diff --git a/MIGRATE.md b/MIGRATE.md
index 6500865076447..710f2d27b2fae 100644
--- a/MIGRATE.md
+++ b/MIGRATE.md
@@ -1,11 +1,11 @@
# Migrating
-Please see the following guides for migratin LangChain code:
+Please see the following guides for migrating LangChain code:
* Migrate to [LangChain v0.3](https://python.langchain.com/docs/versions/v0_3/)
* Migrate to [LangChain v0.2](https://python.langchain.com/docs/versions/v0_2/)
* Migrating from [LangChain 0.0.x Chains](https://python.langchain.com/docs/versions/migrating_chains/)
-* Upgrate to [LangGraph Memory](https://python.langchain.com/docs/versions/migrating_memory/)
+* Upgrade to [LangGraph Memory](https://python.langchain.com/docs/versions/migrating_memory/)
-The [LangChain CLI](https://python.langchain.com/docs/versions/v0_3/#migrate-using-langchain-cli) can help automatically upgrade your code to use non deprecated imports.
+The [LangChain CLI](https://python.langchain.com/docs/versions/v0_3/#migrate-using-langchain-cli) can help you automatically upgrade your code to use non-deprecated imports.
This will be especially helpful if you're still on either version 0.0.x or 0.1.x of LangChain.
diff --git a/docs/cassettes/output_parser_string_28eeace3-3896-497f-93ad-544cbfb7f15c.msgpack.zlib b/docs/cassettes/output_parser_string_28eeace3-3896-497f-93ad-544cbfb7f15c.msgpack.zlib
new file mode 100644
index 0000000000000..1e217f2754185
--- /dev/null
+++ b/docs/cassettes/output_parser_string_28eeace3-3896-497f-93ad-544cbfb7f15c.msgpack.zlib
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/docs/cassettes/output_parser_string_8ac74999-0740-4178-8efd-32a855592f71.msgpack.zlib b/docs/cassettes/output_parser_string_8ac74999-0740-4178-8efd-32a855592f71.msgpack.zlib
new file mode 100644
index 0000000000000..c2023e5c1d562
--- /dev/null
+++ b/docs/cassettes/output_parser_string_8ac74999-0740-4178-8efd-32a855592f71.msgpack.zlib
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/output_parser_string_8c87553e-4f85-46c4-8f1e-666f6a261a50.msgpack.zlib b/docs/cassettes/output_parser_string_8c87553e-4f85-46c4-8f1e-666f6a261a50.msgpack.zlib
new file mode 100644
index 0000000000000..53a3a03a4a273
--- /dev/null
+++ b/docs/cassettes/output_parser_string_8c87553e-4f85-46c4-8f1e-666f6a261a50.msgpack.zlib
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/docs/cassettes/output_parser_string_9cbb8848-9101-465e-b230-0f7af6fb4105.msgpack.zlib b/docs/cassettes/output_parser_string_9cbb8848-9101-465e-b230-0f7af6fb4105.msgpack.zlib
new file mode 100644
index 0000000000000..d2caf2a91615a
--- /dev/null
+++ b/docs/cassettes/output_parser_string_9cbb8848-9101-465e-b230-0f7af6fb4105.msgpack.zlib
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/docs/docs/concepts/chat_history.mdx b/docs/docs/concepts/chat_history.mdx
index 5c7c112460d2b..57d22c2735376 100644
--- a/docs/docs/concepts/chat_history.mdx
+++ b/docs/docs/concepts/chat_history.mdx
@@ -17,7 +17,7 @@ Most conversations start with a **system message** that sets the context for the
The **assistant** may respond directly to the user or if configured with tools request that a [tool](/docs/concepts/tool_calling) be invoked to perform a specific task.
-So a full conversation often involves a combination of two patterns of alternating messages:
+A full conversation often involves a combination of two patterns of alternating messages:
1. The **user** and the **assistant** representing a back-and-forth conversation.
2. The **assistant** and **tool messages** representing an ["agentic" workflow](/docs/concepts/agents) where the assistant is invoking tools to perform specific tasks.
diff --git a/docs/docs/concepts/chat_models.mdx b/docs/docs/concepts/chat_models.mdx
index b42022161f23a..03133a253e582 100644
--- a/docs/docs/concepts/chat_models.mdx
+++ b/docs/docs/concepts/chat_models.mdx
@@ -2,7 +2,7 @@
## Overview
-Large Language Models (LLMs) are advanced machine learning models that excel in a wide range of language-related tasks such as text generation, translation, summarization, question answering, and more, without needing task-specific tuning for every scenario.
+Large Language Models (LLMs) are advanced machine learning models that excel in a wide range of language-related tasks such as text generation, translation, summarization, question answering, and more, without needing task-specific fine tuning for every scenario.
Modern LLMs are typically accessed through a chat model interface that takes a list of [messages](/docs/concepts/messages) as input and returns a [message](/docs/concepts/messages) as output.
@@ -85,7 +85,7 @@ Many chat models have standardized parameters that can be used to configure the
| Parameter | Description |
|----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `model` | The name or identifier of the specific AI model you want to use (e.g., `"gpt-3.5-turbo"` or `"gpt-4"`). |
-| `temperature` | Controls the randomness of the model's output. A higher value (e.g., 1.0) makes responses more creative, while a lower value (e.g., 0.1) makes them more deterministic and focused. |
+| `temperature` | Controls the randomness of the model's output. A higher value (e.g., 1.0) makes responses more creative, while a lower value (e.g., 0.0) makes them more deterministic and focused. |
| `timeout` | The maximum time (in seconds) to wait for a response from the model before canceling the request. Ensures the request doesn’t hang indefinitely. |
| `max_tokens` | Limits the total number of tokens (words and punctuation) in the response. This controls how long the output can be. |
| `stop` | Specifies stop sequences that indicate when the model should stop generating tokens. For example, you might use specific strings to signal the end of a response. |
@@ -97,9 +97,9 @@ Many chat models have standardized parameters that can be used to configure the
Some important things to note:
- Standard parameters only apply to model providers that expose parameters with the intended functionality. For example, some providers do not expose a configuration for maximum output tokens, so max_tokens can't be supported on these.
-- Standard params are currently only enforced on integrations that have their own integration packages (e.g. `langchain-openai`, `langchain-anthropic`, etc.), they're not enforced on models in ``langchain-community``.
+- Standard parameters are currently only enforced on integrations that have their own integration packages (e.g. `langchain-openai`, `langchain-anthropic`, etc.), they're not enforced on models in `langchain-community`.
-ChatModels also accept other parameters that are specific to that integration. To find all the parameters supported by a ChatModel head to the [API reference](https://python.langchain.com/api_reference/) for that model.
+Chat models also accept other parameters that are specific to that integration. To find all the parameters supported by a Chat model head to the their respective [API reference](https://python.langchain.com/api_reference/) for that model.
## Tool calling
@@ -150,7 +150,7 @@ An alternative approach is to use semantic caching, where you cache responses ba
A semantic cache introduces a dependency on another model on the critical path of your application (e.g., the semantic cache may rely on an [embedding model](/docs/concepts/embedding_models) to convert text to a vector representation), and it's not guaranteed to capture the meaning of the input accurately.
-However, there might be situations where caching chat model responses is beneficial. For example, if you have a chat model that is used to answer frequently asked questions, caching responses can help reduce the load on the model provider and improve response times.
+However, there might be situations where caching chat model responses is beneficial. For example, if you have a chat model that is used to answer frequently asked questions, caching responses can help reduce the load on the model provider, costs, and improve response times.
Please see the [how to cache chat model responses](/docs/how_to/chat_model_caching/) guide for more details.
diff --git a/docs/docs/concepts/document_loaders.mdx b/docs/docs/concepts/document_loaders.mdx
index d9b1f13babd00..c38e81610e35d 100644
--- a/docs/docs/concepts/document_loaders.mdx
+++ b/docs/docs/concepts/document_loaders.mdx
@@ -29,7 +29,7 @@ loader = CSVLoader(
data = loader.load()
```
-or if working with large datasets, you can use the `.lazy_load` method:
+When working with large datasets, you can use the `.lazy_load` method:
```python
for document in loader.lazy_load():
diff --git a/docs/docs/concepts/lcel.mdx b/docs/docs/concepts/lcel.mdx
index 020bc6f8aa1ed..da45da268b301 100644
--- a/docs/docs/concepts/lcel.mdx
+++ b/docs/docs/concepts/lcel.mdx
@@ -6,7 +6,7 @@
The **L**ang**C**hain **E**xpression **L**anguage (LCEL) takes a [declarative](https://en.wikipedia.org/wiki/Declarative_programming) approach to building new [Runnables](/docs/concepts/runnables) from existing Runnables.
-This means that you describe what you want to happen, rather than how you want it to happen, allowing LangChain to optimize the run-time execution of the chains.
+This means that you describe what *should* happen, rather than *how* it should happen, allowing LangChain to optimize the run-time execution of the chains.
