mirror of https://github.com/hwchase17/langchain
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# arXiv
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LangChain implements the latest research in the field of Natural Language Processing.
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This page contains `arXiv` papers referenced in the LangChain Documentation and API Reference.
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## Summary
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| arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation and API Reference |
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|------------------|---------|-------------------|-------------------------|
|
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| `2307.03172v3` [Lost in the Middle: How Language Models Use Long Contexts](http://arxiv.org/abs/2307.03172v3) | Nelson F. Liu, Kevin Lin, John Hewitt, et al. | 2023-07-06 | `Docs:` [docs/modules/data_connection/retrievers/long_context_reorder](https://python.langchain.com/docs/modules/data_connection/retrievers/long_context_reorder)
|
||||
| `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://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot)
|
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| `2305.06983v2` [Active Retrieval Augmented Generation](http://arxiv.org/abs/2305.06983v2) | Zhengbao Jiang, Frank F. Xu, Luyu Gao, et al. | 2023-05-11 | `Docs:` [docs/modules/chains](https://python.langchain.com/docs/modules/chains)
|
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| `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://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents)
|
||||
| `2303.08774v6` [GPT-4 Technical Report](http://arxiv.org/abs/2303.08774v6) | OpenAI, Josh Achiam, Steven Adler, et al. | 2023-03-15 | `Docs:` [docs/integrations/vectorstores/mongodb_atlas](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas)
|
||||
| `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.llms...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community.llms...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community.llms...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI)
|
||||
| `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin, et al. | 2022-12-20 | `Docs:` [docs/use_cases/query_analysis/techniques/hyde](https://python.langchain.com/docs/use_cases/query_analysis/techniques/hyde), `API:` [langchain.chains...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
|
||||
| `2212.08073v1` [Constitutional AI: Harmlessness from AI Feedback](http://arxiv.org/abs/2212.08073v1) | Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al. | 2022-12-15 | `Docs:` [docs/guides/productionization/evaluation/string/criteria_eval_chain](https://python.langchain.com/docs/guides/productionization/evaluation/string/criteria_eval_chain)
|
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| `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://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
|
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| `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.example_selectors...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
|
||||
| `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou, et al. | 2022-11-18 | `API:` [langchain_experimental.pal_chain...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain)
|
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| `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)
|
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| `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.embeddings...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
|
||||
| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022-03-15 | `Docs:` [docs/use_cases/sql/quickstart](https://python.langchain.com/docs/use_cases/sql/quickstart), `API:` [langchain_community.utilities...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community.utilities...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
|
||||
| `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher, et al. | 2022-02-01 | `API:` [langchain_community.llms...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community.llms...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
|
||||
| `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://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
|
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| `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_community.llms...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community.llms...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
|
||||
| `1908.10084v1` [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](http://arxiv.org/abs/1908.10084v1) | Nils Reimers, Iryna Gurevych | 2019-08-27 | `Docs:` [docs/integrations/text_embedding/sentence_transformers](https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers)
|
||||
|
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## Lost in the Middle: How Language Models Use Long Contexts
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- **arXiv id:** 2307.03172v3
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- **Title:** Lost in the Middle: How Language Models Use Long Contexts
|
||||
- **Authors:** Nelson F. Liu, Kevin Lin, John Hewitt, et al.
|
||||
- **Published Date:** 2023-07-06
|
||||
- **URL:** http://arxiv.org/abs/2307.03172v3
|
||||
- **LangChain Documentation:** [docs/modules/data_connection/retrievers/long_context_reorder](https://python.langchain.com/docs/modules/data_connection/retrievers/long_context_reorder)
|
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|
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|
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**Abstract:** While recent language models have the ability to take long contexts as input,
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relatively little is known about how well they use longer context. We analyze
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the performance of language models on two tasks that require identifying
|
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relevant information in their input contexts: multi-document question answering
|
||||
and key-value retrieval. We find that performance can degrade significantly
|
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when changing the position of relevant information, indicating that current
|
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language models do not robustly make use of information in long input contexts.
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In particular, we observe that performance is often highest when relevant
|
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information occurs at the beginning or end of the input context, and
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significantly degrades when models must access relevant information in the
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middle of long contexts, even for explicitly long-context models. Our analysis
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provides a better understanding of how language models use their input context
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and provides new evaluation protocols for future long-context language models.
|
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|
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## Large Language Model Guided Tree-of-Thought
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- **arXiv id:** 2305.08291v1
|
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- **Title:** Large Language Model Guided Tree-of-Thought
|
||||
- **Authors:** Jieyi Long
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- **Published Date:** 2023-05-15
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- **URL:** http://arxiv.org/abs/2305.08291v1
|
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|
||||
- **LangChain API Reference:** [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot)
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**Abstract:** In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel
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approach aimed at improving the problem-solving capabilities of auto-regressive
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large language models (LLMs). The ToT technique is inspired by the human mind's
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approach for solving complex reasoning tasks through trial and error. In this
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process, the human mind explores the solution space through a tree-like thought
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process, allowing for backtracking when necessary. To implement ToT as a
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software system, we augment an LLM with additional modules including a prompter
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agent, a checker module, a memory module, and a ToT controller. In order to
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solve a given problem, these modules engage in a multi-round conversation with
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the LLM. The memory module records the conversation and state history of the
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problem solving process, which allows the system to backtrack to the previous
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steps of the thought-process and explore other directions from there. To verify
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the effectiveness of the proposed technique, we implemented a ToT-based solver
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for the Sudoku Puzzle. Experimental results show that the ToT framework can
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significantly increase the success rate of Sudoku puzzle solving. Our
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implementation of the ToT-based Sudoku solver is available on GitHub:
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\url{https://github.com/jieyilong/tree-of-thought-puzzle-solver}.
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|
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## Active Retrieval Augmented Generation
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- **arXiv id:** 2305.06983v2
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- **Title:** Active Retrieval Augmented Generation
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- **Authors:** Zhengbao Jiang, Frank F. Xu, Luyu Gao, et al.
|
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- **Published Date:** 2023-05-11
|
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- **URL:** http://arxiv.org/abs/2305.06983v2
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- **LangChain Documentation:** [docs/modules/chains](https://python.langchain.com/docs/modules/chains)
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|
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**Abstract:** Despite the remarkable ability of large language models (LMs) to comprehend
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and generate language, they have a tendency to hallucinate and create factually
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inaccurate output. Augmenting LMs by retrieving information from external
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knowledge resources is one promising solution. Most existing retrieval
|
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augmented LMs employ a retrieve-and-generate setup that only retrieves
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information once based on the input. This is limiting, however, in more general
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scenarios involving generation of long texts, where continually gathering
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information throughout generation is essential. In this work, we provide a
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generalized view of active retrieval augmented generation, methods that
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actively decide when and what to retrieve across the course of the generation.
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We propose Forward-Looking Active REtrieval augmented generation (FLARE), a
|
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generic method which iteratively uses a prediction of the upcoming sentence to
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anticipate future content, which is then utilized as a query to retrieve
|
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relevant documents to regenerate the sentence if it contains low-confidence
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tokens. We test FLARE along with baselines comprehensively over 4 long-form
|
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knowledge-intensive generation tasks/datasets. FLARE achieves superior or
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competitive performance on all tasks, demonstrating the effectiveness of our
|
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method. Code and datasets are available at https://github.com/jzbjyb/FLARE.
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|
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## HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
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|
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- **arXiv id:** 2303.17580v4
|
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- **Title:** HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
|
||||
- **Authors:** Yongliang Shen, Kaitao Song, Xu Tan, et al.
|
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- **Published Date:** 2023-03-30
|
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- **URL:** http://arxiv.org/abs/2303.17580v4
|
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|
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- **LangChain API Reference:** [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents)
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|
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**Abstract:** Solving complicated AI tasks with different domains and modalities is a key
|
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step toward artificial general intelligence. While there are numerous AI models
|
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available for various domains and modalities, they cannot handle complicated AI
|
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tasks autonomously. Considering large language models (LLMs) have exhibited
|
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exceptional abilities in language understanding, generation, interaction, and
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reasoning, we advocate that LLMs could act as a controller to manage existing
|
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AI models to solve complicated AI tasks, with language serving as a generic
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interface to empower this. Based on this philosophy, we present HuggingGPT, an
|
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LLM-powered agent that leverages LLMs (e.g., ChatGPT) to connect various AI
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models in machine learning communities (e.g., Hugging Face) to solve AI tasks.
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Specifically, we use ChatGPT to conduct task planning when receiving a user
|
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request, select models according to their function descriptions available in
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Hugging Face, execute each subtask with the selected AI model, and summarize
|
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the response according to the execution results. By leveraging the strong
|
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language capability of ChatGPT and abundant AI models in Hugging Face,
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HuggingGPT can tackle a wide range of sophisticated AI tasks spanning different
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modalities and domains and achieve impressive results in language, vision,
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speech, and other challenging tasks, which paves a new way towards the
|
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realization of artificial general intelligence.
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## GPT-4 Technical Report
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- **arXiv id:** 2303.08774v6
|
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- **Title:** GPT-4 Technical Report
|
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- **Authors:** OpenAI, Josh Achiam, Steven Adler, et al.
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- **Published Date:** 2023-03-15
|
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- **URL:** http://arxiv.org/abs/2303.08774v6
|
||||
- **LangChain Documentation:** [docs/integrations/vectorstores/mongodb_atlas](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas)
|
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|
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**Abstract:** We report the development of GPT-4, a large-scale, multimodal model which can
|
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accept image and text inputs and produce text outputs. While less capable than
|
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humans in many real-world scenarios, GPT-4 exhibits human-level performance on
|
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various professional and academic benchmarks, including passing a simulated bar
|
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exam with a score around the top 10% of test takers. GPT-4 is a
|
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Transformer-based model pre-trained to predict the next token in a document.
|
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The post-training alignment process results in improved performance on measures
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||||
of factuality and adherence to desired behavior. A core component of this
|
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project was developing infrastructure and optimization methods that behave
|
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predictably across a wide range of scales. This allowed us to accurately
|
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predict some aspects of GPT-4's performance based on models trained with no
|
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more than 1/1,000th the compute of GPT-4.
|
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|
||||
## A Watermark for Large Language Models
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||||
|
||||
- **arXiv id:** 2301.10226v4
|
||||
- **Title:** A Watermark for Large Language Models
|
||||
- **Authors:** John Kirchenbauer, Jonas Geiping, Yuxin Wen, et al.
|
||||
- **Published Date:** 2023-01-24
|
||||
- **URL:** http://arxiv.org/abs/2301.10226v4
|
||||
|
||||
- **LangChain API Reference:** [langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI)
|
||||
|
||||
**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
|
||||
- **Title:** Precise Zero-Shot Dense Retrieval without Relevance Labels
|
||||
- **Authors:** Luyu Gao, Xueguang Ma, Jimmy Lin, et al.
|
||||
- **Published Date:** 2022-12-20
|
||||
- **URL:** http://arxiv.org/abs/2212.10496v1
|
||||
- **LangChain Documentation:** [docs/use_cases/query_analysis/techniques/hyde](https://python.langchain.com/docs/use_cases/query_analysis/techniques/hyde)
|
||||
- **LangChain API Reference:** [langchain.chains.hyde.base.HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
|
||||
|
||||
**Abstract:** While dense retrieval has been shown effective and efficient across tasks and
|
||||
languages, it remains difficult to create effective fully zero-shot dense
|
||||
retrieval systems when no relevance label is available. In this paper, we
|
||||
recognize the difficulty of zero-shot learning and encoding relevance. Instead,
|
||||
we propose to pivot through Hypothetical Document Embeddings~(HyDE). Given a
|
||||
query, HyDE first zero-shot instructs an instruction-following language model
|
||||
(e.g. InstructGPT) to generate a hypothetical document. The document captures
|
||||
relevance patterns but is unreal and may contain false details. Then, an
|
||||
unsupervised contrastively learned encoder~(e.g. Contriever) encodes the
|
||||
document into an embedding vector. This vector identifies a neighborhood in the
|
||||
corpus embedding space, where similar real documents are retrieved based on
|
||||
vector similarity. This second step ground the generated document to the actual
|
||||
corpus, with the encoder's dense bottleneck filtering out the incorrect
|
||||
details. Our experiments show that HyDE significantly outperforms the
|
||||
state-of-the-art unsupervised dense retriever Contriever and shows strong
|
||||
performance comparable to fine-tuned retrievers, across various tasks (e.g. web
|
||||
search, QA, fact verification) and languages~(e.g. sw, ko, ja).
|
||||
|
||||
## Constitutional AI: Harmlessness from AI Feedback
|
||||
|
||||
- **arXiv id:** 2212.08073v1
|
||||
- **Title:** Constitutional AI: Harmlessness from AI Feedback
|
||||
- **Authors:** Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al.
|
||||
- **Published Date:** 2022-12-15
|
||||
- **URL:** http://arxiv.org/abs/2212.08073v1
|
||||
- **LangChain Documentation:** [docs/guides/productionization/evaluation/string/criteria_eval_chain](https://python.langchain.com/docs/guides/productionization/evaluation/string/criteria_eval_chain)
|
||||
|
||||
|
||||
**Abstract:** As AI systems become more capable, we would like to enlist their help to
|
||||
supervise other AIs. We experiment with methods for training a harmless AI
|
||||
assistant through self-improvement, without any human labels identifying
|
||||
harmful outputs. The only human oversight is provided through a list of rules
|
||||
or principles, and so we refer to the method as 'Constitutional AI'. The
|
||||
process involves both a supervised learning and a reinforcement learning phase.
|
||||
In the supervised phase we sample from an initial model, then generate
|
||||
self-critiques and revisions, and then finetune the original model on revised
|
||||
responses. In the RL phase, we sample from the finetuned model, use a model to
|
||||
evaluate which of the two samples is better, and then train a preference model
|
||||
from this dataset of AI preferences. We then train with RL using the preference
|
||||
model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a
|
||||
result we are able to train a harmless but non-evasive AI assistant that
|
||||
engages with harmful queries by explaining its objections to them. Both the SL
|
||||
and RL methods can leverage chain-of-thought style reasoning to improve the
|
||||
human-judged performance and transparency of AI decision making. These methods
|
||||
make it possible to control AI behavior more precisely and with far fewer human
|
||||
labels.
|
||||
|
||||
## Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
|
||||
|
||||
- **arXiv id:** 2212.07425v3
|
||||
- **Title:** Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
|
||||
- **Authors:** Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande, et al.
|
||||
- **Published Date:** 2022-12-12
|
||||
- **URL:** http://arxiv.org/abs/2212.07425v3
|
||||
|
||||
- **LangChain API Reference:** [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.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
|
||||
- **Title:** Complementary Explanations for Effective In-Context Learning
|
||||
- **Authors:** Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, et al.
|
||||
- **Published Date:** 2022-11-25
|
||||
- **URL:** http://arxiv.org/abs/2211.13892v2
|
||||
|
||||
- **LangChain API Reference:** [langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
|
||||
|
||||
**Abstract:** Large language models (LLMs) have exhibited remarkable capabilities in
|
||||
learning from explanations in prompts, but there has been limited understanding
|
||||
of exactly how these explanations function or why they are effective. This work
|
||||
aims to better understand the mechanisms by which explanations are used for
|
||||
in-context learning. We first study the impact of two different factors on the
|
||||
performance of prompts with explanations: the computation trace (the way the
|
||||
solution is decomposed) and the natural language used to express the prompt. By
|
||||
perturbing explanations on three controlled tasks, we show that both factors
|
||||
contribute to the effectiveness of explanations. We further study how to form
|
||||
maximally effective sets of explanations for solving a given test query. We
|
||||
find that LLMs can benefit from the complementarity of the explanation set:
|
||||
diverse reasoning skills shown by different exemplars can lead to better
|
||||
performance. Therefore, we propose a maximal marginal relevance-based exemplar
|
||||
selection approach for constructing exemplar sets that are both relevant as
|
||||
well as complementary, which successfully improves the in-context learning
|
||||
performance across three real-world tasks on multiple LLMs.
|
||||
|
||||
## PAL: Program-aided Language Models
|
||||
|
||||
- **arXiv id:** 2211.10435v2
|
||||
- **Title:** PAL: Program-aided Language Models
|
||||
- **Authors:** Luyu Gao, Aman Madaan, Shuyan Zhou, et al.
|
||||
- **Published Date:** 2022-11-18
|
||||
- **URL:** http://arxiv.org/abs/2211.10435v2
|
||||
|
||||
- **LangChain API Reference:** [langchain_experimental.pal_chain.base.PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain)
|
||||
|
||||
**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/ .
|
||||
|
||||
## Deep Lake: a Lakehouse for Deep Learning
|
||||
|
||||
- **arXiv id:** 2209.10785v2
|
||||
- **Title:** Deep Lake: a Lakehouse for Deep Learning
|
||||
- **Authors:** Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan, et al.
|
||||
- **Published Date:** 2022-09-22
|
||||
- **URL:** http://arxiv.org/abs/2209.10785v2
|
||||
- **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.
|
||||
|
||||
## Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
|
||||
|
||||
- **arXiv id:** 2205.12654v1
|
||||
- **Title:** Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
|
||||
- **Authors:** Kevin Heffernan, Onur Çelebi, Holger Schwenk
|
||||
- **Published Date:** 2022-05-25
|
||||
- **URL:** http://arxiv.org/abs/2205.12654v1
|
||||
|
||||
- **LangChain API Reference:** [langchain_community.embeddings.laser.LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
|
||||
|
||||
**Abstract:** Scaling multilingual representation learning beyond the hundred most frequent
|
||||
languages is challenging, in particular to cover the long tail of low-resource
|
||||
languages. A promising approach has been to train one-for-all multilingual
|
||||
models capable of cross-lingual transfer, but these models often suffer from
|
||||
insufficient capacity and interference between unrelated languages. Instead, we
|
||||
move away from this approach and focus on training multiple language (family)
|
||||
specific representations, but most prominently enable all languages to still be
|
||||
encoded in the same representational space. To achieve this, we focus on
|
||||
teacher-student training, allowing all encoders to be mutually compatible for
|
||||
bitext mining, and enabling fast learning of new languages. We introduce a new
|
||||
teacher-student training scheme which combines supervised and self-supervised
|
||||
training, allowing encoders to take advantage of monolingual training data,
|
||||
which is valuable in the low-resource setting.
|
||||
Our approach significantly outperforms the original LASER encoder. We study
|
||||
very low-resource languages and handle 50 African languages, many of which are
|
||||
not covered by any other model. For these languages, we train sentence
|
||||
encoders, mine bitexts, and validate the bitexts by training NMT systems.
|
||||
|
||||
## Evaluating the Text-to-SQL Capabilities of Large Language Models
|
||||
|
||||
- **arXiv id:** 2204.00498v1
|
||||
- **Title:** Evaluating the Text-to-SQL Capabilities of Large Language Models
|
||||
- **Authors:** Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau
|
||||
- **Published Date:** 2022-03-15
|
||||
- **URL:** http://arxiv.org/abs/2204.00498v1
|
||||
- **LangChain Documentation:** [docs/use_cases/sql/quickstart](https://python.langchain.com/docs/use_cases/sql/quickstart)
|
||||
- **LangChain API Reference:** [langchain_community.utilities.sql_database.SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community.utilities.spark_sql.SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
|
||||
|
||||
**Abstract:** We perform an empirical evaluation of Text-to-SQL capabilities of the Codex
|
||||
language model. We find that, without any finetuning, Codex is a strong
|
||||
baseline on the Spider benchmark; we also analyze the failure modes of Codex in
|
||||
this setting. Furthermore, we demonstrate on the GeoQuery and Scholar
|
||||
benchmarks that a small number of in-domain examples provided in the prompt
|
||||
enables Codex to perform better than state-of-the-art models finetuned on such
|
||||
few-shot examples.
|
||||
|
||||
## Locally Typical Sampling
|
||||
|
||||
- **arXiv id:** 2202.00666v5
|
||||
- **Title:** Locally Typical Sampling
|
||||
- **Authors:** Clara Meister, Tiago Pimentel, Gian Wiher, et al.
|
||||
- **Published Date:** 2022-02-01
|
||||
- **URL:** http://arxiv.org/abs/2202.00666v5
|
||||
|
||||
- **LangChain API Reference:** [langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
|
||||
|
||||
**Abstract:** Today's probabilistic language generators fall short when it comes to
|
||||
producing coherent and fluent text despite the fact that the underlying models
|
||||
perform well under standard metrics, e.g., perplexity. This discrepancy has
|
||||
puzzled the language generation community for the last few years. In this work,
|
||||
we posit that the abstraction of natural language generation as a discrete
|
||||
stochastic process--which allows for an information-theoretic analysis--can
|
||||
provide new insights into the behavior of probabilistic language generators,
|
||||
e.g., why high-probability texts can be dull or repetitive. Humans use language
|
||||
as a means of communicating information, aiming to do so in a simultaneously
|
||||
efficient and error-minimizing manner; in fact, psycholinguistics research
|
||||
suggests humans choose each word in a string with this subconscious goal in
|
||||
mind. We formally define the set of strings that meet this criterion: those for
|
||||
which each word has an information content close to the expected information
|
||||
content, i.e., the conditional entropy of our model. We then propose a simple
|
||||
and efficient procedure for enforcing this criterion when generating from
|
||||
probabilistic models, which we call locally typical sampling. Automatic and
|
||||
human evaluations show that, in comparison to nucleus and top-k sampling,
|
||||
locally typical sampling offers competitive performance (in both abstractive
|
||||
summarization and story generation) in terms of quality while consistently
|
||||
reducing degenerate repetitions.
|
||||
|
||||
## Learning Transferable Visual Models From Natural Language Supervision
|
||||
|
||||
- **arXiv id:** 2103.00020v1
|
||||
- **Title:** Learning Transferable Visual Models From Natural Language Supervision
|
||||
- **Authors:** Alec Radford, Jong Wook Kim, Chris Hallacy, et al.
|
||||
- **Published Date:** 2021-02-26
|
||||
- **URL:** http://arxiv.org/abs/2103.00020v1
|
||||
|
||||
- **LangChain API Reference:** [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.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
|
||||
- **Title:** CTRL: A Conditional Transformer Language Model for Controllable Generation
|
||||
- **Authors:** Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.
|
||||
- **Published Date:** 2019-09-11
|
||||
- **URL:** http://arxiv.org/abs/1909.05858v2
|
||||
|
||||
- **LangChain API Reference:** [langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
|
||||
|
||||
**Abstract:** Large-scale language models show promising text generation capabilities, but
|
||||
users cannot easily control particular aspects of the generated text. We
|
||||
release CTRL, a 1.63 billion-parameter conditional transformer language model,
|
||||
trained to condition on control codes that govern style, content, and
|
||||
task-specific behavior. Control codes were derived from structure that
|
||||
naturally co-occurs with raw text, preserving the advantages of unsupervised
|
||||
learning while providing more explicit control over text generation. These
|
||||
codes also allow CTRL to predict which parts of the training data are most
|
||||
likely given a sequence. This provides a potential method for analyzing large
|
||||
amounts of data via model-based source attribution. We have released multiple
|
||||
full-sized, pretrained versions of CTRL at https://github.com/salesforce/ctrl.
