mirror of
https://github.com/hwchase17/langchain.git
synced 2026-01-25 22:49:59 +00:00
chore: enrich pyproject.toml files with links to new references, others (#33343)
This commit is contained in:
Mason Daugherty
GitHub
@@ -20,9 +20,12 @@ description = "CLI for interacting with LangChain"
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readme = "README.md"
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[project.urls]
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"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/cli"
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"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-cli%3D%3D0%22&expanded=true"
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repository = "https://github.com/langchain-ai/langchain"
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homepage = "https://docs.langchain.com/"
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repository = "https://github.com/langchain-ai/langchain/tree/master/libs/cli"
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changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-cli%3D%3D1%22"
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twitter = "https://x.com/LangChainAI"
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slack = "https://www.langchain.com/join-community"
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reddit = "https://www.reddit.com/r/LangChain/"
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[project.scripts]
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langchain = "langchain_cli.cli:app"
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@@ -21,9 +21,12 @@ description = "Building applications with LLMs through composability"
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readme = "README.md"
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[project.urls]
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"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/core"
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"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-core%3D%3D0%22&expanded=true"
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repository = "https://github.com/langchain-ai/langchain"
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homepage = "https://docs.langchain.com/"
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repository = "https://github.com/langchain-ai/langchain/tree/master/libs/core"
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changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-core%3D%3D1%22"
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twitter = "https://x.com/LangChainAI"
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slack = "https://www.langchain.com/join-community"
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reddit = "https://www.reddit.com/r/LangChain/"
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[dependency-groups]
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lint = ["ruff>=0.13.1,<0.14.0"]
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@@ -41,9 +41,12 @@ xai = ["langchain-xai"]
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perplexity = ["langchain-perplexity"]
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[project.urls]
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"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/langchain"
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"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain%3D%3D0%22&expanded=true"
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repository = "https://github.com/langchain-ai/langchain"
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homepage = "https://docs.langchain.com/"
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repository = "https://github.com/langchain-ai/langchain/tree/master/libs/langchain"
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changelog = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-classic%3D%3D1%22"
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twitter = "https://x.com/LangChainAI"
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slack = "https://www.langchain.com/join-community"
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reddit = "https://www.reddit.com/r/LangChain/"
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[dependency-groups]
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test = [
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@@ -37,9 +37,12 @@ xai = ["langchain-xai"]
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perplexity = ["langchain-perplexity"]
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[project.urls]
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"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/langchain"
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"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain%3D%3D0%22&expanded=true"
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repository = "https://github.com/langchain-ai/langchain"
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homepage = "https://docs.langchain.com/"
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repository = "https://github.com/langchain-ai/langchain/tree/master/libs/langchain"
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changelog = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain%3D%3D1%22"
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twitter = "https://x.com/LangChainAI"
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slack = "https://www.langchain.com/join-community"
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reddit = "https://www.reddit.com/r/LangChain/"
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[dependency-groups]
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test = [
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@@ -2,42 +2,4 @@
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This package contains the LangChain integration for Anthropic's generative models.
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## Installation
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`pip install -U langchain-anthropic`
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## Chat Models
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Anthropic recommends using their chat models over text completions.
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You can see their recommended models [in the Anthropic docs](https://docs.anthropic.com/claude/docs/models-overview#model-recommendations).
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To use, you should have an Anthropic API key configured. Initialize the model as:
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```python
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from langchain_anthropic import ChatAnthropic
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from langchain_core.messages import AIMessage, HumanMessage
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model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0, max_tokens=1024)
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```
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### Define the input message
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`message = HumanMessage(content="What is the capital of France?")`
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### Generate a response using the model
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`response = model.invoke([message])`
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For a more detailed walkthrough see [here](https://python.langchain.com/docs/integrations/chat/anthropic).
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## LLMs (Legacy)
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You can use the Claude 2 models for text completions.
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```python
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from langchain_anthropic import AnthropicLLM
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model = AnthropicLLM(model="claude-2.1", temperature=0, max_tokens=1024)
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response = model.invoke("The best restaurant in San Francisco is: ")
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```
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View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/anthropic) for more details.