We often refer to a `Runnable` created using LCEL as a "chain". It's important to remember that a "chain" is `Runnable` and it implements the full [Runnable Interface](/docs/concepts/runnables).
@@ -20,8 +20,8 @@ We often refer to a `Runnable` created using LCEL as a "chain". It's important t
LangChain optimizes the run-time execution of chains built with LCEL in a number of ways:
-- **Optimize parallel execution**: Run Runnables in parallel using [RunnableParallel](#runnableparallel) or run multiple inputs through a given chain in parallel using the [Runnable Batch API](/docs/concepts/runnables/#optimized-parallel-execution-batch). Parallel execution can significantly reduce the latency as processing can be done in parallel instead of sequentially.
-- **Guarantee Async support**: Any chain built with LCEL can be run asynchronously using the [Runnable Async API](/docs/concepts/runnables/#asynchronous-support). This can be useful when running chains in a server environment where you want to handle large number of requests concurrently.
+- **Optimized parallel execution**: Run Runnables in parallel using [RunnableParallel](#runnableparallel) or run multiple inputs through a given chain in parallel using the [Runnable Batch API](/docs/concepts/runnables/#optimized-parallel-execution-batch). Parallel execution can significantly reduce the latency as processing can be done in parallel instead of sequentially.
+- **Guaranteed Async support**: Any chain built with LCEL can be run asynchronously using the [Runnable Async API](/docs/concepts/runnables/#asynchronous-support). This can be useful when running chains in a server environment where you want to handle large number of requests concurrently.
- **Simplify streaming**: LCEL chains can be streamed, allowing for incremental output as the chain is executed. LangChain can optimize the streaming of the output to minimize the time-to-first-token(time elapsed until the first chunk of output from a [chat model](/docs/concepts/chat_models) or [llm](/docs/concepts/text_llms) comes out).
Other benefits include:
@@ -38,7 +38,7 @@ LCEL is an [orchestration solution](https://en.wikipedia.org/wiki/Orchestration_
While we have seen users run chains with hundreds of steps in production, we generally recommend using LCEL for simpler orchestration tasks. When the application requires complex state management, branching, cycles or multiple agents, we recommend that users take advantage of [LangGraph](/docs/concepts/architecture#langgraph).
-In LangGraph, users define graphs that specify the flow of the application. This allows users to keep using LCEL within individual nodes when LCEL is needed, while making it easy to define complex orchestration logic that is more readable and maintainable.
+In LangGraph, users define graphs that specify the application's flow. This allows users to keep using LCEL within individual nodes when LCEL is needed, while making it easy to define complex orchestration logic that is more readable and maintainable.
Here are some guidelines:
diff --git a/docs/docs/concepts/messages.mdx b/docs/docs/concepts/messages.mdx
index d1b307b98c1d8..c8765ab3d3471 100644
--- a/docs/docs/concepts/messages.mdx
+++ b/docs/docs/concepts/messages.mdx
@@ -8,7 +8,7 @@
Messages are the unit of communication in [chat models](/docs/concepts/chat_models). They are used to represent the input and output of a chat model, as well as any additional context or metadata that may be associated with a conversation.
-Each message has a **role** (e.g., "user", "assistant"), **content** (e.g., text, multimodal data), and additional metadata that can vary depending on the chat model provider.
+Each message has a **role** (e.g., "user", "assistant") and **content** (e.g., text, multimodal data) with additional metadata that varies depending on the chat model provider.
LangChain provides a unified message format that can be used across chat models, allowing users to work with different chat models without worrying about the specific details of the message format used by each model provider.
@@ -39,6 +39,7 @@ The content of a message text or a list of dictionaries representing [multimodal
Currently, most chat models support text as the primary content type, with some models also supporting multimodal data. However, support for multimodal data is still limited across most chat model providers.
For more information see:
+* [SystemMessage](#systemmessage) -- for content which should be passed to direct the conversation
* [HumanMessage](#humanmessage) -- for content in the input from the user.
* [AIMessage](#aimessage) -- for content in the response from the model.
* [Multimodality](/docs/concepts/multimodality) -- for more information on multimodal content.
diff --git a/docs/docs/concepts/output_parsers.mdx b/docs/docs/concepts/output_parsers.mdx
index d15bc6fdb6894..f2cf62c04713f 100644
--- a/docs/docs/concepts/output_parsers.mdx
+++ b/docs/docs/concepts/output_parsers.mdx
@@ -26,6 +26,7 @@ LangChain has lots of different types of output parsers. This is a list of outpu
| Name | Supports Streaming | Has Format Instructions | Calls LLM | Input Type | Output Type | Description |
|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------|-------------------------|-----------|--------------------|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [Str](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html) | ✅ | | | `str` \| `Message` | String | Parses texts from message objects. Useful for handling variable formats of message content (e.g., extracting text from content blocks). |
| [JSON](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.json.JSONOutputParser.html#langchain_core.output_parsers.json.JSONOutputParser) | ✅ | ✅ | | `str` \| `Message` | JSON object | Returns a JSON object as specified. You can specify a Pydantic model and it will return JSON for that model. Probably the most reliable output parser for getting structured data that does NOT use function calling. |
| [XML](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html#langchain_core.output_parsers.xml.XMLOutputParser) | ✅ | ✅ | | `str` \| `Message` | `dict` | Returns a dictionary of tags. Use when XML output is needed. Use with models that are good at writing XML (like Anthropic's). |
| [CSV](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.list.CommaSeparatedListOutputParser.html#langchain_core.output_parsers.list.CommaSeparatedListOutputParser) | ✅ | ✅ | | `str` \| `Message` | `List[str]` | Returns a list of comma separated values. |
diff --git a/docs/docs/concepts/retrieval.mdx b/docs/docs/concepts/retrieval.mdx
index a69fb8d4f9d54..0ded476b80016 100644
--- a/docs/docs/concepts/retrieval.mdx
+++ b/docs/docs/concepts/retrieval.mdx
@@ -27,7 +27,7 @@ These systems accommodate various data formats:
- Unstructured text (e.g., documents) is often stored in vector stores or lexical search indexes.
- Structured data is typically housed in relational or graph databases with defined schemas.
-Despite this diversity in data formats, modern AI applications increasingly aim to make all types of data accessible through natural language interfaces.
+Despite the growing diversity in data formats, modern AI applications increasingly aim to make all types of data accessible through natural language interfaces.
Models play a crucial role in this process by translating natural language queries into formats compatible with the underlying search index or database.
This translation enables more intuitive and flexible interactions with complex data structures.
@@ -41,7 +41,7 @@ This translation enables more intuitive and flexible interactions with complex d
## Query analysis
-While users typically prefer to interact with retrieval systems using natural language, retrieval systems can specific query syntax or benefit from particular keywords.
+While users typically prefer to interact with retrieval systems using natural language, these systems may require specific query syntax or benefit from certain keywords.
Query analysis serves as a bridge between raw user input and optimized search queries. Some common applications of query analysis include:
1. **Query Re-writing**: Queries can be re-written or expanded to improve semantic or lexical searches.
diff --git a/docs/docs/concepts/runnables.mdx b/docs/docs/concepts/runnables.mdx
index 961942c67d92f..dea928568a735 100644
--- a/docs/docs/concepts/runnables.mdx
+++ b/docs/docs/concepts/runnables.mdx
@@ -1,6 +1,6 @@
# Runnable interface
-The Runnable interface is foundational for working with LangChain components, and it's implemented across many of them, such as [language models](/docs/concepts/chat_models), [output parsers](/docs/concepts/output_parsers), [retrievers](/docs/concepts/retrievers), [compiled LangGraph graphs](
+The Runnable interface is the foundation for working with LangChain components, and it's implemented across many of them, such as [language models](/docs/concepts/chat_models), [output parsers](/docs/concepts/output_parsers), [retrievers](/docs/concepts/retrievers), [compiled LangGraph graphs](
https://langchain-ai.github.io/langgraph/concepts/low_level/#compiling-your-graph) and more.
This guide covers the main concepts and methods of the Runnable interface, which allows developers to interact with various LangChain components in a consistent and predictable manner.
@@ -42,7 +42,7 @@ Some Runnables may provide their own implementations of `batch` and `batch_as_co
rely on a `batch` API provided by a model provider).
:::note
-The async versions of `abatch` and `abatch_as_completed` these rely on asyncio's [gather](https://docs.python.org/3/library/asyncio-task.html#asyncio.gather) and [as_completed](https://docs.python.org/3/library/asyncio-task.html#asyncio.as_completed) functions to run the `ainvoke` method in parallel.
+The async versions of `abatch` and `abatch_as_completed` relies on asyncio's [gather](https://docs.python.org/3/library/asyncio-task.html#asyncio.gather) and [as_completed](https://docs.python.org/3/library/asyncio-task.html#asyncio.as_completed) functions to run the `ainvoke` method in parallel.