|
||||
|
||||
## Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
|
||||
|
||||
- **arXiv id:** 1908.10084v1
|
||||
- **Title:** Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
|
||||
- **Authors:** Nils Reimers, Iryna Gurevych
|
||||
- **Published Date:** 2019-08-27
|
||||
- **URL:** http://arxiv.org/abs/1908.10084v1
|
||||
- **LangChain Documentation:** [docs/integrations/text_embedding/sentence_transformers](https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers)
|
||||
|
||||
|
||||
**Abstract:** BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new
|
||||
state-of-the-art performance on sentence-pair regression tasks like semantic
|
||||
textual similarity (STS). However, it requires that both sentences are fed into
|
||||
the network, which causes a massive computational overhead: Finding the most
|
||||
similar pair in a collection of 10,000 sentences requires about 50 million
|
||||
inference computations (~65 hours) with BERT. The construction of BERT makes it
|
||||
unsuitable for semantic similarity search as well as for unsupervised tasks
|
||||
like clustering.
|
||||
In this publication, we present Sentence-BERT (SBERT), a modification of the
|
||||
pretrained BERT network that use siamese and triplet network structures to
|
||||
derive semantically meaningful sentence embeddings that can be compared using
|
||||
cosine-similarity. This reduces the effort for finding the most similar pair
|
||||
from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while
|
||||
maintaining the accuracy from BERT.
|
||||
We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning
|
||||
tasks, where it outperforms other state-of-the-art sentence embeddings methods.
|
||||
|
||||
@ -0,0 +1,171 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to use callbacks in async environments\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
"This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
"- [Callbacks](/docs/concepts/#callbacks)\n",
|
||||
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"If you are planning to use the async APIs, it is recommended to use and extend [`AsyncCallbackHandler`](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) to avoid blocking the runloop.\n",
|
||||
"\n",
|
||||
"**Note**: if you use a sync `CallbackHandler` while using an async method to run your LLM / Chain / Tool / Agent, it will still work. However, under the hood, it will be called with [`run_in_executor`](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor) which can cause issues if your `CallbackHandler` is not thread-safe."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | output: false\n",
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"%pip install -qU langchain langchain_anthropic\n",
|
||||
"\n",
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"zzzz....\n",
|
||||
"Hi! I just woke up. Your llm is starting\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: Here\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: 's\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: a\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: little\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: joke\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: for\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: you\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: :\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: \n",
|
||||
"\n",
|
||||
"Why\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: can\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: 't\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: a\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: bicycle\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: stan\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: d up\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: by\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: itself\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: ?\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: Because\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: it\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: 's\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: two\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: -\n",
|
||||
"Sync handler being called in a `thread_pool_executor`: token: tire\n",
|
||||
"zzzz....\n",
|
||||
"Hi! I just woke up. Your llm is ending\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"LLMResult(generations=[[ChatGeneration(text=\"Here's a little joke for you:\\n\\nWhy can't a bicycle stand up by itself? Because it's two-tire\", message=AIMessage(content=\"Here's a little joke for you:\\n\\nWhy can't a bicycle stand up by itself? Because it's two-tire\", id='run-8afc89e8-02c0-4522-8480-d96977240bd4-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('8afc89e8-02c0-4522-8480-d96977240bd4'))])"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import asyncio\n",
|
||||
"from typing import Any, Dict, List\n",
|
||||
"\n",
|
||||
"from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler\n",
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.messages import HumanMessage\n",
|
||||
"from langchain_core.outputs import LLMResult\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class MyCustomSyncHandler(BaseCallbackHandler):\n",
|
||||
" def on_llm_new_token(self, token: str, **kwargs) -> None:\n",
|
||||
" print(f\"Sync handler being called in a `thread_pool_executor`: token: {token}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class MyCustomAsyncHandler(AsyncCallbackHandler):\n",
|
||||
" \"\"\"Async callback handler that can be used to handle callbacks from langchain.\"\"\"\n",
|
||||
"\n",
|
||||
" async def on_llm_start(\n",
|
||||
" self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any\n",
|
||||
" ) -> None:\n",
|
||||
" \"\"\"Run when chain starts running.\"\"\"\n",
|
||||
" print(\"zzzz....\")\n",
|
||||
" await asyncio.sleep(0.3)\n",
|
||||
" class_name = serialized[\"name\"]\n",
|
||||
" print(\"Hi! I just woke up. Your llm is starting\")\n",
|
||||
"\n",
|
||||
" async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:\n",
|
||||
" \"\"\"Run when chain ends running.\"\"\"\n",
|
||||
" print(\"zzzz....\")\n",
|
||||
" await asyncio.sleep(0.3)\n",
|
||||
" print(\"Hi! I just woke up. Your llm is ending\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# To enable streaming, we pass in `streaming=True` to the ChatModel constructor\n",
|
||||
"# Additionally, we pass in a list with our custom handler\n",
|
||||
"chat = ChatAnthropic(\n",
|
||||
" model=\"claude-3-sonnet-20240229\",\n",
|
||||
" max_tokens=25,\n",
|
||||
" streaming=True,\n",
|
||||
" callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"await chat.agenerate([[HumanMessage(content=\"Tell me a joke\")]])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
"You've now learned how to create your own custom callback handlers.\n",
|
||||
"\n",
|
||||
"Next, check out the other how-to guides in this section, such as [how to attach callbacks to a runnable](/docs/how_to/callbacks_attach)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,144 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to attach callbacks to a module\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
"This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
"- [Callbacks](/docs/concepts/#callbacks)\n",
|
||||
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
|
||||
"- [Chaining runnables](/docs/how_to/sequence)\n",
|
||||
"- [Attach runtime arguments to a Runnable](/docs/how_to/binding)\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"If you are composing a chain of runnables and want to reuse callbacks across multiple executions, you can attach callbacks with the [`.with_config()`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_config) method. This saves you the need to pass callbacks in each time you invoke the chain.\n",
|
||||
"\n",
|
||||
"Here's an example:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | output: false\n",
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"%pip install -qU langchain langchain_anthropic\n",
|
||||
"\n",
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Chain RunnableSequence started\n",
|
||||
"Chain ChatPromptTemplate started\n",
|
||||
"Chain ended, outputs: messages=[HumanMessage(content='What is 1 + 2?')]\n",
|
||||
"Chat model started\n",
|
||||
"Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01LjC57hgrmzVhEma4yXdLKF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-393950f9-79b9-4fd6-ac6e-50d93d75b906-0'))]] llm_output={'id': 'msg_01LjC57hgrmzVhEma4yXdLKF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None\n",
|
||||
"Chain ended, outputs: content='1 + 2 = 3' response_metadata={'id': 'msg_01LjC57hgrmzVhEma4yXdLKF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} id='run-393950f9-79b9-4fd6-ac6e-50d93d75b906-0'\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01LjC57hgrmzVhEma4yXdLKF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-393950f9-79b9-4fd6-ac6e-50d93d75b906-0')"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from typing import Any, Dict, List\n",
|
||||
"\n",
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.callbacks import BaseCallbackHandler\n",
|
||||
"from langchain_core.messages import BaseMessage\n",
|
||||
"from langchain_core.outputs import LLMResult\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class LoggingHandler(BaseCallbackHandler):\n",
|
||||
" def on_chat_model_start(\n",
|
||||
" self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs\n",
|
||||
" ) -> None:\n",
|
||||
" print(\"Chat model started\")\n",
|
||||
"\n",
|
||||
" def on_llm_end(self, response: LLMResult, **kwargs) -> None:\n",
|
||||
" print(f\"Chat model ended, response: {response}\")\n",
|
||||
"\n",
|
||||
" def on_chain_start(\n",
|
||||
" self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs\n",
|
||||
" ) -> None:\n",
|
||||
" print(f\"Chain {serialized.get('name')} started\")\n",
|
||||
"\n",
|
||||
" def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:\n",
|
||||
" print(f\"Chain ended, outputs: {outputs}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"callbacks = [LoggingHandler()]\n",
|
||||
"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")\n",
|
||||
"prompt = ChatPromptTemplate.from_template(\"What is 1 + {number}?\")\n",
|
||||
"\n",
|
||||
"chain = prompt | llm\n",
|
||||
"\n",
|
||||
"chain_with_callbacks = chain.with_config(callbacks=callbacks)\n",
|
||||
"\n",
|
||||
"chain_with_callbacks.invoke({\"number\": \"2\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The bound callbacks will run for all nested module runs.\n",
|
||||
"\n",
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
"You've now learned how to attach callbacks to a chain.\n",
|
||||
"\n",
|
||||
"Next, check out the other how-to guides in this section, such as how to [pass callbacks in at runtime](/docs/how_to/callbacks_runtime)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,136 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to pass callbacks into a module constructor\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
"This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
"- [Callbacks](/docs/concepts/#callbacks)\n",
|
||||
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"Most LangChain modules allow you to pass `callbacks` directly into the constructor. In this case, the callbacks will only be called for that instance (and any nested runs).\n",
|
||||
"\n",
|
||||
"Here's an example:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | output: false\n",
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"%pip install -qU langchain langchain_anthropic\n",
|
||||
"\n",
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Chat model started\n",
|
||||
"Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01CdKsRmeS9WRb8BWnHDEHm7', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-2d7fdf2a-7405-4e17-97c0-67e6b2a65305-0'))]] llm_output={'id': 'msg_01CdKsRmeS9WRb8BWnHDEHm7', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01CdKsRmeS9WRb8BWnHDEHm7', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-2d7fdf2a-7405-4e17-97c0-67e6b2a65305-0')"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from typing import Any, Dict, List\n",
|
||||
"\n",
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.callbacks import BaseCallbackHandler\n",
|
||||
"from langchain_core.messages import BaseMessage\n",
|
||||
"from langchain_core.outputs import LLMResult\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class LoggingHandler(BaseCallbackHandler):\n",
|
||||
" def on_chat_model_start(\n",
|
||||
" self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs\n",
|
||||
" ) -> None:\n",
|
||||
" print(\"Chat model started\")\n",
|
||||
"\n",
|
||||
" def on_llm_end(self, response: LLMResult, **kwargs) -> None:\n",
|
||||
" print(f\"Chat model ended, response: {response}\")\n",
|
||||
"\n",
|
||||
" def on_chain_start(\n",
|
||||
" self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs\n",
|
||||
" ) -> None:\n",
|
||||
" print(f\"Chain {serialized.get('name')} started\")\n",
|
||||
"\n",
|
||||
" def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:\n",
|
||||
" print(f\"Chain ended, outputs: {outputs}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"callbacks = [LoggingHandler()]\n",
|
||||
"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", callbacks=callbacks)\n",
|
||||
"prompt = ChatPromptTemplate.from_template(\"What is 1 + {number}?\")\n",
|
||||
"\n",
|
||||
"chain = prompt | llm\n",
|
||||
"\n",
|
||||
"chain.invoke({\"number\": \"2\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can see that we only see events from the chat model run - no chain events from the prompt or broader chain.\n",
|
||||
"\n",
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
"You've now learned how to pass callbacks into a constructor.\n",
|
||||
"\n",
|
||||
"Next, check out the other how-to guides in this section, such as how to [pass callbacks at runtime](/docs/how_to/callbacks_runtime)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,140 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to pass callbacks in at runtime\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
"This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
"- [Callbacks](/docs/concepts/#callbacks)\n",
|
||||
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"In many cases, it is advantageous to pass in handlers instead when running the object. When we pass through [`CallbackHandlers`](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) using the `callbacks` keyword arg when executing an run, those callbacks will be issued by all nested objects involved in the execution. For example, when a handler is passed through to an Agent, it will be used for all callbacks related to the agent and all the objects involved in the agent's execution, in this case, the Tools and LLM.\n",
|
||||
"\n",
|
||||
"This prevents us from having to manually attach the handlers to each individual nested object. Here's an example:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | output: false\n",
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"%pip install -qU langchain langchain_anthropic\n",
|
||||
"\n",
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Chain RunnableSequence started\n",
|
||||
"Chain ChatPromptTemplate started\n",
|
||||
"Chain ended, outputs: messages=[HumanMessage(content='What is 1 + 2?')]\n",
|
||||
"Chat model started\n",
|
||||
"Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0'))]] llm_output={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None\n",
|
||||
"Chain ended, outputs: content='1 + 2 = 3' response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0'\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0')"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from typing import Any, Dict, List\n",
|
||||
"\n",
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.callbacks import BaseCallbackHandler\n",
|
||||
"from langchain_core.messages import BaseMessage\n",
|
||||
"from langchain_core.outputs import LLMResult\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class LoggingHandler(BaseCallbackHandler):\n",
|
||||
" def on_chat_model_start(\n",
|
||||
" self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs\n",
|
||||
" ) -> None:\n",
|
||||
" print(\"Chat model started\")\n",
|
||||
"\n",
|
||||
" def on_llm_end(self, response: LLMResult, **kwargs) -> None:\n",
|
||||
" print(f\"Chat model ended, response: {response}\")\n",
|
||||
"\n",
|
||||
" def on_chain_start(\n",
|
||||
" self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs\n",
|
||||
" ) -> None:\n",
|
||||
" print(f\"Chain {serialized.get('name')} started\")\n",
|
||||
"\n",
|
||||
" def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:\n",
|
||||
" print(f\"Chain ended, outputs: {outputs}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"callbacks = [LoggingHandler()]\n",
|
||||
"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")\n",
|
||||
"prompt = ChatPromptTemplate.from_template(\"What is 1 + {number}?\")\n",
|
||||
"\n",
|
||||
"chain = prompt | llm\n",
|
||||
"\n",
|
||||
"chain.invoke({\"number\": \"2\"}, config={\"callbacks\": callbacks})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If there are already existing callbacks associated with a module, these will run in addition to any passed in at runtime.\n",
|
||||
"\n",
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
"You've now learned how to pass callbacks at runtime.\n",
|
||||
"\n",
|
||||
"Next, check out the other how-to guides in this section, such as how to [pass callbacks into a module constructor](/docs/how_to/custom_callbacks)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,141 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to create custom callback handlers\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
"This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
"- [Callbacks](/docs/concepts/#callbacks)\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"LangChain has some built-in callback handlers, but you will often want to create your own handlers with custom logic.\n",
|
||||
"\n",
|
||||
"To create a custom callback handler, we need to determine the [event(s)](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) we want our callback handler to handle as well as what we want our callback handler to do when the event is triggered. Then all we need to do is attach the callback handler to the object, for example via [the constructor](/docs/how_to/callbacks_constructor) or [at runtime](/docs/how_to/callbacks_runtime).\n",
|
||||
"\n",
|
||||
"In the example below, we'll implement streaming with a custom handler.\n",
|
||||
"\n",
|
||||
"In our custom callback handler `MyCustomHandler`, we implement the `on_llm_new_token` handler to print the token we have just received. We then attach our custom handler to the model object as a constructor callback."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | output: false\n",
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"%pip install -qU langchain langchain_anthropic\n",
|
||||
"\n",
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"My custom handler, token: Here\n",
|
||||
"My custom handler, token: 's\n",
|
||||
"My custom handler, token: a\n",
|
||||
"My custom handler, token: bear\n",
|
||||
"My custom handler, token: joke\n",
|
||||
"My custom handler, token: for\n",
|
||||
"My custom handler, token: you\n",
|
||||
"My custom handler, token: :\n",
|
||||
"My custom handler, token: \n",
|
||||
"\n",
|
||||
"Why\n",
|
||||
"My custom handler, token: di\n",
|
||||
"My custom handler, token: d the\n",
|
||||
"My custom handler, token: bear\n",
|
||||
"My custom handler, token: dissol\n",
|
||||
"My custom handler, token: ve\n",
|
||||
"My custom handler, token: in\n",
|
||||
"My custom handler, token: water\n",
|
||||
"My custom handler, token: ?\n",
|
||||
"My custom handler, token: \n",
|
||||
"Because\n",
|
||||
"My custom handler, token: it\n",
|
||||
"My custom handler, token: was\n",
|
||||
"My custom handler, token: a\n",
|
||||
"My custom handler, token: polar\n",
|
||||
"My custom handler, token: bear\n",
|
||||
"My custom handler, token: !\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.callbacks import BaseCallbackHandler\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class MyCustomHandler(BaseCallbackHandler):\n",
|
||||
" def on_llm_new_token(self, token: str, **kwargs) -> None:\n",
|
||||
" print(f\"My custom handler, token: {token}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"prompt = ChatPromptTemplate.from_messages([\"Tell me a joke about {animal}\"])\n",
|
||||
"\n",
|
||||
"# To enable streaming, we pass in `streaming=True` to the ChatModel constructor\n",
|
||||
"# Additionally, we pass in our custom handler as a list to the callbacks parameter\n",
|
||||
"model = ChatAnthropic(\n",
|
||||
" model=\"claude-3-sonnet-20240229\", streaming=True, callbacks=[MyCustomHandler()]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"chain = prompt | model\n",
|
||||
"\n",
|
||||
"response = chain.invoke({\"animal\": \"bears\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can see [this reference page](https://api.python.langchain.com/en/latest/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) for a list of events you can handle. Note that the `handle_chain_*` events run for most LCEL runnables.\n",
|
||||
"\n",
|
||||
"## Next steps\n",
|
||||
"\n",
|
||||
"You've now learned how to create your own custom callback handlers.\n",
|
||||
"\n",
|
||||
"Next, check out the other how-to guides in this section, such as [how to attach callbacks to a runnable](/docs/how_to/callbacks_attach)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,200 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"id": "77bf57fb-e990-45f2-8b5f-c76388b05966",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"---\n",
|
||||
"keywords: [LCEL]\n",
|
||||
"---"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "50d57bf2-7104-4570-b3e5-90fd71e1bea1",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to create a dynamic (self-constructing) chain\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
"This guide assumes familiarity with the following:\n",
|
||||
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
|
||||
"- [How to turn any function into a runnable](/docs/how_to/functions)\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"Sometimes we want to construct parts of a chain at runtime, depending on the chain inputs ([routing](/docs/how_to/routing/) is the most common example of this). We can create dynamic chains like this using a very useful property of RunnableLambda's, which is that if a RunnableLambda returns a Runnable, that Runnable is itself invoked. Let's see an example.\n",
|
||||
"\n",
|
||||
"```{=mdx}\n",
|
||||
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
|
||||
"\n",
|
||||
"<ChatModelTabs\n",
|
||||
" customVarName=\"llm\"\n",
|
||||
"/>\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "406bffc2-86d0-4cb9-9262-5c1e3442397a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"\n",
|
||||
"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "0ae6692b-983e-40b8-aa2a-6c078d945b9e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"According to the context provided, Egypt's population in 2024 is estimated to be about 111 million.\""
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_core.output_parsers import StrOutputParser\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"from langchain_core.runnables import Runnable, RunnablePassthrough, chain\n",
|
||||
"\n",
|
||||
"contextualize_instructions = \"\"\"Convert the latest user question into a standalone question given the chat history. Don't answer the question, return the question and nothing else (no descriptive text).\"\"\"\n",
|
||||
"contextualize_prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [\n",
|
||||
" (\"system\", contextualize_instructions),\n",
|
||||
" (\"placeholder\", \"{chat_history}\"),\n",
|
||||
" (\"human\", \"{question}\"),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"contextualize_question = contextualize_prompt | llm | StrOutputParser()\n",
|
||||
"\n",
|
||||
"qa_instructions = (\n",
|
||||
" \"\"\"Answer the user question given the following context:\\n\\n{context}.\"\"\"\n",
|
||||
")\n",
|
||||
"qa_prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [(\"system\", qa_instructions), (\"human\", \"{question}\")]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@chain\n",
|
||||
"def contextualize_if_needed(input_: dict) -> Runnable:\n",
|
||||
" if input_.get(\"chat_history\"):\n",
|
||||
" # NOTE: This is returning another Runnable, not an actual output.\n",
|
||||
" return contextualize_question\n",
|
||||
" else:\n",
|
||||
" return RunnablePassthrough()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@chain\n",
|
||||
"def fake_retriever(input_: dict) -> str:\n",
|
||||
" return \"egypt's population in 2024 is about 111 million\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"full_chain = (\n",
|
||||
" RunnablePassthrough.assign(question=contextualize_if_needed).assign(\n",
|
||||
" context=fake_retriever\n",
|
||||
" )\n",
|
||||
" | qa_prompt\n",
|
||||
" | llm\n",
|
||||
" | StrOutputParser()\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"full_chain.invoke(\n",
|
||||
" {\n",
|
||||
" \"question\": \"what about egypt\",\n",
|
||||
" \"chat_history\": [\n",
|
||||
" (\"human\", \"what's the population of indonesia\"),\n",
|
||||
" (\"ai\", \"about 276 million\"),\n",
|
||||
" ],\n",
|
||||
" }\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5076ddb4-4a99-47ad-b549-8ac27ca3e2c6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The key here is that `contextualize_if_needed` returns another Runnable and not an actual output. This returned Runnable is itself run when the full chain is executed.\n",
|
||||
"\n",
|
||||
"Looking at the trace we can see that, since we passed in chat_history, we executed the contextualize_question chain as part of the full chain: https://smith.langchain.com/public/9e0ae34c-4082-4f3f-beed-34a2a2f4c991/r"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4fe6ca44-a643-4859-a290-be68403f51f0",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Note that the streaming, batching, etc. capabilities of the returned Runnable are all preserved"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "6def37fa-5105-4090-9b07-77cb488ecd9c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"What\n",
|
||||
" is\n",
|
||||
" the\n",
|
||||
" population\n",
|
||||
" of\n",
|
||||
" Egypt\n",
|
||||
"?\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for chunk in contextualize_if_needed.stream(\n",
|
||||
" {\n",
|
||||
" \"question\": \"what about egypt\",\n",
|
||||
" \"chat_history\": [\n",
|
||||
" (\"human\", \"what's the population of indonesia\"),\n",
|
||||
" (\"ai\", \"about 276 million\"),\n",
|
||||
" ],\n",
|
||||
" }\n",
|
||||
"):\n",
|
||||
" print(chunk)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "poetry-venv-2",
|
||||
"language": "python",
|
||||
"name": "poetry-venv-2"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,351 @@
|
||||
What I Worked On
|
||||
|
||||
February 2021
|
||||
|
||||
Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.
|
||||
|
||||
The first programs I tried writing were on the IBM 1401 that our school district used for what was then called "data processing." This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.
|
||||
|
||||
The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.
|
||||
|
||||
I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear.
|
||||
|
||||
With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1]
|
||||
|
||||
The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer.
|
||||
|
||||
Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter.
|
||||
|
||||
Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored.
|
||||
|
||||
I couldn't have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI.
|
||||
|
||||
AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I don't know how well it has aged, but when I read it I was drawn entirely into its world. It seemed only a matter of time before we'd have Mike, and when I saw Winograd using SHRDLU, it seemed like that time would be a few years at most. All you had to do was teach SHRDLU more words.
|
||||
|
||||
There weren't any classes in AI at Cornell then, not even graduate classes, so I started trying to teach myself. Which meant learning Lisp, since in those days Lisp was regarded as the language of AI. The commonly used programming languages then were pretty primitive, and programmers' ideas correspondingly so. The default language at Cornell was a Pascal-like language called PL/I, and the situation was similar elsewhere. Learning Lisp expanded my concept of a program so fast that it was years before I started to have a sense of where the new limits were. This was more like it; this was what I had expected college to do. It wasn't happening in a class, like it was supposed to, but that was ok. For the next couple years I was on a roll. I knew what I was going to do.
