(langchain.document_loaders.RedditPostsLoader method)
(langchain.document_loaders.RoamLoader method)
(langchain.document_loaders.S3DirectoryLoader method)
(langchain.document_loaders.S3FileLoader method)
(langchain.document_loaders.SeleniumURLLoader method)
(langchain.document_loaders.SitemapLoader method)
(langchain.document_loaders.SlackDirectoryLoader method)
(langchain.document_loaders.SpreedlyLoader method)
(langchain.document_loaders.SRTLoader method)
(langchain.document_loaders.StripeLoader method)
(langchain.document_loaders.TelegramChatApiLoader method)
(langchain.document_loaders.TelegramChatFileLoader method)
(langchain.document_loaders.TextLoader method)
(langchain.document_loaders.ToMarkdownLoader method)
(langchain.document_loaders.TomlLoader method)
(langchain.document_loaders.TwitterTweetLoader method)
(langchain.document_loaders.UnstructuredURLLoader method)
(langchain.document_loaders.WeatherDataLoader method)
(langchain.document_loaders.WebBaseLoader method)
(langchain.document_loaders.WhatsAppChatLoader method)
(langchain.document_loaders.WikipediaLoader method)
(langchain.document_loaders.YoutubeLoader method)
(langchain.utilities.ArxivAPIWrapper method)
(langchain.utilities.WikipediaAPIWrapper method)
load_agent() (in module langchain.agents)
load_all_available_meta (langchain.utilities.ArxivAPIWrapper attribute)
(langchain.utilities.WikipediaAPIWrapper attribute)
load_all_recursively (langchain.document_loaders.BlackboardLoader attribute)
load_chain() (in module langchain.chains)
load_comments() (langchain.document_loaders.HNLoader method)
load_device() (langchain.document_loaders.IFixitLoader method)
load_file() (langchain.document_loaders.DirectoryLoader method)


load_file() (langchain.document_loaders.DirectoryLoader method)
load_fn_kwargs (langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
load_guide() (langchain.document_loaders.IFixitLoader method)
load_huggingface_tool() (in module langchain.agents)
load_local() (langchain.vectorstores.Annoy class method)
(langchain.vectorstores.FAISS class method)
load_max_docs (langchain.utilities.ArxivAPIWrapper attribute)
load_memory_variables() (langchain.experimental.GenerativeAgentMemory method)
(langchain.memory.CombinedMemory method)
(langchain.memory.ConversationBufferMemory method)
(langchain.memory.ConversationBufferWindowMemory 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)
load_messages() (langchain.memory.CosmosDBChatMessageHistory method)
load_page() (langchain.document_loaders.NotionDBLoader method)
load_prompt() (in module langchain.prompts)
load_questions_and_answers() (langchain.document_loaders.IFixitLoader method)
load_results() (langchain.document_loaders.HNLoader method)
load_suggestions() (langchain.document_loaders.IFixitLoader static method)
load_tools() (in module langchain.agents)
load_trashed_files (langchain.document_loaders.GoogleDriveLoader attribute)


load_trashed_files (langchain.document_loaders.GoogleDriveLoader attribute)
locals (langchain.python.PythonREPL attribute)
(langchain.utilities.PythonREPL attribute)
location (langchain.llms.VertexAI attribute)
log_probs (langchain.llms.AlephAlpha attribute)
logit_bias (langchain.llms.AlephAlpha attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenLM attribute)
logitBias (langchain.llms.AI21 attribute)
logits_all (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
logprobs (langchain.llms.LlamaCpp attribute)
(langchain.llms.Writer attribute)
lookup_tool() (langchain.agents.AgentExecutor method)
lora_base (langchain.llms.LlamaCpp attribute)
lora_path (langchain.llms.LlamaCpp attribute)
M
MarkdownTextSplitter (class in langchain.text_splitter)
MastodonTootsLoader (class in langchain.document_loaders)
MathpixPDFLoader (class in langchain.document_loaders)
max_checks (langchain.chains.LLMSummarizationCheckerChain attribute)
max_execution_time (langchain.agents.AgentExecutor attribute)
max_iter (langchain.chains.FlareChain attribute)
max_iterations (langchain.agents.agent_toolkits.PowerBIToolkit attribute)
(langchain.agents.AgentExecutor attribute)
(langchain.tools.QueryPowerBITool attribute)
max_length (langchain.llms.NLPCloud attribute)
(langchain.llms.Petals attribute)
(langchain.prompts.example_selector.LengthBasedExampleSelector attribute)