:::
:::tip
@@ -58,7 +58,7 @@ Runnables expose an asynchronous API, allowing them to be called using the `awai
Please refer to the [Async Programming with LangChain](/docs/concepts/async) guide for more details.
-## Streaming apis
+## Streaming APIs
Streaming is critical in making applications based on LLMs feel responsive to end-users.
@@ -101,7 +101,7 @@ This is an advanced feature that is unnecessary for most users. You should proba
skip this section unless you have a specific need to inspect the schema of a Runnable.
:::
-In some advanced uses, you may want to programmatically **inspect** the Runnable and determine what input and output types the Runnable expects and produces.
+In more advanced use cases, you may want to programmatically **inspect** the Runnable and determine what input and output types the Runnable expects and produces.
The Runnable interface provides methods to get the [JSON Schema](https://json-schema.org/) of the input and output types of a Runnable, as well as [Pydantic schemas](https://docs.pydantic.dev/latest/) for the input and output types.
@@ -315,7 +315,7 @@ the `RunnableConfig` manually to sub-calls in some cases. Please see the
[Propagating RunnableConfig](#propagation-of-runnableconfig) section for more information.
:::
-## Creating a runnable from a function
+## Creating a runnable from a function {#custom-runnables}
You may need to create a custom Runnable that runs arbitrary logic. This is especially
useful if using [LangChain Expression Language (LCEL)](/docs/concepts/lcel) to compose
diff --git a/docs/docs/concepts/structured_outputs.mdx b/docs/docs/concepts/structured_outputs.mdx
index a334ecc1276f4..dad1c1a49cd89 100644
--- a/docs/docs/concepts/structured_outputs.mdx
+++ b/docs/docs/concepts/structured_outputs.mdx
@@ -119,11 +119,11 @@ json_object = json.loads(ai_msg.content)
There are a few challenges when producing structured output with the above methods:
-(1) If using tool calling, tool call arguments needs to be parsed from a dictionary back to the original schema.
+(1) When tool calling is used, tool call arguments needs to be parsed from a dictionary back to the original schema.
(2) In addition, the model needs to be instructed to *always* use the tool when we want to enforce structured output, which is a provider specific setting.
-(3) If using JSON mode, the output needs to be parsed into a JSON object.
+(3) When JSON mode is used, the output needs to be parsed into a JSON object.
With these challenges in mind, LangChain provides a helper function (`with_structured_output()`) to streamline the process.
diff --git a/docs/docs/concepts/tool_calling.mdx b/docs/docs/concepts/tool_calling.mdx
index c3c753ee52570..438c52ccb25a6 100644
--- a/docs/docs/concepts/tool_calling.mdx
+++ b/docs/docs/concepts/tool_calling.mdx
@@ -128,7 +128,7 @@ For more details on usage, see our [how-to guides](/docs/how_to/#tools)!
[Tools](/docs/concepts/tools/) implement the [Runnable](/docs/concepts/runnables/) interface, which means that they can be invoked (e.g., `tool.invoke(args)`) directly.
-[LangGraph](https://langchain-ai.github.io/langgraph/) offers pre-built components (e.g., [`ToolNode`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)) that will often invoke the tool in behalf of the user.
+[LangGraph](https://langchain-ai.github.io/langgraph/) offers pre-built components (e.g., [`ToolNode`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.ToolNode)) that will often invoke the tool in behalf of the user.
:::info[Further reading]
diff --git a/docs/docs/concepts/tools.mdx b/docs/docs/concepts/tools.mdx
index 13bf00d43f4f0..c459a5973b9b3 100644
--- a/docs/docs/concepts/tools.mdx
+++ b/docs/docs/concepts/tools.mdx
@@ -6,7 +6,7 @@
## Overview
-The **tool** abstraction in LangChain associates a python **function** with a **schema** that defines the function's **name**, **description** and **input**.
+The **tool** abstraction in LangChain associates a Python **function** with a **schema** that defines the function's **name**, **description** and **expected arguments**.
**Tools** can be passed to [chat models](/docs/concepts/chat_models) that support [tool calling](/docs/concepts/tool_calling) allowing the model to request the execution of a specific function with specific inputs.
@@ -14,7 +14,7 @@ The **tool** abstraction in LangChain associates a python **function** with a **
- Tools are a way to encapsulate a function and its schema in a way that can be passed to a chat model.
- Create tools using the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator, which simplifies the process of tool creation, supporting the following:
- - Automatically infer the tool's **name**, **description** and **inputs**, while also supporting customization.
+ - Automatically infer the tool's **name**, **description** and **expected arguments**, while also supporting customization.
- Defining tools that return **artifacts** (e.g. images, dataframes, etc.)
- Hiding input arguments from the schema (and hence from the model) using **injected tool arguments**.
diff --git a/docs/docs/concepts/why_langchain.mdx b/docs/docs/concepts/why_langchain.mdx
index c6b1d41da3f5d..584a080c9566b 100644
--- a/docs/docs/concepts/why_langchain.mdx
+++ b/docs/docs/concepts/why_langchain.mdx
@@ -1,9 +1,9 @@
-# Why langchain?
+# Why LangChain?
-The goal of `langchain` the Python package and LangChain the company is to make it as easy possible for developers to build applications that reason.
+The goal of `langchain` the Python package and LangChain the company is to make it as easy as possible for developers to build applications that reason.
While LangChain originally started as a single open source package, it has evolved into a company and a whole ecosystem.
This page will talk about the LangChain ecosystem as a whole.
-Most of the components within in the LangChain ecosystem can be used by themselves - so if you feel particularly drawn to certain components but not others, that is totally fine! Pick and choose whichever components you like best.
+Most of the components within the LangChain ecosystem can be used by themselves - so if you feel particularly drawn to certain components but not others, that is totally fine! Pick and choose whichever components you like best for your own use case!
## Features
@@ -17,8 +17,8 @@ LangChain exposes a standard interface for key components, making it easy to swi
[Orchestration](https://en.wikipedia.org/wiki/Orchestration_(computing)) is crucial for building such applications.
3. **Observability and evaluation:** As applications become more complex, it becomes increasingly difficult to understand what is happening within them.
-Furthermore, the pace of development can become rate-limited by the [paradox of choice](https://en.wikipedia.org/wiki/Paradox_of_choice):
-for example, developers often wonder how to engineer their prompt or which LLM best balances accuracy, latency, and cost.
+Furthermore, the pace of development can become rate-limited by the [paradox of choice](https://en.wikipedia.org/wiki/Paradox_of_choice).
+For example, developers often wonder how to engineer their prompt or which LLM best balances accuracy, latency, and cost.
[Observability](https://en.wikipedia.org/wiki/Observability) and evaluations can help developers monitor their applications and rapidly answer these types of questions with confidence.
@@ -72,11 +72,11 @@ There are several common characteristics of LLM applications that this orchestra
* **[Persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/):** The application needs to maintain [short-term and / or long-term memory](https://langchain-ai.github.io/langgraph/concepts/memory/).
* **[Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/):** The application needs human interaction, e.g., pausing, reviewing, editing, approving certain steps.
-The recommended way to do orchestration for these complex applications is [LangGraph](https://langchain-ai.github.io/langgraph/concepts/high_level/).
+The recommended way to orchestrate components for complex applications is [LangGraph](https://langchain-ai.github.io/langgraph/concepts/high_level/).
LangGraph is a library that gives developers a high degree of control by expressing the flow of the application as a set of nodes and edges.
LangGraph comes with built-in support for [persistence](https://langchain-ai.github.io/langgraph/concepts/persistence/), [human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/), [memory](https://langchain-ai.github.io/langgraph/concepts/memory/), and other features.
-It's particularly well suited for building [agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/) or [multi-agent](https://langchain-ai.github.io/langgraph/concepts/multi_agent/) applications.
-Importantly, individual LangChain components can be used within LangGraph nodes, but you can also use LangGraph **without** using LangChain components.
+It's particularly well suited for building [agents](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/) or [multi-agent](https://langchain-ai.github.io/langgraph/concepts/multi_agent/) applications.
+Importantly, individual LangChain components can be used as LangGraph nodes, but you can also use LangGraph **without** using LangChain components.
:::info[Further reading]
diff --git a/docs/docs/contributing/how_to/documentation/style_guide.mdx b/docs/docs/contributing/how_to/documentation/style_guide.mdx
index 437977573c09f..2eb20d6853786 100644
--- a/docs/docs/contributing/how_to/documentation/style_guide.mdx
+++ b/docs/docs/contributing/how_to/documentation/style_guide.mdx
@@ -4,8 +4,8 @@ sidebar_class_name: "hidden"
# Documentation Style Guide
-As LangChain continues to grow, the surface area of documentation required to cover it continues to grow too.
-This page provides guidelines for anyone writing documentation for LangChain, as well as some of our philosophies around
+As LangChain continues to grow, the amount of documentation required to cover the various concepts and integrations continues to grow too.
+This page provides guidelines for anyone writing documentation for LangChain and outlines some of our philosophies around
organization and structure.
