialized() - self.client.chat.completions.create( - model=self.model, - messages=[{"role": "user", "content": "Say OK"}], - max_tokens=10, - temperature=0, - ) - return { - "status": "healthy", - "provider": "OpenAI", - "model": self.model, - "model_context_tokens": self.model_context_tokens, - } - except Exception as e: - logger.error(f"Health check failed: {str(e)}") - return {"status": "unhealthy", "provider": "OpenAI", "error": str(e)} - - -llm_service = LLMService() + return hashlib.sha256(text.encode("utf-8", errors="ignore")).hexdigest() + + @staticmethod + def _normalize_section_title(title: str) -> str: + t = (title or "").strip() + t = re.sub(r"\s{2,}", " ", t) + t = t.strip(" -:\t\r\n") + if len(t) > 56: + t = t[:56].rstrip() + return t + + def _evict_cache_if_needed(self): + now = time.time() + + expired_docs = [k for k, v in self.doc_store.items() if now - v.get("ts", now) > self.cache_ttl_seconds] + for k in expired_docs: + self.doc_store.pop(k, None) + + if len(self.doc_store) <= self.cache_max_docs: + return + + items = sorted(self.doc_store.items(), key=lambda kv: kv[1].get("ts", 0)) + while len(items) > self.cache_max_docs: + k, _ = items.pop(0) + self.doc_store.pop(k, None) + + def _fit_text_to_context(self, text: str, max_output_tokens: int) -> str: + """ + Truncate input so input + output stays within model_context_tokens. + Keeps head + tail when truncating. + """ + if not text: + return "" + + overhead_tokens = 900 + available_input_tokens = max(500, self.model_context_tokens - int(max_output_tokens) - overhead_tokens) + + est = self._estimate_tokens(text) + if est <= available_input_tokens: + return text + + ratio = available_input_tokens / float(est) + keep_chars = max(2500, int(len(text) * ratio)) + + head = text[: int(keep_chars * 0.72)] + tail = text[-int(keep_chars * 0.28) :] if keep_chars > 4500 else "" + + truncated = head + if tail and tail not in head: + truncated = head + "\n\n[...TRUNCATED...]\n\n" + tail + + return truncated + + def _call_chat(self, system_prompt: str, user_prompt: str, max_tokens: int, temperature: float, stream: bool = False): + try: + return self.client.chat.completions.create( + model=self.model, + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + max_tokens=max_tokens, + temperature=temperature, + stream=stream, + ) + except Exception as e: + logger.exception("OpenAI request failed") + raise RuntimeError(f"OpenAI connection/request failed: {str(e)}") + + # ---------------------------- + # Dynamic section discovery (2 to 5) + # ---------------------------- + def _discover_dynamic_sections( + self, + fitted_text: str, + min_sections: int = 2, + max_sections: int = 5, + temperature: float = 0.2, + ) -> List[Dict[str, Any]]: + """ + Returns 2 to 5 section items derived from the document content. + This is for UI chips. We avoid quotes here; we keep short hints only. + + Output item shape: + - {"title": "Invoice Totals", "hint": "Totals, taxes, payment status"} + """ + self._ensure_initialized() + + min_sections = max(1, min(int(min_sections), 6)) + max_sections = max(min_sections, min(int(max_sections), 8)) + + system_prompt = ( + self._base_system_prompt() + + "\nYou propose section options (chips) for a summarization UI.\n" + "The user can upload ANY document: invoices, payroll, tax returns, audit reports, loan documents, or general articles.\n" + "Propose only sections that are likely supported by the text.\n" + "\nCRITICAL RULES:\n" + f"- Return between {min_sections} and {max_sections} sections.\n" + "- Use short titles (2 to 5 words). No numbering.\n" + "- Provide a short hint (6 to 12 words) describing what to expect.\n" + "- Do not invent specific numbers.\n" + "- Output MUST be strict JSON only.