forked from Archives/langchain
Doc refactor (#6300)
Co-authored-by: jacoblee93 <jacoblee93@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>searx_updates
Davis Chase
11 months ago
committed by
GitHub
parent
94c82a189d
commit
87e502c6bc
@ -1,38 +0,0 @@
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name: linkcheck
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on:
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push:
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branches: [master]
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pull_request:
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paths:
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- 'docs/**'
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env:
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POETRY_VERSION: "1.4.2"
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jobs:
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build:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version:
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- "3.11"
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steps:
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- uses: actions/checkout@v3
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- name: Install poetry
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run: |
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pipx install poetry==$POETRY_VERSION
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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cache: poetry
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- name: Install dependencies
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run: |
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poetry install --with docs
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- name: Build the docs
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run: |
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make docs_build
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- name: Analyzing the docs with linkcheck
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run: |
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make docs_linkcheck
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@ -0,0 +1,4 @@
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[submodule "docs/_docs_skeleton"]
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path = docs/_docs_skeleton
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url = https://github.com/langchain-ai/langchain-shared-docs
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branch = main
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@ -1,16 +1,13 @@
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Indexes
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Data connection
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==============
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Indexes refer to ways to structure documents so that LLMs can best interact with them.
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LangChain has a number of modules that help you load, structure, store, and retrieve documents.
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.. toctree::
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:maxdepth: 1
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:glob:
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modules/docstore
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modules/text_splitter
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modules/document_loaders
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modules/document_transformers
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modules/embeddings
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modules/vectorstores
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modules/retrievers
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modules/document_compressors
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modules/document_transformers
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@ -0,0 +1,29 @@
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API Reference
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==========================
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| Full documentation on all methods, classes, and APIs in the LangChain Python package.
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.. toctree::
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:maxdepth: 1
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:caption: Abstractions
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./modules/base_classes.rst
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.. toctree::
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:maxdepth: 1
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:caption: Core
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./model_io.rst
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./data_connection.rst
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./modules/chains.rst
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./agents.rst
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./modules/memory.rst
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./modules/callbacks.rst
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.. toctree::
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:maxdepth: 1
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:caption: Additional
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./modules/utilities.rst
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./modules/experimental.rst
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@ -0,0 +1,12 @@
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Model I/O
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==============
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LangChain provides interfaces and integrations for working with language models.
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.. toctree::
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:maxdepth: 1
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:glob:
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./prompts.rst
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./models.rst
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./modules/output_parsers.rst
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@ -0,0 +1,5 @@
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Base classes
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========================
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.. automodule:: langchain.schema
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:inherited-members:
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@ -0,0 +1,7 @@
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Callbacks
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=======================
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.. automodule:: langchain.callbacks
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:members:
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:undoc-members:
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@ -1,5 +1,6 @@
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PromptTemplates
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Prompt Templates
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========================
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.. automodule:: langchain.prompts
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:members:
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:undoc-members:
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@ -0,0 +1,14 @@
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Retrievers
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===============================
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.. automodule:: langchain.retrievers
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:members:
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:undoc-members:
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Document compressors
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-------------------------------
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.. automodule:: langchain.retrievers.document_compressors
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:members:
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:undoc-members:
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@ -1,231 +0,0 @@
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# Dependents
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Dependents stats for `hwchase17/langchain`
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[](https://github.com/hwchase17/langchain/network/dependents)
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[&message=212&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
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[&message=7272&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
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[&message=19095&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
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[update: 2023-06-05; only dependent repositories with Stars > 100]
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| Repository | Stars |
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||||
| :-------- | -----: |
|
||||
|[openai/openai-cookbook](https://github.com/openai/openai-cookbook) | 38024 |
|
||||
|[LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant) | 33609 |
|
||||
|[microsoft/TaskMatrix](https://github.com/microsoft/TaskMatrix) | 33136 |
|
||||
|[hpcaitech/ColossalAI](https://github.com/hpcaitech/ColossalAI) | 30032 |
|
||||
|[imartinez/privateGPT](https://github.com/imartinez/privateGPT) | 28094 |
|
||||
|[reworkd/AgentGPT](https://github.com/reworkd/AgentGPT) | 23430 |
|
||||
|[openai/chatgpt-retrieval-plugin](https://github.com/openai/chatgpt-retrieval-plugin) | 17942 |
|
||||
|[jerryjliu/llama_index](https://github.com/jerryjliu/llama_index) | 16697 |
|
||||
|[mindsdb/mindsdb](https://github.com/mindsdb/mindsdb) | 16410 |
|
||||
|[mlflow/mlflow](https://github.com/mlflow/mlflow) | 14517 |
|
||||
|[GaiZhenbiao/ChuanhuChatGPT](https://github.com/GaiZhenbiao/ChuanhuChatGPT) | 10793 |
|
||||
|[databrickslabs/dolly](https://github.com/databrickslabs/dolly) | 10155 |
|
||||
|[openai/evals](https://github.com/openai/evals) | 10076 |
|
||||
|[AIGC-Audio/AudioGPT](https://github.com/AIGC-Audio/AudioGPT) | 8619 |
|
||||
|[logspace-ai/langflow](https://github.com/logspace-ai/langflow) | 8211 |
|
||||
|[imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM) | 8154 |
|
||||
|[PromtEngineer/localGPT](https://github.com/PromtEngineer/localGPT) | 6853 |
|
||||
|[StanGirard/quivr](https://github.com/StanGirard/quivr) | 6830 |
|
||||
|[PipedreamHQ/pipedream](https://github.com/PipedreamHQ/pipedream) | 6520 |
|
||||
|[go-skynet/LocalAI](https://github.com/go-skynet/LocalAI) | 6018 |
|
||||
|[arc53/DocsGPT](https://github.com/arc53/DocsGPT) | 5643 |
|
||||
|[e2b-dev/e2b](https://github.com/e2b-dev/e2b) | 5075 |
|
||||
|[langgenius/dify](https://github.com/langgenius/dify) | 4281 |
|
||||
|[nsarrazin/serge](https://github.com/nsarrazin/serge) | 4228 |
|
||||
|[zauberzeug/nicegui](https://github.com/zauberzeug/nicegui) | 4084 |
|
||||
|[madawei2699/myGPTReader](https://github.com/madawei2699/myGPTReader) | 4039 |
|
||||
|[wenda-LLM/wenda](https://github.com/wenda-LLM/wenda) | 3871 |
|
||||
|[GreyDGL/PentestGPT](https://github.com/GreyDGL/PentestGPT) | 3837 |
|
||||
|[zilliztech/GPTCache](https://github.com/zilliztech/GPTCache) | 3625 |
|
||||
|[csunny/DB-GPT](https://github.com/csunny/DB-GPT) | 3545 |
|
||||
|[gkamradt/langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) | 3404 |
|
||||
|[mmabrouk/chatgpt-wrapper](https://github.com/mmabrouk/chatgpt-wrapper) | 3303 |
|
||||
|[postgresml/postgresml](https://github.com/postgresml/postgresml) | 3052 |
|
||||
|[marqo-ai/marqo](https://github.com/marqo-ai/marqo) | 3014 |
|
||||
|[MineDojo/Voyager](https://github.com/MineDojo/Voyager) | 2945 |
|
||||
|[PrefectHQ/marvin](https://github.com/PrefectHQ/marvin) | 2761 |
|
||||
|[project-baize/baize-chatbot](https://github.com/project-baize/baize-chatbot) | 2673 |
|
||||
|[hwchase17/chat-langchain](https://github.com/hwchase17/chat-langchain) | 2589 |
|
||||
|[whitead/paper-qa](https://github.com/whitead/paper-qa) | 2572 |
|
||||
|[Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo) | 2366 |
|
||||
|[GerevAI/gerev](https://github.com/GerevAI/gerev) | 2330 |
|
||||
|[OpenGVLab/InternGPT](https://github.com/OpenGVLab/InternGPT) | 2289 |
|
||||
|[ParisNeo/gpt4all-ui](https://github.com/ParisNeo/gpt4all-ui) | 2159 |
|
||||
|[OpenBMB/BMTools](https://github.com/OpenBMB/BMTools) | 2158 |
|
||||
|[guangzhengli/ChatFiles](https://github.com/guangzhengli/ChatFiles) | 2005 |
|
||||
|[h2oai/h2ogpt](https://github.com/h2oai/h2ogpt) | 1939 |
|
||||
|[Farama-Foundation/PettingZoo](https://github.com/Farama-Foundation/PettingZoo) | 1845 |
|
||||
|[OpenGVLab/Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) | 1749 |
|
||||
|[IntelligenzaArtificiale/Free-Auto-GPT](https://github.com/IntelligenzaArtificiale/Free-Auto-GPT) | 1740 |
|
||||
|[Unstructured-IO/unstructured](https://github.com/Unstructured-IO/unstructured) | 1628 |
|
||||
|[hwchase17/notion-qa](https://github.com/hwchase17/notion-qa) | 1607 |
|
||||
|[NVIDIA/NeMo-Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) | 1544 |
|
||||
|[SamurAIGPT/privateGPT](https://github.com/SamurAIGPT/privateGPT) | 1543 |
|
||||
|[paulpierre/RasaGPT](https://github.com/paulpierre/RasaGPT) | 1526 |
|
||||
|[yanqiangmiffy/Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) | 1485 |
|
||||
|[Kav-K/GPTDiscord](https://github.com/Kav-K/GPTDiscord) | 1402 |
|
||||
|[vocodedev/vocode-python](https://github.com/vocodedev/vocode-python) | 1387 |
|
||||
|[Chainlit/chainlit](https://github.com/Chainlit/chainlit) | 1336 |
|
||||
|[lunasec-io/lunasec](https://github.com/lunasec-io/lunasec) | 1323 |
|
||||
|[psychic-api/psychic](https://github.com/psychic-api/psychic) | 1248 |
|
||||
|[agiresearch/OpenAGI](https://github.com/agiresearch/OpenAGI) | 1208 |
|
||||
|[jina-ai/thinkgpt](https://github.com/jina-ai/thinkgpt) | 1193 |
|
||||
|[thomas-yanxin/LangChain-ChatGLM-Webui](https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui) | 1182 |
|
||||
|[ttengwang/Caption-Anything](https://github.com/ttengwang/Caption-Anything) | 1137 |
|
||||
|[jina-ai/dev-gpt](https://github.com/jina-ai/dev-gpt) | 1135 |
|
||||
|[greshake/llm-security](https://github.com/greshake/llm-security) | 1086 |
|
||||
|[keephq/keep](https://github.com/keephq/keep) | 1063 |
|
||||
|[juncongmoo/chatllama](https://github.com/juncongmoo/chatllama) | 1037 |
|
||||
|[richardyc/Chrome-GPT](https://github.com/richardyc/Chrome-GPT) | 1035 |
|
||||
|[visual-openllm/visual-openllm](https://github.com/visual-openllm/visual-openllm) | 997 |
|
||||
|[mmz-001/knowledge_gpt](https://github.com/mmz-001/knowledge_gpt) | 995 |
|
||||
|[jina-ai/langchain-serve](https://github.com/jina-ai/langchain-serve) | 949 |
|
||||
|[irgolic/AutoPR](https://github.com/irgolic/AutoPR) | 936 |
|
||||
|[microsoft/X-Decoder](https://github.com/microsoft/X-Decoder) | 908 |
|
||||
|[poe-platform/api-bot-tutorial](https://github.com/poe-platform/api-bot-tutorial) | 902 |
|
||||
|[peterw/Chat-with-Github-Repo](https://github.com/peterw/Chat-with-Github-Repo) | 875 |
|
||||
|[cirediatpl/FigmaChain](https://github.com/cirediatpl/FigmaChain) | 822 |
|
||||
|[homanp/superagent](https://github.com/homanp/superagent) | 806 |
|
||||
|[seanpixel/Teenage-AGI](https://github.com/seanpixel/Teenage-AGI) | 800 |
|
||||
|[chatarena/chatarena](https://github.com/chatarena/chatarena) | 796 |
|
||||
|[hashintel/hash](https://github.com/hashintel/hash) | 795 |
|
||||
|[SamurAIGPT/Camel-AutoGPT](https://github.com/SamurAIGPT/Camel-AutoGPT) | 786 |
|
||||
|[rlancemartin/auto-evaluator](https://github.com/rlancemartin/auto-evaluator) | 770 |
|
||||
|[corca-ai/EVAL](https://github.com/corca-ai/EVAL) | 769 |
|
||||
|[101dotxyz/GPTeam](https://github.com/101dotxyz/GPTeam) | 755 |
|
||||