## Philosophy
@@ -18,9 +18,9 @@ Under this framework, all documentation falls under one of four categories: [Tut
### Tutorials
Tutorials are lessons that take the reader through a practical activity. Their purpose is to help the user
-gain understanding of concepts and how they interact by showing one way to achieve some goal in a hands-on way. They should **avoid** giving
-multiple permutations of ways to achieve that goal in-depth. Instead, it should guide a new user through a recommended path to accomplishing the tutorial's goal. While the end result of a tutorial does not necessarily need to
-be completely production-ready, it should be useful and practically satisfy the the goal that you clearly stated in the tutorial's introduction. Information on how to address additional scenarios
+gain an understanding of concepts and how they interact by showing one way to achieve a specific goal in a hands-on manner. They should **avoid** giving
+multiple permutations of ways to achieve that goal in-depth. Instead, it should guide a new user through a recommended path to accomplish the tutorial's goal. While the end result of a tutorial does not necessarily need to
+be completely production-ready, it should be useful and practically satisfy the goal that is clearly stated in the tutorial's introduction. Information on how to address additional scenarios
belongs in how-to guides.
To quote the Diataxis website:
@@ -53,8 +53,8 @@ Here are some high-level tips on writing a good tutorial:
### How-to guides
A how-to guide, as the name implies, demonstrates how to do something discrete and specific.
-It should assume that the user is already familiar with underlying concepts, and is trying to solve an immediate problem, but
-should still give some background or list the scenarios where the information contained within can be relevant.
+It should assume that the user is already familiar with underlying concepts, and is focused on solving an immediate problem. However,
+it should still provide some background or list certain scenarios where the information may be relevant.
They can and should discuss alternatives if one approach may be better than another in certain cases.
To quote the Diataxis website:
@@ -79,10 +79,10 @@ Here are some high-level tips on writing a good how-to guide:
### Conceptual guide
-LangChain's conceptual guide falls under the **Explanation** quadrant of Diataxis. They should cover LangChain terms and concepts
-in a more abstract way than how-to guides or tutorials, and should be geared towards curious users interested in
-gaining a deeper understanding of the framework. Try to avoid excessively large code examples - the goal here is to
-impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work they way they do.
+LangChain's conceptual guide falls under the **Explanation** quadrant of Diataxis. These guides should cover LangChain terms and concepts
+in a more abstract way than how-to guides or tutorials, targeting curious users interested in
+gaining a deeper understanding and insights of the framework. Try to avoid excessively large code examples as the primary goal is to
+provide perspective to the user rather than to finish a practical project. These guides should cover **why** things work they way they do.
This guide on documentation style is meant to fall under this category.
@@ -137,14 +137,14 @@ be only one (very rarely two), canonical pages for a given concept or feature. I
### Link to other sections
-Because sections of the docs do not exist in a vacuum, it is important to link to other sections as often as possible
-to allow a developer to learn more about an unfamiliar topic inline.
+Because sections of the docs do not exist in a vacuum, it is important to link to other sections frequently,
+to allow a developer to learn more about an unfamiliar topic within the flow of reading.
-This includes linking to the API references as well as conceptual sections!
+This includes linking to the API references and conceptual sections!
### Be concise
-In general, take a less-is-more approach. If a section with a good explanation of a concept already exists, you should link to it rather than
+In general, take a less-is-more approach. If another section with a good explanation of a concept exists, you should link to it rather than
re-explain it, unless the concept you are documenting presents some new wrinkle.
Be concise, including in code samples.
diff --git a/docs/docs/how_to/chatbots_memory.ipynb b/docs/docs/how_to/chatbots_memory.ipynb
index aa6e7002ca706..011609a4a5434 100644
--- a/docs/docs/how_to/chatbots_memory.ipynb
+++ b/docs/docs/how_to/chatbots_memory.ipynb
@@ -15,7 +15,7 @@
"source": [
"# How to add memory to chatbots\n",
"\n",
- "A key feature of chatbots is their ability to use content of previous conversation turns as context. This state management can take several forms, including:\n",
+ "A key feature of chatbots is their ability to use the content of previous conversational turns as context. This state management can take several forms, including:\n",
"\n",
"- Simply stuffing previous messages into a chat model prompt.\n",
"- The above, but trimming old messages to reduce the amount of distracting information the model has to deal with.\n",
@@ -185,7 +185,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- " We'll pass the latest input to the conversation here and let the LangGraph keep track of the conversation history using the checkpointer:"
+ " We'll pass the latest input to the conversation here and let LangGraph keep track of the conversation history using the checkpointer:"
]
},
{
diff --git a/docs/docs/how_to/custom_chat_model.ipynb b/docs/docs/how_to/custom_chat_model.ipynb
index 708a0942c9ec5..4fc502ca171c8 100644
--- a/docs/docs/how_to/custom_chat_model.ipynb
+++ b/docs/docs/how_to/custom_chat_model.ipynb
@@ -503,7 +503,7 @@
"\n",
"Documentation:\n",
"\n",
- "* The model contains doc-strings for all initialization arguments, as these will be surfaced in the [APIReference](https://python.langchain.com/api_reference/langchain/index.html).\n",
+ "* The model contains doc-strings for all initialization arguments, as these will be surfaced in the [API Reference](https://python.langchain.com/api_reference/langchain/index.html).\n",
"* The class doc-string for the model contains a link to the model API if the model is powered by a service.\n",
"\n",
"Tests:\n",
diff --git a/docs/docs/how_to/index.mdx b/docs/docs/how_to/index.mdx
index 8f26f725158d9..76f74934e572b 100644
--- a/docs/docs/how_to/index.mdx
+++ b/docs/docs/how_to/index.mdx
@@ -115,6 +115,7 @@ What LangChain calls [LLMs](/docs/concepts/text_llms) are older forms of languag
[Output Parsers](/docs/concepts/output_parsers) are responsible for taking the output of an LLM and parsing into more structured format.
+- [How to: parse text from message objects](/docs/how_to/output_parser_string)
- [How to: use output parsers to parse an LLM response into structured format](/docs/how_to/output_parser_structured)
- [How to: parse JSON output](/docs/how_to/output_parser_json)
- [How to: parse XML output](/docs/how_to/output_parser_xml)
diff --git a/docs/docs/how_to/output_parser_custom.ipynb b/docs/docs/how_to/output_parser_custom.ipynb
index 26180b3fb6d94..a8cca984b6984 100644
--- a/docs/docs/how_to/output_parser_custom.ipynb
+++ b/docs/docs/how_to/output_parser_custom.ipynb
@@ -238,7 +238,7 @@
"id": "3a96a846-1296-4d92-8e76-e29e583dee22",
"metadata": {},
"source": [
- "Here's a simple parser that can parse a **string** representation of a booealn (e.g., `YES` or `NO`) and convert it into the corresponding `boolean` type."
+ "Here's a simple parser that can parse a **string** representation of a boolean (e.g., `YES` or `NO`) and convert it into the corresponding `boolean` type."
