(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.PredictionGuard attribute)
(langchain.llms.Writer attribute)
max_tokens_for_prompt() (langchain.llms.AzureOpenAI method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenLM method)
(langchain.llms.PromptLayerOpenAI method)
max_tokens_limit (langchain.chains.ConversationalRetrievalChain attribute)
(langchain.chains.RetrievalQAWithSourcesChain attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)
max_tokens_per_generation (langchain.llms.RWKV attribute)
max_tokens_to_sample (langchain.llms.Anthropic attribute)
MaxComputeLoader (class in langchain.document_loaders)
maximum_tokens (langchain.llms.AlephAlpha attribute)
maxTokens (langchain.llms.AI21 attribute)
memories (langchain.memory.CombinedMemory attribute)
(langchain.memory.SimpleMemory attribute)
memory (langchain.chains.ConversationChain attribute)
(langchain.experimental.GenerativeAgent attribute)
(langchain.memory.ReadOnlySharedMemory attribute)
memory_key (langchain.memory.ConversationSummaryBufferMemory attribute)
(langchain.memory.ConversationTokenBufferMemory attribute)
(langchain.memory.VectorStoreRetrieverMemory attribute)
memory_retriever (langchain.experimental.GenerativeAgentMemory attribute)
memory_stream (langchain.retrievers.TimeWeightedVectorStoreRetriever attribute)
memory_variables (langchain.experimental.GenerativeAgentMemory property)
(langchain.memory.CombinedMemory property)
(langchain.memory.ConversationStringBufferMemory property)


(langchain.memory.ConversationStringBufferMemory property)
(langchain.memory.ReadOnlySharedMemory property)
(langchain.memory.SimpleMemory property)
(langchain.memory.VectorStoreRetrieverMemory property)
mentioned (langchain.document_loaders.GitHubIssuesLoader attribute)
merge_from() (langchain.vectorstores.FAISS method)
messages (langchain.memory.CassandraChatMessageHistory property)
(langchain.memory.ChatMessageHistory attribute)
(langchain.memory.DynamoDBChatMessageHistory property)
(langchain.memory.FileChatMessageHistory property)
(langchain.memory.MomentoChatMessageHistory property)
(langchain.memory.MongoDBChatMessageHistory property)
(langchain.memory.PostgresChatMessageHistory property)
(langchain.memory.RedisChatMessageHistory property)
metadata_column (langchain.vectorstores.Clickhouse property)
(langchain.vectorstores.MyScale property)
metadata_key (langchain.retrievers.RemoteLangChainRetriever attribute)
METADATA_KEY (langchain.vectorstores.Qdrant attribute)
MetalRetriever (class in langchain.retrievers)
metaphor_api_key (langchain.utilities.MetaphorSearchAPIWrapper attribute)
method (langchain.tools.APIOperation attribute)
metric (langchain.vectorstores.ClickhouseSettings attribute)
(langchain.vectorstores.MyScaleSettings attribute)
milestone (langchain.document_loaders.GitHubIssuesLoader attribute)
Milvus (class in langchain.vectorstores)
min_chunk_size (langchain.document_loaders.DocugamiLoader attribute)
min_length (langchain.llms.NLPCloud attribute)
min_prob (langchain.chains.FlareChain attribute)
min_token_gap (langchain.chains.FlareChain attribute)
min_tokens (langchain.llms.GooseAI attribute)
(langchain.llms.Writer attribute)
minimax_api_key (langchain.embeddings.MiniMaxEmbeddings attribute)


minimax_api_key (langchain.embeddings.MiniMaxEmbeddings attribute)
minimax_group_id (langchain.embeddings.MiniMaxEmbeddings attribute)
minimum_tokens (langchain.llms.AlephAlpha attribute)
minTokens (langchain.llms.AI21 attribute)
model (langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding attribute)
(langchain.embeddings.CohereEmbeddings attribute)
(langchain.embeddings.MiniMaxEmbeddings attribute)
(langchain.llms.AI21 attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.CTransformers attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.PredictionGuard attribute)
(langchain.llms.RWKV attribute)
(langchain.retrievers.document_compressors.CohereRerank attribute)
model_file (langchain.llms.CTransformers attribute)
model_id (langchain.embeddings.BedrockEmbeddings attribute)
(langchain.embeddings.ModelScopeEmbeddings attribute)
(langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute)
(langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute)
(langchain.llms.Bedrock attribute)
(langchain.llms.HuggingFacePipeline attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.Writer attribute)
model_key (langchain.llms.Banana attribute)
model_kwargs (langchain.chat_models.ChatOpenAI attribute)
(langchain.embeddings.BedrockEmbeddings attribute)
(langchain.embeddings.HuggingFaceEmbeddings attribute)
(langchain.embeddings.HuggingFaceHubEmbeddings attribute)
(langchain.embeddings.HuggingFaceInstructEmbeddings attribute)


(langchain.embeddings.HuggingFaceInstructEmbeddings attribute)
(langchain.embeddings.SagemakerEndpointEmbeddings attribute)
(langchain.llms.Anyscale attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Banana attribute)
(langchain.llms.Beam attribute)
(langchain.llms.Bedrock attribute)
(langchain.llms.CerebriumAI attribute)
(langchain.llms.Databricks attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.HuggingFaceEndpoint attribute)
(langchain.llms.HuggingFaceHub attribute)
(langchain.llms.HuggingFacePipeline attribute)
(langchain.llms.Modal attribute)
(langchain.llms.MosaicML attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.PromptLayerOpenAIChat attribute)
(langchain.llms.SagemakerEndpoint attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.StochasticAI attribute)
model_load_fn (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
model_name (langchain.chains.OpenAIModerationChain attribute)
(langchain.chat_models.ChatGooglePalm attribute)
(langchain.chat_models.ChatOpenAI attribute)
(langchain.chat_models.ChatVertexAI attribute)
(langchain.embeddings.HuggingFaceEmbeddings attribute)
(langchain.embeddings.HuggingFaceInstructEmbeddings attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)


(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.PromptLayerOpenAIChat attribute)
(langchain.tools.SteamshipImageGenerationTool attribute)
model_path (langchain.llms.LlamaCpp attribute)
model_reqs (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute)
(langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
model_type (langchain.llms.CTransformers attribute)
model_url (langchain.embeddings.TensorflowHubEmbeddings attribute)
modelname_to_contextsize() (langchain.llms.AzureOpenAI method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenLM method)
(langchain.llms.PromptLayerOpenAI method)
ModernTreasuryLoader (class in langchain.document_loaders)
    module

langchain.agents
langchain.agents.agent_toolkits
langchain.chains
langchain.chat_models
langchain.docstore
langchain.document_loaders
langchain.document_transformers
langchain.embeddings
langchain.llms
langchain.memory
langchain.output_parsers
langchain.prompts
langchain.prompts.example_selector
langchain.python
langchain.retrievers
langchain.retrievers.document_compressors
langchain.serpapi
langchain.text_splitter
langchain.tools
langchain.utilities
langchain.utilities.searx_search
langchain.vectorstores


langchain.utilities
langchain.utilities.searx_search
langchain.vectorstores
MomentoChatMessageHistory (class in langchain.memory)
MongoDBAtlasVectorSearch (class in langchain.vectorstores)
MongoDBChatMessageHistory (class in langchain.memory)
moving_summary_buffer (langchain.memory.ConversationSummaryBufferMemory attribute)
MWDumpLoader (class in langchain.document_loaders)
MyScale (class in langchain.vectorstores)
N
n (langchain.chat_models.ChatGooglePalm attribute)
(langchain.chat_models.ChatOpenAI attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Writer attribute)
n_batch (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
n_ctx (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
n_gpu_layers (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.LlamaCpp attribute)
n_parts (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
n_predict (langchain.llms.GPT4All attribute)
n_threads (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
name (langchain.agents.agent_toolkits.VectorStoreInfo attribute)
(langchain.experimental.GenerativeAgent attribute)


(langchain.experimental.GenerativeAgent attribute)
(langchain.output_parsers.ResponseSchema attribute)
(langchain.tools.BaseTool attribute)
(langchain.tools.ClickTool attribute)
(langchain.tools.CopyFileTool attribute)
(langchain.tools.CurrentWebPageTool attribute)
(langchain.tools.DeleteFileTool attribute)
(langchain.tools.ExtractHyperlinksTool attribute)
(langchain.tools.ExtractTextTool attribute)
(langchain.tools.FileSearchTool attribute)
(langchain.tools.GetElementsTool attribute)
(langchain.tools.GmailCreateDraft attribute)
(langchain.tools.GmailGetMessage attribute)
(langchain.tools.GmailGetThread attribute)
(langchain.tools.GmailSearch attribute)
(langchain.tools.GmailSendMessage attribute)
(langchain.tools.ListDirectoryTool attribute)
(langchain.tools.MoveFileTool attribute)
(langchain.tools.NavigateBackTool attribute)
(langchain.tools.NavigateTool attribute)
(langchain.tools.ReadFileTool attribute)
(langchain.tools.ShellTool attribute)
(langchain.tools.Tool attribute)
(langchain.tools.WriteFileTool attribute)
nla_tools (langchain.agents.agent_toolkits.NLAToolkit attribute)
NLTKTextSplitter (class in langchain.text_splitter)
no_update_value (langchain.output_parsers.RegexDictParser attribute)
normalize (langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding attribute)
NotebookLoader (class in langchain.document_loaders)
NotionDBLoader (class in langchain.document_loaders)
NotionDirectoryLoader (class in langchain.document_loaders)
num_beams (langchain.llms.NLPCloud attribute)
num_pad_tokens (langchain.chains.FlareChain attribute)
num_results (langchain.tools.BingSearchResults attribute)
(langchain.tools.DuckDuckGoSearchResults attribute)
(langchain.tools.GoogleSearchResults attribute)