|[noahshinn024/reflexion](https://github.com/noahshinn024/reflexion) | 706 |
|
||||
|[eyurtsev/kor](https://github.com/eyurtsev/kor) | 695 |
|
||||
|[cheshire-cat-ai/core](https://github.com/cheshire-cat-ai/core) | 681 |
|
||||
|[e-johnstonn/BriefGPT](https://github.com/e-johnstonn/BriefGPT) | 656 |
|
||||
|[run-llama/llama-lab](https://github.com/run-llama/llama-lab) | 635 |
|
||||
|[griptape-ai/griptape](https://github.com/griptape-ai/griptape) | 583 |
|
||||
|[namuan/dr-doc-search](https://github.com/namuan/dr-doc-search) | 555 |
|
||||
|[getmetal/motorhead](https://github.com/getmetal/motorhead) | 550 |
|
||||
|[kreneskyp/ix](https://github.com/kreneskyp/ix) | 543 |
|
||||
|[hwchase17/chat-your-data](https://github.com/hwchase17/chat-your-data) | 510 |
|
||||
|[Anil-matcha/ChatPDF](https://github.com/Anil-matcha/ChatPDF) | 501 |
|
||||
|[whyiyhw/chatgpt-wechat](https://github.com/whyiyhw/chatgpt-wechat) | 497 |
|
||||
|[SamurAIGPT/ChatGPT-Developer-Plugins](https://github.com/SamurAIGPT/ChatGPT-Developer-Plugins) | 496 |
|
||||
|[microsoft/PodcastCopilot](https://github.com/microsoft/PodcastCopilot) | 492 |
|
||||
|[debanjum/khoj](https://github.com/debanjum/khoj) | 485 |
|
||||
|[akshata29/chatpdf](https://github.com/akshata29/chatpdf) | 485 |
|
||||
|[langchain-ai/langchain-aiplugin](https://github.com/langchain-ai/langchain-aiplugin) | 462 |
|
||||
|[jina-ai/agentchain](https://github.com/jina-ai/agentchain) | 460 |
|
||||
|[alexanderatallah/window.ai](https://github.com/alexanderatallah/window.ai) | 457 |
|
||||
|[yeagerai/yeagerai-agent](https://github.com/yeagerai/yeagerai-agent) | 451 |
|
||||
|[mckaywrigley/repo-chat](https://github.com/mckaywrigley/repo-chat) | 446 |
|
||||
|[michaelthwan/searchGPT](https://github.com/michaelthwan/searchGPT) | 446 |
|
||||
|[mpaepper/content-chatbot](https://github.com/mpaepper/content-chatbot) | 441 |
|
||||
|[freddyaboulton/gradio-tools](https://github.com/freddyaboulton/gradio-tools) | 439 |
|
||||
|[ruoccofabrizio/azure-open-ai-embeddings-qna](https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna) | 429 |
|
||||
|[StevenGrove/GPT4Tools](https://github.com/StevenGrove/GPT4Tools) | 422 |
|
||||
|[jonra1993/fastapi-alembic-sqlmodel-async](https://github.com/jonra1993/fastapi-alembic-sqlmodel-async) | 407 |
|
||||
|[msoedov/langcorn](https://github.com/msoedov/langcorn) | 405 |
|
||||
|[amosjyng/langchain-visualizer](https://github.com/amosjyng/langchain-visualizer) | 395 |
|
||||
|[ajndkr/lanarky](https://github.com/ajndkr/lanarky) | 384 |
|
||||
|[mtenenholtz/chat-twitter](https://github.com/mtenenholtz/chat-twitter) | 376 |
|
||||
|[steamship-core/steamship-langchain](https://github.com/steamship-core/steamship-langchain) | 371 |
|
||||
|[langchain-ai/auto-evaluator](https://github.com/langchain-ai/auto-evaluator) | 365 |
|
||||
|[xuwenhao/geektime-ai-course](https://github.com/xuwenhao/geektime-ai-course) | 358 |
|
||||
|[continuum-llms/chatgpt-memory](https://github.com/continuum-llms/chatgpt-memory) | 357 |
|
||||
|[opentensor/bittensor](https://github.com/opentensor/bittensor) | 347 |
|
||||
|[showlab/VLog](https://github.com/showlab/VLog) | 345 |
|
||||
|[daodao97/chatdoc](https://github.com/daodao97/chatdoc) | 345 |
|
||||
|[logan-markewich/llama_index_starter_pack](https://github.com/logan-markewich/llama_index_starter_pack) | 332 |
|
||||
|[poe-platform/poe-protocol](https://github.com/poe-platform/poe-protocol) | 320 |
|
||||
|[explosion/spacy-llm](https://github.com/explosion/spacy-llm) | 312 |
|
||||
|[andylokandy/gpt-4-search](https://github.com/andylokandy/gpt-4-search) | 311 |
|
||||
|[alejandro-ao/langchain-ask-pdf](https://github.com/alejandro-ao/langchain-ask-pdf) | 310 |
|
||||
|[jupyterlab/jupyter-ai](https://github.com/jupyterlab/jupyter-ai) | 294 |
|
||||
|[BlackHC/llm-strategy](https://github.com/BlackHC/llm-strategy) | 283 |
|
||||
|[itamargol/openai](https://github.com/itamargol/openai) | 281 |
|
||||
|[momegas/megabots](https://github.com/momegas/megabots) | 279 |
|
||||
|[personoids/personoids-lite](https://github.com/personoids/personoids-lite) | 277 |
|
||||
|[yvann-hub/Robby-chatbot](https://github.com/yvann-hub/Robby-chatbot) | 267 |
|
||||
|[Anil-matcha/Website-to-Chatbot](https://github.com/Anil-matcha/Website-to-Chatbot) | 266 |
|
||||
|[Cheems-Seminar/grounded-segment-any-parts](https://github.com/Cheems-Seminar/grounded-segment-any-parts) | 260 |
|
||||
|[sullivan-sean/chat-langchainjs](https://github.com/sullivan-sean/chat-langchainjs) | 248 |
|
||||
|[bborn/howdoi.ai](https://github.com/bborn/howdoi.ai) | 245 |
|
||||
|[daveebbelaar/langchain-experiments](https://github.com/daveebbelaar/langchain-experiments) | 240 |
|
||||
|[MagnivOrg/prompt-layer-library](https://github.com/MagnivOrg/prompt-layer-library) | 237 |
|
||||
|[ur-whitelab/exmol](https://github.com/ur-whitelab/exmol) | 234 |
|
||||
|[conceptofmind/toolformer](https://github.com/conceptofmind/toolformer) | 234 |
|
||||
|[recalign/RecAlign](https://github.com/recalign/RecAlign) | 226 |
|
||||
|[OpenBMB/AgentVerse](https://github.com/OpenBMB/AgentVerse) | 220 |
|
||||
|[alvarosevilla95/autolang](https://github.com/alvarosevilla95/autolang) | 219 |
|
||||
|[JohnSnowLabs/nlptest](https://github.com/JohnSnowLabs/nlptest) | 216 |
|
||||
|[kaleido-lab/dolphin](https://github.com/kaleido-lab/dolphin) | 215 |
|
||||
|[truera/trulens](https://github.com/truera/trulens) | 208 |
|
||||
|[NimbleBoxAI/ChainFury](https://github.com/NimbleBoxAI/ChainFury) | 208 |
|
||||
|[airobotlab/KoChatGPT](https://github.com/airobotlab/KoChatGPT) | 207 |
|
||||
|[monarch-initiative/ontogpt](https://github.com/monarch-initiative/ontogpt) | 200 |
|
||||
|[paolorechia/learn-langchain](https://github.com/paolorechia/learn-langchain) | 195 |
|
||||
|[shaman-ai/agent-actors](https://github.com/shaman-ai/agent-actors) | 185 |
|
||||
|[Haste171/langchain-chatbot](https://github.com/Haste171/langchain-chatbot) | 184 |
|
||||
|[plchld/InsightFlow](https://github.com/plchld/InsightFlow) | 182 |
|
||||
|[su77ungr/CASALIOY](https://github.com/su77ungr/CASALIOY) | 180 |
|
||||
|[jbrukh/gpt-jargon](https://github.com/jbrukh/gpt-jargon) | 177 |
|
||||
|[benthecoder/ClassGPT](https://github.com/benthecoder/ClassGPT) | 174 |
|
||||
|[billxbf/ReWOO](https://github.com/billxbf/ReWOO) | 170 |
|
||||
|[filip-michalsky/SalesGPT](https://github.com/filip-michalsky/SalesGPT) | 168 |
|
||||
|[hwchase17/langchain-streamlit-template](https://github.com/hwchase17/langchain-streamlit-template) | 168 |
|
||||
|[radi-cho/datasetGPT](https://github.com/radi-cho/datasetGPT) | 164 |
|
||||
|[hardbyte/qabot](https://github.com/hardbyte/qabot) | 164 |
|
||||
|[gia-guar/JARVIS-ChatGPT](https://github.com/gia-guar/JARVIS-ChatGPT) | 158 |
|
||||
|[plastic-labs/tutor-gpt](https://github.com/plastic-labs/tutor-gpt) | 154 |
|
||||
|[yasyf/compress-gpt](https://github.com/yasyf/compress-gpt) | 154 |
|
||||
|[fengyuli-dev/multimedia-gpt](https://github.com/fengyuli-dev/multimedia-gpt) | 154 |
|
||||
|[ethanyanjiali/minChatGPT](https://github.com/ethanyanjiali/minChatGPT) | 153 |
|
||||
|[hwchase17/chroma-langchain](https://github.com/hwchase17/chroma-langchain) | 153 |
|
||||
|[edreisMD/plugnplai](https://github.com/edreisMD/plugnplai) | 148 |
|
||||
|[chakkaradeep/pyCodeAGI](https://github.com/chakkaradeep/pyCodeAGI) | 145 |
|
||||
|[ccurme/yolopandas](https://github.com/ccurme/yolopandas) | 145 |
|
||||
|[shamspias/customizable-gpt-chatbot](https://github.com/shamspias/customizable-gpt-chatbot) | 144 |
|
||||
|[realminchoi/babyagi-ui](https://github.com/realminchoi/babyagi-ui) | 143 |
|
||||
|[PradipNichite/Youtube-Tutorials](https://github.com/PradipNichite/Youtube-Tutorials) | 140 |
|
||||
|[gustavz/DataChad](https://github.com/gustavz/DataChad) | 140 |
|
||||
|[Klingefjord/chatgpt-telegram](https://github.com/Klingefjord/chatgpt-telegram) | 140 |
|
||||
|[Jaseci-Labs/jaseci](https://github.com/Jaseci-Labs/jaseci) | 139 |
|
||||
|[handrew/browserpilot](https://github.com/handrew/browserpilot) | 137 |
|
||||
|[jmpaz/promptlib](https://github.com/jmpaz/promptlib) | 137 |
|
||||
|[SamPink/dev-gpt](https://github.com/SamPink/dev-gpt) | 135 |
|
||||
