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langchain_md_files/_templates/integration.mdx

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-[comment: Please, a reference example here "docs/integrations/arxiv.md"]::
-[comment: Use this template to create a new .md file in "docs/integrations/"]::
-
-# Title_REPLACE_ME
-
-[comment: Only one Tile/H1 is allowed!]::
-
->
-[comment: Description: After reading this description, a reader should decide if this integration is good enough to try/follow reading OR]::
-[comment: go to read the next integration doc. ]::
-[comment: Description should include a link to the source for follow reading.]::
-
-## Installation and Setup
-
-[comment: Installation and Setup: All necessary additional package installations and setups for Tokens, etc]::
-
-```bash
-pip install package_name_REPLACE_ME
-```
-
-[comment: OR this text:]::
-
-There isn't any special setup for it.
-
-[comment: The next H2/## sections with names of the integration modules, like "LLM", "Text Embedding Models", etc]::
-[comment: see "Modules" in the "index.html" page]::
-[comment: Each H2 section should include a link to an example(s) and a Python code with the import of the integration class]::
-[comment: Below are several example sections. Remove all unnecessary sections. Add all necessary sections not provided here.]::
-
-## LLM
-
-See a [usage example](/docs/integrations/llms/INCLUDE_REAL_NAME).
-
-```python
-from langchain_community.llms import integration_class_REPLACE_ME
-```
-
-## Text Embedding Models
-
-See a [usage example](/docs/integrations/text_embedding/INCLUDE_REAL_NAME).
-
-```python
-from langchain_community.embeddings import integration_class_REPLACE_ME
-```
-
-## Chat models
-
-See a [usage example](/docs/integrations/chat/INCLUDE_REAL_NAME).
-
-```python
-from langchain_community.chat_models import integration_class_REPLACE_ME
-```
-
-## Document Loader
-
-See a [usage example](/docs/integrations/document_loaders/INCLUDE_REAL_NAME).
-
-```python
-from langchain_community.document_loaders import integration_class_REPLACE_ME
-```

langchain_md_files/additional_resources/arxiv_references.mdx

@@ -1,1101 +0,0 @@
-# arXiv
-            
-LangChain implements the latest research in the field of Natural Language Processing.
-This page contains `arXiv` papers referenced in the LangChain Documentation, API Reference,
- Templates, and Cookbooks.
-
-From the opposite direction, scientists use `LangChain` in research and reference it in the research papers. 
-
-`arXiv` papers with references to:
- [LangChain](https://arxiv.org/search/?query=langchain&searchtype=all&source=header) | [LangGraph](https://arxiv.org/search/?query=langgraph&searchtype=all&source=header) | [LangSmith](https://arxiv.org/search/?query=langsmith&searchtype=all&source=header)
-
-## Summary
-
-| arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation|
-|------------------|---------|-------------------|------------------------|
-| `2403.14403v2` [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](http://arxiv.org/abs/2403.14403v2) | Soyeong Jeong, Jinheon Baek, Sukmin Cho,  et al. | 2024‑03‑21 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
-| `2402.03620v1` [Self-Discover: Large Language Models Self-Compose Reasoning Structures](http://arxiv.org/abs/2402.03620v1) | Pei Zhou, Jay Pujara, Xiang Ren,  et al. | 2024‑02‑06 | `Cookbook:` [Self-Discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
-| `2402.03367v2` [RAG-Fusion: a New Take on Retrieval-Augmented Generation](http://arxiv.org/abs/2402.03367v2) | Zackary Rackauckas | 2024‑01‑31 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
-| `2401.18059v1` [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](http://arxiv.org/abs/2401.18059v1) | Parth Sarthi, Salman Abdullah, Aditi Tuli,  et al. | 2024‑01‑31 | `Cookbook:` [Raptor](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
-| `2401.15884v2` [Corrective Retrieval Augmented Generation](http://arxiv.org/abs/2401.15884v2) | Shi-Qi Yan, Jia-Chen Gu, Yun Zhu,  et al. | 2024‑01‑29 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts), `Cookbook:` [Langgraph Crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
-| `2401.08500v1` [Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering](http://arxiv.org/abs/2401.08500v1) | Tal Ridnik, Dedy Kredo, Itamar Friedman | 2024‑01‑16 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
-| `2401.04088v1` [Mixtral of Experts](http://arxiv.org/abs/2401.04088v1) | Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux,  et al. | 2024‑01‑08 | `Cookbook:` [Together Ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
-| `2312.06648v2` [Dense X Retrieval: What Retrieval Granularity Should We Use?](http://arxiv.org/abs/2312.06648v2) | Tong Chen, Hongwei Wang, Sihao Chen,  et al. | 2023‑12‑11 | `Template:` [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
-| `2311.09210v1` [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](http://arxiv.org/abs/2311.09210v1) | Wenhao Yu, Hongming Zhang, Xiaoman Pan,  et al. | 2023‑11‑15 | `Template:` [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
-| `2310.11511v1` [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](http://arxiv.org/abs/2310.11511v1) | Akari Asai, Zeqiu Wu, Yizhong Wang,  et al. | 2023‑10‑17 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts), `Cookbook:` [Langgraph Self Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
-| `2310.06117v2` [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](http://arxiv.org/abs/2310.06117v2) | Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al. | 2023‑10‑09 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts), `Template:` [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting), `Cookbook:` [Stepback-Qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
-| `2307.15337v3` [Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation](http://arxiv.org/abs/2307.15337v3) | Xuefei Ning, Zinan Lin, Zixuan Zhou,  et al. | 2023‑07‑28 | `Template:` [skeleton-of-thought](https://python.langchain.com/docs/templates/skeleton-of-thought)
-| `2307.09288v2` [Llama 2: Open Foundation and Fine-Tuned Chat Models](http://arxiv.org/abs/2307.09288v2) | Hugo Touvron, Louis Martin, Kevin Stone,  et al. | 2023‑07‑18 | `Cookbook:` [Semi Structured Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
-| `2307.03172v3` [Lost in the Middle: How Language Models Use Long Contexts](http://arxiv.org/abs/2307.03172v3) | Nelson F. Liu, Kevin Lin, John Hewitt,  et al. | 2023‑07‑06 | `Docs:` [docs/how_to/long_context_reorder](https://python.langchain.com/docs/how_to/long_context_reorder)
-| `2305.14283v3` [Query Rewriting for Retrieval-Augmented Large Language Models](http://arxiv.org/abs/2305.14283v3) | Xinbei Ma, Yeyun Gong, Pengcheng He,  et al. | 2023‑05‑23 | `Template:` [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read), `Cookbook:` [Rewrite](https://github.com/langchain-ai/langchain/blob/master/cookbook/rewrite.ipynb)
-| `2305.08291v1` [Large Language Model Guided Tree-of-Thought](http://arxiv.org/abs/2305.08291v1) | Jieyi Long | 2023‑05‑15 | `API:` [langchain_experimental.tot](https://python.langchain.com/api_reference/experimental/tot.html), `Cookbook:` [Tree Of Thought](https://github.com/langchain-ai/langchain/blob/master/cookbook/tree_of_thought.ipynb)
-| `2305.04091v3` [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](http://arxiv.org/abs/2305.04091v3) | Lei Wang, Wanyu Xu, Yihuai Lan,  et al. | 2023‑05‑06 | `Cookbook:` [Plan And Execute Agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
-| `2305.02156v1` [Zero-Shot Listwise Document Reranking with a Large Language Model](http://arxiv.org/abs/2305.02156v1) | Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al. | 2023‑05‑03 | `Docs:` [docs/how_to/contextual_compression](https://python.langchain.com/docs/how_to/contextual_compression), `API:` [langchain...LLMListwiseRerank](https://python.langchain.com/api_reference/langchain/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#)
-| `2304.08485v2` [Visual Instruction Tuning](http://arxiv.org/abs/2304.08485v2) | Haotian Liu, Chunyuan Li, Qingyang Wu,  et al. | 2023‑04‑17 | `Cookbook:` [Semi Structured Multi Modal Rag Llama2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb), [Semi Structured And Multi Modal Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb)
-| `2304.03442v2` [Generative Agents: Interactive Simulacra of Human Behavior](http://arxiv.org/abs/2304.03442v2) | Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai,  et al. | 2023‑04‑07 | `Cookbook:` [Generative Agents Interactive Simulacra Of Human Behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb), [Multiagent Bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb)
-| `2303.17760v2` [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](http://arxiv.org/abs/2303.17760v2) | Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani,  et al. | 2023‑03‑31 | `Cookbook:` [Camel Role Playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
-| `2303.17580v4` [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](http://arxiv.org/abs/2303.17580v4) | Yongliang Shen, Kaitao Song, Xu Tan,  et al. | 2023‑03‑30 | `API:` [langchain_experimental.autonomous_agents](https://python.langchain.com/api_reference/experimental/autonomous_agents.html), `Cookbook:` [Hugginggpt](https://github.com/langchain-ai/langchain/blob/master/cookbook/hugginggpt.ipynb)
-| `2301.10226v4` [A Watermark for Large Language Models](http://arxiv.org/abs/2301.10226v4) | John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al. | 2023‑01‑24 | `API:` [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-| `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin,  et al. | 2022‑12‑20 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts), `API:` [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder), `Template:` [hyde](https://python.langchain.com/docs/templates/hyde), `Cookbook:` [Hypothetical Document Embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
-| `2212.08073v1` [Constitutional AI: Harmlessness from AI Feedback](http://arxiv.org/abs/2212.08073v1) | Yuntao Bai, Saurav Kadavath, Sandipan Kundu,  et al. | 2022‑12‑15 | `Docs:` [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain)
-| `2212.07425v3` [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](http://arxiv.org/abs/2212.07425v3) | Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al. | 2022‑12‑12 | `API:` [langchain_experimental.fallacy_removal](https://python.langchain.com/api_reference/experimental/fallacy_removal.html)
-| `2211.13892v2` [Complementary Explanations for Effective In-Context Learning](http://arxiv.org/abs/2211.13892v2) | Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al. | 2022‑11‑25 | `API:` [langchain_core...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
-| `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou,  et al. | 2022‑11‑18 | `API:` [langchain_experimental.pal_chain](https://python.langchain.com/api_reference/experimental/pal_chain.html), [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), `Cookbook:` [Program Aided Language Model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
-| `2210.11934v2` [An Analysis of Fusion Functions for Hybrid Retrieval](http://arxiv.org/abs/2210.11934v2) | Sebastian Bruch, Siyu Gai, Amir Ingber | 2022‑10‑21 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
-| `2210.03629v3` [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629v3) | Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al. | 2022‑10‑06 | `Docs:` [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/concepts](https://python.langchain.com/docs/concepts), `API:` [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
-| `2209.10785v2` [Deep Lake: a Lakehouse for Deep Learning](http://arxiv.org/abs/2209.10785v2) | Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al. | 2022‑09‑22 | `Docs:` [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
-| `2205.13147v4` [Matryoshka Representation Learning](http://arxiv.org/abs/2205.13147v4) | Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al. | 2022‑05‑26 | `Docs:` [docs/integrations/providers/snowflake](https://python.langchain.com/docs/integrations/providers/snowflake)
-| `2205.12654v1` [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](http://arxiv.org/abs/2205.12654v1) | Kevin Heffernan, Onur Çelebi, Holger Schwenk | 2022‑05‑25 | `API:` [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
-| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022‑03‑15 | `Docs:` [docs/tutorials/sql_qa](https://python.langchain.com/docs/tutorials/sql_qa), `API:` [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
-| `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher,  et al. | 2022‑02‑01 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-| `2112.01488v3` [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](http://arxiv.org/abs/2112.01488v3) | Keshav Santhanam, Omar Khattab, Jon Saad-Falcon,  et al. | 2021‑12‑02 | `Docs:` [docs/integrations/retrievers/ragatouille](https://python.langchain.com/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/docs/integrations/providers/dspy)
-| `2103.00020v1` [Learning Transferable Visual Models From Natural Language Supervision](http://arxiv.org/abs/2103.00020v1) | Alec Radford, Jong Wook Kim, Chris Hallacy,  et al. | 2021‑02‑26 | `API:` [langchain_experimental.open_clip](https://python.langchain.com/api_reference/experimental/open_clip.html)
-| `2005.14165v4` [Language Models are Few-Shot Learners](http://arxiv.org/abs/2005.14165v4) | Tom B. Brown, Benjamin Mann, Nick Ryder,  et al. | 2020‑05‑28 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
-| `2005.11401v4` [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](http://arxiv.org/abs/2005.11401v4) | Patrick Lewis, Ethan Perez, Aleksandra Piktus,  et al. | 2020‑05‑22 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
-| `1909.05858v2` [CTRL: A Conditional Transformer Language Model for Controllable Generation](http://arxiv.org/abs/1909.05858v2) | Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al. | 2019‑09‑11 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-
-## Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
-
-- **Authors:** Soyeong Jeong, Jinheon Baek, Sukmin Cho,  et al.
-- **arXiv id:** [2403.14403v2](http://arxiv.org/abs/2403.14403v2)  **Published Date:** 2024-03-21
-- **LangChain:**
-
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
-
-**Abstract:** Retrieval-Augmented Large Language Models (LLMs), which incorporate the
-non-parametric knowledge from external knowledge bases into LLMs, have emerged
-as a promising approach to enhancing response accuracy in several tasks, such
-as Question-Answering (QA). However, even though there are various approaches
-dealing with queries of different complexities, they either handle simple
-queries with unnecessary computational overhead or fail to adequately address
-complex multi-step queries; yet, not all user requests fall into only one of
-the simple or complex categories. In this work, we propose a novel adaptive QA
-framework, that can dynamically select the most suitable strategy for
-(retrieval-augmented) LLMs from the simplest to the most sophisticated ones
-based on the query complexity. Also, this selection process is operationalized
-with a classifier, which is a smaller LM trained to predict the complexity
-level of incoming queries with automatically collected labels, obtained from
-actual predicted outcomes of models and inherent inductive biases in datasets.
-This approach offers a balanced strategy, seamlessly adapting between the
-iterative and single-step retrieval-augmented LLMs, as well as the no-retrieval
-methods, in response to a range of query complexities. We validate our model on
-a set of open-domain QA datasets, covering multiple query complexities, and
-show that ours enhances the overall efficiency and accuracy of QA systems,
-compared to relevant baselines including the adaptive retrieval approaches.
-Code is available at: https://github.com/starsuzi/Adaptive-RAG.
-                
-## Self-Discover: Large Language Models Self-Compose Reasoning Structures
-
-- **Authors:** Pei Zhou, Jay Pujara, Xiang Ren,  et al.
-- **arXiv id:** [2402.03620v1](http://arxiv.org/abs/2402.03620v1)  **Published Date:** 2024-02-06
-- **LangChain:**
-
-   - **Cookbook:** [self-discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
-
-**Abstract:** We introduce SELF-DISCOVER, a general framework for LLMs to self-discover the
-task-intrinsic reasoning structures to tackle complex reasoning problems that
-are challenging for typical prompting methods. Core to the framework is a
-self-discovery process where LLMs select multiple atomic reasoning modules such
-as critical thinking and step-by-step thinking, and compose them into an
-explicit reasoning structure for LLMs to follow during decoding. SELF-DISCOVER
-substantially improves GPT-4 and PaLM 2's performance on challenging reasoning
-benchmarks such as BigBench-Hard, grounded agent reasoning, and MATH, by as
-much as 32% compared to Chain of Thought (CoT). Furthermore, SELF-DISCOVER
-outperforms inference-intensive methods such as CoT-Self-Consistency by more
-than 20%, while requiring 10-40x fewer inference compute. Finally, we show that
-the self-discovered reasoning structures are universally applicable across
-model families: from PaLM 2-L to GPT-4, and from GPT-4 to Llama2, and share
-commonalities with human reasoning patterns.
-                
-## RAG-Fusion: a New Take on Retrieval-Augmented Generation
-
-- **Authors:** Zackary Rackauckas
-- **arXiv id:** [2402.03367v2](http://arxiv.org/abs/2402.03367v2)  **Published Date:** 2024-01-31
-- **LangChain:**
-
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
-
-**Abstract:** Infineon has identified a need for engineers, account managers, and customers
-to rapidly obtain product information. This problem is traditionally addressed
-with retrieval-augmented generation (RAG) chatbots, but in this study, I
-evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion
-combines RAG and reciprocal rank fusion (RRF) by generating multiple queries,
-reranking them with reciprocal scores and fusing the documents and scores.
-Through manually evaluating answers on accuracy, relevance, and
-comprehensiveness, I found that RAG-Fusion was able to provide accurate and
-comprehensive answers due to the generated queries contextualizing the original
-query from various perspectives. However, some answers strayed off topic when
-the generated queries' relevance to the original query is insufficient. This
-research marks significant progress in artificial intelligence (AI) and natural
-language processing (NLP) applications and demonstrates transformations in a
-global and multi-industry context.
-                
-## RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
-
-- **Authors:** Parth Sarthi, Salman Abdullah, Aditi Tuli,  et al.
-- **arXiv id:** [2401.18059v1](http://arxiv.org/abs/2401.18059v1)  **Published Date:** 2024-01-31
-- **LangChain:**
-
-   - **Cookbook:** [RAPTOR](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
-
-**Abstract:** Retrieval-augmented language models can better adapt to changes in world
-state and incorporate long-tail knowledge. However, most existing methods
-retrieve only short contiguous chunks from a retrieval corpus, limiting
-holistic understanding of the overall document context. We introduce the novel
-approach of recursively embedding, clustering, and summarizing chunks of text,
-constructing a tree with differing levels of summarization from the bottom up.
-At inference time, our RAPTOR model retrieves from this tree, integrating
-information across lengthy documents at different levels of abstraction.
-Controlled experiments show that retrieval with recursive summaries offers
-significant improvements over traditional retrieval-augmented LMs on several
-tasks. On question-answering tasks that involve complex, multi-step reasoning,
-we show state-of-the-art results; for example, by coupling RAPTOR retrieval
-with the use of GPT-4, we can improve the best performance on the QuALITY
-benchmark by 20% in absolute accuracy.