]
},
{
diff --git a/docs/docs/how_to/output_parser_string.ipynb b/docs/docs/how_to/output_parser_string.ipynb
new file mode 100644
index 0000000000000..17a2474e1e942
--- /dev/null
+++ b/docs/docs/how_to/output_parser_string.ipynb
@@ -0,0 +1,202 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "1d6024e0-3847-4418-b8a8-6b8f83adf4c2",
+ "metadata": {},
+ "source": [
+ "# How to parse text from message objects\n",
+ "\n",
+ ":::info Prerequisites\n",
+ "\n",
+ "This guide assumes familiarity with the following concepts:\n",
+ "- [Chat models](/docs/concepts/chat_models/)\n",
+ "- [Messages](/docs/concepts/messages/)\n",
+ "- [Output parsers](/docs/concepts/output_parsers/)\n",
+ "- [LangChain Expression Language (LCEL)](/docs/concepts/lcel/)\n",
+ "\n",
+ ":::\n",
+ "\n",
+ "LangChain [message](/docs/concepts/messages/) objects support content in a [variety of formats](/docs/concepts/messages/#content), including text, [multimodal data](/docs/concepts/multimodality/), and a list of [content block](/docs/concepts/messages/#aimessage) dicts.\n",
+ "\n",
+ "The format of [Chat model](/docs/concepts/chat_models/) response content may depend on the provider. For example, the chat model for [Anthropic](/docs/integrations/chat/anthropic/) will return string content for typical string input:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "8ac74999-0740-4178-8efd-32a855592f71",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Hi there! How are you doing today? Is there anything I can help you with?'"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from langchain_anthropic import ChatAnthropic\n",
+ "\n",
+ "llm = ChatAnthropic(model=\"claude-3-5-haiku-latest\")\n",
+ "\n",
+ "response = llm.invoke(\"Hello\")\n",
+ "response.content"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69b7c3ae-0022-4737-9db7-f44db3402de2",
+ "metadata": {},
+ "source": [
+ "But when tool calls are generated, the response content is structured into content blocks that convey the model's reasoning process:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "8c87553e-4f85-46c4-8f1e-666f6a261a50",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'text': \"I'll help you get the current weather for San Francisco, California. Let me check that for you right away.\",\n",
+ " 'type': 'text'},\n",
+ " {'id': 'toolu_015PwwcKxWYctKfY3pruHFyy',\n",
+ " 'input': {'location': 'San Francisco, CA'},\n",
+ " 'name': 'get_weather',\n",
+ " 'type': 'tool_use'}]"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from langchain_core.tools import tool\n",
+ "\n",
+ "\n",
+ "@tool\n",
+ "def get_weather(location: str) -> str:\n",
+ " \"\"\"Get the weather from a location.\"\"\"\n",
+ "\n",
+ " return \"Sunny.\"\n",
+ "\n",
+ "\n",
+ "llm_with_tools = llm.bind_tools([get_weather])\n",
+ "\n",
+ "response = llm_with_tools.invoke(\"What's the weather in San Francisco, CA?\")\n",
+ "response.content"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "039f6d62-098f-42c9-8b07-43cb1f2a831b",
+ "metadata": {},
+ "source": [
+ "To automatically parse text from message objects irrespective of the format of the underlying content, we can use [StrOutputParser](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html). We can compose it with a chat model as follows:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "0bb9b4dd-64a9-463d-9c71-df147630f3c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain_core.output_parsers import StrOutputParser\n",
+ "\n",
+ "chain = llm_with_tools | StrOutputParser()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4929c724-471f-4f77-a231-36e9af9418a3",
+ "metadata": {},
+ "source": [
+ "[StrOutputParser](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html) simplifies the extraction of text from message objects:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "9cbb8848-9101-465e-b230-0f7af6fb4105",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "I'll help you check the weather in San Francisco, CA right away.\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = chain.invoke(\"What's the weather in San Francisco, CA?\")\n",
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13642ad5-325d-4d9b-b97e-cac40345bfbc",
+ "metadata": {},
+ "source": [
+ "This is particularly useful in streaming contexts:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "28eeace3-3896-497f-93ad-544cbfb7f15c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "|I'll| help| you get| the current| weather for| San Francisco, California|. Let| me retrieve| that| information for you.||||||||||"
+ ]
+ }
+ ],
+ "source": [
+ "for chunk in chain.stream(\"What's the weather in San Francisco, CA?\"):\n",
+ " print(chunk, end=\"|\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "858e2071-a483-404e-9eca-c73a4466fd83",
+ "metadata": {},
+ "source": [
+ "See the [API Reference](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html) for more information."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/how_to/structured_output.ipynb b/docs/docs/how_to/structured_output.ipynb
index e1d6f2b68e82e..04171ac72cb3d 100644
--- a/docs/docs/how_to/structured_output.ipynb
+++ b/docs/docs/how_to/structured_output.ipynb
@@ -56,7 +56,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 1,
"id": "6d55008f",
"metadata": {},
"outputs": [],
@@ -81,7 +81,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
"id": "070bf702",
"metadata": {},
"outputs": [
@@ -91,7 +91,7 @@
"Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=7)"
]
},
- "execution_count": 4,
+ "execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
@@ -147,7 +147,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 3,
"id": "70d82891-42e8-424a-919e-07d83bcfec61",
"metadata": {},
"outputs": [
@@ -159,7 +159,7 @@
" 'rating': 7}"
]
},
- "execution_count": 8,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -199,7 +199,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 4,
"id": "6700994a",
"metadata": {},
"outputs": [
@@ -207,11 +207,10 @@
"data": {
"text/plain": [
"{'setup': 'Why was the cat sitting on the computer?',\n",
- " 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
- " 'rating': 7}"
+ " 'punchline': 'Because it wanted to keep an eye on the mouse!'}"
]
},
- "execution_count": 6,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -250,12 +249,14 @@
"source": [
"### Choosing between multiple schemas\n",
"\n",
- "The simplest way to let the model choose from multiple schemas is to create a parent schema that has a Union-typed attribute:"
+ "The simplest way to let the model choose from multiple schemas is to create a parent schema that has a Union-typed attribute.\n",
+ "\n",
+ "#### Using Pydantic"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 7,
"id": "9194bcf2",
"metadata": {},
"outputs": [
@@ -265,7 +266,7 @@
"FinalResponse(final_output=Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=7))"
]
},
- "execution_count": 19,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -274,7 +275,6 @@
"from typing import Union\n",
"\n",
"\n",
- "# Pydantic\n",
"class Joke(BaseModel):\n",
" \"\"\"Joke to tell user.\"\"\"\n",
"\n",
@@ -302,17 +302,94 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 8,
"id": "84d86132",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "FinalResponse(final_output=ConversationalResponse(response=\"I'm just a bunch of code, so I don't have feelings, but I'm here and ready to help you! How can I assist you today?\"))"
+ "FinalResponse(final_output=ConversationalResponse(response=\"I'm just a computer program, so I don't have feelings, but I'm here and ready to help you with whatever you need!\"))"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "structured_llm.invoke(\"How are you today?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b087112c23bafcd",
+ "metadata": {},
+ "source": [
+ "#### Using TypedDict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "eb0d5855-feba-48fb-84ea-9acb0edb238b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'final_output': {'setup': 'Why was the cat sitting on the computer?',\n",
+ " 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
+ " 'rating': 7}}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from typing import Optional, Union\n",
+ "\n",
+ "from typing_extensions import Annotated, TypedDict\n",
+ "\n",
+ "\n",
+ "class Joke(TypedDict):\n",
+ " \"\"\"Joke to tell user.\"\"\"\n",
+ "\n",
+ " setup: Annotated[str, ..., \"The setup of the joke\"]\n",
+ " punchline: Annotated[str, ..., \"The punchline of the joke\"]\n",
+ " rating: Annotated[Optional[int], None, \"How funny the joke is, from 1 to 10\"]\n",
+ "\n",
+ "\n",
+ "class ConversationalResponse(TypedDict):\n",
+ " \"\"\"Respond in a conversational manner. Be kind and helpful.\"\"\"\n",
+ "\n",
+ " response: Annotated[str, ..., \"A conversational response to the user's query\"]\n",
+ "\n",
+ "\n",
+ "class FinalResponse(TypedDict):\n",
+ " final_output: Union[Joke, ConversationalResponse]\n",
+ "\n",
+ "\n",
+ "structured_llm = llm.with_structured_output(FinalResponse)\n",
+ "\n",
+ "structured_llm.invoke(\"Tell me a joke about cats\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "ec753809-c2c1-41c0-a3c5-69855d65475b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'final_output': {'response': \"I'm just a computer program, so I don't have feelings, but I'm here and ready to help you with whatever you need!\"}}"
]
},
- "execution_count": 20,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -321,6 +398,14 @@
"structured_llm.invoke(\"How are you today?\")"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "dd22149ac9d41d57",
+ "metadata": {},
+ "source": [
+ "Responses shall be identical to the ones shown in the Pydantic example."
+ ]
+ },
{
"cell_type": "markdown",
"id": "e28c14d3",
@@ -347,7 +432,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 11,
"id": "aff89877-28a3-472f-a1aa-eff893fe7736",
"metadata": {},
"outputs": [
@@ -415,7 +500,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"id": "283ba784-2072-47ee-9b2c-1119e3c69e8e",
"metadata": {},
"outputs": [
@@ -423,11 +508,11 @@
"data": {
"text/plain": [
"{'setup': 'Woodpecker',\n",
- " 'punchline': \"Woodpecker who? Woodpecker who can't find a tree is just a bird with a headache!\",\n",
+ " 'punchline': \"Woodpecker you a joke, but I'm afraid it might be too 'hole-some'!\",\n",
" 'rating': 7}"
]
},
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -465,7 +550,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"id": "d7381cb0-b2c3-4302-a319-ed72d0b9e43f",
"metadata": {},
"outputs": [
@@ -474,10 +559,10 @@
"text/plain": [
"{'setup': 'Crocodile',\n",
" 'punchline': 'Crocodile be seeing you later, alligator!',\n",
- " 'rating': 7}"
+ " 'rating': 6}"
]
},
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -579,7 +664,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"id": "df0370e3",
"metadata": {},
"outputs": [
@@ -590,7 +675,7 @@
" 'punchline': 'Because it wanted to keep an eye on the mouse!'}"
]
},
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -733,7 +818,7 @@
"source": [
"query = \"Anna is 23 years old and she is 6 feet tall\"\n",
"\n",
- "print(prompt.invoke(query).to_string())"
+ "print(prompt.invoke({\"query\": query}).to_string())"
]
},
{
@@ -913,9 +998,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "poetry-venv-2",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "poetry-venv-2"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -927,7 +1012,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.9"
+ "version": "3.11.4"
}
},
"nbformat": 4,
diff --git a/docs/docs/integrations/chat/cloudflare_workersai.ipynb b/docs/docs/integrations/chat/cloudflare_workersai.ipynb
index df7c2a1cb667b..571cf32282fb8 100644
--- a/docs/docs/integrations/chat/cloudflare_workersai.ipynb
+++ b/docs/docs/integrations/chat/cloudflare_workersai.ipynb
@@ -17,7 +17,7 @@
"source": [
"# ChatCloudflareWorkersAI\n",
"\n",
- "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all available Cloudflare WorkersAI models head to the [API reference](https://developers.cloudflare.com/workers-ai/).\n",
+ "This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all available Cloudflare WorkersAI models head to the [API reference](https://developers.cloudflare.com/workers-ai/).\n",
"\n",
"\n",
"## Overview\n",
diff --git a/docs/docs/integrations/document_loaders/google_cloud_sql_mssql.ipynb b/docs/docs/integrations/document_loaders/google_cloud_sql_mssql.ipynb
index 1dd568c85c7ea..42ac2892cb6d4 100644
--- a/docs/docs/integrations/document_loaders/google_cloud_sql_mssql.ipynb
+++ b/docs/docs/integrations/document_loaders/google_cloud_sql_mssql.ipynb
@@ -34,7 +34,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -328,7 +328,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The view generated from SQL query can have different schema than default table. In such cases, the behavior of MSSQLLoader is the same as loading from table with non-default schema. Please refer to section [Load documents with customized document page content & metadata](#Load-documents-with-customized-document-page-content-&-metadata)."