|[menloparklab/langchain-cohere-qdrant-doc-retrieval](https://github.com/menloparklab/langchain-cohere-qdrant-doc-retrieval) | 135 |
|
||||
|[hirokidaichi/wanna](https://github.com/hirokidaichi/wanna) | 135 |
|
||||
|[steamship-core/vercel-examples](https://github.com/steamship-core/vercel-examples) | 134 |
|
||||
|[pablomarin/GPT-Azure-Search-Engine](https://github.com/pablomarin/GPT-Azure-Search-Engine) | 133 |
|
||||
|[ibiscp/LLM-IMDB](https://github.com/ibiscp/LLM-IMDB) | 133 |
|
||||
|[shauryr/S2QA](https://github.com/shauryr/S2QA) | 133 |
|
||||
|[jerlendds/osintbuddy](https://github.com/jerlendds/osintbuddy) | 132 |
|
||||
|[yuanjie-ai/ChatLLM](https://github.com/yuanjie-ai/ChatLLM) | 132 |
|
||||
|[yasyf/summ](https://github.com/yasyf/summ) | 132 |
|
||||
|[WongSaang/chatgpt-ui-server](https://github.com/WongSaang/chatgpt-ui-server) | 130 |
|
||||
|[peterw/StoryStorm](https://github.com/peterw/StoryStorm) | 127 |
|
||||
|[Teahouse-Studios/akari-bot](https://github.com/Teahouse-Studios/akari-bot) | 126 |
|
||||
|[vaibkumr/prompt-optimizer](https://github.com/vaibkumr/prompt-optimizer) | 125 |
|
||||
|[preset-io/promptimize](https://github.com/preset-io/promptimize) | 124 |
|
||||
|[homanp/vercel-langchain](https://github.com/homanp/vercel-langchain) | 124 |
|
||||
|[petehunt/langchain-github-bot](https://github.com/petehunt/langchain-github-bot) | 123 |
|
||||
|[eunomia-bpf/GPTtrace](https://github.com/eunomia-bpf/GPTtrace) | 118 |
|
||||
|[nicknochnack/LangchainDocuments](https://github.com/nicknochnack/LangchainDocuments) | 116 |
|
||||
|[jiran214/GPT-vup](https://github.com/jiran214/GPT-vup) | 112 |
|
||||
|[rsaryev/talk-codebase](https://github.com/rsaryev/talk-codebase) | 112 |
|
||||
|[zenml-io/zenml-projects](https://github.com/zenml-io/zenml-projects) | 112 |
|
||||
|[microsoft/azure-openai-in-a-day-workshop](https://github.com/microsoft/azure-openai-in-a-day-workshop) | 112 |
|
||||
|[davila7/file-gpt](https://github.com/davila7/file-gpt) | 112 |
|
||||
|[prof-frink-lab/slangchain](https://github.com/prof-frink-lab/slangchain) | 111 |
|
||||
|[aurelio-labs/arxiv-bot](https://github.com/aurelio-labs/arxiv-bot) | 110 |
|
||||
|[fixie-ai/fixie-examples](https://github.com/fixie-ai/fixie-examples) | 108 |
|
||||
|[miaoshouai/miaoshouai-assistant](https://github.com/miaoshouai/miaoshouai-assistant) | 105 |
|
||||
|[flurb18/AgentOoba](https://github.com/flurb18/AgentOoba) | 103 |
|
||||
|[solana-labs/chatgpt-plugin](https://github.com/solana-labs/chatgpt-plugin) | 102 |
|
||||
|[Significant-Gravitas/Auto-GPT-Benchmarks](https://github.com/Significant-Gravitas/Auto-GPT-Benchmarks) | 102 |
|
||||
|[kaarthik108/snowChat](https://github.com/kaarthik108/snowChat) | 100 |
|
||||
|
||||
|
||||
_Generated by [github-dependents-info](https://github.com/nvuillam/github-dependents-info)_
|
||||
|
||||
`github-dependents-info --repo hwchase17/langchain --markdownfile dependents.md --minstars 100 --sort stars`
|
||||
@ -0,0 +1,7 @@
|
||||
.yarn/
|
||||
|
||||
node_modules/
|
||||
|
||||
.docusaurus
|
||||
.cache-loader
|
||||
docs/api
|
||||
@ -0,0 +1,49 @@
|
||||
# Website
|
||||
|
||||
This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator.
|
||||
|
||||
### Installation
|
||||
|
||||
```
|
||||
$ yarn
|
||||
```
|
||||
|
||||
### Local Development
|
||||
|
||||
```
|
||||
$ yarn start
|
||||
```
|
||||
|
||||
This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
|
||||
|
||||
### Build
|
||||
|
||||
```
|
||||
$ yarn build
|
||||
```
|
||||
|
||||
This command generates static content into the `build` directory and can be served using any static contents hosting service.
|
||||
|
||||
### Deployment
|
||||
|
||||
Using SSH:
|
||||
|
||||
```
|
||||
$ USE_SSH=true yarn deploy
|
||||
```
|
||||
|
||||
Not using SSH:
|
||||
|
||||
```
|
||||
$ GIT_USER=<Your GitHub username> yarn deploy
|
||||
```
|
||||
|
||||
If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.
|
||||
|
||||
### Continuous Integration
|
||||
|
||||
Some common defaults for linting/formatting have been set for you. If you integrate your project with an open source Continuous Integration system (e.g. Travis CI, CircleCI), you may check for issues using the following command.
|
||||
|
||||
```
|
||||
$ yarn ci
|
||||
```
|
||||
@ -0,0 +1,12 @@
|
||||
/**
|
||||
* Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
*
|
||||
* This source code is licensed under the MIT license found in the
|
||||
* LICENSE file in the root directory of this source tree.
|
||||
*
|
||||
* @format
|
||||
*/
|
||||
|
||||
module.exports = {
|
||||
presets: [require.resolve("@docusaurus/core/lib/babel/preset")],
|
||||
};
|
||||
@ -0,0 +1,76 @@
|
||||
/* eslint-disable prefer-template */
|
||||
/* eslint-disable no-param-reassign */
|
||||
// eslint-disable-next-line import/no-extraneous-dependencies
|
||||
const babel = require("@babel/core");
|
||||
const path = require("path");
|
||||
const fs = require("fs");
|
||||
|
||||
/**
|
||||
*
|
||||
* @param {string|Buffer} content Content of the resource file
|
||||
* @param {object} [map] SourceMap data consumable by https://github.com/mozilla/source-map
|
||||
* @param {any} [meta] Meta data, could be anything
|
||||
*/
|
||||
async function webpackLoader(content, map, meta) {
|
||||
const cb = this.async();
|
||||
|
||||
if (!this.resourcePath.endsWith(".ts")) {
|
||||
cb(null, JSON.stringify({ content, imports: [] }), map, meta);
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
const result = await babel.parseAsync(content, {
|
||||
sourceType: "module",
|
||||
filename: this.resourcePath,
|
||||
});
|
||||
|
||||
const imports = [];
|
||||
|
||||
result.program.body.forEach((node) => {
|
||||
if (node.type === "ImportDeclaration") {
|
||||
const source = node.source.value;
|
||||
|
||||
if (!source.startsWith("langchain")) {
|
||||
return;
|
||||
}
|
||||
|
||||
node.specifiers.forEach((specifier) => {
|
||||
if (specifier.type === "ImportSpecifier") {
|
||||
const local = specifier.local.name;
|
||||
const imported = specifier.imported.name;
|
||||
imports.push({ local, imported, source });
|
||||
} else {
|
||||
throw new Error("Unsupported import type");
|
||||
}
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
imports.forEach((imp) => {
|
||||
const { imported, source } = imp;
|
||||
const moduleName = source.split("/").slice(1).join("_");
|
||||
const docsPath = path.resolve(__dirname, "docs", "api", moduleName);
|
||||
const available = fs.readdirSync(docsPath, { withFileTypes: true });
|
||||
const found = available.find(
|
||||
(dirent) =>
|
||||
dirent.isDirectory() &&
|
||||
fs.existsSync(path.resolve(docsPath, dirent.name, imported + ".md"))
|
||||
);
|
||||
if (found) {
|
||||
imp.docs =
|
||||
"/" + path.join("docs", "api", moduleName, found.name, imported);
|
||||
} else {
|
||||
throw new Error(
|
||||
`Could not find docs for ${source}.${imported} in docs/api/`
|
||||
);
|
||||
}
|
||||
});
|
||||
|
||||
cb(null, JSON.stringify({ content, imports }), map, meta);
|
||||
} catch (err) {
|
||||
cb(err);
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = webpackLoader;
|
||||
|
Before Width: | Height: | Size: 559 KiB After Width: | Height: | Size: 559 KiB |
|
Before Width: | Height: | Size: 157 KiB After Width: | Height: | Size: 157 KiB |
|
Before Width: | Height: | Size: 235 KiB After Width: | Height: | Size: 235 KiB |
|
Before Width: | Height: | Size: 148 KiB After Width: | Height: | Size: 148 KiB |
|
Before Width: | Height: | Size: 3.5 MiB After Width: | Height: | Size: 3.5 MiB |
Binary file not shown.
|
After Width: | Height: | Size: 18 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 85 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 16 KiB |
@ -0,0 +1,21 @@
|
||||
pre {
|
||||
white-space: break-spaces;
|
||||
}
|
||||
|
||||
@media (min-width: 1200px) {
|
||||
.container,
|
||||
.container-lg,
|
||||
.container-md,
|
||||
.container-sm,
|
||||
.container-xl {
|
||||
max-width: 2560px !important;
|
||||
}
|
||||
}
|
||||
|
||||
#my-component-root *, #headlessui-portal-root * {
|
||||
z-index: 10000;
|
||||
}
|
||||
|
||||
.content-container p {
|
||||
margin: revert;
|
||||
}
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 542 B |
Binary file not shown.