(langchain.tools.GoogleSearchResults attribute)
num_return_sequences (langchain.llms.NLPCloud attribute)
numResults (langchain.llms.AI21 attribute)
O
object_ids (langchain.document_loaders.OneDriveLoader attribute)
observation_prefix (langchain.agents.Agent property)
(langchain.agents.ConversationalAgent property)
(langchain.agents.ConversationalChatAgent property)
(langchain.agents.StructuredChatAgent property)
(langchain.agents.ZeroShotAgent property)
ObsidianLoader (class in langchain.document_loaders)
OnlinePDFLoader (class in langchain.document_loaders)
openai_api_base (langchain.chat_models.AzureChatOpenAI attribute)
(langchain.chat_models.ChatOpenAI attribute)
openai_api_key (langchain.chains.OpenAIModerationChain attribute)
(langchain.chat_models.AzureChatOpenAI attribute)
(langchain.chat_models.ChatOpenAI attribute)
openai_api_type (langchain.chat_models.AzureChatOpenAI attribute)
openai_api_version (langchain.chat_models.AzureChatOpenAI attribute)
openai_organization (langchain.chains.OpenAIModerationChain attribute)
(langchain.chat_models.AzureChatOpenAI attribute)
(langchain.chat_models.ChatOpenAI attribute)
openai_proxy (langchain.chat_models.AzureChatOpenAI attribute)
(langchain.chat_models.ChatOpenAI attribute)
OpenSearchVectorSearch (class in langchain.vectorstores)
openweathermap_api_key (langchain.utilities.OpenWeatherMapAPIWrapper attribute)
operation_id (langchain.tools.APIOperation attribute)
other_score_keys (langchain.retrievers.TimeWeightedVectorStoreRetriever attribute)
OutlookMessageLoader (class in langchain.document_loaders)
output (langchain.llms.PredictionGuard attribute)
output_key (langchain.chains.QAGenerationChain attribute)


output_key (langchain.chains.QAGenerationChain attribute)
(langchain.memory.ConversationStringBufferMemory attribute)
output_key_to_format (langchain.output_parsers.RegexDictParser attribute)
output_keys (langchain.chains.ConstitutionalChain property)
(langchain.chains.FlareChain property)
(langchain.chains.HypotheticalDocumentEmbedder property)
(langchain.chains.QAGenerationChain property)
(langchain.experimental.BabyAGI property)
(langchain.output_parsers.RegexParser attribute)
output_parser (langchain.agents.Agent attribute)
(langchain.agents.ConversationalAgent attribute)
(langchain.agents.ConversationalChatAgent attribute)
(langchain.agents.LLMSingleActionAgent attribute)
(langchain.agents.StructuredChatAgent attribute)
(langchain.agents.ZeroShotAgent attribute)
(langchain.chains.FlareChain attribute)
(langchain.prompts.BasePromptTemplate attribute)
output_variables (langchain.chains.TransformChain attribute)
owm (langchain.utilities.OpenWeatherMapAPIWrapper attribute)
P
p (langchain.llms.Cohere attribute)
page_content_key (langchain.retrievers.RemoteLangChainRetriever attribute)
PagedPDFSplitter (in module langchain.document_loaders)
paginate_request() (langchain.document_loaders.ConfluenceLoader method)
param_mapping (langchain.chains.OpenAPIEndpointChain attribute)
params (langchain.serpapi.SerpAPIWrapper attribute)
(langchain.tools.ZapierNLARunAction attribute)
(langchain.utilities.searx_search.SearxSearchWrapper attribute)
(langchain.utilities.SearxSearchWrapper attribute)
(langchain.utilities.SerpAPIWrapper attribute)
params_schema (langchain.tools.ZapierNLARunAction attribute)
parse() (langchain.agents.AgentOutputParser method)


parse() (langchain.agents.AgentOutputParser method)
(langchain.output_parsers.CommaSeparatedListOutputParser method)
(langchain.output_parsers.DatetimeOutputParser method)
(langchain.output_parsers.GuardrailsOutputParser method)
(langchain.output_parsers.ListOutputParser method)
(langchain.output_parsers.OutputFixingParser method)
(langchain.output_parsers.PydanticOutputParser method)
(langchain.output_parsers.RegexDictParser method)
(langchain.output_parsers.RegexParser method)
(langchain.output_parsers.RetryOutputParser method)
(langchain.output_parsers.RetryWithErrorOutputParser method)
(langchain.output_parsers.StructuredOutputParser method)
parse_filename() (langchain.document_loaders.BlackboardLoader method)
parse_issue() (langchain.document_loaders.GitHubIssuesLoader method)
parse_obj() (langchain.tools.OpenAPISpec class method)
parse_sitemap() (langchain.document_loaders.SitemapLoader method)
parse_with_prompt() (langchain.output_parsers.RetryOutputParser method)
(langchain.output_parsers.RetryWithErrorOutputParser method)
parser (langchain.output_parsers.OutputFixingParser attribute)
(langchain.output_parsers.RetryOutputParser attribute)
(langchain.output_parsers.RetryWithErrorOutputParser attribute)
partial() (langchain.prompts.BasePromptTemplate method)
(langchain.prompts.ChatPromptTemplate method)
password (langchain.vectorstores.ClickhouseSettings attribute)
(langchain.vectorstores.MyScaleSettings attribute)
patch() (langchain.utilities.TextRequestsWrapper method)
path (langchain.tools.APIOperation attribute)
path_params (langchain.tools.APIOperation property)
pause_to_reflect() (langchain.experimental.GenerativeAgentMemory method)
PDFMinerLoader (class in langchain.document_loaders)


PDFMinerLoader (class in langchain.document_loaders)
PDFMinerPDFasHTMLLoader (class in langchain.document_loaders)
PDFPlumberLoader (class in langchain.document_loaders)
penalty_alpha_frequency (langchain.llms.RWKV attribute)
penalty_alpha_presence (langchain.llms.RWKV attribute)
penalty_bias (langchain.llms.AlephAlpha attribute)
penalty_exceptions (langchain.llms.AlephAlpha attribute)
penalty_exceptions_include_stop_sequences (langchain.llms.AlephAlpha attribute)
persist() (langchain.vectorstores.Chroma method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.SKLearnVectorStore method)
PHP (langchain.text_splitter.Language attribute)
Pinecone (class in langchain.vectorstores)
pipeline_key (langchain.llms.PipelineAI attribute)
pipeline_kwargs (langchain.llms.HuggingFacePipeline attribute)
(langchain.llms.PipelineAI attribute)
pl_tags (langchain.chat_models.PromptLayerChatOpenAI attribute)
plan() (langchain.agents.Agent method)
(langchain.agents.BaseMultiActionAgent method)
(langchain.agents.BaseSingleActionAgent method)
(langchain.agents.LLMSingleActionAgent method)
playwright_strict (langchain.tools.ClickTool attribute)
playwright_timeout (langchain.tools.ClickTool attribute)
PlaywrightURLLoader (class in langchain.document_loaders)
plugin (langchain.tools.AIPluginTool attribute)
port (langchain.vectorstores.ClickhouseSettings attribute)
(langchain.vectorstores.MyScaleSettings attribute)
post() (langchain.utilities.TextRequestsWrapper method)
PostgresChatMessageHistory (class in langchain.memory)
powerbi (langchain.agents.agent_toolkits.PowerBIToolkit attribute)


powerbi (langchain.agents.agent_toolkits.PowerBIToolkit attribute)
(langchain.tools.InfoPowerBITool attribute)
(langchain.tools.ListPowerBITool attribute)
(langchain.tools.QueryPowerBITool attribute)
predict() (langchain.chains.LLMChain method)
(langchain.llms.AI21 method)
(langchain.llms.AlephAlpha method)
(langchain.llms.Anthropic method)
(langchain.llms.Anyscale method)
(langchain.llms.Aviary method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.Beam method)
(langchain.llms.Bedrock method)
(langchain.llms.CerebriumAI method)
(langchain.llms.Cohere method)
(langchain.llms.CTransformers method)
(langchain.llms.Databricks method)
(langchain.llms.DeepInfra method)
(langchain.llms.FakeListLLM method)
(langchain.llms.ForefrontAI method)
(langchain.llms.GooglePalm method)
(langchain.llms.GooseAI method)
(langchain.llms.GPT4All method)
(langchain.llms.HuggingFaceEndpoint method)
(langchain.llms.HuggingFaceHub method)
(langchain.llms.HuggingFacePipeline method)
(langchain.llms.HuggingFaceTextGenInference method)
(langchain.llms.HumanInputLLM method)
(langchain.llms.LlamaCpp method)
(langchain.llms.Modal method)
(langchain.llms.MosaicML method)
(langchain.llms.NLPCloud method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenAIChat method)
(langchain.llms.OpenLM method)
(langchain.llms.Petals method)
(langchain.llms.PipelineAI method)


(langchain.llms.Petals method)
(langchain.llms.PipelineAI method)
(langchain.llms.PredictionGuard method)
(langchain.llms.PromptLayerOpenAI method)
(langchain.llms.PromptLayerOpenAIChat method)
(langchain.llms.Replicate method)
(langchain.llms.RWKV method)
(langchain.llms.SagemakerEndpoint method)
(langchain.llms.SelfHostedHuggingFaceLLM method)
(langchain.llms.SelfHostedPipeline method)
(langchain.llms.StochasticAI method)
(langchain.llms.VertexAI method)
(langchain.llms.Writer method)
predict_and_parse() (langchain.chains.LLMChain method)
predict_messages() (langchain.llms.AI21 method)
(langchain.llms.AlephAlpha method)
(langchain.llms.Anthropic method)
(langchain.llms.Anyscale method)
(langchain.llms.Aviary method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.Beam method)
(langchain.llms.Bedrock method)
(langchain.llms.CerebriumAI method)
(langchain.llms.Cohere method)
(langchain.llms.CTransformers method)
(langchain.llms.Databricks method)
(langchain.llms.DeepInfra method)
(langchain.llms.FakeListLLM method)
(langchain.llms.ForefrontAI method)
(langchain.llms.GooglePalm method)
(langchain.llms.GooseAI method)
(langchain.llms.GPT4All method)
(langchain.llms.HuggingFaceEndpoint method)
(langchain.llms.HuggingFaceHub method)
(langchain.llms.HuggingFacePipeline method)


(langchain.llms.HuggingFacePipeline method)
(langchain.llms.HuggingFaceTextGenInference method)
(langchain.llms.HumanInputLLM method)
(langchain.llms.LlamaCpp method)
(langchain.llms.Modal method)
(langchain.llms.MosaicML method)
(langchain.llms.NLPCloud method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenAIChat method)
(langchain.llms.OpenLM method)
(langchain.llms.Petals method)
(langchain.llms.PipelineAI method)
(langchain.llms.PredictionGuard method)
(langchain.llms.PromptLayerOpenAI method)
(langchain.llms.PromptLayerOpenAIChat method)
(langchain.llms.Replicate method)
(langchain.llms.RWKV method)
(langchain.llms.SagemakerEndpoint method)
(langchain.llms.SelfHostedHuggingFaceLLM method)
(langchain.llms.SelfHostedPipeline method)
(langchain.llms.StochasticAI method)
(langchain.llms.VertexAI method)
(langchain.llms.Writer method)
prefix (langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
prefix_messages (langchain.llms.OpenAIChat attribute)
(langchain.llms.PromptLayerOpenAIChat attribute)
prep_prompts() (langchain.chains.LLMChain method)
prep_streaming_params() (langchain.llms.AzureOpenAI method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenLM method)
(langchain.llms.PromptLayerOpenAI method)
prepare_cosmos() (langchain.memory.CosmosDBChatMessageHistory method)
presence_penalty (langchain.llms.AlephAlpha attribute)


presence_penalty (langchain.llms.AlephAlpha attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Writer attribute)
presencePenalty (langchain.llms.AI21 attribute)
prioritize_tasks() (langchain.experimental.BabyAGI method)
process (langchain.tools.ShellTool attribute)
process_attachment() (langchain.document_loaders.ConfluenceLoader method)
process_doc() (langchain.document_loaders.ConfluenceLoader method)
process_image() (langchain.document_loaders.ConfluenceLoader method)
process_index_results() (langchain.vectorstores.Annoy method)
process_output() (langchain.utilities.BashProcess method)
process_page() (langchain.document_loaders.ConfluenceLoader method)
process_pages() (langchain.document_loaders.ConfluenceLoader method)
process_pdf() (langchain.document_loaders.ConfluenceLoader method)
process_svg() (langchain.document_loaders.ConfluenceLoader method)
process_xls() (langchain.document_loaders.ConfluenceLoader method)
project (langchain.llms.VertexAI attribute)
Prompt (in module langchain.prompts)
prompt (langchain.chains.ConversationChain attribute)
(langchain.chains.LLMBashChain attribute)
(langchain.chains.LLMChain attribute)
(langchain.chains.LLMMathChain attribute)
(langchain.chains.PALChain attribute)
(langchain.chains.SQLDatabaseChain attribute)
prompt_func (langchain.tools.HumanInputRun attribute)
properties (langchain.tools.APIOperation attribute)
PROTO (langchain.text_splitter.Language attribute)
prune() (langchain.memory.ConversationSummaryBufferMemory method)