(langchain.prompts.example_selector.LengthBasedExampleSelector attribute)
max_marginal_relevance_search() (langchain.vectorstores.Annoy method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.Qdrant method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.VectorStore method)
(langchain.vectorstores.Weaviate method)
max_marginal_relevance_search_by_vector() (langchain.vectorstores.Annoy method)
(langchain.vectorstores.Chroma method)
(langchain.vectorstores.DeepLake method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.VectorStore method)
(langchain.vectorstores.Weaviate method)
max_new_tokens (langchain.llms.Petals attribute)
max_output_tokens (langchain.llms.GooglePalm attribute)
(langchain.llms.VertexAI attribute)
max_results (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
max_retries (langchain.chat_models.ChatOpenAI attribute)
(langchain.embeddings.OpenAIEmbeddings attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.OpenAI attribute)
(langchain.llms.OpenAIChat attribute)
(langchain.llms.OpenLM attribute)
(langchain.llms.PromptLayerOpenAIChat attribute)
max_token_limit (langchain.memory.ConversationSummaryBufferMemory attribute)
(langchain.memory.ConversationTokenBufferMemory attribute)
max_tokens (langchain.chat_models.ChatOpenAI attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Cohere attribute)


(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)
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.ReadOnlySharedMemory property)
(langchain.memory.SimpleMemory property)


(langchain.memory.ReadOnlySharedMemory property)
(langchain.memory.SimpleMemory property)
(langchain.memory.VectorStoreRetrieverMemory property)
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.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.MyScaleSettings 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_group_id (langchain.embeddings.MiniMaxEmbeddings attribute)
minimum_tokens (langchain.llms.AlephAlpha attribute)
minTokens (langchain.llms.AI21 attribute)
model (langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding 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.RWKV attribute)
(langchain.retrievers.document_compressors.CohereRerank attribute)
model_file (langchain.llms.CTransformers attribute)
model_id (langchain.embeddings.ModelScopeEmbeddings attribute)
(langchain.embeddings.SelfHostedHuggingFaceEmbeddings attribute)
(langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings 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.HuggingFaceEmbeddings attribute)
(langchain.embeddings.HuggingFaceHubEmbeddings 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.CerebriumAI attribute)
(langchain.llms.Databricks attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.HuggingFaceEndpoint attribute)
(langchain.llms.HuggingFaceHub 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.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)


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
MomentoChatMessageHistory (class in langchain.memory)
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.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.llms.PredictionGuard 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.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)
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)


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_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.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)
(langchain.output_parsers.CommaSeparatedListOutputParser 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)


(langchain.output_parsers.StructuredOutputParser method)
parse_filename() (langchain.document_loaders.BlackboardLoader 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.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)
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)
Pinecone (class in langchain.vectorstores)
pipeline_key (langchain.llms.PipelineAI attribute)
pipeline_kwargs (langchain.llms.HuggingFacePipeline 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.MyScaleSettings attribute)
post() (langchain.utilities.TextRequestsWrapper method)
PostgresChatMessageHistory (class in langchain.memory)
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.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.Beam 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.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.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.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.Beam method)
(langchain.llms.CerebriumAI method)


(langchain.llms.Beam 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.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)


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)
(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)


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)
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)
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_name (langchain.vectorstores.SupabaseVectorStore attribute)
query_params (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
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)
RedisEntityStore (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.SagemakerEndpointEmbeddings attribute)
(langchain.llms.SagemakerEndpoint attribute)
relevancy_threshold (langchain.retrievers.KNNRetriever attribute)
(langchain.retrievers.SVMRetriever attribute)
remove_end_sequence (langchain.llms.NLPCloud 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)
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_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.DuckDuckGoSearchAPIWrapper method)
(langchain.utilities.GoogleSearchAPIWrapper method)
(langchain.utilities.GoogleSerperAPIWrapper 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)
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.BaseMultiActionAgent 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)
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.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.WolframAlphaAPIWrapper method)
run_creation() (langchain.llms.Beam method)


run_creation() (langchain.llms.Beam method)
run_no_throw() (langchain.utilities.SparkSQL method)
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.AzureOpenAI method)
(langchain.llms.Banana method)
(langchain.llms.Beam 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.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)
save_local() (langchain.vectorstores.Annoy method)
(langchain.vectorstores.FAISS method)
schemas (langchain.utilities.PowerBIDataset attribute)
scrape() (langchain.document_loaders.WebBaseLoader method)