|
After Width: | Height: | Size: 1.2 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 15 KiB |
@ -0,0 +1,56 @@
|
||||
document.addEventListener('DOMContentLoaded', () => {
|
||||
// Load the external dependencies
|
||||
function loadScript(src, onLoadCallback) {
|
||||
const script = document.createElement('script');
|
||||
script.src = src;
|
||||
script.onload = onLoadCallback;
|
||||
document.head.appendChild(script);
|
||||
}
|
||||
|
||||
function createRootElement() {
|
||||
const rootElement = document.createElement('div');
|
||||
rootElement.id = 'my-component-root';
|
||||
document.body.appendChild(rootElement);
|
||||
return rootElement;
|
||||
}
|
||||
|
||||
|
||||
|
||||
function initializeMendable() {
|
||||
const rootElement = createRootElement();
|
||||
const { MendableFloatingButton } = Mendable;
|
||||
|
||||
|
||||
const iconSpan1 = React.createElement('span', {
|
||||
}, '🦜');
|
||||
|
||||
const iconSpan2 = React.createElement('span', {
|
||||
}, '🔗');
|
||||
|
||||
const icon = React.createElement('p', {
|
||||
style: { color: '#ffffff', fontSize: '22px',width: '48px', height: '48px', margin: '0px', padding: '0px', display: 'flex', alignItems: 'center', justifyContent: 'center', textAlign: 'center' },
|
||||
}, [iconSpan1, iconSpan2]);
|
||||
|
||||
const mendableFloatingButton = React.createElement(
|
||||
MendableFloatingButton,
|
||||
{
|
||||
style: { darkMode: false, accentColor: '#010810' },
|
||||
floatingButtonStyle: { color: '#ffffff', backgroundColor: '#010810' },
|
||||
anon_key: '82842b36-3ea6-49b2-9fb8-52cfc4bde6bf', // Mendable Search Public ANON key, ok to be public
|
||||
messageSettings: {
|
||||
openSourcesInNewTab: false,
|
||||
prettySources: true // Prettify the sources displayed now
|
||||
},
|
||||
icon: icon,
|
||||
}
|
||||
);
|
||||
|
||||
ReactDOM.render(mendableFloatingButton, rootElement);
|
||||
}
|
||||
|
||||
loadScript('https://unpkg.com/react@17/umd/react.production.min.js', () => {
|
||||
loadScript('https://unpkg.com/react-dom@17/umd/react-dom.production.min.js', () => {
|
||||
loadScript('https://unpkg.com/@mendable/search@0.0.102/dist/umd/mendable.min.js', initializeMendable);
|
||||
});
|
||||
});
|
||||
});
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 103 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 136 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 34 KiB |
@ -0,0 +1,8 @@
|
||||
---
|
||||
sidebar_position: 0
|
||||
---
|
||||
# Integrations
|
||||
|
||||
import DocCardList from "@theme/DocCardList";
|
||||
|
||||
<DocCardList />
|
||||
@ -0,0 +1,5 @@
|
||||
# Installation
|
||||
|
||||
import Installation from "@snippets/get_started/installation.mdx"
|
||||
|
||||
<Installation/>
|
||||
@ -0,0 +1,158 @@
|
||||
# Quickstart
|
||||
|
||||
## Installation
|
||||
|
||||
To install LangChain run:
|
||||
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
import Install from "@snippets/get_started/quickstart/installation.mdx"
|
||||
|
||||
<Install/>
|
||||
|
||||
For more details, see our [Installation guide](/docs/get_started/installation.html).
|
||||
|
||||
## Environment setup
|
||||
|
||||
Using LangChain will usually require integrations with one or more model providers, data stores, APIs, etc. For this example, we'll use OpenAI's model APIs.
|
||||
|
||||
import OpenAISetup from "@snippets/get_started/quickstart/openai_setup.mdx"
|
||||
|
||||
<OpenAISetup/>
|
||||
|
||||
## Building an application
|
||||
|
||||
Now we can start building our language model application. LangChain provides many modules that can be used to build language model applications. Modules can be used as stand-alones in simple applications and they can be combined for more complex use cases.
|
||||
|
||||
## LLMs
|
||||
#### Get predictions from a language model
|
||||
|
||||
The basic building block of LangChain is the LLM, which takes in text and generates more text.
|
||||
|
||||
As an example, suppose we're building an application that generates a company name based on a company description. In order to do this, we need to initialize an OpenAI model wrapper. In this case, since we want the outputs to be MORE random, we'll initialize our model with a HIGH temperature.
|
||||
|
||||
import LLM from "@snippets/get_started/quickstart/llm.mdx"
|
||||
|
||||
<LLM/>
|
||||
|
||||
## Chat models
|
||||
|
||||
Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
|
||||
|
||||
You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`.
|
||||
|
||||
import ChatModel from "@snippets/get_started/quickstart/chat_model.mdx"
|
||||
|
||||
<ChatModel/>
|
||||
|
||||
## Prompt templates
|
||||
|
||||
Most LLM applications do not pass user input directly into to an LLM. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand.
|
||||
|
||||
In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it'd be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions.
|
||||
|
||||
import PromptTemplateLLM from "@snippets/get_started/quickstart/prompt_templates_llms.mdx"
|
||||
import PromptTemplateChatModel from "@snippets/get_started/quickstart/prompt_templates_chat_models.mdx"
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="llms" label="LLMs" default>
|
||||
|
||||
With PromptTemplates this is easy! In this case our template would be very simple:
|
||||
|
||||
<PromptTemplateLLM/>
|
||||
</TabItem>
|
||||
<TabItem value="chat_models" label="Chat models">
|
||||
|
||||
Similar to LLMs, you can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplate`s. You can use `ChatPromptTemplate`'s `format_messages` method to generate the formatted messages.
|
||||
|
||||
Because this is generating a list of messages, it is slightly more complex than the normal prompt template which is generating only a string. Please see the detailed guides on prompts to understand more options available to you here.
|
||||
|
||||
<PromptTemplateChatModel/>
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
## Chains
|
||||
|
||||
Now that we've got a model and a prompt template, we'll want to combine the two. Chains give us a way to link (or chain) together multiple primitives, like models, prompts, and other chains.
|
||||
|
||||
import ChainLLM from "@snippets/get_started/quickstart/chains_llms.mdx"
|
||||
import ChainChatModel from "@snippets/get_started/quickstart/chains_chat_models.mdx"
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="llms" label="LLMs" default>
|
||||
|
||||
The simplest and most common type of chain is an LLMChain, which passes an input first to a PromptTemplate and then to an LLM. We can construct an LLM chain from our existing model and prompt template.
|
||||
|
||||
<ChainLLM/>
|
||||
|
||||
There we go, our first chain! Understanding how this simple chain works will set you up well for working with more complex chains.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="chat_models" label="Chat models">
|
||||
|
||||
The `LLMChain` can be used with chat models as well:
|
||||
|
||||
<ChainChatModel/>
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
## Agents
|
||||
|
||||
import AgentLLM from "@snippets/get_started/quickstart/agents_llms.mdx"
|
||||
import AgentChatModel from "@snippets/get_started/quickstart/agents_chat_models.mdx"
|
||||
|
||||
Our first chain ran a pre-determined sequence of steps. To handle complex workflows, we need to be able to dynamically choose actions based on inputs.
|
||||
|
||||
Agents do just this: they use a language model to determine which actions to take and in what order. Agents are given access to tools, and they repeatedly choose a tool, run the tool, and observe the output until they come up with a final answer.
|
||||
|
||||
To load an agent, you need to choose a(n):
|
||||
- LLM/Chat model: The language model powering the agent.
|
||||
- Tool(s): A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL, other chains. For a list of predefined tools and their specifications, see the [Tools documentation](/docs/modules/agents/tools/).
|
||||
- Agent name: A string that references a supported agent class. An agent class is largely parameterized by the prompt the language model uses to determine which action to take. Because this notebook focuses on the simplest, highest level API, this only covers using the standard supported agents. If you want to implement a custom agent, see [here](/docs/modules/agents/how_to/custom_agent.html). For a list of supported agents and their specifications, see [here](/docs/modules/agents/agent_types/).
|
||||
|
||||
For this example, we'll be using SerpAPI to query a search engine.
|
||||
|
||||
You'll need to install the SerpAPI Python package:
|
||||
|
||||
```bash
|
||||
pip install google-search-results
|
||||
```
|
||||
|
||||
And set the `SERPAPI_API_KEY` environment variable.