prune() (langchain.memory.ConversationSummaryBufferMemory method)
PsychicLoader (class in langchain.document_loaders)
put() (langchain.utilities.TextRequestsWrapper method)
pydantic_object (langchain.output_parsers.PydanticOutputParser attribute)
PyMuPDFLoader (class in langchain.document_loaders)
PyPDFDirectoryLoader (class in langchain.document_loaders)
PyPDFium2Loader (class in langchain.document_loaders)
PyPDFLoader (class in langchain.document_loaders)
PySparkDataFrameLoader (class in langchain.document_loaders)
PYTHON (langchain.text_splitter.Language attribute)
python_globals (langchain.chains.PALChain attribute)
python_locals (langchain.chains.PALChain attribute)
PythonCodeTextSplitter (class in langchain.text_splitter)
PythonLoader (class in langchain.document_loaders)
Q
qa_chain (langchain.chains.GraphCypherQAChain attribute)
(langchain.chains.GraphQAChain attribute)
Qdrant (class in langchain.vectorstores)
query_checker_prompt (langchain.chains.SQLDatabaseChain attribute)
query_instruction (langchain.embeddings.HuggingFaceInstructEmbeddings attribute)
(langchain.embeddings.MosaicMLInstructorEmbeddings attribute)
(langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings attribute)
query_name (langchain.vectorstores.SupabaseVectorStore attribute)
query_params (langchain.document_loaders.GitHubIssuesLoader property)
(langchain.tools.APIOperation property)
query_suffix (langchain.utilities.searx_search.SearxSearchWrapper attribute)
(langchain.utilities.SearxSearchWrapper attribute)
question_generator_chain (langchain.chains.FlareChain attribute)
question_to_checked_assertions_chain (langchain.chains.LLMCheckerChain attribute)
R


question_to_checked_assertions_chain (langchain.chains.LLMCheckerChain attribute)
R
raw_completion (langchain.llms.AlephAlpha attribute)
REACT_DOCSTORE (langchain.agents.AgentType attribute)
ReadTheDocsLoader (class in langchain.document_loaders)
recall_ttl (langchain.memory.RedisEntityStore attribute)
recursive (langchain.document_loaders.GoogleDriveLoader attribute)
RecursiveCharacterTextSplitter (class in langchain.text_splitter)
RedditPostsLoader (class in langchain.document_loaders)
Redis (class in langchain.vectorstores)
redis_client (langchain.memory.RedisEntityStore attribute)
RedisChatMessageHistory (class in langchain.memory)
reduce_k_below_max_tokens (langchain.chains.RetrievalQAWithSourcesChain attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)
reflection_threshold (langchain.experimental.GenerativeAgentMemory attribute)
regex (langchain.output_parsers.RegexParser attribute)
regex_pattern (langchain.output_parsers.RegexDictParser attribute)
region (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
region_name (langchain.embeddings.BedrockEmbeddings attribute)
(langchain.embeddings.SagemakerEndpointEmbeddings attribute)
(langchain.llms.Bedrock attribute)
(langchain.llms.SagemakerEndpoint attribute)
relevancy_threshold (langchain.retrievers.KNNRetriever attribute)
(langchain.retrievers.SVMRetriever attribute)
remove_end_sequence (langchain.llms.NLPCloud attribute)
remove_input (langchain.llms.NLPCloud attribute)
repeat_last_n (langchain.llms.GPT4All attribute)
repeat_penalty (langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)


(langchain.llms.LlamaCpp attribute)
repetition_penalties_include_completion (langchain.llms.AlephAlpha attribute)
repetition_penalties_include_prompt (langchain.llms.AlephAlpha attribute)
repetition_penalty (langchain.llms.ForefrontAI attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.Writer attribute)
repo_id (langchain.embeddings.HuggingFaceHubEmbeddings attribute)
(langchain.llms.HuggingFaceHub attribute)
request_body (langchain.tools.APIOperation attribute)
request_timeout (langchain.chat_models.ChatOpenAI attribute)
(langchain.embeddings.OpenAIEmbeddings attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
request_url (langchain.utilities.PowerBIDataset property)
requests (langchain.chains.OpenAPIEndpointChain attribute)
(langchain.utilities.TextRequestsWrapper property)
requests_kwargs (langchain.document_loaders.WebBaseLoader attribute)
requests_per_second (langchain.document_loaders.WebBaseLoader attribute)
requests_wrapper (langchain.agents.agent_toolkits.OpenAPIToolkit attribute)
(langchain.chains.APIChain attribute)
(langchain.chains.LLMRequestsChain attribute)
response_chain (langchain.chains.FlareChain attribute)
response_key (langchain.retrievers.RemoteLangChainRetriever attribute)
response_schemas (langchain.output_parsers.StructuredOutputParser attribute)
results() (langchain.serpapi.SerpAPIWrapper method)
(langchain.utilities.BingSearchAPIWrapper method)
(langchain.utilities.DuckDuckGoSearchAPIWrapper method)
(langchain.utilities.GoogleSearchAPIWrapper method)
(langchain.utilities.GoogleSerperAPIWrapper method)
(langchain.utilities.MetaphorSearchAPIWrapper method)


(langchain.utilities.MetaphorSearchAPIWrapper method)
(langchain.utilities.searx_search.SearxSearchWrapper method)
(langchain.utilities.SearxSearchWrapper method)
(langchain.utilities.SerpAPIWrapper method)
results_async() (langchain.utilities.MetaphorSearchAPIWrapper method)
retrieve_article() (langchain.utilities.PubMedAPIWrapper method)
retriever (langchain.chains.ConversationalRetrievalChain attribute)
(langchain.chains.FlareChain attribute)
(langchain.chains.RetrievalQA attribute)
(langchain.chains.RetrievalQAWithSourcesChain attribute)
(langchain.memory.VectorStoreRetrieverMemory attribute)
retry_chain (langchain.output_parsers.OutputFixingParser attribute)
(langchain.output_parsers.RetryOutputParser attribute)
(langchain.output_parsers.RetryWithErrorOutputParser attribute)
retry_sleep (langchain.embeddings.MosaicMLInstructorEmbeddings attribute)
(langchain.llms.MosaicML attribute)
return_all (langchain.chains.SequentialChain attribute)
return_direct (langchain.chains.SQLDatabaseChain attribute)
(langchain.tools.BaseTool attribute)
(langchain.tools.Tool attribute)
return_docs (langchain.memory.VectorStoreRetrieverMemory attribute)
return_intermediate_steps (langchain.agents.AgentExecutor attribute)
(langchain.chains.ConstitutionalChain attribute)
(langchain.chains.OpenAPIEndpointChain attribute)
(langchain.chains.PALChain attribute)
(langchain.chains.SQLDatabaseChain attribute)
(langchain.chains.SQLDatabaseSequentialChain attribute)
return_pl_id (langchain.chat_models.PromptLayerChatOpenAI attribute)
return_stopped_response() (langchain.agents.Agent method)
(langchain.agents.BaseMultiActionAgent method)
(langchain.agents.BaseSingleActionAgent method)


(langchain.agents.BaseSingleActionAgent method)
return_urls (langchain.tools.SteamshipImageGenerationTool attribute)
return_values (langchain.agents.Agent property)
(langchain.agents.BaseMultiActionAgent property)
(langchain.agents.BaseSingleActionAgent property)
revised_answer_prompt (langchain.chains.LLMCheckerChain attribute)
revised_summary_prompt (langchain.chains.LLMSummarizationCheckerChain attribute)
revision_chain (langchain.chains.ConstitutionalChain attribute)
RoamLoader (class in langchain.document_loaders)
root_dir (langchain.agents.agent_toolkits.FileManagementToolkit attribute)
RST (langchain.text_splitter.Language attribute)
RUBY (langchain.text_splitter.Language attribute)
run() (langchain.python.PythonREPL method)
(langchain.serpapi.SerpAPIWrapper method)
(langchain.tools.BaseTool method)
(langchain.utilities.ArxivAPIWrapper method)
(langchain.utilities.BashProcess method)
(langchain.utilities.BingSearchAPIWrapper method)
(langchain.utilities.DuckDuckGoSearchAPIWrapper method)
(langchain.utilities.GooglePlacesAPIWrapper method)
(langchain.utilities.GoogleSearchAPIWrapper method)
(langchain.utilities.GoogleSerperAPIWrapper method)
(langchain.utilities.GraphQLAPIWrapper method)
(langchain.utilities.LambdaWrapper method)
(langchain.utilities.OpenWeatherMapAPIWrapper method)
(langchain.utilities.PowerBIDataset method)
(langchain.utilities.PubMedAPIWrapper method)
(langchain.utilities.PythonREPL method)
(langchain.utilities.searx_search.SearxSearchWrapper method)
(langchain.utilities.SearxSearchWrapper method)
(langchain.utilities.SerpAPIWrapper method)
(langchain.utilities.SparkSQL method)
(langchain.utilities.TwilioAPIWrapper method)
(langchain.utilities.WikipediaAPIWrapper method)


(langchain.utilities.WikipediaAPIWrapper method)
(langchain.utilities.WolframAlphaAPIWrapper method)
run_creation() (langchain.llms.Beam method)
run_no_throw() (langchain.utilities.SparkSQL method)
RUST (langchain.text_splitter.Language attribute)
rwkv_verbose (langchain.llms.RWKV attribute)
S
S3DirectoryLoader (class in langchain.document_loaders)
S3FileLoader (class in langchain.document_loaders)
safesearch (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
sample_rows_in_table_info (langchain.utilities.PowerBIDataset attribute)
save() (langchain.agents.AgentExecutor method)
(langchain.agents.BaseMultiActionAgent method)
(langchain.agents.BaseSingleActionAgent method)
(langchain.llms.AI21 method)
(langchain.llms.AlephAlpha method)
(langchain.llms.Anthropic method)
(langchain.llms.Anyscale method)
(langchain.llms.Aviary method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.Beam method)
(langchain.llms.Bedrock method)
(langchain.llms.CerebriumAI method)
(langchain.llms.Cohere method)
(langchain.llms.CTransformers method)
(langchain.llms.Databricks method)
(langchain.llms.DeepInfra method)
(langchain.llms.FakeListLLM method)
(langchain.llms.ForefrontAI method)
(langchain.llms.GooglePalm method)
(langchain.llms.GooseAI method)
(langchain.llms.GPT4All method)
(langchain.llms.HuggingFaceEndpoint method)
(langchain.llms.HuggingFaceHub method)
(langchain.llms.HuggingFacePipeline method)