-                
-## Corrective Retrieval Augmented Generation
-
-- **Authors:** Shi-Qi Yan, Jia-Chen Gu, Yun Zhu,  et al.
-- **arXiv id:** [2401.15884v2](http://arxiv.org/abs/2401.15884v2)  **Published Date:** 2024-01-29
-- **LangChain:**
-
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
-   - **Cookbook:** [langgraph_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
-
-**Abstract:** Large language models (LLMs) inevitably exhibit hallucinations since the
-accuracy of generated texts cannot be secured solely by the parametric
-knowledge they encapsulate. Although retrieval-augmented generation (RAG) is a
-practicable complement to LLMs, it relies heavily on the relevance of retrieved
-documents, raising concerns about how the model behaves if retrieval goes
-wrong. To this end, we propose the Corrective Retrieval Augmented Generation
-(CRAG) to improve the robustness of generation. Specifically, a lightweight
-retrieval evaluator is designed to assess the overall quality of retrieved
-documents for a query, returning a confidence degree based on which different
-knowledge retrieval actions can be triggered. Since retrieval from static and
-limited corpora can only return sub-optimal documents, large-scale web searches
-are utilized as an extension for augmenting the retrieval results. Besides, a
-decompose-then-recompose algorithm is designed for retrieved documents to
-selectively focus on key information and filter out irrelevant information in
-them. CRAG is plug-and-play and can be seamlessly coupled with various
-RAG-based approaches. Experiments on four datasets covering short- and
-long-form generation tasks show that CRAG can significantly improve the
-performance of RAG-based approaches.
-                
-## Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
-
-- **Authors:** Tal Ridnik, Dedy Kredo, Itamar Friedman
-- **arXiv id:** [2401.08500v1](http://arxiv.org/abs/2401.08500v1)  **Published Date:** 2024-01-16
-- **LangChain:**
-
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
-
-**Abstract:** Code generation problems differ from common natural language problems - they
-require matching the exact syntax of the target language, identifying happy
-paths and edge cases, paying attention to numerous small details in the problem
-spec, and addressing other code-specific issues and requirements. Hence, many
-of the optimizations and tricks that have been successful in natural language
-generation may not be effective for code tasks. In this work, we propose a new
-approach to code generation by LLMs, which we call AlphaCodium - a test-based,
-multi-stage, code-oriented iterative flow, that improves the performances of
-LLMs on code problems. We tested AlphaCodium on a challenging code generation
-dataset called CodeContests, which includes competitive programming problems
-from platforms such as Codeforces. The proposed flow consistently and
-significantly improves results. On the validation set, for example, GPT-4
-accuracy (pass@5) increased from 19% with a single well-designed direct prompt
-to 44% with the AlphaCodium flow. Many of the principles and best practices
-acquired in this work, we believe, are broadly applicable to general code
-generation tasks. Full implementation is available at:
-https://github.com/Codium-ai/AlphaCodium
-                
-## Mixtral of Experts
-
-- **Authors:** Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux,  et al.
-- **arXiv id:** [2401.04088v1](http://arxiv.org/abs/2401.04088v1)  **Published Date:** 2024-01-08
-- **LangChain:**
-
-   - **Cookbook:** [together_ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
-
-**Abstract:** We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model.
-Mixtral has the same architecture as Mistral 7B, with the difference that each
-layer is composed of 8 feedforward blocks (i.e. experts). For every token, at
-each layer, a router network selects two experts to process the current state
-and combine their outputs. Even though each token only sees two experts, the
-selected experts can be different at each timestep. As a result, each token has
-access to 47B parameters, but only uses 13B active parameters during inference.
-Mixtral was trained with a context size of 32k tokens and it outperforms or
-matches Llama 2 70B and GPT-3.5 across all evaluated benchmarks. In particular,
-Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and
-multilingual benchmarks. We also provide a model fine-tuned to follow
-instructions, Mixtral 8x7B - Instruct, that surpasses GPT-3.5 Turbo,
-Claude-2.1, Gemini Pro, and Llama 2 70B - chat model on human benchmarks. Both
-the base and instruct models are released under the Apache 2.0 license.
-                
-## Dense X Retrieval: What Retrieval Granularity Should We Use?
-
-- **Authors:** Tong Chen, Hongwei Wang, Sihao Chen,  et al.
-- **arXiv id:** [2312.06648v2](http://arxiv.org/abs/2312.06648v2)  **Published Date:** 2023-12-11
-- **LangChain:**
-
-   - **Template:** [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
-
-**Abstract:** Dense retrieval has become a prominent method to obtain relevant context or
-world knowledge in open-domain NLP tasks. When we use a learned dense retriever
-on a retrieval corpus at inference time, an often-overlooked design choice is
-the retrieval unit in which the corpus is indexed, e.g. document, passage, or
-sentence. We discover that the retrieval unit choice significantly impacts the
-performance of both retrieval and downstream tasks. Distinct from the typical
-approach of using passages or sentences, we introduce a novel retrieval unit,
-proposition, for dense retrieval. Propositions are defined as atomic
-expressions within text, each encapsulating a distinct factoid and presented in
-a concise, self-contained natural language format. We conduct an empirical
-comparison of different retrieval granularity. Our results reveal that
-proposition-based retrieval significantly outperforms traditional passage or
-sentence-based methods in dense retrieval. Moreover, retrieval by proposition
-also enhances the performance of downstream QA tasks, since the retrieved texts
-are more condensed with question-relevant information, reducing the need for
-lengthy input tokens and minimizing the inclusion of extraneous, irrelevant
-information.
-                
-## Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
-
-- **Authors:** Wenhao Yu, Hongming Zhang, Xiaoman Pan,  et al.
-- **arXiv id:** [2311.09210v1](http://arxiv.org/abs/2311.09210v1)  **Published Date:** 2023-11-15
-- **LangChain:**
-
-   - **Template:** [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
-
-**Abstract:** Retrieval-augmented language models (RALMs) represent a substantial
-advancement in the capabilities of large language models, notably in reducing
-factual hallucination by leveraging external knowledge sources. However, the
-reliability of the retrieved information is not always guaranteed. The
-retrieval of irrelevant data can lead to misguided responses, and potentially
-causing the model to overlook its inherent knowledge, even when it possesses
-adequate information to address the query. Moreover, standard RALMs often
-struggle to assess whether they possess adequate knowledge, both intrinsic and
-retrieved, to provide an accurate answer. In situations where knowledge is
-lacking, these systems should ideally respond with "unknown" when the answer is
-unattainable. In response to these challenges, we introduces Chain-of-Noting
-(CoN), a novel approach aimed at improving the robustness of RALMs in facing
-noisy, irrelevant documents and in handling unknown scenarios. The core idea of
-CoN is to generate sequential reading notes for retrieved documents, enabling a
-thorough evaluation of their relevance to the given question and integrating
-this information to formulate the final answer. We employed ChatGPT to create
-training data for CoN, which was subsequently trained on an LLaMa-2 7B model.
-Our experiments across four open-domain QA benchmarks show that RALMs equipped
-with CoN significantly outperform standard RALMs. Notably, CoN achieves an
-average improvement of +7.9 in EM score given entirely noisy retrieved
-documents and +10.5 in rejection rates for real-time questions that fall
-outside the pre-training knowledge scope.
-                
-## Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
-
-- **Authors:** Akari Asai, Zeqiu Wu, Yizhong Wang,  et al.
-- **arXiv id:** [2310.11511v1](http://arxiv.org/abs/2310.11511v1)  **Published Date:** 2023-10-17
-- **LangChain:**
-
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
-   - **Cookbook:** [langgraph_self_rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
-
-**Abstract:** Despite their remarkable capabilities, large language models (LLMs) often
-produce responses containing factual inaccuracies due to their sole reliance on
-the parametric knowledge they encapsulate. Retrieval-Augmented Generation
-(RAG), an ad hoc approach that augments LMs with retrieval of relevant
-knowledge, decreases such issues. However, indiscriminately retrieving and
-incorporating a fixed number of retrieved passages, regardless of whether
-retrieval is necessary, or passages are relevant, diminishes LM versatility or
-can lead to unhelpful response generation. We introduce a new framework called
-Self-Reflective Retrieval-Augmented Generation (Self-RAG) that enhances an LM's
-quality and factuality through retrieval and self-reflection. Our framework
-trains a single arbitrary LM that adaptively retrieves passages on-demand, and
-generates and reflects on retrieved passages and its own generations using
-special tokens, called reflection tokens. Generating reflection tokens makes
-the LM controllable during the inference phase, enabling it to tailor its
-behavior to diverse task requirements. Experiments show that Self-RAG (7B and
-13B parameters) significantly outperforms state-of-the-art LLMs and
-retrieval-augmented models on a diverse set of tasks. Specifically, Self-RAG
-outperforms ChatGPT and retrieval-augmented Llama2-chat on Open-domain QA,
-reasoning and fact verification tasks, and it shows significant gains in
-improving factuality and citation accuracy for long-form generations relative
-to these models.
-                
-## Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
-
-- **Authors:** Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al.
-- **arXiv id:** [2310.06117v2](http://arxiv.org/abs/2310.06117v2)  **Published Date:** 2023-10-09
-- **LangChain:**
-
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
-   - **Template:** [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting)
-   - **Cookbook:** [stepback-qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
-
-**Abstract:** We present Step-Back Prompting, a simple prompting technique that enables
-LLMs to do abstractions to derive high-level concepts and first principles from
-instances containing specific details. Using the concepts and principles to
-guide reasoning, LLMs significantly improve their abilities in following a
-correct reasoning path towards the solution. We conduct experiments of
-Step-Back Prompting with PaLM-2L, GPT-4 and Llama2-70B models, and observe
-substantial performance gains on various challenging reasoning-intensive tasks
-including STEM, Knowledge QA, and Multi-Hop Reasoning. For instance, Step-Back
-Prompting improves PaLM-2L performance on MMLU (Physics and Chemistry) by 7%
-and 11% respectively, TimeQA by 27%, and MuSiQue by 7%.
-                
-## Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation
-
-- **Authors:** Xuefei Ning, Zinan Lin, Zixuan Zhou,  et al.
-- **arXiv id:** [2307.15337v3](http://arxiv.org/abs/2307.15337v3)  **Published Date:** 2023-07-28
-- **LangChain:**
-
-   - **Template:** [skeleton-of-thought](https://python.langchain.com/docs/templates/skeleton-of-thought)
-
-**Abstract:** This work aims at decreasing the end-to-end generation latency of large
-language models (LLMs). One of the major causes of the high generation latency
-is the sequential decoding approach adopted by almost all state-of-the-art
-LLMs. In this work, motivated by the thinking and writing process of humans, we
-propose Skeleton-of-Thought (SoT), which first guides LLMs to generate the
-skeleton of the answer, and then conducts parallel API calls or batched
-decoding to complete the contents of each skeleton point in parallel. Not only
-does SoT provide considerable speed-ups across 12 LLMs, but it can also
-potentially improve the answer quality on several question categories. SoT is
-an initial attempt at data-centric optimization for inference efficiency, and
-showcases the potential of eliciting high-quality answers by explicitly
-planning the answer structure in language.
-                
-## Llama 2: Open Foundation and Fine-Tuned Chat Models
-
-- **Authors:** Hugo Touvron, Louis Martin, Kevin Stone,  et al.
-- **arXiv id:** [2307.09288v2](http://arxiv.org/abs/2307.09288v2)  **Published Date:** 2023-07-18
-- **LangChain:**
-
-   - **Cookbook:** [Semi_Structured_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
-
-**Abstract:** In this work, we develop and release Llama 2, a collection of pretrained and
-fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70
-billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for
-dialogue use cases. Our models outperform open-source chat models on most
-benchmarks we tested, and based on our human evaluations for helpfulness and
-safety, may be a suitable substitute for closed-source models. We provide a
-detailed description of our approach to fine-tuning and safety improvements of
-Llama 2-Chat in order to enable the community to build on our work and
-contribute to the responsible development of LLMs.
-                
-## Lost in the Middle: How Language Models Use Long Contexts
-
-- **Authors:** Nelson F. Liu, Kevin Lin, John Hewitt,  et al.
-- **arXiv id:** [2307.03172v3](http://arxiv.org/abs/2307.03172v3)  **Published Date:** 2023-07-06
-- **LangChain:**
-
-   - **Documentation:** [docs/how_to/long_context_reorder](https://python.langchain.com/docs/how_to/long_context_reorder)
-
-**Abstract:** While recent language models have the ability to take long contexts as input,
-relatively little is known about how well they use longer context. We analyze
-the performance of language models on two tasks that require identifying
-relevant information in their input contexts: multi-document question answering
-and key-value retrieval. We find that performance can degrade significantly
-when changing the position of relevant information, indicating that current
-language models do not robustly make use of information in long input contexts.
-In particular, we observe that performance is often highest when relevant
-information occurs at the beginning or end of the input context, and
-significantly degrades when models must access relevant information in the
-middle of long contexts, even for explicitly long-context models. Our analysis
-provides a better understanding of how language models use their input context
-and provides new evaluation protocols for future long-context language models.
-                
-## Query Rewriting for Retrieval-Augmented Large Language Models
-
-- **Authors:** Xinbei Ma, Yeyun Gong, Pengcheng He,  et al.
-- **arXiv id:** [2305.14283v3](http://arxiv.org/abs/2305.14283v3)  **Published Date:** 2023-05-23
-- **LangChain:**
-
-   - **Template:** [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read)
-   - **Cookbook:** [rewrite](https://github.com/langchain-ai/langchain/blob/master/cookbook/rewrite.ipynb)
-
-**Abstract:** Large Language Models (LLMs) play powerful, black-box readers in the
-retrieve-then-read pipeline, making remarkable progress in knowledge-intensive
-tasks. This work introduces a new framework, Rewrite-Retrieve-Read instead of
-the previous retrieve-then-read for the retrieval-augmented LLMs from the
-perspective of the query rewriting. Unlike prior studies focusing on adapting
-either the retriever or the reader, our approach pays attention to the
-adaptation of the search query itself, for there is inevitably a gap between
-the input text and the needed knowledge in retrieval. We first prompt an LLM to
-generate the query, then use a web search engine to retrieve contexts.
-Furthermore, to better align the query to the frozen modules, we propose a
-trainable scheme for our pipeline. A small language model is adopted as a
-trainable rewriter to cater to the black-box LLM reader. The rewriter is
-trained using the feedback of the LLM reader by reinforcement learning.
-Evaluation is conducted on downstream tasks, open-domain QA and multiple-choice
-QA. Experiments results show consistent performance improvement, indicating
-that our framework is proven effective and scalable, and brings a new framework
-for retrieval-augmented LLM.
-                
-## Large Language Model Guided Tree-of-Thought
-
-- **Authors:** Jieyi Long
-- **arXiv id:** [2305.08291v1](http://arxiv.org/abs/2305.08291v1)  **Published Date:** 2023-05-15
-- **LangChain:**
-
-   - **API Reference:** [langchain_experimental.tot](https://python.langchain.com/api_reference/experimental/tot.html)
-   - **Cookbook:** [tree_of_thought](https://github.com/langchain-ai/langchain/blob/master/cookbook/tree_of_thought.ipynb)
-
-**Abstract:** In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel
-approach aimed at improving the problem-solving capabilities of auto-regressive
-large language models (LLMs). The ToT technique is inspired by the human mind's
-approach for solving complex reasoning tasks through trial and error. In this
-process, the human mind explores the solution space through a tree-like thought
-process, allowing for backtracking when necessary. To implement ToT as a
-software system, we augment an LLM with additional modules including a prompter
-agent, a checker module, a memory module, and a ToT controller. In order to
-solve a given problem, these modules engage in a multi-round conversation with
-the LLM. The memory module records the conversation and state history of the
-problem solving process, which allows the system to backtrack to the previous
-steps of the thought-process and explore other directions from there. To verify
-the effectiveness of the proposed technique, we implemented a ToT-based solver
-for the Sudoku Puzzle. Experimental results show that the ToT framework can
-significantly increase the success rate of Sudoku puzzle solving. Our
-implementation of the ToT-based Sudoku solver is available on [GitHub](https://github.com/jieyilong/tree-of-thought-puzzle-solver).
-                
-## Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
-
-- **Authors:** Lei Wang, Wanyu Xu, Yihuai Lan,  et al.
-- **arXiv id:** [2305.04091v3](http://arxiv.org/abs/2305.04091v3)  **Published Date:** 2023-05-06
-- **LangChain:**
-
-   - **Cookbook:** [plan_and_execute_agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
-
-**Abstract:** Large language models (LLMs) have recently been shown to deliver impressive
-performance in various NLP tasks. To tackle multi-step reasoning tasks,
-few-shot chain-of-thought (CoT) prompting includes a few manually crafted
-step-by-step reasoning demonstrations which enable LLMs to explicitly generate
-reasoning steps and improve their reasoning task accuracy. To eliminate the
-manual effort, Zero-shot-CoT concatenates the target problem statement with
-"Let's think step by step" as an input prompt to LLMs. Despite the success of
-Zero-shot-CoT, it still suffers from three pitfalls: calculation errors,
-missing-step errors, and semantic misunderstanding errors. To address the
-missing-step errors, we propose Plan-and-Solve (PS) Prompting. It consists of
-two components: first, devising a plan to divide the entire task into smaller
-subtasks, and then carrying out the subtasks according to the plan. To address
-the calculation errors and improve the quality of generated reasoning steps, we
-extend PS prompting with more detailed instructions and derive PS+ prompting.
-We evaluate our proposed prompting strategy on ten datasets across three
-reasoning problems. The experimental results over GPT-3 show that our proposed
-zero-shot prompting consistently outperforms Zero-shot-CoT across all datasets
-by a large margin, is comparable to or exceeds Zero-shot-Program-of-Thought
-Prompting, and has comparable performance with 8-shot CoT prompting on the math
-reasoning problem. The code can be found at
-https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting.
