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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

@@ -0,0 +1,863 @@
+# 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. 
+Here you find papers that reference:
+- [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|
+|------------------|---------|-------------------|------------------------|
+| `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)
+| `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 | `Cookbook:` [langgraph_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
+| `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 | `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 | `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)
+| `2307.09288v2` [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)
+| `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/v0.2/api_reference/experimental/index.html#module-langchain_experimental.tot), `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 | `API:` [langchain...LLMListwiseRerank](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
+| `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_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb), [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.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:` [multiagent_bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb), [generative_agents_interactive_simulacra_of_human_behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.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/v0.2/api_reference/experimental/index.html#module-langchain_experimental.autonomous_agents), `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://python.langchain.com/v0.2/api_reference/community/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://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/langchain_community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+| `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 | `API:` [langchain...HypotheticalDocumentEmbedder](https://python.langchain.com/v0.2/api_reference/langchain/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)
+| `2212.07425v3` [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/v0.2/api_reference//arxiv/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
+| `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://python.langchain.com/v0.2/api_reference/core/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/v0.2/api_reference//python/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://python.langchain.com/v0.2/api_reference/experimental/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)
+| `2210.03629v3` [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/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), `API:` [langchain...TrajectoryEvalChain](https://python.langchain.com/v0.2/api_reference/langchain/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain), [langchain...create_react_agent](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent)
+| `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://python.langchain.com/v0.2/api_reference/community/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 | `API:` [langchain_community...SQLDatabase](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://python.langchain.com/v0.2/api_reference/community/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://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+| `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/v0.2/api_reference//arxiv/experimental_api_reference.html#module-langchain_experimental.open_clip)
+| `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://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+
+## Self-Discover: Large Language Models Self-Compose Reasoning Structures
+
+- **arXiv id:** [2402.03620v1](http://arxiv.org/abs/2402.03620v1)  **Published Date:** 2024-02-06
+- **Title:** Self-Discover: Large Language Models Self-Compose Reasoning Structures
+- **Authors:** Pei Zhou, Jay Pujara, Xiang Ren,  et al.
+- **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.
+                
+## RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
+
+- **arXiv id:** [2401.18059v1](http://arxiv.org/abs/2401.18059v1)  **Published Date:** 2024-01-31
+- **Title:** RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
+- **Authors:** Parth Sarthi, Salman Abdullah, Aditi Tuli,  et al.
+- **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
+
+- **arXiv id:** [2401.15884v2](http://arxiv.org/abs/2401.15884v2)  **Published Date:** 2024-01-29
+- **Title:** Corrective Retrieval Augmented Generation
+- **Authors:** Shi-Qi Yan, Jia-Chen Gu, Yun Zhu,  et al.
+- **LangChain:**
+
+   - **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.
+                
+## Mixtral of Experts
+
+- **arXiv id:** [2401.04088v1](http://arxiv.org/abs/2401.04088v1)  **Published Date:** 2024-01-08
+- **Title:** Mixtral of Experts
+- **Authors:** Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux,  et al.
+- **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?
+
+- **arXiv id:** [2312.06648v2](http://arxiv.org/abs/2312.06648v2)  **Published Date:** 2023-12-11
+- **Title:** Dense X Retrieval: What Retrieval Granularity Should We Use?
+- **Authors:** Tong Chen, Hongwei Wang, Sihao Chen,  et al.
+- **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
+
+- **arXiv id:** [2311.09210v1](http://arxiv.org/abs/2311.09210v1)  **Published Date:** 2023-11-15
+- **Title:** Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
+- **Authors:** Wenhao Yu, Hongming Zhang, Xiaoman Pan,  et al.
+- **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
+
+- **arXiv id:** [2310.11511v1](http://arxiv.org/abs/2310.11511v1)  **Published Date:** 2023-10-17
+- **Title:** Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
+- **Authors:** Akari Asai, Zeqiu Wu, Yizhong Wang,  et al.
+- **LangChain:**
+
+   - **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
+
+- **arXiv id:** [2310.06117v2](http://arxiv.org/abs/2310.06117v2)  **Published Date:** 2023-10-09
+- **Title:** Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
+- **Authors:** Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al.
+- **LangChain:**
+
+   - **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%.
+                
+## Llama 2: Open Foundation and Fine-Tuned Chat Models
+
+- **arXiv id:** [2307.09288v2](http://arxiv.org/abs/2307.09288v2)  **Published Date:** 2023-07-18
+- **Title:** Llama 2: Open Foundation and Fine-Tuned Chat Models
+- **Authors:** Hugo Touvron, Louis Martin, Kevin Stone,  et al.
+- **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.
+                
+## Query Rewriting for Retrieval-Augmented Large Language Models
+
+- **arXiv id:** [2305.14283v3](http://arxiv.org/abs/2305.14283v3)  **Published Date:** 2023-05-23
+- **Title:** Query Rewriting for Retrieval-Augmented Large Language Models
+- **Authors:** Xinbei Ma, Yeyun Gong, Pengcheng He,  et al.
+- **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
+
+- **arXiv id:** [2305.08291v1](http://arxiv.org/abs/2305.08291v1)  **Published Date:** 2023-05-15
+- **Title:** Large Language Model Guided Tree-of-Thought
+- **Authors:** Jieyi Long
+- **LangChain:**
+
+   - **API Reference:** [langchain_experimental.tot](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.tot)
+   - **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:
+\url{https://github.com/jieyilong/tree-of-thought-puzzle-solver}.
+                
+## Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
+
+- **arXiv id:** [2305.04091v3](http://arxiv.org/abs/2305.04091v3)  **Published Date:** 2023-05-06
+- **Title:** Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
+- **Authors:** Lei Wang, Wanyu Xu, Yihuai Lan,  et al.
+- **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
+
+- **arXiv id:** [2305.02156v1](http://arxiv.org/abs/2305.02156v1)  **Published Date:** 2023-05-03
+- **Title:** Zero-Shot Listwise Document Reranking with a Large Language Model
+- **Authors:** Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al.
+- **LangChain:**
+
+   - **API Reference:** [langchain...LLMListwiseRerank](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
+
+**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
+
+- **arXiv id:** [2304.08485v2](http://arxiv.org/abs/2304.08485v2)  **Published Date:** 2023-04-17
+- **Title:** Visual Instruction Tuning
+- **Authors:** Haotian Liu, Chunyuan Li, Qingyang Wu,  et al.
+- **LangChain:**
+
+   - **Cookbook:** [Semi_structured_and_multi_modal_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb), [Semi_structured_multi_modal_RAG_LLaMA2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.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
+
+- **arXiv id:** [2304.03442v2](http://arxiv.org/abs/2304.03442v2)  **Published Date:** 2023-04-07
+- **Title:** Generative Agents: Interactive Simulacra of Human Behavior
+- **Authors:** Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai,  et al.
+- **LangChain:**
+
+   - **Cookbook:** [multiagent_bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb), [generative_agents_interactive_simulacra_of_human_behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.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
+
+- **arXiv id:** [2303.17760v2](http://arxiv.org/abs/2303.17760v2)  **Published Date:** 2023-03-31
+- **Title:** CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
+- **Authors:** Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani,  et al.
+- **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
+
+- **arXiv id:** [2303.17580v4](http://arxiv.org/abs/2303.17580v4)  **Published Date:** 2023-03-30
+- **Title:** HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
+- **Authors:** Yongliang Shen, Kaitao Song, Xu Tan,  et al.
+- **LangChain:**
+
+   - **API Reference:** [langchain_experimental.autonomous_agents](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.autonomous_agents)
+   - **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
+
+- **arXiv id:** [2301.10226v4](http://arxiv.org/abs/2301.10226v4)  **Published Date:** 2023-01-24
+- **Title:** A Watermark for Large Language Models
+- **Authors:** John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al.
+- **LangChain:**
+
+   - **API Reference:** [langchain_community...OCIModelDeploymentTGI](https://python.langchain.com/v0.2/api_reference/community/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://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/langchain_community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+
+**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
+
+- **arXiv id:** [2212.10496v1](http://arxiv.org/abs/2212.10496v1)  **Published Date:** 2022-12-20
+- **Title:** Precise Zero-Shot Dense Retrieval without Relevance Labels
+- **Authors:** Luyu Gao, Xueguang Ma, Jimmy Lin,  et al.
+- **LangChain:**
+
+   - **API Reference:** [langchain...HypotheticalDocumentEmbedder](https://python.langchain.com/v0.2/api_reference/langchain/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).