|
||||
|
||||
For my undergraduate thesis, I reverse-engineered SHRDLU. My God did I love working on that program. It was a pleasing bit of code, but what made it even more exciting was my belief — hard to imagine now, but not unique in 1985 — that it was already climbing the lower slopes of intelligence.
|
||||
|
||||
I had gotten into a program at Cornell that didn't make you choose a major. You could take whatever classes you liked, and choose whatever you liked to put on your degree. I of course chose "Artificial Intelligence." When I got the actual physical diploma, I was dismayed to find that the quotes had been included, which made them read as scare-quotes. At the time this bothered me, but now it seems amusingly accurate, for reasons I was about to discover.
|
||||
|
||||
I applied to 3 grad schools: MIT and Yale, which were renowned for AI at the time, and Harvard, which I'd visited because Rich Draves went there, and was also home to Bill Woods, who'd invented the type of parser I used in my SHRDLU clone. Only Harvard accepted me, so that was where I went.
|
||||
|
||||
I don't remember the moment it happened, or if there even was a specific moment, but during the first year of grad school I realized that AI, as practiced at the time, was a hoax. By which I mean the sort of AI in which a program that's told "the dog is sitting on the chair" translates this into some formal representation and adds it to the list of things it knows.
|
||||
|
||||
What these programs really showed was that there's a subset of natural language that's a formal language. But a very proper subset. It was clear that there was an unbridgeable gap between what they could do and actually understanding natural language. It was not, in fact, simply a matter of teaching SHRDLU more words. That whole way of doing AI, with explicit data structures representing concepts, was not going to work. Its brokenness did, as so often happens, generate a lot of opportunities to write papers about various band-aids that could be applied to it, but it was never going to get us Mike.
|
||||
|
||||
So I looked around to see what I could salvage from the wreckage of my plans, and there was Lisp. I knew from experience that Lisp was interesting for its own sake and not just for its association with AI, even though that was the main reason people cared about it at the time. So I decided to focus on Lisp. In fact, I decided to write a book about Lisp hacking. It's scary to think how little I knew about Lisp hacking when I started writing that book. But there's nothing like writing a book about something to help you learn it. The book, On Lisp, wasn't published till 1993, but I wrote much of it in grad school.
|
||||
|
||||
Computer Science is an uneasy alliance between two halves, theory and systems. The theory people prove things, and the systems people build things. I wanted to build things. I had plenty of respect for theory — indeed, a sneaking suspicion that it was the more admirable of the two halves — but building things seemed so much more exciting.
|
||||
|
||||
The problem with systems work, though, was that it didn't last. Any program you wrote today, no matter how good, would be obsolete in a couple decades at best. People might mention your software in footnotes, but no one would actually use it. And indeed, it would seem very feeble work. Only people with a sense of the history of the field would even realize that, in its time, it had been good.
|
||||
|
||||
There were some surplus Xerox Dandelions floating around the computer lab at one point. Anyone who wanted one to play around with could have one. I was briefly tempted, but they were so slow by present standards; what was the point? No one else wanted one either, so off they went. That was what happened to systems work.
|
||||
|
||||
I wanted not just to build things, but to build things that would last.
|
||||
|
||||
In this dissatisfied state I went in 1988 to visit Rich Draves at CMU, where he was in grad school. One day I went to visit the Carnegie Institute, where I'd spent a lot of time as a kid. While looking at a painting there I realized something that might seem obvious, but was a big surprise to me. There, right on the wall, was something you could make that would last. Paintings didn't become obsolete. Some of the best ones were hundreds of years old.
|
||||
|
||||
And moreover this was something you could make a living doing. Not as easily as you could by writing software, of course, but I thought if you were really industrious and lived really cheaply, it had to be possible to make enough to survive. And as an artist you could be truly independent. You wouldn't have a boss, or even need to get research funding.
|
||||
|
||||
I had always liked looking at paintings. Could I make them? I had no idea. I'd never imagined it was even possible. I knew intellectually that people made art — that it didn't just appear spontaneously — but it was as if the people who made it were a different species. They either lived long ago or were mysterious geniuses doing strange things in profiles in Life magazine. The idea of actually being able to make art, to put that verb before that noun, seemed almost miraculous.
|
||||
|
||||
That fall I started taking art classes at Harvard. Grad students could take classes in any department, and my advisor, Tom Cheatham, was very easy going. If he even knew about the strange classes I was taking, he never said anything.
|
||||
|
||||
So now I was in a PhD program in computer science, yet planning to be an artist, yet also genuinely in love with Lisp hacking and working away at On Lisp. In other words, like many a grad student, I was working energetically on multiple projects that were not my thesis.
|
||||
|
||||
I didn't see a way out of this situation. I didn't want to drop out of grad school, but how else was I going to get out? I remember when my friend Robert Morris got kicked out of Cornell for writing the internet worm of 1988, I was envious that he'd found such a spectacular way to get out of grad school.
|
||||
|
||||
Then one day in April 1990 a crack appeared in the wall. I ran into professor Cheatham and he asked if I was far enough along to graduate that June. I didn't have a word of my dissertation written, but in what must have been the quickest bit of thinking in my life, I decided to take a shot at writing one in the 5 weeks or so that remained before the deadline, reusing parts of On Lisp where I could, and I was able to respond, with no perceptible delay "Yes, I think so. I'll give you something to read in a few days."
|
||||
|
||||
I picked applications of continuations as the topic. In retrospect I should have written about macros and embedded languages. There's a whole world there that's barely been explored. But all I wanted was to get out of grad school, and my rapidly written dissertation sufficed, just barely.
|
||||
|
||||
Meanwhile I was applying to art schools. I applied to two: RISD in the US, and the Accademia di Belli Arti in Florence, which, because it was the oldest art school, I imagined would be good. RISD accepted me, and I never heard back from the Accademia, so off to Providence I went.
|
||||
|
||||
I'd applied for the BFA program at RISD, which meant in effect that I had to go to college again. This was not as strange as it sounds, because I was only 25, and art schools are full of people of different ages. RISD counted me as a transfer sophomore and said I had to do the foundation that summer. The foundation means the classes that everyone has to take in fundamental subjects like drawing, color, and design.
|
||||
|
||||
Toward the end of the summer I got a big surprise: a letter from the Accademia, which had been delayed because they'd sent it to Cambridge England instead of Cambridge Massachusetts, inviting me to take the entrance exam in Florence that fall. This was now only weeks away. My nice landlady let me leave my stuff in her attic. I had some money saved from consulting work I'd done in grad school; there was probably enough to last a year if I lived cheaply. Now all I had to do was learn Italian.
|
||||
|
||||
Only stranieri (foreigners) had to take this entrance exam. In retrospect it may well have been a way of excluding them, because there were so many stranieri attracted by the idea of studying art in Florence that the Italian students would otherwise have been outnumbered. I was in decent shape at painting and drawing from the RISD foundation that summer, but I still don't know how I managed to pass the written exam. I remember that I answered the essay question by writing about Cezanne, and that I cranked up the intellectual level as high as I could to make the most of my limited vocabulary. [2]
|
||||
|
||||
I'm only up to age 25 and already there are such conspicuous patterns. Here I was, yet again about to attend some august institution in the hopes of learning about some prestigious subject, and yet again about to be disappointed. The students and faculty in the painting department at the Accademia were the nicest people you could imagine, but they had long since arrived at an arrangement whereby the students wouldn't require the faculty to teach anything, and in return the faculty wouldn't require the students to learn anything. And at the same time all involved would adhere outwardly to the conventions of a 19th century atelier. We actually had one of those little stoves, fed with kindling, that you see in 19th century studio paintings, and a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they'd seen in American art magazines.
|
||||
|
||||
Our model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She'd copy an obscure old painting out of a book, and then he'd take the copy and maltreat it to make it look old. [3]
|
||||
|
||||
While I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time. Painting still lives is different from painting people, because the subject, as its name suggests, can't move. People can't sit for more than about 15 minutes at a time, and when they do they don't sit very still. So the traditional m.o. for painting people is to know how to paint a generic person, which you then modify to match the specific person you're painting. Whereas a still life you can, if you want, copy pixel by pixel from what you're seeing. You don't want to stop there, of course, or you get merely photographic accuracy, and what makes a still life interesting is that it's been through a head. You want to emphasize the visual cues that tell you, for example, that the reason the color changes suddenly at a certain point is that it's the edge of an object. By subtly emphasizing such things you can make paintings that are more realistic than photographs not just in some metaphorical sense, but in the strict information-theoretic sense. [4]
|
||||
|
||||
I liked painting still lives because I was curious about what I was seeing. In everyday life, we aren't consciously aware of much we're seeing. Most visual perception is handled by low-level processes that merely tell your brain "that's a water droplet" without telling you details like where the lightest and darkest points are, or "that's a bush" without telling you the shape and position of every leaf. This is a feature of brains, not a bug. In everyday life it would be distracting to notice every leaf on every bush. But when you have to paint something, you have to look more closely, and when you do there's a lot to see. You can still be noticing new things after days of trying to paint something people usually take for granted, just as you can after days of trying to write an essay about something people usually take for granted.
|
||||
|
||||
This is not the only way to paint. I'm not 100% sure it's even a good way to paint. But it seemed a good enough bet to be worth trying.
|
||||
|
||||
Our teacher, professor Ulivi, was a nice guy. He could see I worked hard, and gave me a good grade, which he wrote down in a sort of passport each student had. But the Accademia wasn't teaching me anything except Italian, and my money was running out, so at the end of the first year I went back to the US.
|
||||
|
||||
I wanted to go back to RISD, but I was now broke and RISD was very expensive, so I decided to get a job for a year and then return to RISD the next fall. I got one at a company called Interleaf, which made software for creating documents. You mean like Microsoft Word? Exactly. That was how I learned that low end software tends to eat high end software. But Interleaf still had a few years to live yet. [5]
|
||||
|
||||
Interleaf had done something pretty bold. Inspired by Emacs, they'd added a scripting language, and even made the scripting language a dialect of Lisp. Now they wanted a Lisp hacker to write things in it. This was the closest thing I've had to a normal job, and I hereby apologize to my boss and coworkers, because I was a bad employee. Their Lisp was the thinnest icing on a giant C cake, and since I didn't know C and didn't want to learn it, I never understood most of the software. Plus I was terribly irresponsible. This was back when a programming job meant showing up every day during certain working hours. That seemed unnatural to me, and on this point the rest of the world is coming around to my way of thinking, but at the time it caused a lot of friction. Toward the end of the year I spent much of my time surreptitiously working on On Lisp, which I had by this time gotten a contract to publish.
|
||||
|
||||
The good part was that I got paid huge amounts of money, especially by art student standards. In Florence, after paying my part of the rent, my budget for everything else had been $7 a day. Now I was getting paid more than 4 times that every hour, even when I was just sitting in a meeting. By living cheaply I not only managed to save enough to go back to RISD, but also paid off my college loans.
|
||||
|
||||
I learned some useful things at Interleaf, though they were mostly about what not to do. I learned that it's better for technology companies to be run by product people than sales people (though sales is a real skill and people who are good at it are really good at it), that it leads to bugs when code is edited by too many people, that cheap office space is no bargain if it's depressing, that planned meetings are inferior to corridor conversations, that big, bureaucratic customers are a dangerous source of money, and that there's not much overlap between conventional office hours and the optimal time for hacking, or conventional offices and the optimal place for it.
|
||||
|
||||
But the most important thing I learned, and which I used in both Viaweb and Y Combinator, is that the low end eats the high end: that it's good to be the "entry level" option, even though that will be less prestigious, because if you're not, someone else will be, and will squash you against the ceiling. Which in turn means that prestige is a danger sign.
|
||||
|
||||
When I left to go back to RISD the next fall, I arranged to do freelance work for the group that did projects for customers, and this was how I survived for the next several years. When I came back to visit for a project later on, someone told me about a new thing called HTML, which was, as he described it, a derivative of SGML. Markup language enthusiasts were an occupational hazard at Interleaf and I ignored him, but this HTML thing later became a big part of my life.
|
||||
|
||||
In the fall of 1992 I moved back to Providence to continue at RISD. The foundation had merely been intro stuff, and the Accademia had been a (very civilized) joke. Now I was going to see what real art school was like. But alas it was more like the Accademia than not. Better organized, certainly, and a lot more expensive, but it was now becoming clear that art school did not bear the same relationship to art that medical school bore to medicine. At least not the painting department. The textile department, which my next door neighbor belonged to, seemed to be pretty rigorous. No doubt illustration and architecture were too. But painting was post-rigorous. Painting students were supposed to express themselves, which to the more worldly ones meant to try to cook up some sort of distinctive signature style.
|
||||
|
||||
A signature style is the visual equivalent of what in show business is known as a "schtick": something that immediately identifies the work as yours and no one else's. For example, when you see a painting that looks like a certain kind of cartoon, you know it's by Roy Lichtenstein. So if you see a big painting of this type hanging in the apartment of a hedge fund manager, you know he paid millions of dollars for it. That's not always why artists have a signature style, but it's usually why buyers pay a lot for such work. [6]
|
||||
|
||||
There were plenty of earnest students too: kids who "could draw" in high school, and now had come to what was supposed to be the best art school in the country, to learn to draw even better. They tended to be confused and demoralized by what they found at RISD, but they kept going, because painting was what they did. I was not one of the kids who could draw in high school, but at RISD I was definitely closer to their tribe than the tribe of signature style seekers.
|
||||
|
||||
I learned a lot in the color class I took at RISD, but otherwise I was basically teaching myself to paint, and I could do that for free. So in 1993 I dropped out. I hung around Providence for a bit, and then my college friend Nancy Parmet did me a big favor. A rent-controlled apartment in a building her mother owned in New York was becoming vacant. Did I want it? It wasn't much more than my current place, and New York was supposed to be where the artists were. So yes, I wanted it! [7]
|
||||
|
||||
Asterix comics begin by zooming in on a tiny corner of Roman Gaul that turns out not to be controlled by the Romans. You can do something similar on a map of New York City: if you zoom in on the Upper East Side, there's a tiny corner that's not rich, or at least wasn't in 1993. It's called Yorkville, and that was my new home. Now I was a New York artist — in the strictly technical sense of making paintings and living in New York.
|
||||
|
||||
I was nervous about money, because I could sense that Interleaf was on the way down. Freelance Lisp hacking work was very rare, and I didn't want to have to program in another language, which in those days would have meant C++ if I was lucky. So with my unerring nose for financial opportunity, I decided to write another book on Lisp. This would be a popular book, the sort of book that could be used as a textbook. I imagined myself living frugally off the royalties and spending all my time painting. (The painting on the cover of this book, ANSI Common Lisp, is one that I painted around this time.)
|
||||
|
||||
The best thing about New York for me was the presence of Idelle and Julian Weber. Idelle Weber was a painter, one of the early photorealists, and I'd taken her painting class at Harvard. I've never known a teacher more beloved by her students. Large numbers of former students kept in touch with her, including me. After I moved to New York I became her de facto studio assistant.
|
||||
|
||||
She liked to paint on big, square canvases, 4 to 5 feet on a side. One day in late 1994 as I was stretching one of these monsters there was something on the radio about a famous fund manager. He wasn't that much older than me, and was super rich. The thought suddenly occurred to me: why don't I become rich? Then I'll be able to work on whatever I want.
|
||||
|
||||
Meanwhile I'd been hearing more and more about this new thing called the World Wide Web. Robert Morris showed it to me when I visited him in Cambridge, where he was now in grad school at Harvard. It seemed to me that the web would be a big deal. I'd seen what graphical user interfaces had done for the popularity of microcomputers. It seemed like the web would do the same for the internet.
|
||||
|
||||
If I wanted to get rich, here was the next train leaving the station. I was right about that part. What I got wrong was the idea. I decided we should start a company to put art galleries online. I can't honestly say, after reading so many Y Combinator applications, that this was the worst startup idea ever, but it was up there. Art galleries didn't want to be online, and still don't, not the fancy ones. That's not how they sell. I wrote some software to generate web sites for galleries, and Robert wrote some to resize images and set up an http server to serve the pages. Then we tried to sign up galleries. To call this a difficult sale would be an understatement. It was difficult to give away. A few galleries let us make sites for them for free, but none paid us.
|
||||
|
||||
Then some online stores started to appear, and I realized that except for the order buttons they were identical to the sites we'd been generating for galleries. This impressive-sounding thing called an "internet storefront" was something we already knew how to build.
|
||||
|
||||
So in the summer of 1995, after I submitted the camera-ready copy of ANSI Common Lisp to the publishers, we started trying to write software to build online stores. At first this was going to be normal desktop software, which in those days meant Windows software. That was an alarming prospect, because neither of us knew how to write Windows software or wanted to learn. We lived in the Unix world. But we decided we'd at least try writing a prototype store builder on Unix. Robert wrote a shopping cart, and I wrote a new site generator for stores — in Lisp, of course.
|
||||
|
||||
We were working out of Robert's apartment in Cambridge. His roommate was away for big chunks of time, during which I got to sleep in his room. For some reason there was no bed frame or sheets, just a mattress on the floor. One morning as I was lying on this mattress I had an idea that made me sit up like a capital L. What if we ran the software on the server, and let users control it by clicking on links? Then we'd never have to write anything to run on users' computers. We could generate the sites on the same server we'd serve them from. Users wouldn't need anything more than a browser.
|
||||
|
||||
This kind of software, known as a web app, is common now, but at the time it wasn't clear that it was even possible. To find out, we decided to try making a version of our store builder that you could control through the browser. A couple days later, on August 12, we had one that worked. The UI was horrible, but it proved you could build a whole store through the browser, without any client software or typing anything into the command line on the server.
|
||||
|
||||
Now we felt like we were really onto something. I had visions of a whole new generation of software working this way. You wouldn't need versions, or ports, or any of that crap. At Interleaf there had been a whole group called Release Engineering that seemed to be at least as big as the group that actually wrote the software. Now you could just update the software right on the server.
|
||||
|
||||
We started a new company we called Viaweb, after the fact that our software worked via the web, and we got $10,000 in seed funding from Idelle's husband Julian. In return for that and doing the initial legal work and giving us business advice, we gave him 10% of the company. Ten years later this deal became the model for Y Combinator's. We knew founders needed something like this, because we'd needed it ourselves.
|
||||
|
||||
At this stage I had a negative net worth, because the thousand dollars or so I had in the bank was more than counterbalanced by what I owed the government in taxes. (Had I diligently set aside the proper proportion of the money I'd made consulting for Interleaf? No, I had not.) So although Robert had his graduate student stipend, I needed that seed funding to live on.
|
||||
|
||||
We originally hoped to launch in September, but we got more ambitious about the software as we worked on it. Eventually we managed to build a WYSIWYG site builder, in the sense that as you were creating pages, they looked exactly like the static ones that would be generated later, except that instead of leading to static pages, the links all referred to closures stored in a hash table on the server.
|
||||
|
||||
It helped to have studied art, because the main goal of an online store builder is to make users look legit, and the key to looking legit is high production values. If you get page layouts and fonts and colors right, you can make a guy running a store out of his bedroom look more legit than a big company.
|
||||
|
||||
(If you're curious why my site looks so old-fashioned, it's because it's still made with this software. It may look clunky today, but in 1996 it was the last word in slick.)
|
||||
|
||||
In September, Robert rebelled. "We've been working on this for a month," he said, "and it's still not done." This is funny in retrospect, because he would still be working on it almost 3 years later. But I decided it might be prudent to recruit more programmers, and I asked Robert who else in grad school with him was really good. He recommended Trevor Blackwell, which surprised me at first, because at that point I knew Trevor mainly for his plan to reduce everything in his life to a stack of notecards, which he carried around with him. But Rtm was right, as usual. Trevor turned out to be a frighteningly effective hacker.
|
||||
|
||||
It was a lot of fun working with Robert and Trevor. They're the two most independent-minded people I know, and in completely different ways. If you could see inside Rtm's brain it would look like a colonial New England church, and if you could see inside Trevor's it would look like the worst excesses of Austrian Rococo.
|
||||
|
||||
We opened for business, with 6 stores, in January 1996. It was just as well we waited a few months, because although we worried we were late, we were actually almost fatally early. There was a lot of talk in the press then about ecommerce, but not many people actually wanted online stores. [8]
|
||||
|
||||
There were three main parts to the software: the editor, which people used to build sites and which I wrote, the shopping cart, which Robert wrote, and the manager, which kept track of orders and statistics, and which Trevor wrote. In its time, the editor was one of the best general-purpose site builders. I kept the code tight and didn't have to integrate with any other software except Robert's and Trevor's, so it was quite fun to work on. If all I'd had to do was work on this software, the next 3 years would have been the easiest of my life. Unfortunately I had to do a lot more, all of it stuff I was worse at than programming, and the next 3 years were instead the most stressful.
|
||||
|
||||
There were a lot of startups making ecommerce software in the second half of the 90s. We were determined to be the Microsoft Word, not the Interleaf. Which meant being easy to use and inexpensive. It was lucky for us that we were poor, because that caused us to make Viaweb even more inexpensive than we realized. We charged $100 a month for a small store and $300 a month for a big one. This low price was a big attraction, and a constant thorn in the sides of competitors, but it wasn't because of some clever insight that we set the price low. We had no idea what businesses paid for things. $300 a month seemed like a lot of money to us.
|
||||
|
||||
We did a lot of things right by accident like that. For example, we did what's now called "doing things that don't scale," although at the time we would have described it as "being so lame that we're driven to the most desperate measures to get users." The most common of which was building stores for them. This seemed particularly humiliating, since the whole reason d'etre of our software was that people could use it to make their own stores. But anything to get users.
|
||||
|
||||
We learned a lot more about retail than we wanted to know. For example, that if you could only have a small image of a man's shirt (and all images were small then by present standards), it was better to have a closeup of the collar than a picture of the whole shirt. The reason I remember learning this was that it meant I had to rescan about 30 images of men's shirts. My first set of scans were so beautiful too.
|
||||
|
||||
Though this felt wrong, it was exactly the right thing to be doing. Building stores for users taught us about retail, and about how it felt to use our software. I was initially both mystified and repelled by "business" and thought we needed a "business person" to be in charge of it, but once we started to get users, I was converted, in much the same way I was converted to fatherhood once I had kids. Whatever users wanted, I was all theirs. Maybe one day we'd have so many users that I couldn't scan their images for them, but in the meantime there was nothing more important to do.
|
||||
|
||||
Another thing I didn't get at the time is that growth rate is the ultimate test of a startup. Our growth rate was fine. We had about 70 stores at the end of 1996 and about 500 at the end of 1997. I mistakenly thought the thing that mattered was the absolute number of users. And that is the thing that matters in the sense that that's how much money you're making, and if you're not making enough, you might go out of business. But in the long term the growth rate takes care of the absolute number. If we'd been a startup I was advising at Y Combinator, I would have said: Stop being so stressed out, because you're doing fine. You're growing 7x a year. Just don't hire too many more people and you'll soon be profitable, and then you'll control your own destiny.
|
||||
|
||||
Alas I hired lots more people, partly because our investors wanted me to, and partly because that's what startups did during the Internet Bubble. A company with just a handful of employees would have seemed amateurish. So we didn't reach breakeven until about when Yahoo bought us in the summer of 1998. Which in turn meant we were at the mercy of investors for the entire life of the company. And since both we and our investors were noobs at startups, the result was a mess even by startup standards.
