mirror of
https://github.com/hwchase17/langchain.git
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docs: integrations
references update (#25217)
Added missed provider pages. Fixed formats and added descriptions and links. --------- Co-authored-by: Chester Curme <chester.curme@gmail.com>
This commit is contained in:
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@ -4,9 +4,23 @@
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"cell_type": "markdown",
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"# ChatLlamaCpp\n",
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"# Llama.cpp\n",
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"\n",
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"\n",
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"This notebook provides a quick overview for getting started with chat model intergrated with [llama cpp python](https://github.com/abetlen/llama-cpp-python)."
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">[llama.cpp python](https://github.com/abetlen/llama-cpp-python) library is a simple Python bindings for `@ggerganov`\n",
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">[llama.cpp](https://github.com/ggerganov/llama.cpp).\n",
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">\n",
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">This package provides:\n",
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">\n",
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"> - Low-level access to C API via ctypes interface.\n",
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"> - High-level Python API for text completion\n",
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"> - `OpenAI`-like API\n",
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"> - `LangChain` compatibility\n",
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"> - `LlamaIndex` compatibility\n",
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"> - OpenAI compatible web server\n",
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"> - Local Copilot replacement\n",
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"> - Function Calling support\n",
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"> - Vision API support\n",
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"> - Multiple Models\n"
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]
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]
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},
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},
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{
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{
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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"version": "3.10.12"
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}
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}
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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@ -99,7 +99,7 @@
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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"version": "3.10.12"
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},
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},
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"vscode": {
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"vscode": {
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"interpreter": {
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"interpreter": {
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@ -17,7 +17,7 @@
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"source": [
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"source": [
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"# ChatPerplexity\n",
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"# ChatPerplexity\n",
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"\n",
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"\n",
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"This notebook covers how to get started with Perplexity chat models."
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"This notebook covers how to get started with `Perplexity` chat models."
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]
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]
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},
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},
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{
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{
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"from langchain_core.prompts import ChatPromptTemplate"
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"from langchain_core.prompts import ChatPromptTemplate"
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]
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]
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},
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},
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{
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"cell_type": "markdown",
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"id": "b26e2035-2f81-4451-ba44-fa2e2d5aeb62",
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"metadata": {},
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"source": [
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"The code provided assumes that your PPLX_API_KEY is set in your environment variables. If you would like to manually specify your API key and also choose a different model, you can use the following code:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d986aac6-1bae-4608-8514-d3ba5b35b10e",
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"metadata": {},
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"outputs": [],
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"source": [
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"chat = ChatPerplexity(\n",
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" temperature=0, pplx_api_key=\"YOUR_API_KEY\", model=\"llama-3-sonar-small-32k-online\"\n",
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")"
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]
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},
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{
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{
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"cell_type": "markdown",
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"cell_type": "markdown",
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"id": "97a8ce3a",
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"id": "97a8ce3a",
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"The code provided assumes that your PPLX_API_KEY is set in your environment variables. If you would like to manually specify your API key and also choose a different model, you can use the following code:\n",
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"\n",
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"```python\n",
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"chat = ChatPerplexity(temperature=0, pplx_api_key=\"YOUR_API_KEY\", model=\"llama-3-sonar-small-32k-online\")\n",
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"```\n",
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"\n",
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"You can check a list of available models [here](https://docs.perplexity.ai/docs/model-cards). For reproducibility, we can set the API key dynamically by taking it as an input in this notebook."
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"You can check a list of available models [here](https://docs.perplexity.ai/docs/model-cards). For reproducibility, we can set the API key dynamically by taking it as an input in this notebook."
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]
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]
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},
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},
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.9.18"
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"version": "3.10.12"
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}
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}
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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# Llama.cpp
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# Llama.cpp
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This page covers how to use [llama.cpp](https://github.com/ggerganov/llama.cpp) within LangChain.
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>[llama.cpp python](https://github.com/abetlen/llama-cpp-python) library is a simple Python bindings for `@ggerganov`
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It is broken into two parts: installation and setup, and then references to specific Llama-cpp wrappers.
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>[llama.cpp](https://github.com/ggerganov/llama.cpp).
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>
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>This package provides:
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>
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> - Low-level access to C API via ctypes interface.
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> - High-level Python API for text completion
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> - `OpenAI`-like API
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> - `LangChain` compatibility
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> - `LlamaIndex` compatibility
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> - OpenAI compatible web server
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> - Local Copilot replacement
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> - Function Calling support
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> - Vision API support
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> - Multiple Models
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## Installation and Setup
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## Installation and Setup
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- Install the Python package with `pip install llama-cpp-python`
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- Install the Python package
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```bash
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pip install llama-cpp-python
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````
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- Download one of the [supported models](https://github.com/ggerganov/llama.cpp#description) and convert them to the llama.cpp format per the [instructions](https://github.com/ggerganov/llama.cpp)
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- Download one of the [supported models](https://github.com/ggerganov/llama.cpp#description) and convert them to the llama.cpp format per the [instructions](https://github.com/ggerganov/llama.cpp)
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## Wrappers
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### LLM
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## Chat models
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See a [usage example](/docs/integrations/chat/llamacpp).