(langchain.llms.HuggingFacePipeline method)
(langchain.llms.HuggingFaceTextGenInference method)
(langchain.llms.HumanInputLLM method)
(langchain.llms.LlamaCpp method)
(langchain.llms.Modal method)
(langchain.llms.MosaicML method)
(langchain.llms.NLPCloud method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenAIChat method)
(langchain.llms.OpenLM method)
(langchain.llms.Petals method)
(langchain.llms.PipelineAI method)
(langchain.llms.PredictionGuard method)
(langchain.llms.PromptLayerOpenAI method)
(langchain.llms.PromptLayerOpenAIChat method)
(langchain.llms.Replicate method)
(langchain.llms.RWKV method)
(langchain.llms.SagemakerEndpoint method)
(langchain.llms.SelfHostedHuggingFaceLLM method)
(langchain.llms.SelfHostedPipeline method)
(langchain.llms.StochasticAI method)
(langchain.llms.VertexAI method)
(langchain.llms.Writer method)
(langchain.prompts.BasePromptTemplate method)
(langchain.prompts.ChatPromptTemplate method)
save_agent() (langchain.agents.AgentExecutor method)
save_context() (langchain.experimental.GenerativeAgentMemory method)
(langchain.memory.CombinedMemory method)
(langchain.memory.ConversationEntityMemory method)
(langchain.memory.ConversationKGMemory method)
(langchain.memory.ConversationStringBufferMemory method)
(langchain.memory.ConversationSummaryBufferMemory method)
(langchain.memory.ConversationSummaryMemory method)
(langchain.memory.ConversationTokenBufferMemory method)
(langchain.memory.ReadOnlySharedMemory method)
(langchain.memory.SimpleMemory method)
(langchain.memory.VectorStoreRetrieverMemory method)


(langchain.memory.SimpleMemory method)
(langchain.memory.VectorStoreRetrieverMemory method)
save_local() (langchain.vectorstores.Annoy method)
(langchain.vectorstores.FAISS method)
SCALA (langchain.text_splitter.Language attribute)
schemas (langchain.utilities.PowerBIDataset attribute)
scrape() (langchain.document_loaders.WebBaseLoader method)
scrape_all() (langchain.document_loaders.WebBaseLoader method)
scrape_page() (langchain.tools.ExtractHyperlinksTool static method)
search() (langchain.docstore.InMemoryDocstore method)
(langchain.docstore.Wikipedia method)
(langchain.vectorstores.VectorStore method)
search_index (langchain.vectorstores.Tigris property)
search_kwargs (langchain.chains.ChatVectorDBChain attribute)
(langchain.chains.VectorDBQA attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
(langchain.retrievers.TimeWeightedVectorStoreRetriever attribute)
search_type (langchain.chains.VectorDBQA attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
search_wrapper (langchain.tools.BraveSearch attribute)
searx_host (langchain.utilities.searx_search.SearxSearchWrapper attribute)
(langchain.utilities.SearxSearchWrapper attribute)
SearxResults (class in langchain.utilities.searx_search)
seed (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
select_examples() (langchain.prompts.example_selector.LengthBasedExampleSelector method)
(langchain.prompts.example_selector.MaxMarginalRelevanceExampleSelector method)
(langchain.prompts.example_selector.SemanticSimilarityExampleSelector method)


(langchain.prompts.example_selector.SemanticSimilarityExampleSelector method)
selected_tools (langchain.agents.agent_toolkits.FileManagementToolkit attribute)
SeleniumURLLoader (class in langchain.document_loaders)
SELF_ASK_WITH_SEARCH (langchain.agents.AgentType attribute)
send_pdf() (langchain.document_loaders.MathpixPDFLoader method)
SentenceTransformerEmbeddings (in module langchain.embeddings)
SentenceTransformersTokenTextSplitter (class in langchain.text_splitter)
sequential_chain (langchain.chains.LLMSummarizationCheckerChain attribute)
serpapi_api_key (langchain.serpapi.SerpAPIWrapper attribute)
(langchain.utilities.SerpAPIWrapper attribute)
serper_api_key (langchain.utilities.GoogleSerperAPIWrapper attribute)
service_account_key (langchain.document_loaders.GoogleDriveLoader attribute)
service_account_path (langchain.document_loaders.GoogleApiClient attribute)
service_name (langchain.retrievers.AzureCognitiveSearchRetriever attribute)
session_cache (langchain.tools.QueryPowerBITool attribute)
session_id (langchain.memory.RedisEntityStore attribute)
(langchain.memory.SQLiteEntityStore attribute)
set() (langchain.memory.InMemoryEntityStore method)
(langchain.memory.RedisEntityStore method)
(langchain.memory.SQLiteEntityStore method)
settings (langchain.document_loaders.OneDriveLoader attribute)
similarity_fn (langchain.document_transformers.EmbeddingsRedundantFilter attribute)
(langchain.retrievers.document_compressors.EmbeddingsFilter attribute)
similarity_search() (langchain.vectorstores.AnalyticDB method)
(langchain.vectorstores.Annoy method)
(langchain.vectorstores.AtlasDB method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.Clickhouse method)
(langchain.vectorstores.DeepLake method)


(langchain.vectorstores.Clickhouse method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.ElasticVectorSearch method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.LanceDB method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.MongoDBAtlasVectorSearch method)
(langchain.vectorstores.MyScale method)
(langchain.vectorstores.OpenSearchVectorSearch method)
(langchain.vectorstores.Pinecone method)
(langchain.vectorstores.Qdrant method)
(langchain.vectorstores.Redis method)
(langchain.vectorstores.SKLearnVectorStore method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.Tair method)
(langchain.vectorstores.Tigris method)
(langchain.vectorstores.Typesense method)
(langchain.vectorstores.Vectara method)
(langchain.vectorstores.VectorStore method)
(langchain.vectorstores.Weaviate method)
similarity_search_by_index() (langchain.vectorstores.Annoy method)
similarity_search_by_text() (langchain.vectorstores.Weaviate method)
similarity_search_by_vector() (langchain.vectorstores.AnalyticDB method)
(langchain.vectorstores.Annoy method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.Clickhouse method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.MyScale method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.VectorStore method)
(langchain.vectorstores.Weaviate method)
similarity_search_by_vector_returning_embeddings() (langchain.vectorstores.SupabaseVectorStore method)
similarity_search_by_vector_with_relevance_scores() (langchain.vectorstores.SupabaseVectorStore method)


similarity_search_limit_score() (langchain.vectorstores.Redis method)
similarity_search_with_relevance_scores() (langchain.vectorstores.Clickhouse method)
(langchain.vectorstores.MyScale method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.VectorStore method)
similarity_search_with_score() (langchain.vectorstores.AnalyticDB method)
(langchain.vectorstores.Annoy method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.ElasticVectorSearch method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.MongoDBAtlasVectorSearch method)
(langchain.vectorstores.OpenSearchVectorSearch method)
(langchain.vectorstores.Pinecone method)
(langchain.vectorstores.Qdrant method)
(langchain.vectorstores.Redis method)
(langchain.vectorstores.SKLearnVectorStore method)
(langchain.vectorstores.Tigris method)
(langchain.vectorstores.Typesense method)
(langchain.vectorstores.Vectara method)
(langchain.vectorstores.Weaviate method)
similarity_search_with_score_by_index() (langchain.vectorstores.Annoy method)
similarity_search_with_score_by_vector() (langchain.vectorstores.AnalyticDB method)
(langchain.vectorstores.Annoy method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Milvus method)
similarity_threshold (langchain.document_transformers.EmbeddingsRedundantFilter attribute)
(langchain.retrievers.document_compressors.EmbeddingsFilter attribute)
since (langchain.document_loaders.GitHubIssuesLoader attribute)
SitemapLoader (class in langchain.document_loaders)
siterestrict (langchain.utilities.GoogleSearchAPIWrapper attribute)


siterestrict (langchain.utilities.GoogleSearchAPIWrapper attribute)
size (langchain.tools.SteamshipImageGenerationTool attribute)
SKLearnVectorStore (class in langchain.vectorstores)
SlackDirectoryLoader (class in langchain.document_loaders)
sort (langchain.document_loaders.GitHubIssuesLoader attribute)
SpacyTextSplitter (class in langchain.text_splitter)
SparkSQL (class in langchain.utilities)
sparse_encoder (langchain.retrievers.PineconeHybridSearchRetriever attribute)
spec (langchain.agents.agent_toolkits.JsonToolkit attribute)
split_documents() (langchain.text_splitter.TextSplitter method)
split_text() (langchain.text_splitter.CharacterTextSplitter method)
(langchain.text_splitter.NLTKTextSplitter method)
(langchain.text_splitter.RecursiveCharacterTextSplitter method)
(langchain.text_splitter.SentenceTransformersTokenTextSplitter method)
(langchain.text_splitter.SpacyTextSplitter method)
(langchain.text_splitter.TextSplitter method)
(langchain.text_splitter.TokenTextSplitter method)
split_text_on_tokens() (in module langchain.text_splitter)
SpreedlyLoader (class in langchain.document_loaders)
sql_chain (langchain.chains.SQLDatabaseSequentialChain attribute)
SRTLoader (class in langchain.document_loaders)
start_with_retrieval (langchain.chains.FlareChain attribute)
state (langchain.document_loaders.GitHubIssuesLoader attribute)
status (langchain.experimental.GenerativeAgent attribute)
steamship (langchain.tools.SteamshipImageGenerationTool attribute)
stop (langchain.agents.LLMSingleActionAgent attribute)
(langchain.chains.PALChain attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)


(langchain.llms.LlamaCpp attribute)
(langchain.llms.Writer attribute)
stop_sequences (langchain.llms.AlephAlpha attribute)
store (langchain.memory.InMemoryEntityStore attribute)
strategy (langchain.llms.RWKV attribute)
stream() (langchain.llms.Anthropic method)
(langchain.llms.AzureOpenAI method)
(langchain.llms.LlamaCpp method)
(langchain.llms.OpenAI method)
(langchain.llms.OpenLM method)
(langchain.llms.PromptLayerOpenAI method)
streaming (langchain.chat_models.ChatOpenAI attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.PromptLayerOpenAIChat attribute)
strip_outputs (langchain.chains.SimpleSequentialChain attribute)
StripeLoader (class in langchain.document_loaders)
STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION (langchain.agents.AgentType attribute)
structured_query_translator (langchain.retrievers.SelfQueryRetriever attribute)
suffix (langchain.llms.LlamaCpp attribute)
(langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
summarize_related_memories() (langchain.experimental.GenerativeAgent method)
summary (langchain.experimental.GenerativeAgent attribute)
summary_message_cls (langchain.memory.ConversationKGMemory attribute)
summary_refresh_seconds (langchain.experimental.GenerativeAgent attribute)
SupabaseVectorStore (class in langchain.vectorstores)
SWIFT (langchain.text_splitter.Language attribute)


SWIFT (langchain.text_splitter.Language attribute)
sync_browser (langchain.agents.agent_toolkits.PlayWrightBrowserToolkit attribute)
T
table (langchain.vectorstores.ClickhouseSettings attribute)
(langchain.vectorstores.MyScaleSettings attribute)
table_info (langchain.utilities.PowerBIDataset property)
table_name (langchain.memory.SQLiteEntityStore attribute)
(langchain.vectorstores.SupabaseVectorStore attribute)
table_names (langchain.utilities.PowerBIDataset attribute)
Tair (class in langchain.vectorstores)
task (langchain.embeddings.HuggingFaceHubEmbeddings attribute)
(langchain.llms.HuggingFaceEndpoint attribute)
(langchain.llms.HuggingFaceHub attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
tbs (langchain.utilities.GoogleSerperAPIWrapper attribute)
TelegramChatApiLoader (class in langchain.document_loaders)
TelegramChatFileLoader (class in langchain.document_loaders)
TelegramChatLoader (in module langchain.document_loaders)
temp (langchain.llms.GPT4All attribute)
temperature (langchain.chat_models.ChatGooglePalm attribute)
(langchain.chat_models.ChatOpenAI attribute)
(langchain.llms.AI21 attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.PredictionGuard attribute)


