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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
Created a chunk of size 1120, which is longer than the specified 1000
Created a chunk of size 1148, which is longer than the specified 1000
Created a chunk of size 1826, which is longer than the specified 1000
Created a chunk of size 1260, which is longer than the specified 1000
Created a chunk of size 1195, which is longer than the specified 1000
Created a chunk of size 2147, which is longer than the specified 1000
Created a chunk of size 1410, which is longer than the specified 1000
Created a chunk of size 1269, which is longer than the specified 1000
Created a chunk of size 1030, which is longer than the specified 1000
Created a chunk of size 1046, which is longer than the specified 1000
Created a chunk of size 1024, which is longer than the specified 1000
Created a chunk of size 1026, which is longer than the specified 1000
Created a chunk of size 1285, which is longer than the specified 1000
Created a chunk of size 1370, which is longer than the specified 1000
Created a chunk of size 1031, which is longer than the specified 1000
Created a chunk of size 1999, which is longer than the specified 1000
Created a chunk of size 1029, which is longer than the specified 1000
Created a chunk of size 1120, which is longer than the specified 1000
Created a chunk of size 1033, which is longer than the specified 1000
Created a chunk of size 1143, which is longer than the specified 1000
Created a chunk of size 1416, which is longer than the specified 1000
Created a chunk of size 2482, which is longer than the specified 1000
Created a chunk of size 1890, which is longer than the specified 1000
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Created a chunk of size 1045, which is longer than the specified 1000
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Created a chunk of size 1208, which is longer than the specified 1000
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Created a chunk of size 1208, which is longer than the specified 1000
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Created a chunk of size 1183, which is longer than the specified 1000
Created a chunk of size 2424, which is longer than the specified 1000
Created a chunk of size 1017, which is longer than the specified 1000
Created a chunk of size 1304, which is longer than the specified 1000
Created a chunk of size 1379, which is longer than the specified 1000
Created a chunk of size 1324, which is longer than the specified 1000
Created a chunk of size 1205, which is longer than the specified 1000
Created a chunk of size 1056, which is longer than the specified 1000
Created a chunk of size 1195, which is longer than the specified 1000
Created a chunk of size 3608, which is longer than the specified 1000
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Created a chunk of size 1440, which is longer than the specified 1000
Created a chunk of size 1046, which is longer than the specified 1000
Created a chunk of size 1220, which is longer than the specified 1000
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Created a chunk of size 1049, which is longer than the specified 1000
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Created a chunk of size 1027, which is longer than the specified 1000
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Created a chunk of size 1382, which is longer than the specified 1000
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Created a chunk of size 1652, which is longer than the specified 1000
Created a chunk of size 1891, which is longer than the specified 1000
Created a chunk of size 1899, which is longer than the specified 1000
Created a chunk of size 1021, which is longer than the specified 1000
Created a chunk of size 1085, which is longer than the specified 1000
Created a chunk of size 1854, which is longer than the specified 1000
Created a chunk of size 1672, which is longer than the specified 1000
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Created a chunk of size 1376, which is longer than the specified 1000
Created a chunk of size 1253, which is longer than the specified 1000
Created a chunk of size 1642, which is longer than the specified 1000
Created a chunk of size 1419, which is longer than the specified 1000
Created a chunk of size 1438, which is longer than the specified 1000
Created a chunk of size 1427, which is longer than the specified 1000
Created a chunk of size 1684, which is longer than the specified 1000
Created a chunk of size 1760, which is longer than the specified 1000
Created a chunk of size 1157, which is longer than the specified 1000
Created a chunk of size 2504, which is longer than the specified 1000
Created a chunk of size 1082, which is longer than the specified 1000
Created a chunk of size 2268, which is longer than the specified 1000
Created a chunk of size 1784, which is longer than the specified 1000
Created a chunk of size 1311, which is longer than the specified 1000
Created a chunk of size 2972, which is longer than the specified 1000
Created a chunk of size 1144, which is longer than the specified 1000
Created a chunk of size 1825, which is longer than the specified 1000
Created a chunk of size 1508, which is longer than the specified 1000
Created a chunk of size 2901, which is longer than the specified 1000
Created a chunk of size 1715, which is longer than the specified 1000
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Created a chunk of size 1102, which is longer than the specified 1000
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Created a chunk of size 1002, which is longer than the specified 1000
Created a chunk of size 1065, which is longer than the specified 1000
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Created a chunk of size 1754, which is longer than the specified 1000
Created a chunk of size 2413, which is longer than the specified 1000
Created a chunk of size 1771, which is longer than the specified 1000
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Created a chunk of size 1419, which is longer than the specified 1000
Created a chunk of size 1368, which is longer than the specified 1000
Created a chunk of size 1008, which is longer than the specified 1000
Created a chunk of size 1227, which is longer than the specified 1000
Created a chunk of size 1745, which is longer than the specified 1000
Created a chunk of size 2296, which is longer than the specified 1000
Created a chunk of size 1083, which is longer than the specified 1000


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_name="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.