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langchain/cookbook/code-analysis-deeplake.ipynb
Levon Ghukasyan 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

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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use LangChain, GPT and Activeloop's Deep Lake to work with code base\n",
"In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT to analyze the code base of the LangChain itself. "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Design"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Prepare data:\n",
" 1. Upload all python project files using the `langchain_community.document_loaders.TextLoader`. We will call these files the **documents**.\n",
" 2. Split all documents to chunks using the `langchain_text_splitters.CharacterTextSplitter`.\n",
" 3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain_community.vectorstores.DeepLake`\n",
"2. Question-Answering:\n",
" 1. Build a chain from `langchain.chat_models.ChatOpenAI` and `langchain.chains.ConversationalRetrievalChain`\n",
" 2. Prepare questions.\n",
" 3. Get answers running the chain.\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementation"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### Integration preparations"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to set up keys for external services and install necessary python libraries."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#!python3 -m pip install --upgrade langchain langchain-deeplake openai"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Set up OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. \n",
"\n",
"For full documentation of Deep Lake please follow https://docs.activeloop.ai/ and API reference https://docs.deeplake.ai/en/latest/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"if \"OPENAI_API_KEY\" not in os.environ:\n",
" os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
"# Please manually enter OpenAI Key"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"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](https://app.activeloop.ai)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"activeloop_token = getpass(\"Activeloop Token:\")\n",
"os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare data "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"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.\n",
"\n",
"If you want to use files from different repo, change `root_dir` to the root dir of your repo."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CITATION.cff MIGRATE.md README.md libs\t poetry.toml\n",
"LICENSE Makefile\t docs\t poetry.lock pyproject.toml\n"
]
}
],
"source": [
"!ls \"../../../../../../libs\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2554\n"
]
}
],
"source": [
"from langchain_community.document_loaders import TextLoader\n",
"\n",
"root_dir = \"../../../../../../libs\"\n",
"\n",
"docs = []\n",
"for dirpath, dirnames, filenames in os.walk(root_dir):\n",
" for file in filenames:\n",
" if file.endswith(\".py\") and \"*venv/\" not in dirpath:\n",
" try:\n",
" loader = TextLoader(os.path.join(dirpath, file), encoding=\"utf-8\")\n",
" docs.extend(loader.load_and_split())\n",
" except Exception:\n",
" pass\n",
"print(f\"{len(docs)}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, chunk the files"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
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"Created a chunk of size 1025, which is longer than the specified 1000\n",
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"Created a chunk of size 1481, which is longer than the specified 1000\n",
"Created a chunk of size 1505, which is longer than the specified 1000\n",
"Created a chunk of size 1497, which is longer than the specified 1000\n",
"Created a chunk of size 1505, which is longer than the specified 1000\n",
"Created a chunk of size 1282, which is longer than the specified 1000\n",
"Created a chunk of size 1224, which is longer than the specified 1000\n",
"Created a chunk of size 1261, which is longer than the specified 1000\n",
"Created a chunk of size 1123, which is longer than the specified 1000\n",
"Created a chunk of size 1137, which is longer than the specified 1000\n",
"Created a chunk of size 2183, which is longer than the specified 1000\n",
"Created a chunk of size 1039, which is longer than the specified 1000\n",
"Created a chunk of size 1135, which is longer than the specified 1000\n",