(langchain.llms.PredictionGuard attribute)
(langchain.llms.RWKV attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
template (langchain.prompts.PromptTemplate attribute)
(langchain.tools.QueryPowerBITool attribute)
template_format (langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
(langchain.prompts.PromptTemplate attribute)
template_tool_response (langchain.agents.ConversationalChatAgent attribute)
text_length (langchain.chains.LLMRequestsChain attribute)
text_splitter (langchain.chains.AnalyzeDocumentChain attribute)
(langchain.chains.MapReduceChain attribute)
(langchain.chains.QAGenerationChain attribute)
TextLoader (class in langchain.document_loaders)
texts (langchain.retrievers.KNNRetriever attribute)
(langchain.retrievers.SVMRetriever attribute)
TextSplitter (class in langchain.text_splitter)
tfidf_array (langchain.retrievers.TFIDFRetriever attribute)
Tigris (class in langchain.vectorstores)
time (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
to_typescript() (langchain.tools.APIOperation method)
token (langchain.llms.PredictionGuard attribute)
(langchain.utilities.PowerBIDataset attribute)
token_path (langchain.document_loaders.GoogleApiClient attribute)
(langchain.document_loaders.GoogleDriveLoader attribute)
Tokenizer (class in langchain.text_splitter)
tokenizer (langchain.llms.Petals attribute)
tokens (langchain.llms.AlephAlpha attribute)
tokens_path (langchain.llms.RWKV attribute)
tokens_per_chunk (langchain.text_splitter.Tokenizer attribute)
TokenTextSplitter (class in langchain.text_splitter)


TokenTextSplitter (class in langchain.text_splitter)
ToMarkdownLoader (class in langchain.document_loaders)
TomlLoader (class in langchain.document_loaders)
tool() (in module langchain.agents)
(in module langchain.tools)
tool_run_logging_kwargs() (langchain.agents.Agent method)
(langchain.agents.BaseMultiActionAgent method)
(langchain.agents.BaseSingleActionAgent method)
(langchain.agents.LLMSingleActionAgent method)
tools (langchain.agents.agent_toolkits.JiraToolkit attribute)
(langchain.agents.agent_toolkits.ZapierToolkit attribute)
(langchain.agents.AgentExecutor attribute)
top_k (langchain.chains.SQLDatabaseChain attribute)
(langchain.chat_models.ChatGooglePalm attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.Petals attribute)
(langchain.llms.VertexAI attribute)
(langchain.retrievers.ChatGPTPluginRetriever attribute)
(langchain.retrievers.DataberryRetriever attribute)
(langchain.retrievers.PineconeHybridSearchRetriever attribute)
top_k_docs_for_context (langchain.chains.ChatVectorDBChain attribute)
top_k_results (langchain.utilities.ArxivAPIWrapper attribute)
(langchain.utilities.GooglePlacesAPIWrapper attribute)
(langchain.utilities.PubMedAPIWrapper attribute)
(langchain.utilities.WikipediaAPIWrapper attribute)
top_n (langchain.retrievers.document_compressors.CohereRerank attribute)


top_n (langchain.retrievers.document_compressors.CohereRerank attribute)
top_p (langchain.chat_models.ChatGooglePalm attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.RWKV attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
topP (langchain.llms.AI21 attribute)
traits (langchain.experimental.GenerativeAgent attribute)
transform (langchain.chains.TransformChain attribute)
transform_documents() (langchain.document_transformers.EmbeddingsRedundantFilter method)
(langchain.text_splitter.TextSplitter method)
transform_input_fn (langchain.llms.Databricks attribute)
transform_output_fn (langchain.llms.Databricks attribute)
transformers (langchain.retrievers.document_compressors.DocumentCompressorPipeline attribute)
TrelloLoader (class in langchain.document_loaders)
truncate (langchain.embeddings.CohereEmbeddings attribute)
(langchain.llms.Cohere attribute)
ts_type_from_python() (langchain.tools.APIOperation static method)
ttl (langchain.memory.RedisEntityStore attribute)
tuned_model_name (langchain.llms.VertexAI attribute)
TwitterTweetLoader (class in langchain.document_loaders)
type (langchain.output_parsers.ResponseSchema attribute)


type (langchain.output_parsers.ResponseSchema attribute)
(langchain.utilities.GoogleSerperAPIWrapper attribute)
Typesense (class in langchain.vectorstores)
U
unsecure (langchain.utilities.searx_search.SearxSearchWrapper attribute)
(langchain.utilities.SearxSearchWrapper attribute)
UnstructuredAPIFileIOLoader (class in langchain.document_loaders)
UnstructuredAPIFileLoader (class in langchain.document_loaders)
UnstructuredEmailLoader (class in langchain.document_loaders)
UnstructuredEPubLoader (class in langchain.document_loaders)
UnstructuredExcelLoader (class in langchain.document_loaders)
UnstructuredFileIOLoader (class in langchain.document_loaders)
UnstructuredFileLoader (class in langchain.document_loaders)
UnstructuredHTMLLoader (class in langchain.document_loaders)
UnstructuredImageLoader (class in langchain.document_loaders)
UnstructuredMarkdownLoader (class in langchain.document_loaders)
UnstructuredODTLoader (class in langchain.document_loaders)
UnstructuredPDFLoader (class in langchain.document_loaders)
UnstructuredPowerPointLoader (class in langchain.document_loaders)
UnstructuredRTFLoader (class in langchain.document_loaders)
UnstructuredURLLoader (class in langchain.document_loaders)
UnstructuredWordDocumentLoader (class in langchain.document_loaders)
update_document() (langchain.vectorstores.Chroma method)
update_forward_refs() (langchain.llms.AI21 class method)
(langchain.llms.AlephAlpha class method)
(langchain.llms.Anthropic class method)
(langchain.llms.Anyscale class method)
(langchain.llms.Aviary class method)
(langchain.llms.AzureOpenAI class method)
(langchain.llms.Banana class method)


(langchain.llms.Banana class method)
(langchain.llms.Beam class method)
(langchain.llms.Bedrock class method)
(langchain.llms.CerebriumAI class method)
(langchain.llms.Cohere class method)
(langchain.llms.CTransformers class method)
(langchain.llms.Databricks class method)
(langchain.llms.DeepInfra class method)
(langchain.llms.FakeListLLM class method)
(langchain.llms.ForefrontAI class method)
(langchain.llms.GooglePalm class method)
(langchain.llms.GooseAI class method)
(langchain.llms.GPT4All class method)
(langchain.llms.HuggingFaceEndpoint class method)
(langchain.llms.HuggingFaceHub class method)
(langchain.llms.HuggingFacePipeline class method)
(langchain.llms.HuggingFaceTextGenInference class method)
(langchain.llms.HumanInputLLM class method)
(langchain.llms.LlamaCpp class method)
(langchain.llms.Modal class method)
(langchain.llms.MosaicML class method)
(langchain.llms.NLPCloud class method)
(langchain.llms.OpenAI class method)
(langchain.llms.OpenAIChat class method)
(langchain.llms.OpenLM class method)
(langchain.llms.Petals class method)
(langchain.llms.PipelineAI class method)
(langchain.llms.PredictionGuard class method)
(langchain.llms.PromptLayerOpenAI class method)
(langchain.llms.PromptLayerOpenAIChat class method)
(langchain.llms.Replicate class method)
(langchain.llms.RWKV class method)
(langchain.llms.SagemakerEndpoint class method)
(langchain.llms.SelfHostedHuggingFaceLLM class method)


(langchain.llms.SelfHostedHuggingFaceLLM class method)
(langchain.llms.SelfHostedPipeline class method)
(langchain.llms.StochasticAI class method)
(langchain.llms.VertexAI class method)
(langchain.llms.Writer class method)
upsert_messages() (langchain.memory.CosmosDBChatMessageHistory method)
url (langchain.document_loaders.GitHubIssuesLoader property)
(langchain.document_loaders.MathpixPDFLoader property)
(langchain.llms.Beam attribute)
(langchain.retrievers.ChatGPTPluginRetriever attribute)
(langchain.retrievers.RemoteLangChainRetriever attribute)
(langchain.tools.IFTTTWebhook attribute)
urls (langchain.document_loaders.PlaywrightURLLoader attribute)
(langchain.document_loaders.SeleniumURLLoader attribute)
use_mlock (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
use_mmap (langchain.llms.LlamaCpp attribute)
use_multiplicative_presence_penalty (langchain.llms.AlephAlpha attribute)
use_query_checker (langchain.chains.SQLDatabaseChain attribute)
username (langchain.vectorstores.ClickhouseSettings attribute)
(langchain.vectorstores.MyScaleSettings attribute)
V
validate_channel_or_videoIds_is_set() (langchain.document_loaders.GoogleApiClient class method)
(langchain.document_loaders.GoogleApiYoutubeLoader class method)
validate_init_args() (langchain.document_loaders.ConfluenceLoader static method)
validate_template (langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
(langchain.prompts.PromptTemplate attribute)
Vectara (class in langchain.vectorstores)
vectorizer (langchain.retrievers.TFIDFRetriever attribute)


vectorizer (langchain.retrievers.TFIDFRetriever attribute)
VectorStore (class in langchain.vectorstores)
vectorstore (langchain.agents.agent_toolkits.VectorStoreInfo attribute)
(langchain.chains.ChatVectorDBChain attribute)
(langchain.chains.VectorDBQA attribute)
(langchain.chains.VectorDBQAWithSourcesChain attribute)
(langchain.prompts.example_selector.SemanticSimilarityExampleSelector attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
(langchain.retrievers.TimeWeightedVectorStoreRetriever attribute)
vectorstore_info (langchain.agents.agent_toolkits.VectorStoreToolkit attribute)
vectorstores (langchain.agents.agent_toolkits.VectorStoreRouterToolkit attribute)
verbose (langchain.llms.AI21 attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.Anyscale attribute)
(langchain.llms.Aviary attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Banana attribute)
(langchain.llms.Beam attribute)
(langchain.llms.Bedrock attribute)
(langchain.llms.CerebriumAI attribute)
(langchain.llms.Cohere attribute)
(langchain.llms.CTransformers attribute)
(langchain.llms.Databricks attribute)
(langchain.llms.DeepInfra attribute)
(langchain.llms.FakeListLLM attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.HuggingFaceEndpoint attribute)
(langchain.llms.HuggingFaceHub attribute)
(langchain.llms.HuggingFacePipeline attribute)
(langchain.llms.HuggingFaceTextGenInference attribute)


