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Merge branch 'master' into sr/init_multiple_times
This commit is contained in:
Sydney Runkle
commit
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|
||||
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
|
||||
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
|
||||
@ -102,7 +102,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 1,
|
||||
"id": "39549336-25f5-4839-9846-f687cd77e59b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -110,43 +110,20 @@
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'is_blocked': False,\n",
|
||||
" 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH',\n",
|
||||
" 'probability_label': 'NEGLIGIBLE',\n",
|
||||
" 'probability_score': 0.046142578125,\n",
|
||||
" 'blocked': False,\n",
|
||||
" 'severity': 'HARM_SEVERITY_NEGLIGIBLE',\n",
|
||||
" 'severity_score': 0.07275390625},\n",
|
||||
" {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT',\n",
|
||||
" 'probability_label': 'NEGLIGIBLE',\n",
|
||||
" 'probability_score': 0.05419921875,\n",
|
||||
" 'blocked': False,\n",
|
||||
" 'severity': 'HARM_SEVERITY_NEGLIGIBLE',\n",
|
||||
" 'severity_score': 0.03955078125},\n",
|
||||
" {'category': 'HARM_CATEGORY_HARASSMENT',\n",
|
||||
" 'probability_label': 'NEGLIGIBLE',\n",
|
||||
" 'probability_score': 0.083984375,\n",
|
||||
" 'blocked': False,\n",
|
||||
" 'severity': 'HARM_SEVERITY_NEGLIGIBLE',\n",
|
||||
" 'severity_score': 0.029296875},\n",
|
||||
" {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n",
|
||||
" 'probability_label': 'NEGLIGIBLE',\n",
|
||||
" 'probability_score': 0.054931640625,\n",
|
||||
" 'blocked': False,\n",
|
||||
" 'severity': 'HARM_SEVERITY_NEGLIGIBLE',\n",
|
||||
" 'severity_score': 0.03466796875}],\n",
|
||||
" 'safety_ratings': [],\n",
|
||||
" 'usage_metadata': {'prompt_token_count': 10,\n",
|
||||
" 'candidates_token_count': 193,\n",
|
||||
" 'total_token_count': 203,\n",
|
||||
" 'candidates_token_count': 55,\n",
|
||||
" 'total_token_count': 65,\n",
|
||||
" 'prompt_tokens_details': [{'modality': 1, 'token_count': 10}],\n",
|
||||
" 'candidates_tokens_details': [{'modality': 1, 'token_count': 193}],\n",
|
||||
" 'candidates_tokens_details': [{'modality': 1, 'token_count': 55}],\n",
|
||||
" 'cached_content_token_count': 0,\n",
|
||||
" 'cache_tokens_details': []},\n",
|
||||
" 'finish_reason': 'STOP',\n",
|
||||
" 'avg_logprobs': -0.5702065976790196,\n",
|
||||
" 'model_name': 'gemini-1.5-flash-001'}"
|
||||
" 'avg_logprobs': -0.251378042047674,\n",
|
||||
" 'model_name': 'gemini-2.0-flash-001'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -154,7 +131,7 @@
|
||||
"source": [
|
||||
"from langchain_google_vertexai import ChatVertexAI\n",
|
||||
"\n",
|
||||
"llm = ChatVertexAI(model=\"gemini-1.5-flash-001\")\n",
|
||||
"llm = ChatVertexAI(model=\"gemini-2.0-flash-001\")\n",
|
||||
"msg = llm.invoke(\"What's the oldest known example of cuneiform\")\n",
|
||||
"msg.response_metadata"
|
||||
]
|
||||
|
||||
@ -18,7 +18,7 @@
|
||||
"# ChatCloudflareWorkersAI\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"This will help you getting started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCloudflareWorkersAI features and configurations head to the [API reference](https://python.langchain.com/docs/integrations/chat/cloudflare_workersai/).\n",
|
||||
"This will help you get started with CloudflareWorkersAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCloudflareWorkersAI features and configurations head to the [API reference](https://python.langchain.com/docs/integrations/chat/cloudflare_workersai/).\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
|
||||
@ -21,7 +21,7 @@
|
||||
"source": [
|
||||
"# ChatContextual\n",
|
||||
"\n",
|
||||
"This will help you getting started with Contextual AI's Grounded Language Model [chat models](/docs/concepts/chat_models/).\n",
|
||||
"This will help you get started with Contextual AI's Grounded Language Model [chat models](/docs/concepts/chat_models/).\n",
|
||||
"\n",
|
||||
"To learn more about Contextual AI, please visit our [documentation](https://docs.contextual.ai/).\n",
|
||||
"\n",
|
||||
|
||||
@ -18,7 +18,7 @@
|
||||
"# ChatDeepSeek\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"This will help you getting started with DeepSeek's hosted [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDeepSeek features and configurations head to the [API reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html).\n",
|
||||
"This will help you get started with DeepSeek's hosted [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDeepSeek features and configurations head to the [API reference](https://python.langchain.com/api_reference/deepseek/chat_models/langchain_deepseek.chat_models.ChatDeepSeek.html).\n",
|
||||
"\n",
|
||||
":::tip\n",
|
||||
"\n",
|
||||
|
||||
@ -22,7 +22,7 @@
|
||||
"# ChatFeatherlessAi\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"This will help you getting started with FeatherlessAi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFeatherlessAi features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.ChatFeatherlessAi.html).\n",
|
||||
"This will help you get started with FeatherlessAi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFeatherlessAi features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.ChatFeatherlessAi.html).\n",
|
||||
"\n",
|
||||
"- See https://featherless.ai/ for an example.\n",
|
||||
"\n",
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
"source": [
|
||||
"# ChatGoodfire\n",
|
||||
"\n",
|
||||
"This will help you getting started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
|
||||
"This will help you get started with Goodfire [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoodfire features and configurations head to the [PyPI project page](https://pypi.org/project/langchain-goodfire/), or go directly to the [Goodfire SDK docs](https://docs.goodfire.ai/sdk-reference/example). All of the Goodfire-specific functionality (e.g. SAE features, variants, etc.) is available via the main `goodfire` package. This integration is a wrapper around the Goodfire SDK.\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
