close

creat.py

@@ -8,7 +8,7 @@
 from langchain.vectorstores import FAISS
 from datetime import datetime, timedelta
 from typing import List
-from .prompt import *
+from prompt import *
 
 
 from langchain_experimental.generative_agents import (
@@ -51,7 +51,7 @@ def create_new_memory_retriever():
     # Define your embedding model
     embeddings_model = HuggingFaceEmbeddings()
     # Initialize the vectorstore as empty
-    embedding_size = 1536
+    embedding_size = 768 # it is a value only for llama-2-7b-chat model
     index = faiss.IndexFlatL2(embedding_size)
     vectorstore = FAISS(
         embeddings_model.embed_query,

langchain_experimental/__init__.py

@@ -0,0 +1,8 @@
+from importlib import metadata
+
+try:
+    __version__ = metadata.version(__package__)
+except metadata.PackageNotFoundError:
+    # Case where package metadata is not available.
+    __version__ = ""
+del metadata  # optional, avoids polluting the results of dir(__package__)

langchain_experimental/agents/__init__.py

@@ -0,0 +1,13 @@
+from langchain_experimental.agents.agent_toolkits import (
+    create_csv_agent,
+    create_pandas_dataframe_agent,
+    create_spark_dataframe_agent,
+    create_xorbits_agent,
+)
+
+__all__ = [
+    "create_csv_agent",
+    "create_pandas_dataframe_agent",
+    "create_spark_dataframe_agent",
+    "create_xorbits_agent",
+]

langchain_experimental/agents/agent_toolkits/__init__.py

@@ -0,0 +1,19 @@
+from langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent
+from langchain_experimental.agents.agent_toolkits.pandas.base import (
+    create_pandas_dataframe_agent,
+)
+from langchain_experimental.agents.agent_toolkits.python.base import create_python_agent
+from langchain_experimental.agents.agent_toolkits.spark.base import (
+    create_spark_dataframe_agent,
+)
+from langchain_experimental.agents.agent_toolkits.xorbits.base import (
+    create_xorbits_agent,
+)
+
+__all__ = [
+    "create_xorbits_agent",
+    "create_pandas_dataframe_agent",
+    "create_spark_dataframe_agent",
+    "create_python_agent",
+    "create_csv_agent",
+]

langchain_experimental/agents/agent_toolkits/csv/__init__.py

@@ -0,0 +1 @@
+"""CSV toolkit."""

langchain_experimental/agents/agent_toolkits/csv/base.py

@@ -0,0 +1,37 @@
+from io import IOBase
+from typing import Any, List, Optional, Union
+
+from langchain.agents.agent import AgentExecutor
+from langchain.schema.language_model import BaseLanguageModel
+
+from langchain_experimental.agents.agent_toolkits.pandas.base import (
+    create_pandas_dataframe_agent,
+)
+
+
+def create_csv_agent(
+    llm: BaseLanguageModel,
+    path: Union[str, IOBase, List[Union[str, IOBase]]],
+    pandas_kwargs: Optional[dict] = None,
+    **kwargs: Any,
+) -> AgentExecutor:
+    """Create csv agent by loading to a dataframe and using pandas agent."""
+    try:
+        import pandas as pd
+    except ImportError:
+        raise ImportError(
+            "pandas package not found, please install with `pip install pandas`"
+        )
+
+    _kwargs = pandas_kwargs or {}
+    if isinstance(path, (str, IOBase)):
+        df = pd.read_csv(path, **_kwargs)
+    elif isinstance(path, list):
+        df = []
+        for item in path:
+            if not isinstance(item, (str, IOBase)):
+                raise ValueError(f"Expected str or file-like object, got {type(path)}")
+            df.append(pd.read_csv(item, **_kwargs))
+    else:
+        raise ValueError(f"Expected str, list, or file-like object, got {type(path)}")
+    return create_pandas_dataframe_agent(llm, df, **kwargs)

langchain_experimental/agents/agent_toolkits/pandas/__init__.py

@@ -0,0 +1 @@
+"""Pandas toolkit."""