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# Use LangChain, GPT and Deep Lake to work with code base

In this tutorial, we are going to use Langchain + Deep Lake with GPT to analyze the code base of the LangChain itself.

## Design

1. Prepare data:

   1. Upload all python project files using the `langchain.document_loaders.TextLoader`. We will call these files the documents.

   2. Split all documents to chunks using the `langchain.text_splitter.CharacterTextSplitter`.

   3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain.vectorstores.DeepLake`

2. Question-Answering:

   1. Build a chain from `langchain.chat_models.ChatOpenAI` and `langchain.chains.ConversationalRetrievalChain`

   2. Prepare questions.

   3. Get answers running the chain.

## Implementation

### Integration preparations

We need to set up keys for external services and install necessary python libraries.

    #!python3 -m pip install --upgrade langchain deeplake openai

Set up OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate.

For full documentation of Deep Lake please follow https://docs.activeloop.ai/ and API reference https://docs.deeplake.ai/en/latest/

    import os
    from getpass import getpass

    os.environ['OPENAI_API_KEY'] = getpass()
    # Please manually enter OpenAI Key

     ········

Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the platform at app.activeloop.ai

    os.environ['ACTIVELOOP_TOKEN'] = getpass.getpass('Activeloop Token:')

     ········

### Prepare data

Load all repository files. Here we assume this notebook is downloaded as the part of the langchain fork and we work with the python files of the `langchain` repo.

If you want to use files from different repo, change `root_dir` to the root dir of your repo.

    from langchain.document_loaders import TextLoader

    root_dir = '../../../..'

    docs = []
    for dirpath, dirnames, filenames in os.walk(root_dir):
        for file in filenames:
            if file.endswith('.py') and '/.venv/' not in dirpath:
                try:
                    loader = TextLoader(os.path.join(dirpath, file), encoding='utf-8')
                    docs.extend(loader.load_and_split())
                except Exception as e:
                    pass
    print(f'{len(docs)}')

    1147

Then, chunk the files

    from langchain.text_splitter import CharacterTextSplitter

    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    texts = text_splitter.split_documents(docs)
    print(f"{len(texts)}")

    Created a chunk of size 1620, which is longer than the specified 1000
    Created a chunk of size 1213, which is longer than the specified 1000
    Created a chunk of size 1263, which is longer than the specified 1000
    Created a chunk of size 1448, which is longer than the specified 1000
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    3477

Then embed chunks and upload them to the DeepLake.

This can take several minutes.

    from langchain.embeddings.openai import OpenAIEmbeddings

    embeddings = OpenAIEmbeddings()
    embeddings

    OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', document_model_name='text-embedding-ada-002', query_model_name='text-embedding-ada-002', embedding_ctx_length=8191, openai_api_key=None, openai_organization=None, allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6)

    from langchain.vectorstores import DeepLake

    db = DeepLake.from_documents(texts, embeddings, dataset_path=f"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code")
    db

### Question Answering

First load the dataset, construct the retriever, then construct the Conversational Chain

    db = DeepLake(dataset_path=f"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code", read_only=True, embedding_function=embeddings)

    -

    This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/user_name/langchain-code

    /

    hub://user_name/langchain-code loaded successfully.

    Deep Lake Dataset in hub://user_name/langchain-code already exists, loading from the storage

    Dataset(path='hub://user_name/langchain-code', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])

      tensor     htype      shape       dtype  compression
      -------   -------    -------     -------  -------
     embedding  generic  (3477, 1536)  float32   None
        ids      text     (3477, 1)      str     None
     metadata    json     (3477, 1)      str     None
       text      text     (3477, 1)      str     None

    retriever = db.as_retriever()
    retriever.search_kwargs['distance_metric'] = 'cos'
    retriever.search_kwargs['fetch_k'] = 20
    retriever.search_kwargs['maximal_marginal_relevance'] = True
    retriever.search_kwargs['k'] = 20

You can also specify user defined functions using Deep Lake filters

    def filter(x):
        # filter based on source code
        if 'something' in x['text'].data()['value']:
            return False

        # filter based on path e.g. extension
        metadata =  x['metadata'].data()['value']
        return 'only_this' in metadata['source'] or 'also_that' in metadata['source']