|
||||
|
||||
It was a huge relief when Yahoo bought us. In principle our Viaweb stock was valuable. It was a share in a business that was profitable and growing rapidly. But it didn't feel very valuable to me; I had no idea how to value a business, but I was all too keenly aware of the near-death experiences we seemed to have every few months. Nor had I changed my grad student lifestyle significantly since we started. So when Yahoo bought us it felt like going from rags to riches. Since we were going to California, I bought a car, a yellow 1998 VW GTI. I remember thinking that its leather seats alone were by far the most luxurious thing I owned.
|
||||
|
||||
The next year, from the summer of 1998 to the summer of 1999, must have been the least productive of my life. I didn't realize it at the time, but I was worn out from the effort and stress of running Viaweb. For a while after I got to California I tried to continue my usual m.o. of programming till 3 in the morning, but fatigue combined with Yahoo's prematurely aged culture and grim cube farm in Santa Clara gradually dragged me down. After a few months it felt disconcertingly like working at Interleaf.
|
||||
|
||||
Yahoo had given us a lot of options when they bought us. At the time I thought Yahoo was so overvalued that they'd never be worth anything, but to my astonishment the stock went up 5x in the next year. I hung on till the first chunk of options vested, then in the summer of 1999 I left. It had been so long since I'd painted anything that I'd half forgotten why I was doing this. My brain had been entirely full of software and men's shirts for 4 years. But I had done this to get rich so I could paint, I reminded myself, and now I was rich, so I should go paint.
|
||||
|
||||
When I said I was leaving, my boss at Yahoo had a long conversation with me about my plans. I told him all about the kinds of pictures I wanted to paint. At the time I was touched that he took such an interest in me. Now I realize it was because he thought I was lying. My options at that point were worth about $2 million a month. If I was leaving that kind of money on the table, it could only be to go and start some new startup, and if I did, I might take people with me. This was the height of the Internet Bubble, and Yahoo was ground zero of it. My boss was at that moment a billionaire. Leaving then to start a new startup must have seemed to him an insanely, and yet also plausibly, ambitious plan.
|
||||
|
||||
But I really was quitting to paint, and I started immediately. There was no time to lose. I'd already burned 4 years getting rich. Now when I talk to founders who are leaving after selling their companies, my advice is always the same: take a vacation. That's what I should have done, just gone off somewhere and done nothing for a month or two, but the idea never occurred to me.
|
||||
|
||||
So I tried to paint, but I just didn't seem to have any energy or ambition. Part of the problem was that I didn't know many people in California. I'd compounded this problem by buying a house up in the Santa Cruz Mountains, with a beautiful view but miles from anywhere. I stuck it out for a few more months, then in desperation I went back to New York, where unless you understand about rent control you'll be surprised to hear I still had my apartment, sealed up like a tomb of my old life. Idelle was in New York at least, and there were other people trying to paint there, even though I didn't know any of them.
|
||||
|
||||
When I got back to New York I resumed my old life, except now I was rich. It was as weird as it sounds. I resumed all my old patterns, except now there were doors where there hadn't been. Now when I was tired of walking, all I had to do was raise my hand, and (unless it was raining) a taxi would stop to pick me up. Now when I walked past charming little restaurants I could go in and order lunch. It was exciting for a while. Painting started to go better. I experimented with a new kind of still life where I'd paint one painting in the old way, then photograph it and print it, blown up, on canvas, and then use that as the underpainting for a second still life, painted from the same objects (which hopefully hadn't rotted yet).
|
||||
|
||||
Meanwhile I looked for an apartment to buy. Now I could actually choose what neighborhood to live in. Where, I asked myself and various real estate agents, is the Cambridge of New York? Aided by occasional visits to actual Cambridge, I gradually realized there wasn't one. Huh.
|
||||
|
||||
Around this time, in the spring of 2000, I had an idea. It was clear from our experience with Viaweb that web apps were the future. Why not build a web app for making web apps? Why not let people edit code on our server through the browser, and then host the resulting applications for them? [9] You could run all sorts of services on the servers that these applications could use just by making an API call: making and receiving phone calls, manipulating images, taking credit card payments, etc.
|
||||
|
||||
I got so excited about this idea that I couldn't think about anything else. It seemed obvious that this was the future. I didn't particularly want to start another company, but it was clear that this idea would have to be embodied as one, so I decided to move to Cambridge and start it. I hoped to lure Robert into working on it with me, but there I ran into a hitch. Robert was now a postdoc at MIT, and though he'd made a lot of money the last time I'd lured him into working on one of my schemes, it had also been a huge time sink. So while he agreed that it sounded like a plausible idea, he firmly refused to work on it.
|
||||
|
||||
Hmph. Well, I'd do it myself then. I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it's now clear is about twenty companies and several open-source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn't so naive as to assume I could spring an overt Lisp on a general audience; we'd hide the parentheses, like Dylan did.
|
||||
|
||||
By then there was a name for the kind of company Viaweb was, an "application service provider," or ASP. This name didn't last long before it was replaced by "software as a service," but it was current for long enough that I named this new company after it: it was going to be called Aspra.
|
||||
|
||||
I started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn't want to run a company — especially not a big one, which it was looking like this would have to be. I'd only started Viaweb because I needed the money. Now that I didn't need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I'd build a subset that could be done as an open-source project.
|
||||
|
||||
Much to my surprise, the time I spent working on this stuff was not wasted after all. After we started Y Combinator, I would often encounter startups working on parts of this new architecture, and it was very useful to have spent so much time thinking about it and even trying to write some of it.
|
||||
|
||||
The subset I would build as an open-source project was the new Lisp, whose parentheses I now wouldn't even have to hide. A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive features of the language is that it has dialects, and partly, I think, because we have in our minds a Platonic form of Lisp that all existing dialects fall short of. I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge.
|
||||
|
||||
The following spring, lightning struck. I was invited to give a talk at a Lisp conference, so I gave one about how we'd used Lisp at Viaweb. Afterward I put a postscript file of this talk online, on paulgraham.com, which I'd created years before using Viaweb but had never used for anything. In one day it got 30,000 page views. What on earth had happened? The referring urls showed that someone had posted it on Slashdot. [10]
|
||||
|
||||
Wow, I thought, there's an audience. If I write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything.
|
||||
|
||||
This had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11]
|
||||
|
||||
In the print era, the channel for publishing essays had been vanishingly small. Except for a few officially anointed thinkers who went to the right parties in New York, the only people allowed to publish essays were specialists writing about their specialties. There were so many essays that had never been written, because there had been no way to publish them. Now they could be, and I was going to write them. [12]
|
||||
|
||||
I've worked on several different things, but to the extent there was a turning point where I figured out what to work on, it was when I started publishing essays online. From then on I knew that whatever else I did, I'd always write essays too.
|
||||
|
||||
I knew that online essays would be a marginal medium at first. Socially they'd seem more like rants posted by nutjobs on their GeoCities sites than the genteel and beautifully typeset compositions published in The New Yorker. But by this point I knew enough to find that encouraging instead of discouraging.
|
||||
|
||||
One of the most conspicuous patterns I've noticed in my life is how well it has worked, for me at least, to work on things that weren't prestigious. Still life has always been the least prestigious form of painting. Viaweb and Y Combinator both seemed lame when we started them. I still get the glassy eye from strangers when they ask what I'm writing, and I explain that it's an essay I'm going to publish on my web site. Even Lisp, though prestigious intellectually in something like the way Latin is, also seems about as hip.
|
||||
|
||||
It's not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it's a sign both that there's something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren't prestigious doesn't guarantee you're on the right track, it at least guarantees you're not on the most common type of wrong one.
|
||||
|
||||
Over the next several years I wrote lots of essays about all kinds of different topics. O'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting. I used to have dinners for a group of friends every thursday night, which taught me how to cook for groups. And I bought another building in Cambridge, a former candy factory (and later, twas said, porn studio), to use as an office.
|
||||
|
||||
One night in October 2003 there was a big party at my house. It was a clever idea of my friend Maria Daniels, who was one of the thursday diners. Three separate hosts would all invite their friends to one party. So for every guest, two thirds of the other guests would be people they didn't know but would probably like. One of the guests was someone I didn't know but would turn out to like a lot: a woman called Jessica Livingston. A couple days later I asked her out.
|
||||
|
||||
Jessica was in charge of marketing at a Boston investment bank. This bank thought it understood startups, but over the next year, as she met friends of mine from the startup world, she was surprised how different reality was. And how colorful their stories were. So she decided to compile a book of interviews with startup founders.
|
||||
|
||||
When the bank had financial problems and she had to fire half her staff, she started looking for a new job. In early 2005 she interviewed for a marketing job at a Boston VC firm. It took them weeks to make up their minds, and during this time I started telling her about all the things that needed to be fixed about venture capital. They should make a larger number of smaller investments instead of a handful of giant ones, they should be funding younger, more technical founders instead of MBAs, they should let the founders remain as CEO, and so on.
|
||||
|
||||
One of my tricks for writing essays had always been to give talks. The prospect of having to stand up in front of a group of people and tell them something that won't waste their time is a great spur to the imagination. When the Harvard Computer Society, the undergrad computer club, asked me to give a talk, I decided I would tell them how to start a startup. Maybe they'd be able to avoid the worst of the mistakes we'd made.
|
||||
|
||||
So I gave this talk, in the course of which I told them that the best sources of seed funding were successful startup founders, because then they'd be sources of advice too. Whereupon it seemed they were all looking expectantly at me. Horrified at the prospect of having my inbox flooded by business plans (if I'd only known), I blurted out "But not me!" and went on with the talk. But afterward it occurred to me that I should really stop procrastinating about angel investing. I'd been meaning to since Yahoo bought us, and now it was 7 years later and I still hadn't done one angel investment.
|
||||
|
||||
Meanwhile I had been scheming with Robert and Trevor about projects we could work on together. I missed working with them, and it seemed like there had to be something we could collaborate on.
|
||||
|
||||
As Jessica and I were walking home from dinner on March 11, at the corner of Garden and Walker streets, these three threads converged. Screw the VCs who were taking so long to make up their minds. We'd start our own investment firm and actually implement the ideas we'd been talking about. I'd fund it, and Jessica could quit her job and work for it, and we'd get Robert and Trevor as partners too. [13]
|
||||
|
||||
Once again, ignorance worked in our favor. We had no idea how to be angel investors, and in Boston in 2005 there were no Ron Conways to learn from. So we just made what seemed like the obvious choices, and some of the things we did turned out to be novel.
|
||||
|
||||
There are multiple components to Y Combinator, and we didn't figure them all out at once. The part we got first was to be an angel firm. In those days, those two words didn't go together. There were VC firms, which were organized companies with people whose job it was to make investments, but they only did big, million dollar investments. And there were angels, who did smaller investments, but these were individuals who were usually focused on other things and made investments on the side. And neither of them helped founders enough in the beginning. We knew how helpless founders were in some respects, because we remembered how helpless we'd been. For example, one thing Julian had done for us that seemed to us like magic was to get us set up as a company. We were fine writing fairly difficult software, but actually getting incorporated, with bylaws and stock and all that stuff, how on earth did you do that? Our plan was not only to make seed investments, but to do for startups everything Julian had done for us.
|
||||
|
||||
YC was not organized as a fund. It was cheap enough to run that we funded it with our own money. That went right by 99% of readers, but professional investors are thinking "Wow, that means they got all the returns." But once again, this was not due to any particular insight on our part. We didn't know how VC firms were organized. It never occurred to us to try to raise a fund, and if it had, we wouldn't have known where to start. [14]
|
||||
|
||||
The most distinctive thing about YC is the batch model: to fund a bunch of startups all at once, twice a year, and then to spend three months focusing intensively on trying to help them. That part we discovered by accident, not merely implicitly but explicitly due to our ignorance about investing. We needed to get experience as investors. What better way, we thought, than to fund a whole bunch of startups at once? We knew undergrads got temporary jobs at tech companies during the summer. Why not organize a summer program where they'd start startups instead? We wouldn't feel guilty for being in a sense fake investors, because they would in a similar sense be fake founders. So while we probably wouldn't make much money out of it, we'd at least get to practice being investors on them, and they for their part would probably have a more interesting summer than they would working at Microsoft.
|
||||
|
||||
We'd use the building I owned in Cambridge as our headquarters. We'd all have dinner there once a week — on tuesdays, since I was already cooking for the thursday diners on thursdays — and after dinner we'd bring in experts on startups to give talks.
|
||||
|
||||
We knew undergrads were deciding then about summer jobs, so in a matter of days we cooked up something we called the Summer Founders Program, and I posted an announcement on my site, inviting undergrads to apply. I had never imagined that writing essays would be a way to get "deal flow," as investors call it, but it turned out to be the perfect source. [15] We got 225 applications for the Summer Founders Program, and we were surprised to find that a lot of them were from people who'd already graduated, or were about to that spring. Already this SFP thing was starting to feel more serious than we'd intended.
|
||||
|
||||
We invited about 20 of the 225 groups to interview in person, and from those we picked 8 to fund. They were an impressive group. That first batch included reddit, Justin Kan and Emmett Shear, who went on to found Twitch, Aaron Swartz, who had already helped write the RSS spec and would a few years later become a martyr for open access, and Sam Altman, who would later become the second president of YC. I don't think it was entirely luck that the first batch was so good. You had to be pretty bold to sign up for a weird thing like the Summer Founders Program instead of a summer job at a legit place like Microsoft or Goldman Sachs.
|
||||
|
||||
The deal for startups was based on a combination of the deal we did with Julian ($10k for 10%) and what Robert said MIT grad students got for the summer ($6k). We invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%. That had to be fair, because it was twice as good as the deal we ourselves had taken. Plus that first summer, which was really hot, Jessica brought the founders free air conditioners. [16]
|
||||
|
||||
Fairly quickly I realized that we had stumbled upon the way to scale startup funding. Funding startups in batches was more convenient for us, because it meant we could do things for a lot of startups at once, but being part of a batch was better for the startups too. It solved one of the biggest problems faced by founders: the isolation. Now you not only had colleagues, but colleagues who understood the problems you were facing and could tell you how they were solving them.
|
||||
|
||||
As YC grew, we started to notice other advantages of scale. The alumni became a tight community, dedicated to helping one another, and especially the current batch, whose shoes they remembered being in. We also noticed that the startups were becoming one another's customers. We used to refer jokingly to the "YC GDP," but as YC grows this becomes less and less of a joke. Now lots of startups get their initial set of customers almost entirely from among their batchmates.
|
||||
|
||||
I had not originally intended YC to be a full-time job. I was going to do three things: hack, write essays, and work on YC. As YC grew, and I grew more excited about it, it started to take up a lot more than a third of my attention. But for the first few years I was still able to work on other things.
|
||||
|
||||
In the summer of 2006, Robert and I started working on a new version of Arc. This one was reasonably fast, because it was compiled into Scheme. To test this new Arc, I wrote Hacker News in it. It was originally meant to be a news aggregator for startup founders and was called Startup News, but after a few months I got tired of reading about nothing but startups. Plus it wasn't startup founders we wanted to reach. It was future startup founders. So I changed the name to Hacker News and the topic to whatever engaged one's intellectual curiosity.
|
||||
|
||||
HN was no doubt good for YC, but it was also by far the biggest source of stress for me. If all I'd had to do was select and help founders, life would have been so easy. And that implies that HN was a mistake. Surely the biggest source of stress in one's work should at least be something close to the core of the work. Whereas I was like someone who was in pain while running a marathon not from the exertion of running, but because I had a blister from an ill-fitting shoe. When I was dealing with some urgent problem during YC, there was about a 60% chance it had to do with HN, and a 40% chance it had do with everything else combined. [17]
|
||||
|
||||
As well as HN, I wrote all of YC's internal software in Arc. But while I continued to work a good deal in Arc, I gradually stopped working on Arc, partly because I didn't have time to, and partly because it was a lot less attractive to mess around with the language now that we had all this infrastructure depending on it. So now my three projects were reduced to two: writing essays and working on YC.
|
||||
|
||||
YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn't have picked a better way to do it.
|
||||
|
||||
There were parts of the job I didn't like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn't like. I was haunted by something Kevin Hale once said about companies: "No one works harder than the boss." He meant it both descriptively and prescriptively, and it was the second part that scared me. I wanted YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I'd better work very hard.
|
||||
|
||||
One day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. "You know," he said, "you should make sure Y Combinator isn't the last cool thing you do."
|
||||
|
||||
At the time I didn't understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed strange advice, because YC was doing great. But if there was one thing rarer than Rtm offering advice, it was Rtm being wrong. So this set me thinking. It was true that on my current trajectory, YC would be the last thing I did, because it was only taking up more of my attention. It had already eaten Arc, and was in the process of eating essays too. Either YC was my life's work or I'd have to leave eventually. And it wasn't, so I would.
|
||||
|
||||
In the summer of 2012 my mother had a stroke, and the cause turned out to be a blood clot caused by colon cancer. The stroke destroyed her balance, and she was put in a nursing home, but she really wanted to get out of it and back to her house, and my sister and I were determined to help her do it. I used to fly up to Oregon to visit her regularly, and I had a lot of time to think on those flights. On one of them I realized I was ready to hand YC over to someone else.
|
||||
|
||||
I asked Jessica if she wanted to be president, but she didn't, so we decided we'd try to recruit Sam Altman. We talked to Robert and Trevor and we agreed to make it a complete changing of the guard. Up till that point YC had been controlled by the original LLC we four had started. But we wanted YC to last for a long time, and to do that it couldn't be controlled by the founders. So if Sam said yes, we'd let him reorganize YC. Robert and I would retire, and Jessica and Trevor would become ordinary partners.
|
||||
|
||||
When we asked Sam if he wanted to be president of YC, initially he said no. He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he'd take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned.
|
||||
|
||||
She died on January 15, 2014. We knew this was coming, but it was still hard when it did.
|
||||
|
||||
I kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I'm interested in, but that only takes a few hours a week.)
|
||||
|
||||
What should I do next? Rtm's advice hadn't included anything about that. I wanted to do something completely different, so I decided I'd paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I was rusty and it took a while to get back into shape, but it was at least completely engaging. [18]
|
||||
|
||||
I spent most of the rest of 2014 painting. I'd never been able to work so uninterruptedly before, and I got to be better than I had been. Not good enough, but better. Then in November, right in the middle of a painting, I ran out of steam. Up till that point I'd always been curious to see how the painting I was working on would turn out, but suddenly finishing this one seemed like a chore. So I stopped working on it and cleaned my brushes and haven't painted since. So far anyway.
|
||||
|
||||
I realize that sounds rather wimpy. But attention is a zero sum game. If you can choose what to work on, and you choose a project that's not the best one (or at least a good one) for you, then it's getting in the way of another project that is. And at 50 there was some opportunity cost to screwing around.
|
||||
|
||||
I started writing essays again, and wrote a bunch of new ones over the next few months. I even wrote a couple that weren't about startups. Then in March 2015 I started working on Lisp again.
|
||||
|
||||
The distinctive thing about Lisp is that its core is a language defined by writing an interpreter in itself. It wasn't originally intended as a programming language in the ordinary sense. It was meant to be a formal model of computation, an alternative to the Turing machine. If you want to write an interpreter for a language in itself, what's the minimum set of predefined operators you need? The Lisp that John McCarthy invented, or more accurately discovered, is an answer to that question. [19]
|
||||
|
||||
McCarthy didn't realize this Lisp could even be used to program computers till his grad student Steve Russell suggested it. Russell translated McCarthy's interpreter into IBM 704 machine language, and from that point Lisp started also to be a programming language in the ordinary sense. But its origins as a model of computation gave it a power and elegance that other languages couldn't match. It was this that attracted me in college, though I didn't understand why at the time.
|
||||
|
||||
McCarthy's 1960 Lisp did nothing more than interpret Lisp expressions. It was missing a lot of things you'd want in a programming language. So these had to be added, and when they were, they weren't defined using McCarthy's original axiomatic approach. That wouldn't have been feasible at the time. McCarthy tested his interpreter by hand-simulating the execution of programs. But it was already getting close to the limit of interpreters you could test that way — indeed, there was a bug in it that McCarthy had overlooked. To test a more complicated interpreter, you'd have had to run it, and computers then weren't powerful enough.
|
||||
|
||||
Now they are, though. Now you could continue using McCarthy's axiomatic approach till you'd defined a complete programming language. And as long as every change you made to McCarthy's Lisp was a discoveredness-preserving transformation, you could, in principle, end up with a complete language that had this quality. Harder to do than to talk about, of course, but if it was possible in principle, why not try? So I decided to take a shot at it. It took 4 years, from March 26, 2015 to October 12, 2019. It was fortunate that I had a precisely defined goal, or it would have been hard to keep at it for so long.
|
||||
|
||||
I wrote this new Lisp, called Bel, in itself in Arc. That may sound like a contradiction, but it's an indication of the sort of trickery I had to engage in to make this work. By means of an egregious collection of hacks I managed to make something close enough to an interpreter written in itself that could actually run. Not fast, but fast enough to test.
|
||||
|
||||
I had to ban myself from writing essays during most of this time, or I'd never have finished. In late 2015 I spent 3 months writing essays, and when I went back to working on Bel I could barely understand the code. Not so much because it was badly written as because the problem is so convoluted. When you're working on an interpreter written in itself, it's hard to keep track of what's happening at what level, and errors can be practically encrypted by the time you get them.
|
||||
|
||||
So I said no more essays till Bel was done. But I told few people about Bel while I was working on it. So for years it must have seemed that I was doing nothing, when in fact I was working harder than I'd ever worked on anything. Occasionally after wrestling for hours with some gruesome bug I'd check Twitter or HN and see someone asking "Does Paul Graham still code?"
|
||||
|
||||
Working on Bel was hard but satisfying. I worked on it so intensively that at any given time I had a decent chunk of the code in my head and could write more there. I remember taking the boys to the coast on a sunny day in 2015 and figuring out how to deal with some problem involving continuations while I watched them play in the tide pools. It felt like I was doing life right. I remember that because I was slightly dismayed at how novel it felt. The good news is that I had more moments like this over the next few years.
|
||||
|
||||
In the summer of 2016 we moved to England. We wanted our kids to see what it was like living in another country, and since I was a British citizen by birth, that seemed the obvious choice. We only meant to stay for a year, but we liked it so much that we still live there. So most of Bel was written in England.
|
||||
|
||||
In the fall of 2019, Bel was finally finished. Like McCarthy's original Lisp, it's a spec rather than an implementation, although like McCarthy's Lisp it's a spec expressed as code.
|
||||
|
||||
Now that I could write essays again, I wrote a bunch about topics I'd had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do? Well, how had I chosen what to work on in the past? I wrote an essay for myself to answer that question, and I was surprised how long and messy the answer turned out to be. If this surprised me, who'd lived it, then I thought perhaps it would be interesting to other people, and encouraging to those with similarly messy lives. So I wrote a more detailed version for others to read, and this is the last sentence of it.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Notes
|
||||
|
||||
[1] My experience skipped a step in the evolution of computers: time-sharing machines with interactive OSes. I went straight from batch processing to microcomputers, which made microcomputers seem all the more exciting.
|
||||
|
||||
[2] Italian words for abstract concepts can nearly always be predicted from their English cognates (except for occasional traps like polluzione). It's the everyday words that differ. So if you string together a lot of abstract concepts with a few simple verbs, you can make a little Italian go a long way.
