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Levon GhukasyanandChester Curme ec403c442a Separate deepale vector store (#29902) 
Thank you for contributing to LangChain!

- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
  - Example: "community: add foobar LLM"

- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>
2025-02-20 17:37:19 +00:00

52 KiB
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Use LangChain, GPT and Activeloop's Deep Lake to work with code base

In this tutorial, we are going to use Langchain + Activeloop's 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_community.document_loaders.TextLoader. We will call these files the documents.
    2. Split all documents to chunks using the langchain_text_splitters.CharacterTextSplitter.
    3. Embed chunks and upload them into the DeepLake using langchain.embeddings.openai.OpenAIEmbeddings and langchain_community.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 [ ]:
#!python3 -m pip install --upgrade langchain 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 [1]:
import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    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

In [2]:
activeloop_token = getpass("Activeloop Token:")
os.environ["ACTIVELOOP_TOKEN"] = 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.

In [10]:
!ls "../../../../../../libs"
CITATION.cff  MIGRATE.md  README.md  libs	  poetry.toml
LICENSE       Makefile	  docs	     poetry.lock  pyproject.toml
In [11]:
from langchain_community.document_loaders import TextLoader

root_dir = "../../../../../../libs"

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:
                pass
print(f"{len(docs)}")
2554

Then, chunk the files

In [12]:
from langchain_text_splitters 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 1010, which is longer than the specified 1000
Created a chunk of size 3466, which is longer than the specified 1000
Created a chunk of size 1375, which is longer than the specified 1000
Created a chunk of size 1928, which is longer than the specified 1000
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Created a chunk of size 1021, which is longer than the specified 1000
Created a chunk of size 1532, which is longer than the specified 1000
Created a chunk of size 1535, which is longer than the specified 1000
Created a chunk of size 1230, which is longer than the specified 1000
Created a chunk of size 2456, which is longer than the specified 1000
Created a chunk of size 1047, which is longer than the specified 1000
Created a chunk of size 1320, 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 1509, which is longer than the specified 1000
Created a chunk of size 1003, which is longer than the specified 1000
Created a chunk of size 1025, which is longer than the specified 1000
Created a chunk of size 1197, which is longer than the specified 1000

8244

Then embed chunks and upload them to the DeepLake.

This can take several minutes.

In [13]:
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
embeddings
Out [13]:
OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', deployment='text-embedding-ada-002', openai_api_version='', openai_api_base='', openai_api_type='', openai_proxy='', embedding_ctx_length=8191, openai_api_key='', openai_organization='', allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6, request_timeout=None, headers=None, tiktoken_model_name=None, show_progress_bar=False, model_kwargs={})
In [ ]:
from langchain_deeplake.vectorstores import DeeplakeVectorStore

username = "<USERNAME_OR_ORG>"


db = DeeplakeVectorStore.from_documents(
    documents=texts,
    embedding=embeddings,
    dataset_path=f"hub://{username}/langchain-code",
    overwrite=True,
)
db

Question Answering

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

In [ ]:
db = DeeplakeVectorStore(
    dataset_path=f"hub://{username}/langchain-code",
    read_only=True,
    embedding_function=embeddings,
)
In [18]:
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
In [20]:
from langchain.chains import ConversationalRetrievalChain
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-3.5-turbo-0613")  # 'ada' 'gpt-3.5-turbo-0613' 'gpt-4',
qa = RetrievalQA.from_llm(model, retriever=retriever)
In [32]:
questions = [
    "What is the class hierarchy?",
    "What classes are derived from the Chain class?",
    "What kind of retrievers does LangChain have?",
]
chat_history = []
qa_dict = {}

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

**Answer**: The class hierarchy for Memory is as follows:

    BaseMemory --> BaseChatMemory --> <name>Memory  # Examples: ZepMemory, MotorheadMemory

The class hierarchy for ChatMessageHistory is as follows:

    BaseChatMessageHistory --> <name>ChatMessageHistory  # Example: ZepChatMessageHistory