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@@ -1,4 +1,4 @@
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"""Anthropic partner package for LangChain."""
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"""Claude (Anthropic) partner package for LangChain."""
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from langchain_anthropic.chat_models import (
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ChatAnthropic,
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@@ -13,13 +13,17 @@ dependencies = [
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]
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name = "langchain-anthropic"
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version = "1.0.0a3"
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description = "An integration package connecting Anthropic and LangChain"
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description = "Integration package connecting Claude (Anthropic) APIs and LangChain"
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readme = "README.md"
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[project.urls]
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"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/anthropic"
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"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-anthropic%3D%3D0%22&expanded=true"
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repository = "https://github.com/langchain-ai/langchain"
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homepage = "https://docs.langchain.com/oss/python/integrations/providers/anthropic"
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repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/anthropic"
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changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-anthropic%22"
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docs = "https://reference.langchain.com/python/integrations/langchain_anthropic/"
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twitter = "https://x.com/LangChainAI"
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slack = "https://www.langchain.com/join-community"
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reddit = "https://www.reddit.com/r/LangChain/"
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[dependency-groups]
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test = [
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@@ -2,20 +2,4 @@
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This package contains the LangChain integration with Chroma.
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## Installation
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```bash
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pip install -U langchain-chroma
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```
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## Usage
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The `Chroma` class exposes the connection to the Chroma vector store.
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```python
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from langchain_chroma import Chroma
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embeddings = ... # use a LangChain Embeddings class
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vectorstore = Chroma(embeddings=embeddings)
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```
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View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/chroma) for more details.
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@@ -18,9 +18,13 @@ description = "An integration package connecting Chroma and LangChain."
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readme = "README.md"
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[project.urls]
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"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/chroma"
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"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-chroma%3D%3D0%22&expanded=true"
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repository = "https://github.com/langchain-ai/langchain"
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homepage = "https://docs.langchain.com/oss/python/integrations/providers/chroma"
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repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/chroma"
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changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-chroma%22"
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docs = "https://reference.langchain.com/python/integrations/langchain_chroma/"
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twitter = "https://x.com/LangChainAI"
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slack = "https://www.langchain.com/join-community"
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reddit = "https://www.reddit.com/r/LangChain/"
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[dependency-groups]
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test = [
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@@ -2,23 +2,4 @@
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This package contains the LangChain integration with the DeepSeek API
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## Installation
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```bash
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pip install -U langchain-deepseek
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```
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And you should configure credentials by setting the following environment variables:
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* `DEEPSEEK_API_KEY`
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## Chat Models
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`ChatDeepSeek` class exposes chat models from DeepSeek.
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```python
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from langchain_deepseek import ChatDeepSeek
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llm = ChatDeepSeek(model="deepseek-chat")
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llm.invoke("Sing a ballad of LangChain.")
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```
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View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/deepseek) for more details.
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@@ -16,9 +16,13 @@ description = "An integration package connecting DeepSeek and LangChain"
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readme = "README.md"
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[project.urls]
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"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/deepseek"
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"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-deepseek%3D%3D0%22&expanded=true"
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repository = "https://github.com/langchain-ai/langchain"
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homepage = "https://docs.langchain.com/oss/python/integrations/providers/deepseek"
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repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/deepseek"
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changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-deepseek%22"
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docs = "https://reference.langchain.com/python/integrations/langchain_deepseek/"
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twitter = "https://x.com/LangChainAI"
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slack = "https://www.langchain.com/join-community"
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reddit = "https://www.reddit.com/r/LangChain/"
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[dependency-groups]
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test = [
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@@ -2,104 +2,4 @@
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This package contains the LangChain integrations for Exa Cloud generative models.
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## Installation
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```bash
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pip install -U langchain-exa
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```
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## Exa Search Retriever
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You can retrieve search results as follows
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```python
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from langchain_exa import ExaSearchRetriever
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exa_api_key = "YOUR API KEY"
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# Create a new instance of the ExaSearchRetriever
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exa = ExaSearchRetriever(exa_api_key=exa_api_key)
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# Search for a query and save the results
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results = exa.invoke("What is the capital of France?")
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# Print the results
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print(results)
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```
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### Advanced Features
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You can use advanced features like text limits, summaries, and live crawling:
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|
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```python
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from langchain_exa import ExaSearchRetriever, TextContentsOptions
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# Create a new instance with advanced options
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exa = ExaSearchRetriever(
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exa_api_key="YOUR API KEY",
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k=20, # Number of results (1-100)
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type="auto", # Can be "neural", "keyword", or "auto"
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livecrawl="always", # Can be "always", "fallback", or "never"
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summary=True, # Get an AI-generated summary of each result
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text_contents_options={"max_characters": 3000} # Limit text length
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)
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# Search for a query with custom summary prompt
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exa_with_custom_summary = ExaSearchRetriever(
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exa_api_key="YOUR API KEY",
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summary={"query": "generate one line summary in simple words."} # Custom summary prompt
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)
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```
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## Exa Search Results
|
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You can run the ExaSearchResults module as follows
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```python
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from langchain_exa import ExaSearchResults
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# Initialize the ExaSearchResults tool
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search_tool = ExaSearchResults(exa_api_key="YOUR API KEY")
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|
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# Perform a search query
|
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search_results = search_tool._run(
|
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query="When was the last time the New York Knicks won the NBA Championship?",
|
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num_results=5,
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text_contents_options=True,
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highlights=True
|
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)
|
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print("Search Results:", search_results)
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```
|
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## Exa Find Similar Results
|
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You can run the ExaFindSimilarResults module as follows
|
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|
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```python
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from langchain_exa import ExaFindSimilarResults
|
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|
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# Initialize the ExaFindSimilarResults tool
|
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find_similar_tool = ExaFindSimilarResults(exa_api_key="YOUR API KEY")
|
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|
||||
# Find similar results based on a URL
|
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similar_results = find_similar_tool._run(
|
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url="http://espn.com",
|
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num_results=5,
|
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text_contents_options=True,
|
||||
highlights=True
|
||||
)
|
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|
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print("Similar Results:", similar_results)
|
||||
```
|
||||
|
||||
## Configuration Options
|
||||
|
||||
All Exa tools support the following common parameters:
|
||||
|
||||
- `num_results` (1-100): Number of search results to return
|
||||
- `type`: Search type - "neural", "keyword", or "auto"
|
||||
- `livecrawl`: Live crawling mode - "always", "fallback", or "never"
|
||||
- `summary`: Get AI-generated summaries (True/False or custom prompt dict)
|
||||
- `text_contents_options`: Dict to limit text length (e.g. `{"max_characters": 2000}`)
|
||||
- `highlights`: Include highlighted text snippets (True/False)
|
||||
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/exa_search) for more details.
|
||||
|
||||
@@ -16,9 +16,13 @@ description = "An integration package connecting Exa and LangChain"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/exa"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-exa%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/oss/python/integrations/providers/exa_search"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/exa"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-exa%22"
|
||||
docs = "https://reference.langchain.com/python/integrations/langchain_exa/"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[dependency-groups]
|
||||
test = [
|
||||
|
||||
@@ -2,77 +2,4 @@
|
||||
|
||||
This is the partner package for tying Fireworks.ai and LangChain. Fireworks really strive to provide good support for LangChain use cases, so if you run into any issues please let us know. You can reach out to us [in our Discord channel](https://discord.com/channels/1137072072808472616/)
|
||||
|
||||
## Installation
|
||||
|
||||
To use the `langchain-fireworks` package, follow these installation steps:
|
||||
|
||||
```bash
|
||||
pip install langchain-fireworks
|
||||
```
|
||||
|
||||
## Basic usage
|
||||
|
||||
### Setting up
|
||||
|
||||
1. Sign in to [Fireworks AI](http://fireworks.ai/) to obtain an API Key to access the models, and make sure it is set as the `FIREWORKS_API_KEY` environment variable.
|
||||
|
||||
Once you've signed in and obtained an API key, follow these steps to set the `FIREWORKS_API_KEY` environment variable:
|
||||
- **Linux/macOS:** Open your terminal and execute the following command:
|
||||
|
||||
```bash
|
||||
export FIREWORKS_API_KEY='your_api_key'
|
||||
```
|
||||
|
||||
**Note:** To make this environment variable persistent across terminal sessions, add the above line to your `~/.bashrc`, `~/.bash_profile`, or `~/.zshrc` file.
|
||||
|
||||
- **Windows:** For Command Prompt, use:
|
||||
|
||||
```cmd
|
||||
set FIREWORKS_API_KEY=your_api_key
|
||||
```
|
||||
|
||||
2. Set up your model using a model id. If the model is not set, the default model is `fireworks-llama-v2-7b-chat`. See the full, most up-to-date model list on [fireworks.ai](https://fireworks.ai/models).
|
||||
|
||||
```python
|
||||
import getpass
|
||||
import os
|
||||
|
||||
# Initialize a Fireworks model
|
||||
llm = Fireworks(
|
||||
model="accounts/fireworks/models/llama-v3p1-8b-instruct",
|
||||
base_url="https://api.fireworks.ai/inference/v1/completions",
|
||||
)
|
||||
```
|
||||
|
||||
### Calling the Model Directly
|
||||
|
||||
You can call the model directly with string prompts to get completions.
|
||||
|
||||
```python
|
||||
# Single prompt
|
||||
output = llm.invoke("Who's the best quarterback in the NFL?")
|
||||
print(output)
|
||||
```
|
||||
|
||||
```python
|
||||
# Calling multiple prompts
|
||||
output = llm.generate(
|
||||
[
|
||||
"Who's the best cricket player in 2016?",
|
||||
"Who's the best basketball player in the league?",
|
||||
]
|
||||
)
|
||||
print(output.generations)
|
||||
```
|
||||
|
||||
## Advanced usage
|
||||
|
||||
### Tool use: LangChain Agent + Fireworks function calling model
|
||||
|
||||
Please checkout how to teach Fireworks function calling model to use a calculator [in this notebook](https://github.com/fw-ai/cookbook/blob/main/learn/function-calling/notebooks_langchain/fireworks_langchain_tool_usage.ipynb).
|
||||
|
||||
Fireworks focus on delivering the best experience for fast model inference as well as tool use. You can check out [our blog](https://fireworks.ai/blog/firefunction-v1-gpt-4-level-function-calling) for more details on how it compares to GPT-4, the punchline is that it is on par with GPT-4 in terms of function calling use cases, but it is way faster and much cheaper.
|
||||
|
||||
### RAG: LangChain agent + Fireworks function calling model + MongoDB + Nomic AI embeddings
|
||||
|
||||
Please check out the [cookbook here](https://github.com/fw-ai/cookbook/blob/main/integrations/MongoDB/project_rag_with_mongodb/mongodb_agent.ipynb) for an end to end flow
|
||||
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/fireworks) for more details.
|
||||
|
||||
@@ -19,9 +19,13 @@ description = "An integration package connecting Fireworks and LangChain"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/fireworks"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-fireworks%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/oss/python/integrations/providers/fireworks"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/fireworks"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-fireworks%22"
|
||||
docs = "https://reference.langchain.com/python/integrations/langchain_fireworks/"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[dependency-groups]
|
||||
test = [
|
||||
|
||||
@@ -1,71 +1,3 @@
|
||||
# langchain-groq
|
||||
|
||||
## Welcome to Groq! 🚀
|
||||
|
||||
At Groq, we've developed the world's first Language Processing Unit™, or LPU. The Groq LPU has a deterministic, single core streaming architecture that sets the standard for GenAI inference speed with predictable and repeatable performance for any given workload.
|
||||
|
||||
Beyond the architecture, our software is designed to empower developers like you with the tools you need to create innovative, powerful AI applications. With Groq as your engine, you can:
|
||||
|
||||
* Achieve uncompromised low latency and performance for real-time AI and HPC inferences 🔥
|
||||
* Know the exact performance and compute time for any given workload 🔮
|
||||
* Take advantage of our cutting-edge technology to stay ahead of the competition 💪
|
||||
|
||||
Want more Groq? Check out our [website](https://groq.com) for more resources and join our [Discord community](https://discord.gg/JvNsBDKeCG) to connect with our developers!
|
||||
|
||||
## Installation and Setup
|
||||
|
||||
Install the integration package:
|
||||
|
||||
```bash
|
||||
pip install langchain-groq
|
||||
```
|
||||
|
||||
Request an [API key](https://console.groq.com/login?utm_source=langchain&utm_content=package_readme) and set it as an environment variable
|
||||
|
||||
```bash
|
||||
export GROQ_API_KEY=gsk_...
|
||||
```
|
||||
|
||||
## Chat Model
|
||||
|
||||
See a [usage example](https://python.langchain.com/docs/integrations/chat/groq).
|
||||
|
||||
## Development
|
||||
|
||||
To develop the `langchain-groq` package, you'll need to follow these instructions:
|
||||
|
||||
### Install dev dependencies
|
||||
|
||||
```bash
|
||||
uv sync --group lint --group test
|
||||
```
|
||||
|
||||
### Build the package
|
||||
|
||||
```bash
|
||||
uv build
|
||||
```
|
||||
|
||||
### Run unit tests
|
||||
|
||||
Unit tests live in `tests/unit_tests` and SHOULD NOT require an internet connection or a valid API KEY. Run unit tests with
|
||||
|
||||
```bash
|
||||
make tests
|
||||
```
|
||||
|
||||
### Run integration tests
|
||||
|
||||
Integration tests live in `tests/integration_tests` and require a connection to the Groq API and a valid API KEY.
|
||||
|
||||
```bash
|
||||
make integration_tests
|
||||
```
|
||||
|
||||
### Lint & Format
|
||||
|
||||
Run additional tests and linters to ensure your code is up to standard.
|
||||
|
||||
```bash
|
||||
make lint check_imports
|
||||
```
|
||||
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/groq) for more details.
|
||||
|
||||
@@ -16,9 +16,13 @@ description = "An integration package connecting Groq and LangChain"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/groq"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-groq%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/oss/python/integrations/providers/groq"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/groq"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-groq%22"
|
||||
docs = "https://reference.langchain.com/python/integrations/langchain_groq/"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[dependency-groups]
|
||||
test = [
|
||||
|
||||
@@ -2,10 +2,4 @@
|
||||
|
||||
This package contains the LangChain integrations for huggingface related classes.
|
||||
|
||||
## Installation and Setup
|
||||
|
||||
- Install the LangChain partner package
|
||||
|
||||
```bash
|
||||
pip install langchain-huggingface
|
||||
```
|
||||
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/groq) for more details.
|
||||
|
||||
@@ -17,9 +17,13 @@ description = "An integration package connecting Hugging Face and LangChain."
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/huggingface"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-huggingface%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/oss/python/integrations/providers/huggingface"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/huggingface"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-huggingface%22"
|
||||
docs = "https://reference.langchain.com/python/integrations/langchain_huggingface/"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[project.optional-dependencies]
|
||||
full = [
|
||||
|
||||
@@ -1,57 +1,3 @@
|
||||
# langchain-mistralai
|
||||
|
||||
This package contains the LangChain integrations for [MistralAI](https://docs.mistral.ai) through their [mistralai](https://pypi.org/project/mistralai/) SDK.
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install -U langchain-mistralai
|
||||
```
|
||||
|
||||
## Chat Models
|
||||
|
||||
This package contains the `ChatMistralAI` class, which is the recommended way to interface with MistralAI models.
|
||||
|
||||
To use, install the requirements, and configure your environment.
|
||||
|
||||
```bash
|
||||
export MISTRAL_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
Then initialize
|
||||
|
||||
```python
|
||||
from langchain_core.messages import HumanMessage
|
||||
from langchain_mistralai.chat_models import ChatMistralAI
|
||||
|
||||
chat = ChatMistralAI(model="mistral-small")
|
||||
messages = [HumanMessage(content="say a brief hello")]
|
||||
chat.invoke(messages)
|
||||
```
|
||||
|
||||
`ChatMistralAI` also supports async and streaming functionality:
|
||||
|
||||
```python
|
||||
# For async...
|
||||
await chat.ainvoke(messages)
|
||||
|
||||
# For streaming...
|
||||
for chunk in chat.stream(messages):
|
||||
print(chunk.content, end="", flush=True)
|
||||
```
|
||||
|
||||
## Embeddings
|
||||
|
||||
With `MistralAIEmbeddings`, you can directly use the default model 'mistral-embed', or set a different one if available.
|
||||
|
||||
### Choose model
|
||||
|
||||
`embedding.model = 'mistral-embed'`
|
||||
|
||||
### Simple query
|
||||
|
||||
`res_query = embedding.embed_query("The test information")`
|
||||
|
||||
### Documents
|
||||
|
||||
`res_document = embedding.embed_documents(["test1", "another test"])`
|
||||
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/mistralai) for more details.
|
||||
|
||||
@@ -19,9 +19,13 @@ description = "An integration package connecting Mistral and LangChain"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/mistralai"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-mistralai%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/oss/python/integrations/providers/mistralai"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/mistralai"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-mistralai%22"
|
||||
docs = "https://reference.langchain.com/python/integrations/langchain_mistralai/"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[dependency-groups]
|
||||
test = [
|
||||
|
||||
@@ -2,22 +2,4 @@
|
||||
|
||||
This package contains the LangChain integration with Nomic
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install -U langchain-nomic
|
||||
```
|
||||
|
||||
And you should configure credentials by setting the following environment variables:
|
||||
|
||||
* `NOMIC_API_KEY`: your nomic API key
|
||||
|
||||
## Embeddings
|
||||
|
||||
`NomicEmbeddings` class exposes embeddings from Nomic.
|
||||
|
||||
```python
|
||||
from langchain_nomic import NomicEmbeddings
|
||||
|
||||
embeddings = NomicEmbeddings()
|
||||
embeddings.embed_query("What is the meaning of life?")
|
||||
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/nomic) for more details.
|
||||
|
||||
@@ -17,9 +17,13 @@ description = "An integration package connecting Nomic and LangChain"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/nomic"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-nomic%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/oss/python/integrations/providers/nomic"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/nomic"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-nomic%22"
|
||||
docs = "https://reference.langchain.com/python/integrations/langchain_nomic/"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[dependency-groups]
|
||||
test = [
|
||||
|
||||
@@ -2,46 +2,7 @@
|
||||
|
||||
This package contains the LangChain integration with Ollama
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install -U langchain-ollama
|
||||
```
|
||||
|
||||
For the package to work, you will need to install and run the Ollama server locally ([download](https://ollama.com/download)).
|
||||
|
||||
## [Chat Models](https://python.langchain.com/api_reference/ollama/chat_models/langchain_ollama.chat_models.ChatOllama.html#chatollama)
|
||||
|
||||
`ChatOllama` class exposes chat models from Ollama.
|
||||
|
||||
```python
|
||||
from langchain_ollama import ChatOllama
|
||||
|
||||
llm = ChatOllama(model="llama3.1")
|
||||
llm.invoke("Sing a ballad of LangChain.")
|
||||
```
|
||||
|
||||
## [Embeddings](https://python.langchain.com/api_reference/ollama/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html#ollamaembeddings)
|
||||
|
||||
`OllamaEmbeddings` class exposes embeddings from Ollama.
|
||||
|
||||
```python
|
||||
from langchain_ollama import OllamaEmbeddings
|
||||
|
||||
embeddings = OllamaEmbeddings(model="llama3.1")
|
||||
embeddings.embed_query("What is the meaning of life?")
|
||||
```
|
||||
|
||||
## [LLMs](https://python.langchain.com/api_reference/ollama/llms/langchain_ollama.llms.OllamaLLM.html#ollamallm)
|
||||
|
||||
`OllamaLLM` class exposes traditional LLMs from Ollama.
|
||||
|
||||
```python
|
||||
from langchain_ollama import OllamaLLM
|
||||
|
||||
llm = OllamaLLM(model="llama3.1")
|
||||
llm.invoke("The meaning of life is")
|
||||
```
|
||||
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/ollama) for more details.
|
||||
|
||||
## Development
|
||||
|
||||
|
||||
@@ -16,9 +16,13 @@ description = "An integration package connecting Ollama and LangChain"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/ollama"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-ollama%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/oss/python/integrations/providers/ollama"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/ollama"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-ollama%22"
|
||||
docs = "https://reference.langchain.com/python/integrations/langchain_ollama/"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[dependency-groups]
|
||||
test = [
|
||||
|
||||
@@ -2,60 +2,4 @@
|
||||
|
||||
This package contains the LangChain integrations for OpenAI through their `openai` SDK.
|
||||
|
||||
## Installation and Setup
|
||||
|
||||
- Install the LangChain partner package
|
||||
|
||||
```bash
|
||||
pip install langchain-openai
|
||||
```
|
||||
|
||||
- Get an OpenAI api key and set it as an environment variable (`OPENAI_API_KEY`)
|
||||
|
||||
## Chat model
|
||||
|
||||
See a [usage example](https://python.langchain.com/docs/integrations/chat/openai).
|
||||
|
||||
```python
|
||||
from langchain_openai import ChatOpenAI
|
||||
```
|
||||
|
||||
If you are using a model hosted on `Azure`, you should use different wrapper for that:
|
||||
|
||||
```python
|
||||
from langchain_openai import AzureChatOpenAI
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of the `Azure` wrapper, see [AzureChatOpenAI](https://python.langchain.com/docs/integrations/chat/azure_chat_openai)
|
||||
|
||||
## Text Embedding Model
|
||||
|
||||
See a [usage example](https://python.langchain.com/docs/integrations/text_embedding/openai)
|
||||
|
||||
```python
|
||||
from langchain_openai import OpenAIEmbeddings
|
||||
```
|
||||
|
||||
If you are using a model hosted on `Azure`, you should use different wrapper for that:
|
||||
|
||||
```python
|
||||
from langchain_openai import AzureOpenAIEmbeddings
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of the `Azure` wrapper, see [AzureOpenAIEmbeddings](https://python.langchain.com/docs/integrations/text_embedding/azureopenai)
|
||||
|
||||
## LLM (Legacy)
|
||||
|
||||
LLM refers to the legacy text-completion models that preceded chat models. See a [usage example](https://python.langchain.com/docs/integrations/llms/openai).
|
||||
|
||||
```python
|
||||
from langchain_openai import OpenAI
|
||||
```
|
||||
|
||||
If you are using a model hosted on `Azure`, you should use different wrapper for that:
|
||||
|
||||
```python
|
||||
from langchain_openai import AzureOpenAI
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of the `Azure` wrapper, see [Azure OpenAI](https://python.langchain.com/docs/integrations/llms/azure_openai)
|
||||
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/openai) for more details.
|
||||
|
||||
@@ -17,9 +17,13 @@ description = "An integration package connecting OpenAI and LangChain"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/openai"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-openai%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/oss/python/integrations/providers/openai"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/openai"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-openai%22"
|
||||
docs = "https://reference.langchain.com/python/integrations/langchain_openai/"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[dependency-groups]
|
||||
test = [
|
||||
|
||||
@@ -2,28 +2,4 @@
|
||||
|
||||
This package contains the LangChain integration with Perplexity.
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install -U langchain-perplexity
|
||||
```
|
||||
|
||||
And you should [configure your perplexity credentials](https://docs.perplexity.ai/guides/getting-started)
|
||||
and then set the `PPLX_API_KEY` environment variable.
|
||||
|
||||
## Usage
|
||||
|
||||
This package contains the `ChatPerplexity` class, which is the recommended way to interface with Perplexity chat models.
|
||||
|
||||
```python
|
||||
import getpass
|
||||
import os
|
||||
|
||||
if not os.environ.get("PPLX_API_KEY"):
|
||||
os.environ["PPLX_API_KEY"] = getpass.getpass("Enter API key for Perplexity: ")
|
||||
|
||||
from langchain.chat_models import init_chat_model
|
||||
|
||||
llm = init_chat_model("llama-3.1-sonar-small-128k-online", model_provider="perplexity")
|
||||
llm.invoke("Hello, world!")
|
||||
```
|
||||
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/perplexity) for more details.
|
||||
|
||||
@@ -16,9 +16,13 @@ description = "An integration package connecting Perplexity and LangChain"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/perplexity"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-perplexity%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/oss/python/integrations/providers/perplexity"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/perplexity"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-perplexity%22"
|
||||
docs = "https://reference.langchain.com/python/integrations/langchain_perplexity/"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[dependency-groups]
|
||||
test = [
|
||||
|
||||
@@ -2,6 +2,8 @@
|
||||
|
||||
This package contains the LangChain integration with Microsoft Prompty.
|
||||
|
||||
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/microsoft) for more details.