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_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)
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)
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)
sequential_chain (langchain.chains.LLMSummarizationCheckerChain attribute)


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)
set() (langchain.memory.InMemoryEntityStore method)
(langchain.memory.RedisEntityStore 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.DeepLake method)
(langchain.vectorstores.ElasticVectorSearch method)
(langchain.vectorstores.FAISS method)
(langchain.vectorstores.LanceDB method)
(langchain.vectorstores.Milvus method)
(langchain.vectorstores.MyScale method)
(langchain.vectorstores.OpenSearchVectorSearch method)
(langchain.vectorstores.Pinecone method)
(langchain.vectorstores.Qdrant method)
(langchain.vectorstores.Redis method)
(langchain.vectorstores.SupabaseVectorStore method)
(langchain.vectorstores.Tair method)
(langchain.vectorstores.Typesense method)
(langchain.vectorstores.Vectara method)
(langchain.vectorstores.VectorStore 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.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.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.OpenSearchVectorSearch method)
(langchain.vectorstores.Pinecone method)
(langchain.vectorstores.Qdrant method)
(langchain.vectorstores.Redis method)
(langchain.vectorstores.Typesense method)


(langchain.vectorstores.Redis 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)
SitemapLoader (class in langchain.document_loaders)
siterestrict (langchain.utilities.GoogleSearchAPIWrapper attribute)
size (langchain.tools.SteamshipImageGenerationTool attribute)
SlackDirectoryLoader (class in langchain.document_loaders)
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.SpacyTextSplitter method)
(langchain.text_splitter.TextSplitter method)
(langchain.text_splitter.TokenTextSplitter method)
SpreedlyLoader (class in langchain.document_loaders)
sql_chain (langchain.chains.SQLDatabaseSequentialChain attribute)
SRTLoader (class in langchain.document_loaders)


SRTLoader (class in langchain.document_loaders)
start_with_retrieval (langchain.chains.FlareChain 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.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.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)
sync_browser (langchain.agents.agent_toolkits.PlayWrightBrowserToolkit attribute)
T
table (langchain.vectorstores.MyScaleSettings attribute)
table_info (langchain.utilities.PowerBIDataset property)
table_name (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.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.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)
time (langchain.utilities.DuckDuckGoSearchAPIWrapper attribute)
to_typescript() (langchain.tools.APIOperation method)
token (langchain.utilities.PowerBIDataset attribute)
token_path (langchain.document_loaders.GoogleApiClient attribute)
(langchain.document_loaders.GoogleDriveLoader attribute)
tokenizer (langchain.llms.Petals attribute)


tokenizer (langchain.llms.Petals attribute)
tokens (langchain.llms.AlephAlpha attribute)
tokens_path (langchain.llms.RWKV attribute)
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.WikipediaAPIWrapper attribute)


(langchain.utilities.GooglePlacesAPIWrapper attribute)
(langchain.utilities.WikipediaAPIWrapper 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)
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.utilities.GoogleSerperAPIWrapper attribute)


type (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)
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.AzureOpenAI class method)
(langchain.llms.Banana class method)
(langchain.llms.Beam class method)
(langchain.llms.CerebriumAI class method)
(langchain.llms.Cohere 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.SelfHostedPipeline class method)
(langchain.llms.StochasticAI class method)
(langchain.llms.VertexAI class method)
(langchain.llms.Writer class method)


(langchain.llms.VertexAI class method)
(langchain.llms.Writer class method)
upsert_messages() (langchain.memory.CosmosDBChatMessageHistory method)
url (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.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)
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.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.AzureOpenAI attribute)
(langchain.llms.Banana attribute)
(langchain.llms.Beam 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.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.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.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)


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 May 28, 2023.