+                
+## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
+
+- **arXiv id:** [2212.07425v3](http://arxiv.org/abs/2212.07425v3)  **Published Date:** 2022-12-12
+- **Title:** Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
+- **Authors:** Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al.
+- **LangChain:**
+
+   - **API Reference:** [langchain_experimental.fallacy_removal](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.fallacy_removal)
+
+**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
+
+- **arXiv id:** [2211.13892v2](http://arxiv.org/abs/2211.13892v2)  **Published Date:** 2022-11-25
+- **Title:** Complementary Explanations for Effective In-Context Learning
+- **Authors:** Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al.
+- **LangChain:**
+
+   - **API Reference:** [langchain_core...MaxMarginalRelevanceExampleSelector](https://python.langchain.com/v0.2/api_reference/core/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
+
+- **arXiv id:** [2211.10435v2](http://arxiv.org/abs/2211.10435v2)  **Published Date:** 2022-11-18
+- **Title:** PAL: Program-aided Language Models
+- **Authors:** Luyu Gao, Aman Madaan, Shuyan Zhou,  et al.
+- **LangChain:**
+
+   - **API Reference:** [langchain_experimental.pal_chain](https://python.langchain.com/v0.2/api_reference//python/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://python.langchain.com/v0.2/api_reference/experimental/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/ .
+                
+## ReAct: Synergizing Reasoning and Acting in Language Models
+
+- **arXiv id:** [2210.03629v3](http://arxiv.org/abs/2210.03629v3)  **Published Date:** 2022-10-06
+- **Title:** ReAct: Synergizing Reasoning and Acting in Language Models
+- **Authors:** Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al.
+- **LangChain:**
+
+   - **Documentation:** [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping)
+   - **API Reference:** [langchain...TrajectoryEvalChain](https://python.langchain.com/v0.2/api_reference/langchain/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain), [langchain...create_react_agent](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent)
+
+**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
+
+- **arXiv id:** [2209.10785v2](http://arxiv.org/abs/2209.10785v2)  **Published Date:** 2022-09-22
+- **Title:** Deep Lake: a Lakehouse for Deep Learning
+- **Authors:** Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al.
+- **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
+
+- **arXiv id:** [2205.13147v4](http://arxiv.org/abs/2205.13147v4)  **Published Date:** 2022-05-26
+- **Title:** Matryoshka Representation Learning
+- **Authors:** Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al.
+- **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
+
+- **arXiv id:** [2205.12654v1](http://arxiv.org/abs/2205.12654v1)  **Published Date:** 2022-05-25
+- **Title:** Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
+- **Authors:** Kevin Heffernan, Onur Çelebi, Holger Schwenk
+- **LangChain:**
+
+   - **API Reference:** [langchain_community...LaserEmbeddings](https://python.langchain.com/v0.2/api_reference/community/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
+
+- **arXiv id:** [2204.00498v1](http://arxiv.org/abs/2204.00498v1)  **Published Date:** 2022-03-15
+- **Title:** Evaluating the Text-to-SQL Capabilities of Large Language Models
+- **Authors:** Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau
+- **LangChain:**
+
+   - **API Reference:** [langchain_community...SQLDatabase](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://python.langchain.com/v0.2/api_reference/community/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
+
+- **arXiv id:** [2202.00666v5](http://arxiv.org/abs/2202.00666v5)  **Published Date:** 2022-02-01
+- **Title:** Locally Typical Sampling
+- **Authors:** Clara Meister, Tiago Pimentel, Gian Wiher,  et al.
+- **LangChain:**
+
+   - **API Reference:** [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+
+**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.
+                
+## Learning Transferable Visual Models From Natural Language Supervision
+
+- **arXiv id:** [2103.00020v1](http://arxiv.org/abs/2103.00020v1)  **Published Date:** 2021-02-26
+- **Title:** Learning Transferable Visual Models From Natural Language Supervision
+- **Authors:** Alec Radford, Jong Wook Kim, Chris Hallacy,  et al.
+- **LangChain:**
+
+   - **API Reference:** [langchain_experimental.open_clip](https://python.langchain.com/v0.2/api_reference/experimental/index.html#module-langchain_experimental.open_clip)
+
+**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.
+                
+## CTRL: A Conditional Transformer Language Model for Controllable Generation
+
+- **arXiv id:** [1909.05858v2](http://arxiv.org/abs/1909.05858v2)  **Published Date:** 2019-09-11
+- **Title:** CTRL: A Conditional Transformer Language Model for Controllable Generation
+- **Authors:** Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al.
+- **LangChain:**
+
+   - **API Reference:** [langchain_huggingface...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/huggingface/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceEndpoint](https://python.langchain.com/v0.2/api_reference/langchain_community/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
+
+**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

