diff --git a/docs/docs/expression_language/how_to/functions.ipynb b/docs/docs/expression_language/how_to/functions.ipynb index 87a890e3fe..a11fc0b510 100644 --- a/docs/docs/expression_language/how_to/functions.ipynb +++ b/docs/docs/expression_language/how_to/functions.ipynb @@ -82,7 +82,7 @@ "source": [ "## Accepting a Runnable Config\n", "\n", - "Runnable lambdas can optionally accept a [RunnableConfig](https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.config.RunnableConfig.html?highlight=runnableconfig#langchain.schema.runnable.config.RunnableConfig), which they can use to pass callbacks, tags, and other configuration information to nested runs." + "Runnable lambdas can optionally accept a [RunnableConfig](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig), which they can use to pass callbacks, tags, and other configuration information to nested runs." ] }, { diff --git a/docs/docs/expression_language/interface.ipynb b/docs/docs/expression_language/interface.ipynb index 8161a92a14..39023210d6 100644 --- a/docs/docs/expression_language/interface.ipynb +++ b/docs/docs/expression_language/interface.ipynb @@ -16,7 +16,7 @@ "id": "9a9acd2e", "metadata": {}, "source": [ - "To make it as easy as possible to create custom chains, we've implemented a [\"Runnable\"](https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.base.Runnable.html#langchain.schema.runnable.base.Runnable) protocol. The `Runnable` protocol is implemented for most components. \n", + "To make it as easy as possible to create custom chains, we've implemented a [\"Runnable\"](https://api.python.langchain.com/en/stable/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) protocol. The `Runnable` protocol is implemented for most components. \n", "This is a standard interface, which makes it easy to define custom chains as well as invoke them in a standard way. \n", "The standard interface includes:\n", "\n", diff --git a/docs/docs/modules/data_connection/retrievers/multi_vector.ipynb b/docs/docs/modules/data_connection/retrievers/multi_vector.ipynb index 5eb0df6410..9d6aea1a13 100644 --- a/docs/docs/modules/data_connection/retrievers/multi_vector.ipynb +++ b/docs/docs/modules/data_connection/retrievers/multi_vector.ipynb @@ -183,7 +183,7 @@ "id": "cdef8339-f9fa-4b3b-955f-ad9dbdf2734f", "metadata": {}, "source": [ - "The default search type the retriever performs on the vector database is a similarity search. LangChain Vector Stores also support searching via [Max Marginal Relevance](https://api.python.langchain.com/en/latest/schema/langchain.schema.vectorstore.VectorStore.html#langchain.schema.vectorstore.VectorStore.max_marginal_relevance_search) so if you want this instead you can just set the `search_type` property as follows:" + "The default search type the retriever performs on the vector database is a similarity search. LangChain Vector Stores also support searching via [Max Marginal Relevance](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.max_marginal_relevance_search) so if you want this instead you can just set the `search_type` property as follows:" ] }, { diff --git a/docs/docs/modules/memory/adding_memory.ipynb b/docs/docs/modules/memory/adding_memory.ipynb index c2168c638a..a4a5c89be5 100644 --- a/docs/docs/modules/memory/adding_memory.ipynb +++ b/docs/docs/modules/memory/adding_memory.ipynb @@ -190,9 +190,9 @@ "id": "a237bbb8-e448-4238-8420-004e046ef84e", "metadata": {}, "source": [ - "We will use the [ChatPromptTemplate](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html) class to set up the chat prompt.\n", + "We will use the [ChatPromptTemplate](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html?highlight=chatprompttemplate) class to set up the chat prompt.\n", "\n", - "The [from_messages](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html#langchain.prompts.chat.ChatPromptTemplate.from_messages) method creates a `ChatPromptTemplate` from a list of messages (e.g., `SystemMessage`, `HumanMessage`, `AIMessage`, `ChatMessage`, etc.) or message templates, such as the [MessagesPlaceholder](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.MessagesPlaceholder.html#langchain.prompts.chat.MessagesPlaceholder) below.\n", + "The [from_messages](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html#langchain_core.prompts.chat.ChatPromptTemplate.from_messages) method creates a `ChatPromptTemplate` from a list of messages (e.g., `SystemMessage`, `HumanMessage`, `AIMessage`, `ChatMessage`, etc.) or message templates, such as the [MessagesPlaceholder](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.chat.MessagesPlaceholder.html#langchain_core.prompts.chat.MessagesPlaceholder) below.