|
||||
|
||||
[3] I lived at Piazza San Felice 4, so my walk to the Accademia went straight down the spine of old Florence: past the Pitti, across the bridge, past Orsanmichele, between the Duomo and the Baptistery, and then up Via Ricasoli to Piazza San Marco. I saw Florence at street level in every possible condition, from empty dark winter evenings to sweltering summer days when the streets were packed with tourists.
|
||||
|
||||
[4] You can of course paint people like still lives if you want to, and they're willing. That sort of portrait is arguably the apex of still life painting, though the long sitting does tend to produce pained expressions in the sitters.
|
||||
|
||||
[5] Interleaf was one of many companies that had smart people and built impressive technology, and yet got crushed by Moore's Law. In the 1990s the exponential growth in the power of commodity (i.e. Intel) processors rolled up high-end, special-purpose hardware and software companies like a bulldozer.
|
||||
|
||||
[6] The signature style seekers at RISD weren't specifically mercenary. In the art world, money and coolness are tightly coupled. Anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive.
|
||||
|
||||
[7] Technically the apartment wasn't rent-controlled but rent-stabilized, but this is a refinement only New Yorkers would know or care about. The point is that it was really cheap, less than half market price.
|
||||
|
||||
[8] Most software you can launch as soon as it's done. But when the software is an online store builder and you're hosting the stores, if you don't have any users yet, that fact will be painfully obvious. So before we could launch publicly we had to launch privately, in the sense of recruiting an initial set of users and making sure they had decent-looking stores.
|
||||
|
||||
[9] We'd had a code editor in Viaweb for users to define their own page styles. They didn't know it, but they were editing Lisp expressions underneath. But this wasn't an app editor, because the code ran when the merchants' sites were generated, not when shoppers visited them.
|
||||
|
||||
[10] This was the first instance of what is now a familiar experience, and so was what happened next, when I read the comments and found they were full of angry people. How could I claim that Lisp was better than other languages? Weren't they all Turing complete? People who see the responses to essays I write sometimes tell me how sorry they feel for me, but I'm not exaggerating when I reply that it has always been like this, since the very beginning. It comes with the territory. An essay must tell readers things they don't already know, and some people dislike being told such things.
|
||||
|
||||
[11] People put plenty of stuff on the internet in the 90s of course, but putting something online is not the same as publishing it online. Publishing online means you treat the online version as the (or at least a) primary version.
|
||||
|
||||
[12] There is a general lesson here that our experience with Y Combinator also teaches: Customs continue to constrain you long after the restrictions that caused them have disappeared. Customary VC practice had once, like the customs about publishing essays, been based on real constraints. Startups had once been much more expensive to start, and proportionally rare. Now they could be cheap and common, but the VCs' customs still reflected the old world, just as customs about writing essays still reflected the constraints of the print era.
|
||||
|
||||
Which in turn implies that people who are independent-minded (i.e. less influenced by custom) will have an advantage in fields affected by rapid change (where customs are more likely to be obsolete).
|
||||
|
||||
Here's an interesting point, though: you can't always predict which fields will be affected by rapid change. Obviously software and venture capital will be, but who would have predicted that essay writing would be?
|
||||
|
||||
[13] Y Combinator was not the original name. At first we were called Cambridge Seed. But we didn't want a regional name, in case someone copied us in Silicon Valley, so we renamed ourselves after one of the coolest tricks in the lambda calculus, the Y combinator.
|
||||
|
||||
I picked orange as our color partly because it's the warmest, and partly because no VC used it. In 2005 all the VCs used staid colors like maroon, navy blue, and forest green, because they were trying to appeal to LPs, not founders. The YC logo itself is an inside joke: the Viaweb logo had been a white V on a red circle, so I made the YC logo a white Y on an orange square.
|
||||
|
||||
[14] YC did become a fund for a couple years starting in 2009, because it was getting so big I could no longer afford to fund it personally. But after Heroku got bought we had enough money to go back to being self-funded.
|
||||
|
||||
[15] I've never liked the term "deal flow," because it implies that the number of new startups at any given time is fixed. This is not only false, but it's the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed.
|
||||
|
||||
[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than she could carry now.
|
||||
|
||||
[17] Another problem with HN was a bizarre edge case that occurs when you both write essays and run a forum. When you run a forum, you're assumed to see if not every conversation, at least every conversation involving you. And when you write essays, people post highly imaginative misinterpretations of them on forums. Individually these two phenomena are tedious but bearable, but the combination is disastrous. You actually have to respond to the misinterpretations, because the assumption that you're present in the conversation means that not responding to any sufficiently upvoted misinterpretation reads as a tacit admission that it's correct. But that in turn encourages more; anyone who wants to pick a fight with you senses that now is their chance.
|
||||
|
||||
[18] The worst thing about leaving YC was not working with Jessica anymore. We'd been working on YC almost the whole time we'd known each other, and we'd neither tried nor wanted to separate it from our personal lives, so leaving was like pulling up a deeply rooted tree.
|
||||
|
||||
[19] One way to get more precise about the concept of invented vs discovered is to talk about space aliens. Any sufficiently advanced alien civilization would certainly know about the Pythagorean theorem, for example. I believe, though with less certainty, that they would also know about the Lisp in McCarthy's 1960 paper.
|
||||
|
||||
But if so there's no reason to suppose that this is the limit of the language that might be known to them. Presumably aliens need numbers and errors and I/O too. So it seems likely there exists at least one path out of McCarthy's Lisp along which discoveredness is preserved.
|
||||
|
||||
|
||||
|
||||
Thanks to Trevor Blackwell, John Collison, Patrick Collison, Daniel Gackle, Ralph Hazell, Jessica Livingston, Robert Morris, and Harj Taggar for reading drafts of this.
|
||||
@ -1,450 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"id": "7f219241",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"---\n",
|
||||
"sidebar_position: 4\n",
|
||||
"sidebar_class_name: hidden\n",
|
||||
"---"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "15780a65",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to use LangChain tools\n",
|
||||
"\n",
|
||||
"Tools are interfaces that an agent, chain, or LLM can use to interact with the world.\n",
|
||||
"They combine a few things:\n",
|
||||
"\n",
|
||||
"1. The name of the tool\n",
|
||||
"2. A description of what the tool is\n",
|
||||
"3. JSON schema of what the inputs to the tool are\n",
|
||||
"4. The function to call \n",
|
||||
"5. Whether the result of a tool should be returned directly to the user\n",
|
||||
"\n",
|
||||
"It is useful to have all this information because this information can be used to build action-taking systems! The name, description, and JSON schema can be used to prompt the LLM so it knows how to specify what action to take, and then the function to call is equivalent to taking that action.\n",
|
||||
"\n",
|
||||
"The simpler the input to a tool is, the easier it is for an LLM to be able to use it.\n",
|
||||
"Many agents will only work with tools that have a single string input.\n",
|
||||
"For a list of agent types and which ones work with more complicated inputs, please see [this documentation](https://python.langchain.com/v0.1/docs/modules/agents/agent_types/)\n",
|
||||
"\n",
|
||||
"Importantly, the name, description, and JSON schema (if used) are all used in the prompt. Therefore, it is really important that they are clear and describe exactly how the tool should be used. You may need to change the default name, description, or JSON schema if the LLM is not understanding how to use the tool.\n",
|
||||
"\n",
|
||||
"## Default Tools\n",
|
||||
"\n",
|
||||
"Let's take a look at how to work with tools. To do this, we'll work with a built in tool."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "19297004",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_community.tools import WikipediaQueryRun\n",
|
||||
"from langchain_community.utilities import WikipediaAPIWrapper"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1098e51a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now we initialize the tool. This is where we can configure it as we please"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"id": "27a48655",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100)\n",
|
||||
"tool = WikipediaQueryRun(api_wrapper=api_wrapper)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "7db48439",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This is the default name"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"id": "50f1ece1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Wikipedia'"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tool.name"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "075499b1",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This is the default description"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"id": "e9be09e2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tool.description"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "89c86b00",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This is the default JSON schema of the inputs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"id": "963a2e8c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'query': {'title': 'Query', 'type': 'string'}}"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tool.args"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5c467a35",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can see if the tool should return directly to the user"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"id": "039334b3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"False"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tool.return_direct"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "fc421b02",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can call this tool with a dictionary input"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"id": "6669a13c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Page: LangChain\\nSummary: LangChain is a framework designed to simplify the creation of applications '"
|
||||
]
|
||||
},
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tool.run({\"query\": \"langchain\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "587d6a58",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can also call this tool with a single string input. \n",
|
||||
"We can do this because this tool expects only a single input.\n",
|
||||
"If it required multiple inputs, we would not be able to do that."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"id": "8cb23935",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Page: LangChain\\nSummary: LangChain is a framework designed to simplify the creation of applications '"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tool.run(\"langchain\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "19eee1d5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Customizing Default Tools\n",
|
||||
"We can also modify the built in name, description, and JSON schema of the arguments.\n",
|
||||
"\n",
|
||||
"When defining the JSON schema of the arguments, it is important that the inputs remain the same as the function, so you shouldn't change that. But you can define custom descriptions for each input easily."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"id": "599c4da7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class WikiInputs(BaseModel):\n",
|
||||
" \"\"\"Inputs to the wikipedia tool.\"\"\"\n",
|
||||
"\n",
|
||||
" query: str = Field(\n",
|
||||
" description=\"query to look up in Wikipedia, should be 3 or less words\"\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"id": "6bde63e1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tool = WikipediaQueryRun(\n",
|
||||
" name=\"wiki-tool\",\n",
|
||||
" description=\"look up things in wikipedia\",\n",
|
||||
" args_schema=WikiInputs,\n",
|
||||
" api_wrapper=api_wrapper,\n",
|
||||
" return_direct=True,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"id": "eeaa1d9a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'wiki-tool'"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tool.name"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"id": "7599d88c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'look up things in wikipedia'"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tool.description"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"id": "80042cb1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'query': {'title': 'Query',\n",
|
||||
" 'description': 'query to look up in Wikipedia, should be 3 or less words',\n",
|
||||
" 'type': 'string'}}"
|
||||
]
|
||||
},
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tool.args"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"id": "8455fb9e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tool.return_direct"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"id": "86f731a8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Page: LangChain\\nSummary: LangChain is a framework designed to simplify the creation of applications '"
|
||||
]
|
||||
},
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tool.run(\"langchain\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c5b8b6bc",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## More Topics\n",
|
||||
"\n",
|
||||
"This was a quick introduction to tools in LangChain, but there is a lot more to learn\n",
|
||||
"\n",
|
||||
"**[Built-In Tools](/docs/integrations/tools/)**: For a list of all built-in tools, see [this page](/docs/integrations/tools/)\n",
|
||||
" \n",
|
||||
"**[Custom Tools](/docs/how_to/custom_tools)**: Although built-in tools are useful, it's highly likely that you'll have to define your own tools. See [this guide](/docs/how_to/custom_tools) for instructions on how to do so.\n",
|
||||
" \n",
|
||||
"**[Toolkits](/docs/how_to/toolkits)**: Toolkits are collections of tools that work well together. For a more in depth description as well as a list of all built-in toolkits, see [this page](/docs/how_to/toolkits)\n",
|
||||
"\n",
|
||||
"**[Tools as OpenAI Functions](/docs/how_to/tools_as_openai_functions/)**: Tools are very similar to OpenAI Functions, and can easily be converted to that format. See [this notebook](/docs/how_to/tools_as_openai_functions) for instructions on how to do that.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "78e2d0b3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,236 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"id": "7f219241",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"---\n",
|
||||
"sidebar_position: 4\n",
|
||||
"sidebar_class_name: hidden\n",
|
||||
"---"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"id": "e8f68de0-7df7-4bfd-9207-3258431426ef",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to use built-in tools and toolkits\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
"This guide assumes familiarity with the following concepts:\n",
|
||||
"\n",
|
||||
"- [LangChain Tools](/docs/concepts/#tools)\n",
|
||||
"- [LangChain Toolkits](/docs/concepts/#tools)\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"## Tools\n",
|
||||
"\n",
|
||||
"LangChain has a large collection of 3rd party tools. Please visit [Tool Integrations](/docs/integrations/tools/) for a list of the available tools.\n",
|
||||
"\n",
|
||||
":::{.callout-important}\n",
|
||||
"\n",
|
||||
"When using 3rd party tools, make sure that you understand how the tool works, what permissions\n",
|
||||
"it has. Read over its documentation and check if anything is required from you\n",
|
||||
"from a security point of view. Please see our [security](https://python.langchain.com/v0.1/docs/security/) \n",
|
||||
"guidelines for more information.\n",
|
||||
"\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"Let's try out the [Wikipedia integration](/docs/integrations/tools/wikipedia/)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "84f70856-b865-4658-9930-7577fb4712ce",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install -qU wikipedia"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"id": "b4eaed85-c5a6-4ba9-b401-40258b0131c2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Page: LangChain\n",
|
||||
"Summary: LangChain is a framework designed to simplify the creation of applications \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_community.tools import WikipediaQueryRun\n",
|
||||
"from langchain_community.utilities import WikipediaAPIWrapper\n",
|
||||
"\n",
|
||||
"api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100)\n",
|
||||
"tool = WikipediaQueryRun(api_wrapper=api_wrapper)\n",
|
||||
"\n",
|
||||
"print(tool.invoke({\"query\": \"langchain\"}))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cb870984-52d5-4453-be35-7072a08c6c14",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The tool has the following defaults associated with it:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"id": "7f094f01-2e98-4947-acc4-0846963a96e0",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Name: wiki-tool\n",
|
||||
"Description: look up things in wikipedia\n",
|
||||
"args schema: {'query': {'title': 'Query', 'description': 'query to look up in Wikipedia, should be 3 or less words', 'type': 'string'}}\n",
|
||||
"returns directly?: True\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(f\"Name: {tool.name}\")\n",
|
||||
"print(f\"Description: {tool.description}\")\n",
|
||||
"print(f\"args schema: {tool.args}\")\n",
|
||||
"print(f\"returns directly?: {tool.return_direct}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "19eee1d5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Customizing Default Tools\n",
|
||||
"We can also modify the built in name, description, and JSON schema of the arguments.\n",
|
||||
"\n",
|
||||
"When defining the JSON schema of the arguments, it is important that the inputs remain the same as the function, so you shouldn't change that. But you can define custom descriptions for each input easily."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"id": "1365784c-e666-41c8-a1bb-e50f822b5936",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Page: LangChain\n",
|
||||
"Summary: LangChain is a framework designed to simplify the creation of applications \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_community.tools import WikipediaQueryRun\n",
|
||||
"from langchain_community.utilities import WikipediaAPIWrapper\n",
|
||||
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class WikiInputs(BaseModel):\n",
|
||||
" \"\"\"Inputs to the wikipedia tool.\"\"\"\n",
|
||||
"\n",
|
||||
" query: str = Field(\n",
|
||||
" description=\"query to look up in Wikipedia, should be 3 or less words\"\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"tool = WikipediaQueryRun(\n",
|
||||
" name=\"wiki-tool\",\n",
|
||||
" description=\"look up things in wikipedia\",\n",
|
||||
" args_schema=WikiInputs,\n",
|
||||
" api_wrapper=api_wrapper,\n",
|
||||
" return_direct=True,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(tool.run(\"langchain\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"id": "6e8850d6-6840-443e-a2be-adf64b30975c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Name: wiki-tool\n",
|
||||
"Description: look up things in wikipedia\n",
|
||||
"args schema: {'query': {'title': 'Query', 'description': 'query to look up in Wikipedia, should be 3 or less words', 'type': 'string'}}\n",
|
||||
"returns directly?: True\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(f\"Name: {tool.name}\")\n",
|
||||
"print(f\"Description: {tool.description}\")\n",
|
||||
"print(f\"args schema: {tool.args}\")\n",
|
||||
"print(f\"returns directly?: {tool.return_direct}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"id": "acf0c2f7-ddc6-4633-8cef-59f234321e5c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## How to use built-in toolkits\n",
|
||||
"\n",
|
||||
"Toolkits are collections of tools that are designed to be used together for specific tasks. They have convenient loading methods.\n",
|
||||
"\n",
|
||||
"For a complete list of available ready-made toolkits, visit [Integrations](/docs/integrations/toolkits/).\n",
|
||||
"\n",
|
||||
"All Toolkits expose a `get_tools` method which returns a list of tools.\n",
|
||||
"\n",
|
||||
"You're usually meant to use them this way:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"# Initialize a toolkit\n",
|
||||
"toolkit = ExampleTookit(...)\n",
|
||||
"\n",
|
||||
"# Get list of tools\n",
|
||||
"tools = toolkit.get_tools()\n",
|
||||
"```"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,273 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"id": "1ea1fe24-fe1e-463b-a52c-79f0ef02328e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"---\n",
|
||||
"sidebar_position: 2\n",
|
||||
"---"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "95982bf1-7d9d-4dd6-a4ad-9de0719fe17f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to use an LLM to choose between multiple tools\n",
|
||||
"\n",
|
||||
"In our [Quickstart](/docs/how_to/tool_calling) we went over how to build a Chain that calls a single `multiply` tool. Now let's take a look at how we might augment this chain so that it can pick from a number of tools to call. We'll focus on Chains since [Agents](/docs/tutorials/agents) can route between multiple tools by default."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3fafec38-443a-42ad-a913-5be7667e3734",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"We'll need to install the following packages for this guide:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "78411bf1-0117-4f33-a3d7-f3d77a97bb78",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install --upgrade --quiet langchain-core"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "59d08fd0-ddd9-4c74-bcea-a5ca3a86e542",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "4185e74b-0500-4cad-ace0-bac37de466ac",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
|
||||
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d28159f5-b7d0-4385-aa44-4cd1b64507bb",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Tools\n",
|
||||
"\n",
|
||||
"Recall we already had a `multiply` tool:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "e13ec98c-8521-4d63-b521-caf92da87b70",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.tools import tool\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
"def multiply(first_int: int, second_int: int) -> int:\n",
|
||||
" \"\"\"Multiply two integers together.\"\"\"\n",
|
||||
" return first_int * second_int"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3de233af-b3bd-4f0c-8b1a-83527143a8db",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"And now we can add to it an `exponentiate` and `add` tool:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "e93661cd-a2ba-4ada-91ad-baf1b60879ec",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@tool\n",
|
||||
"def add(first_int: int, second_int: int) -> int:\n",
|
||||
" \"Add two integers.\"\n",
|
||||
" return first_int + second_int\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
"def exponentiate(base: int, exponent: int) -> int:\n",
|
||||
" \"Exponentiate the base to the exponent power.\"\n",
|
||||
" return base**exponent"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "bbea4555-ed10-4a18-b802-e9a3071f132b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The main difference between using one Tool and many is that we can't be sure which Tool the model will invoke upfront, so we cannot hardcode, like we did in the [Quickstart](/docs/how_to/tool_calling), a specific tool into our chain. Instead we'll add `call_tools`, a `RunnableLambda` that takes the output AI message with tools calls and routes to the correct tools.\n",
|
||||
"\n",
|
||||
"```{=mdx}\n",
|
||||
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
|
||||
"\n",
|
||||
"<ChatModelTabs customVarName=\"llm\"/>\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "f00f0f3f-8530-4c1d-a26c-d20824e31faf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"\n",
|
||||
"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "c35359ae-a740-48c5-b5e7-1a377fb25aa2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from operator import itemgetter\n",
|
||||
"from typing import Dict, List, Union\n",
|
||||
"\n",
|
||||
"from langchain_core.messages import AIMessage\n",
|
||||
"from langchain_core.runnables import (\n",
|
||||
" Runnable,\n",
|
||||
" RunnableLambda,\n",
|
||||
" RunnableMap,\n",
|
||||
" RunnablePassthrough,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"tools = [multiply, exponentiate, add]\n",
|
||||
"llm_with_tools = llm.bind_tools(tools)\n",
|
||||
"tool_map = {tool.name: tool for tool in tools}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def call_tools(msg: AIMessage) -> Runnable:\n",
|
||||
" \"\"\"Simple sequential tool calling helper.\"\"\"\n",
|
||||
" tool_map = {tool.name: tool for tool in tools}\n",
|
||||
" tool_calls = msg.tool_calls.copy()\n",
|
||||
" for tool_call in tool_calls:\n",
|
||||
" tool_call[\"output\"] = tool_map[tool_call[\"name\"]].invoke(tool_call[\"args\"])\n",
|
||||
" return tool_calls\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"chain = llm_with_tools | call_tools"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "ea6dbb32-ec9b-4c70-a90f-a2db93978cf1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[{'name': 'multiply',\n",
|
||||
" 'args': {'first_int': 23, 'second_int': 7},\n",
|
||||
" 'id': 'toolu_01Wf8kUs36kxRKLDL8vs7G8q',\n",
|
||||
" 'output': 161}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"chain.invoke(\"What's 23 times 7\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "b1c6c0f8-6d04-40d4-a40e-8719ca7b27c2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[{'name': 'add',\n",
|
||||
" 'args': {'first_int': 1000000, 'second_int': 1000000000},\n",