"### Integration details\n",
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
"source": [
|
||||
"# ChatGroq\n",
|
||||
"\n",
|
||||
"This will help you getting started with Groq [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models?utm_source=langchain).\n",
|
||||
"This will help you get started with Groq [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models?utm_source=langchain).\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
"### Integration details\n",
|
||||
|
||||
@ -6,7 +6,7 @@
|
||||
"source": [
|
||||
"# ChatHuggingFace\n",
|
||||
"\n",
|
||||
"This will help you getting started with `langchain_huggingface` [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatHuggingFace` features and configurations head to the [API reference](https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html). For a list of models supported by Hugging Face check out [this page](https://huggingface.co/models).\n",
|
||||
"This will help you get started with `langchain_huggingface` [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatHuggingFace` features and configurations head to the [API reference](https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html). For a list of models supported by Hugging Face check out [this page](https://huggingface.co/models).\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
"### Integration details\n",
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
"source": [
|
||||
"# ChatMistralAI\n",
|
||||
"\n",
|
||||
"This will help you getting started with Mistral [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
|
||||
"This will help you get started with Mistral [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
"### Integration details\n",
|
||||
|
||||
@ -20,7 +20,7 @@
|
||||
"\n",
|
||||
"ModelScope ([Home](https://www.modelscope.cn/) | [GitHub](https://github.com/modelscope/modelscope)) is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation. \n",
|
||||
"\n",
|
||||
"This will help you getting started with ModelScope Chat Endpoint.\n",
|
||||
"This will help you get started with ModelScope Chat Endpoint.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
"source": [
|
||||
"# ChatNetmind\n",
|
||||
"\n",
|
||||
"This will help you getting started with Netmind [chat models](https://www.netmind.ai/). For detailed documentation of all ChatNetmind features and configurations head to the [API reference](https://github.com/protagolabs/langchain-netmind).\n",
|
||||
"This will help you get started with Netmind [chat models](https://www.netmind.ai/). For detailed documentation of all ChatNetmind features and configurations head to the [API reference](https://github.com/protagolabs/langchain-netmind).\n",
|
||||
"\n",
|
||||
"- See https://www.netmind.ai/ for an example.\n",
|
||||
"\n",
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
"source": [
|
||||
"# ChatNVIDIA\n",
|
||||
"\n",
|
||||
"This will help you getting started with NVIDIA [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatNVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html).\n",
|
||||
"This will help you get started with NVIDIA [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatNVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html).\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
"The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on\n",
|
||||
|
||||
@ -19,7 +19,7 @@
|
||||
"source": [
|
||||
"# ChatOCIModelDeployment\n",
|
||||
"\n",
|
||||
"This will help you getting started with OCIModelDeployment [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatOCIModelDeployment features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.oci_data_science.ChatOCIModelDeployment.html).\n",
|
||||
"This will help you get started with OCIModelDeployment [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatOCIModelDeployment features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.oci_data_science.ChatOCIModelDeployment.html).\n",
|
||||
"\n",
|
||||
"[OCI Data Science](https://docs.oracle.com/en-us/iaas/data-science/using/home.htm) is a fully managed and serverless platform for data science teams to build, train, and manage machine learning models in the Oracle Cloud Infrastructure. You can use [AI Quick Actions](https://blogs.oracle.com/ai-and-datascience/post/ai-quick-actions-in-oci-data-science) to easily deploy LLMs on [OCI Data Science Model Deployment Service](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm). You may choose to deploy the model with popular inference frameworks such as vLLM or TGI. By default, the model deployment endpoint mimics the OpenAI API protocol.\n",
|
||||
"\n",
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
"source": [
|
||||
"# ChatOutlines\n",
|
||||
"\n",
|
||||
"This will help you getting started with Outlines [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatOutlines features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.outlines.ChatOutlines.html).\n",
|
||||
"This will help you get started with Outlines [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatOutlines features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.outlines.ChatOutlines.html).\n",
|
||||
"\n",
|
||||
"[Outlines](https://github.com/outlines-dev/outlines) is a library for constrained language generation. It allows you to use large language models (LLMs) with various backends while applying constraints to the generated output.\n",
|
||||
"\n",
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
"source": [
|
||||
"# ChatPipeshift\n",
|
||||
"\n",
|
||||
"This will help you getting started with Pipeshift [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatPipeshift features and configurations head to the [API reference](https://dashboard.pipeshift.com/docs).\n",
|
||||
"This will help you get started with Pipeshift [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatPipeshift features and configurations head to the [API reference](https://dashboard.pipeshift.com/docs).\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
"### Integration details\n",
|
||||
|
||||
@ -21,7 +21,7 @@
|
||||
"source": [
|
||||
"# ChatQwQ\n",
|
||||
"\n",