\n", "\n", "The configuration below makes it so the memory will be injected to the middle of the chat prompt, in the `chat_history` key, and the user's inputs will be added in a human/user message to the end of the chat prompt." ] diff --git a/docs/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat.ipynb b/docs/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat.ipynb index aaf00e2bc4..a7c08084ad 100644 --- a/docs/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat.ipynb +++ b/docs/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat.ipynb @@ -7,7 +7,7 @@ "source": [ "# Few-shot examples for chat models\n", "\n", - "This notebook covers how to use few-shot examples in chat models. There does not appear to be solid consensus on how best to do few-shot prompting, and the optimal prompt compilation will likely vary by model. Because of this, we provide few-shot prompt templates like the [FewShotChatMessagePromptTemplate](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.few_shot.FewShotChatMessagePromptTemplate.html) as a flexible starting point, and you can modify or replace them as you see fit.\n", + "This notebook covers how to use few-shot examples in chat models. There does not appear to be solid consensus on how best to do few-shot prompting, and the optimal prompt compilation will likely vary by model. Because of this, we provide few-shot prompt templates like the [FewShotChatMessagePromptTemplate](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.few_shot.FewShotChatMessagePromptTemplate.html?highlight=fewshot#langchain_core.prompts.few_shot.FewShotChatMessagePromptTemplate) as a flexible starting point, and you can modify or replace them as you see fit.\n", "\n", "The goal of few-shot prompt templates are to dynamically select examples based on an input, and then format the examples in a final prompt to provide for the model.\n", "\n", @@ -28,7 +28,7 @@ "\n", "The basic components of the template are:\n", "- `examples`: A list of dictionary examples to include in the final prompt.\n", - "- `example_prompt`: converts each example into 1 or more messages through its [`format_messages`](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html#langchain.prompts.chat.ChatPromptTemplate.format_messages) method. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.\n", + "- `example_prompt`: converts each example into 1 or more messages through its [`format_messages`](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html?highlight=format_messages#langchain_core.prompts.chat.ChatPromptTemplate.format_messages) method. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.\n", "\n", "Below is a simple demonstration. First, import the modules for this example:" ] @@ -176,8 +176,8 @@ "\n", "Sometimes you may want to condition which examples are shown based on the input. For this, you can replace the `examples` with an `example_selector`. The other components remain the same as above! To review, the dynamic few-shot prompt template would look like:\n", "\n", - "- `example_selector`: responsible for selecting few-shot examples (and the order in which they are returned) for a given input. These implement the [BaseExampleSelector](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.example_selector.base.BaseExampleSelector.html#langchain.prompts.example_selector.base.BaseExampleSelector) interface. A common example is the vectorstore-backed [SemanticSimilarityExampleSelector](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.example_selector.semantic_similarity.SemanticSimilarityExampleSelector.html#langchain.prompts.example_selector.semantic_similarity.SemanticSimilarityExampleSelector)\n", - "- `example_prompt`: convert each example into 1 or more messages through its [`format_messages`](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html#langchain.prompts.chat.ChatPromptTemplate.format_messages) method. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.\n", + "- `example_selector`: responsible for selecting few-shot examples (and the order in which they are returned) for a given input. These implement the [BaseExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.base.BaseExampleSelector.html?highlight=baseexampleselector#langchain_core.example_selectors.base.BaseExampleSelector) interface. A common example is the vectorstore-backed [SemanticSimilarityExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector.html?highlight=semanticsimilarityexampleselector#langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector)\n", + "- `example_prompt`: convert each example into 1 or more messages through its [`format_messages`](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html?highlight=chatprompttemplate#langchain_core.prompts.chat.ChatPromptTemplate.format_messages) method. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.\n", "\n", "These once again can be composed with other messages and chat templates to assemble your final prompt." ]