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="llms" label="LLMs" default>
|
||||
<AgentLLM/>
|
||||
</TabItem>
|
||||
<TabItem value="chat_models" label="Chat models">
|
||||
|
||||
Agents can also be used with chat models, you can initialize one using `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION` as the agent type.
|
||||
|
||||
<AgentChatModel/>
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
## Memory
|
||||
|
||||
The chains and agents we've looked at so far have been stateless, but for many applications it's necessary to reference past interactions. This is clearly the case with a chatbot for example, where you want it to understand new messages in the context of past messages.
|
||||
|
||||
The Memory module gives you a way to maintain application state. The base Memory interface is simple: it lets you update state given the latest run inputs and outputs and it lets you modify (or contextualize) the next input using the stored state.
|
||||
|
||||
There are a number of built-in memory systems. The simplest of these are is a buffer memory which just prepends the last few inputs/outputs to the current input - we will use this in the example below.
|
||||
|
||||
import MemoryLLM from "@snippets/get_started/quickstart/memory_llms.mdx"
|
||||
import MemoryChatModel from "@snippets/get_started/quickstart/memory_chat_models.mdx"
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="llms" label="LLMs" default>
|
||||
|
||||
<MemoryLLM/>
|
||||
</TabItem>
|
||||
<TabItem value="chat_models" label="Chat models">
|
||||
|
||||
You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
|
||||
|
||||
<MemoryChatModel/>
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
@ -0,0 +1,13 @@
|
||||
# Conversational
|
||||
|
||||
This walkthrough demonstrates how to use an agent optimized for conversation. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well.
|
||||
|
||||
import Example from "@snippets/modules/agents/agent_types/conversational_agent.mdx"
|
||||
|
||||
<Example/>
|
||||
|
||||
import ChatExample from "@snippets/modules/agents/agent_types/chat_conversation_agent.mdx"
|
||||
|
||||
## Using a chat model
|
||||
|
||||
<ChatExample/>
|
||||
@ -0,0 +1,57 @@
|
||||
---
|
||||
sidebar_position: 0
|
||||
---
|
||||
|
||||
# Agent types
|
||||
|
||||
## Action agents
|
||||
|
||||
Agents use an LLM to determine which actions to take and in what order.
|
||||
An action can either be using a tool and observing its output, or returning a response to the user.
|
||||
Here are the agents available in LangChain.
|
||||
|
||||
### [Zero-shot ReAct](/docs/modules/agents/agent_types/react.html)
|
||||
|
||||
This agent uses the [ReAct](https://arxiv.org/pdf/2205.00445.pdf) framework to determine which tool to use
|
||||
based solely on the tool's description. Any number of tools can be provided.
|
||||
This agent requires that a description is provided for each tool.
|
||||
|
||||
**Note**: This is the most general purpose action agent.
|
||||
|
||||
### [Structured input ReAct](/docs/modules/agents/agent_types/structured_chat.html)
|
||||
|
||||
The structured tool chat agent is capable of using multi-input tools.
|
||||
Older agents are configured to specify an action input as a single string, but this agent can use a tools' argument
|
||||
schema to create a structured action input. This is useful for more complex tool usage, like precisely
|
||||
navigating around a browser.
|
||||
|
||||
### [OpenAI Functions](/docs/modules/agents/agent_types/openai_functions_agent.html)
|
||||
|
||||
Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been explicitly fine-tuned to detect when a
|
||||
function should to be called and respond with the inputs that should be passed to the function.
|
||||
The OpenAI Functions Agent is designed to work with these models.
|
||||
|
||||
### [Conversational](/docs/modules/agents/agent_types/chat_conversation_agent.html)
|
||||
|
||||
This agent is designed to be used in conversational settings.
|
||||
The prompt is designed to make the agent helpful and conversational.
|
||||
It uses the ReAct framework to decide which tool to use, and uses memory to remember the previous conversation interactions.
|
||||
|
||||
### [Self ask with search](/docs/modules/agents/agent_types/self_ask_with_search.html)
|
||||
|
||||
This agent utilizes a single tool that should be named `Intermediate Answer`.
|
||||
This tool should be able to lookup factual answers to questions. This agent
|
||||
is equivalent to the original [self ask with search paper](https://ofir.io/self-ask.pdf),
|
||||
where a Google search API was provided as the tool.
|
||||
|
||||
### [ReAct document store](/docs/modules/agents/agent_types/react_docstore.html)
|
||||
|
||||
This agent uses the ReAct framework to interact with a docstore. Two tools must
|
||||
be provided: a `Search` tool and a `Lookup` tool (they must be named exactly as so).
|
||||
The `Search` tool should search for a document, while the `Lookup` tool should lookup
|
||||
a term in the most recently found document.
|
||||
This agent is equivalent to the
|
||||
original [ReAct paper](https://arxiv.org/pdf/2210.03629.pdf), specifically the Wikipedia example.
|
||||
|
||||
## [Plan-and-execute agents](/docs/modules/agents/agent_types/plan_and_execute.html)
|
||||
Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).
|
||||
@ -0,0 +1,11 @@
|
||||
# OpenAI functions
|
||||
|
||||
Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to detect when a function should to be called and respond with the inputs that should be passed to the function.
|
||||
In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call those functions.
|
||||
The goal of the OpenAI Function APIs is to more reliably return valid and useful function calls than a generic text completion or chat API.
|
||||
|
||||
The OpenAI Functions Agent is designed to work with these models.
|
||||
|
||||
import Example from "@snippets/modules/agents/agent_types/openai_functions_agent.mdx";
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,11 @@
|
||||
# Plan and execute
|
||||
|
||||
Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).
|
||||
|
||||
The planning is almost always done by an LLM.
|
||||
|
||||
The execution is usually done by a separate agent (equipped with tools).
|
||||
|
||||
import Example from "@snippets/modules/agents/agent_types/plan_and_execute.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,15 @@
|
||||
# ReAct
|
||||
|
||||
This walkthrough showcases using an agent to implement the [ReAct](https://react-lm.github.io/) logic.
|
||||
|
||||
import Example from "@snippets/modules/agents/agent_types/react.mdx"
|
||||
|
||||
<Example/>
|
||||
|
||||
## Using chat models
|
||||
|
||||
You can also create ReAct agents that use chat models instead of LLMs as the agent driver.
|
||||
|
||||
import ChatExample from "@snippets/modules/agents/agent_types/react_chat.mdx"
|
||||
|
||||
<ChatExample/>
|
||||
@ -0,0 +1,10 @@
|
||||
# Structured tool chat
|
||||
|
||||
The structured tool chat agent is capable of using multi-input tools.
|
||||
|
||||
Older agents are configured to specify an action input as a single string, but this agent can use the provided tools' `args_schema` to populate the action input.
|
||||
|
||||
|
||||
import Example from "@snippets/modules/agents/agent_types/structured_chat.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,2 @@
|
||||
label: 'How-to'
|
||||
position: 1
|
||||
@ -0,0 +1,14 @@
|
||||
# Custom LLM Agent
|
||||
|
||||
This notebook goes through how to create your own custom LLM agent.
|
||||
|
||||
An LLM agent consists of three parts:
|
||||
|
||||
- PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do
|
||||
- LLM: This is the language model that powers the agent
|
||||
- `stop` sequence: Instructs the LLM to stop generating as soon as this string is found
|
||||
- OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object
|
||||
|
||||
import Example from "@snippets/modules/agents/how_to/custom_llm_agent.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,14 @@
|
||||
# Custom LLM Agent (with a ChatModel)
|
||||
|
||||
This notebook goes through how to create your own custom agent based on a chat model.