(langchain.llms.HuggingFaceTextGenInference attribute)
(langchain.llms.HumanInputLLM attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llms.Modal attribute)
(langchain.llms.MosaicML attribute)
(langchain.llms.NLPCloud attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.Petals attribute)
(langchain.llms.PipelineAI attribute)
(langchain.llms.PredictionGuard attribute)
(langchain.llms.Replicate attribute)
(langchain.llms.RWKV attribute)
(langchain.llms.SagemakerEndpoint attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
(langchain.llms.StochasticAI attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
(langchain.retrievers.SelfQueryRetriever attribute)
(langchain.tools.BaseTool attribute)
(langchain.tools.Tool attribute)
VespaRetriever (class in langchain.retrievers)
video_ids (langchain.document_loaders.GoogleApiYoutubeLoader attribute)
visible_only (langchain.tools.ClickTool attribute)
vocab_only (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
W
wait_for_processing() (langchain.document_loaders.MathpixPDFLoader method)
WeatherDataLoader (class in langchain.document_loaders)
Weaviate (class in langchain.vectorstores)
WeaviateHybridSearchRetriever (class in langchain.retrievers)


WeaviateHybridSearchRetriever (class in langchain.retrievers)
WeaviateHybridSearchRetriever.Config (class in langchain.retrievers)
web_path (langchain.document_loaders.WebBaseLoader property)
web_paths (langchain.document_loaders.WebBaseLoader attribute)
WebBaseLoader (class in langchain.document_loaders)
WhatsAppChatLoader (class in langchain.document_loaders)
Wikipedia (class in langchain.docstore)
WikipediaLoader (class in langchain.document_loaders)
wolfram_alpha_appid (langchain.utilities.WolframAlphaAPIWrapper attribute)
writer_api_key (langchain.llms.Writer attribute)
writer_org_id (langchain.llms.Writer attribute)
Y
YoutubeLoader (class in langchain.document_loaders)
Z
zapier_description (langchain.tools.ZapierNLARunAction attribute)
ZepRetriever (class in langchain.retrievers)
ZERO_SHOT_REACT_DESCRIPTION (langchain.agents.AgentType attribute)
Zilliz (class in langchain.vectorstores)
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 07, 2023.


.rst
.pdf
API References
API References#
Full documentation on all methods, classes, and APIs in LangChain.
Models
Prompts
Indexes
Memory
Chains
Agents
Utilities
Experimental Modules
previous
Installation
next
Models
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 07, 2023.


.rst
.pdf
Welcome to LangChain
 Contents
Getting Started
Modules
Use Cases
Reference Docs
Ecosystem
Additional Resources
Welcome to LangChain#
LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model, but will also be:
Data-aware: connect a language model to other sources of data
Agentic: allow a language model to interact with its environment
The LangChain framework is designed around these principles.
This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see here. For the JavaScript documentation, see here.
Getting Started#
How to get started using LangChain to create an Language Model application.
Quickstart Guide
Concepts and terminology.
Concepts and terminology
Tutorials created by community experts and presented on YouTube.
Tutorials
Modules#
These modules are the core abstractions which we view as the building blocks of any LLM-powered application.
For each module LangChain provides standard, extendable interfaces. LangChain also provides external integrations and even end-to-end implementations for off-the-shelf use.
The docs for each module contain quickstart examples, how-to guides, reference docs, and conceptual guides.
The modules are (from least to most complex):
Models: Supported model types and integrations.
Prompts: Prompt management, optimization, and serialization.
Memory: Memory refers to state that is persisted between calls of a chain/agent.
Indexes: Language models become much more powerful when combined with application-specific data - this module contains interfaces and integrations for loading, querying and updating external data.
Chains: Chains are structured sequences of calls (to an LLM or to a different utility).


Agents: An agent is a Chain in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes the action and observes the outcome until the high-level directive is complete.
Callbacks: Callbacks let you log and stream the intermediate steps of any chain, making it easy to observe, debug, and evaluate the internals of an application.
Use Cases#
Best practices and built-in implementations for common LangChain use cases:
Autonomous Agents: Autonomous agents are long-running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.
Agent Simulations: Putting agents in a sandbox and observing how they interact with each other and react to events can be an effective way to evaluate their long-range reasoning and planning abilities.
Personal Assistants: One of the primary LangChain use cases. Personal assistants need to take actions, remember interactions, and have knowledge about your data.
Question Answering: Another common LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.
Chatbots: Language models love to chat, making this a very natural use of them.
Querying Tabular Data: Recommended reading if you want to use language models to query structured data (CSVs, SQL, dataframes, etc).
Code Understanding: Recommended reading if you want to use language models to analyze code.
Interacting with APIs: Enabling language models to interact with APIs is extremely powerful. It gives them access to up-to-date information and allows them to take actions.
Extraction: Extract structured information from text.
Summarization: Compressing longer documents. A type of Data-Augmented Generation.
Evaluation: Generative models are hard to evaluate with traditional metrics. One promising approach is to use language models themselves to do the evaluation.
Reference Docs#


Reference Docs#
Full documentation on all methods, classes, installation methods, and integration setups for LangChain.
LangChain Installation
Reference Documentation
Ecosystem#
LangChain integrates a lot of different LLMs, systems, and products.
From the other side, many systems and products depend on LangChain.
It creates a vibrant and thriving ecosystem.
Integrations: Guides for how other products can be used with LangChain.
Dependents: List of repositories that use LangChain.
Deployments: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.
Additional Resources#
Additional resources we think may be useful as you develop your application!
LangChainHub: The LangChainHub is a place to share and explore other prompts, chains, and agents.
Gallery: A collection of great projects that use Langchain, compiled by the folks at Kyrolabs. Useful for finding inspiration and example implementations.
Deploying LLMs in Production: A collection of best practices and tutorials for deploying LLMs in production.
Tracing: A guide on using tracing in LangChain to visualize the execution of chains and agents.
Model Laboratory: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.
Discord: Join us on our Discord to discuss all things LangChain!
YouTube: A collection of the LangChain tutorials and videos.
Production Support: As you move your LangChains into production, we’d love to offer more comprehensive support. Please fill out this form and we’ll set up a dedicated support Slack channel.
next
Quickstart Guide
 Contents

Getting Started
Modules
Use Cases
Reference Docs
Ecosystem
Additional Resources
By Harrison Chase

      © Copyright 2023, Harrison Chase.


By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 07, 2023.


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By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 07, 2023.


.md
.pdf
Dependents
Dependents#
Dependents stats for hwchase17/langchain
[update: 2023-06-05; only dependent repositories with Stars > 100]
Repository
Stars
openai/openai-cookbook
38024
LAION-AI/Open-Assistant
33609
microsoft/TaskMatrix
33136
hpcaitech/ColossalAI
30032
imartinez/privateGPT
28094
reworkd/AgentGPT
23430
openai/chatgpt-retrieval-plugin
17942
jerryjliu/llama_index
16697
mindsdb/mindsdb
16410
mlflow/mlflow
14517
GaiZhenbiao/ChuanhuChatGPT
10793
databrickslabs/dolly
10155
openai/evals
10076
AIGC-Audio/AudioGPT
8619
logspace-ai/langflow
8211
imClumsyPanda/langchain-ChatGLM
8154
PromtEngineer/localGPT
6853
StanGirard/quivr
6830
PipedreamHQ/pipedream
6520
go-skynet/LocalAI
6018
arc53/DocsGPT
5643
e2b-dev/e2b
5075
langgenius/dify
4281
nsarrazin/serge
4228
zauberzeug/nicegui
4084
madawei2699/myGPTReader
4039
wenda-LLM/wenda
3871
GreyDGL/PentestGPT
3837
zilliztech/GPTCache
3625
csunny/DB-GPT
3545
gkamradt/langchain-tutorials
3404


3545
gkamradt/langchain-tutorials
3404
mmabrouk/chatgpt-wrapper
3303
postgresml/postgresml
3052
marqo-ai/marqo
3014
MineDojo/Voyager
2945
PrefectHQ/marvin
2761
project-baize/baize-chatbot
2673
hwchase17/chat-langchain
2589
whitead/paper-qa
2572
Azure-Samples/azure-search-openai-demo
2366
GerevAI/gerev
2330
OpenGVLab/InternGPT
2289
ParisNeo/gpt4all-ui
2159
OpenBMB/BMTools
2158
guangzhengli/ChatFiles
2005
h2oai/h2ogpt
1939
Farama-Foundation/PettingZoo
1845
OpenGVLab/Ask-Anything
1749
IntelligenzaArtificiale/Free-Auto-GPT
1740
Unstructured-IO/unstructured
1628
hwchase17/notion-qa
1607
NVIDIA/NeMo-Guardrails
1544
SamurAIGPT/privateGPT
1543
paulpierre/RasaGPT
1526
yanqiangmiffy/Chinese-LangChain
1485
Kav-K/GPTDiscord
1402
vocodedev/vocode-python
1387
Chainlit/chainlit
1336
lunasec-io/lunasec
1323
psychic-api/psychic
1248
agiresearch/OpenAGI
1208
jina-ai/thinkgpt
1193
thomas-yanxin/LangChain-ChatGLM-Webui
1182


thomas-yanxin/LangChain-ChatGLM-Webui
1182
ttengwang/Caption-Anything
1137
jina-ai/dev-gpt
1135
greshake/llm-security
1086
keephq/keep
1063
juncongmoo/chatllama
1037
richardyc/Chrome-GPT
1035
visual-openllm/visual-openllm
997
mmz-001/knowledge_gpt
995
jina-ai/langchain-serve
949
irgolic/AutoPR
936
microsoft/X-Decoder
908
poe-platform/api-bot-tutorial
902
peterw/Chat-with-Github-Repo
875
cirediatpl/FigmaChain
822
homanp/superagent
806
seanpixel/Teenage-AGI
800
chatarena/chatarena
796
hashintel/hash
795
SamurAIGPT/Camel-AutoGPT
786
rlancemartin/auto-evaluator
770
corca-ai/EVAL
769
101dotxyz/GPTeam
755
noahshinn024/reflexion
706
eyurtsev/kor
695
cheshire-cat-ai/core
681
e-johnstonn/BriefGPT
656
run-llama/llama-lab
635
griptape-ai/griptape
583
namuan/dr-doc-search
555
getmetal/motorhead
550
kreneskyp/ix
543
hwchase17/chat-your-data
510
Anil-matcha/ChatPDF
501
whyiyhw/chatgpt-wechat
497
SamurAIGPT/ChatGPT-Developer-Plugins
496
microsoft/PodcastCopilot
492
debanjum/khoj


496
microsoft/PodcastCopilot
492
debanjum/khoj
485
akshata29/chatpdf
485
langchain-ai/langchain-aiplugin
462
jina-ai/agentchain
460
alexanderatallah/window.ai
457
yeagerai/yeagerai-agent
451
mckaywrigley/repo-chat
446
michaelthwan/searchGPT
446
mpaepper/content-chatbot
441
freddyaboulton/gradio-tools
439
ruoccofabrizio/azure-open-ai-embeddings-qna
429
StevenGrove/GPT4Tools
422
jonra1993/fastapi-alembic-sqlmodel-async
407
msoedov/langcorn
405
amosjyng/langchain-visualizer
395
ajndkr/lanarky
384
mtenenholtz/chat-twitter
376
steamship-core/steamship-langchain
371
langchain-ai/auto-evaluator
365
xuwenhao/geektime-ai-course
358
continuum-llms/chatgpt-memory
357
opentensor/bittensor
347
showlab/VLog
345
daodao97/chatdoc
345
logan-markewich/llama_index_starter_pack
332
poe-platform/poe-protocol
320
explosion/spacy-llm
312
andylokandy/gpt-4-search
311
alejandro-ao/langchain-ask-pdf
310
jupyterlab/jupyter-ai
294
BlackHC/llm-strategy
283
itamargol/openai
281
momegas/megabots
279
personoids/personoids-lite
277
yvann-hub/Robby-chatbot
267
Anil-matcha/Website-to-Chatbot