|
||||
"This will help you getting started with QwQ [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatQwQ features and configurations head to the [API reference](https://pypi.org/project/langchain-qwq/).\n",
|
||||
"This will help you get started with QwQ [chat models](../../concepts/chat_models.mdx). For detailed documentation of all ChatQwQ features and configurations head to the [API reference](https://pypi.org/project/langchain-qwq/).\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
"### Integration details\n",
|
||||
|
||||
@ -19,7 +19,7 @@
|
||||
"source": [
|
||||
"# ChatSambaNovaCloud\n",
|
||||
"\n",
|
||||
"This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).\n",
|
||||
"This will help you get started with SambaNovaCloud [chat models](/docs/concepts/chat_models/). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).\n",
|
||||
"\n",
|
||||
"**[SambaNova](https://sambanova.ai/)'s** [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models\n",
|
||||
"\n",
|
||||
|
||||
@ -19,7 +19,7 @@
|
||||
"source": [
|
||||
"# ChatSambaStudio\n",
|
||||
"\n",
|
||||
"This will help you getting started with SambaStudio [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://docs.sambanova.ai/sambastudio/latest/index.html).\n",
|
||||
"This will help you get started with SambaStudio [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://docs.sambanova.ai/sambastudio/latest/index.html).\n",
|
||||
"\n",
|
||||
"**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) SambaStudio is a rich, GUI-based platform that provides the functionality to train, deploy, and manage models in SambaNova [DataScale](https://sambanova.ai/products/datascale) systems.\n",
|
||||
"\n",
|
||||
|
||||
@ -5,14 +5,14 @@
|
||||
"id": "134a0785",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"[Vectara](https://vectara.com/) is the trusted AI Assistant and Agent platform which focuses on enterprise readiness for mission-critical applications.\n",
|
||||
"[Vectara](https://vectara.com/) is the trusted AI Assistant and Agent platform, which focuses on enterprise readiness for mission-critical applications.\n",
|
||||
"Vectara serverless RAG-as-a-service provides all the components of RAG behind an easy-to-use API, including:\n",
|
||||
"1. A way to extract text from files (PDF, PPT, DOCX, etc)\n",
|
||||
"2. ML-based chunking that provides state of the art performance.\n",
|
||||
"2. ML-based chunking that provides state-of-the-art performance.\n",
|
||||
"3. The [Boomerang](https://vectara.com/how-boomerang-takes-retrieval-augmented-generation-to-the-next-level-via-grounded-generation/) embeddings model.\n",
|
||||
"4. Its own internal vector database where text chunks and embedding vectors are stored.\n",
|
||||
"5. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments, including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching) as well as multiple reranking options such as the [multi-lingual relevance reranker](https://www.vectara.com/blog/deep-dive-into-vectara-multilingual-reranker-v1-state-of-the-art-reranker-across-100-languages), [MMR](https://vectara.com/get-diverse-results-and-comprehensive-summaries-with-vectaras-mmr-reranker/), [UDF reranker](https://www.vectara.com/blog/rag-with-user-defined-functions-based-reranking). \n",
|
||||
"6. An LLM to for creating a [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents (context), including citations.\n",
|
||||
"6. An LLM for creating a [generative summary](https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-overview), based on the retrieved documents (context), including citations.\n",
|
||||
"\n",
|
||||
"For more information:\n",
|
||||
"- [Documentation](https://docs.vectara.com/docs/)\n",
|
||||
@ -24,7 +24,7 @@
|
||||
"\n",
|
||||
"### Setup\n",
|
||||
"\n",
|
||||
"To use the `VectaraVectorStore` you first need to install the partner package.\n"
|
||||
"To use the `VectaraVectorStore`, you first need to install the partner package.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -46,8 +46,8 @@
|
||||
"\n",
|
||||
"To get started, use the following steps:\n",
|
||||
"1. If you don't already have one, [Sign up](https://www.vectara.com/integrations/langchain) for your free Vectara trial.\n",
|
||||
"2. Within your account you can create one or more corpora. Each corpus represents an area that stores text data upon ingest from input documents. To create a corpus, use the **\"Create Corpus\"** button. You then provide a name to your corpus as well as a description. Optionally you can define filtering attributes and apply some advanced options. If you click on your created corpus, you can see its name and corpus ID right on the top.\n",
|
||||
"3. Next you'll need to create API keys to access the corpus. Click on the **\"Access Control\"** tab in the corpus view and then the **\"Create API Key\"** button. Give your key a name, and choose whether you want query-only or query+index for your key. Click \"Create\" and you now have an active API key. Keep this key confidential. \n",
|
||||
"2. Within your account, you can create one or more corpora. Each corpus represents an area that stores text data upon ingestion from input documents. To create a corpus, use the **\"Create Corpus\"** button. You then provide a name to your corpus as well as a description. Optionally, you can define filtering attributes and apply some advanced options. If you click on your created corpus, you can see its name and corpus ID right on the top.\n",
|
||||
"3. Next, you'll need to create API keys to access the corpus. Click on the **\"Access Control\"** tab in the corpus view and then the **\"Create API Key\"** button. Give your key a name, and choose whether you want query-only or query+index for your key. Click \"Create\", and you now have an active API key. Keep this key confidential. \n",
|
||||
"\n",
|
||||
"To use LangChain with Vectara, you'll need to have these two values: `corpus_key` and `api_key`.\n",
|
||||
"You can provide `VECTARA_API_KEY` to LangChain in two ways:\n",
|
||||
@ -73,7 +73,7 @@
|
||||
")\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"In this notebook we assume they are provided in the environment."