|
||||
|
||||
An LLM chat agent consists of three parts:
|
||||
|
||||
- PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do
|
||||
- ChatModel: This is the language model that powers the agent
|
||||
- `stop` sequence: Instructs the LLM to stop generating as soon as this string is found
|
||||
- OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object
|
||||
|
||||
import Example from "@snippets/modules/agents/how_to/custom_llm_chat_agent.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,16 @@
|
||||
# Replicating MRKL
|
||||
|
||||
This walkthrough demonstrates how to replicate the [MRKL](https://arxiv.org/pdf/2205.00445.pdf) system using agents.
|
||||
|
||||
This uses the example Chinook database.
|
||||
To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository.
|
||||
|
||||
import Example from "@snippets/modules/agents/how_to/mrkl.mdx"
|
||||
|
||||
<Example/>
|
||||
|
||||
## With a chat model
|
||||
|
||||
import ChatExample from "@snippets/modules/agents/how_to/mrkl_chat.mdx"
|
||||
|
||||
<ChatExample/>
|
||||
@ -0,0 +1,51 @@
|
||||
---
|
||||
sidebar_position: 4
|
||||
---
|
||||
# Agents
|
||||
|
||||
Some applications require a flexible chain of calls to LLMs and other tools based on user input. The **Agent** interface provides the flexibility for such applications. An agent has access to a suite of tools, and determines which ones to use depending on the user input. Agents can use multiple tools, and use the output of one tool as the input to the next.
|
||||
|
||||
There are two main types of agents:
|
||||
|
||||
- **Action agents**: at each timestep, decide on the next action using the outputs of all previous actions
|
||||
- **Plan-and-execute agents**: decide on the full sequence of actions up front, then execute them all without updating the plan
|
||||
|
||||
Action agents are suitable for small tasks, while plan-and-execute agents are better for complex or long-running tasks that require maintaining long-term objectives and focus. Often the best approach is to combine the dynamism of an action agent with the planning abilities of a plan-and-execute agent by letting the plan-and-execute agent use action agents to execute plans.
|
||||
|
||||
For a full list of agent types see [agent types](/docs/modules/agents/agent_types/). Additional abstractions involved in agents are:
|
||||
- [**Tools**](/docs/modules/agents/tools/): the actions an agent can take. What tools you give an agent highly depend on what you want the agent to do
|
||||
- [**Toolkits**](/docs/modules/agents/toolkits/): wrappers around collections of tools that can be used together a specific use case. For example, in order for an agent to
|
||||
interact with a SQL database it will likely need one tool to execute queries and another to inspect tables
|
||||
|
||||
## Action agents
|
||||
|
||||
At a high-level an action agent:
|
||||
1. Receives user input
|
||||
2. Decides which tool, if any, to use and the tool input
|
||||
3. Calls the tool and records the output (also known as an "observation")
|
||||
4. Decides the next step using the history of tools, tool inputs, and observations
|
||||
5. Repeats 3-4 until it determines it can respond directly to the user
|
||||
|
||||
Action agents are wrapped in **agent executors**, which are responsible for calling the agent, getting back an action and action input, calling the tool that the action references with the generated input, getting the output of the tool, and then passing all that information back into the agent to get the next action it should take.
|
||||
|
||||
Although an agent can be constructed in many ways, it typically involves these components:
|
||||
|
||||
- **Prompt template**: Responsible for taking the user input and previous steps and constructing a prompt
|
||||
to send to the language model
|
||||
- **Language model**: Takes the prompt with use input and action history and decides what to do next
|
||||
- **Output parser**: Takes the output of the language model and parses it into the next action or a final answer
|
||||
|
||||
## Plan-and-execute agents
|
||||
|
||||
At a high-level a plan-and-execute agent:
|
||||
1. Receives user input
|
||||
2. Plans the full sequence of steps to take
|
||||
3. Executes the steps in order, passing the outputs of past steps as inputs to future steps
|
||||
|
||||
The most typical implementation is to have the planner be a language model, and the executor be an action agent. Read more [here](/docs/modules/agents/agent_types/plan_and_execute.html).
|
||||
|
||||
## Get started
|
||||
|
||||
import GetStarted from "@snippets/modules/agents/get_started.mdx"
|
||||
|
||||
<GetStarted/>
|
||||
@ -0,0 +1,10 @@
|
||||
---
|
||||
sidebar_position: 3
|
||||
---
|
||||
# Toolkits
|
||||
|
||||
Toolkits are collections of tools that are designed to be used together for specific tasks and have convenience loading methods.
|
||||
|
||||
import DocCardList from "@theme/DocCardList";
|
||||
|
||||
<DocCardList />
|
||||
@ -0,0 +1,2 @@
|
||||
label: 'How-to'
|
||||
position: 0
|
||||
@ -0,0 +1,17 @@
|
||||
---
|
||||
sidebar_position: 2
|
||||
---
|
||||
# Tools
|
||||
|
||||
Tools are interfaces that an agent can use to interact with the world.
|
||||
|
||||
## Get started
|
||||
|
||||
Tools are functions that agents can use to interact with the world.
|
||||
These tools can be generic utilities (e.g. search), other chains, or even other agents.
|
||||
|
||||
Currently, tools can be loaded with the following snippet:
|
||||
|
||||
import GetStarted from "@snippets/modules/agents/tools/get_started.mdx"
|
||||
|
||||
<GetStarted/>
|
||||
@ -0,0 +1 @@
|
||||
label: 'Integrations'
|
||||
@ -0,0 +1,2 @@
|
||||
label: 'How-to'
|
||||
position: 0
|
||||
@ -0,0 +1,10 @@
|
||||
---
|
||||
sidebar_position: 5
|
||||
---
|
||||
# Callbacks
|
||||
|
||||
LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
|
||||
|
||||
import GetStarted from "@snippets/modules/callbacks/get_started.mdx"
|
||||
|
||||
<GetStarted/>
|
||||
@ -0,0 +1 @@
|
||||
label: 'Integrations'
|
||||
@ -0,0 +1,7 @@
|
||||
# Analyze Document
|
||||
|
||||
The AnalyzeDocumentChain can be used as an end-to-end to chain. This chain takes in a single document, splits it up, and then runs it through a CombineDocumentsChain.
|
||||
|
||||
import Example from "@snippets/modules/chains/additional/analyze_document.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,7 @@
|
||||
# Self-critique chain with constitutional AI
|
||||
The ConstitutionalChain is a chain that ensures the output of a language model adheres to a predefined set of constitutional principles. By incorporating specific rules and guidelines, the ConstitutionalChain filters and modifies the generated content to align with these principles, thus providing more controlled, ethical, and contextually appropriate responses. This mechanism helps maintain the integrity of the output while minimizing the risk of generating content that may violate guidelines, be offensive, or deviate from the desired context.
|
||||
|
||||
|
||||
import Example from "@snippets/modules/chains/additional/constitutional_chain.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,8 @@
|
||||
---
|
||||
sidebar_position: 4
|
||||
---
|
||||
# Additional
|
||||
|
||||
import DocCardList from "@theme/DocCardList";
|
||||
|
||||
<DocCardList />
|
||||
@ -0,0 +1,8 @@
|
||||
# Moderation
|
||||
This notebook walks through examples of how to use a moderation chain, and several common ways for doing so. Moderation chains are useful for detecting text that could be hateful, violent, etc. This can be useful to apply on both user input, but also on the output of a Language Model. Some API providers, like OpenAI, [specifically prohibit](https://beta.openai.com/docs/usage-policies/use-case-policy) you, or your end users, from generating some types of harmful content. To comply with this (and to just generally prevent your application from being harmful) you may often want to append a moderation chain to any LLMChains, in order to make sure any output the LLM generates is not harmful.
|
||||
|
||||
If the content passed into the moderation chain is harmful, there is not one best way to handle it, it probably depends on your application. Sometimes you may want to throw an error in the Chain (and have your application handle that). Other times, you may want to return something to the user explaining that the text was harmful. There could even be other ways to handle it! We will cover all these ways in this walkthrough.
|
||||
|
||||
import Example from "@snippets/modules/chains/additional/moderation.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,7 @@
|
||||
# Dynamically selecting from multiple prompts
|
||||
|
||||
This notebook demonstrates how to use the `RouterChain` paradigm to create a chain that dynamically selects the prompt to use for a given input. Specifically we show how to use the `MultiPromptChain` to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt.
