🔧 chore(chains.mdx): add import statement for Admonition component to improve code organization and readability
🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 chore(chains.mdx): update verbose parameter description to improve clarity 🔧 chore(chains.mdx): fix formatting and indentation for better code readability 🔧 📝 chore(docs): update import statements for Admonition component in examples 📝 chore(docs): update link in Prompts component to use Admonition component 📝 chore(docs): update import statements for Admonition component in examples 📝 chore(docs): update link in Conversation Chain component to use Admonition component 📝 chore(docs): update import statements for Admonition component in examples 📝 chore(docs): update link in CSV Loader component to use Admonition component 📝 chore(docs): update import statements for Admonition component in examples 📝 chore(docs): update link in MidJourney Prompt Chain component to use Admonition component 📝 chore(docs): update import statements for Admonition component in examples 📝 chore(docs): update link in Multiple Vector Stores component to use Admonition component 📝 docs(examples/python-function.mdx): add import statement for Admonition component 📝 docs(examples/python-function.mdx): improve readability of tip admonition by breaking lines 📝 docs(examples/python-function.mdx): improve readability of info admonition by breaking lines 📝 docs(examples/serp-api-tool.mdx): add import statement for Admonition component 📝 docs(examples/serp-api-tool.mdx): improve readability of info admonition by breaking lines 📝 docs(guidelines/features.mdx): add import statement for Admonition component 📝 docs(guidelines/features.mdx): improve readability of caution admonition by breaking lines
This commit is contained in:
Gabriel Luiz Freitas Almeida
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10 changed files with 122 additions and 74 deletions
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@ -1,6 +1,7 @@
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import ThemedImage from "@theme/ThemedImage";
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import useBaseUrl from "@docusaurus/useBaseUrl";
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import ZoomableImage from "/src/theme/ZoomableImage.js";
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import Admonition from "@theme/Admonition";
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# Chains
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@ -12,22 +13,23 @@ Chains, in the context of language models, refer to a series of calls made to a
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The `CombineDocsChain` incorporates methods to combine or aggregate loaded documents for question-answering functionality.
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:::info
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<Admonition type="info">
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Works as a proxy of LangChain’s [documents](https://python.langchain.com/docs/modules/chains/document/) chains generated by the `load_qa_chain` function.
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:::
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</Admonition>
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**Params**
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- **LLM:** Language Model to use in the chain.
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- **chain_type:** The chain type to be used. Each one of them applies a different “combination strategy”.
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- **stuff**: The stuff [documents](https://python.langchain.com/docs/modules/chains/document/stuff) 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.
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- **map_reduce**: The map-reduce [documents](https://python.langchain.com/docs/modules/chains/document/map_reduce) 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 combined 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 combined documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.
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- **map_rerank**: The map re-rank [documents](https://python.langchain.com/docs/modules/chains/document/map_rerank) 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.
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- **refine**: The refine [documents](https://python.langchain.com/docs/modules/chains/document/refine) 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.
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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 that 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.
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- **stuff**: The stuff [documents](https://python.langchain.com/docs/modules/chains/document/stuff) 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.
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- **map_reduce**: The map-reduce [documents](https://python.langchain.com/docs/modules/chains/document/map_reduce) 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 combined 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 combined documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.
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- **map_rerank**: The map re-rank [documents](https://python.langchain.com/docs/modules/chains/document/map_rerank) 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.
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- **refine**: The refine [documents](https://python.langchain.com/docs/modules/chains/document/refine) 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.
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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 that 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.
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---
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@ -41,7 +43,7 @@ The `ConversationChain` is a straightforward chain for interactive conversations
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- **Memory:** Default memory store.
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- **input_key:** Used to specify the key under which the user input will be stored in the conversation memory. It allows you to provide the user's input to the chain for processing and generating a response.
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- **output_key:** Used to specify the key under which the generated response will be stored in the conversation memory. It allows you to retrieve the response using the specified key.
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- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can be helpful for debugging and understanding the chain's behavior. If set to False, it will suppress the verbose output — defaults to `False`.
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- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can be helpful for debugging and understanding the chain's behavior. If set to False, it will suppress the verbose output — defaults to `False`.
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---
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@ -49,11 +51,11 @@ The `ConversationChain` is a straightforward chain for interactive conversations
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The `ConversationalRetrievalChain` extracts information and provides answers by combining document search and question-answering abilities.