|
||||
" 'id': 'toolu_012aK4xZBQg2sXARsFZnqxHh',\n",
|
||||
" 'output': 1001000000}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"chain.invoke(\"add a million plus a billion\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "ce76f299-1a4d-421c-afa4-a6346e34285c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[{'name': 'exponentiate',\n",
|
||||
" 'args': {'base': 37, 'exponent': 3},\n",
|
||||
" 'id': 'toolu_01VDU6X3ugDb9cpnnmCZFPbC',\n",
|
||||
" 'output': 50653}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"chain.invoke(\"cube thirty-seven\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,215 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "95982bf1-7d9d-4dd6-a4ad-9de0719fe17f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to call tools in parallel\n",
|
||||
"\n",
|
||||
"In the [Chains with multiple tools](/docs/how_to/tools_multiple) guide we saw how to build function-calling chains that select between multiple tools. Some models, like the OpenAI models released in Fall 2023, also support parallel function calling, which allows you to invoke multiple functions (or the same function multiple times) in a single model call. Our previous chain from the multiple tools guides actually already supports this."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3fafec38-443a-42ad-a913-5be7667e3734",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"We'll need to install the following packages for this guide:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "78411bf1-0117-4f33-a3d7-f3d77a97bb78",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install --upgrade --quiet langchain-core"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "59d08fd0-ddd9-4c74-bcea-a5ca3a86e542",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4185e74b-0500-4cad-ace0-bac37de466ac",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
|
||||
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d28159f5-b7d0-4385-aa44-4cd1b64507bb",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Tools"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "e13ec98c-8521-4d63-b521-caf92da87b70",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.tools import tool\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
"def multiply(first_int: int, second_int: int) -> int:\n",
|
||||
" \"\"\"Multiply two integers together.\"\"\"\n",
|
||||
" return first_int * second_int\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
"def add(first_int: int, second_int: int) -> int:\n",
|
||||
" \"Add two integers.\"\n",
|
||||
" return first_int + second_int\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
"def exponentiate(base: int, exponent: int) -> int:\n",
|
||||
" \"Exponentiate the base to the exponent power.\"\n",
|
||||
" return base**exponent"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "119d419c-1c61-4e0d-834a-5dabb72f5514",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Chain\n",
|
||||
"\n",
|
||||
"```{=mdx}\n",
|
||||
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
|
||||
"\n",
|
||||
"<ChatModelTabs customVarName=\"llm\" hideGoogle=\"true\"/>\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "f67d91d8-cc38-4065-8f80-901e079954dd",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | echo: false\n",
|
||||
"# | output: false\n",
|
||||
"\n",
|
||||
"from langchain_openai import ChatOpenAI\n",
|
||||
"\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "c35359ae-a740-48c5-b5e7-1a377fb25aa2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from operator import itemgetter\n",
|
||||
"from typing import Dict, List, Union\n",
|
||||
"\n",
|
||||
"from langchain_core.messages import AIMessage\n",
|
||||
"from langchain_core.runnables import (\n",
|
||||
" Runnable,\n",
|
||||
" RunnableLambda,\n",
|
||||
" RunnableMap,\n",
|
||||
" RunnablePassthrough,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"tools = [multiply, exponentiate, add]\n",
|
||||
"llm_with_tools = llm.bind_tools(tools)\n",
|
||||
"tool_map = {tool.name: tool for tool in tools}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def call_tools(msg: AIMessage) -> Runnable:\n",
|
||||
" \"\"\"Simple sequential tool calling helper.\"\"\"\n",
|
||||
" tool_map = {tool.name: tool for tool in tools}\n",
|
||||
" tool_calls = msg.tool_calls.copy()\n",
|
||||
" for tool_call in tool_calls:\n",
|
||||
" tool_call[\"output\"] = tool_map[tool_call[\"name\"]].invoke(tool_call[\"args\"])\n",
|
||||
" return tool_calls\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"chain = llm_with_tools | call_tools"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "ea6dbb32-ec9b-4c70-a90f-a2db93978cf1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[{'name': 'multiply',\n",
|
||||
" 'args': {'first_int': 23, 'second_int': 7},\n",
|
||||
" 'id': 'call_22tgOrsVLyLMsl2RLbUhtycw',\n",
|
||||
" 'output': 161},\n",
|
||||
" {'name': 'multiply',\n",
|
||||
" 'args': {'first_int': 5, 'second_int': 18},\n",
|
||||
" 'id': 'call_EbKHEG3TjqBhEwb7aoxUtgzf',\n",
|
||||
" 'output': 90},\n",
|
||||
" {'name': 'add',\n",
|
||||
" 'args': {'first_int': 1000000, 'second_int': 1000000000},\n",
|
||||
" 'id': 'call_LUhu2IT3vINxlTc5fCVY6Nhi',\n",
|
||||
" 'output': 1001000000},\n",
|
||||
" {'name': 'exponentiate',\n",
|
||||
" 'args': {'base': 37, 'exponent': 3},\n",
|
||||
" 'id': 'call_bnCZIXelOKkmcyd4uGXId9Ct',\n",
|
||||
" 'output': 50653}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"chain.invoke(\n",
|
||||
" \"What's 23 times 7, and what's five times 18 and add a million plus a billion and cube thirty-seven\"\n",
|
||||
")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@ -0,0 +1,502 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "bf37a837-7a6a-447b-8779-38f26c585887",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Vector stores and retrievers\n",
|
||||
"\n",
|
||||
"This tutorial will familiarize you with LangChain's vector store and retriever abstractions. These abstractions are designed to support retrieval of data-- from (vector) databases and other sources-- for integration with LLM workflows. They are important for applications that fetch data to be reasoned over as part of model inference, as in the case of retrieval-augmented generation, or RAG (see our RAG tutorial [here](/docs/tutorials/rag)).\n",
|
||||
"\n",
|
||||
"## Concepts\n",
|
||||
"\n",
|
||||
"This guide focuses on retrieval of text data. We will cover the following concepts:\n",
|
||||
"\n",
|
||||
"- Documents;\n",
|
||||
"- Vector stores;\n",
|
||||
"- Retrievers.\n",
|
||||
"\n",
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"### Jupyter Notebook\n",
|
||||
"\n",
|
||||
"This and other tutorials are perhaps most conveniently run in a Jupyter notebook. See [here](https://jupyter.org/install) for instructions on how to install.\n",
|
||||
"\n",
|
||||
"### Installation\n",
|
||||
"\n",
|
||||
"This tutorial requires the `langchain`, `langchain-chroma`, and `langchain-openai` packages:\n",
|
||||
"\n",
|
||||
"```{=mdx}\n",
|
||||
"import Tabs from '@theme/Tabs';\n",
|
||||
"import TabItem from '@theme/TabItem';\n",
|
||||
"import CodeBlock from \"@theme/CodeBlock\";\n",
|
||||
"\n",
|
||||
"<Tabs>\n",
|
||||
" <TabItem value=\"pip\" label=\"Pip\" default>\n",
|
||||
" <CodeBlock language=\"bash\">pip install langchain langchain-chroma langchain-openai</CodeBlock>\n",
|
||||
" </TabItem>\n",
|
||||
" <TabItem value=\"conda\" label=\"Conda\">\n",
|
||||
" <CodeBlock language=\"bash\">conda install langchain langchain-chroma langchain-openai -c conda-forge</CodeBlock>\n",
|
||||
" </TabItem>\n",
|
||||
"</Tabs>\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"For more details, see our [Installation guide](/docs/how_to/installation).\n",
|
||||
"\n",
|
||||
"### LangSmith\n",
|
||||
"\n",
|
||||
"Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls.\n",
|
||||
"As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent.\n",
|
||||
"The best way to do this is with [LangSmith](https://smith.langchain.com).\n",
|
||||
"\n",
|
||||
"After you sign up at the link above, make sure to set your environment variables to start logging traces:\n",
|
||||
"\n",
|
||||
"```shell\n",
|
||||
"export LANGCHAIN_TRACING_V2=\"true\"\n",
|
||||
"export LANGCHAIN_API_KEY=\"...\"\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Or, if in a notebook, you can set them with:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
|
||||
"os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Documents\n",
|
||||
"\n",
|
||||
"LangChain implements a [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) abstraction, which is intended to represent a unit of text and associated metadata. It has two attributes:\n",
|
||||
"\n",
|
||||
"- `page_content`: a string representing the content;\n",
|
||||
"- `metadata`: a dict containing arbitrary metadata.\n",
|
||||
"\n",
|
||||
"The `metadata` attribute can capture information about the source of the document, its relationship to other documents, and other information. Note that an individual `Document` object often represents a chunk of a larger document.\n",
|
||||
"\n",
|
||||
"Let's generate some sample documents:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "9f3dc151-7b2f-4d94-9558-7a84f7eab100",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.documents import Document\n",
|
||||
"\n",
|
||||
"documents = [\n",
|
||||
" Document(\n",
|
||||
" page_content=\"Dogs are great companions, known for their loyalty and friendliness.\",\n",
|
||||
" metadata={\"source\": \"mammal-pets-doc\"},\n",
|
||||
" ),\n",
|
||||
" Document(\n",
|
||||
" page_content=\"Cats are independent pets that often enjoy their own space.\",\n",
|
||||
" metadata={\"source\": \"mammal-pets-doc\"},\n",
|
||||
" ),\n",
|
||||
" Document(\n",
|
||||
" page_content=\"Goldfish are popular pets for beginners, requiring relatively simple care.\",\n",
|
||||
" metadata={\"source\": \"fish-pets-doc\"},\n",
|
||||
" ),\n",
|
||||
" Document(\n",
|
||||
" page_content=\"Parrots are intelligent birds capable of mimicking human speech.\",\n",
|
||||
" metadata={\"source\": \"bird-pets-doc\"},\n",
|
||||
" ),\n",
|
||||
" Document(\n",
|
||||
" page_content=\"Rabbits are social animals that need plenty of space to hop around.\",\n",
|
||||
" metadata={\"source\": \"mammal-pets-doc\"},\n",
|
||||
" ),\n",
|
||||
"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1cac19bd-27d1-40f1-9c27-7a586b685b4e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Here we've generated five documents, containing metadata indicating three distinct \"sources\".\n",
|
||||
"\n",
|
||||
"## Vector stores\n",
|
||||
"\n",
|
||||
"Vector search is a common way to store and search over unstructured data (such as unstructured text). The idea is to store numeric vectors that are associated with the text. Given a query, we can [embed](/docs/concepts#embedding-models) it as a vector of the same dimension and use vector similarity metrics to identify related data in the store.\n",
|
||||
"\n",
|
||||
"LangChain [VectorStore](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html) objects contain methods for adding text and `Document` objects to the store, and querying them using various similarity metrics. They are often initialized with [embedding](/docs/how_to/embed_text) models, which determine how text data is translated to numeric vectors.\n",
|
||||
"\n",
|
||||
"LangChain includes a suite of [integrations](/docs/integrations/vectorstores) with different vector store technologies. Some vector stores are hosted by a provider (e.g., various cloud providers) and require specific credentials to use; some (such as [Postgres](/docs/integrations/vectorstores/pgvector)) run in separate infrastructure that can be run locally or via a third-party; others can run in-memory for lightweight workloads. Here we will demonstrate usage of LangChain VectorStores using [Chroma](/docs/integrations/vectorstores/chroma), which includes an in-memory implementation.\n",
|
||||
"\n",
|
||||
"To instantiate a vector store, we often need to provide an [embedding](/docs/how_to/embed_text) model to specify how text should be converted into a numeric vector. Here we will use [OpenAI embeddings](/docs/integrations/text_embedding/openai/)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "d48acc28-1a34-414b-8e08-fbdef3a2a60b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_chroma import Chroma\n",
|
||||
"from langchain_openai import OpenAIEmbeddings\n",
|
||||
"\n",
|
||||
"vectorstore = Chroma.from_documents(\n",
|
||||
" documents,\n",
|
||||
" embedding=OpenAIEmbeddings(),\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ff0f0b43-e5b8-4c79-b782-a02f17345487",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Calling `.from_documents` here will add the documents to the vector store. [VectorStore](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html) implements methods for adding documents that can also be called after the object is instantiated. Most implementations will allow you to connect to an existing vector store-- e.g., by providing a client, index name, or other information. See the documentation for a specific [integration](/docs/integrations/vectorstores) for more detail.\n",
|
||||
"\n",
|
||||
"Once we've instantiated a `VectorStore` that contains documents, we can query it. [VectorStore](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html) includes methods for querying:\n",
|
||||
"- Synchronously and asynchronously;\n",
|
||||
"- By string query and by vector;\n",
|
||||
"- With and without returning similarity scores;\n",
|
||||
"- By similarity and [maximum marginal relevance](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.max_marginal_relevance_search) (to balance similarity with query to diversity in retrieved results).\n",
|
||||
"\n",
|
||||
"The methods will generally include a list of [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects in their outputs.\n",
|
||||
"\n",
|
||||
"### Examples\n",
|
||||
"\n",
|
||||
"Return documents based on similarity to a string query:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "7e01ed91-1a98-4221-960a-bd7a2541a548",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Cats are independent pets that often enjoy their own space.', metadata={'source': 'mammal-pets-doc'}),\n",
|
||||
" Document(page_content='Dogs are great companions, known for their loyalty and friendliness.', metadata={'source': 'mammal-pets-doc'}),\n",
|
||||
" Document(page_content='Rabbits are social animals that need plenty of space to hop around.', metadata={'source': 'mammal-pets-doc'}),\n",
|
||||
" Document(page_content='Parrots are intelligent birds capable of mimicking human speech.', metadata={'source': 'bird-pets-doc'})]"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"vectorstore.similarity_search(\"cat\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4d4f9857-5a7d-4b5f-82b8-ff76539143c2",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Async query:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "618af196-6182-4a7d-8b09-07493fcdc868",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Cats are independent pets that often enjoy their own space.', metadata={'source': 'mammal-pets-doc'}),\n",
|
||||
" Document(page_content='Dogs are great companions, known for their loyalty and friendliness.', metadata={'source': 'mammal-pets-doc'}),\n",
|
||||
" Document(page_content='Rabbits are social animals that need plenty of space to hop around.', metadata={'source': 'mammal-pets-doc'}),\n",
|
||||
" Document(page_content='Parrots are intelligent birds capable of mimicking human speech.', metadata={'source': 'bird-pets-doc'})]"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"await vectorstore.asimilarity_search(\"cat\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d4172698-9ad7-4422-99b2-bdc268e99c75",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Return scores:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "4ed24af2-0d82-478c-949b-b389348d4e9f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[(Document(page_content='Cats are independent pets that often enjoy their own space.', metadata={'source': 'mammal-pets-doc'}),\n",
|
||||
" 0.3751849830150604),\n",
|
||||
" (Document(page_content='Dogs are great companions, known for their loyalty and friendliness.', metadata={'source': 'mammal-pets-doc'}),\n",
|
||||
" 0.48316916823387146),\n",
|
||||
" (Document(page_content='Rabbits are social animals that need plenty of space to hop around.', metadata={'source': 'mammal-pets-doc'}),\n",
|
||||
" 0.49601367115974426),\n",
|
||||
" (Document(page_content='Parrots are intelligent birds capable of mimicking human speech.', metadata={'source': 'bird-pets-doc'}),\n",
|
||||
" 0.4972994923591614)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Note that providers implement different scores; Chroma here\n",
|
||||
"# returns a distance metric that should vary inversely with\n",
|
||||
"# similarity.\n",
|
||||
"\n",
|
||||
"vectorstore.similarity_search_with_score(\"cat\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b4991642-7275-40a9-b11a-e3beccbf2614",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Return documents based on similarity to a embedded query:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "b1a5eabb-a821-48cc-917e-cc27f03e4bcc",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Cats are independent pets that often enjoy their own space.', metadata={'source': 'mammal-pets-doc'}),\n",
|
||||
" Document(page_content='Dogs are great companions, known for their loyalty and friendliness.', metadata={'source': 'mammal-pets-doc'}),\n",
|
||||
" Document(page_content='Rabbits are social animals that need plenty of space to hop around.', metadata={'source': 'mammal-pets-doc'}),\n",
|
||||
" Document(page_content='Parrots are intelligent birds capable of mimicking human speech.', metadata={'source': 'bird-pets-doc'})]"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"embedding = OpenAIEmbeddings().embed_query(\"cat\")\n",
|
||||
"\n",
|
||||
"vectorstore.similarity_search_by_vector(embedding)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "168dbbec-ea97-4cc9-bb1a-75519c2d08af",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Learn more:\n",
|
||||
"\n",
|
||||
"- [API reference](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html)\n",
|
||||
"- [How-to guide](/docs/how_to/vectorstores)\n",
|
||||
"- [Integration-specific docs](/docs/integrations/vectorstores)\n",
|
||||
"\n",
|
||||
"## Retrievers\n",
|
||||
"\n",
|
||||
"LangChain `VectorStore` objects do not subclass [Runnable](https://api.python.langchain.com/en/latest/core_api_reference.html#module-langchain_core.runnables), and so cannot immediately be integrated into LangChain Expression Language [chains](/docs/concepts/#langchain-expression-language-lcel).\n",
|
||||
"\n",
|
||||
"LangChain [Retrievers](https://api.python.langchain.com/en/latest/core_api_reference.html#module-langchain_core.retrievers) are Runnables, so they implement a standard set of methods (e.g., synchronous and asynchronous `invoke` and `batch` operations) and are designed to be incorporated in LCEL chains.\n",
|
||||
"\n",
|
||||
"We can create a simple version of this ourselves, without subclassing `Retriever`. If we choose what method we wish to use to retrieve documents, we can create a runnable easily. Below we will build one around the `similarity_search` method:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "f1461582-e569-4326-bd95-510f72edf019",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[[Document(page_content='Cats are independent pets that often enjoy their own space.', metadata={'source': 'mammal-pets-doc'})],\n",
|
||||
" [Document(page_content='Goldfish are popular pets for beginners, requiring relatively simple care.', metadata={'source': 'fish-pets-doc'})]]"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from typing import List\n",
|
||||
"\n",
|
||||
"from langchain_core.documents import Document\n",
|
||||
"from langchain_core.runnables import RunnableLambda\n",
|
||||
"\n",
|
||||
"retriever = RunnableLambda(vectorstore.similarity_search).bind(k=1) # select top result\n",
|
||||
"\n",
|
||||
"retriever.batch([\"cat\", \"shark\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a36d3f64-a8bc-4baa-b2ea-07e324a0143e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Vectorstores implement an `as_retriever` method that will generate a Retriever, specifically a [VectorStoreRetriever](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStoreRetriever.html). These retrievers include specific `search_type` and `search_kwargs` attributes that identify what methods of the underlying vector store to call, and how to parameterize them. For instance, we can replicate the above with the following:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "4989fe5e-ac58-4751-bc35-f53ff885860c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[[Document(page_content='Cats are independent pets that often enjoy their own space.', metadata={'source': 'mammal-pets-doc'})],\n",
|
||||
" [Document(page_content='Goldfish are popular pets for beginners, requiring relatively simple care.', metadata={'source': 'fish-pets-doc'})]]"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"retriever = vectorstore.as_retriever(\n",
|
||||
" search_type=\"similarity\",\n",
|
||||
" search_kwargs={\"k\": 1},\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"retriever.batch([\"cat\", \"shark\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "6b79ded3-39ed-4aeb-8b70-cd36795ae239",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"`VectorStoreRetriever` supports search types of `\"similarity\"` (default), `\"mmr\"` (maximum marginal relevance, described above), and `\"similarity_score_threshold\"`. We can use the latter to threshold documents output by the retriever by similarity score.\n",
|
||||
"\n",
|
||||
"Retrievers can easily be incorporated into more complex applications, such as retrieval-augmented generation (RAG) applications that combine a given question with retrieved context into a prompt for a LLM. Below we show a minimal example.\n",
|
||||
"\n",
|
||||
"```{=mdx}\n",
|
||||
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
|
||||
"\n",
|
||||
"<ChatModelTabs customVarName=\"llm\" />\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "c77b68bf-59f3-4416-9877-960f934c374d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# | output: false\n",
|
||||
"# | echo: false\n",
|
||||
"\n",
|
||||
"from langchain_openai import ChatOpenAI\n",
|
||||
"\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "6f1ae0d0-0b4b-4da0-80ce-f82913052a83",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"from langchain_core.runnables import RunnablePassthrough\n",
|
||||
"\n",
|
||||
"message = \"\"\"\n",
|
||||
"Answer this question using the provided context only.\n",
|
||||
"\n",
|
||||
"{question}\n",
|
||||
"\n",
|
||||
"Context:\n",
|
||||
"{context}\n",
|
||||
"\"\"\"\n",
|
||||
"\n",
|
||||
"prompt = ChatPromptTemplate.from_messages([(\"human\", message)])\n",
|
||||
"\n",
|
||||
"rag_chain = {\"context\": retriever, \"question\": RunnablePassthrough()} | prompt | llm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "b3c0d625-61e0-492e-b3a6-c40d383fca03",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Cats are independent pets that often enjoy their own space.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"response = rag_chain.invoke(\"tell me about cats\")\n",
|
||||
"\n",
|
||||
"print(response.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3d9be7cb-2081-48a4-b6e4-d5e2d562ffd4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Learn more:\n",
|
||||
"\n",
|
||||
"Retrieval strategies can be rich and complex. For example:\n",
|
||||
"\n",
|
||||
"- We can [infer hard rules and filters](/docs/how_to/self_query/) from a query (e.g., \"using documents published after 2020\");\n",
|
||||
"- We can [return documents that are linked](/docs/how_to/parent_document_retriever/) to the retrieved context in some way (e.g., via some document taxonomy);\n",
|
||||
"- We can generate [multiple embeddings](/docs/how_to/multi_vector) for each unit of context;\n",
|
||||
"- We can [ensemble results](/docs/how_to/ensemble_retriever) from multiple retrievers;\n",
|
||||
"- We can assign weights to documents, e.g., to weigh [recent documents](/docs/how_to/time_weighted_vectorstore/) higher.\n",
|
||||
"\n",
|
||||
"The [retrievers](/docs/how_to#retrievers) section of the how-to guides covers these and other built-in retrieval strategies.\n",
|
||||
"\n",
|
||||
"It is also straightforward to extend the [BaseRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_core.retrievers.BaseRetriever.html) class in order to implement custom retrievers. See our how-to guide [here](/docs/how_to/custom_retriever)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,124 @@
|
||||
---
|
||||
sidebar_position: 2
|
||||
sidebar_label: astream_events v2
|
||||
---
|
||||
|
||||
# Migrating to Astream Events v2
|
||||
|
||||
:::danger
|
||||
|
||||
This migration guide is a work in progress and is not complete. Please wait to migrate astream_events.
|
||||
|
||||
:::
|
||||
|
||||
We've added a `v2` of the astream_events API with the release of `0.2.0`. You can see this [PR](https://github.com/langchain-ai/langchain/pull/21638) for more details.
|
||||
|
||||
The `v2` version is a re-write of the `v1` version, and should be more efficient, with more consistent output for the events. The `v1` version of the API will be deprecated in favor of the `v2` version and will be removed in `0.4.0`.
|
||||
|
||||
Below is a list of changes between the `v1` and `v2` versions of the API.
|
||||
|
||||
|
||||
### output for `on_chat_model_end`
|
||||
|
||||
In `v1`, the outputs associated with `on_chat_model_end` changed depending on whether the
|
||||
chat model was run as a root level runnable or as part of a chain.
|
||||
|
||||
As a root level runnable the output was:
|
||||
|
||||
```python
|
||||
"data": {"output": AIMessageChunk(content="hello world!", id='some id')}
|
||||
```
|
||||
|
||||
As part of a chain the output was:
|
||||
|
||||
```
|
||||
"data": {
|
||||
"output": {
|
||||
"generations": [
|
||||
[
|
||||
{
|
||||
"generation_info": None,
|
||||
"message": AIMessageChunk(
|
||||
content="hello world!", id=AnyStr()
|
||||
),
|
||||
"text": "hello world!",
|
||||
"type": "ChatGenerationChunk",
|
||||
}
|
||||
]
|
||||
],
|
||||
"llm_output": None,
|
||||
}
|
||||
},
|
||||
```
|
||||
|
||||
|
||||
As of `v2`, the output will always be the simpler representation:
|
||||
|
||||
```python
|
||||
"data": {"output": AIMessageChunk(content="hello world!", id='some id')}
|
||||
```
|
||||
|
||||
:::note
|
||||
Non chat models (i.e., regular LLMs) are will be consistently associated with the more verbose format for now.
|
||||
:::
|
||||
|
||||
### output for `on_retriever_end`
|
||||
|
||||
`on_retriever_end` output will always return a list of `Documents`.
|
||||
|
||||
Before:
|
||||
```python
|
||||
{
|
||||
"data": {
|
||||
"output": [
|
||||
Document(...),
|
||||
Document(...),
|
||||
...
