### Install langchain-yantrikdb

Source: https://github.com/yantrikos/langchain-yantrikdb/blob/main/README.md

Install the package using pip.

```bash
pip install langchain-yantrikdb
```

--------------------------------

### Explain search results with store.explain_search()

Source: https://github.com/yantrikos/langchain-yantrikdb/blob/main/README.md

Retrieve search results along with the reasoning behind why they were retrieved.

```python
for doc, why in store.explain_search("what is the rate limit?", k=2):
    print(doc.page_content, why["why_retrieved"])
# ... ['semantically similar (0.93)', 'recent', 'important (decay=0.80)']
```

--------------------------------

### Initialize YantrikDBVectorStore and perform similarity search

Source: https://github.com/yantrikos/langchain-yantrikdb/blob/main/README.md

Initialize the vector store, add texts, and perform a similarity search.

```python
from langchain_yantrikdb import YantrikDBVectorStore

store = YantrikDBVectorStore(db_path="./memory.db")

store.add_texts([
    "The deploy target is eu-west-1",
    "The database is PostgreSQL 16",
])

docs = store.similarity_search("where do we deploy?", k=1)
print(docs[0].page_content)   # The deploy target is eu-west-1

retriever = store.as_retriever()  # drop into any chain
```

--------------------------------

### Initialize YantrikDBVectorStore with OpenAIEmbeddings

Source: https://github.com/yantrikos/langchain-yantrikdb/blob/main/README.md

Configures the vector store with a specific database path, embedding model, and namespace. The embedding dimension is probed at construction and must remain consistent for the database file's lifetime.

```python
from langchain_openai import OpenAIEmbeddings

store = YantrikDBVectorStore(
    db_path="./memory.db",
    embedding=OpenAIEmbeddings(model="text-embedding-3-small"),
    namespace="docs",
)
```

--------------------------------

### Detect contradictions with store.think()

Source: https://github.com/yantrikos/langchain-yantrikdb/blob/main/README.md

Add contradictory facts and use the think method to identify potential redundancies.

```python
store.add_texts([
    "The API rate limit is 100 requests per minute",
    "The API rate limit is 500 requests per minute",
])

report = store.think()
for trigger in report["triggers"]:
    print(trigger["reason"])
# Two memories are 97% similar and may be redundant (rid_a=..., rid_b=...):
# 'The API rate limit is 500 requests per minute' vs
# 'The API rate limit is 100 requests per minute'
# suggested_action: consolidate_or_forget
```

--------------------------------

### Configure YantrikDBChatMessageHistory

Source: https://github.com/yantrikos/langchain-yantrikdb/blob/main/README.md

Use YantrikDBChatMessageHistory with RunnableWithMessageHistory to persist chat sessions.

```python
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_yantrikdb import YantrikDBChatMessageHistory

chain_with_history = RunnableWithMessageHistory(
    chain,
    lambda session_id: YantrikDBChatMessageHistory(
        session_id, db_path="./memory.db"
    ),
    input_messages_key="input",
    history_messages_key="history",
)
```

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