\n" + ) + + user_prompt = f"""Read the document and propose section chips. + +Return JSON: +{{ + "sections": [ + {{ + "title": "Short Title", + "hint": "Short description" + }} + ] +}} + +Document: +{fitted_text} +""" + + resp = self._call_chat( + system_prompt, + user_prompt, + max_tokens=350, + temperature=max(0.0, min(float(temperature), 0.35)), + stream=False, + ) + raw = (resp.choices[0].message.content or "").strip() + + items: List[Dict[str, Any]] = [] + try: + data = json.loads(raw) + secs = data.get("sections", []) + if isinstance(secs, list): + for s in secs: + if not isinstance(s, dict): + continue + title = self._normalize_section_title(str(s.get("title", "")).strip()) + hint = str(s.get("hint", "") or "").strip() + hint = re.sub(r"\s{2,}", " ", hint) + if not title: + continue + if len(hint) > 90: + hint = hint[:90].rstrip() + items.append({"title": title, "hint": hint}) + except Exception: + logger.exception("Failed to parse dynamic sections JSON; using fallback.") + + # de-dupe titles + out: List[Dict[str, Any]] = [] + seen = set() + for it in items: + key = it["title"].lower() + if key in seen: + continue + seen.add(key) + out.append(it) + + if len(out) >= min_sections: + return out[:max_sections] + + # Fallback for low-signal docs + fallback = [ + {"title": "General Summary", "hint": "What the document is about"}, + {"title": "Key Extracts", "hint": "Important names, dates, numbers"}, + ] + return fallback[:max_sections] + + # ---------------------------- + # Fact extraction with anchors (internal) + # ---------------------------- + def _extract_facts_with_anchors(self, section_title: str, section_hint: str, fitted_text: str) -> List[Dict[str, Any]]: + """ + Extract readable facts (paraphrased) relevant to a requested section. + Each fact must include at least one short anchor that appears in the document text. + Anchors are used ONLY for validation and are not shown in the final user output. + """ + title = self._normalize_section_title(section_title) + hint = (section_hint or "").strip() + + if not title: + return [] + + system_prompt = ( + self._base_system_prompt() + + "\nTask: extract readable key facts for a requested section.\n" + "Facts must be supported by the document.\n" + "\nCRITICAL RULES:\n" + "- Return STRICT JSON only.\n" + "- Facts must be written in plain English (not copied verbatim).\n" + "- Each fact MUST include at least one anchor.\n" + "- Anchors must be SHORT strings that appear verbatim in the document (examples: a number, a date, an entity name, a short label).\n" + "- Do NOT return long quotes.\n" + ) + + user_prompt = f"""Requested section: {title} +Section hint (if any): {hint} + +Return JSON: +{{ + "facts": [ + {{ + "point": "Readable summarized point", + "anchors": ["anchor1", "anchor2"] + }} + ] +}} + +Rules: +- Provide up to {self.facts_max_items} facts. +- Each fact must include 1 to {self.anchor_max_items} anchors. +- Anchors must be <= {self.anchor_max_chars_each} characters. +- Prefer anchors that include numbers, dates, totals, ratios, names, account labels, or table row labels. +- Do not invent any numbers. + +Document: +{fitted_text} +""" + + resp = self._call_chat(system_prompt, user_prompt, max_tokens=650, temperature=0.15, stream=False) + raw = (resp.choices[0].message.content or "").strip() + + facts: List[Dict[str, Any]] = [] + try: + data = json.loads(raw) + items = data.get("facts", []) + if isinstance(items, list): + for it in items: + if not isinstance(it, dict): + continue + point = str(it.get("point", "") or "").strip() + anchors = it.get("anchors", []) + if not point or not isinstance(anchors, list): + continue + + # normalize anchors + norm_anchors: List[str] = [] + for a in anchors[: self.anchor_max_items]: + s = str(a or "").strip() + if not s: + continue + if len(s) > self.anchor_max_chars_each: + s = s[: self.anchor_max_chars_each].rstrip() + norm_anchors.append(s) + + if not norm_anchors: + continue + + facts.append({"point": point, "anchors": norm_anchors}) + except Exception: + logger.exception("Failed to parse facts JSON from extractor") + return [] + + # de-dupe similar points + out: List[Dict[str, Any]] = [] + seen = set() + for f in facts: + key = re.sub(r"\s+", " ", f["point"]).strip().lower() + if key in seen: + continue + seen.add(key) + out.append(f) + return out + + def _validate_anchored_facts(self, facts: List[Dict[str, Any]], fitted_text: str) -> List[Dict[str, Any]]: + """ + Keep only facts that have at least one anchor substring present in fitted_text. + """ + if not facts: + return [] + + valid: List[Dict[str, Any]] = [] + for f in facts: + anchors = f.get("anchors", []) + if not isinstance(anchors, list): + continue + ok = False + for a in anchors: + if not a: + continue + if str(a) in fitted_text: + ok = True + break + if ok: + valid.append(f) + return valid + + # ---------------------------- + # Final section writing (user-facing, no anchors shown) + # ---------------------------- + def _write_section_from_facts(self, section_title: str, facts: List[Dict[str, Any]], max_tokens: int, temperature: float) -> str: + title = self._normalize_section_title(section_title) + + if not facts: + return f"{title}\n- No supported information found in the text for this section.\n" + + # Build fact lines (anchors not shown) + fact_lines: List[str] = [] + for f in facts: + p = str(f.get("point", "") or "").strip() + if p: + fact_lines.append(f"- {p}") + + if not fact_lines: + return f"{title}\n- No supported information found in the text for this section.\n" + + system_prompt = self._base_system_prompt() + ( + "\nWrite the requested section using ONLY the provided facts.\n" + "Do not invent.\n" + "\nOUTPUT RULES:\n" + "- Start with the heading exactly as provided.\n" + "- Keep it easy to read.\n" + "- Use bullets (recommended) or short paragraphs.\n" + "- Keep numbers when present in facts.\n" + "- If facts are weak, explicitly say it is limited.\n" + ) + + user_prompt = f"""Section heading: {title} + +Facts (use only these): +{chr(10).join(fact_lines)} + +Write the section now. +""" + + resp = self._call_chat( + system_prompt, + user_prompt, + max_tokens=max_tokens, + temperature=max(0.0, min(float(temperature), 0.3)), + stream=False, + ) + out = clean_text(resp.choices[0].message.content or "") + out = _dedupe_section_heading(out, title) + return out + + # ---------------------------- + # Public API + # ---------------------------- + def summarize_financial( + self, + text: str, + mode: str = "financial_initial", + section: str = None, + max_tokens: int = None, + temperature: float = None, + stream: bool = False, + doc_id: str = None, + ) -> Union[str, Iterator[str]]: + self._ensure_initialized() + self._evict_cache_if_needed() + + max_tokens = max_tokens or config.LLM_MAX_TOKENS + temperature = temperature if temperature is not None else config.LLM_TEMPERATURE + + if mode not in ("financial_initial", "financial_section", "financial_overall", "financial_sectionwise"): + raise ValueError( + "mode must be one of: financial_initial, financial_section, financial_overall, financial_sectionwise" + ) + + if stream and mode != "financial_overall": + raise ValueError("stream=True is only supported for mode='financial_overall'") + + if not text or not text.strip(): + return "No text found to summarize." + + fitted_text = self._fit_text_to_context(text, max_output_tokens=max_tokens) + + if mode == "financial_initial": + out = self._financial_initial(fitted_text, max_tokens=max_tokens, temperature=temperature) + return _normalize_money(clean_text(out)) + + if mode == "financial_overall": + out = self._financial_overall(fitted_text, max_tokens=max_tokens, temperature=temperature, stream=stream) + if isinstance(out, str): + return _normalize_money(clean_text(out)) + return