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```python
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from langchain_community.chat_models import ChatLlamaCpp
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```
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## LLMs
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See a [usage example](/docs/integrations/llms/llamacpp).
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There exists a LlamaCpp LLM wrapper, which you can access with
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```python
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```python
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from langchain_community.llms import LlamaCpp
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from langchain_community.llms import LlamaCpp
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```
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```
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For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/llamacpp)
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### Embeddings
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## Embedding models
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See a [usage example](/docs/integrations/text_embedding/llamacpp).
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There exists a LlamaCpp Embeddings wrapper, which you can access with
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```python
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```python
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from langchain_community.embeddings import LlamaCppEmbeddings
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from langchain_community.embeddings import LlamaCppEmbeddings
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```
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```
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For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/llamacpp)
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docs/docs/integrations/providers/maritalk.mdx
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# MariTalk
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>[MariTalk](https://www.maritaca.ai/en) is an LLM-based chatbot trained to meet the needs of Brazil.
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## Installation and Setup
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You have to get the MariTalk API key.
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You also need to install the `httpx` Python package.
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```bash
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pip install httpx
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```
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## Chat models
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See a [usage example](/docs/integrations/chat/maritalk).
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```python
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from langchain_community.chat_models import ChatMaritalk
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```
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docs/docs/integrations/providers/mlx.mdx
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docs/docs/integrations/providers/mlx.mdx
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# MLX
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>[MLX](https://ml-explore.github.io/mlx/build/html/index.html) is a `NumPy`-like array framework
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> designed for efficient and flexible machine learning on `Apple` silicon,
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> brought to you by `Apple machine learning research`.
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## Installation and Setup
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Install several Python packages:
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```bash
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pip install mlx-lm transformers huggingface_hub
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````
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## Chat models
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See a [usage example](/docs/integrations/chat/mlx).
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```python
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from langchain_community.chat_models.mlx import ChatMLX
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```
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## LLMs
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### MLX Local Pipelines
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See a [usage example](/docs/integrations/llms/mlx_pipelines).
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```python
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from langchain_community.llms.mlx_pipeline import MLXPipeline
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```
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37
docs/docs/integrations/providers/octoai.mdx
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docs/docs/integrations/providers/octoai.mdx
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# OctoAI
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>[OctoAI](https://docs.octoai.cloud/docs) offers easy access to efficient compute
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> and enables users to integrate their choice of AI models into applications.
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> The `OctoAI` compute service helps you run, tune, and scale AI applications easily.
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## Installation and Setup
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- Install the `openai` Python package:
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```bash
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pip install openai
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````
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- Register on `OctoAI` and get an API Token from [your OctoAI account page](https://octoai.cloud/settings).
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## Chat models
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See a [usage example](/docs/integrations/chat/octoai).
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```python
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from langchain_community.chat_models import ChatOctoAI
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```
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## LLMs
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See a [usage example](/docs/integrations/llms/octoai).
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```python
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from langchain_community.llms.octoai_endpoint import OctoAIEndpoint
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```
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## Embedding models
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```python
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from langchain_community.embeddings.octoai_embeddings import OctoAIEmbeddings
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```
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25
docs/docs/integrations/providers/perplexity.mdx
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docs/docs/integrations/providers/perplexity.mdx
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# Perplexity
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>[Perplexity](https://www.perplexity.ai/pro) is the most powerful way to search
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> the internet with unlimited Pro Search, upgraded AI models, unlimited file upload,
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> image generation, and API credits.
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>
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> You can check a [list of available models](https://docs.perplexity.ai/docs/model-cards).
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## Installation and Setup
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Install a Python package:
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```bash
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pip install openai
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````
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Get your API key from [here](https://docs.perplexity.ai/docs/getting-started).
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## Chat models
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See a [usage example](/docs/integrations/chat/perplexity).
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```python
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from langchain_community.chat_models import ChatPerplexity
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```
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"cell_type": "markdown",
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"cell_type": "markdown",
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"# Llama-cpp\n",
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"# Llama.cpp\n",
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"\n",
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"\n",
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"This notebook goes over how to use Llama-cpp embeddings within LangChain"
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">[llama.cpp python](https://github.com/abetlen/llama-cpp-python) library is a simple Python bindings for `@ggerganov`\n",
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">[llama.cpp](https://github.com/ggerganov/llama.cpp).\n",
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">\n",
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">This package provides:\n",
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">\n",
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"> - Low-level access to C API via ctypes interface.\n",
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"> - High-level Python API for text completion\n",
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"> - `OpenAI`-like API\n",
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"> - `LangChain` compatibility\n",
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"> - `LlamaIndex` compatibility\n",
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"> - OpenAI compatible web server\n",
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"> - Local Copilot replacement\n",
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"> - Function Calling support\n",
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"> - Vision API support\n",
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"> - Multiple Models\n"
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]
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]
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},
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},
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{
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{
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.10.12"
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}
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}
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}
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}
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