"Created a chunk of size 1254, which is longer than the specified 1000\n",
"Created a chunk of size 1234, which is longer than the specified 1000\n",
"Created a chunk of size 1111, which is longer than the specified 1000\n",
"Created a chunk of size 1135, which is longer than the specified 1000\n",
"Created a chunk of size 2023, which is longer than the specified 1000\n",
"Created a chunk of size 1216, which is longer than the specified 1000\n",
"Created a chunk of size 1013, which is longer than the specified 1000\n",
"Created a chunk of size 1152, which is longer than the specified 1000\n",
"Created a chunk of size 1087, which is longer than the specified 1000\n",
"Created a chunk of size 1040, which is longer than the specified 1000\n",
"Created a chunk of size 1330, which is longer than the specified 1000\n",
"Created a chunk of size 2342, which is longer than the specified 1000\n",
"Created a chunk of size 1940, which is longer than the specified 1000\n",
"Created a chunk of size 1621, which is longer than the specified 1000\n",
"Created a chunk of size 2169, which is longer than the specified 1000\n",
"Created a chunk of size 1824, which is longer than the specified 1000\n",
"Created a chunk of size 1554, which is longer than the specified 1000\n",
"Created a chunk of size 1457, which is longer than the specified 1000\n",
"Created a chunk of size 1486, which is longer than the specified 1000\n",
"Created a chunk of size 1556, which is longer than the specified 1000\n",
"Created a chunk of size 1012, which is longer than the specified 1000\n",
"Created a chunk of size 1484, which is longer than the specified 1000\n",
"Created a chunk of size 1039, which is longer than the specified 1000\n",
"Created a chunk of size 1335, which is longer than the specified 1000\n",
"Created a chunk of size 1684, which is longer than the specified 1000\n",
"Created a chunk of size 1537, which is longer than the specified 1000\n",
"Created a chunk of size 1136, which is longer than the specified 1000\n",
"Created a chunk of size 1219, which is longer than the specified 1000\n",
"Created a chunk of size 1011, which is longer than the specified 1000\n",
"Created a chunk of size 1055, which is longer than the specified 1000\n",
"Created a chunk of size 1433, which is longer than the specified 1000\n",
"Created a chunk of size 1263, which is longer than the specified 1000\n",
"Created a chunk of size 1014, which is longer than the specified 1000\n",
"Created a chunk of size 1107, which is longer than the specified 1000\n",
"Created a chunk of size 2702, which is longer than the specified 1000\n",
"Created a chunk of size 1237, which is longer than the specified 1000\n",
"Created a chunk of size 1172, which is longer than the specified 1000\n",
"Created a chunk of size 1517, which is longer than the specified 1000\n",
"Created a chunk of size 1589, which is longer than the specified 1000\n",
"Created a chunk of size 1681, which is longer than the specified 1000\n",
"Created a chunk of size 2244, which is longer than the specified 1000\n",
"Created a chunk of size 1505, which is longer than the specified 1000\n",
"Created a chunk of size 1228, which is longer than the specified 1000\n",
"Created a chunk of size 1801, which is longer than the specified 1000\n",
"Created a chunk of size 1856, which is longer than the specified 1000\n",
"Created a chunk of size 2171, which is longer than the specified 1000\n",
"Created a chunk of size 2450, which is longer than the specified 1000\n",
"Created a chunk of size 1110, which is longer than the specified 1000\n",
"Created a chunk of size 1148, which is longer than the specified 1000\n",
"Created a chunk of size 1050, which is longer than the specified 1000\n",
"Created a chunk of size 1014, which is longer than the specified 1000\n",
"Created a chunk of size 1458, which is longer than the specified 1000\n",
"Created a chunk of size 1270, which is longer than the specified 1000\n",
"Created a chunk of size 1287, which is longer than the specified 1000\n",
"Created a chunk of size 1127, which is longer than the specified 1000\n",
"Created a chunk of size 1576, which is longer than the specified 1000\n",
"Created a chunk of size 1350, which is longer than the specified 1000\n",
"Created a chunk of size 2283, which is longer than the specified 1000\n",
"Created a chunk of size 2211, which is longer than the specified 1000\n",
"Created a chunk of size 1167, which is longer than the specified 1000\n",