|
||||
"In this notebook, we assume they are provided in the environment."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -109,9 +109,9 @@
|
||||
"\n",
|
||||
"In most uses of LangChain to create chatbots, one must integrate a special `memory` component that maintains the history of chat sessions and then uses that history to ensure the chatbot is aware of conversation history.\n",
|
||||
"\n",
|
||||
"With Vectara Chat - all of that is performed in the backend by Vectara automatically. You can look at the [Chat](https://docs.vectara.com/docs/api-reference/chat-apis/chat-apis-overview) documentation for the details, to learn more about the internals of how this is implemented, but with LangChain all you have to do is turn that feature on in the Vectara vectorstore.\n",
|
||||
"With Vectara Chat, all of that is performed in the backend by Vectara automatically. You can look at the [Chat](https://docs.vectara.com/docs/api-reference/chat-apis/chat-apis-overview) documentation for the details, to learn more about the internals of how this is implemented, but with LangChain, all you have to do is turn that feature on in the Vectara vectorstore.\n",
|
||||
"\n",
|
||||
"Let's see an example. First we load the SOTU document (remember, text extraction and chunking all occurs automatically on the Vectara platform):"
|
||||
"Let's see an example. First, we load the SOTU document (remember, text extraction and chunking all occur automatically on the Vectara platform):"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -241,8 +241,8 @@
|
||||
"source": [
|
||||
"## Chat with streaming\n",
|
||||
"\n",
|
||||
"Of course the chatbot interface also supports streaming.\n",
|
||||
"Instead of the `invoke` method you simply use `stream`:"
|
||||
"Of course, the chatbot interface also supports streaming.\n",
|
||||
"Instead of the `invoke` method, you simply use `stream`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -279,12 +279,15 @@
|
||||
"cell_type": "markdown",
|
||||
"id": "cefdf72b1d90085a",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## Chaining\n",
|
||||
"\n",
|
||||
"For additional capabilities you can use chaining."
|
||||
"For additional capabilities, you can use chaining."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -344,7 +347,10 @@
|
||||
"cell_type": "markdown",
|
||||
"id": "3b8bb761-db4a-436c-8939-41e9f8652083",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
"collapsed": false,
|
||||
"jupyter": {
|
||||
"outputs_hidden": false
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## API reference\n",
|
||||
@ -369,7 +375,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.0"
|
||||
"version": "3.12.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -20,7 +20,7 @@
|
||||
"vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
"This will help you getting started with vLLM [chat models](/docs/concepts/chat_models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
|
||||
"This will help you get started with vLLM [chat models](/docs/concepts/chat_models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
|
||||
"\n",
|
||||
"### Integration details\n",
|
||||
"\n",
|
||||
|
||||
@ -18,7 +18,7 @@
|
||||
"# ChatXAI\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"This page will help you get started with xAI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all `ChatXAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html).\n",
|
||||
"This page will help you get started with xAI [chat models](../../concepts/chat_models.mdx). For detailed documentation of all `ChatXAI` features and configurations, head to the [API reference](https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html).\n",
|
||||
"\n",
|
||||
"[xAI](https://console.x.ai/) offers an API to interact with Grok models.\n",
|
||||
"\n",
|
||||
@ -36,11 +36,11 @@
|
||||
"\n",
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"To access xAI models you'll need to create an xAI account, get an API key, and install the `langchain-xai` integration package.\n",
|
||||
"To access xAI models, you'll need to create an xAI account, get an API key, and install the `langchain-xai` integration package.\n",
|
||||
"\n",
|
||||
"### Credentials\n",
|
||||
"\n",
|
||||
"Head to [this page](https://console.x.ai/) to sign up for xAI and generate an API key. Once you've done this set the `XAI_API_KEY` environment variable:"
|
||||
"Head to [this page](https://console.x.ai/) to sign up for xAI and generate an API key. Once you've done this, set the `XAI_API_KEY` environment variable:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -243,7 +243,7 @@
|
||||
"\n",
|
||||
"### ChatXAI.bind_tools()\n",
|
||||
"\n",
|
||||
"With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an OpenAI tool schemas, which looks like:\n",
|
||||
"With `ChatXAI.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood, these are converted to an OpenAI tool schema, which looks like:\n",
|
||||
"```\n",
|
||||
"{\n",
|
||||
" \"name\": \"...\",\n",
|
||||
@ -345,7 +345,7 @@
|
||||
"source": [
|
||||
"## API reference\n",
|
||||
"\n",
|
||||
"For detailed documentation of all `ChatXAI` features and configurations head to the API reference: https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html"
|
||||
"For detailed documentation of all `ChatXAI` features and configurations, head to the API reference: https://python.langchain.com/api_reference/xai/chat_models/langchain_xai.chat_models.ChatXAI.html"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -365,7 +365,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.4"
|
||||
"version": "3.12.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -6,7 +6,7 @@
|
||||
"source": [
|
||||
"# ChatYI\n",
|
||||
"\n",
|
||||
"This will help you getting started with Yi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
|
||||
"This will help you get started with Yi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
|
||||
"\n",
|
||||
"[01.AI](https://www.lingyiwanwu.com/en), founded by Dr. Kai-Fu Lee, is a global company at the forefront of AI 2.0. They offer cutting-edge large language models, including the Yi series, which range from 6B to hundreds of billions of parameters. 01.AI also provides multimodal models, an open API platform, and open-source options like Yi-34B/9B/6B and Yi-VL.\n",
|
||||
"\n",
|
||||
|
||||
@ -6,7 +6,7 @@
|
||||
"source": [
|
||||
"# NVIDIA\n",
|
||||
"\n",
|
||||
"This will help you getting started with NVIDIA [models](/docs/concepts/text_llms). For detailed documentation of all `NVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/llms/langchain_nvidia_ai_endpoints.chat_models.NVIDIA.html).\n",
|
||||
"This will help you get started with NVIDIA [models](/docs/concepts/text_llms). For detailed documentation of all `NVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/llms/langchain_nvidia_ai_endpoints.chat_models.NVIDIA.html).\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
"The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on \n",
|
||||
|
||||
@ -6,7 +6,7 @@
|
||||
"source": [
|
||||
"# Outlines\n",
|
||||
"\n",
|
||||
"This will help you getting started with Outlines LLM. For detailed documentation of all Outlines features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.outlines.Outlines.html).\n",
|
||||
"This will help you get started with Outlines LLM. For detailed documentation of all Outlines features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.outlines.Outlines.html).\n",
|
||||
"\n",
|
||||
"[Outlines](https://github.com/outlines-dev/outlines) is a library for constrained language generation. It allows you to use large language models (LLMs) with various backends while applying constraints to the generated output.\n",
|
||||
"\n",
|
||||
|
||||
@ -68,7 +68,7 @@
|
||||
"source": [
|
||||
"## Document Compressor\n",
|
||||
"\n",
|
||||
"We can also use RAGatouille off-the-shelf as a reranker. This will allow us to use ColBERT to rerank retrieved results from any generic retriever. The benefits of this are that we can do this on top of any existing index, so that we don't need to create a new idex. We can do this by using the [document compressor](/docs/how_to/contextual_compression) abstraction in LangChain."
