# Conversation Memory Module (06_conversation_memory)

Stores conversation outputs as embeddings for context-aware retrieval.

## Files
- `memory_processor.py`: Parses LLM's output, builds chunks, and stores them in Pinecone.
- `config.py`: Configuration for embedding model and Pinecone namespace.
- `error_handling.py`: Error handling for Pinecone operations (only needed for full pipeline, not for test_multi_retrieval.py)


## Features
- **Deduplication**: Checks similarity before storing. If a memory is >95% similar to an existing one, it's skipped.
- **Session Parsing**: Automatically parses LLM's `Query`/`Answer`/`Evidence` format.
- **Unified Retrieval**: Designed to work with `04_vectoredb/retriever_multi.py`.

## Usage

### 1. Storing Memory
```python
from memory_processor import store_memory_sync

raw_output = '''
Query: "What is the patient's age?"
Answer: The patient is 45 years old.
Evidence: Patient is a 45-year-old male.
'''

# Automatically checks for duplicates before storing
result = store_memory_sync(raw_output, pinecone_index)
if result:
    print(f"Stored: {result['chunk_id']}")
else:
    print("Skipped (duplicate or error)")
```

### 2. Retrieval Configuration
Adjust thresholds in `04_vectoredb/config.py`:
- `MEMORY_MIN_SCORE`: Minimum impact (default 0.4)
- `MEMORY_BOOST`: Ranking boost (default 0.05)

## Integration Flow
```
1. LLM generates answer
2. memory_processor parses & embeds
3. Checks Pinecone for duplicates (>0.95)
4. Stores if unique
5. Future queries retrieve this memory via retriever_multi.py
```