Solves https://github.com/hwchase17/langchain/issues/1412
Currently `OpenAIChat` inherits the way it calculates the number of
tokens, `get_num_token`, from `BaseLLM`.
In the other hand `OpenAI` inherits from `BaseOpenAI`.
`BaseOpenAI` and `BaseLLM` uses different methodologies for doing this.
The first relies on `tiktoken` while the second on `GPT2TokenizerFast`.
The motivation of this PR is:
1. Bring consistency about the way of calculating number of tokens
`get_num_token` to the `OpenAI` family, regardless of `Chat` vs `non
Chat` scenarios.
2. Give preference to the `tiktoken` method as it's serverless friendly.
It doesn't require downloading models which might make it incompatible
with `readonly` filesystems.
Fix an issue that occurs when using OpenAIChat for llm_math, refer to
the code style of the "Final Answer:" in Mrkl。 the reason is I found a
issue when I try OpenAIChat for llm_math, when I try the question in
Chinese, the model generate the format like "\n\nQuestion: What is the
square of 29?\nAnswer: 841", it translate the question first , then
answer. below is my snapshot:
<img width="945" alt="snapshot"
src="https://user-images.githubusercontent.com/82029664/222642193-10ecca77-db7b-4759-bc46-32a8f8ddc48f.png">
Hello! Thank you for the amazing library you've created!
While following the tutorial at [the link(`Using an example
selector`)](https://langchain.readthedocs.io/en/latest/modules/prompts/examples/few_shot_examples.html#using-an-example-selector),
I noticed that passing Chroma as an argument to from_examples results in
a type hint error.
Error message(mypy):
```
Argument 3 to "from_examples" of "SemanticSimilarityExampleSelector" has incompatible type "Type[Chroma]"; expected "VectorStore" [arg-type]mypy(error)
```
This pull request fixes the type hint and allows the VectorStore class
to be specified as an argument.
Different PDF libraries have different strengths and weaknesses. PyMuPDF
does a good job at extracting the most amount of content from the doc,
regardless of the source quality, extremely fast (especially compared to
Unstructured).
https://pymupdf.readthedocs.io/en/latest/index.html
- Added instructions on setting up self hosted searx
- Add notebook example with agent
- Use `localhost:8888` as example url to stay consistent since public
instances are not really usable.
Co-authored-by: blob42 <spike@w530>
% is being misinterpreted by sqlalchemy as parameter passing, so any
`LIKE 'asdf%'` will result in a value error with mysql, mariadb, and
maybe some others. This is one way to fix it - the alternative is to
simply double up %, like `LIKE 'asdf%%'` but this seemed cleaner in
terms of output.
Fixes#1383
Updating documentation in initialize_agent.
One thing that could benefit from further clarification is the
responsibility
breakdown by between an AgentExecutor vs. an Agent. The documentation
for an
AgentExecutor does not clarify that. From the class attributes, it
appears that
executor has access to the tools, while the agent is only aware of the
tool
names. Anyway, additional clarification would be beneficial on the
AgentExecutor class.
This PR fixes the types returned by Cohere embeddings. Currently, Cohere
client returns instances of `cohere.embeddings.Embeddings`. Since the
transport layer relies on JSON, some numbers might be represented as
ints, not floats, which happens quite often. While that doesn't seem to
be an issue, it breaks some pydantic models if they require strict
floats.
The YAML and JSON examples of prompt serialization now give a strange
`No '_type' key found, defaulting to 'prompt'` message when you try to
run them yourself or copy the format of the files. The reason for this
harmless warning is that the _type key was not in the config files,
which means they are parsed as a standard prompt.
This could be confusing to new users (like it was confusing to me after
upgrading from 0.0.85 to 0.0.86+ for my few_shot prompts that needed a
_type added to the example_prompt config), so this update includes the
_type key just for clarity.
Obviously this is not critical as the warning is harmless, but it could
be confusing to track down or be interpreted as an error by a new user,
so this update should resolve that.
This PR:
- Increases `qdrant-client` version to 1.0.4
- Introduces custom content and metadata keys (as requested in #1087)
- Moves all the `QdrantClient` parameters into the method parameters to
simplify code completion
This PR adds
* `ZeroShotAgent.as_sql_agent`, which returns an agent for interacting
with a sql database. This builds off of `SQLDatabaseChain`. The main
advantages are 1) answering general questions about the db, 2) access to
a tool for double checking queries, and 3) recovering from errors
* `ZeroShotAgent.as_json_agent` which returns an agent for interacting
with json blobs.
* Several examples in notebooks
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