"Created a chunk of size 1038, which is longer than the specified 1000\n",
"Created a chunk of size 1117, which is longer than the specified 1000\n",
"Created a chunk of size 1160, which is longer than the specified 1000\n",
"Created a chunk of size 1163, which is longer than the specified 1000\n",
"Created a chunk of size 1013, which is longer than the specified 1000\n",
"Created a chunk of size 1226, which is longer than the specified 1000\n",
"Created a chunk of size 1336, which is longer than the specified 1000\n",
"Created a chunk of size 1012, which is longer than the specified 1000\n",
"Created a chunk of size 2833, which is longer than the specified 1000\n",
"Created a chunk of size 1201, which is longer than the specified 1000\n",
"Created a chunk of size 1172, which is longer than the specified 1000\n",
"Created a chunk of size 1438, which is longer than the specified 1000\n",
"Created a chunk of size 1259, which is longer than the specified 1000\n",
"Created a chunk of size 1452, which is longer than the specified 1000\n",
"Created a chunk of size 1377, which is longer than the specified 1000\n",
"Created a chunk of size 1001, which is longer than the specified 1000\n",
"Created a chunk of size 1240, which is longer than the specified 1000\n",
"Created a chunk of size 1142, which is longer than the specified 1000\n",
"Created a chunk of size 1338, which is longer than the specified 1000\n",
"Created a chunk of size 1057, which is longer than the specified 1000\n",
"Created a chunk of size 1040, which is longer than the specified 1000\n",
"Created a chunk of size 1579, which is longer than the specified 1000\n",
"Created a chunk of size 1176, which is longer than the specified 1000\n",
"Created a chunk of size 1081, which is longer than the specified 1000\n",
"Created a chunk of size 1751, which is longer than the specified 1000\n",
"Created a chunk of size 1064, which is longer than the specified 1000\n",
"Created a chunk of size 1029, which is longer than the specified 1000\n",
"Created a chunk of size 1937, which is longer than the specified 1000\n",
"Created a chunk of size 1972, which is longer than the specified 1000\n",
"Created a chunk of size 1417, which is longer than the specified 1000\n",
"Created a chunk of size 1203, which is longer than the specified 1000\n",
"Created a chunk of size 1314, which is longer than the specified 1000\n",
"Created a chunk of size 1088, which is longer than the specified 1000\n",
"Created a chunk of size 1455, which is longer than the specified 1000\n",
"Created a chunk of size 1467, which is longer than the specified 1000\n",
"Created a chunk of size 1476, which is longer than the specified 1000\n",
"Created a chunk of size 1354, which is longer than the specified 1000\n",
"Created a chunk of size 1403, which is longer than the specified 1000\n",
"Created a chunk of size 1366, which is longer than the specified 1000\n",
"Created a chunk of size 1112, which is longer than the specified 1000\n",
"Created a chunk of size 1512, which is longer than the specified 1000\n",
"Created a chunk of size 1262, which is longer than the specified 1000\n",
"Created a chunk of size 1405, which is longer than the specified 1000\n",
"Created a chunk of size 2221, which is longer than the specified 1000\n",
"Created a chunk of size 1128, which is longer than the specified 1000\n",
"Created a chunk of size 1021, which is longer than the specified 1000\n",
"Created a chunk of size 1532, which is longer than the specified 1000\n",
"Created a chunk of size 1535, which is longer than the specified 1000\n",
"Created a chunk of size 1230, which is longer than the specified 1000\n",
"Created a chunk of size 2456, which is longer than the specified 1000\n",
"Created a chunk of size 1047, which is longer than the specified 1000\n",
"Created a chunk of size 1320, which is longer than the specified 1000\n",
"Created a chunk of size 1144, which is longer than the specified 1000\n",
"Created a chunk of size 1509, which is longer than the specified 1000\n",
"Created a chunk of size 1003, which is longer than the specified 1000\n",
"Created a chunk of size 1025, which is longer than the specified 1000\n",
"Created a chunk of size 1197, which is longer than the specified 1000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"8244\n"
]
}
],
"source": [
"from langchain_text_splitters import CharacterTextSplitter\n",
"\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_documents(docs)\n",
