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parambharat authored Nov 14, 2024
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1 change: 0 additions & 1 deletion .github/scripts/check_diff.py
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PY_312_MAX_PACKAGES = [
f"libs/partners/{integration}"
for integration in [
"anthropic",
"chroma",
"couchbase",
"huggingface",
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1 change: 1 addition & 0 deletions .github/workflows/_integration_test.yml
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Expand Up @@ -79,6 +79,7 @@ jobs:
VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
run: |
make integration_tests
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1 change: 1 addition & 0 deletions .github/workflows/_release.yml
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Expand Up @@ -308,6 +308,7 @@ jobs:
VOYAGE_API_KEY: ${{ secrets.VOYAGE_API_KEY }}
UPSTAGE_API_KEY: ${{ secrets.UPSTAGE_API_KEY }}
FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
run: make integration_tests
working-directory: ${{ inputs.working-directory }}

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6 changes: 3 additions & 3 deletions MIGRATE.md
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@@ -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.
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2 changes: 1 addition & 1 deletion docs/docs/concepts/chat_history.mdx
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Expand Up @@ -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.
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14 changes: 7 additions & 7 deletions docs/docs/concepts/chat_models.mdx
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Expand Up @@ -2,13 +2,13 @@

## 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.

The newest generation of chat models offer additional capabilities:

* [Tool calling](/docs/concepts/tool_calling): Many popular chat models offer a native [tool calling](/docs/concepts/tool_calling) API. This API allows developers to build rich applications that enable AI to interact with external services, APIs, and databases. Tool calling can also be used to extract structured information from unstructured data and perform various other tasks.
* [Tool calling](/docs/concepts/tool_calling): Many popular chat models offer a native [tool calling](/docs/concepts/tool_calling) API. This API allows developers to build rich applications that enable LLMs to interact with external services, APIs, and databases. Tool calling can also be used to extract structured information from unstructured data and perform various other tasks.
* [Structured output](/docs/concepts/structured_outputs): A technique to make a chat model respond in a structured format, such as JSON that matches a given schema.
* [Multimodality](/docs/concepts/multimodality): The ability to work with data other than text; for example, images, audio, and video.

Expand All @@ -19,7 +19,7 @@ LangChain provides a consistent interface for working with chat models from diff
* Integrations with many chat model providers (e.g., Anthropic, OpenAI, Ollama, Microsoft Azure, Google Vertex, Amazon Bedrock, Hugging Face, Cohere, Groq). Please see [chat model integrations](/docs/integrations/chat/) for an up-to-date list of supported models.
* Use either LangChain's [messages](/docs/concepts/messages) format or OpenAI format.
* Standard [tool calling API](/docs/concepts/tool_calling): standard interface for binding tools to models, accessing tool call requests made by models, and sending tool results back to the model.
* Standard API for structuring outputs (/docs/concepts/structured_outputs) via the `with_structured_output` method.
* Standard API for [structuring outputs](/docs/concepts/structured_outputs/#structured-output-method) via the `with_structured_output` method.
* Provides support for [async programming](/docs/concepts/async), [efficient batching](/docs/concepts/runnables/#optimized-parallel-execution-batch), [a rich streaming API](/docs/concepts/streaming).
* Integration with [LangSmith](https://docs.smith.langchain.com) for monitoring and debugging production-grade applications based on LLMs.
* Additional features like standardized [token usage](/docs/concepts/messages/#aimessage), [rate limiting](#rate-limiting), [caching](#caching) and more.
Expand Down Expand Up @@ -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. |
Expand All @@ -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

Expand Down Expand Up @@ -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.

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2 changes: 1 addition & 1 deletion docs/docs/concepts/document_loaders.mdx
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Expand Up @@ -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():
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