|
||||
|
||||
import Example from "@snippets/modules/chains/additional/multi_prompt_router.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,7 @@
|
||||
# Dynamically selecting from multiple retrievers
|
||||
|
||||
This notebook demonstrates how to use the `RouterChain` paradigm to create a chain that dynamically selects which Retrieval system to use. Specifically we show how to use the `MultiRetrievalQAChain` to create a question-answering chain that selects the retrieval QA chain which is most relevant for a given question, and then answers the question using it.
|
||||
|
||||
import Example from "@snippets/modules/chains/additional/multi_retrieval_qa_router.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,13 @@
|
||||
# Document QA
|
||||
|
||||
Here we walk through how to use LangChain for question answering over a list of documents. Under the hood we'll be using our [Document chains](../document.html).
|
||||
|
||||
import Example from "@snippets/modules/chains/additional/question_answering.mdx"
|
||||
|
||||
<Example/>
|
||||
|
||||
## Document QA with sources
|
||||
|
||||
import ExampleWithSources from "@snippets/modules/chains/additional/qa_with_sources.mdx"
|
||||
|
||||
<ExampleWithSources/>
|
||||
@ -0,0 +1,16 @@
|
||||
---
|
||||
sidebar_position: 2
|
||||
---
|
||||
# Documents
|
||||
|
||||
These are the core chains for working with Documents. They are useful for summarizing documents, answering questions over documents, extracting information from documents, and more.
|
||||
|
||||
These chains all implement a common interface:
|
||||
|
||||
import Interface from "@snippets/modules/chains/document/combine_docs.mdx"
|
||||
|
||||
<Interface/>
|
||||
|
||||
import DocCardList from "@theme/DocCardList";
|
||||
|
||||
<DocCardList />
|
||||
@ -0,0 +1,5 @@
|
||||
# Map reduce
|
||||
|
||||
The map reduce documents chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. It then passes all the new documents to a separate combine documents chain to get a single output (the Reduce step). It can optionally first compress, or collapse, the mapped documents to make sure that they fit in the combine documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.
|
||||
|
||||
|
||||
@ -0,0 +1,5 @@
|
||||
# Map re-rank
|
||||
|
||||
The map re-rank documents chain runs an initial prompt on each document, that not only tries to complete a task but also gives a score for how certain it is in its answer. The highest scoring response is returned.
|
||||
|
||||
|
||||
@ -0,0 +1,12 @@
|
||||
---
|
||||
sidebar_position: 1
|
||||
---
|
||||
# Refine
|
||||
|
||||
The refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.
|
||||
|
||||
Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context.
|
||||
The obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain.
|
||||
There are also certain tasks which are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents.
|
||||
|
||||
|
||||
@ -0,0 +1,12 @@
|
||||
---
|
||||
sidebar_position: 0
|
||||
---
|
||||
# Stuff
|
||||
|
||||
The stuff documents chain ("stuff" as in "to stuff" or "to fill") is the most straightforward of the document chains. It takes a list of documents, inserts them all into a prompt and passes that prompt to an LLM.
|
||||
|
||||
This chain is well-suited for applications where documents are small and only a few are passed in for most calls.
|
||||
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,8 @@
|
||||
---
|
||||
sidebar_position: 1
|
||||
---
|
||||
# Foundational
|
||||
|
||||
import DocCardList from "@theme/DocCardList";
|
||||
|
||||
<DocCardList />
|
||||
@ -0,0 +1,11 @@
|
||||
# LLM
|
||||
|
||||
An LLMChain is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents.
|
||||
|
||||
An LLMChain consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.
|
||||
|
||||
## Get started
|
||||
|
||||
import Example from "@snippets/modules/chains/foundational/llm_chain.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,14 @@
|
||||
# Sequential
|
||||
|
||||
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! Instead, edit the notebook w/the location & name as this file. -->
|
||||
|
||||
The next step after calling a language model is make a series of calls to a language model. This is particularly useful when you want to take the output from one call and use it as the input to another.
|
||||
|
||||
In this notebook we will walk through some examples for how to do this, using sequential chains. Sequential chains allow you to connect multiple chains and compose them into pipelines that execute some specific scenario.. There are two types of sequential chains:
|
||||
|
||||
- `SimpleSequentialChain`: The simplest form of sequential chains, where each step has a singular input/output, and the output of one step is the input to the next.
|
||||
- `SequentialChain`: A more general form of sequential chains, allowing for multiple inputs/outputs.
|
||||
|
||||
import Example from "@snippets/modules/chains/foundational/sequential_chains.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,8 @@
|
||||
# Debugging chains
|
||||
|
||||
It can be hard to debug a `Chain` object solely from its output as most `Chain` objects involve a fair amount of input prompt preprocessing and LLM output post-processing.
|
||||
|
||||
import Example from "@snippets/modules/chains/how_to/debugging.mdx"
|
||||
|
||||
<Example/>
|
||||
|
||||
@ -0,0 +1,8 @@
|
||||
---
|
||||
sidebar_position: 0
|
||||
---
|
||||
# How to
|
||||
|
||||
import DocCardList from "@theme/DocCardList";
|
||||
|
||||
<DocCardList />
|
||||
@ -0,0 +1,10 @@
|
||||
# Adding memory (state)
|
||||
|
||||
Chains can be initialized with a Memory object, which will persist data across calls to the chain. This makes a Chain stateful.
|
||||
|
||||
## Get started
|
||||
|
||||
import GetStarted from "@snippets/modules/chains/how_to/memory.mdx"
|
||||
|
||||
<GetStarted/>
|
||||
|
||||
@ -0,0 +1,33 @@
|
||||
---
|
||||
sidebar_position: 2
|
||||
---
|
||||
|
||||
# Chains
|
||||
|
||||
Using an LLM in isolation is fine for simple applications,
|
||||
but more complex applications require chaining LLMs - either with each other or with other components.
|
||||
|
||||
LangChain provides the **Chain** interface for such "chained" applications. We define a Chain very generically as a sequence of calls to components, which can include other chains. The base interface is simple:
|
||||
|
||||
import BaseClass from "@snippets/modules/chains/base_class.mdx"
|
||||
|
||||
<BaseClass/>
|
||||
|
||||
This idea of composing components together in a chain is simple but powerful. It drastically simplifies and makes more modular the implementation of complex applications, which in turn makes it much easier to debug, maintain, and improve your applications.
|
||||
|
||||
For more specifics check out:
|
||||
- [How-to](/docs/modules/chains/how_to/) for walkthroughs of different chain features
|
||||
- [Foundational](/docs/modules/chains/foundational/) to get acquainted with core building block chains
|
||||
- [Document](/docs/modules/chains/document/) to learn how to incorporate documents into chains
|
||||
- [Popular](/docs/modules/chains/popular/) chains for the most common use cases
|
||||
- [Additional](/docs/modules/chains/additional/) to see some of the more advanced chains and integrations that you can use out of the box
|
||||
|
||||
## Why do we need chains?
|
||||
|
||||
Chains allow us to combine multiple components together to create a single, coherent application. For example, we can create a chain that takes user input, formats it with a PromptTemplate, and then passes the formatted response to an LLM. We can build more complex chains by combining multiple chains together, or by combining chains with other components.
|
||||
|
||||
## Get started
|
||||
|
||||
import GetStarted from "@snippets/modules/chains/get_started.mdx"
|
||||
|
||||
<GetStarted/>
|
||||
@ -0,0 +1,9 @@
|
||||
---
|
||||
sidebar_position: 0
|
||||
---
|
||||
# API chains
|
||||
APIChain enables using LLMs to interact with APIs to retrieve relevant information. Construct the chain by providing a question relevant to the provided API documentation.
|
||||
|
||||
import Example from "@snippets/modules/chains/popular/api.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,14 @@
|
||||
---
|
||||
sidebar_position: 2
|
||||
---
|
||||
|
||||
# Conversational Retrieval QA
|
||||
The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component.
|
||||
|
||||
It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question answering chain to return a response.
|
||||
|
||||
To create one, you will need a retriever. In the below example, we will create one from a vector store, which can be created from embeddings.
|
||||
|
||||
import Example from "@snippets/modules/chains/popular/chat_vector_db.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -0,0 +1,8 @@
|
||||
---
|
||||
sidebar_position: 3
|
||||
---
|
||||
# Popular
|
||||
|
||||
import DocCardList from "@theme/DocCardList";
|
||||
|
||||
<DocCardList />
|
||||
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Reference in New Issue