dima/langchain
1
0 Fork 0
mirror of https://github.com/langchain-ai/langchain.git synced 2026-10-06 01:45:27 +03:00
Code Issues Packages Projects Releases Wiki Activity
Files
dev2049/fmt_nbs
langchain/docs/use_cases/code/code-analysis-deeplake.ipynb
T
Dev 2049 82174bf176 fmt
2023-04-14 10:59:27 -07:00

27 KiB
Raw Permalink Blame History

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.

In [3]:
#!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/

In [5]:
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 https://app.activeloop.ai

In [6]:
DEEPLAKE_ACCOUNT_NAME = getpass()
 ········
In [7]:
os.environ["DEEPLAKE_KEY"] = getpass()
 ········
In [22]:
!activeloop login -t $DEEPLAKE_KEY

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.

In [8]:
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

In [13]:
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
Created a chunk of size 1418, which is longer than the specified 1000
Created a chunk of size 1848, which is longer than the specified 1000
Created a chunk of size 1069, which is longer than the specified 1000
Created a chunk of size 2369, which is longer than the specified 1000
Created a chunk of size 1045, which is longer than the specified 1000
Created a chunk of size 1501, which is longer than the specified 1000
Created a chunk of size 1208, which is longer than the specified 1000
Created a chunk of size 1950, which is longer than the specified 1000
Created a chunk of size 1283, which is longer than the specified 1000
Created a chunk of size 1414, 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 1224, which is longer than the specified 1000
Created a chunk of size 1060, which is longer than the specified 1000
Created a chunk of size 2461, which is longer than the specified 1000
Created a chunk of size 1099, which is longer than the specified 1000
Created a chunk of size 1178, which is longer than the specified 1000
Created a chunk of size 1449, which is longer than the specified 1000
Created a chunk of size 1345, which is longer than the specified 1000
Created a chunk of size 3359, which is longer than the specified 1000
Created a chunk of size 2248, which is longer than the specified 1000
Created a chunk of size 1589, which is longer than the specified 1000
Created a chunk of size 2104, which is longer than the specified 1000
Created a chunk of size 1505, which is longer than the specified 1000
Created a chunk of size 1387, which is longer than the specified 1000
Created a chunk of size 1215, which is longer than the specified 1000
Created a chunk of size 1240, which is longer than the specified 1000
Created a chunk of size 1635, which is longer than the specified 1000
Created a chunk of size 1075, which is longer than the specified 1000
Created a chunk of size 2180, which is longer than the specified 1000
Created a chunk of size 1791, which is longer than the specified 1000
Created a chunk of size 1555, 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 1225, which is longer than the specified 1000
Created a chunk of size 1287, 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 1117, which is longer than the specified 1000
Created a chunk of size 1966, which is longer than the specified 1000
Created a chunk of size 1150, 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 1150, which is longer than the specified 1000
Created a chunk of size 1585, which is longer than the specified 1000
Created a chunk of size 1208, which is longer than the specified 1000
Created a chunk of size 1267, which is longer than the specified 1000
Created a chunk of size 1542, which is longer than the specified 1000
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
Created a chunk of size 1058, which is longer than the specified 1000
Created a chunk of size 1075, which is longer than the specified 1000
Created a chunk of size 1217, which is longer than the specified 1000
Created a chunk of size 1109, which is longer than the specified 1000
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
Created a chunk of size 1403, which is longer than the specified 1000
Created a chunk of size 1241, 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 1049, which is longer than the specified 1000
Created a chunk of size 1580, which is longer than the specified 1000
Created a chunk of size 1565, which is longer than the specified 1000
Created a chunk of size 1131, which is longer than the specified 1000
Created a chunk of size 1425, which is longer than the specified 1000
Created a chunk of size 1054, which is longer than the specified 1000
Created a chunk of size 1027, which is longer than the specified 1000
Created a chunk of size 2559, which is longer than the specified 1000
Created a chunk of size 1028, which is longer than the specified 1000
Created a chunk of size 1382, which is longer than the specified 1000
Created a chunk of size 1888, which is longer than the specified 1000
Created a chunk of size 1475, which is longer than the specified 1000
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
Created a chunk of size 2537, which is longer than the specified 1000
Created a chunk of size 1251, which is longer than the specified 1000
Created a chunk of size 1734, 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 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
Created a chunk of size 1062, which is longer than the specified 1000
Created a chunk of size 1206, which is longer than the specified 1000
Created a chunk of size 1102, which is longer than the specified 1000
Created a chunk of size 1184, which is longer than the specified 1000
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
Created a chunk of size 1871, which is longer than the specified 1000
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
Created a chunk of size 2054, which is longer than the specified 1000
Created a chunk of size 2000, which is longer than the specified 1000
Created a chunk of size 2061, which is longer than the specified 1000
Created a chunk of size 1066, 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 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.

In [14]:
from langchain.embeddings.openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
embeddings
Out [14]:
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)
In [ ]:
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

In [16]:
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
In [17]:
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

In [18]:
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
In [19]:
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)
In [ ]:
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.

Reference in new issue View Git Blame Copy Permalink