The class hierarchy for Prompt is as follows:

    BasePromptTemplate --> PipelinePromptTemplate
                           StringPromptTemplate --> PromptTemplate
                                                    FewShotPromptTemplate
                                                    FewShotPromptWithTemplates
                           BaseChatPromptTemplate --> AutoGPTPrompt
                                                      ChatPromptTemplate --> AgentScratchPadChatPromptTemplate
 

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

**Answer**: The classes derived from the Chain class are:

- APIChain
- OpenAPIEndpointChain
- AnalyzeDocumentChain
- MapReduceDocumentsChain
- MapRerankDocumentsChain
- ReduceDocumentsChain
- RefineDocumentsChain
- StuffDocumentsChain
- ConstitutionalChain
- ConversationChain
- ChatVectorDBChain
- ConversationalRetrievalChain
- FalkorDBQAChain
- FlareChain
- ArangoGraphQAChain
- GraphQAChain
- GraphCypherQAChain
- HugeGraphQAChain
- KuzuQAChain
- NebulaGraphQAChain
- NeptuneOpenCypherQAChain
- GraphSparqlQAChain
- HypotheticalDocumentEmbedder
- LLMChain
- LLMBashChain
- LLMCheckerChain
- LLMMathChain
- LLMRequestsChain
- LLMSummarizationCheckerChain
- MapReduceChain
- OpenAIModerationChain
- NatBotChain
- QAGenerationChain
- QAWithSourcesChain
- RetrievalQAWithSourcesChain
- VectorDBQAWithSourcesChain
- RetrievalQA
- VectorDBQA
- LLMRouterChain
- MultiPromptChain
- MultiRetrievalQAChain
- MultiRouteChain
- RouterChain
- SequentialChain
- SimpleSequentialChain
- TransformChain
- TaskPlaningChain
- QueryChain
- CPALChain
 

-> **Question**: What kind of retrievers does LangChain have? 

**Answer**: The LangChain class includes various types of retrievers such as:

- ArxivRetriever
- AzureAISearchRetriever
- BM25Retriever
- ChaindeskRetriever
- ChatGPTPluginRetriever
- ContextualCompressionRetriever
- DocArrayRetriever
- ElasticSearchBM25Retriever
- EnsembleRetriever
- GoogleVertexAISearchRetriever
- AmazonKendraRetriever
- KNNRetriever
- LlamaIndexGraphRetriever and LlamaIndexRetriever
- MergerRetriever
- MetalRetriever
- MilvusRetriever
- MultiQueryRetriever
- ParentDocumentRetriever
- PineconeHybridSearchRetriever
- PubMedRetriever
- RePhraseQueryRetriever
- RemoteLangChainRetriever
- SelfQueryRetriever
- SVMRetriever
- TFIDFRetriever
- TimeWeightedVectorStoreRetriever
- VespaRetriever
- WeaviateHybridSearchRetriever
- WebResearchRetriever
- WikipediaRetriever
- ZepRetriever
- ZillizRetriever
In [31]:
qa_dict
Out [31]:
{'question': 'LangChain possesses a variety of retrievers including:\n\n1. ArxivRetriever\n2. AzureAISearchRetriever\n3. BM25Retriever\n4. ChaindeskRetriever\n5. ChatGPTPluginRetriever\n6. ContextualCompressionRetriever\n7. DocArrayRetriever\n8. ElasticSearchBM25Retriever\n9. EnsembleRetriever\n10. GoogleVertexAISearchRetriever\n11. AmazonKendraRetriever\n12. KNNRetriever\n13. LlamaIndexGraphRetriever\n14. LlamaIndexRetriever\n15. MergerRetriever\n16. MetalRetriever\n17. MilvusRetriever\n18. MultiQueryRetriever\n19. ParentDocumentRetriever\n20. PineconeHybridSearchRetriever\n21. PubMedRetriever\n22. RePhraseQueryRetriever\n23. RemoteLangChainRetriever\n24. SelfQueryRetriever\n25. SVMRetriever\n26. TFIDFRetriever\n27. TimeWeightedVectorStoreRetriever\n28. VespaRetriever\n29. WeaviateHybridSearchRetriever\n30. WebResearchRetriever\n31. WikipediaRetriever\n32. ZepRetriever\n33. ZillizRetriever\n\nIt also includes self query translators like:\n\n1. ChromaTranslator\n2. DeepLakeTranslator\n3. MyScaleTranslator\n4. PineconeTranslator\n5. QdrantTranslator\n6. WeaviateTranslator\n\nAnd remote retrievers like:\n\n1. RemoteLangChainRetriever'}
In [33]:
print(qa_dict["What is the class hierarchy?"])
The class hierarchy for Memory is as follows:

    BaseMemory --> BaseChatMemory --> <name>Memory  # Examples: ZepMemory, MotorheadMemory

The class hierarchy for ChatMessageHistory is as follows:

    BaseChatMessageHistory --> <name>ChatMessageHistory  # Example: ZepChatMessageHistory

The class hierarchy for Prompt is as follows:

    BasePromptTemplate --> PipelinePromptTemplate
                           StringPromptTemplate --> PromptTemplate
                                                    FewShotPromptTemplate
                                                    FewShotPromptWithTemplates
                           BaseChatPromptTemplate --> AutoGPTPrompt
                                                      ChatPromptTemplate --> AgentScratchPadChatPromptTemplate
In [34]:
print(qa_dict["What classes are derived from the Chain class?"])
The classes derived from the Chain class are:

- APIChain
- OpenAPIEndpointChain
- AnalyzeDocumentChain
- MapReduceDocumentsChain
- MapRerankDocumentsChain
- ReduceDocumentsChain
- RefineDocumentsChain
- StuffDocumentsChain
- ConstitutionalChain
- ConversationChain
- ChatVectorDBChain
- ConversationalRetrievalChain
- FlareChain
- ArangoGraphQAChain
- GraphQAChain
- GraphCypherQAChain
- HugeGraphQAChain
- KuzuQAChain
- NebulaGraphQAChain
- NeptuneOpenCypherQAChain
- GraphSparqlQAChain
- HypotheticalDocumentEmbedder
- LLMChain
- LLMBashChain
- LLMCheckerChain
- LLMMathChain
- LLMRequestsChain
- LLMSummarizationCheckerChain
- MapReduceChain
- OpenAIModerationChain
- NatBotChain
- QAGenerationChain
- QAWithSourcesChain
- RetrievalQAWithSourcesChain
- VectorDBQAWithSourcesChain
- RetrievalQA
- VectorDBQA
- LLMRouterChain
- MultiPromptChain
- MultiRetrievalQAChain
- MultiRouteChain
- RouterChain
- SequentialChain
- SimpleSequentialChain
- TransformChain
- TaskPlaningChain
- QueryChain
- CPALChain
In [35]:
print(qa_dict["What kind of retrievers does LangChain have?"])
The LangChain class includes various types of retrievers such as:

- ArxivRetriever
- AzureAISearchRetriever
- BM25Retriever
- ChaindeskRetriever
- ChatGPTPluginRetriever
- ContextualCompressionRetriever
- DocArrayRetriever
- ElasticSearchBM25Retriever
- EnsembleRetriever
- GoogleVertexAISearchRetriever
- AmazonKendraRetriever
- KNNRetriever
- LlamaIndexGraphRetriever and LlamaIndexRetriever
- MergerRetriever
- MetalRetriever
- MilvusRetriever
- MultiQueryRetriever
- ParentDocumentRetriever
- PineconeHybridSearchRetriever
- PubMedRetriever
- RePhraseQueryRetriever
- RemoteLangChainRetriever
- SelfQueryRetriever
- SVMRetriever
- TFIDFRetriever
- TimeWeightedVectorStoreRetriever
- VespaRetriever
- WeaviateHybridSearchRetriever
- WebResearchRetriever
- WikipediaRetriever
- ZepRetriever
- ZillizRetriever
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