+ "The view generated from SQL query can have different schema than default table. In such cases, the behavior of MSSQLLoader is the same as loading from table with non-default schema. Please refer to section [Load documents with customized document page content & metadata](#load-documents-with-customized-document-page-content--metadata)."
]
},
{
@@ -633,7 +633,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.6"
+ "version": "3.11.4"
}
},
"nbformat": 4,
diff --git a/docs/docs/integrations/document_loaders/google_cloud_sql_mysql.ipynb b/docs/docs/integrations/document_loaders/google_cloud_sql_mysql.ipynb
index d656b8642f47e..5743fdedc543a 100644
--- a/docs/docs/integrations/document_loaders/google_cloud_sql_mysql.ipynb
+++ b/docs/docs/integrations/document_loaders/google_cloud_sql_mysql.ipynb
@@ -317,7 +317,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The view generated from SQL query can have different schema than default table. In such cases, the behavior of MySQLLoader is the same as loading from table with non-default schema. Please refer to section [Load documents with customized document page content & metadata](#Load-documents-with-customized-document-page-content-&-metadata)."
+ "The view generated from SQL query can have different schema than default table. In such cases, the behavior of MySQLLoader is the same as loading from table with non-default schema. Please refer to section [Load documents with customized document page content & metadata](#load-documents-with-customized-document-page-content--metadata)."
]
},
{
@@ -619,7 +619,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.6"
+ "version": "3.11.4"
}
},
"nbformat": 4,
diff --git a/docs/docs/integrations/llms/aleph_alpha.ipynb b/docs/docs/integrations/llms/aleph_alpha.ipynb
index 70fc18af07f8f..1c7f264dbbd83 100644
--- a/docs/docs/integrations/llms/aleph_alpha.ipynb
+++ b/docs/docs/integrations/llms/aleph_alpha.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Aleph Alpha\n",
"\n",
- "[The Luminous series](https://docs.aleph-alpha.com/docs/introduction/luminous/) is a family of large language models.\n",
+ "[The Luminous series](https://docs.aleph-alpha.com/docs/category/luminous/) is a family of large language models.\n",
"\n",
"This example goes over how to use LangChain to interact with Aleph Alpha models"
]
diff --git a/docs/docs/integrations/llms/cloudflare_workersai.ipynb b/docs/docs/integrations/llms/cloudflare_workersai.ipynb
index 5c6652eb336f4..023268353591a 100644
--- a/docs/docs/integrations/llms/cloudflare_workersai.ipynb
+++ b/docs/docs/integrations/llms/cloudflare_workersai.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Cloudflare Workers AI\n",
"\n",
- "[Cloudflare AI documentation](https://developers.cloudflare.com/workers-ai/models/text-generation/) listed all generative text models available.\n",
+ "[Cloudflare AI documentation](https://developers.cloudflare.com/workers-ai/models/) listed all generative text models available.\n",
"\n",
"Both Cloudflare account ID and API token are required. Find how to obtain them from [this document](https://developers.cloudflare.com/workers-ai/get-started/rest-api/)."
]
diff --git a/docs/docs/integrations/llms/forefrontai.ipynb b/docs/docs/integrations/llms/forefrontai.ipynb
index 34dec0be5ed93..c06988e6e3cc6 100644
--- a/docs/docs/integrations/llms/forefrontai.ipynb
+++ b/docs/docs/integrations/llms/forefrontai.ipynb
@@ -7,7 +7,7 @@
"# ForefrontAI\n",
"\n",
"\n",
- "The `Forefront` platform gives you the ability to fine-tune and use [open-source large language models](https://docs.forefront.ai/forefront/master/models).\n",
+ "The `Forefront` platform gives you the ability to fine-tune and use [open-source large language models](https://docs.forefront.ai/get-started/models).\n",
"\n",
"This notebook goes over how to use Langchain with [ForefrontAI](https://www.forefront.ai/).\n"
]
diff --git a/docs/docs/integrations/providers/astradb.mdx b/docs/docs/integrations/providers/astradb.mdx
index d545d1ea02625..853eafcc8ff5d 100644
--- a/docs/docs/integrations/providers/astradb.mdx
+++ b/docs/docs/integrations/providers/astradb.mdx
@@ -133,7 +133,7 @@ store = AstraDBStore(
)
```
-Learn more in the [example notebook](/docs/integrations/stores/astradb#astradbstore).
+See the API Reference for the [AstraDBStore](https://python.langchain.com/api_reference/astradb/storage/langchain_astradb.storage.AstraDBStore.html).
## Byte Store
@@ -147,4 +147,4 @@ store = AstraDBByteStore(
)
```
-Learn more in the [example notebook](/docs/integrations/stores/astradb#astradbbytestore).
+See the API reference for the [AstraDBByteStore](https://python.langchain.com/api_reference/astradb/storage/langchain_astradb.storage.AstraDBByteStore.html).
diff --git a/docs/docs/integrations/providers/nvidia.mdx b/docs/docs/integrations/providers/nvidia.mdx
index 0f02b3522367e..2dc6bf2f43837 100644
--- a/docs/docs/integrations/providers/nvidia.mdx
+++ b/docs/docs/integrations/providers/nvidia.mdx
@@ -51,7 +51,7 @@ result = llm.invoke("Write a ballad about LangChain.")
print(result.content)
```
-Using the API, you can query live endpoints available on the NVIDIA API Catalog to get quick results from a DGX-hosted cloud compute environment. All models are source-accessible and can be deployed on your own compute cluster using NVIDIA NIM which is part of NVIDIA AI Enterprise, shown in the next section [Working with NVIDIA NIMs](##working-with-nvidia-nims).
+Using the API, you can query live endpoints available on the NVIDIA API Catalog to get quick results from a DGX-hosted cloud compute environment. All models are source-accessible and can be deployed on your own compute cluster using NVIDIA NIM which is part of NVIDIA AI Enterprise, shown in the next section [Working with NVIDIA NIMs](#working-with-nvidia-nims).
## Working with NVIDIA NIMs
When ready to deploy, you can self-host models with NVIDIA NIM—which is included with the NVIDIA AI Enterprise software license—and run them anywhere, giving you ownership of your customizations and full control of your intellectual property (IP) and AI applications.
diff --git a/docs/docs/integrations/providers/unstructured.mdx b/docs/docs/integrations/providers/unstructured.mdx
index 33510cf5e4803..312a28d6f6815 100644
--- a/docs/docs/integrations/providers/unstructured.mdx
+++ b/docs/docs/integrations/providers/unstructured.mdx
@@ -164,7 +164,7 @@ from langchain_community.document_loaders import UnstructuredOrgModeLoader
### UnstructuredPDFLoader
-See a [usage example](/docs/how_to/document_loader_pdf#using-unstructured).
+See a [usage example](/docs/how_to/document_loader_pdf/#layout-analysis-and-extraction-of-text-from-images).
```python
from langchain_community.document_loaders import UnstructuredPDFLoader
diff --git a/docs/docs/integrations/tools/google_books.ipynb b/docs/docs/integrations/tools/google_books.ipynb
index 57446c435f638..0954a1f15067c 100644
--- a/docs/docs/integrations/tools/google_books.ipynb
+++ b/docs/docs/integrations/tools/google_books.ipynb
@@ -139,7 +139,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### [Invoke directly with args](/docs/concepts/#invoke-with-just-the-arguments)\n",
+ "### [Invoke directly with args](/docs/concepts/tools)\n",
"\n",
"See below for an direct invocation example."
