# Chat on CSV

## Python Implementation

### Importing Components

```py
from genai_stack.stack.stack import Stack
from genai_stack.etl.langchain import LangchainETL
from genai_stack.embedding.langchain import LangchainEmbedding
from genai_stack.vectordb.chromadb import ChromaDB
from genai_stack.prompt_engine.engine import PromptEngine
from genai_stack.model.gpt3_5 import OpenAIGpt35Model
from genai_stack.retriever.langchain import LangChainRetriever
from genai_stack.memory.langchain import ConversationBufferMemory
```

## Initializing Stack Components

### ETL

```py
etl = LangchainETL.from_kwargs(name="CSVLoader", fields={"file_path": "/path/sample.csv"})
```

### Embeddings

```py
config = {
    "model_name": "sentence-transformers/all-mpnet-base-v2",
    "model_kwargs": {"device": "cpu"},
    "encode_kwargs": {"normalize_embeddings": False},
}
embedding = LangchainEmbedding.from_kwargs(name="HuggingFaceEmbeddings", fields=config)
```

### VectorDB

```py
chromadb = ChromaDB.from_kwargs()
```

### Model

```py
llm = OpenAIGpt35Model.from_kwargs(parameters={"openai_api_key": "your-api-key"})
```

### Prompt Engine

```py
prompt_engine = PromptEngine.from_kwargs(should_validate=False)
```

### Retriever

```py
retriever = LangChainRetriever.from_kwargs()
```

### Memory

```py
memory = ConversationBufferMemory.from_kwargs()
```

## Initializing Stack

### Stack

```py
Stack(
    etl=etl,
    embedding=embedding,
    vectordb=chromadb,
    model=llm,
    prompt_engine=prompt_engine,
    retriever=retriever,
    memory=memory
)
```

## Performing ETL operations

`run()` will execute Extract, Transform and Load operations.

```py
etl.run()
```

## Now you can start asking your queries.

```py
response = retriever.retrieve("your query")
print(response)
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://genaistack.aiplanet.com/example-use-cases/chat-on-csv.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.