.md
.pdf
Tutorials
 Contents
Tutorials#
This is a collection of LangChain tutorials mostly on YouTube.
⛓ icon marks a new video [last update 2023-05-15]
#
LangChain AI Handbook By James Briggs and Francisco Ingham
#
LangChain Tutorials by Edrick:
⛓ LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF
LangChain Crash Course: Build an AutoGPT app in 25 minutes by Nicholas Renotte
LangChain Crash Course - Build apps with language models by Patrick Loeber
LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners by Rabbitmetrics
#
LangChain for Gen AI and LLMs by James Briggs:
#1 Getting Started with GPT-3 vs. Open Source LLMs
#2 Prompt Templates for GPT 3.5 and other LLMs
#3 LLM Chains using GPT 3.5 and other LLMs
#4 Chatbot Memory for Chat-GPT, Davinci + other LLMs
#5 Chat with OpenAI in LangChain
⛓ #6 Fixing LLM Hallucinations with Retrieval Augmentation in LangChain
⛓ #7 LangChain Agents Deep Dive with GPT 3.5
⛓ #8 Create Custom Tools for Chatbots in LangChain
⛓ #9 Build Conversational Agents with Vector DBs
#
LangChain 101 by Data Independent:
What Is LangChain? - LangChain + ChatGPT Overview
Quickstart Guide
Beginner Guide To 7 Essential Concepts
OpenAI + Wolfram Alpha
Ask Questions On Your Custom (or Private) Files
Connect Google Drive Files To OpenAI
YouTube Transcripts + OpenAI
Question A 300 Page Book (w/ OpenAI + Pinecone)


Question A 300 Page Book (w/ OpenAI + Pinecone)
Workaround OpenAI's Token Limit With Chain Types
Build Your Own OpenAI + LangChain Web App in 23 Minutes
Working With The New ChatGPT API
OpenAI + LangChain Wrote Me 100 Custom Sales Emails
Structured Output From OpenAI (Clean Dirty Data)
Connect OpenAI To +5,000 Tools (LangChain + Zapier)
Use LLMs To Extract Data From Text (Expert Mode)
⛓ Extract Insights From Interview Transcripts Using LLMs
⛓ 5 Levels Of LLM Summarizing: Novice to Expert
#
LangChain How to and guides by Sam Witteveen:
LangChain Basics - LLMs & PromptTemplates with Colab
LangChain Basics - Tools and Chains
ChatGPT API Announcement & Code Walkthrough with LangChain
Conversations with Memory (explanation & code walkthrough)
Chat with Flan20B
Using Hugging Face Models locally (code walkthrough)
PAL : Program-aided Language Models with LangChain code
Building a Summarization System with LangChain and GPT-3 - Part 1
Building a Summarization System with LangChain and GPT-3 - Part 2
Microsoft’s Visual ChatGPT using LangChain
LangChain Agents - Joining Tools and Chains with Decisions
Comparing LLMs with LangChain
Using Constitutional AI in LangChain
Talking to Alpaca with LangChain - Creating an Alpaca Chatbot
Talk to your CSV & Excel with LangChain
BabyAGI: Discover the Power of Task-Driven Autonomous Agents!
Improve your BabyAGI with LangChain
⛓ Master PDF Chat with LangChain - Your essential guide to queries on documents
⛓ Using LangChain with DuckDuckGO Wikipedia & PythonREPL Tools


⛓ Using LangChain with DuckDuckGO Wikipedia & PythonREPL Tools
⛓ Building Custom Tools and Agents with LangChain (gpt-3.5-turbo)
⛓ LangChain Retrieval QA Over Multiple Files with ChromaDB
⛓ LangChain Retrieval QA with Instructor Embeddings & ChromaDB for PDFs
⛓ LangChain + Retrieval Local LLMs for Retrieval QA - No OpenAI!!!
#
LangChain by Prompt Engineering:
LangChain Crash Course — All You Need to Know to Build Powerful Apps with LLMs
Working with MULTIPLE PDF Files in LangChain: ChatGPT for your Data
ChatGPT for YOUR OWN PDF files with LangChain
Talk to YOUR DATA without OpenAI APIs: LangChain
⛓ CHATGPT For WEBSITES: Custom ChatBOT
#
LangChain by Chat with data
LangChain Beginner’s Tutorial for Typescript/Javascript
GPT-4 Tutorial: How to Chat With Multiple PDF Files (~1000 pages of Tesla’s 10-K Annual Reports)
GPT-4 & LangChain Tutorial: How to Chat With A 56-Page PDF Document (w/Pinecone)
⛓ LangChain & Supabase Tutorial: How to Build a ChatGPT Chatbot For Your Website
#
Get SH*T Done with Prompt Engineering and LangChain by Venelin Valkov
Getting Started with LangChain: Load Custom Data, Run OpenAI Models, Embeddings and ChatGPT
Loaders, Indexes & Vectorstores in LangChain: Question Answering on PDF files with ChatGPT
LangChain Models: ChatGPT, Flan Alpaca, OpenAI Embeddings, Prompt Templates & Streaming
LangChain Chains: Use ChatGPT to Build Conversational Agents, Summaries and Q&A on Text With LLMs