"print(f\"{len(texts)}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Then embed chunks and upload them to the DeepLake.\n",
"\n",
"This can take several minutes. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"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={})"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"embeddings = OpenAIEmbeddings()\n",
"embeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain_deeplake.vectorstores import DeeplakeVectorStore\n",
"\n",
"username = \"<USERNAME_OR_ORG>\"\n",
"\n",
"\n",
"db = DeeplakeVectorStore.from_documents(\n",
" documents=texts,\n",
" embedding=embeddings,\n",
" dataset_path=f\"hub://{username}/langchain-code\",\n",
" overwrite=True,\n",
")\n",
"db"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question Answering\n",
"First load the dataset, construct the retriever, then construct the Conversational Chain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"db = DeeplakeVectorStore(\n",
" dataset_path=f\"hub://{username}/langchain-code\",\n",
" read_only=True,\n",
" embedding_function=embeddings,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"retriever = db.as_retriever()\n",
"retriever.search_kwargs[\"distance_metric\"] = \"cos\"\n",
"retriever.search_kwargs[\"fetch_k\"] = 20\n",
"retriever.search_kwargs[\"maximal_marginal_relevance\"] = True\n",
"retriever.search_kwargs[\"k\"] = 20"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chains import ConversationalRetrievalChain\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(model=\"gpt-3.5-turbo-0613\") # 'ada' 'gpt-3.5-turbo-0613' 'gpt-4',\n",
"qa = RetrievalQA.from_llm(model, retriever=retriever)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-> **Question**: What is the class hierarchy? \n",
"\n",
"**Answer**: The class hierarchy for Memory is as follows:\n",
"\n",
" BaseMemory --> BaseChatMemory --> <name>Memory # Examples: ZepMemory, MotorheadMemory\n",
"\n",
"The class hierarchy for ChatMessageHistory is as follows:\n",
"\n",
" BaseChatMessageHistory --> <name>ChatMessageHistory # Example: ZepChatMessageHistory\n",
"\n",
"The class hierarchy for Prompt is as follows:\n",
"\n",
" BasePromptTemplate --> PipelinePromptTemplate\n",
" StringPromptTemplate --> PromptTemplate\n",
" FewShotPromptTemplate\n",
" FewShotPromptWithTemplates\n",
" BaseChatPromptTemplate --> AutoGPTPrompt\n",
" ChatPromptTemplate --> AgentScratchPadChatPromptTemplate\n",
" \n",
"\n",
"-> **Question**: What classes are derived from the Chain class? \n",
"\n",
"**Answer**: The classes derived from the Chain class are:\n",
"\n",
"- APIChain\n",
"- OpenAPIEndpointChain\n",
"- AnalyzeDocumentChain\n",
"- MapReduceDocumentsChain\n",
"- MapRerankDocumentsChain\n",
"- ReduceDocumentsChain\n",
"- RefineDocumentsChain\n",
"- StuffDocumentsChain\n",
"- ConstitutionalChain\n",
"- ConversationChain\n",
"- ChatVectorDBChain\n",
"- ConversationalRetrievalChain\n",
"- FalkorDBQAChain\n",
"- FlareChain\n",
"- ArangoGraphQAChain\n",
"- GraphQAChain\n",
"- GraphCypherQAChain\n",
"- HugeGraphQAChain\n",
"- KuzuQAChain\n",
"- NebulaGraphQAChain\n",
"- NeptuneOpenCypherQAChain\n",
"- GraphSparqlQAChain\n",
"- HypotheticalDocumentEmbedder\n",
"- LLMChain\n",
"- LLMBashChain\n",
"- LLMCheckerChain\n",
"- LLMMathChain\n",
"- LLMRequestsChain\n",
"- LLMSummarizationCheckerChain\n",
"- MapReduceChain\n",
"- OpenAIModerationChain\n",
"- NatBotChain\n",
"- QAGenerationChain\n",
"- QAWithSourcesChain\n",
"- RetrievalQAWithSourcesChain\n",
"- VectorDBQAWithSourcesChain\n",
"- RetrievalQA\n",
"- VectorDBQA\n",
"- LLMRouterChain\n",
"- MultiPromptChain\n",
"- MultiRetrievalQAChain\n",
"- MultiRouteChain\n",
"- RouterChain\n",
"- SequentialChain\n",
"- SimpleSequentialChain\n",
"- TransformChain\n",
"- TaskPlaningChain\n",
"- QueryChain\n",
"- CPALChain\n",
" \n",
"\n",
"-> **Question**: What kind of retrievers does LangChain have? \n",
"\n",
"**Answer**: The LangChain class includes various types of retrievers such as:\n",
"\n",
"- ArxivRetriever\n",
"- AzureAISearchRetriever\n",
"- BM25Retriever\n",
"- ChaindeskRetriever\n",
"- ChatGPTPluginRetriever\n",
"- ContextualCompressionRetriever\n",
"- DocArrayRetriever\n",
"- ElasticSearchBM25Retriever\n",
"- EnsembleRetriever\n",
"- GoogleVertexAISearchRetriever\n",
"- AmazonKendraRetriever\n",
"- KNNRetriever\n",
"- LlamaIndexGraphRetriever and LlamaIndexRetriever\n",
"- MergerRetriever\n",
"- MetalRetriever\n",
"- MilvusRetriever\n",
"- MultiQueryRetriever\n",
"- ParentDocumentRetriever\n",