out + + if mode == "financial_section": + # Dynamic: accept ANY section title. + sec = self._normalize_section_title(section or "") + if not sec: + raise ValueError("section must be provided for mode='financial_section'") + + # Optional hint from discovery (helps retrieval) + hint = "" + try: + if doc_id: + hint = self.get_doc_section_hint(doc_id, sec) + except Exception: + hint = "" + + # 1) Extract facts + anchors (internal) + facts = self._extract_facts_with_anchors(sec, hint, fitted_text) + + # 2) Validate anchors exist in document + valid_facts = self._validate_anchored_facts(facts, fitted_text) + + # If too few facts are anchored, treat as unsupported + if facts: + ratio = (len(valid_facts) / float(len(facts))) if len(facts) > 0 else 0.0 + else: + ratio = 0.0 + + if not valid_facts or ratio < self.min_anchored_fact_ratio: + out = f"{sec}\n- No supported information found in the text for this section.\n" + return _normalize_money(clean_text(out)) + + # 3) Write final section from validated facts (anchors NOT shown) + out = self._write_section_from_facts(sec, valid_facts, max_tokens=max_tokens, temperature=temperature) + out = _normalize_money(clean_text(out)) + out = _dedupe_section_heading(out, sec) + return out + + # financial_sectionwise: keep a generic structured brief (still useful for some flows) + out = self._financial_sectionwise(fitted_text, max_tokens=max_tokens, temperature=temperature) + return _normalize_money(clean_text(out)) + + # ---------------------------- + # Implementations + # ---------------------------- + def _financial_initial(self, text: str, max_tokens: int, temperature: float) -> str: + init_max = max(160, min(int(max_tokens), 280)) + temperature_init = max(0.0, min(float(temperature), 0.25)) + + system_prompt = self._base_system_prompt() + ( + "\nWrite a short generalized summary that tells the user what this document is about.\n" + "Focus on: document type, company/entity, reporting period/date, and purpose ONLY if explicitly stated.\n" + "Rules:\n" + "- Plain text only.\n" + "- 3 to 5 sentences.\n" + "- Prefer exact names, dates, and periods when present.\n" + "- Include key numeric highlights ONLY if explicitly present.\n" + ) + + user_prompt = f"""Write the generalized summary for this document. + +Document: +{text} +""" + resp = self._call_chat(system_prompt, user_prompt, max_tokens=init_max, temperature=temperature_init, stream=False) + return resp.choices[0].message.content or "" + + def _financial_overall(self, text: str, max_tokens: int, temperature: float, stream: bool) -> Union[str, Iterator[str]]: + temperature_overall = max(0.0, min(float(temperature), 0.35)) + + system_prompt = self._base_system_prompt() + ( + "\nCreate a concise summary for a non-expert user.\n" + "Prefer concrete numbers and dates when present.\n" + "Keep it readable.\n" + "\nRules:\n" + "- Do not invent.\n" + "- If the document is an article or non-financial, summarize its key claims and any numbers.\n" + ) + + user_prompt = f"""Create a neat summary of the document below. + +Document: +{text} + +Write 8 to 14 bullet points max. +""" + resp = self._call_chat(system_prompt, user_prompt, max_tokens=max_tokens, temperature=temperature_overall, stream=stream) + if stream: + return self._stream_response(resp) + return resp.choices[0].message.content or "" + + def _financial_sectionwise(self, text: str, max_tokens: int, temperature: float) -> str: + temperature_structured = max(0.0, min(float(temperature), 0.35)) + + system_prompt = self._base_system_prompt() + ( + "\nCreate a short section-wise brief.\n" + "Since the document type may be unknown, choose sensible headings that match the content.\n" + "Do not invent.\n" + "Plain text only.