Analyze Custom CSV Data with GPT-4 using Langchain
⛓ Build ChatGPT Chatbots with LangChain Memory: Understanding and Implementing Memory in Conversations
⛓ icon marks a new video [last update 2023-05-15]
previous
Concepts
next
Models
 Contents

By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.md
.pdf
Quickstart Guide
 Contents
Installation
Environment Setup
Building a Language Model Application: LLMs
LLMs: Get predictions from a language model
Prompt Templates: Manage prompts for LLMs
Chains: Combine LLMs and prompts in multi-step workflows
Agents: Dynamically Call Chains Based on User Input
Memory: Add State to Chains and Agents
Building a Language Model Application: Chat Models
Get Message Completions from a Chat Model
Chat Prompt Templates
Chains with Chat Models
Agents with Chat Models
Memory: Add State to Chains and Agents
Quickstart Guide#
This tutorial gives you a quick walkthrough about building an end-to-end language model application with LangChain.
Installation#
To get started, install LangChain with the following command:
pip install langchain
# or
conda install langchain -c conda-forge
Environment Setup#
Using LangChain will usually require integrations with one or more model providers, data stores, apis, etc.
For this example, we will be using OpenAI’s APIs, so we will first need to install their SDK:
pip install openai
We will then need to set the environment variable in the terminal.
export OPENAI_API_KEY="..."
Alternatively, you could do this from inside the Jupyter notebook (or Python script):
import os
os.environ["OPENAI_API_KEY"] = "..."
If you want to set the API key dynamically, you can use the openai_api_key parameter when initiating OpenAI class—for instance, each user’s API key.
from langchain.llms import OpenAI
llm = OpenAI(openai_api_key="OPENAI_API_KEY")
Building a Language Model Application: LLMs#
Now that we have installed LangChain and set up our environment, we can start building our language model application.


LangChain provides many modules that can be used to build language model applications. Modules can be combined to create more complex applications, or be used individually for simple applications.
LLMs: Get predictions from a language model#
The most basic building block of LangChain is calling an LLM on some input.
Let’s walk through a simple example of how to do this.
For this purpose, let’s pretend we are building a service that generates a company name based on what the company makes.
In order to do this, we first need to import the LLM wrapper.
from langchain.llms import OpenAI
We can then initialize the wrapper with any arguments.
In this example, we probably want the outputs to be MORE random, so we’ll initialize it with a HIGH temperature.
llm = OpenAI(temperature=0.9)
We can now call it on some input!
text = "What would be a good company name for a company that makes colorful socks?"
print(llm(text))
Feetful of Fun
For more details on how to use LLMs within LangChain, see the LLM getting started guide.
Prompt Templates: Manage prompts for LLMs#
Calling an LLM is a great first step, but it’s just the beginning.
Normally when you use an LLM in an application, you are not sending user input directly to the LLM.
Instead, you are probably taking user input and constructing a prompt, and then sending that to the LLM.
For example, in the previous example, the text we passed in was hardcoded to ask for a name for a company that made colorful socks.
In this imaginary service, what we would want to do is take only the user input describing what the company does, and then format the prompt with that information.
This is easy to do with LangChain!
First lets define the prompt template:


This is easy to do with LangChain!
First lets define the prompt template:
from langchain.prompts import PromptTemplate
prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)
Let’s now see how this works! We can call the .format method to format it.
print(prompt.format(product="colorful socks"))
What is a good name for a company that makes colorful socks?
For more details, check out the getting started guide for prompts.
Chains: Combine LLMs and prompts in multi-step workflows#
Up until now, we’ve worked with the PromptTemplate and LLM primitives by themselves. But of course, a real application is not just one primitive, but rather a combination of them.
A chain in LangChain is made up of links, which can be either primitives like LLMs or other chains.
The most core type of chain is an LLMChain, which consists of a PromptTemplate and an LLM.
Extending the previous example, we can construct an LLMChain which takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM.
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)
We can now create a very simple chain that will take user input, format the prompt with it, and then send it to the LLM:
from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
Now we can run that chain only specifying the product!
chain.run("colorful socks")