267
Anil-matcha/Website-to-Chatbot
266
Cheems-Seminar/grounded-segment-any-parts
260
sullivan-sean/chat-langchainjs
248
bborn/howdoi.ai
245
daveebbelaar/langchain-experiments
240
MagnivOrg/prompt-layer-library
237
ur-whitelab/exmol
234
conceptofmind/toolformer
234
recalign/RecAlign
226
OpenBMB/AgentVerse
220
alvarosevilla95/autolang
219
JohnSnowLabs/nlptest
216
kaleido-lab/dolphin
215
truera/trulens
208
NimbleBoxAI/ChainFury
208
airobotlab/KoChatGPT
207
monarch-initiative/ontogpt
200
paolorechia/learn-langchain
195
shaman-ai/agent-actors
185
Haste171/langchain-chatbot
184
plchld/InsightFlow
182
su77ungr/CASALIOY
180
jbrukh/gpt-jargon
177
benthecoder/ClassGPT
174
billxbf/ReWOO
170
filip-michalsky/SalesGPT
168
hwchase17/langchain-streamlit-template
168
radi-cho/datasetGPT
164
hardbyte/qabot
164
gia-guar/JARVIS-ChatGPT
158
plastic-labs/tutor-gpt
154
yasyf/compress-gpt
154
fengyuli-dev/multimedia-gpt
154
ethanyanjiali/minChatGPT
153
hwchase17/chroma-langchain
153
edreisMD/plugnplai
148
chakkaradeep/pyCodeAGI
145


148
chakkaradeep/pyCodeAGI
145
ccurme/yolopandas
145
shamspias/customizable-gpt-chatbot
144
realminchoi/babyagi-ui
143
PradipNichite/Youtube-Tutorials
140
gustavz/DataChad
140
Klingefjord/chatgpt-telegram
140
Jaseci-Labs/jaseci
139
handrew/browserpilot
137
jmpaz/promptlib
137
SamPink/dev-gpt
135
menloparklab/langchain-cohere-qdrant-doc-retrieval
135
hirokidaichi/wanna
135
steamship-core/vercel-examples
134
pablomarin/GPT-Azure-Search-Engine
133
ibiscp/LLM-IMDB
133
shauryr/S2QA
133
jerlendds/osintbuddy
132
yuanjie-ai/ChatLLM
132
yasyf/summ
132
WongSaang/chatgpt-ui-server
130
peterw/StoryStorm
127
Teahouse-Studios/akari-bot
126
vaibkumr/prompt-optimizer
125
preset-io/promptimize
124
homanp/vercel-langchain
124
petehunt/langchain-github-bot
123
eunomia-bpf/GPTtrace
118
nicknochnack/LangchainDocuments
116
jiran214/GPT-vup
112
rsaryev/talk-codebase
112
zenml-io/zenml-projects
112
microsoft/azure-openai-in-a-day-workshop
112
davila7/file-gpt
112
prof-frink-lab/slangchain
111
aurelio-labs/arxiv-bot
110


111
aurelio-labs/arxiv-bot
110
fixie-ai/fixie-examples
108
miaoshouai/miaoshouai-assistant
105
flurb18/AgentOoba
103
solana-labs/chatgpt-plugin
102
Significant-Gravitas/Auto-GPT-Benchmarks
102
kaarthik108/snowChat
100
Generated by github-dependents-info
github-dependents-info --repo hwchase17/langchain --markdownfile dependents.md --minstars 100 --sort stars
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.rst
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Indexes
 Contents
Index Types
Indexes#
Note
Conceptual Guide
Indexes refer to ways to structure documents so that LLMs can best interact with them.
The most common way that indexes are used in chains is in a “retrieval” step.
This step refers to taking a user’s query and returning the most relevant documents.
We draw this distinction because (1) an index can be used for other things besides retrieval, and
(2) retrieval can use other logic besides an index to find relevant documents.
We therefore have a concept of a Retriever interface - this is the interface that most chains work with.
Most of the time when we talk about indexes and retrieval we are talking about indexing and retrieving
unstructured data (like text documents).
For interacting with structured data (SQL tables, etc) or APIs, please see the corresponding use case
sections for links to relevant functionality.
Getting Started: An overview of the indexes.
Index Types#
Document Loaders: How to load documents from a variety of sources.
Text Splitters: An overview and different types of the Text Splitters.
VectorStores: An overview and different types of the  Vector Stores.
Retrievers: An overview and different types of the Retrievers.
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Index Types
By Harrison Chase

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  Last updated on Jun 07, 2023.


.rst
.pdf
Prompts
Prompts#
Note
Conceptual Guide
The new way of programming models is through prompts.
A prompt refers to the input to the model.
This input is often constructed from multiple components.
A PromptTemplate is responsible for the construction of this input.
LangChain provides several classes and functions to make constructing and working with prompts easy.
Getting Started: An overview of the prompts.
LLM Prompt Templates: How to use PromptTemplates to prompt Language Models.
Chat Prompt Templates: How to use PromptTemplates to prompt Chat Models.
Example Selectors: Often times it is useful to include examples in prompts.
These examples can be dynamically selected. This section goes over example selection.
Output Parsers: Language models (and Chat Models) output text.
But many times you may want to get more structured information. This is where output parsers come in.
Output Parsers:
instruct the model how output should be formatted,
parse output into the desired formatting (including retrying if necessary).
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Models
 Contents
Model Types
Models#
Note
Conceptual Guide
This section of the documentation deals with different types of models that are used in LangChain.
On this page we will go over the model types at a high level,
but we have individual pages for each model type.
The pages contain more detailed “how-to” guides for working with that model,
as well as a list of different model providers.
Getting Started: An overview of the models.
Model Types#
LLMs: Large Language Models (LLMs) take a text string as input and return a text string as output.
Chat Models: Chat Models are usually backed by a language model, but their APIs are more structured.
Specifically, these models take a list of Chat Messages as input, and return a Chat Message.
Text Embedding Models: Text embedding models take text as input and return a list of floats.
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By Harrison Chase

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Memory
Memory#
Note
Conceptual Guide
By default, Chains and Agents are stateless,
meaning that they treat each incoming query independently (as are the underlying LLMs and chat models).
In some applications (chatbots being a GREAT example) it is highly important
to remember previous interactions, both at a short term but also at a long term level.
The Memory does exactly that.
LangChain provides memory components in two forms.
First, LangChain provides helper utilities for managing and manipulating previous chat messages.
These are designed to be modular and useful regardless of how they are used.
Secondly, LangChain provides easy ways to incorporate these utilities into chains.
Getting Started: An overview of different types of memory.
How-To Guides: A collection of how-to guides. These highlight different types of memory, as well as how to use memory in chains.
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Agents
 Contents
Action Agents
Plan-and-Execute Agents
Agents#
Note
Conceptual Guide
Some applications require not just a predetermined chain of calls to LLMs/other tools,
but potentially an unknown chain that depends on the user’s input.
In these types of chains, there is an agent which has access to a suite of tools.
Depending on the user input, the agent can then decide which, if any, of these tools to call.
At the moment, there are two main types of agents:
Action Agents: these agents decide the actions to take and execute that actions one action at a time.
Plan-and-Execute Agents: these agents first decide a plan of actions to take, and then execute those actions one at a time.
When should you use each one? Action Agents are more conventional, and good for small tasks.
For more complex or long running tasks, the initial planning step helps to maintain long term objectives and focus.
However, that comes at the expense of generally more calls and higher latency.
These two agents are also not mutually exclusive - in fact, it is often best to have an Action Agent be in charge
of the execution for the Plan and Execute agent.
Action Agents#
High level pseudocode of the Action Agents:
The user input is received
The agent decides which tool - if any - to use, and what the tool input should be
That tool is then called with the tool input, and an observation is recorded (the output of this calling)
That history of tool, tool input, and observation is passed back into the agent, and it decides the next step
This is repeated until the agent decides it no longer needs to use a tool, and then it responds directly to the user.
The different abstractions involved in agents are:


The different abstractions involved in agents are:
Agent: this is where the logic of the application lives. Agents expose an interface that takes in user input
along with a list of previous steps the agent has taken, and returns either an AgentAction or AgentFinish
AgentAction corresponds to the tool to use and the input to that tool
AgentFinish means the agent is done, and has information around what to return to the user
Tools: these are the actions an agent can take. What tools you give an agent highly depend on what you want the agent to do
Toolkits: these are groups of tools designed for a specific use case. For example, in order for an agent to
interact with a SQL database in the best way it may need access to one tool to execute queries and another tool to inspect tables.
Agent Executor: this wraps an agent and a list of tools. This is responsible for the loop of running the agent
iteratively until the stopping criteria is met.
Getting Started: An overview of agents. It covers how to use all things related to agents in an end-to-end manner.
Agent Construction:
Although an agent can be constructed in many way, the typical way to construct an agent is with:
PromptTemplate: this is responsible for taking the user input and previous steps and constructing a prompt
to send to the language model
Language Model: this takes the prompt constructed by the PromptTemplate and returns some output
Output Parser: this takes the output of the Language Model and parses it into an AgentAction or AgentFinish object.
Additional Documentation:
Tools: Different types of tools LangChain supports natively. We also cover how to add your own tools.
Agents: Different types of agents LangChain supports natively. We also cover how to
modify and create your own agents.
Toolkits: Various toolkits that LangChain supports out of the box, and how to
create an agent from them.


create an agent from them.
Agent Executor: The Agent Executor class, which is responsible for calling
the agent and tools in a loop. We go over different ways to customize this, and options you can use for more control.
Plan-and-Execute Agents#
High level pseudocode of the Plan-and-Execute Agents:
The user input is received
The planner lists out the steps to take
The executor goes through the list of steps, executing them
The most typical implementation is to have the planner be a language model, and the executor be an action agent.
Plan-and-Execute Agents
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.rst
.pdf
Chains
Chains#
Note
Conceptual Guide
Using an LLM in isolation is fine for some simple applications,
but more complex applications require chaining LLMs - either with each other or with other experts.
LangChain provides a standard interface for Chains, as well as several common implementations of chains.
Getting Started: An overview of chains.
How-To Guides: How-to guides about various types of chains.
Reference: API reference documentation for all Chain classes.
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Document Loaders
 Contents
Transform loaders
Public dataset or service loaders
Proprietary dataset or service loaders
Document Loaders#
Note
Conceptual Guide
Combining language models with your own text data is a powerful way to differentiate them.
The first step in doing this is to load the data into “Documents” - a fancy way of say some pieces of text.
The document loader is aimed at making this easy.
The following document loaders are provided:
Transform loaders#
These transform loaders transform data from a specific format into the Document format.
For example, there are transformers for CSV and SQL.
Mostly, these loaders input data from files but sometime from URLs.
A primary driver of a lot of these transformers is the Unstructured python package.
This package transforms many types of files - text, powerpoint, images, html, pdf, etc - into text data.
For detailed instructions on how to get set up with Unstructured, see installation guidelines here.
OpenAIWhisperParser
CoNLL-U
Copy Paste
CSV
Email
EPub
EverNote
Microsoft Excel
Facebook Chat
File Directory
HTML
Images
Jupyter Notebook
JSON
Markdown
Microsoft PowerPoint
Microsoft Word
Open Document Format (ODT)
Pandas DataFrame
PDF
Sitemap
Subtitle
Telegram
TOML
Unstructured File
URL
Selenium URL Loader
Playwright URL Loader
WebBaseLoader
Weather
WhatsApp Chat
Public dataset or service loaders#
These datasets and sources are created for public domain and we use queries to search there
and download necessary documents.
For example, Hacker News service.
We don’t need any access permissions to these datasets and services.
Arxiv
AZLyrics
BiliBili
College Confidential
Gutenberg
Hacker News
HuggingFace dataset
iFixit
IMSDb