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:::info
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<Admonition type="info">
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A retriever is a component that finds documents based on a query. It doesn't store the documents themselves, but it returns the ones that match the query.
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:::
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</Admonition >
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**Params**
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@ -61,12 +63,13 @@ A retriever is a component that finds documents based on a query. It doesn't sto
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- **Memory:** Default memory store.
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- **Retriever:** The retriever used to fetch relevant documents.
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- **chain_type:** The chain type to be used. Each one of them applies a different “combination strategy”.
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- **stuff**: The stuff [documents](https://python.langchain.com/docs/modules/chains/document/stuff) 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.
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- **map_reduce**: The map-reduce [documents](https://python.langchain.com/docs/modules/chains/document/map_reduce) 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 combined 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 combined documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.
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- **map_rerank**: The map re-rank [documents](https://python.langchain.com/docs/modules/chains/document/map_rerank) 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.
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- **refine**: The refine [documents](https://python.langchain.com/docs/modules/chains/document/refine) 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.
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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 that 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.
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- **stuff**: The stuff [documents](https://python.langchain.com/docs/modules/chains/document/stuff) 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.
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- **map_reduce**: The map-reduce [documents](https://python.langchain.com/docs/modules/chains/document/map_reduce) 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 combined 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 combined documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.
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- **map_rerank**: The map re-rank [documents](https://python.langchain.com/docs/modules/chains/document/map_rerank) 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.
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- **refine**: The refine [documents](https://python.langchain.com/docs/modules/chains/document/refine) 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.
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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 that 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.
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- **return_source_documents:** Used to specify whether or not to include the source documents that were used to answer the question in the output. When set to `True`, source documents will be included in the output along with the generated answer. This can be useful for providing additional context or references to the user — defaults to `True`.
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- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
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`RetrievalQA` is a chain used to find relevant documents or information to answer a given query. The retriever is responsible for returning the relevant documents based on the query, and the QA component then extracts the answer from those documents. The retrieval QA system combines the capabilities of both the retriever and the QA component to provide accurate and relevant answers to user queries.
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:::info
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<Admonition type="info">
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A retriever is a component that finds documents based on a query. It doesn't store the documents themselves, but it returns the ones that match the query.
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:::
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</Admonition >
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**Params**
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- **Combine Documents Chain:** Chain to use to combine the documents.
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- **Memory:** Default memory store.
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- **Retriever:** The retriever used to fetch relevant documents.
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- **Retriever:** The retriever used to fetch relevant documents.
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- **input_key:** This parameter is used to specify the key in the input data that contains the question. It is used to retrieve the question from the input data and pass it to the question-answering model for generating the answer — defaults to `query`.
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- **output_key:** This parameter is used to specify the key in the output data where the generated answer will be stored. It is used to retrieve the answer from the output data after the question-answering model has generated it — defaults to `result`.
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- **return_source_documents:** Used to specify whether or not to include the source documents that were used to answer the question in the output. When set to `True`, source documents will be included in the output along with the generated answer. This can be useful for providing additional context or references to the user — defaults to `True`.
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@ -134,4 +137,4 @@ The `SQLDatabaseChain` finds answers to questions using a SQL database. It works
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- **Db:** SQL Database to connect to.
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- **LLM:** Language Model to use in the chain.
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- **Prompt:** Prompt template to translate natural language to SQL.
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- **Prompt:** Prompt template to translate natural language to SQL.
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import Admonition from "@theme/Admonition";
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# Prompts
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A prompt refers to the input given to a language model. It is constructed from multiple components and can be parametrized using prompt templates. A prompt template is a reproducible way to generate prompts and allow for easy customization through input variables.
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The `PromptTemplate` component allows users to create prompts and define variables that provide control over instructing the model. The template can take in a set of variables from the end user and generates the prompt once the conversation is initiated.
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:::info
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Once a variable is defined in the prompt template, it becomes a component input of its own. Check out [Prompt Customization](../guidelines/prompt-customization.mdx) to learn more.
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:::
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<Admonition type="info">
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Once a variable is defined in the prompt template, it becomes a component
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input of its own. Check out [Prompt
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Customization](../guidelines/prompt-customization.mdx) to learn more.
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</Admonition>
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- **template:** Template used to format an individual request.
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- **template:** Template used to format an individual request.
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