Now we can run that chain only specifying the product!
chain.run("colorful socks")
# -> '\n\nSocktastic!'
There we go! There’s the first chain - an LLM Chain.
This is one of the simpler types of chains, but understanding how it works will set you up well for working with more complex chains.
For more details, check out the getting started guide for chains.
Agents: Dynamically Call Chains Based on User Input#
So far the chains we’ve looked at run in a predetermined order.
Agents no longer do: they use an LLM to determine which actions to take and in what order. An action can either be using a tool and observing its output, or returning to the user.
When used correctly agents can be extremely powerful. In this tutorial, we show you how to easily use agents through the simplest, highest level API.
In order to load agents, you should understand the following concepts:
Tool: A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL, other chains. The interface for a tool is currently a function that is expected to have a string as an input, with a string as an output.
LLM: The language model powering the agent.
Agent: The agent to use. This should be a string that references a support agent class. Because this notebook focuses on the simplest, highest level API, this only covers using the standard supported agents. If you want to implement a custom agent, see the documentation for custom agents (coming soon).
Agents: For a list of supported agents and their specifications, see here.
Tools: For a list of predefined tools and their specifications, see here.
For this example, you will also need to install the SerpAPI Python package.
pip install google-search-results
And set the appropriate environment variables.
import os


pip install google-search-results
And set the appropriate environment variables.
import os
os.environ["SERPAPI_API_KEY"] = "..."
Now we can get started!
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import OpenAI
# First, let's load the language model we're going to use to control the agent.
llm = OpenAI(temperature=0)
# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
tools = load_tools(["serpapi", "llm-math"], llm=llm)
# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
# Now let's test it out!
agent.run("What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?")
> Entering new AgentExecutor chain...
 I need to find the temperature first, then use the calculator to raise it to the .023 power.
Action: Search
Action Input: "High temperature in SF yesterday"
Observation: San Francisco Temperature Yesterday. Maximum temperature yesterday: 57 °F (at 1:56 pm) Minimum temperature yesterday: 49 °F (at 1:56 am) Average temperature ...
Thought: I now have the temperature, so I can use the calculator to raise it to the .023 power.
Action: Calculator
Action Input: 57^.023
Observation: Answer: 1.0974509573251117
Thought: I now know the final answer


Thought: I now know the final answer
Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .023 power is 1.0974509573251117.
> Finished chain.
Memory: Add State to Chains and Agents#
So far, all the chains and agents we’ve gone through have been stateless. But often, you may want a chain or agent to have some concept of “memory” so that it may remember information about its previous interactions. The clearest and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of “short-term memory”. On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of “long-term memory”. For more concrete ideas on the latter, see this awesome paper.
LangChain provides several specially created chains just for this purpose. This notebook walks through using one of those chains (the ConversationChain) with two different types of memory.
By default, the ConversationChain has a simple type of memory that remembers all previous inputs/outputs and adds them to the context that is passed. Let’s take a look at using this chain (setting verbose=True so we can see the prompt).
from langchain import OpenAI, ConversationChain
llm = OpenAI(temperature=0)
conversation = ConversationChain(llm=llm, verbose=True)
output = conversation.predict(input="Hi there!")
print(output)
> Entering new chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
Human: Hi there!
AI:


Current conversation:
Human: Hi there!
AI:
> Finished chain.
' Hello! How are you today?'
output = conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
print(output)
> Entering new chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
Human: Hi there!
AI:  Hello! How are you today?
Human: I'm doing well! Just having a conversation with an AI.
AI:
> Finished chain.
" That's great! What would you like to talk about?"
Building a Language Model Application: Chat Models#
Similarly, you can use chat models instead of LLMs. Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a “text in, text out” API, they expose an interface where “chat messages” are the inputs and outputs.
Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
Get Message Completions from a Chat Model#
You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are AIMessage, HumanMessage, SystemMessage, and ChatMessage – ChatMessage takes in an arbitrary role parameter. Most of the time, you’ll just be dealing with HumanMessage, AIMessage, and SystemMessage.
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
    AIMessage,
    HumanMessage,
    SystemMessage
)


AIMessage,
    HumanMessage,
    SystemMessage
)
chat = ChatOpenAI(temperature=0)
You can get completions by passing in a single message.
chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
You can also pass in multiple messages for OpenAI’s gpt-3.5-turbo and gpt-4 models.
messages = [
    SystemMessage(content="You are a helpful assistant that translates English to French."),
    HumanMessage(content="I love programming.")
]
chat(messages)
# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
You can go one step further and generate completions for multiple sets of messages using generate. This returns an LLMResult with an additional message parameter:
batch_messages = [
    [
        SystemMessage(content="You are a helpful assistant that translates English to French."),
        HumanMessage(content="I love programming.")
    ],
    [
        SystemMessage(content="You are a helpful assistant that translates English to French."),
        HumanMessage(content="I love artificial intelligence.")
    ],
]
result = chat.generate(batch_messages)
result
# -> LLMResult(generations=[[ChatGeneration(text="J'aime programmer.", generation_info=None, message=AIMessage(content="J'aime programmer.", additional_kwargs={}))], [ChatGeneration(text="J'aime l'intelligence artificielle.", generation_info=None, message=AIMessage(content="J'aime l'intelligence artificielle.", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}})
You can recover things like token usage from this LLMResult:


You can recover things like token usage from this LLMResult:
result.llm_output['token_usage']
# -> {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}
Chat Prompt Templates#
Similar to LLMs, you can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate’s format_prompt – this returns a PromptValue, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.
For convenience, there is a from_template method exposed on the template. If you were to use this template, this is what it would look like:
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
chat = ChatOpenAI(temperature=0)
template = "You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
# get a chat completion from the formatted messages
chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
Chains with Chat Models#
The LLMChain discussed in the above section can be used with chat models as well:
from langchain.chat_models import ChatOpenAI
from langchain import LLMChain
from langchain.prompts.chat import (


from langchain import LLMChain
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
chat = ChatOpenAI(temperature=0)
template = "You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chain = LLMChain(llm=chat, prompt=chat_prompt)
chain.run(input_language="English", output_language="French", text="I love programming.")
# -> "J'aime programmer."
Agents with Chat Models#
Agents can also be used with chat models, you can initialize one using AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION as the agent type.
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
# First, let's load the language model we're going to use to control the agent.
chat = ChatOpenAI(temperature=0)
# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.


agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
# Now let's test it out!
agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
> Entering new AgentExecutor chain...
Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power.
Action:
{
  "action": "Search",
  "action_input": "Olivia Wilde boyfriend"
}
Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling.
Thought:I need to use a search engine to find Harry Styles' current age.
Action:
{
  "action": "Search",
  "action_input": "Harry Styles age"
}
Observation: 29 years
Thought:Now I need to calculate 29 raised to the 0.23 power.
Action:
{
  "action": "Calculator",
  "action_input": "29^0.23"
}
Observation: Answer: 2.169459462491557
Thought:I now know the final answer.
Final Answer: 2.169459462491557
> Finished chain.
'2.169459462491557'
Memory: Add State to Chains and Agents#


'2.169459462491557'
Memory: Add State to Chains and Agents#
You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
from langchain.prompts import (
    ChatPromptTemplate,
    MessagesPlaceholder,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate
)
from langchain.chains import ConversationChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
prompt = ChatPromptTemplate.from_messages([
    SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."),
    MessagesPlaceholder(variable_name="history"),
    HumanMessagePromptTemplate.from_template("{input}")
])
llm = ChatOpenAI(temperature=0)
memory = ConversationBufferMemory(return_messages=True)
conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)
conversation.predict(input="Hi there!")
# -> 'Hello! How can I assist you today?'
conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
# -> "That sounds like fun! I'm happy to chat with you. Is there anything specific you'd like to talk about?"
conversation.predict(input="Tell me about yourself.")


conversation.predict(input="Tell me about yourself.")
# -> "Sure! I am an AI language model created by OpenAI. I was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. I can answer questions, provide information, and even have conversations like this one. Is there anything else you'd like to know about me?"
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Welcome to LangChain
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Concepts
 Contents

Installation
Environment Setup
Building a Language Model Application: LLMs
LLMs: Get predictions from a language model
Prompt Templates: Manage prompts for LLMs
Chains: Combine LLMs and prompts in multi-step workflows
Agents: Dynamically Call Chains Based on User Input
Memory: Add State to Chains and Agents
Building a Language Model Application: Chat Models
Get Message Completions from a Chat Model
Chat Prompt Templates
Chains with Chat Models
Agents with Chat Models
Memory: Add State to Chains and Agents
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.md
.pdf
Concepts
 Contents
Chain of Thought
Action Plan Generation
ReAct
Self-ask
Prompt Chaining
Memetic Proxy
Self Consistency
Inception
MemPrompt
Concepts#
These are concepts and terminology commonly used when developing LLM applications.
It contains reference to external papers or sources where the concept was first introduced,
as well as to places in LangChain where the concept is used.
Chain of Thought#
Chain of Thought (CoT) is a prompting technique used to encourage the model to generate a series of intermediate reasoning steps.
A less formal way to induce this behavior is to include “Let’s think step-by-step” in the prompt.
Chain-of-Thought Paper
Step-by-Step Paper
Action Plan Generation#
Action Plan Generation is a prompting technique that uses a language model to generate actions to take.
The results of these actions can then be fed back into the language model to generate a subsequent action.
WebGPT Paper
SayCan Paper
ReAct#
ReAct is a prompting technique that combines Chain-of-Thought prompting with action plan generation.
This induces the model to think about what action to take, then take it.
Paper
LangChain Example
Self-ask#
Self-ask is a prompting method that builds on top of chain-of-thought prompting.
In this method, the model explicitly asks itself follow-up questions, which are then answered by an external search engine.
Paper
LangChain Example
Prompt Chaining#
Prompt Chaining is combining multiple LLM calls, with the output of one-step being the input to the next.
PromptChainer Paper
Language Model Cascades
ICE Primer Book
Socratic Models
Memetic Proxy#
Memetic Proxy is encouraging the LLM
to respond in a certain way framing the discussion in a context that the model knows of and that