]
@@ -165,7 +165,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### [Invoke with ToolCall](/docs/concepts/#invoke-with-toolcall)\n",
+ "### [Invoke with ToolCall](/docs/concepts/tools)\n",
"\n",
"See below for a tool call example."
]
diff --git a/docs/docs/integrations/tools/zapier.ipynb b/docs/docs/integrations/tools/zapier.ipynb
index a6deab263082e..3c73d1f15ac11 100644
--- a/docs/docs/integrations/tools/zapier.ipynb
+++ b/docs/docs/integrations/tools/zapier.ipynb
@@ -110,19 +110,19 @@
"text": [
"\n",
"\n",
- "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
- "\u001b[32;1m\u001b[1;3m I need to find the email and summarize it.\n",
+ "\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
+ "\u001B[32;1m\u001B[1;3m I need to find the email and summarize it.\n",
"Action: Gmail: Find Email\n",
- "Action Input: Find the latest email from Silicon Valley Bank\u001b[0m\n",
- "Observation: \u001b[31;1m\u001b[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos Finished chain.\u001b[0m\n"
+ "\u001B[1m> Finished chain.\u001B[0m\n"
]
},
{
@@ -286,18 +286,18 @@
"text": [
"\n",
"\n",
- "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
- "\u001b[36;1m\u001b[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos Entering new SimpleSequentialChain chain...\u001B[0m\n",
+ "\u001B[36;1m\u001B[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos Finished chain.\u001b[0m\n"
+ "\u001B[1m> Finished chain.\u001B[0m\n"
]
},
{
@@ -325,7 +325,7 @@
"id": "09ff954e-45f2-4595-92ea-91627abde4a0",
"metadata": {},
"source": [
- "## Example Using OAuth Access Token\n",
+ "## Example Using OAuth Access Token{#oauth}\n",
"The below snippet shows how to initialize the wrapper with a procured OAuth access token. Note the argument being passed in as opposed to setting an environment variable. Review the [authentication docs](https://nla.zapier.com/docs/authentication/#oauth-credentials) for full user-facing oauth developer support.\n",
"\n",
"The developer is tasked with handling the OAuth handshaking to procure and refresh the access token."
diff --git a/docs/docs/troubleshooting/errors/INVALID_TOOL_RESULTS.ipynb b/docs/docs/troubleshooting/errors/INVALID_TOOL_RESULTS.ipynb
index 4a1b9b3bdf3dc..f3e62badc49f9 100644
--- a/docs/docs/troubleshooting/errors/INVALID_TOOL_RESULTS.ipynb
+++ b/docs/docs/troubleshooting/errors/INVALID_TOOL_RESULTS.ipynb
@@ -6,7 +6,7 @@
"source": [
"# INVALID_TOOL_RESULTS\n",
"\n",
- "You are passing too many, too few, or mismatched [`ToolMessages`](https://api.js.langchain.com/classes/_langchain_core.messages_tool.ToolMessage.html) to a model.\n",
+ "You are passing too many, too few, or mismatched [`ToolMessages`](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.tool.ToolMessage.html#toolmessage) to a model.\n",
"\n",
"When [using a model to call tools](/docs/concepts/tool_calling), the [`AIMessage`](https://api.js.langchain.com/classes/_langchain_core.messages.AIMessage.html)\n",
"the model responds with will contain a `tool_calls` array. To continue the flow, the next messages you pass back to the model must\n",
diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js
index 305b14df135ac..fc00559683ac9 100644
--- a/docs/docusaurus.config.js
+++ b/docs/docusaurus.config.js
@@ -26,6 +26,7 @@ const config = {
trailingSlash: true,
onBrokenLinks: "throw",
onBrokenMarkdownLinks: "throw",
+ onBrokenAnchors: "throw",
themes: ["@docusaurus/theme-mermaid"],
markdown: {
diff --git a/libs/community/langchain_community/callbacks/tracers/wandb.py b/libs/community/langchain_community/callbacks/tracers/wandb.py
index 76dbb3d202847..fcc2312f47e9b 100644
--- a/libs/community/langchain_community/callbacks/tracers/wandb.py
+++ b/libs/community/langchain_community/callbacks/tracers/wandb.py
@@ -16,6 +16,7 @@
Union,
)
+from langchain_core._api import warn_deprecated
from langchain_core.output_parsers.pydantic import PydanticBaseModel
from langchain_core.tracers.base import BaseTracer
from langchain_core.tracers.schemas import Run
@@ -325,6 +326,22 @@ def __init__(
self._run_args = run_args
self._ensure_run(should_print_url=(wandb.run is None))
self._io_serializer = io_serializer
+ warn_deprecated(
+ "0.3.8",
+ pending=False,
+ message=(
+ "Please use the `WeaveTracer` from the `weave` package instead of this."
+ "The `WeaveTracer` is a more flexible and powerful tool for logging "
+ "and tracing your LangChain callables."
+ "Find more information at https://weave-docs.wandb.ai/guides/integrations/langchain"
+ ),
+ alternative=(
+ "Please instantiate the WeaveTracer from "
+ "`weave.integrations.langchain import WeaveTracer` ."
+ "For autologging simply use `weave.init()` and log all traces "
+ "from your LangChain callables."
+ ),
+ )
def finish(self) -> None:
"""Waits for all asynchronous processes to finish and data to upload.
diff --git a/libs/core/langchain_core/callbacks/file.py b/libs/core/langchain_core/callbacks/file.py
index 7ea1ff76f8aa2..961b0c9bc241e 100644
--- a/libs/core/langchain_core/callbacks/file.py
+++ b/libs/core/langchain_core/callbacks/file.py
@@ -13,7 +13,8 @@ class FileCallbackHandler(BaseCallbackHandler):
"""Callback Handler that writes to a file.
Parameters:
- file: The file to write to.
+ filename: The file to write to.
+ mode: The mode to open the file in. Defaults to "a".
color: The color to use for the text.
"""
diff --git a/libs/core/langchain_core/callbacks/manager.py b/libs/core/langchain_core/callbacks/manager.py
index 9a25734f9371f..821939d1a609c 100644
--- a/libs/core/langchain_core/callbacks/manager.py
+++ b/libs/core/langchain_core/callbacks/manager.py
@@ -1298,7 +1298,7 @@ def on_chat_model_start(
run_id: Optional[UUID] = None,
**kwargs: Any,
) -> list[CallbackManagerForLLMRun]:
- """Run when LLM starts running.
+ """Run when chat model starts running.
Args:
serialized (Dict[str, Any]): The serialized LLM.
diff --git a/libs/core/langchain_core/indexing/__init__.py b/libs/core/langchain_core/indexing/__init__.py
index 786914c00e1ea..472f41e11a846 100644
--- a/libs/core/langchain_core/indexing/__init__.py
+++ b/libs/core/langchain_core/indexing/__init__.py
@@ -7,6 +7,7 @@
from langchain_core.indexing.api import IndexingResult, aindex, index
from langchain_core.indexing.base import (
+ DeleteResponse,
DocumentIndex,
InMemoryRecordManager,
RecordManager,
@@ -15,6 +16,7 @@
__all__ = [
"aindex",
+ "DeleteResponse",
"DocumentIndex",
"index",
"IndexingResult",
diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock
index 3b7f361f70464..167daa9f8b31a 100644
--- a/libs/core/poetry.lock
+++ b/libs/core/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
name = "annotated-types"
@@ -208,21 +208,20 @@ lxml = ["lxml"]
[[package]]
name = "bleach"
-version = "6.1.0"
+version = "6.2.0"
description = "An easy safelist-based HTML-sanitizing tool."
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"},
- {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"},
+ {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"},
+ {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"},
]
[package.dependencies]
-six = ">=1.9.0"
webencodings = "*"
[package.extras]
-css = ["tinycss2 (>=1.1.0,<1.3)"]
+css = ["tinycss2 (>=1.1.0,<1.5)"]
[[package]]
name = "certifi"
@@ -458,37 +457,37 @@ test = ["pytest"]
[[package]]
name = "debugpy"
-version = "1.8.7"
+version = "1.8.8"
description = "An implementation of the Debug Adapter Protocol for Python"
optional = false
python-versions = ">=3.8"
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[[package]]
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optional = false
python-versions = ">=3.8"
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[[package]]
@@ -2850,41 +2839,41 @@ zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "watchdog"
-version = "5.0.3"
+version = "6.0.0"
description = "Filesystem events monitoring"
optional = false
python-versions = ">=3.9"
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+ {file = "watchdog-6.0.0.tar.gz", hash = "sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282"},
]
[package.extras]
@@ -2903,19 +2892,15 @@ files = [
[[package]]
name = "webcolors"
-version = "24.8.0"
+version = "24.11.1"
description = "A library for working with the color formats defined by HTML and CSS."