"- PineconeHybridSearchRetriever\n",
"- PubMedRetriever\n",
"- RePhraseQueryRetriever\n",
"- RemoteLangChainRetriever\n",
"- SelfQueryRetriever\n",
"- SVMRetriever\n",
"- TFIDFRetriever\n",
"- TimeWeightedVectorStoreRetriever\n",
"- VespaRetriever\n",
"- WeaviateHybridSearchRetriever\n",
"- WebResearchRetriever\n",
"- WikipediaRetriever\n",
"- ZepRetriever\n",
"- ZillizRetriever \n",
"\n"
]
}
],
"source": [
"questions = [\n",
" \"What is the class hierarchy?\",\n",
" \"What classes are derived from the Chain class?\",\n",
" \"What kind of retrievers does LangChain have?\",\n",
"]\n",
"chat_history = []\n",
"qa_dict = {}\n",
"\n",
"for question in questions:\n",
" result = qa({\"question\": question, \"chat_history\": chat_history})\n",
" chat_history.append((question, result[\"answer\"]))\n",
" qa_dict[question] = result[\"answer\"]\n",
" print(f\"-> **Question**: {question} \\n\")\n",
" print(f\"**Answer**: {result['answer']} \\n\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'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'}"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qa_dict"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The class hierarchy for Memory is as follows:\n",
"\n",
" BaseMemory --> BaseChatMemory --> <name>Memory # Examples: ZepMemory, MotorheadMemory\n",
"\n",
"The class hierarchy for ChatMessageHistory is as follows:\n",
"\n",
" BaseChatMessageHistory --> <name>ChatMessageHistory # Example: ZepChatMessageHistory\n",
"\n",
"The class hierarchy for Prompt is as follows:\n",
"\n",
" BasePromptTemplate --> PipelinePromptTemplate\n",
" StringPromptTemplate --> PromptTemplate\n",
" FewShotPromptTemplate\n",
" FewShotPromptWithTemplates\n",
" BaseChatPromptTemplate --> AutoGPTPrompt\n",
" ChatPromptTemplate --> AgentScratchPadChatPromptTemplate\n",
"\n"
]
}
],
"source": [
"print(qa_dict[\"What is the class hierarchy?\"])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The classes derived from the Chain class are:\n",
"\n",
"- APIChain\n",
"- OpenAPIEndpointChain\n",
"- AnalyzeDocumentChain\n",
"- MapReduceDocumentsChain\n",
"- MapRerankDocumentsChain\n",
"- ReduceDocumentsChain\n",
"- RefineDocumentsChain\n",
"- StuffDocumentsChain\n",
"- ConstitutionalChain\n",
"- ConversationChain\n",
"- ChatVectorDBChain\n",
"- ConversationalRetrievalChain\n",
"- FlareChain\n",
"- ArangoGraphQAChain\n",
"- GraphQAChain\n",
"- GraphCypherQAChain\n",
"- HugeGraphQAChain\n",
"- KuzuQAChain\n",
"- NebulaGraphQAChain\n",
"- NeptuneOpenCypherQAChain\n",
"- GraphSparqlQAChain\n",
"- HypotheticalDocumentEmbedder\n",
"- LLMChain\n",
"- LLMBashChain\n",
"- LLMCheckerChain\n",
"- LLMMathChain\n",
"- LLMRequestsChain\n",
"- LLMSummarizationCheckerChain\n",
"- MapReduceChain\n",
"- OpenAIModerationChain\n",
"- NatBotChain\n",
"- QAGenerationChain\n",
"- QAWithSourcesChain\n",
"- RetrievalQAWithSourcesChain\n",
"- VectorDBQAWithSourcesChain\n",
"- RetrievalQA\n",
"- VectorDBQA\n",
"- LLMRouterChain\n",
"- MultiPromptChain\n",
"- MultiRetrievalQAChain\n",
"- MultiRouteChain\n",
"- RouterChain\n",
"- SequentialChain\n",
"- SimpleSequentialChain\n",
"- TransformChain\n",
"- TaskPlaningChain\n",
"- QueryChain\n",
"- CPALChain\n",
"\n"
]
}
],
"source": [
"print(qa_dict[\"What classes are derived from the Chain class?\"])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The LangChain class includes various types of retrievers such as:\n",
"\n",
"- ArxivRetriever\n",
"- AzureAISearchRetriever\n",
"- BM25Retriever\n",
"- ChaindeskRetriever\n",
"- ChatGPTPluginRetriever\n",
"- ContextualCompressionRetriever\n",
"- DocArrayRetriever\n",
"- ElasticSearchBM25Retriever\n",
"- EnsembleRetriever\n",
"- GoogleVertexAISearchRetriever\n",
"- AmazonKendraRetriever\n",
"- KNNRetriever\n",
"- LlamaIndexGraphRetriever and LlamaIndexRetriever\n",
"- MergerRetriever\n",
"- MetalRetriever\n",
"- MilvusRetriever\n",
"- MultiQueryRetriever\n",
"- ParentDocumentRetriever\n",
"- PineconeHybridSearchRetriever\n",
"- PubMedRetriever\n",
"- RePhraseQueryRetriever\n",
"- RemoteLangChainRetriever\n",
"- SelfQueryRetriever\n",
"- SVMRetriever\n",
"- TFIDFRetriever\n",
"- TimeWeightedVectorStoreRetriever\n",
"- VespaRetriever\n",
"- WeaviateHybridSearchRetriever\n",
"- WebResearchRetriever\n",
"- WikipediaRetriever\n",
"- ZepRetriever\n",
"- ZillizRetriever\n"
]
}
],
"source": [
"print(qa_dict[\"What kind of retrievers does LangChain have?\"])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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