feat: Add FAISS search tool

- Implement FAISSSearchTool for vector similarity search
- Add comprehensive unit tests
- Update documentation with usage examples
- Add FAISS dependency

Closes #2118

Co-Authored-By: Joe Moura <joao@crewai.com>

src/crewai/tools/faiss_search_tool.py

@@ -0,0 +1,64 @@
+from typing import List, Dict, Any, Optional
+import faiss
+import numpy as np
+from pydantic import BaseModel, Field
+
+from crewai.tools import BaseTool
+from crewai.utilities import EmbeddingConfigurator
+
+class FAISSSearchTool(BaseTool):
+    name: str = "FAISS Search Tool"
+    description: str = "Search through documents using FAISS vector similarity search"
+    
+    def __init__(
+        self,
+        index_type: str = "L2",
+        dimension: int = 384,  # Default for BAAI/bge-small-en-v1.5
+        embedder_config: Optional[Dict[str, Any]] = None,
+    ):
+        super().__init__()
+        self.embedder_config = embedder_config
+        self.dimension = dimension
+        self.index = self._create_index(index_type)
+        self.texts = []
+        self._initialize_embedder()
+
+    def _create_index(self, index_type: str) -> faiss.Index:
+        if index_type == "L2":
+            return faiss.IndexFlatL2(self.dimension)
+        elif index_type == "IP":
+            return faiss.IndexFlatIP(self.dimension)
+        else:
+            raise ValueError(f"Unsupported index type: {index_type}")
+
+    def _initialize_embedder(self):
+        configurator = EmbeddingConfigurator()
+        self.embedder = configurator.configure_embedder(self.embedder_config)
+
+    def _run(
+        self,
+        query: str,
+        k: int = 3,
+        score_threshold: float = 0.6
+    ) -> List[Dict[str, Any]]:
+        query_embedding = self.embedder.embed_text(query)
+        D, I = self.index.search(
+            np.array([query_embedding], dtype=np.float32),
+            k
+        )
+        
+        results = []
+        for i, (dist, idx) in enumerate(zip(D[0], I[0])):
+            if idx < len(self.texts):
+                score = 1.0 / (1.0 + dist)  # Convert distance to similarity score
+                if score >= score_threshold:
+                    results.append({
+                        "text": self.texts[idx],
+                        "score": score
+                    })
+        return results
+
+    def add_texts(self, texts: List[str]) -> None:
+        embeddings = self.embedder.embed_texts(texts)
+        self.index.add(np.array(embeddings, dtype=np.float32))
+        self.texts.extend(texts)