@@ -0,0 +1,554 @@
+# 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

@@ -0,0 +1,51 @@
+# 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)
+
+## 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

@@ -0,0 +1,63 @@
+# 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

@@ -0,0 +1,10 @@
+# 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

@@ -0,0 +1,93 @@
+# 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                  |

langchain_md_files/contributing/code/guidelines.mdx

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+# General guidelines
+
+Here are some things to keep in mind for all types of contributions:
+
+- Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
+- Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
+- Ensure your PR passes formatting, linting, and testing checks before requesting a review.
+  - If you would like comments or feedback on your current progress, please open an issue or discussion and tag a maintainer.
+  - See the sections on [Testing](/docs/contributing/code/setup#testing) and [Formatting and Linting](/docs/contributing/code/setup#formatting-and-linting) for how to run these checks locally.
+- Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
+- Look for duplicate PRs or issues that have already been opened before opening a new one.
+- Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
+
+## Bugfixes
+
+We encourage and appreciate bugfixes. We ask that you:
+
+- Explain the bug in enough detail for maintainers to be able to reproduce it.
+  - If an accompanying issue exists, link to it. Prefix with `Fixes` so that the issue will close automatically when the PR is merged.
+- Avoid breaking changes if possible.
+- Include unit tests that fail without the bugfix.
+
+If you come across a bug and don't know how to fix it, we ask that you open an issue for it describing in detail the environment in which you encountered the bug.
+
+## New features
+
+We aim to keep the bar high for new features. We generally don't accept new core abstractions, changes to infra, changes to dependencies,
+or new agents/chains from outside contributors without an existing GitHub discussion or issue that demonstrates an acute need for them.
+
+- New features must come with docs, unit tests, and (if appropriate) integration tests.
+- New integrations must come with docs, unit tests, and (if appropriate) integration tests.
+  - See [this page](/docs/contributing/integrations) for more details on contributing new integrations.
+- New functionality should not inherit from or use deprecated methods or classes.
+- We will reject features that are likely to lead to security vulnerabilities or reports.
+- Do not add any hard dependencies. Integrations may add optional dependencies.

langchain_md_files/contributing/code/index.mdx

@@ -0,0 +1,6 @@
+# Contribute Code
+
+If you would like to add a new feature or update an existing one, please read the resources below before getting started:
+
+- [General guidelines](/docs/contributing/code/guidelines/)
+- [Setup](/docs/contributing/code/setup/)