\n" + ) + + user_prompt = f"""Create a short section-wise brief for this document. + +Document: +{text} + +Return the section-wise brief now. +""" + resp = self._call_chat(system_prompt, user_prompt, max_tokens=max_tokens, temperature=temperature_structured, stream=False) + return resp.choices[0].message.content or "" + + def _stream_response(self, response) -> Iterator[str]: + accumulated = "" + for chunk in response: + delta = chunk.choices[0].delta + if delta and getattr(delta, "content", None): + accumulated += delta.content + if accumulated.endswith((".", "!", "?", "\n")): + yield _normalize_money(clean_text(accumulated)) + accumulated = "" + if accumulated: + yield _normalize_money(clean_text(accumulated)) + + def health_check(self) -> Dict[str, Any]: + try: + if not config.OPENAI_API_KEY: + return {"status": "not_configured", "provider": "OpenAI", "message": "OPENAI_API_KEY not configured"} + + self._ensure_initialized() + self.client.chat.completions.create( + model=self.model, + messages=[{"role": "user", "content": "Say OK"}], + max_tokens=10, + temperature=0, + ) + return { + "status": "healthy", + "provider": "OpenAI", + "model": self.model, + "model_context_tokens": self.model_context_tokens, + } + except Exception as e: + logger.error(f"Health check failed: {str(e)}") + return {"status": "unhealthy", "provider": "OpenAI", "error": str(e)} + + +llm_service = LLMService() diff --git a/docker-compose.yml b/docker-compose.yml index 7abc7bf..1c35f3e 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,56 +1,56 @@ -services: - # Backend Gateway (Python) - backend: - build: - context: ./backend - dockerfile: Dockerfile - container_name: backend - ports: - - "8001:8000" - env_file: - - ./backend/.env - volumes: - - ./backend:/app - networks: - - app_network - restart: unless-stopped - - # Frontend (React) - frontend: - build: - context: ./frontend - dockerfile: Dockerfile +services: + # Backend Gateway (Python) + backend: + build: + context: ./backend + dockerfile: Dockerfile + container_name: backend + ports: + - "8001:8000" + env_file: + - ./backend/.env + volumes: + - ./backend:/app + networks: + - app_network + restart: unless-stopped + + # Frontend (React) + frontend: + build: + context: ./frontend + dockerfile: Dockerfile container_name: frontend ports: - - "5173:80" - depends_on: - - backend - networks: - - app_network - restart: unless-stopped - -networks: - app_network: - driver: bridge - -# ============================================================================= -# Usage Instructions: -# ============================================================================= -# -# 1. Copy backend/.env.example to backend/.env and configure your Keycloak credentials -# -# 2. Start the application: -# docker-compose up -d -# -# 3. Access the application: -# - Frontend: http://localhost:5173 -# - Backend API: http://localhost:8000 -# - API Docs: http://localhost:8000/docs -# -# 4. View logs: -# docker-compose logs -f -# -# 5. Stop the application: -# docker-compose down -# -# ============================================================================= + - "5173:8080" + depends_on: + - backend + networks: + - app_network + restart: unless-stopped + +networks: + app_network: + driver: bridge + +# ============================================================================= +# Usage Instructions: +# ============================================================================= +# +# 1. Copy backend/.env.example to backend/.env and configure your Keycloak credentials +# +# 2. Start the application: +# docker-compose up -d +# +# 3. Access the application: +# - Frontend: http://localhost:5173 +# - Backend API: http://localhost:8000 +# - API Docs: http://localhost:8000/docs +# +# 4. View logs: +# docker-compose logs -f +# +# 5. Stop the application: +# docker-compose down +# +# ============================================================================= diff --git a/frontend/Dockerfile