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Removed `on_retriever_stream`
|
||||
|
||||
The `on_retriever_stream` event was an artifact of the implementation and has been removed.
|
||||
|
||||
Full information associated with the event is already available in the `on_retriever_end` event.
|
||||
|
||||
Please use `on_retriever_end` instead.
|
||||
|
||||
### Removed `on_tool_stream`
|
||||
|
||||
The `on_tool_stream` event was an artifact of the implementation and has been removed.
|
||||
|
||||
Full information associated with the event is already available in the `on_tool_end` event.
|
||||
|
||||
Please use `on_tool_end` instead.
|
||||
|
||||
### Propagating Names
|
||||
|
||||
Names of runnables have been updated to be more consistent.
|
||||
|
||||
```python
|
||||
model = GenericFakeChatModel(messages=infinite_cycle).configurable_fields(
|
||||
messages=ConfigurableField(
|
||||
id="messages",
|
||||
name="Messages",
|
||||
description="Messages return by the LLM",
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
In `v1`, the event name was `RunnableConfigurableFields`.
|
||||
|
||||
In `v2`, the event name is `GenericFakeChatModel`.
|
||||
|
||||
If you're filtering by event names, check if you need to update your filters.
|
||||
|
||||
### RunnableRetry
|
||||
|
||||
Usage of [RunnableRetry](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.retry.RunnableRetry.html)
|
||||
within an LCEL chain being streamed generated an incorrect `on_chain_end` event in `v1` corresponding
|
||||
to the failed runnable invocation that was being retried. This event has been removed in `v2`.
|
||||
|
||||
No action is required for this change.
|
||||
@ -0,0 +1,465 @@
|
||||
"""Parse arXiv references from the documentation.
|
||||
Generate a page with a table of the arXiv references with links to the documentation pages.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Set
|
||||
|
||||
from pydantic.v1 import BaseModel, root_validator
|
||||
|
||||
# TODO parse docstrings for arXiv references
|
||||
# TODO Generate a page with a table of the references with correspondent modules/classes/functions.
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_ROOT_DIR = Path(os.path.abspath(__file__)).parents[2]
|
||||
DOCS_DIR = _ROOT_DIR / "docs" / "docs"
|
||||
CODE_DIR = _ROOT_DIR / "libs"
|
||||
ARXIV_ID_PATTERN = r"https://arxiv\.org/(abs|pdf)/(\d+\.\d+)"
|
||||
|
||||
|
||||
@dataclass
|
||||
class ArxivPaper:
|
||||
"""ArXiv paper information."""
|
||||
|
||||
arxiv_id: str
|
||||
referencing_docs: list[str] # TODO: Add the referencing docs
|
||||
referencing_api_refs: list[str] # TODO: Add the referencing docs
|
||||
title: str
|
||||
authors: list[str]
|
||||
abstract: str
|
||||
url: str
|
||||
published_date: str
|
||||
|
||||
|
||||
def search_documentation_for_arxiv_references(docs_dir: Path) -> dict[str, set[str]]:
|
||||
"""Search the documentation for arXiv references.
|
||||
|
||||
Search for the arXiv references in the documentation pages.
|
||||
Note: It finds only the first arXiv reference in a line.
|
||||
|
||||
Args:
|
||||
docs_dir: Path to the documentation root folder.
|
||||
Returns:
|
||||
dict: Dictionary with arxiv_id as key and set of file names as value.
|
||||
"""
|
||||
arxiv_url_pattern = re.compile(ARXIV_ID_PATTERN)
|
||||
exclude_strings = {"file_path", "metadata", "link", "loader", "PyPDFLoader"}
|
||||
|
||||
# loop all the files (ipynb, mdx, md) in the docs folder
|
||||
files = (
|
||||
p.resolve()
|
||||
for p in Path(docs_dir).glob("**/*")
|
||||
if p.suffix in {".ipynb", ".mdx", ".md"}
|
||||
)
|
||||
arxiv_id2file_names: dict[str, set[str]] = {}
|
||||
for file in files:
|
||||
if "-checkpoint.ipynb" in file.name:
|
||||
continue
|
||||
with open(file, "r", encoding="utf-8") as f:
|
||||
lines = f.readlines()
|
||||
for line in lines:
|
||||
if any(exclude_string in line for exclude_string in exclude_strings):
|
||||
continue
|
||||
matches = arxiv_url_pattern.search(line)
|
||||
if matches:
|
||||
arxiv_id = matches.group(2)
|
||||
file_name = _get_doc_path(file.parts, file.suffix)
|
||||
if arxiv_id not in arxiv_id2file_names:
|
||||
arxiv_id2file_names[arxiv_id] = {file_name}
|
||||
else:
|
||||
arxiv_id2file_names[arxiv_id].add(file_name)
|
||||
return arxiv_id2file_names
|
||||
|
||||
|
||||
def convert_module_name_and_members_to_urls(
|
||||
arxiv_id2module_name_and_members: dict[str, set[str]],
|
||||
) -> dict[str, set[str]]:
|
||||
arxiv_id2urls = {}
|
||||
for arxiv_id, module_name_and_members in arxiv_id2module_name_and_members.items():
|
||||
urls = set()
|
||||
for module_name_and_member in module_name_and_members:
|
||||
module_name, type_and_member = module_name_and_member.split(":")
|
||||
if "$" in type_and_member:
|
||||
type, member = type_and_member.split("$")
|
||||
else:
|
||||
type = type_and_member
|
||||
member = ""
|
||||
_namespace_parts = module_name.split(".")
|
||||
if type == "module":
|
||||
first_namespace_part = _namespace_parts[0]
|
||||
if first_namespace_part.startswith("langchain_"):
|
||||
first_namespace_part = first_namespace_part.replace(
|
||||
"langchain_", ""
|
||||
)
|
||||
url = f"{first_namespace_part}_api_reference.html#module-{module_name}"
|
||||
elif type in ["class", "function"]:
|
||||
second_namespace_part = _namespace_parts[1]
|
||||
url = f"{second_namespace_part}/{module_name}.{member}.html#{module_name}.{member}"
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unknown type: {type} in the {module_name_and_member}."
|
||||
)
|
||||
urls.add(url)
|
||||
arxiv_id2urls[arxiv_id] = urls
|
||||
return arxiv_id2urls
|
||||
|
||||
|
||||
def search_code_for_arxiv_references(code_dir: Path) -> dict[str, set[str]]:
|
||||
"""Search the code for arXiv references.
|
||||
|
||||
Search for the arXiv references in the code.
|
||||
Note: It finds only the first arXiv reference in a line.
|
||||
|
||||
Args:
|
||||
code_dir: Path to the code root folder.
|
||||
Returns:
|
||||
dict: Dictionary with arxiv_id as key and set of module names as value.
|
||||
module names encoded as:
|
||||
<module_name>:module
|
||||
<module_name>:class$<ClassName>
|
||||
<module_name>:function$<function_name>
|
||||
"""
|
||||
arxiv_url_pattern = re.compile(ARXIV_ID_PATTERN)
|
||||
# exclude_strings = {"file_path", "metadata", "link", "loader"}
|
||||
class_pattern = re.compile(r"\s*class\s+(\w+).*:")
|
||||
function_pattern = re.compile(r"\s*def\s+(\w+)")
|
||||
|
||||
# loop all the files (ipynb, mdx, md) in the docs folder
|
||||
files = (
|
||||
p.resolve()
|
||||
for p in Path(code_dir).glob("**/*")
|
||||
if p.suffix in {".py"} and "tests" not in p.parts and "scripts" not in p.parts
|
||||
# ".md" files are excluded
|
||||
)
|
||||
arxiv_id2module_name_and_members: dict[str, set[str]] = {}
|
||||
for file in files:
|
||||
try:
|
||||
with open(file, "r", encoding="utf-8") as f:
|
||||
module_name = _get_module_name(file.parts)
|
||||
class_or_function_started = "module"
|
||||
for line in f.readlines():
|
||||
# class line:
|
||||
matches = class_pattern.search(line)
|
||||
if matches:
|
||||
class_name = matches.group(1)
|
||||
class_or_function_started = f"class${class_name}"
|
||||
|
||||
# function line:
|
||||
# not inside a class!
|
||||
if "class" not in class_or_function_started:
|
||||
matches = function_pattern.search(line)
|
||||
if matches:
|
||||
func_name = matches.group(1)
|
||||
class_or_function_started = f"function${func_name}"
|
||||
|
||||
# arxiv line:
|
||||
matches = arxiv_url_pattern.search(line)
|
||||
if matches:
|
||||
arxiv_id = matches.group(2)
|
||||
module_name_and_member = (
|
||||
f"{module_name}:{class_or_function_started}"
|
||||
)
|
||||
if arxiv_id not in arxiv_id2module_name_and_members:
|
||||
arxiv_id2module_name_and_members[arxiv_id] = {
|
||||
module_name_and_member
|
||||
}
|
||||
else:
|
||||
arxiv_id2module_name_and_members[arxiv_id].add(
|
||||
module_name_and_member
|
||||
)
|
||||
except UnicodeDecodeError:
|
||||
# Skip files like this 'tests/integration_tests/examples/non-utf8-encoding.py'
|
||||
logger.warning(f"Could not read the file {file}.")
|
||||
|
||||
# handle border cases:
|
||||
# 1. {'langchain_experimental.pal_chain.base:class$PALChain', 'langchain_experimental.pal_chain.base:module' - remove}
|
||||
for arxiv_id, module_name_and_members in arxiv_id2module_name_and_members.items():
|
||||
module_name_and_member_deduplicated = set()
|
||||
non_module_members = set()
|
||||
for module_name_and_member in module_name_and_members:
|
||||
if not module_name_and_member.endswith(":module"):
|
||||
module_name_and_member_deduplicated.add(module_name_and_member)
|
||||
non_module_members.add(module_name_and_member.split(":")[0])
|
||||
for module_name_and_member in module_name_and_members:
|
||||
if module_name_and_member.endswith(":module"):
|
||||
if module_name_and_member.split(":")[0] in non_module_members:
|
||||
continue
|
||||
module_name_and_member_deduplicated.add(module_name_and_member)
|
||||
arxiv_id2module_name_and_members[arxiv_id] = module_name_and_member_deduplicated
|
||||
|
||||
# 2. {'langchain.evaluation.scoring.prompt:module', 'langchain.evaluation.comparison.prompt:module'}
|
||||
# only modules with 2-part namespaces are parsed into API Reference now! TODO fix this behavior
|
||||
# leave only the modules with 2-part namespaces
|
||||
arxiv_id2module_name_and_members_reduced = {}
|
||||
for arxiv_id, module_name_and_members in arxiv_id2module_name_and_members.items():
|
||||
module_name_and_member_reduced = set()
|
||||
removed_modules = set()
|
||||
for module_name_and_member in module_name_and_members:
|
||||
if module_name_and_member.endswith(":module"):
|
||||
if module_name_and_member.split(":")[0].count(".") <= 1:
|
||||
module_name_and_member_reduced.add(module_name_and_member)
|
||||
else:
|
||||
removed_modules.add(module_name_and_member)
|
||||
else:
|
||||
module_name_and_member_reduced.add(module_name_and_member)
|
||||
if module_name_and_member_reduced:
|
||||
arxiv_id2module_name_and_members_reduced[arxiv_id] = (
|
||||
module_name_and_member_reduced
|
||||
)
|
||||
if removed_modules:
|
||||
logger.warning(
|
||||
f"{arxiv_id}: Removed the following modules with 2+ -part namespaces: {removed_modules}."
|
||||
)
|
||||
return arxiv_id2module_name_and_members_reduced
|
||||
|
||||
|
||||
def _get_doc_path(file_parts: tuple[str, ...], file_extension) -> str:
|
||||
"""Get the relative path to the documentation page
|
||||
from the absolute path of the file.
|
||||
Remove file_extension
|
||||
"""
|
||||
res = []
|
||||
for el in file_parts[::-1]:
|
||||
res.append(el)
|
||||
if el == "docs":
|
||||
break
|
||||
ret = "/".join(reversed(res))
|
||||
return ret[: -len(file_extension)] if ret.endswith(file_extension) else ret
|
||||
|
||||
|
||||
def _get_code_path(file_parts: tuple[str, ...]) -> str:
|
||||
"""Get the relative path to the documentation page
|
||||
from the absolute path of the file.
|
||||
"""
|
||||
res = []
|
||||
for el in file_parts[::-1]:
|
||||
res.append(el)
|
||||
if el == "libs":
|
||||
break
|
||||
return "/".join(reversed(res))
|
||||
|
||||
|
||||
def _get_module_name(file_parts: tuple[str, ...]) -> str:
|
||||
"""Get the module name from the absolute path of the file."""
|
||||
ns_parts = []
|
||||
for el in file_parts[::-1]:
|
||||
if str(el) == "__init__.py":
|
||||
continue
|
||||
ns_parts.insert(0, str(el).replace(".py", ""))
|
||||
if el.startswith("langchain"):
|
||||
break
|
||||
return ".".join(ns_parts)
|
||||
|
||||
|
||||
def compound_urls(
|
||||
arxiv_id2file_names: dict[str, set[str]], arxiv_id2code_urls: dict[str, set[str]]
|
||||
) -> dict[str, dict[str, set[str]]]:
|
||||
arxiv_id2urls = dict()
|
||||
for arxiv_id, code_urls in arxiv_id2code_urls.items():
|
||||
arxiv_id2urls[arxiv_id] = {"api": code_urls}
|
||||
# intersection of the two sets
|
||||
if arxiv_id in arxiv_id2file_names:
|
||||
arxiv_id2urls[arxiv_id]["docs"] = arxiv_id2file_names[arxiv_id]
|
||||
for arxiv_id, file_names in arxiv_id2file_names.items():
|
||||
if arxiv_id not in arxiv_id2code_urls:
|
||||
arxiv_id2urls[arxiv_id] = {"docs": file_names}
|
||||
# reverse sort by the arxiv_id (the newest papers first)
|
||||
ret = dict(sorted(arxiv_id2urls.items(), key=lambda item: item[0], reverse=True))
|
||||
return ret
|
||||
|
||||
|
||||
def _format_doc_link(doc_paths: list[str]) -> list[str]:
|
||||
return [
|
||||
f"[{doc_path}](https://python.langchain.com/{doc_path})"
|
||||
for doc_path in doc_paths
|
||||
]
|
||||
|
||||
|
||||
def _format_api_ref_link(
|
||||
doc_paths: list[str], compact: bool = False
|
||||
) -> list[str]: # TODO
|
||||
# agents/langchain_core.agents.AgentAction.html#langchain_core.agents.AgentAction
|
||||
ret = []
|
||||
for doc_path in doc_paths:
|
||||
module = doc_path.split("#")[1].replace("module-", "")
|
||||
if compact and module.count(".") > 2:
|
||||
# langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI
|
||||
# -> langchain_community.llms...OCIModelDeploymentTGI
|
||||
module_parts = module.split(".")
|
||||
module = f"{module_parts[0]}.{module_parts[1]}...{module_parts[-1]}"
|
||||
ret.append(
|
||||
f"[{module}](https://api.python.langchain.com/en/latest/{doc_path.split('langchain.com/')[-1]})"
|
||||
)
|
||||
return ret
|
||||
|
||||
|
||||
def log_results(arxiv_id2urls):
|
||||
arxiv_ids = arxiv_id2urls.keys()
|
||||
doc_number, api_number = 0, 0
|
||||
for urls in arxiv_id2urls.values():
|
||||
if "docs" in urls:
|
||||
doc_number += len(urls["docs"])
|
||||
if "api" in urls:
|
||||
api_number += len(urls["api"])
|
||||
logger.info(
|
||||
f"Found {len(arxiv_ids)} arXiv references in the {doc_number} docs and in {api_number} API Refs."
|
||||
)
|
||||
|
||||
|
||||
class ArxivAPIWrapper(BaseModel):
|
||||
arxiv_search: Any #: :meta private:
|
||||
arxiv_exceptions: Any # :meta private:
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that the python package exists in environment."""
|
||||
try:
|
||||
import arxiv
|
||||
|
||||
values["arxiv_search"] = arxiv.Search
|
||||
values["arxiv_exceptions"] = (
|
||||
arxiv.ArxivError,
|
||||
arxiv.UnexpectedEmptyPageError,
|
||||
arxiv.HTTPError,
|
||||
)
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import arxiv python package. "
|
||||
"Please install it with `pip install arxiv`."
|
||||
)
|
||||
return values
|
||||
|
||||
def get_papers(
|
||||
self, arxiv_id2urls: dict[str, dict[str, set[str]]]
|
||||
) -> list[ArxivPaper]:
|
||||
"""
|
||||
Performs an arxiv search and returns information about the papers found.
|
||||
|
||||
If an error occurs or no documents found, error text
|
||||
is returned instead.
|
||||
Args:
|
||||
arxiv_id2urls: Dictionary with arxiv_id as key and dictionary
|
||||
with sets of doc file names and API Ref urls.
|
||||
|
||||
Returns:
|
||||
List of ArxivPaper objects.
|
||||
""" # noqa: E501
|
||||
|
||||
def cut_authors(authors: list) -> list[str]:
|
||||
if len(authors) > 3:
|
||||
return [str(a) for a in authors[:3]] + [" et al."]
|
||||
else:
|
||||
return [str(a) for a in authors]
|
||||
|
||||
if not arxiv_id2urls:
|
||||
return []
|
||||
try:
|
||||
arxiv_ids = list(arxiv_id2urls.keys())
|
||||
results = self.arxiv_search(
|
||||
id_list=arxiv_ids,
|
||||
max_results=len(arxiv_ids),
|
||||
).results()
|
||||
except self.arxiv_exceptions as ex:
|
||||
raise ex
|
||||
papers = [
|
||||
ArxivPaper(
|
||||
arxiv_id=result.entry_id.split("/")[-1],
|
||||
title=result.title,
|
||||
authors=cut_authors(result.authors),
|
||||
abstract=result.summary,
|
||||
url=result.entry_id,
|
||||
published_date=str(result.published.date()),
|
||||
referencing_docs=urls["docs"] if "docs" in urls else [],
|
||||
referencing_api_refs=urls["api"] if "api" in urls else [],
|
||||
)
|
||||
for result, urls in zip(results, arxiv_id2urls.values())
|
||||
]
|
||||
return papers
|
||||
|
||||
|
||||
def generate_arxiv_references_page(file_name: str, papers: list[ArxivPaper]) -> None:
|
||||
with open(file_name, "w") as f:
|
||||
# Write the table headers
|
||||
f.write("""# arXiv
|
||||
|
||||
LangChain implements the latest research in the field of Natural Language Processing.
|
||||
This page contains `arXiv` papers referenced in the LangChain Documentation and API Reference.
|
||||
|
||||
## Summary
|
||||
|
||||
| arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation and API Reference |
|
||||
|------------------|---------|-------------------|-------------------------|
|
||||
""")
|
||||
for paper in papers:
|
||||
refs = []
|
||||
if paper.referencing_docs:
|
||||
refs += [
|
||||
"`Docs:` " + ", ".join(_format_doc_link(paper.referencing_docs))
|
||||
]
|
||||
if paper.referencing_api_refs:
|
||||
refs += [
|
||||
"`API:` "
|
||||
+ ", ".join(
|
||||
_format_api_ref_link(paper.referencing_api_refs, compact=True)
|
||||
)
|
||||
]
|
||||
refs_str = ", ".join(refs)
|
||||
|
||||
title_link = f"[{paper.title}]({paper.url})"
|
||||
f.write(
|
||||
f"| {' | '.join([f'`{paper.arxiv_id}` {title_link}', ', '.join(paper.authors), paper.published_date, refs_str])}\n"
|
||||
)
|
||||
|
||||
for paper in papers:
|
||||
docs_refs = (
|
||||
f"- **LangChain Documentation:** {', '.join(_format_doc_link(paper.referencing_docs))}"
|
||||
if paper.referencing_docs
|
||||
else ""
|
||||
)
|
||||
api_ref_refs = (
|
||||
f"- **LangChain API Reference:** {', '.join(_format_api_ref_link(paper.referencing_api_refs))}"
|
||||
if paper.referencing_api_refs
|
||||
else ""
|
||||
)
|
||||
f.write(f"""
|
||||
## {paper.title}
|
||||
|
||||
- **arXiv id:** {paper.arxiv_id}
|
||||
- **Title:** {paper.title}
|
||||
- **Authors:** {', '.join(paper.authors)}
|
||||
- **Published Date:** {paper.published_date}
|
||||
- **URL:** {paper.url}
|
||||
{docs_refs}
|
||||
{api_ref_refs}
|
||||
|
||||
**Abstract:** {paper.abstract}
|
||||
""")
|
||||
|
||||
logger.info(f"Created the {file_name} file with {len(papers)} arXiv references.")
|
||||
|
||||
|
||||
def main():
|
||||
# search the documentation and the API Reference for arXiv references:
|
||||
arxiv_id2module_name_and_members = search_code_for_arxiv_references(CODE_DIR)
|
||||
arxiv_id2code_urls = convert_module_name_and_members_to_urls(
|
||||
arxiv_id2module_name_and_members
|
||||
)
|
||||
arxiv_id2file_names = search_documentation_for_arxiv_references(DOCS_DIR)
|
||||
arxiv_id2urls = compound_urls(arxiv_id2file_names, arxiv_id2code_urls)
|
||||
log_results(arxiv_id2urls)
|
||||
|
||||
# get the arXiv paper information
|
||||
papers = ArxivAPIWrapper().get_papers(arxiv_id2urls)
|
||||
|
||||
# generate the arXiv references page
|
||||
output_file = str(DOCS_DIR / "additional_resources" / "arxiv_references.mdx")
|
||||
generate_arxiv_references_page(output_file, papers)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,22 @@
|
||||
import React from "react";
|
||||
import DocPaginator from "@theme-original/DocPaginator";
|
||||
|
||||
const BLACKLISTED_PATHS = ["/docs/how_to/", "/docs/tutorials/"];
|
||||
|
||||
export default function DocPaginatorWrapper(props) {
|
||||
const [shouldHide, setShouldHide] = React.useState(false);
|
||||
|
||||
React.useEffect(() => {
|
||||
if (typeof window === "undefined") return;
|
||||
const currentPath = window.location.pathname;
|
||||
if (BLACKLISTED_PATHS.some((path) => currentPath.includes(path))) {
|
||||
setShouldHide(true);
|
||||
}
|
||||
}, []);
|
||||
|
||||
if (!shouldHide) {
|
||||
// eslint-disable-next-line react/jsx-props-no-spreading
|
||||
return <DocPaginator {...props} />;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
@ -0,0 +1,24 @@
|
||||
"""
|
||||
Chains module for langchain_community
|
||||
|
||||
This module contains the community chains.
|
||||
"""
|
||||
|
||||
import importlib
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langchain_community.chains.pebblo_retrieval.base import PebbloRetrievalQA
|
||||
|
||||
__all__ = ["PebbloRetrievalQA"]
|
||||
|
||||
_module_lookup = {
|
||||
"PebbloRetrievalQA": "langchain_community.chains.pebblo_retrieval.base"
|
||||
}
|
||||
|
||||
|
||||
def __getattr__(name: str) -> Any:
|
||||
if name in _module_lookup:
|
||||
module = importlib.import_module(_module_lookup[name])
|
||||
return getattr(module, name)
|
||||
raise AttributeError(f"module {__name__} has no attribute {name}")
|
||||
@ -0,0 +1,218 @@
|
||||
"""
|
||||
Pebblo Retrieval Chain with Identity & Semantic Enforcement for question-answering
|
||||
against a vector database.
|
||||
"""
|
||||
|
||||
import inspect
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
|
||||
from langchain_core.callbacks import (
|
||||
AsyncCallbackManagerForChainRun,
|
||||
CallbackManagerForChainRun,
|
||||
)
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.language_models import BaseLanguageModel
|
||||
from langchain_core.pydantic_v1 import Extra, Field, validator
|
||||
from langchain_core.vectorstores import VectorStoreRetriever
|
||||
|
||||
from langchain_community.chains.pebblo_retrieval.enforcement_filters import (
|
||||
SUPPORTED_VECTORSTORES,
|
||||
set_enforcement_filters,
|
||||
)
|
||||
from langchain_community.chains.pebblo_retrieval.models import (
|
||||
AuthContext,
|
||||
SemanticContext,
|
||||
)
|
||||
|
||||
|
||||
class PebbloRetrievalQA(Chain):
|
||||
"""
|
||||
Retrieval Chain with Identity & Semantic Enforcement for question-answering
|
||||
against a vector database.