-                
-## Zero-Shot Listwise Document Reranking with a Large Language Model
-
-- **Authors:** Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al.
-- **arXiv id:** [2305.02156v1](http://arxiv.org/abs/2305.02156v1)  **Published Date:** 2023-05-03
-- **LangChain:**
-
-   - **Documentation:** [docs/how_to/contextual_compression](https://python.langchain.com/docs/how_to/contextual_compression)
-   - **API Reference:** [langchain...LLMListwiseRerank](https://python.langchain.com/api_reference/langchain/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#)
-
-**Abstract:** Supervised ranking methods based on bi-encoder or cross-encoder architectures
-have shown success in multi-stage text ranking tasks, but they require large
-amounts of relevance judgments as training data. In this work, we propose
-Listwise Reranker with a Large Language Model (LRL), which achieves strong
-reranking effectiveness without using any task-specific training data.
-Different from the existing pointwise ranking methods, where documents are
-scored independently and ranked according to the scores, LRL directly generates
-a reordered list of document identifiers given the candidate documents.
-Experiments on three TREC web search datasets demonstrate that LRL not only
-outperforms zero-shot pointwise methods when reranking first-stage retrieval
-results, but can also act as a final-stage reranker to improve the top-ranked
-results of a pointwise method for improved efficiency. Additionally, we apply
-our approach to subsets of MIRACL, a recent multilingual retrieval dataset,
-with results showing its potential to generalize across different languages.
-                
-## Visual Instruction Tuning
-
-- **Authors:** Haotian Liu, Chunyuan Li, Qingyang Wu,  et al.
-- **arXiv id:** [2304.08485v2](http://arxiv.org/abs/2304.08485v2)  **Published Date:** 2023-04-17
-- **LangChain:**
-
-   - **Cookbook:** [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb), [Semi_structured_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb)
-
-**Abstract:** Instruction tuning large language models (LLMs) using machine-generated
-instruction-following data has improved zero-shot capabilities on new tasks,
-but the idea is less explored in the multimodal field. In this paper, we
-present the first attempt to use language-only GPT-4 to generate multimodal
-language-image instruction-following data. By instruction tuning on such
-generated data, we introduce LLaVA: Large Language and Vision Assistant, an
-end-to-end trained large multimodal model that connects a vision encoder and
-LLM for general-purpose visual and language understanding.Our early experiments
-show that LLaVA demonstrates impressive multimodel chat abilities, sometimes
-exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and
-yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal
-instruction-following dataset. When fine-tuned on Science QA, the synergy of
-LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make
-GPT-4 generated visual instruction tuning data, our model and code base
-publicly available.
-                
-## Generative Agents: Interactive Simulacra of Human Behavior
-
-- **Authors:** Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai,  et al.
-- **arXiv id:** [2304.03442v2](http://arxiv.org/abs/2304.03442v2)  **Published Date:** 2023-04-07
-- **LangChain:**
-
-   - **Cookbook:** [generative_agents_interactive_simulacra_of_human_behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb), [multiagent_bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb)
-
-**Abstract:** Believable proxies of human behavior can empower interactive applications
-ranging from immersive environments to rehearsal spaces for interpersonal
-communication to prototyping tools. In this paper, we introduce generative
-agents--computational software agents that simulate believable human behavior.
-Generative agents wake up, cook breakfast, and head to work; artists paint,
-while authors write; they form opinions, notice each other, and initiate
-conversations; they remember and reflect on days past as they plan the next
-day. To enable generative agents, we describe an architecture that extends a
-large language model to store a complete record of the agent's experiences
-using natural language, synthesize those memories over time into higher-level
-reflections, and retrieve them dynamically to plan behavior. We instantiate
-generative agents to populate an interactive sandbox environment inspired by
-The Sims, where end users can interact with a small town of twenty five agents
-using natural language. In an evaluation, these generative agents produce
-believable individual and emergent social behaviors: for example, starting with
-only a single user-specified notion that one agent wants to throw a Valentine's
-Day party, the agents autonomously spread invitations to the party over the
-next two days, make new acquaintances, ask each other out on dates to the
-party, and coordinate to show up for the party together at the right time. We
-demonstrate through ablation that the components of our agent
-architecture--observation, planning, and reflection--each contribute critically
-to the believability of agent behavior. By fusing large language models with
-computational, interactive agents, this work introduces architectural and
-interaction patterns for enabling believable simulations of human behavior.
-                
-## CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
-
-- **Authors:** Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani,  et al.
-- **arXiv id:** [2303.17760v2](http://arxiv.org/abs/2303.17760v2)  **Published Date:** 2023-03-31
-- **LangChain:**
-
-   - **Cookbook:** [camel_role_playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
-
-**Abstract:** The rapid advancement of chat-based language models has led to remarkable
-progress in complex task-solving. However, their success heavily relies on
-human input to guide the conversation, which can be challenging and
-time-consuming. This paper explores the potential of building scalable
-techniques to facilitate autonomous cooperation among communicative agents, and
-provides insight into their "cognitive" processes. To address the challenges of
-achieving autonomous cooperation, we propose a novel communicative agent
-framework named role-playing. Our approach involves using inception prompting
-to guide chat agents toward task completion while maintaining consistency with
-human intentions. We showcase how role-playing can be used to generate
-conversational data for studying the behaviors and capabilities of a society of
-agents, providing a valuable resource for investigating conversational language
-models. In particular, we conduct comprehensive studies on
-instruction-following cooperation in multi-agent settings. Our contributions
-include introducing a novel communicative agent framework, offering a scalable
-approach for studying the cooperative behaviors and capabilities of multi-agent
-systems, and open-sourcing our library to support research on communicative
-agents and beyond: https://github.com/camel-ai/camel.
-                
-## HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
-
-- **Authors:** Yongliang Shen, Kaitao Song, Xu Tan,  et al.
-- **arXiv id:** [2303.17580v4](http://arxiv.org/abs/2303.17580v4)  **Published Date:** 2023-03-30
-- **LangChain:**
-
-   - **API Reference:** [langchain_experimental.autonomous_agents](https://python.langchain.com/api_reference/experimental/autonomous_agents.html)
-   - **Cookbook:** [hugginggpt](https://github.com/langchain-ai/langchain/blob/master/cookbook/hugginggpt.ipynb)
-
-**Abstract:** Solving complicated AI tasks with different domains and modalities is a key
-step toward artificial general intelligence. While there are numerous AI models
-available for various domains and modalities, they cannot handle complicated AI
-tasks autonomously. Considering large language models (LLMs) have exhibited
-exceptional abilities in language understanding, generation, interaction, and
-reasoning, we advocate that LLMs could act as a controller to manage existing
-AI models to solve complicated AI tasks, with language serving as a generic
-interface to empower this. Based on this philosophy, we present HuggingGPT, an
-LLM-powered agent that leverages LLMs (e.g., ChatGPT) to connect various AI
-models in machine learning communities (e.g., Hugging Face) to solve AI tasks.
-Specifically, we use ChatGPT to conduct task planning when receiving a user
-request, select models according to their function descriptions available in
-Hugging Face, execute each subtask with the selected AI model, and summarize
-the response according to the execution results. By leveraging the strong
-language capability of ChatGPT and abundant AI models in Hugging Face,
-HuggingGPT can tackle a wide range of sophisticated AI tasks spanning different
-modalities and domains and achieve impressive results in language, vision,
-speech, and other challenging tasks, which paves a new way towards the
-realization of artificial general intelligence.
-                
-## A Watermark for Large Language Models
-
-- **Authors:** John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al.
-- **arXiv id:** [2301.10226v4](http://arxiv.org/abs/2301.10226v4)  **Published Date:** 2023-01-24
-- **LangChain:**
-
-   - **API Reference:** [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-
-**Abstract:** Potential harms of large language models can be mitigated by watermarking
-model output, i.e., embedding signals into generated text that are invisible to
-humans but algorithmically detectable from a short span of tokens. We propose a
-watermarking framework for proprietary language models. The watermark can be
-embedded with negligible impact on text quality, and can be detected using an
-efficient open-source algorithm without access to the language model API or
-parameters. The watermark works by selecting a randomized set of "green" tokens
-before a word is generated, and then softly promoting use of green tokens
-during sampling. We propose a statistical test for detecting the watermark with
-interpretable p-values, and derive an information-theoretic framework for
-analyzing the sensitivity of the watermark. We test the watermark using a
-multi-billion parameter model from the Open Pretrained Transformer (OPT)
-family, and discuss robustness and security.
-                
-## Precise Zero-Shot Dense Retrieval without Relevance Labels
-
-- **Authors:** Luyu Gao, Xueguang Ma, Jimmy Lin,  et al.
-- **arXiv id:** [2212.10496v1](http://arxiv.org/abs/2212.10496v1)  **Published Date:** 2022-12-20
-- **LangChain:**
-
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
-   - **API Reference:** [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
-   - **Template:** [hyde](https://python.langchain.com/docs/templates/hyde)
-   - **Cookbook:** [hypothetical_document_embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
-
-**Abstract:** While dense retrieval has been shown effective and efficient across tasks and
-languages, it remains difficult to create effective fully zero-shot dense
-retrieval systems when no relevance label is available. In this paper, we
-recognize the difficulty of zero-shot learning and encoding relevance. Instead,
-we propose to pivot through Hypothetical Document Embeddings~(HyDE). Given a
-query, HyDE first zero-shot instructs an instruction-following language model
-(e.g. InstructGPT) to generate a hypothetical document. The document captures
-relevance patterns but is unreal and may contain false details. Then, an
-unsupervised contrastively learned encoder~(e.g. Contriever) encodes the
-document into an embedding vector. This vector identifies a neighborhood in the
-corpus embedding space, where similar real documents are retrieved based on
-vector similarity. This second step ground the generated document to the actual
-corpus, with the encoder's dense bottleneck filtering out the incorrect
-details. Our experiments show that HyDE significantly outperforms the
-state-of-the-art unsupervised dense retriever Contriever and shows strong
-performance comparable to fine-tuned retrievers, across various tasks (e.g. web
-search, QA, fact verification) and languages~(e.g. sw, ko, ja).
-                
-## Constitutional AI: Harmlessness from AI Feedback
-
-- **Authors:** Yuntao Bai, Saurav Kadavath, Sandipan Kundu,  et al.
-- **arXiv id:** [2212.08073v1](http://arxiv.org/abs/2212.08073v1)  **Published Date:** 2022-12-15
-- **LangChain:**
-
-   - **Documentation:** [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain)
-
-**Abstract:** As AI systems become more capable, we would like to enlist their help to
-supervise other AIs. We experiment with methods for training a harmless AI
-assistant through self-improvement, without any human labels identifying
-harmful outputs. The only human oversight is provided through a list of rules
-or principles, and so we refer to the method as 'Constitutional AI'. The
-process involves both a supervised learning and a reinforcement learning phase.
-In the supervised phase we sample from an initial model, then generate
-self-critiques and revisions, and then finetune the original model on revised
-responses. In the RL phase, we sample from the finetuned model, use a model to
-evaluate which of the two samples is better, and then train a preference model
-from this dataset of AI preferences. We then train with RL using the preference
-model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a
-result we are able to train a harmless but non-evasive AI assistant that
-engages with harmful queries by explaining its objections to them. Both the SL
-and RL methods can leverage chain-of-thought style reasoning to improve the
-human-judged performance and transparency of AI decision making. These methods
-make it possible to control AI behavior more precisely and with far fewer human
-labels.
-                
-## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
-
-- **Authors:** Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al.
-- **arXiv id:** [2212.07425v3](http://arxiv.org/abs/2212.07425v3)  **Published Date:** 2022-12-12
-- **LangChain:**
-
-   - **API Reference:** [langchain_experimental.fallacy_removal](https://python.langchain.com/api_reference/experimental/fallacy_removal.html)
-
-**Abstract:** The spread of misinformation, propaganda, and flawed argumentation has been
-amplified in the Internet era. Given the volume of data and the subtlety of
-identifying violations of argumentation norms, supporting information analytics
-tasks, like content moderation, with trustworthy methods that can identify
-logical fallacies is essential. In this paper, we formalize prior theoretical
-work on logical fallacies into a comprehensive three-stage evaluation framework
-of detection, coarse-grained, and fine-grained classification. We adapt
-existing evaluation datasets for each stage of the evaluation. We employ three
-families of robust and explainable methods based on prototype reasoning,
-instance-based reasoning, and knowledge injection. The methods combine language
-models with background knowledge and explainable mechanisms. Moreover, we
-address data sparsity with strategies for data augmentation and curriculum
-learning. Our three-stage framework natively consolidates prior datasets and
-methods from existing tasks, like propaganda detection, serving as an
-overarching evaluation testbed. We extensively evaluate these methods on our
-datasets, focusing on their robustness and explainability. Our results provide
-insight into the strengths and weaknesses of the methods on different
-components and fallacy classes, indicating that fallacy identification is a
-challenging task that may require specialized forms of reasoning to capture
-various classes. We share our open-source code and data on GitHub to support
-further work on logical fallacy identification.
-                
-## Complementary Explanations for Effective In-Context Learning
-
-- **Authors:** Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al.
-- **arXiv id:** [2211.13892v2](http://arxiv.org/abs/2211.13892v2)  **Published Date:** 2022-11-25
-- **LangChain:**
-
-   - **API Reference:** [langchain_core...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
-
-**Abstract:** Large language models (LLMs) have exhibited remarkable capabilities in
-learning from explanations in prompts, but there has been limited understanding
-of exactly how these explanations function or why they are effective. This work
-aims to better understand the mechanisms by which explanations are used for
-in-context learning. We first study the impact of two different factors on the
-performance of prompts with explanations: the computation trace (the way the
-solution is decomposed) and the natural language used to express the prompt. By
-perturbing explanations on three controlled tasks, we show that both factors
-contribute to the effectiveness of explanations. We further study how to form
-maximally effective sets of explanations for solving a given test query. We
-find that LLMs can benefit from the complementarity of the explanation set:
-diverse reasoning skills shown by different exemplars can lead to better
-performance. Therefore, we propose a maximal marginal relevance-based exemplar
-selection approach for constructing exemplar sets that are both relevant as
-well as complementary, which successfully improves the in-context learning
-performance across three real-world tasks on multiple LLMs.
-                
-## PAL: Program-aided Language Models
-
-- **Authors:** Luyu Gao, Aman Madaan, Shuyan Zhou,  et al.
-- **arXiv id:** [2211.10435v2](http://arxiv.org/abs/2211.10435v2)  **Published Date:** 2022-11-18
-- **LangChain:**
-
-   - **API Reference:** [langchain_experimental.pal_chain](https://python.langchain.com/api_reference/experimental/pal_chain.html), [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain)
-   - **Cookbook:** [program_aided_language_model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
-
-**Abstract:** Large language models (LLMs) have recently demonstrated an impressive ability
-to perform arithmetic and symbolic reasoning tasks, when provided with a few
-examples at test time ("few-shot prompting"). Much of this success can be
-attributed to prompting methods such as "chain-of-thought'', which employ LLMs
-for both understanding the problem description by decomposing it into steps, as
-well as solving each step of the problem. While LLMs seem to be adept at this
-sort of step-by-step decomposition, LLMs often make logical and arithmetic
-mistakes in the solution part, even when the problem is decomposed correctly.
-In this paper, we present Program-Aided Language models (PAL): a novel approach
-that uses the LLM to read natural language problems and generate programs as
-the intermediate reasoning steps, but offloads the solution step to a runtime
-such as a Python interpreter. With PAL, decomposing the natural language
-problem into runnable steps remains the only learning task for the LLM, while
-solving is delegated to the interpreter. We demonstrate this synergy between a
-neural LLM and a symbolic interpreter across 13 mathematical, symbolic, and
-algorithmic reasoning tasks from BIG-Bench Hard and other benchmarks. In all
-these natural language reasoning tasks, generating code using an LLM and
-reasoning using a Python interpreter leads to more accurate results than much
-larger models. For example, PAL using Codex achieves state-of-the-art few-shot
-accuracy on the GSM8K benchmark of math word problems, surpassing PaLM-540B
-which uses chain-of-thought by absolute 15% top-1. Our code and data are
-publicly available at http://reasonwithpal.com/ .
-                
-## An Analysis of Fusion Functions for Hybrid Retrieval
-
-- **Authors:** Sebastian Bruch, Siyu Gai, Amir Ingber
-- **arXiv id:** [2210.11934v2](http://arxiv.org/abs/2210.11934v2)  **Published Date:** 2022-10-21
-- **LangChain:**
-
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
-
-**Abstract:** We study hybrid search in text retrieval where lexical and semantic search
-are fused together with the intuition that the two are complementary in how
-they model relevance. In particular, we examine fusion by a convex combination
-(CC) of lexical and semantic scores, as well as the Reciprocal Rank Fusion
-(RRF) method, and identify their advantages and potential pitfalls. Contrary to
-existing studies, we find RRF to be sensitive to its parameters; that the
-learning of a CC fusion is generally agnostic to the choice of score
-normalization; that CC outperforms RRF in in-domain and out-of-domain settings;
-and finally, that CC is sample efficient, requiring only a small set of
-training examples to tune its only parameter to a target domain.
-                
-## ReAct: Synergizing Reasoning and Acting in Language Models
-
-- **Authors:** Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al.
-- **arXiv id:** [2210.03629v3](http://arxiv.org/abs/2210.03629v3)  **Published Date:** 2022-10-06
-- **LangChain:**
-
-   - **Documentation:** [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/concepts](https://python.langchain.com/docs/concepts)
-   - **API Reference:** [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
-
-**Abstract:** While large language models (LLMs) have demonstrated impressive capabilities
-across tasks in language understanding and interactive decision making, their
-abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g.
-action plan generation) have primarily been studied as separate topics. In this
-paper, we explore the use of LLMs to generate both reasoning traces and
-task-specific actions in an interleaved manner, allowing for greater synergy
-between the two: reasoning traces help the model induce, track, and update
-action plans as well as handle exceptions, while actions allow it to interface
-with external sources, such as knowledge bases or environments, to gather
-additional information. We apply our approach, named ReAct, to a diverse set of
-language and decision making tasks and demonstrate its effectiveness over
-state-of-the-art baselines, as well as improved human interpretability and
-trustworthiness over methods without reasoning or acting components.
-Concretely, on question answering (HotpotQA) and fact verification (Fever),
-ReAct overcomes issues of hallucination and error propagation prevalent in
-chain-of-thought reasoning by interacting with a simple Wikipedia API, and
-generates human-like task-solving trajectories that are more interpretable than
-baselines without reasoning traces. On two interactive decision making
-benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and
-reinforcement learning methods by an absolute success rate of 34% and 10%
-respectively, while being prompted with only one or two in-context examples.
-Project site with code: https://react-lm.github.io
-                
-## Deep Lake: a Lakehouse for Deep Learning
-
-- **Authors:** Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al.
-- **arXiv id:** [2209.10785v2](http://arxiv.org/abs/2209.10785v2)  **Published Date:** 2022-09-22
-- **LangChain:**
-
-   - **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
-
-**Abstract:** Traditional data lakes provide critical data infrastructure for analytical
-workloads by enabling time travel, running SQL queries, ingesting data with
-ACID transactions, and visualizing petabyte-scale datasets on cloud storage.
-They allow organizations to break down data silos, unlock data-driven
-decision-making, improve operational efficiency, and reduce costs. However, as
-deep learning usage increases, traditional data lakes are not well-designed for
-applications such as natural language processing (NLP), audio processing,
-computer vision, and applications involving non-tabular datasets. This paper
-presents Deep Lake, an open-source lakehouse for deep learning applications
-developed at Activeloop. Deep Lake maintains the benefits of a vanilla data
-lake with one key difference: it stores complex data, such as images, videos,
-annotations, as well as tabular data, in the form of tensors and rapidly
-streams the data over the network to (a) Tensor Query Language, (b) in-browser
-visualization engine, or (c) deep learning frameworks without sacrificing GPU
-utilization. Datasets stored in Deep Lake can be accessed from PyTorch,
-TensorFlow, JAX, and integrate with numerous MLOps tools.
-                
-## Matryoshka Representation Learning
-
-- **Authors:** Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al.
-- **arXiv id:** [2205.13147v4](http://arxiv.org/abs/2205.13147v4)  **Published Date:** 2022-05-26
-- **LangChain:**
-
-   - **Documentation:** [docs/integrations/providers/snowflake](https://python.langchain.com/docs/integrations/providers/snowflake)
-
-**Abstract:** Learned representations are a central component in modern ML systems, serving
-a multitude of downstream tasks. When training such representations, it is
-often the case that computational and statistical constraints for each
-downstream task are unknown. In this context rigid, fixed capacity
-representations can be either over or under-accommodating to the task at hand.
-This leads us to ask: can we design a flexible representation that can adapt to
-multiple downstream tasks with varying computational resources? Our main
-contribution is Matryoshka Representation Learning (MRL) which encodes
-information at different granularities and allows a single embedding to adapt
-to the computational constraints of downstream tasks. MRL minimally modifies
-existing representation learning pipelines and imposes no additional cost
-during inference and deployment. MRL learns coarse-to-fine representations that
-are at least as accurate and rich as independently trained low-dimensional
-representations. The flexibility within the learned Matryoshka Representations
-offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at
-the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale
-retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for
-long-tail few-shot classification, all while being as robust as the original
-representations. Finally, we show that MRL extends seamlessly to web-scale
-datasets (ImageNet, JFT) across various modalities -- vision (ViT, ResNet),
-vision + language (ALIGN) and language (BERT). MRL code and pretrained models
-are open-sourced at https://github.com/RAIVNLab/MRL.
-                
-## Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
-
-- **Authors:** Kevin Heffernan, Onur Çelebi, Holger Schwenk
-- **arXiv id:** [2205.12654v1](http://arxiv.org/abs/2205.12654v1)  **Published Date:** 2022-05-25
-- **LangChain:**
-
-   - **API Reference:** [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
-
-**Abstract:** Scaling multilingual representation learning beyond the hundred most frequent
-languages is challenging, in particular to cover the long tail of low-resource
-languages. A promising approach has been to train one-for-all multilingual
-models capable of cross-lingual transfer, but these models often suffer from
-insufficient capacity and interference between unrelated languages. Instead, we
-move away from this approach and focus on training multiple language (family)
-specific representations, but most prominently enable all languages to still be
-encoded in the same representational space. To achieve this, we focus on
-teacher-student training, allowing all encoders to be mutually compatible for
-bitext mining, and enabling fast learning of new languages. We introduce a new
-teacher-student training scheme which combines supervised and self-supervised
-training, allowing encoders to take advantage of monolingual training data,
-which is valuable in the low-resource setting.
-  Our approach significantly outperforms the original LASER encoder. We study
-very low-resource languages and handle 50 African languages, many of which are
-not covered by any other model. For these languages, we train sentence
-encoders, mine bitexts, and validate the bitexts by training NMT systems.
-                
-## Evaluating the Text-to-SQL Capabilities of Large Language Models
-
-- **Authors:** Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau
-- **arXiv id:** [2204.00498v1](http://arxiv.org/abs/2204.00498v1)  **Published Date:** 2022-03-15
-- **LangChain:**
-
-   - **Documentation:** [docs/tutorials/sql_qa](https://python.langchain.com/docs/tutorials/sql_qa)
-   - **API Reference:** [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
-
-**Abstract:** We perform an empirical evaluation of Text-to-SQL capabilities of the Codex
-language model. We find that, without any finetuning, Codex is a strong
-baseline on the Spider benchmark; we also analyze the failure modes of Codex in
-this setting. Furthermore, we demonstrate on the GeoQuery and Scholar
-benchmarks that a small number of in-domain examples provided in the prompt
-enables Codex to perform better than state-of-the-art models finetuned on such
-few-shot examples.
-                
-## Locally Typical Sampling
-
-- **Authors:** Clara Meister, Tiago Pimentel, Gian Wiher,  et al.
-- **arXiv id:** [2202.00666v5](http://arxiv.org/abs/2202.00666v5)  **Published Date:** 2022-02-01
-- **LangChain:**
-
-   - **API Reference:** [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-
-**Abstract:** Today's probabilistic language generators fall short when it comes to
-producing coherent and fluent text despite the fact that the underlying models
-perform well under standard metrics, e.g., perplexity. This discrepancy has
-puzzled the language generation community for the last few years. In this work,
-we posit that the abstraction of natural language generation as a discrete
-stochastic process--which allows for an information-theoretic analysis--can
-provide new insights into the behavior of probabilistic language generators,
-e.g., why high-probability texts can be dull or repetitive. Humans use language
-as a means of communicating information, aiming to do so in a simultaneously
-efficient and error-minimizing manner; in fact, psycholinguistics research
-suggests humans choose each word in a string with this subconscious goal in
-mind. We formally define the set of strings that meet this criterion: those for
-which each word has an information content close to the expected information
-content, i.e., the conditional entropy of our model. We then propose a simple
-and efficient procedure for enforcing this criterion when generating from
-probabilistic models, which we call locally typical sampling. Automatic and
-human evaluations show that, in comparison to nucleus and top-k sampling,
-locally typical sampling offers competitive performance (in both abstractive
-summarization and story generation) in terms of quality while consistently
-reducing degenerate repetitions.
-                
-## ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
-
-- **Authors:** Keshav Santhanam, Omar Khattab, Jon Saad-Falcon,  et al.
-- **arXiv id:** [2112.01488v3](http://arxiv.org/abs/2112.01488v3)  **Published Date:** 2021-12-02
-- **LangChain:**
-
-   - **Documentation:** [docs/integrations/retrievers/ragatouille](https://python.langchain.com/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/docs/integrations/providers/dspy)
-
-**Abstract:** Neural information retrieval (IR) has greatly advanced search and other
-knowledge-intensive language tasks. While many neural IR methods encode queries
-and documents into single-vector representations, late interaction models
-produce multi-vector representations at the granularity of each token and
-decompose relevance modeling into scalable token-level computations. This
-decomposition has been shown to make late interaction more effective, but it
-inflates the space footprint of these models by an order of magnitude. In this
-work, we introduce ColBERTv2, a retriever that couples an aggressive residual
-compression mechanism with a denoised supervision strategy to simultaneously
-improve the quality and space footprint of late interaction. We evaluate
-ColBERTv2 across a wide range of benchmarks, establishing state-of-the-art
-quality within and outside the training domain while reducing the space
-footprint of late interaction models by 6--10$\times$.
-                
-## Learning Transferable Visual Models From Natural Language Supervision
-
-- **Authors:** Alec Radford, Jong Wook Kim, Chris Hallacy,  et al.
-- **arXiv id:** [2103.00020v1](http://arxiv.org/abs/2103.00020v1)  **Published Date:** 2021-02-26
-- **LangChain:**
-
-   - **API Reference:** [langchain_experimental.open_clip](https://python.langchain.com/api_reference/experimental/open_clip.html)
-
-**Abstract:** State-of-the-art computer vision systems are trained to predict a fixed set
-of predetermined object categories. This restricted form of supervision limits
-their generality and usability since additional labeled data is needed to
-specify any other visual concept. Learning directly from raw text about images
-is a promising alternative which leverages a much broader source of
-supervision. We demonstrate that the simple pre-training task of predicting
-which caption goes with which image is an efficient and scalable way to learn
-SOTA image representations from scratch on a dataset of 400 million (image,
-text) pairs collected from the internet. After pre-training, natural language
-is used to reference learned visual concepts (or describe new ones) enabling
-zero-shot transfer of the model to downstream tasks. We study the performance
-of this approach by benchmarking on over 30 different existing computer vision
-datasets, spanning tasks such as OCR, action recognition in videos,
-geo-localization, and many types of fine-grained object classification. The
-model transfers non-trivially to most tasks and is often competitive with a
-fully supervised baseline without the need for any dataset specific training.
-For instance, we match the accuracy of the original ResNet-50 on ImageNet
-zero-shot without needing to use any of the 1.28 million training examples it
-was trained on. We release our code and pre-trained model weights at
-https://github.com/OpenAI/CLIP.
-                
-## Language Models are Few-Shot Learners
-
-- **Authors:** Tom B. Brown, Benjamin Mann, Nick Ryder,  et al.
-- **arXiv id:** [2005.14165v4](http://arxiv.org/abs/2005.14165v4)  **Published Date:** 2020-05-28
-- **LangChain:**
-
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
-
-**Abstract:** Recent work has demonstrated substantial gains on many NLP tasks and
-benchmarks by pre-training on a large corpus of text followed by fine-tuning on
-a specific task. While typically task-agnostic in architecture, this method
-still requires task-specific fine-tuning datasets of thousands or tens of
-thousands of examples. By contrast, humans can generally perform a new language
-task from only a few examples or from simple instructions - something which
-current NLP systems still largely struggle to do. Here we show that scaling up
-language models greatly improves task-agnostic, few-shot performance, sometimes
-even reaching competitiveness with prior state-of-the-art fine-tuning
-approaches. Specifically, we train GPT-3, an autoregressive language model with
-175 billion parameters, 10x more than any previous non-sparse language model,
-and test its performance in the few-shot setting. For all tasks, GPT-3 is
-applied without any gradient updates or fine-tuning, with tasks and few-shot
-demonstrations specified purely via text interaction with the model. GPT-3
-achieves strong performance on many NLP datasets, including translation,
-question-answering, and cloze tasks, as well as several tasks that require
-on-the-fly reasoning or domain adaptation, such as unscrambling words, using a
-novel word in a sentence, or performing 3-digit arithmetic. At the same time,
-we also identify some datasets where GPT-3's few-shot learning still struggles,
-as well as some datasets where GPT-3 faces methodological issues related to
-training on large web corpora. Finally, we find that GPT-3 can generate samples
-of news articles which human evaluators have difficulty distinguishing from
-articles written by humans. We discuss broader societal impacts of this finding
-and of GPT-3 in general.
-                
-## Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
-
-- **Authors:** Patrick Lewis, Ethan Perez, Aleksandra Piktus,  et al.
-- **arXiv id:** [2005.11401v4](http://arxiv.org/abs/2005.11401v4)  **Published Date:** 2020-05-22
-- **LangChain:**
-
-   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
-
-**Abstract:** Large pre-trained language models have been shown to store factual knowledge
-in their parameters, and achieve state-of-the-art results when fine-tuned on
-downstream NLP tasks. However, their ability to access and precisely manipulate
-knowledge is still limited, and hence on knowledge-intensive tasks, their
-performance lags behind task-specific architectures. Additionally, providing
-provenance for their decisions and updating their world knowledge remain open
-research problems. Pre-trained models with a differentiable access mechanism to
-explicit non-parametric memory can overcome this issue, but have so far been
-only investigated for extractive downstream tasks. We explore a general-purpose
-fine-tuning recipe for retrieval-augmented generation (RAG) -- models which
-combine pre-trained parametric and non-parametric memory for language
-generation. We introduce RAG models where the parametric memory is a
-pre-trained seq2seq model and the non-parametric memory is a dense vector index
-of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG
-formulations, one which conditions on the same retrieved passages across the
-whole generated sequence, the other can use different passages per token. We
-fine-tune and evaluate our models on a wide range of knowledge-intensive NLP
-tasks and set the state-of-the-art on three open domain QA tasks, outperforming
-parametric seq2seq models and task-specific retrieve-and-extract architectures.
-For language generation tasks, we find that RAG models generate more specific,
-diverse and factual language than a state-of-the-art parametric-only seq2seq
-baseline.
-                
-## CTRL: A Conditional Transformer Language Model for Controllable Generation
-
-- **Authors:** Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al.
-- **arXiv id:** [1909.05858v2](http://arxiv.org/abs/1909.05858v2)  **Published Date:** 2019-09-11
-- **LangChain:**
-
-   - **API Reference:** [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-
-**Abstract:** Large-scale language models show promising text generation capabilities, but
-users cannot easily control particular aspects of the generated text. We
-release CTRL, a 1.63 billion-parameter conditional transformer language model,
-trained to condition on control codes that govern style, content, and
-task-specific behavior. Control codes were derived from structure that
-naturally co-occurs with raw text, preserving the advantages of unsupervised
-learning while providing more explicit control over text generation. These
-codes also allow CTRL to predict which parts of the training data are most
-likely given a sequence. This provides a potential method for analyzing large
-amounts of data via model-based source attribution. We have released multiple
-full-sized, pretrained versions of CTRL at https://github.com/salesforce/ctrl.
-                

langchain_md_files/additional_resources/dependents.mdx

@@ -1,554 +0,0 @@
-# Dependents
-
-Dependents stats for `langchain-ai/langchain`
-
-[![](https://img.shields.io/static/v1?label=Used%20by&message=41717&color=informational&logo=slickpic)](https://github.com/langchain-ai/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(public)&message=538&color=informational&logo=slickpic)](https://github.com/langchain-ai/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(private)&message=41179&color=informational&logo=slickpic)](https://github.com/langchain-ai/langchain/network/dependents)
-
-
-[update: `2023-12-08`; only dependent repositories with Stars > 100]
-
-
-| Repository | Stars  |
-| :--------  | -----: |
-|[AntonOsika/gpt-engineer](https://github.com/AntonOsika/gpt-engineer) | 46514 |
-|[imartinez/privateGPT](https://github.com/imartinez/privateGPT) | 44439 |
-|[LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant) | 35906 |
-|[hpcaitech/ColossalAI](https://github.com/hpcaitech/ColossalAI) | 35528 |
-|[moymix/TaskMatrix](https://github.com/moymix/TaskMatrix) | 34342 |
-|[geekan/MetaGPT](https://github.com/geekan/MetaGPT) | 31126 |
-|[streamlit/streamlit](https://github.com/streamlit/streamlit) | 28911 |
-|[reworkd/AgentGPT](https://github.com/reworkd/AgentGPT) | 27833 |
-|[StanGirard/quivr](https://github.com/StanGirard/quivr) | 26032 |
-|[OpenBB-finance/OpenBBTerminal](https://github.com/OpenBB-finance/OpenBBTerminal) | 24946 |
-|[run-llama/llama_index](https://github.com/run-llama/llama_index) | 24859 |
-|[jmorganca/ollama](https://github.com/jmorganca/ollama) | 20849 |
-|[openai/chatgpt-retrieval-plugin](https://github.com/openai/chatgpt-retrieval-plugin) | 20249 |
-|[chatchat-space/Langchain-Chatchat](https://github.com/chatchat-space/Langchain-Chatchat) | 19305 |
-|[mindsdb/mindsdb](https://github.com/mindsdb/mindsdb) | 19172 |
-|[PromtEngineer/localGPT](https://github.com/PromtEngineer/localGPT) | 17528 |
-|[cube-js/cube](https://github.com/cube-js/cube) | 16575 |
-|[mlflow/mlflow](https://github.com/mlflow/mlflow) | 16000 |
-|[mudler/LocalAI](https://github.com/mudler/LocalAI) | 14067 |
-|[logspace-ai/langflow](https://github.com/logspace-ai/langflow) | 13679 |
-|[GaiZhenbiao/ChuanhuChatGPT](https://github.com/GaiZhenbiao/ChuanhuChatGPT) | 13648 |
-|[arc53/DocsGPT](https://github.com/arc53/DocsGPT) | 13423 |
-|[openai/evals](https://github.com/openai/evals) | 12649 |
-|[airbytehq/airbyte](https://github.com/airbytehq/airbyte) | 12460 |
-|[langgenius/dify](https://github.com/langgenius/dify) | 11859 |
-|[databrickslabs/dolly](https://github.com/databrickslabs/dolly) | 10672 |
-|[AIGC-Audio/AudioGPT](https://github.com/AIGC-Audio/AudioGPT) | 9437 |
-|[langchain-ai/langchainjs](https://github.com/langchain-ai/langchainjs) | 9227 |
-|[gventuri/pandas-ai](https://github.com/gventuri/pandas-ai) | 9203 |
-|[aws/amazon-sagemaker-examples](https://github.com/aws/amazon-sagemaker-examples) | 9079 |
-|[h2oai/h2ogpt](https://github.com/h2oai/h2ogpt) | 8945 |
-|[PipedreamHQ/pipedream](https://github.com/PipedreamHQ/pipedream) | 7550 |
-|[bentoml/OpenLLM](https://github.com/bentoml/OpenLLM) | 6957 |
-|[THUDM/ChatGLM3](https://github.com/THUDM/ChatGLM3) | 6801 |
-|[microsoft/promptflow](https://github.com/microsoft/promptflow) | 6776 |
-|[cpacker/MemGPT](https://github.com/cpacker/MemGPT) | 6642 |
-|[joshpxyne/gpt-migrate](https://github.com/joshpxyne/gpt-migrate) | 6482 |
-|[zauberzeug/nicegui](https://github.com/zauberzeug/nicegui) | 6037 |
-|[embedchain/embedchain](https://github.com/embedchain/embedchain) | 6023 |
-|[mage-ai/mage-ai](https://github.com/mage-ai/mage-ai) | 6019 |
-|[assafelovic/gpt-researcher](https://github.com/assafelovic/gpt-researcher) | 5936 |
-|[sweepai/sweep](https://github.com/sweepai/sweep) | 5855 |
-|[wenda-LLM/wenda](https://github.com/wenda-LLM/wenda) | 5766 |
-|[zilliztech/GPTCache](https://github.com/zilliztech/GPTCache) | 5710 |
-|[pdm-project/pdm](https://github.com/pdm-project/pdm) | 5665 |
-|[GreyDGL/PentestGPT](https://github.com/GreyDGL/PentestGPT) | 5568 |
-|[gkamradt/langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) | 5507 |
-|[Shaunwei/RealChar](https://github.com/Shaunwei/RealChar) | 5501 |
-|[facebookresearch/llama-recipes](https://github.com/facebookresearch/llama-recipes) | 5477 |
-|[serge-chat/serge](https://github.com/serge-chat/serge) | 5221 |
-|[run-llama/rags](https://github.com/run-llama/rags) | 4916 |
-|[openchatai/OpenChat](https://github.com/openchatai/OpenChat) | 4870 |
-|[danswer-ai/danswer](https://github.com/danswer-ai/danswer) | 4774 |
-|[langchain-ai/opengpts](https://github.com/langchain-ai/opengpts) | 4709 |
-|[postgresml/postgresml](https://github.com/postgresml/postgresml) | 4639 |
-|[MineDojo/Voyager](https://github.com/MineDojo/Voyager) | 4582 |
-|[intel-analytics/BigDL](https://github.com/intel-analytics/BigDL) | 4581 |
-|[yihong0618/xiaogpt](https://github.com/yihong0618/xiaogpt) | 4359 |
-|[RayVentura/ShortGPT](https://github.com/RayVentura/ShortGPT) | 4357 |
-|[Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo) | 4317 |
-|[madawei2699/myGPTReader](https://github.com/madawei2699/myGPTReader) | 4289 |
-|[apache/nifi](https://github.com/apache/nifi) | 4098 |
-|[langchain-ai/chat-langchain](https://github.com/langchain-ai/chat-langchain) | 4091 |
-|[aiwaves-cn/agents](https://github.com/aiwaves-cn/agents) | 4073 |
-|[krishnaik06/The-Grand-Complete-Data-Science-Materials](https://github.com/krishnaik06/The-Grand-Complete-Data-Science-Materials) | 4065 |
-|[khoj-ai/khoj](https://github.com/khoj-ai/khoj) | 4016 |
-|[Azure/azure-sdk-for-python](https://github.com/Azure/azure-sdk-for-python) | 3941 |
-|[PrefectHQ/marvin](https://github.com/PrefectHQ/marvin) | 3915 |
-|[OpenBMB/ToolBench](https://github.com/OpenBMB/ToolBench) | 3799 |
-|[marqo-ai/marqo](https://github.com/marqo-ai/marqo) | 3771 |
-|[kyegomez/tree-of-thoughts](https://github.com/kyegomez/tree-of-thoughts) | 3688 |
-|[Unstructured-IO/unstructured](https://github.com/Unstructured-IO/unstructured) | 3543 |
-|[llm-workflow-engine/llm-workflow-engine](https://github.com/llm-workflow-engine/llm-workflow-engine) | 3515 |
-|[shroominic/codeinterpreter-api](https://github.com/shroominic/codeinterpreter-api) | 3425 |
-|[openchatai/OpenCopilot](https://github.com/openchatai/OpenCopilot) | 3418 |
-|[josStorer/RWKV-Runner](https://github.com/josStorer/RWKV-Runner) | 3297 |
-|[whitead/paper-qa](https://github.com/whitead/paper-qa) | 3280 |
-|[homanp/superagent](https://github.com/homanp/superagent) | 3258 |
-|[ParisNeo/lollms-webui](https://github.com/ParisNeo/lollms-webui) | 3199 |
-|[OpenBMB/AgentVerse](https://github.com/OpenBMB/AgentVerse) | 3099 |
-|[project-baize/baize-chatbot](https://github.com/project-baize/baize-chatbot) | 3090 |
-|[OpenGVLab/InternGPT](https://github.com/OpenGVLab/InternGPT) | 2989 |
-|[xlang-ai/OpenAgents](https://github.com/xlang-ai/OpenAgents) | 2825 |
-|[dataelement/bisheng](https://github.com/dataelement/bisheng) | 2797 |
-|[Mintplex-Labs/anything-llm](https://github.com/Mintplex-Labs/anything-llm) | 2784 |
-|[OpenBMB/BMTools](https://github.com/OpenBMB/BMTools) | 2734 |
-|[run-llama/llama-hub](https://github.com/run-llama/llama-hub) | 2721 |
-|[SamurAIGPT/EmbedAI](https://github.com/SamurAIGPT/EmbedAI) | 2647 |
-|[NVIDIA/NeMo-Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) | 2637 |
-|[X-D-Lab/LangChain-ChatGLM-Webui](https://github.com/X-D-Lab/LangChain-ChatGLM-Webui) | 2532 |
-|[GerevAI/gerev](https://github.com/GerevAI/gerev) | 2517 |
-|[keephq/keep](https://github.com/keephq/keep) | 2448 |
-|[yanqiangmiffy/Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) | 2397 |
-|[OpenGVLab/Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) | 2324 |
-|[IntelligenzaArtificiale/Free-Auto-GPT](https://github.com/IntelligenzaArtificiale/Free-Auto-GPT) | 2241 |
-|[YiVal/YiVal](https://github.com/YiVal/YiVal) | 2232 |
-|[jupyterlab/jupyter-ai](https://github.com/jupyterlab/jupyter-ai) | 2189 |
-|[Farama-Foundation/PettingZoo](https://github.com/Farama-Foundation/PettingZoo) | 2136 |
-|[microsoft/TaskWeaver](https://github.com/microsoft/TaskWeaver) | 2126 |
-|[hwchase17/notion-qa](https://github.com/hwchase17/notion-qa) | 2083 |
-|[FlagOpen/FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) | 2053 |
-|[paulpierre/RasaGPT](https://github.com/paulpierre/RasaGPT) | 1999 |
-|[hegelai/prompttools](https://github.com/hegelai/prompttools) | 1984 |
-|[mckinsey/vizro](https://github.com/mckinsey/vizro) | 1951 |
-|[vocodedev/vocode-python](https://github.com/vocodedev/vocode-python) | 1868 |
-|[dot-agent/openAMS](https://github.com/dot-agent/openAMS) | 1796 |
-|[explodinggradients/ragas](https://github.com/explodinggradients/ragas) | 1766 |
-|[AI-Citizen/SolidGPT](https://github.com/AI-Citizen/SolidGPT) | 1761 |
-|[Kav-K/GPTDiscord](https://github.com/Kav-K/GPTDiscord) | 1696 |
-|[run-llama/sec-insights](https://github.com/run-llama/sec-insights) | 1654 |
-|[avinashkranjan/Amazing-Python-Scripts](https://github.com/avinashkranjan/Amazing-Python-Scripts) | 1635 |
-|[microsoft/WhatTheHack](https://github.com/microsoft/WhatTheHack) | 1629 |
-|[noahshinn/reflexion](https://github.com/noahshinn/reflexion) | 1625 |
-|[psychic-api/psychic](https://github.com/psychic-api/psychic) | 1618 |
-|[Forethought-Technologies/AutoChain](https://github.com/Forethought-Technologies/AutoChain) | 1611 |
-|[pinterest/querybook](https://github.com/pinterest/querybook) | 1586 |
-|[refuel-ai/autolabel](https://github.com/refuel-ai/autolabel) | 1553 |
-|[jina-ai/langchain-serve](https://github.com/jina-ai/langchain-serve) | 1537 |
-|[jina-ai/dev-gpt](https://github.com/jina-ai/dev-gpt) | 1522 |
-|[agiresearch/OpenAGI](https://github.com/agiresearch/OpenAGI) | 1493 |
-|[ttengwang/Caption-Anything](https://github.com/ttengwang/Caption-Anything) | 1484 |
-|[greshake/llm-security](https://github.com/greshake/llm-security) | 1483 |
-|[promptfoo/promptfoo](https://github.com/promptfoo/promptfoo) | 1480 |
-|[milvus-io/bootcamp](https://github.com/milvus-io/bootcamp) | 1477 |
-|[richardyc/Chrome-GPT](https://github.com/richardyc/Chrome-GPT) | 1475 |
-|[melih-unsal/DemoGPT](https://github.com/melih-unsal/DemoGPT) | 1428 |
-|[YORG-AI/Open-Assistant](https://github.com/YORG-AI/Open-Assistant) | 1419 |
-|[101dotxyz/GPTeam](https://github.com/101dotxyz/GPTeam) | 1416 |
-|[jina-ai/thinkgpt](https://github.com/jina-ai/thinkgpt) | 1408 |
-|[mmz-001/knowledge_gpt](https://github.com/mmz-001/knowledge_gpt) | 1398 |
-|[intel/intel-extension-for-transformers](https://github.com/intel/intel-extension-for-transformers) | 1387 |
-|[Azure/azureml-examples](https://github.com/Azure/azureml-examples) | 1385 |
-|[lunasec-io/lunasec](https://github.com/lunasec-io/lunasec) | 1367 |
-|[eyurtsev/kor](https://github.com/eyurtsev/kor) | 1355 |
-|[xusenlinzy/api-for-open-llm](https://github.com/xusenlinzy/api-for-open-llm) | 1325 |
-|[griptape-ai/griptape](https://github.com/griptape-ai/griptape) | 1323 |
-|[SuperDuperDB/superduperdb](https://github.com/SuperDuperDB/superduperdb) | 1290 |
-|[cofactoryai/textbase](https://github.com/cofactoryai/textbase) | 1284 |
-|[psychic-api/rag-stack](https://github.com/psychic-api/rag-stack) | 1260 |
-|[filip-michalsky/SalesGPT](https://github.com/filip-michalsky/SalesGPT) | 1250 |
-|[nod-ai/SHARK](https://github.com/nod-ai/SHARK) | 1237 |
-|[pluralsh/plural](https://github.com/pluralsh/plural) | 1234 |
-|[cheshire-cat-ai/core](https://github.com/cheshire-cat-ai/core) | 1194 |
-|[LC1332/Chat-Haruhi-Suzumiya](https://github.com/LC1332/Chat-Haruhi-Suzumiya) | 1184 |
-|[poe-platform/server-bot-quick-start](https://github.com/poe-platform/server-bot-quick-start) | 1182 |
-|[microsoft/X-Decoder](https://github.com/microsoft/X-Decoder) | 1180 |
-|[juncongmoo/chatllama](https://github.com/juncongmoo/chatllama) | 1171 |
-|[visual-openllm/visual-openllm](https://github.com/visual-openllm/visual-openllm) | 1156 |
-|[alejandro-ao/ask-multiple-pdfs](https://github.com/alejandro-ao/ask-multiple-pdfs) | 1153 |
-|[ThousandBirdsInc/chidori](https://github.com/ThousandBirdsInc/chidori) | 1152 |
-|[irgolic/AutoPR](https://github.com/irgolic/AutoPR) | 1137 |
-|[SamurAIGPT/Camel-AutoGPT](https://github.com/SamurAIGPT/Camel-AutoGPT) | 1083 |
-|[ray-project/llm-applications](https://github.com/ray-project/llm-applications) | 1080 |
-|[run-llama/llama-lab](https://github.com/run-llama/llama-lab) | 1072 |
-|[jiran214/GPT-vup](https://github.com/jiran214/GPT-vup) | 1041 |
-|[MetaGLM/FinGLM](https://github.com/MetaGLM/FinGLM) | 1035 |
-|[peterw/Chat-with-Github-Repo](https://github.com/peterw/Chat-with-Github-Repo) | 1020 |
-|[Anil-matcha/ChatPDF](https://github.com/Anil-matcha/ChatPDF) | 991 |
-|[langchain-ai/langserve](https://github.com/langchain-ai/langserve) | 983 |
-|[THUDM/AgentTuning](https://github.com/THUDM/AgentTuning) | 976 |
-|[rlancemartin/auto-evaluator](https://github.com/rlancemartin/auto-evaluator) | 975 |
-|[codeacme17/examor](https://github.com/codeacme17/examor) | 964 |
-|[all-in-aigc/gpts-works](https://github.com/all-in-aigc/gpts-works) | 946 |
-|[Ikaros-521/AI-Vtuber](https://github.com/Ikaros-521/AI-Vtuber) | 946 |
-|[microsoft/Llama-2-Onnx](https://github.com/microsoft/Llama-2-Onnx) | 898 |
-|[cirediatpl/FigmaChain](https://github.com/cirediatpl/FigmaChain) | 895 |
-|[ricklamers/shell-ai](https://github.com/ricklamers/shell-ai) | 893 |
-|[modelscope/modelscope-agent](https://github.com/modelscope/modelscope-agent) | 893 |
-|[seanpixel/Teenage-AGI](https://github.com/seanpixel/Teenage-AGI) | 886 |
-|[ajndkr/lanarky](https://github.com/ajndkr/lanarky) | 880 |
-|[kennethleungty/Llama-2-Open-Source-LLM-CPU-Inference](https://github.com/kennethleungty/Llama-2-Open-Source-LLM-CPU-Inference) | 872 |
-|[corca-ai/EVAL](https://github.com/corca-ai/EVAL) | 846 |
-|[hwchase17/chat-your-data](https://github.com/hwchase17/chat-your-data) | 841 |
-|[kreneskyp/ix](https://github.com/kreneskyp/ix) | 821 |
-|[Link-AGI/AutoAgents](https://github.com/Link-AGI/AutoAgents) | 820 |
-|[truera/trulens](https://github.com/truera/trulens) | 794 |
-|[Dataherald/dataherald](https://github.com/Dataherald/dataherald) | 788 |
-|[sunlabuiuc/PyHealth](https://github.com/sunlabuiuc/PyHealth) | 783 |
-|[jondurbin/airoboros](https://github.com/jondurbin/airoboros) | 783 |
-|[pyspark-ai/pyspark-ai](https://github.com/pyspark-ai/pyspark-ai) | 782 |
-|[confident-ai/deepeval](https://github.com/confident-ai/deepeval) | 780 |
-|[billxbf/ReWOO](https://github.com/billxbf/ReWOO) | 777 |
-|[langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent) | 776 |
-|[akshata29/entaoai](https://github.com/akshata29/entaoai) | 771 |
-|[LambdaLabsML/examples](https://github.com/LambdaLabsML/examples) | 770 |
-|[getmetal/motorhead](https://github.com/getmetal/motorhead) | 768 |
-|[Dicklesworthstone/swiss_army_llama](https://github.com/Dicklesworthstone/swiss_army_llama) | 757 |
-|[ruoccofabrizio/azure-open-ai-embeddings-qna](https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna) | 757 |
-|[msoedov/langcorn](https://github.com/msoedov/langcorn) | 754 |
-|[e-johnstonn/BriefGPT](https://github.com/e-johnstonn/BriefGPT) | 753 |
-|[microsoft/sample-app-aoai-chatGPT](https://github.com/microsoft/sample-app-aoai-chatGPT) | 749 |
-|[explosion/spacy-llm](https://github.com/explosion/spacy-llm) | 731 |
-|[MiuLab/Taiwan-LLM](https://github.com/MiuLab/Taiwan-LLM) | 716 |
-|[whyiyhw/chatgpt-wechat](https://github.com/whyiyhw/chatgpt-wechat) | 702 |
-|[Azure-Samples/openai](https://github.com/Azure-Samples/openai) | 692 |
-|[iusztinpaul/hands-on-llms](https://github.com/iusztinpaul/hands-on-llms) | 687 |
-|[safevideo/autollm](https://github.com/safevideo/autollm) | 682 |
-|[OpenGenerativeAI/GenossGPT](https://github.com/OpenGenerativeAI/GenossGPT) | 669 |
-|[NoDataFound/hackGPT](https://github.com/NoDataFound/hackGPT) | 663 |
-|[AILab-CVC/GPT4Tools](https://github.com/AILab-CVC/GPT4Tools) | 662 |
-|[langchain-ai/auto-evaluator](https://github.com/langchain-ai/auto-evaluator) | 657 |
-|[yvann-ba/Robby-chatbot](https://github.com/yvann-ba/Robby-chatbot) | 639 |
-|[alexanderatallah/window.ai](https://github.com/alexanderatallah/window.ai) | 635 |
-|[amosjyng/langchain-visualizer](https://github.com/amosjyng/langchain-visualizer) | 630 |
-|[microsoft/PodcastCopilot](https://github.com/microsoft/PodcastCopilot) | 621 |
-|[aws-samples/aws-genai-llm-chatbot](https://github.com/aws-samples/aws-genai-llm-chatbot) | 616 |
-|[NeumTry/NeumAI](https://github.com/NeumTry/NeumAI) | 605 |
-|[namuan/dr-doc-search](https://github.com/namuan/dr-doc-search) | 599 |
-|[plastic-labs/tutor-gpt](https://github.com/plastic-labs/tutor-gpt) | 595 |
-|[marimo-team/marimo](https://github.com/marimo-team/marimo) | 591 |
-|[yakami129/VirtualWife](https://github.com/yakami129/VirtualWife) | 586 |
-|[xuwenhao/geektime-ai-course](https://github.com/xuwenhao/geektime-ai-course) | 584 |
-|[jonra1993/fastapi-alembic-sqlmodel-async](https://github.com/jonra1993/fastapi-alembic-sqlmodel-async) | 573 |
-|[dgarnitz/vectorflow](https://github.com/dgarnitz/vectorflow) | 568 |
-|[yeagerai/yeagerai-agent](https://github.com/yeagerai/yeagerai-agent) | 564 |
-|[daveebbelaar/langchain-experiments](https://github.com/daveebbelaar/langchain-experiments) | 563 |
-|[traceloop/openllmetry](https://github.com/traceloop/openllmetry) | 559 |
-|[Agenta-AI/agenta](https://github.com/Agenta-AI/agenta) | 546 |
-|[michaelthwan/searchGPT](https://github.com/michaelthwan/searchGPT) | 545 |
-|[jina-ai/agentchain](https://github.com/jina-ai/agentchain) | 544 |
-|[mckaywrigley/repo-chat](https://github.com/mckaywrigley/repo-chat) | 533 |
-|[marella/chatdocs](https://github.com/marella/chatdocs) | 532 |
-|[opentensor/bittensor](https://github.com/opentensor/bittensor) | 532 |
-|[DjangoPeng/openai-quickstart](https://github.com/DjangoPeng/openai-quickstart) | 527 |
-|[freddyaboulton/gradio-tools](https://github.com/freddyaboulton/gradio-tools) | 517 |
-|[sidhq/Multi-GPT](https://github.com/sidhq/Multi-GPT) | 515 |
-|[alejandro-ao/langchain-ask-pdf](https://github.com/alejandro-ao/langchain-ask-pdf) | 514 |
-|[sajjadium/ctf-archives](https://github.com/sajjadium/ctf-archives) | 507 |
-|[continuum-llms/chatgpt-memory](https://github.com/continuum-llms/chatgpt-memory) | 502 |
-|[steamship-core/steamship-langchain](https://github.com/steamship-core/steamship-langchain) | 494 |
-|[mpaepper/content-chatbot](https://github.com/mpaepper/content-chatbot) | 493 |
-|[langchain-ai/langchain-aiplugin](https://github.com/langchain-ai/langchain-aiplugin) | 492 |
-|[logan-markewich/llama_index_starter_pack](https://github.com/logan-markewich/llama_index_starter_pack) | 483 |
-|[datawhalechina/llm-universe](https://github.com/datawhalechina/llm-universe) | 475 |
-|[leondz/garak](https://github.com/leondz/garak) | 464 |
-|[RedisVentures/ArXivChatGuru](https://github.com/RedisVentures/ArXivChatGuru) | 461 |
-|[Anil-matcha/Chatbase](https://github.com/Anil-matcha/Chatbase) | 455 |
-|[Aiyu-awa/luna-ai](https://github.com/Aiyu-awa/luna-ai) | 450 |
-|[DataDog/dd-trace-py](https://github.com/DataDog/dd-trace-py) | 450 |
-|[Azure-Samples/miyagi](https://github.com/Azure-Samples/miyagi) | 449 |
-|[poe-platform/poe-protocol](https://github.com/poe-platform/poe-protocol) | 447 |
-|[onlyphantom/llm-python](https://github.com/onlyphantom/llm-python) | 446 |
-|[junruxiong/IncarnaMind](https://github.com/junruxiong/IncarnaMind) | 441 |
-|[CarperAI/OpenELM](https://github.com/CarperAI/OpenELM) | 441 |
-|[daodao97/chatdoc](https://github.com/daodao97/chatdoc) | 437 |
-|[showlab/VLog](https://github.com/showlab/VLog) | 436 |
-|[wandb/weave](https://github.com/wandb/weave) | 420 |
-|[QwenLM/Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) | 419 |
-|[huchenxucs/ChatDB](https://github.com/huchenxucs/ChatDB) | 416 |
-|[jerlendds/osintbuddy](https://github.com/jerlendds/osintbuddy) | 411 |
-|[monarch-initiative/ontogpt](https://github.com/monarch-initiative/ontogpt) | 408 |
-|[mallorbc/Finetune_LLMs](https://github.com/mallorbc/Finetune_LLMs) | 406 |
-|[JayZeeDesign/researcher-gpt](https://github.com/JayZeeDesign/researcher-gpt) | 405 |
-|[rsaryev/talk-codebase](https://github.com/rsaryev/talk-codebase) | 401 |
-|[langchain-ai/langsmith-cookbook](https://github.com/langchain-ai/langsmith-cookbook) | 398 |
-|[mtenenholtz/chat-twitter](https://github.com/mtenenholtz/chat-twitter) | 398 |
-|[morpheuslord/GPT_Vuln-analyzer](https://github.com/morpheuslord/GPT_Vuln-analyzer) | 391 |
-|[MagnivOrg/prompt-layer-library](https://github.com/MagnivOrg/prompt-layer-library) | 387 |
-|[JohnSnowLabs/langtest](https://github.com/JohnSnowLabs/langtest) | 384 |
-|[mrwadams/attackgen](https://github.com/mrwadams/attackgen) | 381 |
-|[codefuse-ai/Test-Agent](https://github.com/codefuse-ai/Test-Agent) | 380 |
-|[personoids/personoids-lite](https://github.com/personoids/personoids-lite) | 379 |
-|[mosaicml/examples](https://github.com/mosaicml/examples) | 378 |
-|[steamship-packages/langchain-production-starter](https://github.com/steamship-packages/langchain-production-starter) | 370 |
-|[FlagAI-Open/Aquila2](https://github.com/FlagAI-Open/Aquila2) | 365 |
-|[Mintplex-Labs/vector-admin](https://github.com/Mintplex-Labs/vector-admin) | 365 |
-|[NimbleBoxAI/ChainFury](https://github.com/NimbleBoxAI/ChainFury) | 357 |
-|[BlackHC/llm-strategy](https://github.com/BlackHC/llm-strategy) | 354 |
-|[lilacai/lilac](https://github.com/lilacai/lilac) | 352 |
-|[preset-io/promptimize](https://github.com/preset-io/promptimize) | 351 |
-|[yuanjie-ai/ChatLLM](https://github.com/yuanjie-ai/ChatLLM) | 347 |
-|[andylokandy/gpt-4-search](https://github.com/andylokandy/gpt-4-search) | 346 |
-|[zhoudaquan/ChatAnything](https://github.com/zhoudaquan/ChatAnything) | 343 |
-|[rgomezcasas/dotfiles](https://github.com/rgomezcasas/dotfiles) | 343 |
-|[tigerlab-ai/tiger](https://github.com/tigerlab-ai/tiger) | 342 |
-|[HumanSignal/label-studio-ml-backend](https://github.com/HumanSignal/label-studio-ml-backend) | 334 |
-|[nasa-petal/bidara](https://github.com/nasa-petal/bidara) | 334 |
-|[momegas/megabots](https://github.com/momegas/megabots) | 334 |
-|[Cheems-Seminar/grounded-segment-any-parts](https://github.com/Cheems-Seminar/grounded-segment-any-parts) | 330 |
-|[CambioML/pykoi](https://github.com/CambioML/pykoi) | 326 |
-|[Nuggt-dev/Nuggt](https://github.com/Nuggt-dev/Nuggt) | 326 |
-|[wandb/edu](https://github.com/wandb/edu) | 326 |
-|[Haste171/langchain-chatbot](https://github.com/Haste171/langchain-chatbot) | 324 |
-|[sugarforever/LangChain-Tutorials](https://github.com/sugarforever/LangChain-Tutorials) | 322 |
-|[liangwq/Chatglm_lora_multi-gpu](https://github.com/liangwq/Chatglm_lora_multi-gpu) | 321 |
-|[ur-whitelab/chemcrow-public](https://github.com/ur-whitelab/chemcrow-public) | 320 |
-|[itamargol/openai](https://github.com/itamargol/openai) | 318 |
-|[gia-guar/JARVIS-ChatGPT](https://github.com/gia-guar/JARVIS-ChatGPT) | 304 |
-|[SpecterOps/Nemesis](https://github.com/SpecterOps/Nemesis) | 302 |
-|[facebookresearch/personal-timeline](https://github.com/facebookresearch/personal-timeline) | 302 |
-|[hnawaz007/pythondataanalysis](https://github.com/hnawaz007/pythondataanalysis) | 301 |
-|[Chainlit/cookbook](https://github.com/Chainlit/cookbook) | 300 |
-|[airobotlab/KoChatGPT](https://github.com/airobotlab/KoChatGPT) | 300 |
-|[GPT-Fathom/GPT-Fathom](https://github.com/GPT-Fathom/GPT-Fathom) | 299 |
-|[kaarthik108/snowChat](https://github.com/kaarthik108/snowChat) | 299 |
-|[kyegomez/swarms](https://github.com/kyegomez/swarms) | 296 |
-|[LangStream/langstream](https://github.com/LangStream/langstream) | 295 |
-|[genia-dev/GeniA](https://github.com/genia-dev/GeniA) | 294 |
-|[shamspias/customizable-gpt-chatbot](https://github.com/shamspias/customizable-gpt-chatbot) | 291 |
-|[TsinghuaDatabaseGroup/DB-GPT](https://github.com/TsinghuaDatabaseGroup/DB-GPT) | 290 |
-|[conceptofmind/toolformer](https://github.com/conceptofmind/toolformer) | 283 |
-|[sullivan-sean/chat-langchainjs](https://github.com/sullivan-sean/chat-langchainjs) | 283 |
-|[AutoPackAI/beebot](https://github.com/AutoPackAI/beebot) | 282 |
-|[pablomarin/GPT-Azure-Search-Engine](https://github.com/pablomarin/GPT-Azure-Search-Engine) | 282 |
-|[gkamradt/LLMTest_NeedleInAHaystack](https://github.com/gkamradt/LLMTest_NeedleInAHaystack) | 280 |
-|[gustavz/DataChad](https://github.com/gustavz/DataChad) | 280 |
-|[Safiullah-Rahu/CSV-AI](https://github.com/Safiullah-Rahu/CSV-AI) | 278 |
-|[hwchase17/chroma-langchain](https://github.com/hwchase17/chroma-langchain) | 275 |
-|[AkshitIreddy/Interactive-LLM-Powered-NPCs](https://github.com/AkshitIreddy/Interactive-LLM-Powered-NPCs) | 268 |
-|[ennucore/clippinator](https://github.com/ennucore/clippinator) | 267 |
-|[artitw/text2text](https://github.com/artitw/text2text) | 264 |
-|[anarchy-ai/LLM-VM](https://github.com/anarchy-ai/LLM-VM) | 263 |
-|[wpydcr/LLM-Kit](https://github.com/wpydcr/LLM-Kit) | 262 |
-|[streamlit/llm-examples](https://github.com/streamlit/llm-examples) | 262 |
-|[paolorechia/learn-langchain](https://github.com/paolorechia/learn-langchain) | 262 |
-|[yym68686/ChatGPT-Telegram-Bot](https://github.com/yym68686/ChatGPT-Telegram-Bot) | 261 |
-|[PradipNichite/Youtube-Tutorials](https://github.com/PradipNichite/Youtube-Tutorials) | 259 |
-|[radi-cho/datasetGPT](https://github.com/radi-cho/datasetGPT) | 259 |
-|[ur-whitelab/exmol](https://github.com/ur-whitelab/exmol) | 259 |
-|[ml6team/fondant](https://github.com/ml6team/fondant) | 254 |
-|[bborn/howdoi.ai](https://github.com/bborn/howdoi.ai) | 254 |
-|[rahulnyk/knowledge_graph](https://github.com/rahulnyk/knowledge_graph) | 253 |
-|[recalign/RecAlign](https://github.com/recalign/RecAlign) | 248 |
-|[hwchase17/langchain-streamlit-template](https://github.com/hwchase17/langchain-streamlit-template) | 248 |
-|[fetchai/uAgents](https://github.com/fetchai/uAgents) | 247 |
-|[arthur-ai/bench](https://github.com/arthur-ai/bench) | 247 |
-|[miaoshouai/miaoshouai-assistant](https://github.com/miaoshouai/miaoshouai-assistant) | 246 |
-|[RoboCoachTechnologies/GPT-Synthesizer](https://github.com/RoboCoachTechnologies/GPT-Synthesizer) | 244 |
-|[langchain-ai/web-explorer](https://github.com/langchain-ai/web-explorer) | 242 |
-|[kaleido-lab/dolphin](https://github.com/kaleido-lab/dolphin) | 242 |
-|[PJLab-ADG/DriveLikeAHuman](https://github.com/PJLab-ADG/DriveLikeAHuman) | 241 |
-|[stepanogil/autonomous-hr-chatbot](https://github.com/stepanogil/autonomous-hr-chatbot) | 238 |
-|[WongSaang/chatgpt-ui-server](https://github.com/WongSaang/chatgpt-ui-server) | 236 |
-|[nexus-stc/stc](https://github.com/nexus-stc/stc) | 235 |
-|[yeagerai/genworlds](https://github.com/yeagerai/genworlds) | 235 |
-|[Gentopia-AI/Gentopia](https://github.com/Gentopia-AI/Gentopia) | 235 |
-|[alphasecio/langchain-examples](https://github.com/alphasecio/langchain-examples) | 235 |
-|[grumpyp/aixplora](https://github.com/grumpyp/aixplora) | 232 |
-|[shaman-ai/agent-actors](https://github.com/shaman-ai/agent-actors) | 232 |
-|[darrenburns/elia](https://github.com/darrenburns/elia) | 231 |
-|[orgexyz/BlockAGI](https://github.com/orgexyz/BlockAGI) | 231 |
-|[handrew/browserpilot](https://github.com/handrew/browserpilot) | 226 |
-|[su77ungr/CASALIOY](https://github.com/su77ungr/CASALIOY) | 225 |
-|[nicknochnack/LangchainDocuments](https://github.com/nicknochnack/LangchainDocuments) | 225 |
-|[dbpunk-labs/octogen](https://github.com/dbpunk-labs/octogen) | 224 |
-|[langchain-ai/weblangchain](https://github.com/langchain-ai/weblangchain) | 222 |
-|[CL-lau/SQL-GPT](https://github.com/CL-lau/SQL-GPT) | 222 |
-|[alvarosevilla95/autolang](https://github.com/alvarosevilla95/autolang) | 221 |
-|[showlab/UniVTG](https://github.com/showlab/UniVTG) | 220 |
-|[edreisMD/plugnplai](https://github.com/edreisMD/plugnplai) | 219 |
-|[hardbyte/qabot](https://github.com/hardbyte/qabot) | 216 |
-|[microsoft/azure-openai-in-a-day-workshop](https://github.com/microsoft/azure-openai-in-a-day-workshop) | 215 |
-|[Azure-Samples/chat-with-your-data-solution-accelerator](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator) | 214 |
-|[amadad/agentcy](https://github.com/amadad/agentcy) | 213 |
-|[snexus/llm-search](https://github.com/snexus/llm-search) | 212 |
-|[afaqueumer/DocQA](https://github.com/afaqueumer/DocQA) | 206 |
-|[plchld/InsightFlow](https://github.com/plchld/InsightFlow) | 205 |
-|[yasyf/compress-gpt](https://github.com/yasyf/compress-gpt) | 205 |
-|[benthecoder/ClassGPT](https://github.com/benthecoder/ClassGPT) | 205 |
-|[voxel51/voxelgpt](https://github.com/voxel51/voxelgpt) | 204 |
-|[jbrukh/gpt-jargon](https://github.com/jbrukh/gpt-jargon) | 204 |
-|[emarco177/ice_breaker](https://github.com/emarco177/ice_breaker) | 204 |
-|[tencentmusic/supersonic](https://github.com/tencentmusic/supersonic) | 202 |
-|[Azure-Samples/azure-search-power-skills](https://github.com/Azure-Samples/azure-search-power-skills) | 202 |
-|[blob42/Instrukt](https://github.com/blob42/Instrukt) | 201 |
-|[langchain-ai/langsmith-sdk](https://github.com/langchain-ai/langsmith-sdk) | 200 |
-|[SamPink/dev-gpt](https://github.com/SamPink/dev-gpt) | 200 |
-|[ju-bezdek/langchain-decorators](https://github.com/ju-bezdek/langchain-decorators) | 198 |
-|[KMnO4-zx/huanhuan-chat](https://github.com/KMnO4-zx/huanhuan-chat) | 196 |
-|[Azure-Samples/jp-azureopenai-samples](https://github.com/Azure-Samples/jp-azureopenai-samples) | 192 |
-|[hongbo-miao/hongbomiao.com](https://github.com/hongbo-miao/hongbomiao.com) | 190 |
-|[CakeCrusher/openplugin](https://github.com/CakeCrusher/openplugin) | 190 |
-|[PaddlePaddle/ERNIE-Bot-SDK](https://github.com/PaddlePaddle/ERNIE-Bot-SDK) | 189 |
-|[retr0reg/Ret2GPT](https://github.com/retr0reg/Ret2GPT) | 189 |
-|[AmineDiro/cria](https://github.com/AmineDiro/cria) | 187 |
-|[lancedb/vectordb-recipes](https://github.com/lancedb/vectordb-recipes) | 186 |
-|[vaibkumr/prompt-optimizer](https://github.com/vaibkumr/prompt-optimizer) | 185 |
-|[aws-ia/ecs-blueprints](https://github.com/aws-ia/ecs-blueprints) | 184 |
-|[ethanyanjiali/minChatGPT](https://github.com/ethanyanjiali/minChatGPT) | 183 |
-|[MuhammadMoinFaisal/LargeLanguageModelsProjects](https://github.com/MuhammadMoinFaisal/LargeLanguageModelsProjects) | 182 |
-|[shauryr/S2QA](https://github.com/shauryr/S2QA) | 181 |
-|[summarizepaper/summarizepaper](https://github.com/summarizepaper/summarizepaper) | 180 |
-|[NomaDamas/RAGchain](https://github.com/NomaDamas/RAGchain) | 179 |
-|[pnkvalavala/repochat](https://github.com/pnkvalavala/repochat) | 179 |
-|[ibiscp/LLM-IMDB](https://github.com/ibiscp/LLM-IMDB) | 177 |
-|[fengyuli-dev/multimedia-gpt](https://github.com/fengyuli-dev/multimedia-gpt) | 177 |
-|[langchain-ai/text-split-explorer](https://github.com/langchain-ai/text-split-explorer) | 175 |
-|[iMagist486/ElasticSearch-Langchain-Chatglm2](https://github.com/iMagist486/ElasticSearch-Langchain-Chatglm2) | 175 |
-|[limaoyi1/Auto-PPT](https://github.com/limaoyi1/Auto-PPT) | 175 |
-|[Open-Swarm-Net/GPT-Swarm](https://github.com/Open-Swarm-Net/GPT-Swarm) | 175 |
-|[morpheuslord/HackBot](https://github.com/morpheuslord/HackBot) | 174 |
-|[v7labs/benchllm](https://github.com/v7labs/benchllm) | 174 |
-|[Coding-Crashkurse/Langchain-Full-Course](https://github.com/Coding-Crashkurse/Langchain-Full-Course) | 174 |
-|[dongyh20/Octopus](https://github.com/dongyh20/Octopus) | 173 |
-|[kimtth/azure-openai-llm-vector-langchain](https://github.com/kimtth/azure-openai-llm-vector-langchain) | 173 |
-|[mayooear/private-chatbot-mpt30b-langchain](https://github.com/mayooear/private-chatbot-mpt30b-langchain) | 173 |
-|[zilliztech/akcio](https://github.com/zilliztech/akcio) | 172 |
-|[jmpaz/promptlib](https://github.com/jmpaz/promptlib) | 172 |
-|[ccurme/yolopandas](https://github.com/ccurme/yolopandas) | 172 |
-|[joaomdmoura/CrewAI](https://github.com/joaomdmoura/CrewAI) | 170 |
-|[katanaml/llm-mistral-invoice-cpu](https://github.com/katanaml/llm-mistral-invoice-cpu) | 170 |
-|[chakkaradeep/pyCodeAGI](https://github.com/chakkaradeep/pyCodeAGI) | 170 |
-|[mudler/LocalAGI](https://github.com/mudler/LocalAGI) | 167 |
-|[dssjon/biblos](https://github.com/dssjon/biblos) | 165 |
-|[kjappelbaum/gptchem](https://github.com/kjappelbaum/gptchem) | 165 |
-|[xxw1995/chatglm3-finetune](https://github.com/xxw1995/chatglm3-finetune) | 164 |
-|[ArjanCodes/examples](https://github.com/ArjanCodes/examples) | 163 |
-|[AIAnytime/Llama2-Medical-Chatbot](https://github.com/AIAnytime/Llama2-Medical-Chatbot) | 163 |
-|[RCGAI/SimplyRetrieve](https://github.com/RCGAI/SimplyRetrieve) | 162 |
-|[langchain-ai/langchain-teacher](https://github.com/langchain-ai/langchain-teacher) | 162 |
-|[menloparklab/falcon-langchain](https://github.com/menloparklab/falcon-langchain) | 162 |
-|[flurb18/AgentOoba](https://github.com/flurb18/AgentOoba) | 162 |
-|[homanp/vercel-langchain](https://github.com/homanp/vercel-langchain) | 161 |
-|[jiran214/langup-ai](https://github.com/jiran214/langup-ai) | 160 |
-|[JorisdeJong123/7-Days-of-LangChain](https://github.com/JorisdeJong123/7-Days-of-LangChain) | 160 |
-|[GoogleCloudPlatform/data-analytics-golden-demo](https://github.com/GoogleCloudPlatform/data-analytics-golden-demo) | 159 |
-|[positive666/Prompt-Can-Anything](https://github.com/positive666/Prompt-Can-Anything) | 159 |
-|[luisroque/large_laguage_models](https://github.com/luisroque/large_laguage_models) | 159 |
-|[mlops-for-all/mlops-for-all.github.io](https://github.com/mlops-for-all/mlops-for-all.github.io) | 158 |
-|[wandb/wandbot](https://github.com/wandb/wandbot) | 158 |
-|[elastic/elasticsearch-labs](https://github.com/elastic/elasticsearch-labs) | 157 |
-|[shroominic/funcchain](https://github.com/shroominic/funcchain) | 157 |
-|[deeppavlov/dream](https://github.com/deeppavlov/dream) | 156 |
-|[mluogh/eastworld](https://github.com/mluogh/eastworld) | 154 |
-|[georgesung/llm_qlora](https://github.com/georgesung/llm_qlora) | 154 |
-|[RUC-GSAI/YuLan-Rec](https://github.com/RUC-GSAI/YuLan-Rec) | 153 |
-|[KylinC/ChatFinance](https://github.com/KylinC/ChatFinance) | 152 |
-|[Dicklesworthstone/llama2_aided_tesseract](https://github.com/Dicklesworthstone/llama2_aided_tesseract) | 152 |
-|[c0sogi/LLMChat](https://github.com/c0sogi/LLMChat) | 152 |
-|[eunomia-bpf/GPTtrace](https://github.com/eunomia-bpf/GPTtrace) | 152 |
-|[ErikBjare/gptme](https://github.com/ErikBjare/gptme) | 152 |
-|[Klingefjord/chatgpt-telegram](https://github.com/Klingefjord/chatgpt-telegram) | 152 |
-|[RoboCoachTechnologies/ROScribe](https://github.com/RoboCoachTechnologies/ROScribe) | 151 |
-|[Aggregate-Intellect/sherpa](https://github.com/Aggregate-Intellect/sherpa) | 151 |
-|[3Alan/DocsMind](https://github.com/3Alan/DocsMind) | 151 |
-|[tangqiaoyu/ToolAlpaca](https://github.com/tangqiaoyu/ToolAlpaca) | 150 |
-|[kulltc/chatgpt-sql](https://github.com/kulltc/chatgpt-sql) | 150 |
-|[mallahyari/drqa](https://github.com/mallahyari/drqa) | 150 |
-|[MedalCollector/Orator](https://github.com/MedalCollector/Orator) | 149 |
-|[Teahouse-Studios/akari-bot](https://github.com/Teahouse-Studios/akari-bot) | 149 |
-|[realminchoi/babyagi-ui](https://github.com/realminchoi/babyagi-ui) | 148 |
-|[ssheng/BentoChain](https://github.com/ssheng/BentoChain) | 148 |
-|[solana-labs/chatgpt-plugin](https://github.com/solana-labs/chatgpt-plugin) | 147 |
-|[aurelio-labs/arxiv-bot](https://github.com/aurelio-labs/arxiv-bot) | 147 |
-|[Jaseci-Labs/jaseci](https://github.com/Jaseci-Labs/jaseci) | 146 |
-|[menloparklab/langchain-cohere-qdrant-doc-retrieval](https://github.com/menloparklab/langchain-cohere-qdrant-doc-retrieval) | 146 |
-|[trancethehuman/entities-extraction-web-scraper](https://github.com/trancethehuman/entities-extraction-web-scraper) | 144 |
-|[peterw/StoryStorm](https://github.com/peterw/StoryStorm) | 144 |
-|[grumpyp/chroma-langchain-tutorial](https://github.com/grumpyp/chroma-langchain-tutorial) | 144 |
-|[gh18l/CrawlGPT](https://github.com/gh18l/CrawlGPT) | 142 |
-|[langchain-ai/langchain-aws-template](https://github.com/langchain-ai/langchain-aws-template) | 142 |
-|[yasyf/summ](https://github.com/yasyf/summ) | 141 |
-|[petehunt/langchain-github-bot](https://github.com/petehunt/langchain-github-bot) | 141 |
-|[hirokidaichi/wanna](https://github.com/hirokidaichi/wanna) | 140 |
-|[jina-ai/fastapi-serve](https://github.com/jina-ai/fastapi-serve) | 139 |
-|[zenml-io/zenml-projects](https://github.com/zenml-io/zenml-projects) | 139 |
-|[jlonge4/local_llama](https://github.com/jlonge4/local_llama) | 139 |
-|[smyja/blackmaria](https://github.com/smyja/blackmaria) | 138 |
-|[ChuloAI/BrainChulo](https://github.com/ChuloAI/BrainChulo) | 137 |
-|[log1stics/voice-generator-webui](https://github.com/log1stics/voice-generator-webui) | 137 |
-|[davila7/file-gpt](https://github.com/davila7/file-gpt) | 137 |
-|[dcaribou/transfermarkt-datasets](https://github.com/dcaribou/transfermarkt-datasets) | 136 |
-|[ciare-robotics/world-creator](https://github.com/ciare-robotics/world-creator) | 135 |
-|[Undertone0809/promptulate](https://github.com/Undertone0809/promptulate) | 134 |
-|[fixie-ai/fixie-examples](https://github.com/fixie-ai/fixie-examples) | 134 |
-|[run-llama/ai-engineer-workshop](https://github.com/run-llama/ai-engineer-workshop) | 133 |
-|[definitive-io/code-indexer-loop](https://github.com/definitive-io/code-indexer-loop) | 131 |
-|[mortium91/langchain-assistant](https://github.com/mortium91/langchain-assistant) | 131 |
-|[baidubce/bce-qianfan-sdk](https://github.com/baidubce/bce-qianfan-sdk) | 130 |
-|[Ngonie-x/langchain_csv](https://github.com/Ngonie-x/langchain_csv) | 130 |
-|[IvanIsCoding/ResuLLMe](https://github.com/IvanIsCoding/ResuLLMe) | 130 |
-|[AnchoringAI/anchoring-ai](https://github.com/AnchoringAI/anchoring-ai) | 129 |
-|[Azure/business-process-automation](https://github.com/Azure/business-process-automation) | 128 |
-|[athina-ai/athina-sdk](https://github.com/athina-ai/athina-sdk) | 126 |
-|[thunlp/ChatEval](https://github.com/thunlp/ChatEval) | 126 |
-|[prof-frink-lab/slangchain](https://github.com/prof-frink-lab/slangchain) | 126 |
-|[vietanhdev/pautobot](https://github.com/vietanhdev/pautobot) | 125 |
-|[awslabs/generative-ai-cdk-constructs](https://github.com/awslabs/generative-ai-cdk-constructs) | 124 |
-|[sdaaron/QueryGPT](https://github.com/sdaaron/QueryGPT) | 124 |
-|[rabbitmetrics/langchain-13-min](https://github.com/rabbitmetrics/langchain-13-min) | 124 |
-|[AutoLLM/AutoAgents](https://github.com/AutoLLM/AutoAgents) | 122 |
-|[nicknochnack/Nopenai](https://github.com/nicknochnack/Nopenai) | 122 |
-|[wombyz/HormoziGPT](https://github.com/wombyz/HormoziGPT) | 122 |
-|[dotvignesh/PDFChat](https://github.com/dotvignesh/PDFChat) | 122 |
-|[topoteretes/PromethAI-Backend](https://github.com/topoteretes/PromethAI-Backend) | 121 |
-|[nftblackmagic/flask-langchain](https://github.com/nftblackmagic/flask-langchain) | 121 |
-|[vishwasg217/finsight](https://github.com/vishwasg217/finsight) | 120 |
-|[snap-stanford/MLAgentBench](https://github.com/snap-stanford/MLAgentBench) | 120 |
-|[Azure/app-service-linux-docs](https://github.com/Azure/app-service-linux-docs) | 120 |
-|[nyanp/chat2plot](https://github.com/nyanp/chat2plot) | 120 |
-|[ant4g0nist/polar](https://github.com/ant4g0nist/polar) | 119 |
-|[aws-samples/cdk-eks-blueprints-patterns](https://github.com/aws-samples/cdk-eks-blueprints-patterns) | 119 |
-|[aws-samples/amazon-kendra-langchain-extensions](https://github.com/aws-samples/amazon-kendra-langchain-extensions) | 119 |
-|[Xueheng-Li/SynologyChatbotGPT](https://github.com/Xueheng-Li/SynologyChatbotGPT) | 119 |
-|[CodeAlchemyAI/ViLT-GPT](https://github.com/CodeAlchemyAI/ViLT-GPT) | 117 |
-|[Lin-jun-xiang/docGPT-langchain](https://github.com/Lin-jun-xiang/docGPT-langchain) | 117 |
-|[ademakdogan/ChatSQL](https://github.com/ademakdogan/ChatSQL) | 116 |
-|[aniketmaurya/llm-inference](https://github.com/aniketmaurya/llm-inference) | 115 |
-|[xuwenhao/mactalk-ai-course](https://github.com/xuwenhao/mactalk-ai-course) | 115 |
-|[cmooredev/RepoReader](https://github.com/cmooredev/RepoReader) | 115 |
-|[abi/autocommit](https://github.com/abi/autocommit) | 115 |
-|[MIDORIBIN/langchain-gpt4free](https://github.com/MIDORIBIN/langchain-gpt4free) | 114 |
-|[finaldie/auto-news](https://github.com/finaldie/auto-news) | 114 |
-|[Anil-matcha/Youtube-to-chatbot](https://github.com/Anil-matcha/Youtube-to-chatbot) | 114 |
-|[avrabyt/MemoryBot](https://github.com/avrabyt/MemoryBot) | 114 |
-|[Capsize-Games/airunner](https://github.com/Capsize-Games/airunner) | 113 |
-|[atisharma/llama_farm](https://github.com/atisharma/llama_farm) | 113 |
-|[mbchang/data-driven-characters](https://github.com/mbchang/data-driven-characters) | 112 |
-|[fiddler-labs/fiddler-auditor](https://github.com/fiddler-labs/fiddler-auditor) | 112 |
-|[dirkjbreeuwer/gpt-automated-web-scraper](https://github.com/dirkjbreeuwer/gpt-automated-web-scraper) | 111 |
-|[Appointat/Chat-with-Document-s-using-ChatGPT-API-and-Text-Embedding](https://github.com/Appointat/Chat-with-Document-s-using-ChatGPT-API-and-Text-Embedding) | 111 |
-|[hwchase17/langchain-gradio-template](https://github.com/hwchase17/langchain-gradio-template) | 111 |
-|[artas728/spelltest](https://github.com/artas728/spelltest) | 110 |
-|[NVIDIA/GenerativeAIExamples](https://github.com/NVIDIA/GenerativeAIExamples) | 109 |
-|[Azure/aistudio-copilot-sample](https://github.com/Azure/aistudio-copilot-sample) | 108 |
-|[codefuse-ai/codefuse-chatbot](https://github.com/codefuse-ai/codefuse-chatbot) | 108 |
-|[apirrone/Memento](https://github.com/apirrone/Memento) | 108 |
-|[e-johnstonn/GPT-Doc-Summarizer](https://github.com/e-johnstonn/GPT-Doc-Summarizer) | 108 |
-|[salesforce/BOLAA](https://github.com/salesforce/BOLAA) | 107 |
-|[Erol444/gpt4-openai-api](https://github.com/Erol444/gpt4-openai-api) | 106 |
-|[linjungz/chat-with-your-doc](https://github.com/linjungz/chat-with-your-doc) | 106 |
-|[crosleythomas/MirrorGPT](https://github.com/crosleythomas/MirrorGPT) | 106 |
-|[panaverse/learn-generative-ai](https://github.com/panaverse/learn-generative-ai) | 105 |
-|[Azure/azure-sdk-tools](https://github.com/Azure/azure-sdk-tools) | 105 |
-|[malywut/gpt_examples](https://github.com/malywut/gpt_examples) | 105 |
-|[ritun16/chain-of-verification](https://github.com/ritun16/chain-of-verification) | 104 |
-|[langchain-ai/langchain-benchmarks](https://github.com/langchain-ai/langchain-benchmarks) | 104 |
-|[lightninglabs/LangChainBitcoin](https://github.com/lightninglabs/LangChainBitcoin) | 104 |
-|[flepied/second-brain-agent](https://github.com/flepied/second-brain-agent) | 103 |
-|[llmapp/openai.mini](https://github.com/llmapp/openai.mini) | 102 |
-|[gimlet-ai/tddGPT](https://github.com/gimlet-ai/tddGPT) | 102 |
-|[jlonge4/gpt_chatwithPDF](https://github.com/jlonge4/gpt_chatwithPDF) | 102 |
-|[agentification/RAFA_code](https://github.com/agentification/RAFA_code) | 101 |
-|[pacman100/DHS-LLM-Workshop](https://github.com/pacman100/DHS-LLM-Workshop) | 101 |
-|[aws-samples/private-llm-qa-bot](https://github.com/aws-samples/private-llm-qa-bot) | 101 |
-
-
-_Generated by [github-dependents-info](https://github.com/nvuillam/github-dependents-info)_
-
-`github-dependents-info --repo "langchain-ai/langchain" --markdownfile dependents.md --minstars 100 --sort stars`

langchain_md_files/additional_resources/tutorials.mdx

@@ -1,52 +0,0 @@
-# 3rd Party Tutorials
-
-##  Tutorials
-
-### [LangChain v 0.1 by LangChain.ai](https://www.youtube.com/playlist?list=PLfaIDFEXuae0gBSJ9T0w7cu7iJZbH3T31)
-### [Build with Langchain - Advanced by LangChain.ai](https://www.youtube.com/playlist?list=PLfaIDFEXuae06tclDATrMYY0idsTdLg9v)
-### [LangGraph by LangChain.ai](https://www.youtube.com/playlist?list=PLfaIDFEXuae16n2TWUkKq5PgJ0w6Pkwtg)
-### [by Greg Kamradt](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5)
-### [by Sam Witteveen](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ)
-### [by James Briggs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F)
-### [by Prompt Engineering](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr)
-### [by Mayo Oshin](https://www.youtube.com/@chatwithdata/search?query=langchain)
-### [by 1 little Coder](https://www.youtube.com/playlist?list=PLpdmBGJ6ELUK-v0MK-t4wZmVEbxM5xk6L)
-### [by BobLin (Chinese language)](https://www.youtube.com/playlist?list=PLbd7ntv6PxC3QMFQvtWfk55p-Op_syO1C)
-### [by Total Technology Zonne](https://youtube.com/playlist?list=PLI8raxzYtfGyE02fAxiM1CPhLUuqcTLWg&si=fkAye16rQKBJVHc9)
-
-## Courses
-
-### Featured courses on Deeplearning.AI
-
-- [LangChain for LLM Application Development](https://www.deeplearning.ai/short-courses/langchain-for-llm-application-development/)
-- [LangChain Chat with Your Data](https://www.deeplearning.ai/short-courses/langchain-chat-with-your-data/)
-- [Functions, Tools and Agents with LangChain](https://www.deeplearning.ai/short-courses/functions-tools-agents-langchain/)
-- [Build LLM Apps with LangChain.js](https://www.deeplearning.ai/short-courses/build-llm-apps-with-langchain-js/)
-
-### Online courses
-
-- [Udemy](https://www.udemy.com/courses/search/?q=langchain)
-- [DataCamp](https://www.datacamp.com/courses/developing-llm-applications-with-langchain)
-- [Pluralsight](https://www.pluralsight.com/search?q=langchain)
-- [Coursera](https://www.coursera.org/search?query=langchain)
-- [Maven](https://maven.com/courses?query=langchain)
-- [Udacity](https://www.udacity.com/catalog/all/any-price/any-school/any-skill/any-difficulty/any-duration/any-type/relevance/page-1?searchValue=langchain)
-- [LinkedIn Learning](https://www.linkedin.com/search/results/learning/?keywords=langchain)
-- [edX](https://www.edx.org/search?q=langchain)
-- [freeCodeCamp](https://www.youtube.com/@freecodecamp/search?query=langchain)
-
-## Short Tutorials
-
-- [by Nicholas Renotte](https://youtu.be/MlK6SIjcjE8)
-- [by Patrick Loeber](https://youtu.be/LbT1yp6quS8)
-- [by Rabbitmetrics](https://youtu.be/aywZrzNaKjs)
-- [by Ivan Reznikov](https://medium.com/@ivanreznikov/langchain-101-course-updated-668f7b41d6cb)
-
-## Books and Handbooks
-
-- [Generative AI with LangChain](https://www.amazon.com/Generative-AI-LangChain-language-ChatGPT/dp/1835083463/ref=sr_1_1?crid=1GMOMH0G7GLR&keywords=generative+ai+with+langchain&qid=1703247181&sprefix=%2Caps%2C298&sr=8-1) by [Ben Auffrath](https://www.amazon.com/stores/Ben-Auffarth/author/B08JQKSZ7D?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true), ©️ 2023 Packt Publishing
-- [LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
-- [LangChain Cheatsheet](https://pub.towardsai.net/langchain-cheatsheet-all-secrets-on-a-single-page-8be26b721cde) by **Ivan Reznikov**
-- [Dive into Langchain (Chinese language)](https://langchain.boblin.app/)
-
----------------------

langchain_md_files/additional_resources/youtube.mdx

@@ -1,63 +0,0 @@
-# YouTube videos
-
-[Updated 2024-05-16]
-
-### [Official LangChain YouTube channel](https://www.youtube.com/@LangChain)
-
-### [Tutorials on YouTube](/docs/additional_resources/tutorials/#tutorials)
-
-## Videos (sorted by views)
-
-Only videos with 40K+ views:
-
-- [Using `ChatGPT` with YOUR OWN Data. This is magical. (LangChain `OpenAI API`)](https://youtu.be/9AXP7tCI9PI)
-- [Chat with Multiple `PDFs` | LangChain App Tutorial in Python (Free LLMs and Embeddings)](https://youtu.be/dXxQ0LR-3Hg?si=pjXKhsHRzn10vOqX)
-- [`Hugging Face` + Langchain in 5 mins | Access 200k+ FREE AI models for your AI apps](https://youtu.be/_j7JEDWuqLE?si=psimQscN3qo2dOa9)
-- [LangChain Crash Course For Beginners | LangChain Tutorial](https://youtu.be/nAmC7SoVLd8?si=qJdvyG5-rnjqfdj1)
-- [Vector Embeddings Tutorial – Code Your Own AI Assistant with GPT-4 API + LangChain + NLP](https://youtu.be/yfHHvmaMkcA?si=UBP3yw50cLm3a2nj)
-- [Development with Large Language Models Tutorial – `OpenAI`, Langchain, Agents, `Chroma`](https://youtu.be/xZDB1naRUlk?si=v8J1q6oFHRyTkf7Y)
-- [Langchain: `PDF` Chat App (GUI) | ChatGPT for Your PDF FILES | Step-by-Step Tutorial](https://youtu.be/RIWbalZ7sTo?si=LbKsCcuyv0BtnrTY)
-- [Vector Search `RAG` Tutorial – Combine Your Data with LLMs with Advanced Search](https://youtu.be/JEBDfGqrAUA?si=pD7oxpfwWeJCxfBt)
-- [LangChain Crash Course for Beginners](https://youtu.be/lG7Uxts9SXs?si=Yte4S5afN7KNCw0F)
-- [Learn `RAG` From Scratch – Python AI Tutorial from a LangChain Engineer](https://youtu.be/sVcwVQRHIc8?si=_LN4g0vOgSdtlB3S)
-- [`Llama 2` in LangChain — FIRST Open Source Conversational Agent!](https://youtu.be/6iHVJyX2e50?si=rtq1maPrzWKHbwVV)
-- [LangChain Tutorial for Beginners | Generative AI Series](https://youtu.be/cQUUkZnyoD0?si=KYz-bvcocdqGh9f_)
-- [Chatbots with `RAG`: LangChain Full Walkthrough](https://youtu.be/LhnCsygAvzY?si=yS7T98VLfcWdkDek)
-- [LangChain Explained In 15 Minutes - A MUST Learn For Python Programmers](https://youtu.be/mrjq3lFz23s?si=wkQGcSKUJjuiiEPf)
-- [LLM Project | End to End LLM Project Using Langchain, `OpenAI` in Finance Domain](https://youtu.be/MoqgmWV1fm8?si=oVl-5kJVgd3a07Y_)
-- [What is LangChain?](https://youtu.be/1bUy-1hGZpI?si=NZ0D51VM5y-DhjGe)
-- [`RAG` + Langchain Python Project: Easy AI/Chat For Your Doc](https://youtu.be/tcqEUSNCn8I?si=RLcWPBVLIErRqdmU)
-- [Getting Started With LangChain In 20 Minutes- Build Celebrity Search Application](https://youtu.be/_FpT1cwcSLg?si=X9qVazlXYucN_JBP)
-- [LangChain GEN AI Tutorial – 6 End-to-End Projects using OpenAI, Google `Gemini Pro`, `LLAMA2`](https://youtu.be/x0AnCE9SE4A?si=_92gJYm7kb-V2bi0)
-- [Complete Langchain GEN AI Crash Course With 6 End To End LLM Projects With OPENAI, `LLAMA2`, `Gemini Pro`](https://youtu.be/aWKrL4z5H6w?si=NVLi7Yiq0ccE7xXE)
-- [AI Leader Reveals The Future of AI AGENTS (LangChain CEO)](https://youtu.be/9ZhbA0FHZYc?si=1r4P6kRvKVvEhRgE)
-- [Learn How To Query Pdf using Langchain Open AI in 5 min](https://youtu.be/5Ghv-F1wF_0?si=ZZRjrWfeiFOVrcvu)
-- [Reliable, fully local RAG agents with `LLaMA3`](https://youtu.be/-ROS6gfYIts?si=75CXA8W_BbnkIxcV)
-- [Learn `LangChain.js` - Build LLM apps with JavaScript and `OpenAI`](https://youtu.be/HSZ_uaif57o?si=Icj-RAhwMT-vHaYA)
-- [LLM Project | End to End LLM Project Using LangChain, Google Palm In Ed-Tech Industry](https://youtu.be/AjQPRomyd-k?si=eC3NT6kn02Lhpz-_)
-- [Chatbot Answering from Your Own Knowledge Base: Langchain, `ChatGPT`, `Pinecone`, and `Streamlit`: | Code](https://youtu.be/nAKhxQ3hcMA?si=9Zd_Nd_jiYhtml5w)
-- [LangChain is AMAZING | Quick Python Tutorial](https://youtu.be/I4mFqyqFkxg?si=aJ66qh558OfNAczD)
-- [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw?si=kZR-lnJwixeVrjmh)
-- [Using NEW `MPT-7B` in `Hugging Face` and LangChain](https://youtu.be/DXpk9K7DgMo?si=99JDpV_ueimwJhMi)
-- [LangChain - COMPLETE TUTORIAL - Basics to advanced concept!](https://youtu.be/a89vqgK-Qcs?si=0aVO2EOqsw7GE5e3)
-- [LangChain Agents: Simply Explained!](https://youtu.be/Xi9Ui-9qcPw?si=DCuG7nGx8dxcfhkx)
-- [Chat With Multiple `PDF` Documents With Langchain And Google `Gemini Pro`](https://youtu.be/uus5eLz6smA?si=YUwvHtaZsGeIl0WD)
-- [LLM Project | End to end LLM project Using Langchain, `Google Palm` in Retail Industry](https://youtu.be/4wtrl4hnPT8?si=_eOKPpdLfWu5UXMQ)
-- [Tutorial | Chat with any Website using Python and Langchain](https://youtu.be/bupx08ZgSFg?si=KRrjYZFnuLsstGwW)
-- [Prompt Engineering And LLM's With LangChain In One Shot-Generative AI](https://youtu.be/t2bSApmPzU4?si=87vPQQtYEWTyu2Kx)
-- [Build a Custom Chatbot with `OpenAI`: `GPT-Index` & LangChain | Step-by-Step Tutorial](https://youtu.be/FIDv6nc4CgU?si=gR1u3DUG9lvzBIKK)
-- [Search Your `PDF` App using Langchain, `ChromaDB`, and Open Source LLM: No OpenAI API (Runs on CPU)](https://youtu.be/rIV1EseKwU4?si=UxZEoXSiPai8fXgl)
-- [Building a `RAG` application from scratch using Python, LangChain, and the `OpenAI API`](https://youtu.be/BrsocJb-fAo?si=hvkh9iTGzJ-LnsX-)
-- [Function Calling via `ChatGPT API` - First Look With LangChain](https://youtu.be/0-zlUy7VUjg?si=Vc6LFseckEc6qvuk)
-- [Private GPT, free deployment! Langchain-Chachat helps you easily play with major mainstream AI models! | Zero Degree Commentary](https://youtu.be/3LLUyaHP-3I?si=AZumEeFXsvqaLl0f)
-- [Create a ChatGPT clone using `Streamlit` and LangChain](https://youtu.be/IaTiyQ2oYUQ?si=WbgsYmqPDnMidSUK)
-- [What's next for AI agents ft. LangChain's Harrison Chase](https://youtu.be/pBBe1pk8hf4?si=H4vdBF9nmkNZxiHt)
-- [`LangFlow`: Build Chatbots without Writing Code - LangChain](https://youtu.be/KJ-ux3hre4s?si=TJuDu4bAlva1myNL)
-- [Building a LangChain Custom Medical Agent with Memory](https://youtu.be/6UFtRwWnHws?si=wymYad26VgigRkHy)
-- [`Ollama` meets LangChain](https://youtu.be/k_1pOF1mj8k?si=RlBiCrmaR3s7SnMK)
-- [End To End LLM Langchain Project using `Pinecone` Vector Database](https://youtu.be/erUfLIi9OFM?si=aHpuHXdIEmAfS4eF)
-- [`LLaMA2` with LangChain - Basics | LangChain TUTORIAL](https://youtu.be/cIRzwSXB4Rc?si=FUs0OLVJpzKhut0h)
-- [Understanding `ReACT` with LangChain](https://youtu.be/Eug2clsLtFs?si=imgj534ggxlypS0d)
-
----------------------
-[Updated 2024-05-16]

langchain_md_files/changes/changelog/core.mdx

@@ -1,10 +0,0 @@
-# langchain-core
-
-## 0.1.x
-
-#### Deprecated
-
-- `BaseChatModel` methods `__call__`, `call_as_llm`, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.invoke` instead.
-- `BaseChatModel` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.ainvoke` instead.
-- `BaseLLM` methods `__call__, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseLLM.invoke` instead.
-- `BaseLLM` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseLLM.ainvoke` instead.

langchain_md_files/changes/changelog/langchain.mdx

@@ -1,93 +0,0 @@
-# langchain
-
-## 0.2.0
-
-### Deleted
-
-As of release 0.2.0, `langchain` is required to be integration-agnostic. This means that code in `langchain`  should not by default instantiate any specific chat models, llms, embedding models, vectorstores etc; instead, the user will be required to specify those explicitly.
-
-The following functions and classes require an explicit LLM to be passed as an argument:
-
-- `langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreToolkit`
-- `langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreRouterToolkit`
-- `langchain.chains.openai_functions.get_openapi_chain`
-- `langchain.chains.router.MultiRetrievalQAChain.from_retrievers`
-- `langchain.indexes.VectorStoreIndexWrapper.query`
-- `langchain.indexes.VectorStoreIndexWrapper.query_with_sources`
-- `langchain.indexes.VectorStoreIndexWrapper.aquery_with_sources`
-- `langchain.chains.flare.FlareChain`
-
-The following classes now require passing an explicit Embedding model as an argument:
-
-- `langchain.indexes.VectostoreIndexCreator`
-
-The following code has been removed:
-
-- `langchain.natbot.NatBotChain.from_default` removed in favor of the `from_llm` class method.
-
-### Deprecated
-
-We have two main types of deprecations:
-
-1. Code that was moved from `langchain` into another package (e.g, `langchain-community`)
-
-If you try to import it from `langchain`, the import will keep on working, but will raise a deprecation warning. The warning will provide a replacement import statement.
-
-```python
-python -c "from langchain.document_loaders.markdown import UnstructuredMarkdownLoader"
-
-```
-
-```python
-LangChainDeprecationWarning: Importing UnstructuredMarkdownLoader from langchain.document_loaders is deprecated. Please replace deprecated imports:
-
->> from langchain.document_loaders import UnstructuredMarkdownLoader
-
-with new imports of:
-
->> from langchain_community.document_loaders import UnstructuredMarkdownLoader
-```
-
-We will continue supporting the imports in `langchain` until release 0.4 as long as the relevant package where the code lives is installed. (e.g., as long as `langchain_community` is installed.)
-
-However, we advise for users to not rely on these imports and instead migrate to the new imports. To help with this process, we’re releasing a migration script via the LangChain CLI. See further instructions in migration guide.
-
-1. Code that has better alternatives available and will eventually be removed, so there’s only a single way to do things. (e.g., `predict_messages` method in ChatModels has been deprecated in favor of `invoke`).
-
-Many of these were marked for removal in 0.2. We have bumped the removal to 0.3.
-
-
-## 0.1.0 (Jan 5, 2024)
-
-### Deleted
-
-No deletions.
-
-### Deprecated
-
-Deprecated classes and methods will be removed in 0.2.0
-
-| Deprecated                      | Alternative                       | Reason                                         |
-|---------------------------------|-----------------------------------|------------------------------------------------|
-| ChatVectorDBChain               | ConversationalRetrievalChain      | More general to all retrievers                 |
-| create_ernie_fn_chain           | create_ernie_fn_runnable          | Use LCEL under the hood                        |
-| created_structured_output_chain | create_structured_output_runnable | Use LCEL under the hood                        |
-| NatBotChain                     |                                   | Not used                                       |
-| create_openai_fn_chain          | create_openai_fn_runnable         | Use LCEL under the hood                        |
-| create_structured_output_chain  | create_structured_output_runnable | Use LCEL under the hood                        |
-| load_query_constructor_chain    | load_query_constructor_runnable   | Use LCEL under the hood                        |
-| VectorDBQA                      | RetrievalQA                       | More general to all retrievers                 |
-| Sequential Chain                | LCEL                              | Obviated by LCEL                               |
-| SimpleSequentialChain           | LCEL                              | Obviated by LCEL                               |
-| TransformChain                  | LCEL/RunnableLambda               | Obviated by LCEL                               |
-| create_tagging_chain            | create_structured_output_runnable | Use LCEL under the hood                        |
-| ChatAgent                       | create_react_agent                | Use LCEL builder over a class                  |
-| ConversationalAgent             | create_react_agent                | Use LCEL builder over a class                  |
-| ConversationalChatAgent         | create_json_chat_agent            | Use LCEL builder over a class                  |
-| initialize_agent                | Individual create agent methods   | Individual create agent methods are more clear |
-| ZeroShotAgent                   | create_react_agent                | Use LCEL builder over a class                  |
-| OpenAIFunctionsAgent            | create_openai_functions_agent     | Use LCEL builder over a class                  |
-| OpenAIMultiFunctionsAgent       | create_openai_tools_agent         | Use LCEL builder over a class                  |
-| SelfAskWithSearchAgent          | create_self_ask_with_search       | Use LCEL builder over a class                  |
-| StructuredChatAgent             | create_structured_chat_agent      | Use LCEL builder over a class                  |
-| XMLAgent                        | create_xml_agent                  | Use LCEL builder over a class                  |