Gutenberg
Hacker News
HuggingFace dataset
iFixit
IMSDb
MediaWikiDump
Wikipedia
YouTube transcripts
Proprietary dataset or service loaders#
These datasets and services are not from the public domain.
These loaders mostly transform data from specific formats of applications or cloud services,
for example Google Drive.
We need access tokens and sometime other parameters to get access to these datasets and services.
Airbyte JSON
Apify Dataset
AWS S3 Directory
AWS S3 File
Azure Blob Storage Container
Azure Blob Storage File
Blackboard
Blockchain
ChatGPT Data
Confluence
Examples
Diffbot
Docugami
DuckDB
Figma
GitBook
Git
Google BigQuery
Google Cloud Storage Directory
Google Cloud Storage File
Google Drive
Image captions
Iugu
Joplin
Microsoft OneDrive
Modern Treasury
Notion DB 2/2
Notion DB 1/2
Obsidian
Psychic
PySpark DataFrame Loader
ReadTheDocs Documentation
Reddit
Roam
Slack
Spreedly
Stripe
2Markdown
Twitter
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Transform loaders
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By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Jun 07, 2023.


.rst
.pdf
Vectorstores
Vectorstores#
Note
Conceptual Guide
Vectorstores are one of the most important components of building indexes.
For an introduction to vectorstores and generic functionality see:
Getting Started
We also have documentation for all the types of vectorstores that are supported.
Please see below for that list.
AnalyticDB
Annoy
Atlas
Chroma
ClickHouse Vector Search
Deep Lake
DocArrayHnswSearch
DocArrayInMemorySearch
ElasticSearch
ElasticVectorSearch class
ElasticKnnSearch Class
FAISS
LanceDB
MatchingEngine
Milvus
Commented out until further notice
MyScale
OpenSearch
PGVector
Pinecone
Qdrant
Redis
SKLearnVectorStore
Supabase (Postgres)
Tair
Tigris
Typesense
Vectara
Weaviate
Persistance
Retriever options
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.rst
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Text Splitters
Text Splitters#
Note
Conceptual Guide
When you want to deal with long pieces of text, it is necessary to split up that text into chunks.
As simple as this sounds, there is a lot of potential complexity here. Ideally, you want to keep the semantically related pieces of text together. What “semantically related” means could depend on the type of text.
This notebook showcases several ways to do that.
At a high level, text splitters work as following:
Split the text up into small, semantically meaningful chunks (often sentences).
Start combining these small chunks into a larger chunk until you reach a certain size (as measured by some function).
Once you reach that size, make that chunk its own piece of text and then start creating a new chunk of text with some overlap (to keep context between chunks).
That means there are two different axes along which you can customize your text splitter:
How the text is split
How the chunk size is measured
For an introduction to the default text splitter and generic functionality see:
Getting Started
Usage examples for the text splitters:
Character
Code (including HTML, Markdown, Latex, Python, etc)
NLTK
Recursive Character
spaCy
tiktoken (OpenAI)
Most LLMs are constrained by the number of tokens that you can pass in, which is not the same as the number of characters.
In order to get a more accurate estimate, we can use tokenizers to count the number of tokens in the text.
We use this number inside the ..TextSplitter classes.
This implemented as the from_<tokenizer> methods of the ..TextSplitter classes:
Hugging Face tokenizer
tiktoken (OpenAI) tokenizer
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Getting Started
 Contents
One Line Index Creation
Walkthrough
Getting Started#
LangChain primarily focuses on constructing indexes with the goal of using them as a Retriever. In order to best understand what this means, it’s worth highlighting what the base Retriever interface is. The BaseRetriever class in LangChain is as follows:
from abc import ABC, abstractmethod
from typing import List
from langchain.schema import Document
class BaseRetriever(ABC):
    @abstractmethod
    def get_relevant_documents(self, query: str) -> List[Document]:
        """Get texts relevant for a query.
        Args:
            query: string to find relevant texts for
        Returns:
            List of relevant documents
        """
It’s that simple! The get_relevant_documents method can be implemented however you see fit.
Of course, we also help construct what we think useful Retrievers are. The main type of Retriever that we focus on is a Vectorstore retriever. We will focus on that for the rest of this guide.
In order to understand what a vectorstore retriever is, it’s important to understand what a Vectorstore is. So let’s look at that.
By default, LangChain uses Chroma as the vectorstore to index and search embeddings. To walk through this tutorial, we’ll first need to install chromadb.
pip install chromadb
This example showcases question answering over documents.
We have chosen this as the example for getting started because it nicely combines a lot of different elements (Text splitters, embeddings, vectorstores) and then also shows how to use them in a chain.
Question answering over documents consists of four steps:
Create an index
Create a Retriever from that index
Create a question answering chain
Ask questions!


Create a Retriever from that index
Create a question answering chain
Ask questions!
Each of the steps has multiple sub steps and potential configurations. In this notebook we will primarily focus on (1). We will start by showing the one-liner for doing so, but then break down what is actually going on.
First, let’s import some common classes we’ll use no matter what.
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
Next in the generic setup, let’s specify the document loader we want to use. You can download the state_of_the_union.txt file here
from langchain.document_loaders import TextLoader
loader = TextLoader('../state_of_the_union.txt', encoding='utf8')
One Line Index Creation#
To get started as quickly as possible, we can use the VectorstoreIndexCreator.
from langchain.indexes import VectorstoreIndexCreator
index = VectorstoreIndexCreator().from_loaders([loader])
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
Now that the index is created, we can use it to ask questions of the data! Note that under the hood this is actually doing a few steps as well, which we will cover later in this guide.
query = "What did the president say about Ketanji Brown Jackson"
index.query(query)
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
query = "What did the president say about Ketanji Brown Jackson"
index.query_with_sources(query)


index.query_with_sources(query)
{'question': 'What did the president say about Ketanji Brown Jackson',
 'answer': " The president said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, one of the nation's top legal minds, to continue Justice Breyer's legacy of excellence, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\n",
 'sources': '../state_of_the_union.txt'}
What is returned from the VectorstoreIndexCreator is VectorStoreIndexWrapper, which provides these nice query and query_with_sources functionality. If we just wanted to access the vectorstore directly, we can also do that.
index.vectorstore
<langchain.vectorstores.chroma.Chroma at 0x119aa5940>
If we then want to access the VectorstoreRetriever, we can do that with:
index.vectorstore.as_retriever()
VectorStoreRetriever(vectorstore=<langchain.vectorstores.chroma.Chroma object at 0x119aa5940>, search_kwargs={})
Walkthrough#
Okay, so what’s actually going on? How is this index getting created?
A lot of the magic is being hid in this VectorstoreIndexCreator. What is this doing?
There are three main steps going on after the documents are loaded:
Splitting documents into chunks
Creating embeddings for each document
Storing documents and embeddings in a vectorstore
Let’s walk through this in code
documents = loader.load()
Next, we will split the documents into chunks.
from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
We will then select which embeddings we want to use.


We will then select which embeddings we want to use.
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
We now create the vectorstore to use as the index.
from langchain.vectorstores import Chroma
db = Chroma.from_documents(texts, embeddings)
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
So that’s creating the index. Then, we expose this index in a retriever interface.
retriever = db.as_retriever()
Then, as before, we create a chain and use it to answer questions!
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=retriever)
query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)
" The President said that Judge Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He said she is a consensus builder and has received a broad range of support from organizations such as the Fraternal Order of Police and former judges appointed by Democrats and Republicans."
VectorstoreIndexCreator is just a wrapper around all this logic. It is configurable in the text splitter it uses, the embeddings it uses, and the vectorstore it uses. For example, you can configure it as below:
index_creator = VectorstoreIndexCreator(
    vectorstore_cls=Chroma,
    embedding=OpenAIEmbeddings(),
    text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
)


)
Hopefully this highlights what is going on under the hood of VectorstoreIndexCreator. While we think it’s important to have a simple way to create indexes, we also think it’s important to understand what’s going on under the hood.
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Retrievers
Retrievers#
Note
Conceptual Guide
The retriever interface is a generic interface that makes it easy to combine documents with
language models. This interface exposes a get_relevant_documents method which takes in a query
(a string) and returns a list of documents.
Please see below for a list of all the retrievers supported.
Arxiv
Azure Cognitive Search
ChatGPT Plugin
Self-querying with Chroma
Cohere Reranker
Contextual Compression
Stringing compressors and document transformers together
Databerry
ElasticSearch BM25
kNN
Metal
Pinecone Hybrid Search
PubMed Retriever
Self-querying with Qdrant
Self-querying
SVM
TF-IDF
Time Weighted VectorStore
VectorStore
Vespa
Weaviate Hybrid Search
Self-querying with Weaviate
Wikipedia
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Wikipedia
 Contents
Installation
Examples
Wikipedia#
Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.
This notebook shows how to load wiki pages from wikipedia.org into the Document format that we use downstream.
Installation#
First, you need to install wikipedia python package.
#!pip install wikipedia
Examples#
WikipediaLoader has these arguments:
query: free text which used to find documents in Wikipedia
optional lang: default=”en”. Use it to search in a specific language part of Wikipedia
optional load_max_docs: default=100. Use it to limit number of downloaded documents. It takes time to download all 100 documents, so use a small number for experiments. There is a hard limit of 300 for now.
optional load_all_available_meta: default=False. By default only the most important fields downloaded: Published (date when document was published/last updated), title, Summary. If True, other fields also downloaded.
from langchain.document_loaders import WikipediaLoader
docs = WikipediaLoader(query='HUNTER X HUNTER', load_max_docs=2).load()
len(docs)
docs[0].metadata  # meta-information of the Document
docs[0].page_content[:400]  # a content of the Document
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Roam
 Contents
🧑 Instructions for ingesting your own dataset
Roam#
ROAM is a note-taking tool for networked thought, designed to create a personal knowledge base.
This notebook covers how to load documents from a Roam database. This takes a lot of inspiration from the example repo here.
🧑 Instructions for ingesting your own dataset#
Export your dataset from Roam Research. You can do this by clicking on the three dots in the upper right hand corner and then clicking Export.
When exporting, make sure to select the Markdown & CSV format option.
This will produce a .zip file in your Downloads folder. Move the .zip file into this repository.
Run the following command to unzip the zip file (replace the Export... with your own file name as needed).
unzip Roam-Export-1675782732639.zip -d Roam_DB
from langchain.document_loaders import RoamLoader
loader = RoamLoader("Roam_DB")
docs = loader.load()
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Slack
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🧑 Instructions for ingesting your own dataset
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  Last updated on Jun 07, 2023.