b/frontend/Dockerfile index a2cffd1..c6099db 100644 --- a/frontend/Dockerfile +++ b/frontend/Dockerfile @@ -1,58 +1,60 @@ -# Build stage -FROM node:18-alpine AS build - -WORKDIR /app - -COPY package*.json ./ -RUN npm install - -COPY . . -RUN npm run build - +# Build stage +FROM node:18-alpine AS build + +WORKDIR /app + +COPY package*.json ./ +RUN npm install + +COPY . . +RUN npm run build + # Production stage -FROM nginx:alpine - -COPY --from=build /app/dist /usr/share/nginx/html - -# Create nginx config with increased file upload limit and no-cache headers +FROM nginxinc/nginx-unprivileged:alpine + +COPY --from=build /app/dist /usr/share/nginx/html + +# Create nginx config with increased file upload limit and no-cache headers RUN echo 'server { \ - listen 80; \ + listen 8080; \ server_name localhost; \ - client_max_body_size 500M; \ - \ - location / { \ - root /usr/share/nginx/html; \ - index index.html; \ - try_files $uri $uri/ /index.html; \ - \ - # Disable cache for HTML \ - add_header Cache-Control "no-store, no-cache, must-revalidate, proxy-revalidate, max-age=0"; \ - add_header Pragma "no-cache"; \ - add_header Expires "0"; \ - } \ - \ - location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg)$ { \ - root /usr/share/nginx/html; \ - expires -1; \ - add_header Cache-Control "no-store, no-cache, must-revalidate, proxy-revalidate, max-age=0"; \ - } \ - \ - location /v1/ { \ - proxy_pass http://backend:8000; \ - proxy_http_version 1.1; \ - proxy_set_header Upgrade $http_upgrade; \ - proxy_set_header Connection "upgrade"; \ - proxy_set_header Host $host; \ - proxy_set_header X-Real-IP $remote_addr; \ - proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; \ - proxy_set_header X-Forwarded-Proto $scheme; \ - client_max_body_size 500M; \ - proxy_read_timeout 600s; \ - proxy_connect_timeout 600s; \ - proxy_send_timeout 600s; \ - } \ -}' > /etc/nginx/conf.d/default.conf - -EXPOSE 80 + client_max_body_size 500M; \ + \ + location / { \ + root /usr/share/nginx/html; \ + index index.html; \ + try_files $uri $uri/ /index.html; \ + \ + # Disable cache for HTML \ + add_header Cache-Control "no-store, no-cache, must-revalidate, proxy-revalidate, max-age=0"; \ + add_header Pragma "no-cache"; \ + add_header Expires "0"; \ + } \ + \ + location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg)$ { \ + root /usr/share/nginx/html; \ + expires -1; \ + add_header Cache-Control "no-store, no-cache, must-revalidate, proxy-revalidate, max-age=0"; \ + } \ + \ + location /v1/ { \ + proxy_pass http://backend:8000; \ + proxy_http_version 1.1; \ + proxy_set_header Upgrade $http_upgrade; \ + proxy_set_header Connection "upgrade"; \ + proxy_set_header Host $host; \ + proxy_set_header X-Real-IP $remote_addr; \ + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; \ + proxy_set_header X-Forwarded-Proto $scheme; \ + client_max_body_size 500M; \ + proxy_read_timeout 600s; \ + proxy_connect_timeout 600s; \ + proxy_send_timeout 600s; \ + } \ +}' > /etc/nginx/conf.d/default.conf + +EXPOSE 8080 + +USER 101 CMD ["nginx", "-g", "daemon off;"] diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 7f04c86..84fc541 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -1,2675 +1,2689 @@ -{ - "name": "docsum-react-ui", - "version": "1.0.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "docsum-react-ui", - "version": "1.0.0", - "dependencies": { - "jspdf": "^4.0.0", - "lucide-react": "^0.294.0", - "react": "^18.2.0", - "react-dom": "^18.2.0", - "react-hot-toast": "^2.4.1", - "react-router-dom": "^6.20.0" - }, - "devDependencies": { - "@vitejs/plugin-react": "^4.2.0", - "autoprefixer": "^10.4.16", - "postcss": "^8.4.32", - "tailwindcss": "^3.3.5", - "vite": "^5.0.0" - } - }, - "node_modules/@alloc/quick-lru": { - "version": "5.2.0", 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