|
||||
"""
|
||||
|
||||
combine_documents_chain: BaseCombineDocumentsChain
|
||||
"""Chain to use to combine the documents."""
|
||||
input_key: str = "query" #: :meta private:
|
||||
output_key: str = "result" #: :meta private:
|
||||
return_source_documents: bool = False
|
||||
"""Return the source documents or not."""
|
||||
|
||||
retriever: VectorStoreRetriever = Field(exclude=True)
|
||||
"""VectorStore to use for retrieval."""
|
||||
auth_context_key: str = "auth_context" #: :meta private:
|
||||
"""Authentication context for identity enforcement."""
|
||||
semantic_context_key: str = "semantic_context" #: :meta private:
|
||||
"""Semantic context for semantic enforcement."""
|
||||
|
||||
def _call(
|
||||
self,
|
||||
inputs: Dict[str, Any],
|
||||
run_manager: Optional[CallbackManagerForChainRun] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""Run get_relevant_text and llm on input query.
|
||||
|
||||
If chain has 'return_source_documents' as 'True', returns
|
||||
the retrieved documents as well under the key 'source_documents'.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
res = indexqa({'query': 'This is my query'})
|
||||
answer, docs = res['result'], res['source_documents']
|
||||
"""
|
||||
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
|
||||
question = inputs[self.input_key]
|
||||
auth_context = inputs.get(self.auth_context_key)
|
||||
semantic_context = inputs.get(self.semantic_context_key)
|
||||
accepts_run_manager = (
|
||||
"run_manager" in inspect.signature(self._get_docs).parameters
|
||||
)
|
||||
if accepts_run_manager:
|
||||
docs = self._get_docs(
|
||||
question, auth_context, semantic_context, run_manager=_run_manager
|
||||
)
|
||||
else:
|
||||
docs = self._get_docs(question, auth_context, semantic_context) # type: ignore[call-arg]
|
||||
answer = self.combine_documents_chain.run(
|
||||
input_documents=docs, question=question, callbacks=_run_manager.get_child()
|
||||
)
|
||||
|
||||
if self.return_source_documents:
|
||||
return {self.output_key: answer, "source_documents": docs}
|
||||
else:
|
||||
return {self.output_key: answer}
|
||||
|
||||
async def _acall(
|
||||
self,
|
||||
inputs: Dict[str, Any],
|
||||
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""Run get_relevant_text and llm on input query.
|
||||
|
||||
If chain has 'return_source_documents' as 'True', returns
|
||||
the retrieved documents as well under the key 'source_documents'.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
res = indexqa({'query': 'This is my query'})
|
||||
answer, docs = res['result'], res['source_documents']
|
||||
"""
|
||||
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
|
||||
question = inputs[self.input_key]
|
||||
auth_context = inputs.get(self.auth_context_key)
|
||||
semantic_context = inputs.get(self.semantic_context_key)
|
||||
accepts_run_manager = (
|
||||
"run_manager" in inspect.signature(self._aget_docs).parameters
|
||||
)
|
||||
if accepts_run_manager:
|
||||
docs = await self._aget_docs(
|
||||
question, auth_context, semantic_context, run_manager=_run_manager
|
||||
)
|
||||
else:
|
||||
docs = await self._aget_docs(question, auth_context, semantic_context) # type: ignore[call-arg]
|
||||
answer = await self.combine_documents_chain.arun(
|
||||
input_documents=docs, question=question, callbacks=_run_manager.get_child()
|
||||
)
|
||||
|
||||
if self.return_source_documents:
|
||||
return {self.output_key: answer, "source_documents": docs}
|
||||
else:
|
||||
return {self.output_key: answer}
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
arbitrary_types_allowed = True
|
||||
allow_population_by_field_name = True
|
||||
|
||||
@property
|
||||
def input_keys(self) -> List[str]:
|
||||
"""Input keys.
|
||||
|
||||
:meta private:
|
||||
"""
|
||||
return [self.input_key, self.auth_context_key, self.semantic_context_key]
|
||||
|
||||
@property
|
||||
def output_keys(self) -> List[str]:
|
||||
"""Output keys.
|
||||
|
||||
:meta private:
|
||||
"""
|
||||
_output_keys = [self.output_key]
|
||||
if self.return_source_documents:
|
||||
_output_keys += ["source_documents"]
|
||||
return _output_keys
|
||||
|
||||
@property
|
||||
def _chain_type(self) -> str:
|
||||
"""Return the chain type."""
|
||||
return "pebblo_retrieval_qa"
|
||||
|
||||
@classmethod
|
||||
def from_chain_type(
|
||||
cls,
|
||||
llm: BaseLanguageModel,
|
||||
chain_type: str = "stuff",
|
||||
chain_type_kwargs: Optional[dict] = None,
|
||||
**kwargs: Any,
|
||||
) -> "PebbloRetrievalQA":
|
||||
"""Load chain from chain type."""
|
||||
from langchain.chains.question_answering import load_qa_chain
|
||||
|
||||
_chain_type_kwargs = chain_type_kwargs or {}
|
||||
combine_documents_chain = load_qa_chain(
|
||||
llm, chain_type=chain_type, **_chain_type_kwargs
|
||||
)
|
||||
return cls(combine_documents_chain=combine_documents_chain, **kwargs)
|
||||
|
||||
@validator("retriever", pre=True, always=True)
|
||||
def validate_vectorstore(
|
||||
cls, retriever: VectorStoreRetriever
|
||||
) -> VectorStoreRetriever:
|
||||
"""
|
||||
Validate that the vectorstore of the retriever is supported vectorstores.
|
||||
"""
|
||||
if not any(
|
||||
isinstance(retriever.vectorstore, supported_class)
|
||||
for supported_class in SUPPORTED_VECTORSTORES
|
||||
):
|
||||
raise ValueError(
|
||||
f"Vectorstore must be an instance of one of the supported "
|
||||
f"vectorstores: {SUPPORTED_VECTORSTORES}. "
|
||||
f"Got {type(retriever.vectorstore).__name__} instead."
|
||||
)
|
||||
return retriever
|
||||
|
||||
def _get_docs(
|
||||
self,
|
||||
question: str,
|
||||
auth_context: Optional[AuthContext],
|
||||
semantic_context: Optional[SemanticContext],
|
||||
*,
|
||||
run_manager: CallbackManagerForChainRun,
|
||||
) -> List[Document]:
|
||||
"""Get docs."""
|
||||
set_enforcement_filters(self.retriever, auth_context, semantic_context)
|
||||
return self.retriever.get_relevant_documents(
|
||||
question, callbacks=run_manager.get_child()
|
||||
)
|
||||
|
||||
async def _aget_docs(
|
||||
self,
|
||||
question: str,
|
||||
auth_context: Optional[AuthContext],
|
||||
semantic_context: Optional[SemanticContext],
|
||||
*,
|
||||
run_manager: AsyncCallbackManagerForChainRun,
|
||||
) -> List[Document]:
|
||||
"""Get docs."""
|
||||
set_enforcement_filters(self.retriever, auth_context, semantic_context)
|
||||
return await self.retriever.aget_relevant_documents(
|
||||
question, callbacks=run_manager.get_child()
|
||||
)
|
||||
@ -0,0 +1,265 @@
|
||||
"""
|
||||
Identity & Semantic Enforcement filters for PebbloRetrievalQA chain:
|
||||
|
||||
This module contains methods for applying Identity and Semantic Enforcement filters
|
||||
in the PebbloRetrievalQA chain.
|
||||
These filters are used to control the retrieval of documents based on authorization and
|
||||
semantic context.
|
||||
The Identity Enforcement filter ensures that only authorized identities can access
|
||||
certain documents, while the Semantic Enforcement filter controls document retrieval
|
||||
based on semantic context.
|
||||
|
||||
The methods in this module are designed to work with different types of vector stores.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from typing import List, Optional, Union
|
||||
|
||||
from langchain_core.vectorstores import VectorStoreRetriever
|
||||
|
||||
from langchain_community.chains.pebblo_retrieval.models import (
|
||||
AuthContext,
|
||||
SemanticContext,
|
||||
)
|
||||
from langchain_community.vectorstores import Pinecone, Qdrant
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
SUPPORTED_VECTORSTORES = [Pinecone, Qdrant]
|
||||
|
||||
|
||||
def set_enforcement_filters(
|
||||
retriever: VectorStoreRetriever,
|
||||
auth_context: Optional[AuthContext],
|
||||
semantic_context: Optional[SemanticContext],
|
||||
) -> None:
|
||||
"""
|
||||
Set identity and semantic enforcement filters in the retriever.
|
||||
"""
|
||||
if auth_context is not None:
|
||||
_set_identity_enforcement_filter(retriever, auth_context)
|
||||
if semantic_context is not None:
|
||||
_set_semantic_enforcement_filter(retriever, semantic_context)
|
||||
|
||||
|
||||
def _apply_qdrant_semantic_filter(
|
||||
search_kwargs: dict, semantic_context: Optional[SemanticContext]
|
||||
) -> None:
|
||||
"""
|
||||
Set semantic enforcement filter in search_kwargs for Qdrant vectorstore.
|
||||
"""
|
||||
try:
|
||||
from qdrant_client.http import models as rest
|
||||
except ImportError as e:
|
||||
raise ValueError(
|
||||
"Could not import `qdrant-client.http` python package. "
|
||||
"Please install it with `pip install qdrant-client`."
|
||||
) from e
|
||||
|
||||
# Create a semantic enforcement filter condition
|
||||
semantic_filters: List[
|
||||
Union[
|
||||
rest.FieldCondition,
|
||||
rest.IsEmptyCondition,
|
||||
rest.IsNullCondition,
|
||||
rest.HasIdCondition,
|
||||
rest.NestedCondition,
|
||||
rest.Filter,
|
||||
]
|
||||
] = []
|
||||
|
||||
if (
|
||||
semantic_context is not None
|
||||
and semantic_context.pebblo_semantic_topics is not None
|
||||
):
|
||||
semantic_topics_filter = rest.FieldCondition(
|
||||
key="metadata.pebblo_semantic_topics",
|
||||
match=rest.MatchAny(any=semantic_context.pebblo_semantic_topics.deny),
|
||||
)
|
||||
semantic_filters.append(semantic_topics_filter)
|
||||
if (
|
||||
semantic_context is not None
|
||||
and semantic_context.pebblo_semantic_entities is not None
|
||||
):
|
||||
semantic_entities_filter = rest.FieldCondition(
|
||||
key="metadata.pebblo_semantic_entities",
|
||||
match=rest.MatchAny(any=semantic_context.pebblo_semantic_entities.deny),
|
||||
)
|
||||
semantic_filters.append(semantic_entities_filter)
|
||||
|
||||
# If 'filter' already exists in search_kwargs
|
||||
if "filter" in search_kwargs:
|
||||
existing_filter: rest.Filter = search_kwargs["filter"]
|
||||
|
||||
# Check if existing_filter is a qdrant-client filter
|
||||
if isinstance(existing_filter, rest.Filter):
|
||||
# If 'must_not' condition exists in the existing filter
|
||||
if isinstance(existing_filter.must_not, list):
|
||||
# Warn if 'pebblo_semantic_topics' or 'pebblo_semantic_entities'
|
||||
# filter is overridden
|
||||
new_must_not_conditions: List[
|
||||
Union[
|
||||
rest.FieldCondition,
|
||||
rest.IsEmptyCondition,
|
||||
rest.IsNullCondition,
|
||||
rest.HasIdCondition,
|
||||
rest.NestedCondition,
|
||||
rest.Filter,
|
||||
]
|
||||
] = []
|
||||
# Drop semantic filter conditions if already present
|
||||
for condition in existing_filter.must_not:
|
||||
if hasattr(condition, "key"):
|
||||
if condition.key == "metadata.pebblo_semantic_topics":
|
||||
continue
|
||||
if condition.key == "metadata.pebblo_semantic_entities":
|
||||
continue
|
||||
new_must_not_conditions.append(condition)
|
||||
# Add semantic enforcement filters to 'must_not' conditions
|
||||
existing_filter.must_not = new_must_not_conditions
|
||||
existing_filter.must_not.extend(semantic_filters)
|
||||
else:
|
||||
# Set 'must_not' condition with semantic enforcement filters
|
||||
existing_filter.must_not = semantic_filters
|
||||
else:
|
||||
raise TypeError(
|
||||
"Using dict as a `filter` is deprecated. "
|
||||
"Please use qdrant-client filters directly: "
|
||||
"https://qdrant.tech/documentation/concepts/filtering/"
|
||||
)
|
||||
else:
|
||||
# If 'filter' does not exist in search_kwargs, create it
|
||||
search_kwargs["filter"] = rest.Filter(must_not=semantic_filters)
|
||||
|
||||
|
||||
def _apply_qdrant_authorization_filter(
|
||||
search_kwargs: dict, auth_context: Optional[AuthContext]
|
||||
) -> None:
|
||||
"""
|
||||
Set identity enforcement filter in search_kwargs for Qdrant vectorstore.
|
||||
"""
|
||||
try:
|
||||
from qdrant_client.http import models as rest
|
||||
except ImportError as e:
|
||||
raise ValueError(
|
||||
"Could not import `qdrant-client.http` python package. "
|
||||
"Please install it with `pip install qdrant-client`."
|
||||
) from e
|
||||
|
||||
if auth_context is not None:
|
||||
# Create a identity enforcement filter condition
|
||||
identity_enforcement_filter = rest.FieldCondition(
|
||||
key="metadata.authorized_identities",
|
||||
match=rest.MatchAny(any=auth_context.user_auth),
|
||||
)
|
||||
else:
|
||||
return
|
||||
|
||||
# If 'filter' already exists in search_kwargs
|
||||
if "filter" in search_kwargs:
|
||||
existing_filter: rest.Filter = search_kwargs["filter"]
|
||||
|
||||
# Check if existing_filter is a qdrant-client filter
|
||||
if isinstance(existing_filter, rest.Filter):
|
||||
# If 'must' exists in the existing filter
|
||||
if existing_filter.must:
|
||||
new_must_conditions: List[
|
||||
Union[
|
||||
rest.FieldCondition,
|
||||
rest.IsEmptyCondition,
|
||||
rest.IsNullCondition,
|
||||
rest.HasIdCondition,
|
||||
rest.NestedCondition,
|
||||
rest.Filter,
|
||||
]
|
||||
] = []
|
||||
# Drop 'authorized_identities' filter condition if already present
|
||||
for condition in existing_filter.must:
|
||||
if (
|
||||
hasattr(condition, "key")
|
||||
and condition.key == "metadata.authorized_identities"
|
||||
):
|
||||
continue
|
||||
new_must_conditions.append(condition)
|
||||
|
||||
# Add identity enforcement filter to 'must' conditions
|
||||
existing_filter.must = new_must_conditions
|
||||
existing_filter.must.append(identity_enforcement_filter)
|
||||
else:
|
||||
# Set 'must' condition with identity enforcement filter
|
||||
existing_filter.must = [identity_enforcement_filter]
|
||||
else:
|
||||
raise TypeError(
|
||||
"Using dict as a `filter` is deprecated. "
|
||||
"Please use qdrant-client filters directly: "
|
||||
"https://qdrant.tech/documentation/concepts/filtering/"
|
||||
)
|
||||
else:
|
||||
# If 'filter' does not exist in search_kwargs, create it
|
||||
search_kwargs["filter"] = rest.Filter(must=[identity_enforcement_filter])
|
||||
|
||||
|
||||
def _apply_pinecone_semantic_filter(
|
||||
search_kwargs: dict, semantic_context: Optional[SemanticContext]
|
||||
) -> None:
|
||||
"""
|
||||
Set semantic enforcement filter in search_kwargs for Pinecone vectorstore.
|
||||
"""
|
||||
# Check if semantic_context is provided
|
||||
semantic_context = semantic_context
|
||||
if semantic_context is not None:
|
||||
if semantic_context.pebblo_semantic_topics is not None:
|
||||
# Add pebblo_semantic_topics filter to search_kwargs
|
||||
search_kwargs.setdefault("filter", {})["pebblo_semantic_topics"] = {
|
||||
"$nin": semantic_context.pebblo_semantic_topics.deny
|
||||
}
|
||||
|
||||
if semantic_context.pebblo_semantic_entities is not None:
|
||||
# Add pebblo_semantic_entities filter to search_kwargs
|
||||
search_kwargs.setdefault("filter", {})["pebblo_semantic_entities"] = {
|
||||
"$nin": semantic_context.pebblo_semantic_entities.deny
|
||||
}
|
||||
|
||||
|
||||
def _apply_pinecone_authorization_filter(
|
||||
search_kwargs: dict, auth_context: Optional[AuthContext]
|
||||
) -> None:
|
||||
"""
|
||||
Set identity enforcement filter in search_kwargs for Pinecone vectorstore.
|
||||
"""
|
||||
if auth_context is not None:
|
||||
search_kwargs.setdefault("filter", {})["authorized_identities"] = {
|
||||
"$in": auth_context.user_auth
|
||||
}
|
||||
|
||||
|
||||
def _set_identity_enforcement_filter(
|
||||
retriever: VectorStoreRetriever, auth_context: Optional[AuthContext]
|
||||
) -> None:
|
||||
"""
|
||||
Set identity enforcement filter in search_kwargs.
|
||||
|
||||
This method sets the identity enforcement filter in the search_kwargs
|
||||
of the retriever based on the type of the vectorstore.
|
||||
"""
|
||||
search_kwargs = retriever.search_kwargs
|
||||
if isinstance(retriever.vectorstore, Pinecone):
|
||||
_apply_pinecone_authorization_filter(search_kwargs, auth_context)
|
||||
elif isinstance(retriever.vectorstore, Qdrant):
|
||||
_apply_qdrant_authorization_filter(search_kwargs, auth_context)
|
||||
|
||||
|
||||
def _set_semantic_enforcement_filter(
|
||||
retriever: VectorStoreRetriever, semantic_context: Optional[SemanticContext]
|
||||
) -> None:
|
||||
"""
|
||||
Set semantic enforcement filter in search_kwargs.
|
||||
|
||||
This method sets the semantic enforcement filter in the search_kwargs
|
||||
of the retriever based on the type of the vectorstore.
|
||||
"""
|
||||
search_kwargs = retriever.search_kwargs
|
||||
if isinstance(retriever.vectorstore, Pinecone):
|
||||
_apply_pinecone_semantic_filter(search_kwargs, semantic_context)
|
||||
elif isinstance(retriever.vectorstore, Qdrant):
|
||||
_apply_qdrant_semantic_filter(search_kwargs, semantic_context)
|
||||
@ -0,0 +1,62 @@
|
||||
"""Models for the PebbloRetrievalQA chain."""
|
||||
|
||||
from typing import Any, List, Optional
|
||||
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
|
||||
|
||||
class AuthContext(BaseModel):
|
||||
"""Class for an authorization context."""
|
||||
|
||||
name: Optional[str] = None
|
||||
user_id: str
|
||||
user_auth: List[str]
|
||||
"""List of user authorizations, which may include their User ID and
|
||||
the groups they are part of"""
|
||||
|
||||
|
||||
class SemanticEntities(BaseModel):
|
||||
"""Class for a semantic entity filter."""
|
||||
|
||||
deny: List[str]
|
||||
|
||||
|
||||
class SemanticTopics(BaseModel):
|
||||
"""Class for a semantic topic filter."""
|
||||
|
||||
deny: List[str]
|
||||
|
||||
|
||||
class SemanticContext(BaseModel):
|
||||
"""Class for a semantic context."""
|
||||
|
||||
pebblo_semantic_entities: Optional[SemanticEntities] = None
|
||||
pebblo_semantic_topics: Optional[SemanticTopics] = None
|
||||
|
||||
def __init__(self, **data: Any) -> None:
|
||||
super().__init__(**data)
|
||||
|
||||
# Validate semantic_context
|
||||
if (
|
||||
self.pebblo_semantic_entities is None
|
||||
and self.pebblo_semantic_topics is None
|
||||
):
|
||||
raise ValueError(
|
||||
"semantic_context must contain 'pebblo_semantic_entities' or "
|
||||
"'pebblo_semantic_topics'"
|
||||
)
|
||||
|
||||
|
||||
class ChainInput(BaseModel):
|
||||
"""Input for PebbloRetrievalQA chain."""
|
||||
|
||||
query: str
|
||||
auth_context: Optional[AuthContext] = None
|
||||
semantic_context: Optional[SemanticContext] = None
|
||||
|
||||
def dict(self, **kwargs: Any) -> dict:
|
||||
base_dict = super().dict(**kwargs)
|
||||
# Keep auth_context and semantic_context as it is(Pydantic models)
|
||||
base_dict["auth_context"] = self.auth_context
|
||||
base_dict["semantic_context"] = self.semantic_context
|
||||
return base_dict
|
||||
@ -0,0 +1,76 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, Dict, Optional, Sequence
|
||||
|
||||
from langchain_core.callbacks.manager import Callbacks
|
||||
from langchain_core.documents import BaseDocumentCompressor, Document
|
||||
from langchain_core.pydantic_v1 import Extra, root_validator
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from flashrank import Ranker, RerankRequest
|
||||
else:
|
||||
# Avoid pydantic annotation issues when actually instantiating
|
||||
# while keeping this import optional
|
||||
try:
|
||||
from flashrank import Ranker, RerankRequest
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
DEFAULT_MODEL_NAME = "ms-marco-MultiBERT-L-12"
|
||||
|
||||
|
||||
class FlashrankRerank(BaseDocumentCompressor):
|
||||
"""Document compressor using Flashrank interface."""
|
||||
|
||||
client: Ranker
|
||||
"""Flashrank client to use for compressing documents"""
|
||||
top_n: int = 3
|
||||
"""Number of documents to return."""
|
||||
model: Optional[str] = None
|
||||
"""Model to use for reranking."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@root_validator(pre=True)
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that api key and python package exists in environment."""
|
||||
try:
|
||||
from flashrank import Ranker
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import flashrank python package. "
|
||||
"Please install it with `pip install flashrank`."
|
||||
)
|
||||
|
||||
values["model"] = values.get("model", DEFAULT_MODEL_NAME)
|
||||
values["client"] = Ranker(model_name=values["model"])
|
||||
return values
|
||||
|
||||
def compress_documents(
|
||||
self,
|
||||
documents: Sequence[Document],
|
||||
query: str,
|
||||
callbacks: Optional[Callbacks] = None,
|
||||
) -> Sequence[Document]:
|
||||
passages = [
|
||||
{"id": i, "text": doc.page_content, "meta": doc.metadata}
|
||||
for i, doc in enumerate(documents)
|
||||
]
|
||||
|
||||
rerank_request = RerankRequest(query=query, passages=passages)
|
||||
rerank_response = self.client.rerank(rerank_request)[: self.top_n]
|
||||
final_results = []
|
||||
|
||||
for r in rerank_response:
|
||||
metadata = r["meta"]
|
||||
metadata["relevance_score"] = r["score"]
|
||||
doc = Document(
|
||||
page_content=r["text"],
|
||||
metadata=metadata,
|
||||
)
|
||||
final_results.append(doc)
|
||||
return final_results
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue