Complete Memora implementation with AI integration and file processing

src/memora/ai/conversational_ai.py

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+import ollama
+from pathlib import Path
+from memora.core.ingestion import extract_facts
+from memora.core.store import ObjectStore
+
+
+class MemoraChat:
+
+    def __init__(self):
+        self.client = ollama.Client()
+        self.store = ObjectStore(Path(".memora"))
+
+    def extract_code_blocks(self, message: str):
+        code_blocks = []
+
+        if "```" in message:
+            parts = message.split("```")
+            for i in range(1, len(parts), 2):
+                block = parts[i].strip()
+                if block:
+                    code_blocks.append(block)
+
+        if not code_blocks:
+            if any(k in message for k in ["def ", "return ", "class ", "import "]):
+                code_blocks.append(message.strip())
+
+        return code_blocks
+
+    def needs_memory(self, message: str) -> bool:
+        message = message.lower()
+
+        triggers = [
+            "my", "i", "me",
+            "what did i", "what do i",
+            "who am i", "remember",
+            "code", "earlier", "before"
+        ]
+
+        return any(t in message for t in triggers)
+
+    def chat(self, message: str):
+
+        message_lower = message.lower()
+
+        # ✅ STEP 1: store normal facts
+        if len(message.strip()) > 3:
+            facts = extract_facts(message, "user")
+            for f in facts:
+                self.store.write(f)
+
+        # ✅ STEP 2: store code (SAFE METHOD)
+        code_blocks = self.extract_code_blocks(message)
+
+        for code in code_blocks:
+            fact_text = f"user wrote code {code}"
+            facts = extract_facts(fact_text, "user")
+
+            for f in facts:
+                self.store.write(f)
+
+        # ✅ STEP 3: retrieve memory
+        if self.needs_memory(message):
+
+            all_hashes = self.store.list_all_hashes()
+
+            # 🔥 CODE RETRIEVAL
+            if "code" in message_lower:
+                for h in reversed(all_hashes):
+                    try:
+                        fact = self.store.read_fact(h)
+
+                        content = getattr(fact, "content", "").lower()
+
+                        if "code" in content or "def " in content:
+                            return f"Here is your code:\n{fact.content}"
+
+                    except:
+                        continue
+
+        # ✅ STEP 4: fallback AI
+        try:
+            response = self.client.chat(
+                model="llama3.2",
+                messages=[{"role": "user", "content": message}]
+            )
+
+            return response["message"]["content"].strip()
+
+        except Exception:
+            return "Something went wrong."

src/memora/ai/file_processor.py

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+from pathlib import Path
+from memora.core.ingestion import extract_facts
+from memora.core.store import ObjectStore
+
+
+class FileProcessor:
+
+    def __init__(self):
+        self.store = ObjectStore(Path(".memora"))
+
+    def process_file(self, file_path: str):
+
+        path = Path(file_path)
+
+        if not path.exists():
+            raise ValueError("File not found")
+
+        # 1. Read file
+        if path.suffix == ".txt" or path.suffix == ".md":
+            with open(path, "r", encoding="utf-8") as f:
+                content = f.read()
+        else:
+            raise ValueError("Only .txt and .md supported")
+
+        # 2. Extract facts
+        facts = extract_facts(content, f"file:{path.name}")
+
+        # 3. Store facts
+        for f in facts:
+            self.store.write(f)
+
+        return len(facts)