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codespell: workflow, config + some (quite a few) typos fixed (#6785)

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Probably the most  boring PR to review ;)

Individual commits might be easier to digest

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This commit is contained in:
Yaroslav Halchenko
2023-07-12 16:20:08 -04:00
committed by GitHub
parent 931e68692e
commit 0d92a7f357
100 changed files with 213 additions and 127 deletions
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12
docs/snippets/modules/chains/popular/chat_vector_db.mdx
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@@ -44,7 +44,7 @@ vectorstore = Chroma.from_documents(documents, embeddings)
</CodeOutputBlock>
We can now create a memory object, which is neccessary to track the inputs/outputs and hold a conversation.
We can now create a memory object, which is necessary to track the inputs/outputs and hold a conversation.
```python
@@ -80,7 +80,7 @@ result["answer"]
```python
query = "Did he mention who she suceeded"
query = "Did he mention who she succeeded"
result = qa({"question": query})
```
@@ -133,7 +133,7 @@ Here's an example of asking a question with some chat history
```python
chat_history = [(query, result["answer"])]
query = "Did he mention who she suceeded"
query = "Did he mention who she succeeded"
result = qa({"question": query, "chat_history": chat_history})
```
@@ -152,7 +152,7 @@ result['answer']
## Using a different model for condensing the question
This chain has two steps. First, it condenses the current question and the chat history into a standalone question. This is neccessary to create a standanlone vector to use for retrieval. After that, it does retrieval and then answers the question using retrieval augmented generation with a separate model. Part of the power of the declarative nature of LangChain is that you can easily use a separate language model for each call. This can be useful to use a cheaper and faster model for the simpler task of condensing the question, and then a more expensive model for answering the question. Here is an example of doing so.
This chain has two steps. First, it condenses the current question and the chat history into a standalone question. This is necessary to create a standanlone vector to use for retrieval. After that, it does retrieval and then answers the question using retrieval augmented generation with a separate model. Part of the power of the declarative nature of LangChain is that you can easily use a separate language model for each call. This can be useful to use a cheaper and faster model for the simpler task of condensing the question, and then a more expensive model for answering the question. Here is an example of doing so.
```python
@@ -178,7 +178,7 @@ result = qa({"question": query, "chat_history": chat_history})
```python
chat_history = [(query, result["answer"])]
query = "Did he mention who she suceeded"
query = "Did he mention who she succeeded"
result = qa({"question": query, "chat_history": chat_history})
```
@@ -352,7 +352,7 @@ result = qa({"question": query, "chat_history": chat_history})
```python
chat_history = [(query, result["answer"])]
query = "Did he mention who she suceeded"
query = "Did he mention who she succeeded"
result = qa({"question": query, "chat_history": chat_history})
```