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "webcolors-24.8.0-py3-none-any.whl", hash = "sha256:fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a"},
- {file = "webcolors-24.8.0.tar.gz", hash = "sha256:08b07af286a01bcd30d583a7acadf629583d1f79bfef27dd2c2c5c263817277d"},
+ {file = "webcolors-24.11.1-py3-none-any.whl", hash = "sha256:515291393b4cdf0eb19c155749a096f779f7d909f7cceea072791cb9095b92e9"},
+ {file = "webcolors-24.11.1.tar.gz", hash = "sha256:ecb3d768f32202af770477b8b65f318fa4f566c22948673a977b00d589dd80f6"},
]
-[package.extras]
-docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
-tests = ["coverage[toml]"]
-
[[package]]
name = "webencodings"
version = "0.5.1"
@@ -2956,13 +2941,13 @@ files = [
[[package]]
name = "zipp"
-version = "3.20.2"
+version = "3.21.0"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
- {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
+ {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
+ {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
]
[package.extras]
diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml
index b47ab26e1c5bb..281560993c764 100644
--- a/libs/core/pyproject.toml
+++ b/libs/core/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-core"
-version = "0.3.17"
+version = "0.3.18"
description = "Building applications with LLMs through composability"
authors = []
license = "MIT"
@@ -84,17 +84,20 @@ classmethod-decorators = [ "classmethod", "langchain_core.utils.pydantic.pre_ini
[tool.poetry.group.lint.dependencies]
ruff = "^0.5"
+
[tool.poetry.group.typing.dependencies]
mypy = ">=1.10,<1.11"
types-pyyaml = "^6.0.12.2"
types-requests = "^2.28.11.5"
types-jinja2 = "^2.11.9"
+
[tool.poetry.group.dev.dependencies]
jupyter = "^1.0.0"
setuptools = "^67.6.1"
grandalf = "^0.8"
+
[tool.poetry.group.test.dependencies]
pytest = "^8"
freezegun = "^1.2.2"
@@ -113,12 +116,15 @@ python = "<3.12"
version = "^1.26.0"
python = ">=3.12"
+
[tool.poetry.group.test_integration.dependencies]
+
[tool.poetry.group.typing.dependencies.langchain-text-splitters]
path = "../text-splitters"
develop = true
+
[tool.poetry.group.test.dependencies.langchain-standard-tests]
path = "../standard-tests"
develop = true
diff --git a/libs/core/tests/unit_tests/indexing/test_public_api.py b/libs/core/tests/unit_tests/indexing/test_public_api.py
index fce3d4f4f9623..24ac092eb2d4a 100644
--- a/libs/core/tests/unit_tests/indexing/test_public_api.py
+++ b/libs/core/tests/unit_tests/indexing/test_public_api.py
@@ -6,6 +6,7 @@ def test_all() -> None:
assert __all__ == sorted(__all__, key=str.lower)
assert set(__all__) == {
"aindex",
+ "DeleteResponse",
"DocumentIndex",
"index",
"IndexingResult",
diff --git a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
index c8312104b0d1d..38a99073107d6 100644
--- a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
+++ b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
@@ -480,7 +480,7 @@
# ---
# name: test_combining_sequences.3
list([
- RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000002-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000004')}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000005'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:4'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000005', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000006'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'foobar'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nicer assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='foobar', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:5'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000006', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000007'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['baz, qux'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000006-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:6'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000007', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000008'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='baz, qux', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000009')}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:7'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000008', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')),
+ RunTree(id=00000000-0000-4000-8000-000000000000, name='RunnableSequence', run_type='chain', dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
])
# ---
# name: test_configurable_fields[schema2]
@@ -1119,7 +1119,7 @@
# ---
# name: test_prompt_with_chat_model.2
list([
- RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000003')}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000002-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')),
+ RunTree(id=00000000-0000-4000-8000-000000000000, name='RunnableSequence', run_type='chain', dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
])
# ---
# name: test_prompt_with_chat_model_and_parser
@@ -1243,7 +1243,7 @@
# ---
# name: test_prompt_with_chat_model_and_parser.1
list([
- RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000002-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000004')}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')),
+ RunTree(id=00000000-0000-4000-8000-000000000000, name='RunnableSequence', run_type='chain', dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
])
# ---
# name: test_prompt_with_chat_model_async
@@ -1359,7 +1359,7 @@
# ---
# name: test_prompt_with_chat_model_async.2
list([
- RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo', additional_kwargs={}, response_metadata={}, id='00000000-0000-4000-8000-000000000003')}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelistchatmodel', 'ls_model_type': 'chat'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake_chat_models', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo'])", 'name': 'FakeListChatModel'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo', 'type': 'ai', 'id': 'run-00000000-0000-4000-8000-000000000002-0', 'tool_calls': [], 'invalid_tool_calls': []}}}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')),
+ RunTree(id=00000000-0000-4000-8000-000000000000, name='RunnableSequence', run_type='chain', dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
])
# ---
# name: test_prompt_with_llm
@@ -1469,13 +1469,13 @@
# ---
# name: test_prompt_with_llm.1
list([
- RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])", 'name': 'FakeListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')),
+ RunTree(id=00000000-0000-4000-8000-000000000000, name='RunnableSequence', run_type='chain', dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
])
# ---
# name: test_prompt_with_llm.2
list([
- RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': 'bar'}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2, 'metadata': {'ls_provider': 'fakelist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])", 'name': 'FakeListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bar', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')),
- RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000004'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your favorite color?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000003.20230101T000000000000Z00000000-0000-4000-8000-000000000004', trace_id=UUID('00000000-0000-4000-8000-000000000003')), RunTree(id=UUID('00000000-0000-4000-8000-000000000005'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2, 'metadata': {'ls_provider': 'fakelist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])", 'name': 'FakeListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000003.20230101T000000000000Z00000000-0000-4000-8000-000000000005', trace_id=UUID('00000000-0000-4000-8000-000000000003'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000003')),
+ RunTree(id=00000000-0000-4000-8000-000000000000, name='RunnableSequence', run_type='chain', dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
+ RunTree(id=00000000-0000-4000-8000-000000000003, name='RunnableSequence', run_type='chain', dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000003'),
])
# ---
# name: test_prompt_with_llm_and_async_lambda
@@ -1598,7 +1598,7 @@
# ---
# name: test_prompt_with_llm_and_async_lambda.1
list([
- RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakelist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])", 'name': 'FakeListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='passthrough', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'foo'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')),
+ RunTree(id=00000000-0000-4000-8000-000000000000, name='RunnableSequence', run_type='chain', dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
])
# ---
# name: test_prompt_with_llm_parser
@@ -1722,7 +1722,7 @@
# ---
# name: test_prompt_with_llm_parser.1
list([
- RunTree(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=None, tags=[], attachments={}, child_runs=[RunTree(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'input_variables': ['question'], 'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string'}, 'name': 'PromptTemplate'}}}]}, 'name': 'ChatPromptTemplate'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ChatPromptValue(messages=[SystemMessage(content='You are a nice assistant.', additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1, 'metadata': {'ls_provider': 'fakestreaminglist', 'ls_model_type': 'llm'}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'language_models', 'fake', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])", 'name': 'FakeStreamingListLLM'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bear, dog, cat', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None, 'type': 'LLMResult'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002', trace_id=UUID('00000000-0000-4000-8000-000000000000')), RunTree(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized=None, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'bear, dog, cat'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], attachments={}, child_runs=[], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003', trace_id=UUID('00000000-0000-4000-8000-000000000000'))], session_name='default', session_id=None, dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000', trace_id=UUID('00000000-0000-4000-8000-000000000000')),
+ RunTree(id=00000000-0000-4000-8000-000000000000, name='RunnableSequence', run_type='chain', dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
])
# ---
# name: test_router_runnable
diff --git a/libs/partners/xai/poetry.lock b/libs/partners/xai/poetry.lock
index 95ba6848aecc0..0502c9065a3b8 100644
--- a/libs/partners/xai/poetry.lock
+++ b/libs/partners/xai/poetry.lock
@@ -720,7 +720,7 @@ files = [
[[package]]
name = "langchain-core"
-version = "0.3.17"
+version = "0.3.18"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"
@@ -2071,4 +2071,4 @@ propcache = ">=0.2.0"
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
-content-hash = "954aeccc9bb5a2c79b1fd5affaab2303d588dcda6447db5e866430de7f759823"
+content-hash = "1f77714ec9420ce148ab90de835585ddf87ce68f2d28240f63fcea47c9bccf6c"
diff --git a/libs/partners/xai/pyproject.toml b/libs/partners/xai/pyproject.toml
index 819223c97db67..a6dbc23286c5c 100644
--- a/libs/partners/xai/pyproject.toml
+++ b/libs/partners/xai/pyproject.toml
@@ -21,7 +21,7 @@ disallow_untyped_defs = "True"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-openai = ">=0.2,<0.3"
-langchain-core = ">=0.3,<0.4"
+langchain-core = ">=0.3.18,<0.4"
requests = ">=2,<3"
aiohttp = ">=3.9.1,<4"