to respond in a certain way framing the discussion in a context that the model knows of and that
will result in that type of response.
For example, as a conversation between a student and a teacher.
Paper
Self Consistency#
Self Consistency is a decoding strategy that samples a diverse set of reasoning paths and then selects the most consistent answer.
Is most effective when combined with Chain-of-thought prompting.
Paper
Inception#
Inception is also called First Person Instruction.
It is encouraging the model to think a certain way by including the start of the model’s response in the prompt.
Example
MemPrompt#
MemPrompt maintains a memory of errors and user feedback, and uses them to prevent repetition of mistakes.
Paper
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Quickstart Guide
next
Tutorials
 Contents

Chain of Thought
Action Plan Generation
ReAct
Self-ask
Prompt Chaining
Memetic Proxy
Self Consistency
Inception
MemPrompt
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.rst
.pdf
Chains
Chains#
Note
Conceptual Guide
Using an LLM in isolation is fine for some simple applications,
but many more complex ones require chaining LLMs - either with each other or with other experts.
LangChain provides a standard interface for Chains, as well as some common implementations of chains for ease of use.
The following sections of documentation are provided:
Getting Started: A getting started guide for chains, to get you up and running quickly.
How-To Guides: A collection of how-to guides. These highlight how to use various types of chains.
Reference: API reference documentation for all Chain classes.
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Zep Memory
next
Getting Started
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.rst
.pdf
Agents
 Contents
Action Agents
Plan-and-Execute Agents
Agents#
Note
Conceptual Guide
Some applications will 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 a “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 an action to take and take that action one step 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 agents looks something like:
Some user input is received
The agent decides which tool - if any - to use, and what the input to that tool should be
That tool is then called with that tool input, and an observation is recorded (this is just the output of calling that tool with that tool input)
That history of tool, tool input, and observation is passed back into the agent, and it decides what step to take next
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 as follows:


The different abstractions involved in agents are as follows:
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.
The most important abstraction of the four above to understand is that of the agent.
Although an agent can be defined in whatever way one chooses, 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.
In this section of documentation, we first start with a Getting Started notebook to cover how to use all things related to agents in an end-to-end manner.
We then split the documentation into the following sections:
Tools
In this section we cover the different types of tools LangChain supports natively.
We then cover how to add your own tools.
Agents
In this section we cover the different types of agents LangChain supports natively.


Agents
In this section we cover the different types of agents LangChain supports natively.
We then cover how to modify and create your own agents.
Toolkits
In this section we go over the various toolkits that LangChain supports out of the box,
and how to create an agent from them.
Agent Executor
In this section we go over 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.
Go Deeper
Tools
Agents
Toolkits
Agent Executors
Plan-and-Execute Agents#
High level pseudocode of agents looks something like:
Some 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.
Go Deeper
Plan and Execute
Imports
Tools
Planner, Executor, and Agent
Run Example
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Chains
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Getting Started
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Action Agents
Plan-and-Execute Agents
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.


.rst
.pdf
Prompts
 Contents
Getting Started
Go Deeper
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 rarely hard coded, but rather 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.
This section of documentation is split into four sections:
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 hardcoded, but it is often more powerful if they are 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 than just text back.
This is where output parsers come in.
Output Parsers are responsible for (1) instructing the model how output should be formatted,
(2) parsing output into the desired formatting (including retrying if necessary).
Getting Started#
Getting Started
Go Deeper#
Prompt Templates
Chat Prompt Template
Example Selectors
Output Parsers
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TensorflowHub
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Getting Started
 Contents

Getting Started
Go Deeper
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on May 28, 2023.