|
||||
"We can also use RAGatouille off-the-shelf as a reranker. This will allow us to use ColBERT to rerank retrieved results from any generic retriever. The benefits of this are that we can do this on top of any existing index, so that we don't need to create a new index. We can do this by using the [document compressor](/docs/how_to/contextual_compression) abstraction in LangChain."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@ -21,7 +21,7 @@
|
||||
"\n",
|
||||
"`AzureAISearchRetriever` is an integration module that returns documents from an unstructured query. It's based on the BaseRetriever class and it targets the 2023-11-01 stable REST API version of Azure AI Search, which means it supports vector indexing and queries.\n",
|
||||
"\n",
|
||||
"This guide will help you getting started with the Azure AI Search [retriever](/docs/concepts/retrievers). For detailed documentation of all `AzureAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html).\n",
|
||||
"This guide will help you get started with the Azure AI Search [retriever](/docs/concepts/retrievers). For detailed documentation of all `AzureAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html).\n",
|
||||
"\n",
|
||||
"`AzureAISearchRetriever` replaces `AzureCognitiveSearchRetriever`, which will soon be deprecated. We recommend switching to the newer version that's based on the most recent stable version of the search APIs.\n",
|
||||
"\n",
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
"source": [
|
||||
"# Bedrock (Knowledge Bases) Retriever\n",
|
||||
"\n",
|
||||
"This guide will help you getting started with the AWS Knowledge Bases [retriever](/docs/concepts/retrievers).\n",
|
||||
"This guide will help you get started with the AWS Knowledge Bases [retriever](/docs/concepts/retrievers).\n",
|
||||
"\n",
|
||||
"[Knowledge Bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an Amazon Web Services (AWS) offering which lets you quickly build RAG applications by using your private data to customize FM response.\n",
|
||||
"\n",
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
"source": [
|
||||
"# BoxRetriever\n",
|
||||
"\n",
|
||||
"This will help you getting started with the Box [retriever](/docs/concepts/retrievers). For detailed documentation of all BoxRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html).\n",
|
||||
"This will help you get started with the Box [retriever](/docs/concepts/retrievers). For detailed documentation of all BoxRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html).\n",
|
||||
"\n",
|
||||
"# Overview\n",
|
||||
"\n",
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
"source": [
|
||||
"# CogneeRetriever\n",
|
||||
"\n",
|
||||
"This will help you getting started with the Cognee [retriever](/docs/concepts/retrievers). For detailed documentation of all CogneeRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.cognee.CogneeRetriever.html).\n",
|
||||
"This will help you get started with the Cognee [retriever](/docs/concepts/retrievers). For detailed documentation of all CogneeRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.cognee.CogneeRetriever.html).\n",
|
||||
"\n",
|
||||
"### Integration details\n",
|
||||
"\n",
|
||||
|
||||
@ -23,7 +23,7 @@
|
||||
"source": [
|
||||
"# DappierRetriever\n",
|
||||
"\n",
|
||||
"This will help you getting started with the Dappier [retriever](https://python.langchain.com/docs/concepts/retrievers/). For detailed documentation of all DappierRetriever features and configurations head to the [API reference](https://python.langchain.com/en/latest/retrievers/langchain_dappier.retrievers.Dappier.DappierRetriever.html).\n",
|
||||
"This will help you get started with the Dappier [retriever](https://python.langchain.com/docs/concepts/retrievers/). For detailed documentation of all DappierRetriever features and configurations head to the [API reference](https://python.langchain.com/en/latest/retrievers/langchain_dappier.retrievers.Dappier.DappierRetriever.html).\n",
|
||||
"\n",
|
||||
"### Setup\n",
|
||||
"\n",
|
||||
|
||||
@ -21,7 +21,7 @@
|
||||
"\n",
|
||||
"The `ElasticsearchRetriever` is a generic wrapper to enable flexible access to all `Elasticsearch` features through the [Query DSL](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html). For most use cases the other classes (`ElasticsearchStore`, `ElasticsearchEmbeddings`, etc.) should suffice, but if they don't you can use `ElasticsearchRetriever`.\n",
|
||||
"\n",
|
||||
"This guide will help you getting started with the Elasticsearch [retriever](/docs/concepts/retrievers). For detailed documentation of all `ElasticsearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/elasticsearch/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html).\n",
|
||||
"This guide will help you get started with the Elasticsearch [retriever](/docs/concepts/retrievers). For detailed documentation of all `ElasticsearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/elasticsearch/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html).\n",
|
||||
"\n",
|
||||
"### Integration details\n",
|
||||
"\n",
|
||||
|
||||
@ -19,7 +19,7 @@
|
||||
"\n",
|
||||
"> [Linkup](https://www.linkup.so/) provides an API to connect LLMs to the web and the Linkup Premium Partner sources.\n",
|
||||
"\n",
|
||||
"This will help you getting started with the LinkupSearchRetriever [retriever](/docs/concepts/retrievers/). For detailed documentation of all LinkupSearchRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/linkup/retrievers/linkup_langchain.search_retriever.LinkupSearchRetriever.html).\n",
|
||||
"This will help you get started with the LinkupSearchRetriever [retriever](/docs/concepts/retrievers/). For detailed documentation of all LinkupSearchRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/linkup/retrievers/linkup_langchain.search_retriever.LinkupSearchRetriever.html).\n",
|
||||
"\n",
|
||||
"### Integration details\n",
|
||||
"\n",
|
||||
|
||||
@ -21,7 +21,7 @@
|
||||
"source": [
|
||||
"# ZoteroRetriever\n",
|
||||
"\n",
|
||||
"This will help you getting started with the Zotero [retriever](/docs/concepts/retrievers). For detailed documentation of all ZoteroRetriever features and configurations head to the [Github page](https://github.com/TimBMK/langchain-zotero-retriever).\n",
|
||||
"This will help you get started with the Zotero [retriever](/docs/concepts/retrievers). For detailed documentation of all ZoteroRetriever features and configurations head to the [Github page](https://github.com/TimBMK/langchain-zotero-retriever).\n",
|
||||
"\n",
|
||||
"### Integration details\n",
|
||||
"\n",
|
||||
|
||||
@ -21,7 +21,7 @@
|
||||
"source": [
|
||||
"# SambaNovaCloudEmbeddings\n",
|
||||
"\n",
|
||||
"This will help you getting started with SambaNovaCloud embedding models using LangChain. For detailed documentation on `SambaNovaCloudEmbeddings` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).\n",
|
||||
"This will help you get started with SambaNovaCloud embedding models using LangChain. For detailed documentation on `SambaNovaCloudEmbeddings` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).\n",
|
||||
"\n",
|
||||
"**[SambaNova](https://sambanova.ai/)'s** [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models\n",
|
||||
"\n",
|
||||
|
||||
@ -21,7 +21,7 @@
|
||||
"source": [
|
||||
"# Dappier Tool\n",
|
||||
"\n",
|
||||
"This will help you getting started with the Dappier [tool](https://python.langchain.com/docs/concepts/tools/). For detailed documentation of all DappierRetriever features and configurations head to the [API reference](https://python.langchain.com/en/latest/tools/langchain_dappier.tools.Dappier.DappierRealTimeSearchTool.html).\n",
|
||||
"This will help you get started with the Dappier [tool](https://python.langchain.com/docs/concepts/tools/). For detailed documentation of all DappierRetriever features and configurations head to the [API reference](https://python.langchain.com/en/latest/tools/langchain_dappier.tools.Dappier.DappierRealTimeSearchTool.html).\n",
|
||||
"\n",
|
||||
"## Overview\n",
|
||||
"\n",
|
||||
|
||||
@ -6,7 +6,7 @@
|
||||
"source": [
|
||||
"# Gmail Toolkit\n",
|
||||
"\n",
|
||||
"This will help you getting started with the GMail [toolkit](/docs/concepts/tools/#toolkits). This toolkit interacts with the GMail API to read messages, draft and send messages, and more. For detailed documentation of all GmailToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.toolkit.GmailToolkit.html).\n",
|
||||
"This will help you get started with the GMail [toolkit](/docs/concepts/tools/#toolkits). This toolkit interacts with the GMail API to read messages, draft and send messages, and more. For detailed documentation of all GmailToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.toolkit.GmailToolkit.html).\n",
|
||||
"\n",
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
"source": [
|
||||
"# MemgraphToolkit\n",
|
||||
"\n",
|
||||
"This will help you getting started with the Memgraph [toolkit](/docs/concepts/tools/#toolkits). \n",
|
||||
"This will help you get started with the Memgraph [toolkit](/docs/concepts/tools/#toolkits). \n",
|
||||
"\n",
|
||||
"Tools within `MemgraphToolkit` are designed for the interaction with the `Memgraph` database.\n",
|
||||
"\n",
|
||||
|
||||
@ -6,7 +6,7 @@
|
||||
"source": [
|
||||
"# Slack Toolkit\n",
|
||||
"\n",
|
||||
"This will help you getting started with the Slack [toolkit](/docs/concepts/tools/#toolkits). For detailed documentation of all SlackToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html).\n",
|
||||
"This will help you get started with the Slack [toolkit](/docs/concepts/tools/#toolkits). For detailed documentation of all SlackToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html).\n",
|
||||
"\n",
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
|
||||
@ -7,7 +7,7 @@
|
||||
"source": [
|
||||
"# SQLDatabase Toolkit\n",
|
||||
"\n",
|
||||
"This will help you getting started with the SQL Database [toolkit](/docs/concepts/tools/#toolkits). For detailed documentation of all `SQLDatabaseToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.html).\n",
|
||||
"This will help you get started with the SQL Database [toolkit](/docs/concepts/tools/#toolkits). For detailed documentation of all `SQLDatabaseToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.html).\n",
|
||||
"\n",
|
||||
"Tools within the `SQLDatabaseToolkit` are designed to interact with a `SQL` database. \n",
|
||||
"\n",
|
||||
|
||||
@ -30,8 +30,6 @@ From time to time, we will version packages as **release candidates**. These are
|
||||
|
||||
### `langchain-community`
|
||||
|
||||
`langchain-community` is currently on version `0.2.x`.
|
||||
|
||||
Minor version increases will occur for:
|
||||
|
||||
- Updates to the major/minor versions of required `langchain-x` dependencies. E.g., when updating the required version of `langchain-core` from `^0.2.x` to `0.3.0`.
|
||||
@ -49,7 +47,7 @@ However, if an external API makes a breaking change then breaking changes to the
|
||||
|
||||
### `langchain-experimental`
|
||||
|
||||
`langchain-experimental` is currently on version `0.0.x`. All changes will be accompanied with patch version increases.
|
||||
All changes will be accompanied with patch version increases.
|
||||
|
||||
## Release cadence
|
||||
|
||||
@ -99,4 +97,4 @@ When a feature is deprecated, it will continue to work in the current and next m
|
||||
|
||||
Since we're expecting to space out minor releases by at least 2-3 months, this means that a feature can be removed within 2-6 months of being deprecated.
|
||||
|
||||
In some situations, we may allow the feature to remain in the code base for longer periods of time, if it's not causing issues in the packages, to reduce the burden on users.
|
||||
In some situations, we may allow the feature to remain in the code base for longer periods of time, if it's not causing issues in the packages, to reduce the burden on users.
|
||||
|
||||
@ -19,7 +19,7 @@
|
||||
"\n",
|
||||
"- TODO: Make sure API reference link is correct.\n",
|
||||
"\n",
|
||||
"This will help you getting started with __ModuleName__ [chat models](/docs/concepts/chat_models). For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html).\n",
|
||||
"This will help you get started with __ModuleName__ [chat models](/docs/concepts/chat_models). For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html).\n",
|
||||
"\n",
|
||||
"- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/docs/integrations/chat/openai/ for an example.\n",
|
||||
"\n",
|
||||
|
||||
@ -19,7 +19,7 @@
|
||||
"\n",
|
||||
"- TODO: Make sure API reference link is correct.\n",
|
||||
"\n",
|
||||
"This will help you getting started with the __ModuleName__ [retriever](/docs/concepts/retrievers). For detailed documentation of all __ModuleName__Retriever features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/retrievers/__module_name__.retrievers.__ModuleName__.__ModuleName__Retriever.html).\n",
|
||||
"This will help you get started with the __ModuleName__ [retriever](/docs/concepts/retrievers). For detailed documentation of all __ModuleName__Retriever features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/retrievers/__module_name__.retrievers.__ModuleName__.__ModuleName__Retriever.html).\n",
|
||||
"\n",
|
||||
"### Integration details\n",
|
||||
"\n",
|
||||
|
||||
@ -19,7 +19,7 @@
|
||||
"\n",
|
||||
"- TODO: Make sure API reference link is correct.\n",
|
||||
"\n",
|
||||
"This will help you getting started with the __ModuleName__ [toolkit](/docs/concepts/tools/#toolkits). For detailed documentation of all __ModuleName__Toolkit features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/agent_toolkits/__module_name__.agent_toolkits.__ModuleName__.toolkit.__ModuleName__Toolkit.html).\n",
|
||||
"This will help you get started with the __ModuleName__ [toolkit](/docs/concepts/tools/#toolkits). For detailed documentation of all __ModuleName__Toolkit features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/agent_toolkits/__module_name__.agent_toolkits.__ModuleName__.toolkit.__ModuleName__Toolkit.html).\n",
|
||||
"\n",
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
|
||||
@ -2129,6 +2129,13 @@ def _create_usage_metadata(anthropic_usage: BaseModel) -> UsageMetadata:
|
||||
"cache_read": getattr(anthropic_usage, "cache_read_input_tokens", None),
|
||||
"cache_creation": getattr(anthropic_usage, "cache_creation_input_tokens", None),
|
||||
}
|
||||
# Add (beta) cache TTL information if available
|
||||
cache_creation = getattr(anthropic_usage, "cache_creation", None)
|
||||
if cache_creation:
|
||||
for k in ["ephemeral_1h_input_tokens", "ephemeral_5m_input_tokens"]:
|
||||
v = getattr(cache_creation, k, None)
|
||||
if v:
|
||||
input_token_details[k] = v
|
||||
|
||||
# Anthropic input_tokens exclude cached token counts.
|
||||
input_tokens = (
|
||||
|
||||
@ -24,6 +24,7 @@ from pydantic.v1 import (
|
||||
ValidationError as ValidationErrorV1,
|
||||
)
|
||||
from pytest_benchmark.fixture import BenchmarkFixture # type: ignore[import-untyped]
|
||||
from pytest_benchmark.fixture import BenchmarkFixture # type: ignore[import-untyped]
|
||||
from syrupy import SnapshotAssertion
|
||||
|
||||
from langchain_tests.base import BaseStandardTests
|
||||
@ -211,6 +212,16 @@ class ChatModelTests(BaseStandardTests):
|
||||
"""
|
||||
return False
|
||||
|
||||
@property
|
||||
def enable_vcr_tests(self) -> bool:
|
||||
"""(bool) whether to enable VCR tests for the chat model.
|
||||
|
||||
.. important::
|
||||
See ``enable_vcr_tests`` dropdown :class:`above <ChatModelTests>` for more
|
||||
information.
|
||||
"""
|
||||
return False
|
||||
|
||||
@property
|
||||
def supported_usage_metadata_details(
|
||||
self,
|
||||
@ -768,6 +779,151 @@ class ChatModelUnitTests(ChatModelTests):
|
||||
Makefile commands).
|
||||
|
||||
|
||||
.. dropdown:: enable_vcr_tests
|
||||
|
||||
Property controlling whether to enable select tests that rely on
|
||||
`VCR <https://vcrpy.readthedocs.io/en/latest/>`_ caching of HTTP calls, such
|
||||
as benchmarking tests.