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
|
||||
@@ -16,9 +16,13 @@ description = "An integration package connecting Prompty and LangChain"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/prompty"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-prompty%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/oss/python/integrations/providers/microsoft"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/prompty"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-prompty%22"
|
||||
docs = "https://reference.langchain.com/python/integrations/langchain_prompty/"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[dependency-groups]
|
||||
test = [
|
||||
|
||||
@@ -2,24 +2,4 @@
|
||||
|
||||
This package contains the LangChain integration with [Qdrant](https://qdrant.tech/).
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install -U langchain-qdrant
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
The `Qdrant` class exposes the connection to the Qdrant vector store.
|
||||
|
||||
```python
|
||||
from langchain_qdrant import Qdrant
|
||||
|
||||
embeddings = ... # use a LangChain Embeddings class
|
||||
|
||||
vectorstore = Qdrant.from_existing_collection(
|
||||
embeddings=embeddings,
|
||||
collection_name="<COLLECTION_NAME>",
|
||||
url="http://localhost:6333",
|
||||
)
|
||||
```
|
||||
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/qdrant) for more details.
|
||||
|
||||
@@ -17,9 +17,13 @@ description = "An integration package connecting Qdrant and LangChain"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/qdrant"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-qdrant%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/oss/python/integrations/providers/qdrant"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/qdrant"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-qdrant%22"
|
||||
docs = "https://reference.langchain.com/python/integrations/langchain_qdrant/"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[project.optional-dependencies]
|
||||
fastembed = [
|
||||
|
||||
@@ -2,16 +2,4 @@
|
||||
|
||||
This package contains the LangChain integrations for [xAI](https://x.ai/) through their [APIs](https://console.x.ai).
|
||||
|
||||
## Installation and Setup
|
||||
|
||||
- Install the LangChain partner package
|
||||
|
||||
```bash
|
||||
pip install -U langchain-xai
|
||||
```
|
||||
|
||||
- Get your xAI api key from the [xAI Dashboard](https://console.x.ai) and set it as an environment variable (`XAI_API_KEY`)
|
||||
|
||||
## Chat Completions
|
||||
|
||||
This package contains the `ChatXAI` class, which is the recommended way to interface with xAI chat models.
|
||||
View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/xai) for more details.
|
||||
|
||||
@@ -18,9 +18,13 @@ description = "An integration package connecting xAI and LangChain"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/xai"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-xai%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/oss/python/integrations/providers/xai"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/xai"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-xai%22"
|
||||
docs = "https://reference.langchain.com/python/integrations/langchain_xai/"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[dependency-groups]
|
||||
test = [
|
||||
|
||||
@@ -26,8 +26,12 @@ description = "Standard tests for LangChain implementations"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/standard-tests"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/standard-tests"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-tests%3D%3D1%22"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[dependency-groups]
|
||||
test = ["langchain-core"]
|
||||
|
||||
@@ -15,9 +15,12 @@ description = "LangChain text splitting utilities"
|
||||
readme = "README.md"
|
||||
|
||||
[project.urls]
|
||||
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/text-splitters"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-text-splitters%3D%3D0%22&expanded=true"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
homepage = "https://docs.langchain.com/"
|
||||
repository = "https://github.com/langchain-ai/langchain/tree/master/libs/text-splitters"
|
||||
changelog = "https://github.com/langchain-ai/langchain/releases?q=%22langchain-text-splitters%22"
|
||||
twitter = "https://x.com/LangChainAI"
|
||||
slack = "https://www.langchain.com/join-community"
|
||||
reddit = "https://www.reddit.com/r/LangChain/"
|
||||
|
||||
[dependency-groups]
|
||||
lint = [
|
||||
|
||||
Reference in New Issue
Block a user