    ### turn on below for custom filtering
    # retriever.search_kwargs['filter'] = filter

    from langchain.chat_models import ChatOpenAI
    from langchain.chains import ConversationalRetrievalChain

    model = ChatOpenAI(model='gpt-3.5-turbo') # 'ada' 'gpt-3.5-turbo' 'gpt-4',
    qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)

    questions = [
        "What is the class hierarchy?",
        # "What classes are derived from the Chain class?",
        # "What classes and functions in the ./langchain/utilities/ forlder are not covered by unit tests?",
        # "What one improvement do you propose in code in relation to the class herarchy for the Chain class?",
    ]
    chat_history = []

    for question in questions:
        result = qa({"question": question, "chat_history": chat_history})
        chat_history.append((question, result['answer']))
        print(f"-> **Question**: {question} \n")
        print(f"**Answer**: {result['answer']} \n")

-> Question: What is the class hierarchy?

Answer: There are several class hierarchies in the provided code, so I’ll list a few:

1. `BaseModel` -> `ConstitutionalPrinciple`: `ConstitutionalPrinciple` is a subclass of `BaseModel`.

2. `BasePromptTemplate` -> `StringPromptTemplate`, `AIMessagePromptTemplate`, `BaseChatPromptTemplate`, `ChatMessagePromptTemplate`, `ChatPromptTemplate`, `HumanMessagePromptTemplate`, `MessagesPlaceholder`, `SystemMessagePromptTemplate`, `FewShotPromptTemplate`, `FewShotPromptWithTemplates`, `Prompt`, `PromptTemplate`: All of these classes are subclasses of `BasePromptTemplate`.

3. `APIChain`, `Chain`, `MapReduceDocumentsChain`, `MapRerankDocumentsChain`, `RefineDocumentsChain`, `StuffDocumentsChain`, `HypotheticalDocumentEmbedder`, `LLMChain`, `LLMBashChain`, `LLMCheckerChain`, `LLMMathChain`, `LLMRequestsChain`, `PALChain`, `QAWithSourcesChain`, `VectorDBQAWithSourcesChain`, `VectorDBQA`, `SQLDatabaseChain`: All of these classes are subclasses of `Chain`.

4. `BaseLoader`: `BaseLoader` is a subclass of `ABC`.

5. `BaseTracer` -> `ChainRun`, `LLMRun`, `SharedTracer`, `ToolRun`, `Tracer`, `TracerException`, `TracerSession`: All of these classes are subclasses of `BaseTracer`.

6. `OpenAIEmbeddings`, `HuggingFaceEmbeddings`, `CohereEmbeddings`, `JinaEmbeddings`, `LlamaCppEmbeddings`, `HuggingFaceHubEmbeddings`, `TensorflowHubEmbeddings`, `SagemakerEndpointEmbeddings`, `HuggingFaceInstructEmbeddings`, `SelfHostedEmbeddings`, `SelfHostedHuggingFaceEmbeddings`, `SelfHostedHuggingFaceInstructEmbeddings`, `FakeEmbeddings`, `AlephAlphaAsymmetricSemanticEmbedding`, `AlephAlphaSymmetricSemanticEmbedding`: All of these classes are subclasses of `BaseLLM`.

-> Question: What classes are derived from the Chain class?

Answer: There are multiple classes that are derived from the Chain class. Some of them are:

- APIChain

- AnalyzeDocumentChain

- ChatVectorDBChain

- CombineDocumentsChain

- ConstitutionalChain

- ConversationChain

- GraphQAChain

- HypotheticalDocumentEmbedder

- LLMChain

- LLMCheckerChain

- LLMRequestsChain

- LLMSummarizationCheckerChain

- MapReduceChain

- OpenAPIEndpointChain

- PALChain

- QAWithSourcesChain

- RetrievalQA

- RetrievalQAWithSourcesChain

- SequentialChain

- SQLDatabaseChain

- TransformChain

- VectorDBQA

- VectorDBQAWithSourcesChain

There might be more classes that are derived from the Chain class as it is possible to create custom classes that extend the Chain class.

-> Question: What classes and functions in the ./langchain/utilities/ forlder are not covered by unit tests?

Answer: All classes and functions in the `./langchain/utilities/` folder seem to have unit tests written for them.