|
||||
|
||||
To enable these tests, follow these steps:
|
||||
|
||||
1. Override the ``enable_vcr_tests`` property to return ``True``:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
@property
|
||||
def enable_vcr_tests(self) -> bool:
|
||||
return True
|
||||
|
||||
2. Configure VCR to exclude sensitive headers and other information from cassettes.
|
||||
|
||||
.. important::
|
||||
VCR will by default record authentication headers and other sensitive
|
||||
information in cassettes. Read below for how to configure what
|
||||
information is recorded in cassettes.
|
||||
|
||||
To add configuration to VCR, add a ``conftest.py`` file to the ``tests/``
|
||||
directory and implement the ``vcr_config`` fixture there.
|
||||
|
||||
``langchain-tests`` excludes the headers ``"authorization"``,
|
||||
``"x-api-key"``, and ``"api-key"`` from VCR cassettes. To pick up this
|
||||
configuration, you will need to add ``conftest.py`` as shown below. You can
|
||||
also exclude additional headers, override the default exclusions, or apply
|
||||
other customizations to the VCR configuration. See example below:
|
||||
|
||||
.. code-block:: python
|
||||
:caption: tests/conftest.py
|
||||
|
||||
import pytest
|
||||
from langchain_tests.conftest import _base_vcr_config as _base_vcr_config
|
||||
|
||||
_EXTRA_HEADERS = [
|
||||
# Specify additional headers to redact
|
||||
("user-agent", "PLACEHOLDER"),
|
||||
]
|
||||
|
||||
|
||||
def remove_response_headers(response: dict) -> dict:
|
||||
# If desired, remove or modify headers in the response.
|
||||
response["headers"] = {}
|
||||
return response
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def vcr_config(_base_vcr_config: dict) -> dict: # noqa: F811
|
||||
\"\"\"Extend the default configuration from langchain_tests.\"\"\"
|
||||
config = _base_vcr_config.copy()
|
||||
config.setdefault("filter_headers", []).extend(_EXTRA_HEADERS)
|
||||
config["before_record_response"] = remove_response_headers
|
||||
|
||||
return config
|
||||
|
||||
.. dropdown:: Compressing cassettes
|
||||
|
||||
``langchain-tests`` includes a custom VCR serializer that compresses
|
||||
cassettes using gzip. To use it, register the ``"yaml.gz"`` serializer
|
||||
to your VCR fixture and enable this serializer in the config. See
|
||||
example below:
|
||||
|
||||
.. code-block:: python
|
||||
:caption: tests/conftest.py
|
||||
|
||||
import pytest
|
||||
from langchain_tests.conftest import YamlGzipSerializer
|
||||
from langchain_tests.conftest import _base_vcr_config as _base_vcr_config
|
||||
from vcr import VCR
|
||||
|
||||
_EXTRA_HEADERS = [
|
||||
# Specify additional headers to redact
|
||||
("user-agent", "PLACEHOLDER"),
|
||||
]
|
||||
|
||||
|
||||
def remove_response_headers(response: dict) -> dict:
|
||||
# If desired, remove or modify headers in the response.
|
||||
response["headers"] = {}
|
||||
return response
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def vcr_config(_base_vcr_config: dict) -> dict: # noqa: F811
|
||||
\"\"\"Extend the default configuration from langchain_tests.\"\"\"
|
||||
config = _base_vcr_config.copy()
|
||||
config.setdefault("filter_headers", []).extend(_EXTRA_HEADERS)
|
||||
config["before_record_response"] = remove_response_headers
|
||||
# New: enable serializer and set file extension
|
||||
config["serializer"] = "yaml.gz"
|
||||
config["path_transformer"] = VCR.ensure_suffix(".yaml.gz")
|
||||
|
||||
return config
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def vcr(vcr_config: dict) -> VCR:
|
||||
\"\"\"Override the default vcr fixture to include custom serializers\"\"\"
|
||||
my_vcr = VCR(**vcr_config)
|
||||
my_vcr.register_serializer("yaml.gz", YamlGzipSerializer)
|
||||
return my_vcr
|
||||
|
||||
You can inspect the contents of the compressed cassettes (e.g., to
|
||||
ensure no sensitive information is recorded) using the serializer:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_tests.conftest import YamlGzipSerializer
|
||||
|
||||
with open("/path/to/tests/cassettes/TestClass_test.yaml.gz", "r") as f:
|
||||
data = f.read()
|
||||
|
||||
YamlGzipSerializer.deserialize(data)
|
||||
|
||||
3. Run tests to generate VCR cassettes.
|
||||
|
||||
Example:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
uv run python -m pytest tests/integration_tests/test_chat_models.py::TestMyModel::test_stream_time
|
||||
|
||||
This will generate a VCR cassette for the test in
|
||||
``tests/integration_tests/cassettes/``.
|
||||
|
||||
.. important::
|
||||
You should inspect the generated cassette to ensure that it does not
|
||||
contain sensitive information. If it does, you can modify the
|
||||
``vcr_config`` fixture to exclude headers or modify the response
|
||||
before it is recorded.
|
||||
|
||||
You can then commit the cassette to your repository. Subsequent test runs
|
||||
will use the cassette instead of making HTTP calls.
|
||||
|
||||
.. tip::
|
||||
Adding ``--vcr-record=none`` to the pytest command will ensure that
|
||||
no new cassettes are recorded, and only existing cassettes are used.
|
||||
Consider adding this to your CI configuration (e.g., modify relevant
|
||||
Makefile commands).
|
||||
|
||||
|
||||
Testing initialization from environment variables
|
||||
Some unit tests may require testing initialization from environment variables.
|
||||
These tests can be enabled by overriding the ``init_from_env_params``
|
||||
|
||||
Loading…
Reference in New Issue
Block a user